European business statistics compiler’s manual for ICT usage and e-commerce in enterprises, 2024 survey : 2025 edition.
Date: 2025-04-24
Institutions: Eurostat
Subjects: EU law, EU statistics, economic indicator, electronic commerce, goods and services, information technology, invoicing, national competent authority, small and medium-sized enterprises, statistical method, user guide

European business statistics 
compiler’s manual for ICT 
usage and e-commerce in 
enterprises, 2024 survey

2025 edition

M A N UA L S   A N D 
G U I D E L I N E S

2023 editionEuropean business statistics 
compiler’s manual for ICT 
usage and e-commerce in 
 enterprises, 2024 survey 

2025 edition 

 
 
 
 
 
 
 
 
Manuscript completed in March 2025 

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Theme: Science, technology, and digital society 
Collection: Manuals and guidelines 

PDF: 

ISBN 978-92-68-25680-0 

  ISSN 2315-0815 

doi: 10.2785/7657533  KS-01-25-012-EN-N 

Abbreviations 

ADSL  

AI  
B2B  
B2C  

B2G  

CDMA  

CRM  

DDoS  

DSL  
EBS  

eDAMIS  

EDI  

EEA  

EFTA  

ERP 

ESS  

ESS-MH  

Asymmetric Digital 
Subscriber Line 
Artificial Intelligence 
Business To Business 
Business To Consum-
ers 
Business To Govern-
ment 
Code Division Multiple 
Access 
Customer Relationship 
Management  
Distributed Denial Of 
Service 
Digital Subscriber Line 
European Business 
Statistics 
electronic Dataflow 
Administration And 
Management Infor-
mation System 
Electronic Data Inter-
change 
European Economic 
Area 
European Free Trade 
Association 
Electronic Resource 
Planning 
European Statistical 
System 
European Statistical 
System Metadata 
Handler 

EU  
FTE  
FTTP 

GIA  

HDSL  

ICT  

IDS  

IEEE  

INFOSOC  
ISO  

MH  
MS  
NACE  

NGFW  

European Union 
Full-Time Equivalent 
Fiber optics technol-
ogy 
General Implementing 
Act 
High-Bit-Rate Digital 
Subscriber Line 
Information And Com-
munication Technol-
ogy 
Intrusion Detection 
System 
Institute Of Electrical 
And Electronics Engi-
neers 
Information Society 
International Organi-
zation for Standardiza-
tion 
Metadata Handler 
Member States 
Statistical Classifica-
tion Of Economic Acti-
vities In The European 
Community (Nomen-
clature Generale Des 
Activites Economiques 
Dans Les Commu-
nautes Europeennes 
Next Generation Fire-
wall 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

3 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
NGIPS  

NSIs  

NUTS  

OECD  

OTP  
PIN  

R&D  

SBR  

SDMX  

SDSL  

Next Generation Intru-
sion Prevention Sys-
tem 
National Statistical In-
stitutes 
Nomenclature Of Ter-
ritorial Units For Sta-
tistics 
Organisation for Eco-
nomic Co-Operation 
And Development 
One-Time Password 
Personal Identification 
Number 
Research And Devel-
opment 
Statistical Business 
Register 
Statistical Data and 
Metadata Exchange 
Symmetric Digital Sub-
scriber Line 

SIEM  

SIMS  

SME  

SSL  
UMTS  

UNECE  

VAT  
VPN  

VTL  

XML 

Abbreviations 

Security Information 
And Event Manage-
ment 
Single Integrated 
Metadata Structure 
Small and Medium-
Sized Enterprises 
Secure Sockets Layer 
Universal Mobile Tele-
phone System 
United Nations Eco-
nomic Commission for 
Europe 
Value-Added Tax 
Virtual Private Net-
work 
Validation and Trans-
formation Language 
Extensible Markup 
Language 

4 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Contents 

Introduction ............................................................................................................................................... 7 
1.1.What are statistics on ICT usage and e-commerce in enterprises? .................................................... 7 

1.2. Usage and importance of statistics on ICT usage and e-commerce in enterprises ............................ 7 

1.3. What is the purpose of this compilers’ manual .................................................................................... 8 

1.4. Where can I find further guidance? ..................................................................................................... 9 

Data compilation ..................................................................................................................................... 10 
2.1. Legislative background ...................................................................................................................... 10 

2.2. Data requirements ............................................................................................................................. 11 
2.2.1. Introduction ................................................................................................................................. 11 
2.2.2. Statistical unit .............................................................................................................................. 11 
2.2.3. Statistical unit enterprise ............................................................................................................. 12 
2.2.3.1. The concept of enterprise in European business statistics ................................................ 12 
2.2.3.2. Recommendation for the correct implementation of the statistical unit enterprise in the ICT 
domain ............................................................................................................................................. 13 
2.2.4. Statistical population ................................................................................................................... 18 
2.2.5. Accuracy ..................................................................................................................................... 20 
2.2.6. Periodicity ................................................................................................................................... 20 
2.2.7. Variables ..................................................................................................................................... 21 

2.3. Model questionnaire .......................................................................................................................... 22 
2.3.1. Development of model questionnaires ....................................................................................... 22 
2.3.2. Modules and questions ............................................................................................................... 22 
2.3.2.1. General remark: ‘use’, ‘have’ or ‘have another enterprise use for you’ ............................. 22 
2.3.2.2. Module A: Access and use of the internet .......................................................................... 23 
2.3.2.3. Module B: e-Commerce sales ............................................................................................ 34 
2.3.2.4. Module C: ICT specialists and skills................................................................................... 45 
2.3.2.5. Module D: ICT Security ...................................................................................................... 50 
2.3.2.6. Module E: Artificial Intelligence .......................................................................................... 59 
2.3.2.7. Module X: Background information .................................................................................... 77 
Data transmission .................................................................................................................................. 84 
3.1. General description ........................................................................................................................... 84 

3.2. Codification, indicators and breakdown aggregates ......................................................................... 84 
3.2.1. Codification of microdata ............................................................................................................ 84 
3.2.2. Digital intensity index ................................................................................................................ 123 
3.2.3. Indicator coding and scope ....................................................................................................... 124 
3.2.4. Activity and employee number codes ....................................................................................... 308 
3.2.4.1. Economic activity breakdown ........................................................................................... 309 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

5 

Contents 

3.2.4.2. Enterprise size class ........................................................................................................ 312 
3.2.4.3. Sector enterprise size class ............................................................................................. 312 
3.2.4.4. Economic activity breakdowns for micro-enterprises ....................................................... 313 
3.2.4.5. Sector enterprise size classes for enterprises with 0-9 employees and self-employed 
persons .......................................................................................................................................... 315 
3.2.4.6. Optional sector breakdowns by size class for enterprises with 0-1 and 2-9 employees and 
self-employed persons .................................................................................................................. 315 
3.2.4.7. Optional breakdowns by economic activity for enterprises with 0-1 employees and self-
employed persons ......................................................................................................................... 316 
3.2.4.8. Optional breakdowns by economic activity for enterprises with 2-9 employees and self-
employed persons ......................................................................................................................... 317 
3.2.5. Regional data ............................................................................................................................ 318 

3.3. Transmission format for the ICT usage and e-commerce in enterprises survey............................. 327 
3.3.1. SDMX ........................................................................................................................................ 327 
3.3.2. SDMX format for the ICT usage and e-commerce in enterprises survey ................................. 328 
3.3.3. Example .................................................................................................................................... 333 

3.4. Data confidentiality .......................................................................................................................... 334 

3.5. Transmission deadlines ................................................................................................................... 335 

3.6. Transmission channels .................................................................................................................... 335 

3.7. Data revisions .................................................................................................................................. 336 

3.8. Support of data providers ................................................................................................................ 336 

Data quality ........................................................................................................................................... 337 
4.1. Quality framework............................................................................................................................ 337 
4.1.1. European business statistics quality framework ....................................................................... 337 
4.1.2. Implementing the framework for data on ICT usage and e-commerce in enterprises.............. 339 

4.2. Metadata and quality reports ........................................................................................................... 341 
4.2.1. Metadata and quality report on ICT usage and e-commerce in enterprises ............................ 342 
4.2.2. European Statistical System metadata handler ........................................................................ 343 

4.3. Data validation by Eurostat ............................................................................................................. 345 

Data dissemination ............................................................................................................................... 347 
5.1. Data description ............................................................................................................................... 347 

5.2. Confidentiality and flags .................................................................................................................. 347 

5.3. Dissemination channels .................................................................................................................. 348 

Annex 1 – Model questionnaire .......................................................................................................... 354 
Annex 2 – Validation rules ................................................................................................................... 380 
Annex 3 – Metadata reporting template ............................................................................................. 448 

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European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises     

1 

Introduction 

1Introduction 

1.1. What are statistics on ICT usage and e-

commerce in enterprises? 

Information and communications technologies (ICT) usage and e-commerce in enterprises 
statistics measure the uptake of technologies and the digitalisation of the European Union (EU) 
economy. These statistics are gathered through the annual ‘ICT usage and e-commerce in 
enterprises’ survey. The 2024 survey measured enterprises’ access to and use of the internet 
and e-commerce (i.e. sale of goods and services online), ICT specialists and skills, ICT security, 
use of artificial intelligence.  

This exercise has been carried out since 2002 when the European Commission (EC) established 
annual ‘information society’ surveys to benchmark ICT-driven development in enterprises and by 
individuals.  

Eurostat is responsible for coordinating the ‘ICT usage and e-commerce in enterprises’ survey 
which is conducted at national level. Survey questions are developed every year, in close 
collaboration with Member States (MS) and the Organisation for Economic Cooperation and 
Development (OECD), in line with the changing needs of data users and policy makers. To 
facilitate the data collection a model questionnaire is provided, which is accompanied by 
methodological guidelines for its implementation. 

1.2. Usage and importance of statistics on ICT 
usage and e-commerce in enterprises 
ICT account for a significant part of EU productivity and growth and are transforming our societies 
and economies in a profound and unprecedented way. Official statistics are indispensable for an 
informed understanding of the implications of the transformations under way. Selecting 
appropriate indicators is a crucial step. Measuring the development of the information society with 
relevant statistics on business processes and the digital economy requires continuous revision 
and improvement. 

The current compilers’ manual refers to the model questionnaire used for collecting part of the 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

7 

 
1 

Introduction 

statistical data for monitoring progress towards the EU’s digital targets for 2030, set by the Digital 
Decade Policy Programme. Four of the key performance indicators of this policy programme are 
based on the statistics on ICT usage and e-commerce in enterprises: artificial intelligence (AI), 
cloud computing, big data (data analytics) and the digital intensity index - a composite indicator 
reflecting the digital transformation of business. 

1.3. What is the purpose of this compilers’ 
manual 
This compilers’ manual (hereafter referred to as the ‘Manual’) is meant to serve as a practical 
reference document for all National Statistical Institutes (NSIs) involved in the compilation of data 
on the use of ICT and e-commerce in enterprises. As such, its main objectives are:  

• 

• 

• 

• 

to help NSIs translate Eurostat model questionnaires into national languages and to ensure 
that the same methodology is used by all countries when conducting national surveys;  

to set out the concepts, definitions and compilation methods, guiding the compilation of data;  

to explain the validation and quality rules, and metadata reporting;  

to explain the concepts and methods of data transmission to Eurostat.  

In order to do so, each chapter of this manual describes a step of the production process of 
statistics on ICT usage and e-commerce in enterprises.  

The second chapter is on data compilation. Data compilation is carried out by the NSIs on the 
basis of the model questionnaire. This chapter introduces the legislative background 
underpinning the process and explains how to interpret the model questionnaire to ensure the 
comparability of data between MS.  

The third chapter is on data transmission. Once the data has been collected by the NSIs, they 
must transmit them to Eurostat. To that effect, this chapter sets out how to transfer the data to 
Eurostat (codification, transmission channel, deadlines, flags, confidentiality…).  

The fourth chapter is on data quality. Once the data are transmitted, Eurostat applies validation 
rules to assess their quality. This chapter sets out the quality framework and the validation rules 
used by Eurostat.  

Finally, the fifth chapter is on data dissemination. Once the whole data collection process is 
complete, Eurostat publishes the data. This chapter describes the type of data that is published 
and how to access them. 

Note that this edition of the Manual serves as a reference for the compilation and 
transmission to Eurostat of data relating to 2024 as reference year.  

To keep up with technological advances in digitalisation and changes in data requirements, this 
Manual will be updated every year. 
This Manual focuses on issues relevant to ICT and e-commerce in enterprises. It does not 
provide an exhaustive list of all concepts and tools underpinning European statistics. For this 
information, please refer to the manuals, guidelines and other references listed in Section 1.4 
below. Notably, the European Business Statistics Manual, 2021 edition (EBS Manual) covers the 

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European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

1 

Introduction 

concepts that are shared across the business statistics domains. 

1.4. Where can I find further guidance? 
Further guidance is available from the following sources:  

•  The Digital economy and society webpage provides a global and regularly updated overview 

of Digital economy and society statistics, including statistics on the use of ICT and e-
commerce in enterprises. 

•  The European Business Statistics Manual provides an overview of business statistics while 

highlighting the features of the regulatory framework. The manual also describes the various 
statistical tools and activities supporting EBS production such as statistical units and profiling, 
classifications, data processing and statistical disclosure control. 

•  The European Statistical System handbook for quality and metadata reports sets out 

guidelines for the preparation of producer and user reports for the full range of statistical 
processes and their outputs within Member States, EFTA countries and Eurostat.  

•  The ESS Handbook Methodology for data validation 2.0 provides a generic reference 

framework and metrics for data validation.  

•  The Business Architecture for ESS Validation manual sets out a common understanding of 

how ESS validation should be conducted.  

•  The European Statistics Code of Practice sets out principles that aim to ensure that statistics 
produced within the European Statistical System are relevant, timely and accurate, and that 
they comply with the principles of professional independence, impartiality and objectivity. 

•  The Quality Assurance Framework of the European Statistical System provides a collection of 
methods, tools and good practices on how to implement the European Statistics Code of 
Practice.

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

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2 

Data compilation 

2 

Data compilation 

Data compilation is done by the NSIs on the basis of the model questionnaire. This chapter 
explains the legislative background and the data requirements, and provides guidance on the 
interpretation of the model questionnaire. 

2.1. Legislative background 
Statistics on the usage of ICT and e-commerce in enterprises are based on EU legislation to 
ensure a harmonised approach for the production of statistics by all reporting countries. Three 
regulations currently form the legal basis of the survey on ICT usage and e-commerce in 
enterprises for the 2024 survey: 

•  Regulation (EU) 2019/2152 of the European Parliament and of the Council of 

27 November 2019 on European business statistics, repealing 10 legal acts in the field of 
business statistics (‘EBS Regulation’);  

•  Commission Implementing Regulation (EU) 2020/1197 of 30 July 2020 laying down technical 
specifications and arrangements pursuant to Regulation (EU) 2019/2152 of the European 
Parliament and of the Council on European business statistics, repealing 10 legal acts in the 
field of business statistics (‘General Implementing Act’ or ‘EBS GIA Regulation’); and  

•  Commission Implementing Regulation (EU) 2023/1507 of 20 July 2023 laying down the 

technical specifications of data requirements for the topic ICT usage and e-commerce for the 
reference year 2024, pursuant to Regulation (EU) 2019/2152 of the European Parliament and 
of the Council (‘Implementing Act’). 

Regulation (EU) 2019/2152 is a framework regulation. It therefore provides flexibility to adapt the 
survey on ICT usage and e-commerce in enterprises to the evolving needs of users and decision-
makers. Annual implementing measures, such as Commission Implementing Regulation (EU) 
2023/1507, are the basis for the Eurostat surveys. This ensures harmonised data for all EU-27 
MS. The annual implementing Regulation is of relevance for the European Economic Area (EEA). 

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European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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Data compilation 

2.2. Data requirements 

2.2.1. Introduction 

The statistical product is the clear and precise definition of the statistical information to be 
produced. It is distinct from the production methodology. The production methodology is the way 
or method of doing, while the statistical product is its direct result. Different methodologies can 
produce the same statistical product, being only different ways of doing the same thing. This 
means that as long as it is guaranteed that two figures concern the same statistical product – for 
instance for two different countries – they are comparable. Distinguishing between the statistical 
product and the statistical methodology thus helps to focus on the aspects that are more 
important for ensuring comparability between national statistics and for producing new ones at 
EU level. NSIs may choose the most appropriate statistical methodology to be applied taking into 
account national particularities.  

Elements that make up the statistical product, at input level, are the statistical unit, the target 
population and the observation variables. Elements at output level are the periodicity, variables, 
aggregates and tabulation. Covering all the elements of the statistical product, the statistical 
concepts and the nomenclatures are the additional elements ensuring that statistics are 
harmonised and comparable.  

This chapter provides a detailed description of data requirements on ICT usage and e-commerce 
in enterprises. These are based on the EBS Regulation and the Implementing Act. 

2.2.2. Statistical unit 

The statistical unit is the base type of the elements of a group (also called population) that is 
intended for observation or analysis. The basic statistical operations of classification, aggregation 
and ordering are done on the statistical unit.  

The choice of the statistical unit is a matter of both the data collection process (namely the 
operational restrictions associated with collecting data from each type of statistical unit) and the 
conceptual framework chosen to observe and analyse the phenomenon. The statistical unit is the 
bearer of statistical characteristics or attributes that are ultimately intended to be measured.  

There are several types of statistical units, according to their usage. An observation unit 
represents an identifiable entity, about which data can be obtained. During the collection of data, 
this is the unit for which data are recorded. It should be noted that this may, or may not be, the 
same as the reporting unit. The reporting unit is the unit that reports to the survey authority. It 
reports information for the observation unit(s). In certain cases, it may be different from the 
observation unit. A reporting unit is a unit that supplies the data for a given survey instance. 

The observation unit in the survey on ICT usage and e-commerce in enterprises is the enterprise, 
as defined in Council Regulation (EEC) No 696/93 of 15 March 1993 on the statistical units for 
the observation and analysis of the production system in the Community. The Regulation defines 
the enterprise as follows: 

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Data compilation 

‘The enterprise is the smallest combination of legal units that is an organisational unit 
producing goods or services, which benefits from a certain degree of autonomy in 
decision-making, especially for the allocation of its current resources. An enterprise 
carries out one or more activities at one or more locations. An enterprise may be a sole 
legal unit.’ 

The enterprise, therefore, is an economic entity that can, under certain circumstances, 
correspond to a grouping of several legal units. Some legal units, in fact, perform activities 
exclusively for other legal units and their existence can only be explained by administrative 
factors (e.g. tax reasons), without them being of any economic significance. A large proportion of 
legal units with no employees or self-employed persons also belong to this category. In many 
cases, the activities of these legal units should be seen as ancillary activities of the parent legal 
unit they serve, to which they belong and to which they must be attached to form an enterprise 
used for economic analysis.  

However, the definition of the enterprise as the appropriate statistical unit poses some limitations. 
Some enterprises, especially larger ones, are composed of several local units (establishments). 
Because of this, a geographical breakdown of the results (although still possible using the 
location of the enterprise’s main headquarters) is of limited use. Nevertheless, ICT usage is not 
easily attributable to an enterprise’s various establishments, and for this reason the enterprise is 
the statistical unit adopted.  

Implementation of the statistical unit ‘enterprise’ in business statistics requires specific 
considerations for the delineation of enterprises in business registers (profiling), the consolidation 
of the data on legal units, etc. Nevertheless, for surveys with qualitative variables specifically, the 
consolidation is more challenging.  

Specific guidelines have been developed for this survey and are described in Section 2.2.3 
below. 

2.2.3. Statistical unit enterprise 

2.2.3.1. The concept of enterprise in European business sta-
tistics 
The enterprise is the most important statistical unit in EBS. The enterprise concept is applied in 
almost all domains of business statistics. It is also the core unit, given that the other statistical 
units, such as the kind-of-activity unit (KAU), the local kind-of-activity unit (LKAU)1 and the local 

1 The kind-of-activity unit is a part of an enterprise. It groups together all the offices, production facilities etc. of an enterprise that con-
tribute to the performance of a specific economic activity defined at class level (four digits) of the European classification of economic 
activities (NACE Rev. 2.) For example, a kind-of-activity unit might be the combination of all parts of a metal-producing enterprise that 
produce copper (class 24.44 in NACE Rev. 2); within the same enterprise, there might be another kind-of-activity unit consisting of 
those parts that produce aluminium (class 24.42 in NACE Rev. 2). The local part of kind of activity units is called local kind-of-activity 
unit; the term establishment is common as well, e.g. in SNA or ISIC (Glossary: Kind-of-activity unit and local-kind-of-activity unit - Sta-
tistics Explained (europa.eu)). 

12 

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Data compilation 

unit2, are defined in relation to the enterprise. An enterprise may carry out one or more economic 
activities at one or more locations. The enterprise is thus not a homogeneous unit, neither with 
respect to its activity nor to its geographical location. However, in most enterprises, the principal 
activity accounts for quite a large share of the value added generated, and thus these enterprises 
come closer to the homogeneity of the economic activity. Also, most of the enterprises only have 
one location, which means that, for those enterprises, the regional attribution would be close to 
correct. Larger enterprises, however, perform various activities and have more than one location.  

In business statistics, the term enterprise should always be used in the sense of a ‘statistical 
enterprise’. This means, by definition, one aims to create a statistical unit that would allow to 
compile harmonised data, irrespective of the actual organisational and legal structures of the 
economic agents. The criteria that a statistical enterprise should satisfy are: operating the 
necessary factors of production; having an organisational and managerial structure; autonomy of 
decision-making; and producing and selling goods and services on the market (see European 
business statistics methodological manual for statistical business registers, 2021 edition).  

The relationships between legal units and enterprises may be like in the two examples below. 

An enterprise consisting of more than one legal unit under the same management can be 
referred to as a ‘complex enterprise’. 

2.2.3.2. Recommendation for the correct implementation of 
the statistical unit enterprise in the ICT domain 
In general, the share of complex enterprises or enterprises consisting of more than one legal unit 
is low, at around 5% or even lower, depending on the country. However, complex enterprises are 
usually those with a high turnover or/and employment. So, an effort should be made to provide 
data for those enterprises in a harmonised way.  

Consistency is especially encouraged with structural business statistics but also with other annual 
business statistics at national level. 

2 The local unit is an enterprise or part thereof (e.g. a workshop, factory, warehouse, office, mine or depot) situated in a geographically 
identified place. At or from this place, economic activity is carried out for which - save for certain exceptions - one or more persons 
work (even if only part-time) for one and the same enterprise (Glossary: Local unit - Statistics Explained (europa.eu)). 

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Data compilation 

2.2.3.2.1. Target population 

In the case of complex enterprises, the attributes for the complex enterprise as a whole should be 
relevant for including the enterprise and its underlying legal units in the sample, even if one or 
more legal units are out of scope.  

Therefore, the target population should comprise enterprises, including complex ones, having the 
main attributes (e.g. given NACE3 code, number of employees and self-employed persons) in the 
scope of the ICT survey. 

2.2.3.2.2. Sampling 

The sample should be drawn at the level of enterprise. The attributes of the enterprise should be 
used as they are registered in the statistical business registers (SBR).  

Unit consistency with structural business statistics and preferably also other national annual 
business statistics at the national level should be ensured.  

If the variables ‘employees and self-employed persons’ and ‘turnover’ from existing data sources 
prove to be outdated, it could be considered to collect those variables from the enterprises, pre-fill 
them for verification by the enterprise, or check against the latest information of the SBR when 
processing the data. It should be considered to ask for intra-enterprise flows on a voluntary basis. 

2.2.3.2.3. Data collection 

It is up to NSIs to collect the data either from legal units or from enterprises.  

3 NACE is the acronym for Nomenclature statistique des activités économiques dans la Communauté européenne. For more infor-
mation on NACE Rev. 2. 

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Data compilation 

Data collection 

If an enterprise consists of more than one legal unit, the 
NSI should make an agreement with the enterprise as to 
who should be the contact person. If that is not possible, 
the legal unit that is best suited to reply should be chosen.            

At enterprise 
level 

Contact      
person 

If it is not known which legal unit is best suited to reply, the 
group head or top management should be contacted.  

At enterprise 
level 

Measures to 
ensure     
quality 

If they cannot reply, they should forward the questionnaire 
for the individual modules to the person who is best suited 
to reply. 

The contact person should be given a list of legal units for 
which they should respond.  

If it turns out that the information in the SBR as regards the 
legal units belonging to the enterprise is not correct, it 
should be reported back to colleagues in the responsible 
unit for profiling or the SBR. 

Ideally, data should be collected from all legal units 
belonging to the enterprise in the sample.  

Contact        
person 

If data cannot be collected from all legal units, the legal 
units that report should report for all legal units belonging to 
the enterprise; they should be explicitly made aware for 
which legal units they should provide the data. 

From legal 
units 

Measures to 
ensure     
quality 

Special care should be taken to include the legal units 
having e-commerce in order not to underestimate the e-
commerce. These would include, for example, legal units 
that, in previous years, reported having e-commerce or 
websites with functionalities to order goods or services, or 
legal units known from other sources to have e-commerce. 

For non-additive variables, e.g. variables with intra-
enterprise flows, it might be better to collect them from 
a contact person/legal unit nominated or agreed or legally 
best-suited to reply for the whole enterprise, or the group 
head or top management. For non-additive variables, legal 
units should be asked to deduct the intra-enterprise flows. 

2.2.3.2.4. Consolidation of variables 

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Data compilation 

Variable type 

Consolidation 

Qualitative      
dichotomous 
questions (yes/no       
questions) 

As a general rule, if one answer is ‘yes’, the answer should be ‘yes’ 
for the whole enterprise. 

Qualitative 
questions with 
different answer      
options 

In the 2024 survey, this concerns the question on internet speed, 
where the fastest connection in the enterprise should be chosen, 
and the question on the definition of the most recent review of 
documents on measures, practices or procedures on ICT security, 
where it should be the most recent update in the enterprise. 

Additive variables are those for which the simple sum of the 
amounts of the legal units yields the consolidated amount for the 
enterprise. This must be the case for all enterprises, regardless of 
the way in which the legal units are combined to form the 
enterprise.  

In principle, variables such as ‘number of persons employed having 
access to the internet for business purposes’ and ’number of 
persons employed using a portable device provided by the 
enterprise, that allows internet connection via mobile telephone 
networks’, are additive variables. However, in line with the 
Summary note on additive and non-additive SBS variables, the 
number of persons employed are headcounts and not additive. For 
example, if one person (employee) is part-time employed by 
different legal units of the enterprise, that person should be counted 
once. If further information is not available, the variable should be 
treated as additive for practical reasons. 

Non-additive variables are those that 
cannot be simply added up to calculate the 
total amount at enterprise level, if the 
enterprise consists of several legal units. 
However, a consolidation of the amounts of 
the variable in question (e.g. turnover) of 
the underlying legal units must be carried 
out, involving the elimination of values that 
are related to internal flows. Non-additive 

Data collection 
from enterprises  

If data are collected 
from enterprises, 
they should be 
asked to deduct the 
intra-enterprise 
flows. 

Quantitative 
additive variables 

Quantitative non-
additive variables 

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Variable type 

Consolidation 

variables in the survey on ICT usage and e-
commerce in enterprises are variables with 
internal flows, such as e-commerce or 
turnover.  

Non-additive variables should be collected 
from the contact person, the legal unit best 
suited to reply for the whole enterprise or 
the group head/top management, or if the 
consolidation is done in the national 
statistical institute, an attempt should be 
made to deduct the intra-enterprise flows in 
the case of web sales or EDI-type 
(electronic data interchange) sales. 

Data collection 
from legal units  

If data are collected 
from legal units, 
information on the 
existence of intra-
enterprise flows from 
the (statistical) 
business register 
should be used if 
available. Legal units 
could be asked to 
deduct the intra-
enterprise flows. 

Data           
collection  
from           
enterprises 

If data are collected from the enterprise, the contact 
person, the legal unit best suited to reply for the 
whole enterprise or the group head/ top 
management in the enterprise should provide the 
percentage or an estimate for the whole enterprise. 

Percentages 

If data are collected from legal units, the NSI should 
consolidate by dividing the absolute values (e.g. 
persons employed having access to the internet for 
business purposes) from all legal units by the total 
value for the denominator (e.g. persons employed in 
all legal units).  

If data from any legal unit is not available, it should 
be imputed. 

Information from SBR can be used, if available or 
useful.  

Data           
collection               
from legal 
units 

Care should be taken if the percentages are for non-
additive variables, where, for example, intra-
enterprise flows should be excluded if possible. 

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Variable type 

Consolidation 

Mutually          
exclusive    
questions 

Mutually exclusive questions are, for example, questions about 
using AI, where there is a different set of questions for those who 
are using AI and for those who don’t (such as the reasons for not 
using AI). 

In that case, the answers for such a question (e.g. reasons for not 
using AI) should only be counted if no legal unit in the enterprise 
has used the relevant technology (AI technologies). Therefore, the 
replies should be counted for the enterprise as whole; a ‘yes’ 
answer in at least one legal unit should be counted as a ‘yes’ for the 
whole enterprise. 

2.2.4. Statistical population 

A population is a collection of objects of the same class. In statistical terms, this means a group 
of elements of the same statistical unit. There are two types of populations to be considered 
when producing statistics: the target population and the frame population.  

The target population is the population of interest. It is identified by clearly delimiting the group 
of statistical elements about which information is desired. That delimitation is based on one or 
more attributes of the statistical unit. For example, for the enterprise, some commonly used 
attributes to delimit the target population are the size (e.g. number of employees and self-
employed persons), the economic activity and its location. An example of a target population 
could be ‘enterprises with 10 or more employees and self-employed persons, classified in 
Divisions 41–43 of NACE Rev. 2 (Construction), localised in the EU’. 

The target population of the survey on ICT usage and e-commerce in enterprises is the group of 
enterprises delimited by the following attributes: 

•  Economic activity  

NACE is the standard for the statistical classification of economic activities in the EU. The present 
NACE Rev. 2, which is the new revised version of the NACE Rev. 1 and of its minor update 
NACE Rev. 1.1, was implemented for the first time in the 2009 survey.  

Enterprises are classified in the following categories of NACE Rev. 2: 

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NACE Rev. 2 

Description 

Section C 

Manufacturing 

Section D, E 

Section F 

Section G 

Section H 

Section I 

Section J 

Section L 

Section M 

Section N 

Electricity, gas and steam and air-conditioning supply, water 
supply, sewerage, waste management and remediation 
activities 

Construction 

Wholesale and retail trade; repair of motor vehicles and 
motorcycles 

Transportation and storage 

Accommodation and food service activities 

Information and communication 

Real estate activities 

Professional, scientific and technical activities 

Administrative and support service activities 

Group 95.1 

Repair of computers and communication equipment 

Enterprises are classified in one of these categories based on their principle economic activity. 
Since 2021, in accordance with the provisions of the EBS Regulation, Division 75 Veterinary 
activities is also included in the target population. 

•  Enterprise size  

The target population consists of enterprises with 10 or more employees and self-employed 
persons.  

Optionally, the target population can be extended to enterprises with numbers of employees and 
self-employed persons between 0 and 9.  

With the adoption of the EBS Regulation, the variable 'persons employed' was replaced by the 
variable 'employees and self-employed persons'. This change of name of the variable does not 
imply any change in the scope. The two variables represent exactly the same concept. For the 
sake of user-friendliness, the term 'employees and self-employed persons' is only used in the 
introductory part of the model questionnaire and in Module X; in the rest of the questionnaire, the 
term 'persons employed' is still used.  

The variable number of employees and self-employed persons is defined in Commission 
Implementing Regulation (EC) No 2020/1197 of 30 July 2020 (p. 92, Variable 120101: Number of 
employees and self-employed persons) and should not be confused with the number of 

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employees or with full-time equivalents (FTE)4. In what follows, from the statistical definition point 
of view, even though the word ‘employees’ is used, it always refers to ‘employees and self-
employed persons’. 

•  Geographic scope  

Enterprises located in any part of the territory of the country. 

The frame population is an operationalisation of the target population, consisting ideally of the 
complete list of the target population elements. A target population can be easily defined but, in 
practice, a list of all its elements is needed for its complete or partial observation (if a sample is 
used). This can be very difficult to obtain. The list should be complete and include each element 
of the target population only once. However, it will usually suffer from both under-coverage and 
over-coverage. Generally, files of statistical elements (registers) are maintained and updated, 
containing lists of statistical elements and also information on some attributes, usually used for 
delimiting target populations. Normally, frame populations are extracted from those registers. 

2.2.5. Accuracy 

The accuracy of statistical information refers to the closeness of estimates to the unknown true 
values. In practice, it is the degree to which the information correctly describes the phenomena it 
was designed to measure. The accuracy of statistical information is broken down into bias 
(systematic error) and variance (random error).  

Sampling error is one of the quality indicators related to accuracy and, for most of the sampling 
surveys, is the most indicative quality information. The quality aspect is covered in Article 17 of 
the EBS Regulation, Articles 10 and 11 of EBS Regulation General Implementing Act, and Article 
2 of the Implementing Act. The quantitative element for evaluating the data accuracy, established 
in agreement with MS, has been the standard error. The maximum standard error (commonly 
used for evaluating quality) has been set to 2 percentage points for the whole coverage and 
5 percentage points for the breakdowns, for the proportions and ratios that are calculated on the 
basis of the aggregate data that MS transmit to Eurostat. It is an obligation of the NSIs, as 
producers of national official statistics, and of Eurostat, being the provider of official statistics on 
the EU, to estimate and present these statistical errors to the users. 

2.2.6. Periodicity 

The periodicity is annual. The data are collected and compiled once a year.  

4 A full-time equivalent (FTE) is a unit to measure employed persons or students in a way that makes them comparable although they 
may work or study a different number of hours per week. The unit is obtained by comparing an employee’s or student’s average num-
ber of hours worked to the average number of hours of a full-time worker or student. A full-time person is therefore counted as one 
FTE, while a part-time worker / student gets a score in proportion to the hours worked or studied. For example, a part-time worker 
employed for 20 hours a week where full-time work consists of 40 hours is counted as 0.5 FTE (Glossary: Full-time equivalent (FTE) - 
Statistics Explained (europa.eu)). 

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However, the content of the survey and questionnaire can be adapted to accommodate the 
evolving needs of data users. In practice, the content is changed every year. In addition, to 
minimise the burden on NSIs and respondents, some variables can be observed with a lower 
frequency, e.g. variables that tend to be stable over time.  

The coverage of the survey is 2023-2024. Most variables of the 2024 survey are collected for the 
year 2024. However, the 2024 survey also collects certain variables for the year 2023. For 
example, questions of the 2024 survey relating to web sales of goods and services, ICT 
specialists and skills, ICT security incidents and background information refer to variables for the 
year 2023.  

This periodicity is laid down in Article 6 and Annex II to the EBS Regulation. 

2.2.7. Variables 

Ultimately, the attributes of the statistical unit are intended to be observed; the observation 
variables hold the numerical measures of these attributes. Attributes and observation variables 
should not be confused. An attribute is some property of the statistical unit and each attribute 
may have one or more observation variables with qualitative or quantitative information.  

For example, for the statistical unit ‘enterprise’, an attribute can be its size. The observation 
variable is then the number of employees and self-employed persons in its service.  

There are many ways of classifying observation variables; the most relevant one for this survey is 
the distinction between qualitative and quantitative variables.  

Qualitative variables concern non-numerical information. They serve merely as labels or names 
for identifying attributes of the statistical unit. An example is the ‘use (Yes/No) of any type of fixed 
line connection to the internet’ by the enterprise. Sometimes qualitative variables can be turned 
into numerical ones by coding the non-numeric values. Binary (or dichotomous) variables are an 
important type of qualitative variable. Binary variables assume only two different values, which 
are usually turned into numerical ones by attributing the values ‘0’ and ‘1’ in a meaningful way. 

Quantitative variables contain information as to how much or how many. An example is the 
number of persons employed who have access to the internet.  

In the survey on ICT usage and e-commerce in enterprises, the observation variables are mainly 
qualitative (binary variables).  

The operational version of the observation variables are the questions in the 2024 model 
questionnaire that can be found in Annex 1 – Model questionnaire.  

The definition of the observation variables or the model questions are discussed in more detail in 
Section 2.3 below. 

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2.3. Model questionnaire  

2.3.1. Development of model questionnaires 

The legal background presented in Section 2.1 provides the context for developing the model 
questionnaires. The legal basis is output oriented, i.e. the Implementing Act defines the variables 
and their characteristics but does not impose a specific method for data collection.  

In order to reach a higher degree of harmonisation, Eurostat - together with a task force of 
experts in the survey on ICT usage and e-commerce in enterprises – prepares an annual model 
questionnaire that is recommended to the NSIs.  

The EBS Regulation limits the response burden of the annual Implementing Act for the topic of 
ICT usage and e-commerce to 73 variables, including background variables. On the other hand, 
the need for measuring digitalisation is ever increasing. To collect relevant statistics measuring 
the significant technological changes of the ICT landscape, but at the same time not to increase 
the burden on respondents, topics and/or variables are replaced or updated to collect more 
relevant or more in-depth information every year.  

In addition to the compulsory data collection, some variables are proposed to be collected by the 
NSIs on a voluntary basis. 

2.3.2. Modules and questions 

This chapter refers to the questions in the 2024 model questionnaire (MQ) (See Annex 1 – Model 
questionnaire). The structure of the chapter follows the model questionnaire, i.e. the explanatory 
notes are grouped by module (title) and question. It is recommended to have the model 
questionnaire at hand while reading this section. 

2.3.2.1. General remark: ‘use’, ‘have’ or ‘have another enter-
prise use for you’ 
In many of the questions and the corresponding notes, reference is made to the use of networks, 
systems, software, etc. The term ‘use’ does not refer to the ownership of such goods and 
infrastructure. For example, AI technologies may belong to the enterprise, may be leased or may 
be shared with another organisation. In the case where AI technologies are used by the 
responding enterprise, but provided or maintained by another enterprise, they should be 
considered as used by the responding enterprise.  

Additionally, enterprises often buy ICT services or services which relate to the use of ICT in the 
context of the survey (like accounting). In cases where ICT services or other services which 
include use of ICT are provided totally by another enterprise and the responding enterprise itself 
is not using ICT for that function, then it should not be counted as use of ICT of the responding 
enterprise.  

In some cases there will be a mixture of ICT usage by the responding enterprise and the ICT 

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service provider (another enterprise). In these cases it should be made clear from the question 
what kind of activities will be counted as ICT usage of the responding enterprise.  

Examples on the interpretation of special cases: In some questions the ICT involvement of 
the enterprise is not about ICT usage, but rather about whether the enterprise has or offers some 
digital solutions to its partners (customers or business partners). One example is the question 
about having a website (or websites). Here the question is about having, not using, and the 
subject is the solution that is offered to customers regardless of how the website(s) is technically 
maintained. The main issue about having a website is about control and responsibility for the 
contents in a separate area on the web. The enterprise’s web pages can be maintained and 
designed by a service provider, but if the responding enterprise ‘owns’ the contents, it is 
considered to be its own website. However, not all presence in the web means that the 
respondent has a website. Presence in certain service catalogues, e.g. yellow pages, address 
lists etc., are not counted as the enterprise’s website. E-commerce systems can also be provided 
and maintained by service providers. Similarly to the case of the website, if it is the responding 
enterprise’s e-commerce facility, the responding enterprise has control over the content of the 
site and is the one who is selling. If it is the owner of the products sold, then it is the respondent’s 
e-commerce. Internet marketplaces — where enterprises can sell their products — are a special 
case. Here the respondent does not own or control the site, but it does control the sale and own 
the products being sold and therefore it is their e-commerce (if e-commerce is a valid definition in 
the situation). 

2.3.2.2. Module A: Access and use of the internet 

Question A1: How many persons employed have access to the internet for business 
purposes? (including fixed line, fixed wireless and mobile telephone network connection) 

If you can’t provide this value, 

Please indicate an estimate of the percentage of the total number of persons employed who 
have access to the internet for business purposes. 

[Scope: all enterprises] 

[Type: numerical, absolute or percentage values; filter question] 

This variable refers to persons employed (employees and self-employed), including people 
working remotely, who have access to the World Wide Web (WWW) for business purposes from 
their workstation or from a computer to which they have free access. The following definitions 
applies: Computers include personal computers, portable computers, tablets, and other portable 
devices such as smartphones. 

Remote workers are persons employed who work from a remote location outside of the traditional 
office environment. This means that they telework and carry out work remotely with the help of 
digital tools. In most cases it refers to working from home, but it is not necessary, and based on 
enterprise’s remote work policy, which set of guidelines or an agreement between an employer 
and person employed. 

The aim is to identify how many persons employed have access to the WWW for business 

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purposes. This also includes the usage of private internet connections where the enterprise may 
reimburse fully or part of the costs. The reason for applying this specific internet service is that, 
from the several services that can be run on the internet, the WWW (as well as email) is the most 
common. Accessing the internet only through the email service is not considered in this variable, 
because email is already very common in Europe and it doesn’t effectively represent the potential 
in terms of access to information as much as the use of the WWW. 

This variable can be collected in absolute or percentage values. Eurostat recommends using a 
combination of both, giving the respondent the opportunity to choose to answer in absolute or 
percentage values. 

Independently of how this variable is collected, the background variable ‘Average number of 
persons employed, during the previous year’ (X2) is needed for grossing up the overall 
percentage of persons employed having access to the World Wide Web for business purposes. 

It can be observed in smaller enterprises that – in some cases – the owners of the enterprises 
are not considering themselves as “persons employed”. Make sure that those are included as 
well. 

Question A2: Does your enterprise use any type of fixed connection to the internet? (e.g. 
ADSL, SDSL, VDSL, fibre optics technology (FTTP), cable technology, fixed wireless) (add 
national examples) 

[Scope: enterprises with access to the internet, i.e. A1 > 0] 

[Type: single answer (i.e. Tick only one); binary (Yes/No); filter question] 

‘Connection’ means the type of the last mile connection of the enterprise (e.g. enterprise’s 
computer-mediated network) to the network of the internet service provider (ISP). The last mile is 
the final segment between an ISP’s infrastructure and a subscriber’s location for delivering 
communications (connectivity) to the enterprise. 

Note: From the 2020 model questionnaire onwards, the question does not explicitly mention 
‘broadband connection’ as few countries mentioned the marginal use of ‘narrowband’ for the 
connection to the internet. Narrowband connections are not within the scope of this question (all 
examples mentioned refer to broadband connections). Enterprises that still use only narrowband 
connections to the internet should tick ‘No’ to this question.  

Type of fixed internet connection: 

•  DSL connection e.g. xDSL, ADSL, SDSL, VDSL 

xDSL digital subscriber line (DSL) is a family of technologies that provide digital data 
transmission over the wires of a local telephone network. DSL service is delivered simultaneously 
with a regular telephone service on the same telephone line as it uses a higher frequency band 
that is separated by filtering. A DSL line can carry both data and voice signals and the data part 
of the line is continuously connected. 

The asymmetric digital subscriber line (ADSL), where more bandwidth is allocated to download 
than upload, and the high-bit-rate digital subscriber line (HDSL) are considered dominant DSL 
technologies. Symmetric digital subscriber line (SDSL) refers to either a DSL technology that 
offers symmetric bandwidth for upload and download or to a particular DSL variant where data 

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are only supported on a single line that does not support analogue calls. Very-high-bit-rate digital 
subscriber line (VDSL) is a DSL technology that offers faster data transmission. VDSL is capable 
of supporting high-definition television, telephone services (voice over IP (internet protocol)) and 
internet access over a single connection, among others. 

•  Fibre optics technology (FTTP), cable technology, etc. 

FTTP refers to ‘Fibre to the Premises’, also called Fibre to the Home (FTTH). It may be more 
correct to use FTTP: ‘fibre to the premises’ referring to both ‘home’ and ‘business’.  

•  Other high capacity ‘speed’ fixed (wired or wireless) connection includes the following 

types of internet connections: 

•  Cable modem ‘cable TV network connection’; 
•  High capacity leased lines ‘frame relay, automatic teller machine (ATM), digital multiplex’; 
•  Ethernet LAN connection; 
•  Optical fibre connection; 
•  Fixed wireless access (FWA) connections, e.g. satellite connection, public Wi-Fi connec-

tion, WiMax. 

Cable modem uses modems attached to cable television networks (cable TV lines) for perma-
nent fixed access to the internet. The term cable internet (or simply cable) refers to the delivery of 
an internet service over this infrastructure. A cable modem is a device that enables to hook up a 
personal computer (PC) to a local cable TV line. It is considered as one of the high-capacity 
speed permanent fixed internet connections. 

A leased line (dedicated line) is a telephone line that has been leased for private use. A leased 
line is usually contrasted with a switched line or dial-up line. Leased lines are usually available at 
speeds of 64k, 128k, 256k, 512k, 2 Mb (megabyte) and provided to the customer on X.21 presen-
tation. Frame relay protocol and T-1 and T-3 (in Europe called E1 and E3) lines are used for in-
ternet connection via leased lines. Higher speeds are available on alternative interfaces. 

High-capacity leased line is a permanent telephone connection between two points set up by a 
telecommunications common carrier. Typically, leased lines are used by businesses to connect 
geographically distant offices. Unlike normal dial-up connections, a leased line is always active. 
Because the connection doesn’t carry anybody else’s communications, the carrier can provide a 
given level of quality. For example, a T-1 channel is a type of leased line that provides a maxi-
mum transmission speed of 1 544 Mbit/s (megabits per second). The connection can be divided 
into different lines for data and voice communication or the channel can be used for one high-
speed data circuit. Dividing the connection is called multiplexing. Increasingly, leased lines are 
being used by enterprises, and even individuals, for internet access because they support faster 
data transfer rates and are cost-effective if the internet connection is heavily used. 

Fixed wireless internet connection (FWA) is a technology which uses radio frequency, infra-
red, microwave, or other types of electromagnetic or acoustic waves in place of wires, cables, or 
fibre optics to transmit signals or data (provide internet access) between stationary (fixed) points. 
It includes for example a satellite internet connection (long-range wireless transmission) or public 
Wi-Fi (medium-range wireless transmission). 

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Wi-Fi (or Wi-fi, WiFi, Wifi, wifi), short for ‘Wireless Fidelity’, is a set of Ethernet standards for 
wireless local area networks (WLAN) currently based on the IEEE5 802.11 specifications. New 
standards beyond the 802.11 specifications, such as 802.16 have been developed. They offer 
many improvements, from longer range to greater transfer speeds. Wi-Fi was intended to be 
used for wireless devices and local area networks (LAN), but is now often used for internet ac-
cess (one of the main international standards for wireless broadband internet access and net-
working, with widespread use in business, homes and public spaces). It is based on radio signals 
with a frequency of 2.4 GHz and theoretically capable of speeds of over 54 Mbit/s (at close range, 
some versions of Wi-Fi, running on suitable hardware, can achieve speeds of over 1 Gbit/s). It 
enables a person with a wireless-enabled computer or personal digital assistant to connect to the 
internet when close to an access point called a hotspot. Internet connection via mobile telephone 
networks is not included under this category. 

By public Wi-Fi, essentially refers not to the enterprise’s Wi-Fi (that may be xDSL, cable or fibre 
optic) but to public Wi-Fi, hotspots, hotzones that have different names in different countries. For 
example, in Luxembourg it is called HotCity and the coverage is very wide. National examples 
(i.e. WiFi or WiMax with their national brand names) would help respondents. 

Note: In case the enterprises are using only a mobile internet connection, e.g. 4G or 5G technol-
ogy as the only connection in the office (instead of a fixed line connection), then they should an-
swer NO in question A2, because it is mobile broadband technology even though it is used like 
fixed line connection to the internet only on one address. However, it was considered that a sepa-
rate question on the use of mobile internet connections was not necessary, as the information 
can be derived from the information provided by enterprises in questions A1 and A2. 

Question A3: What is the maximum contracted download speed of the fastest fixed line 
internet connection of your enterprise? 

(additional categories at national level can be added, if needed) 

[Scope: enterprises with fixed line connection to the internet, i.e. A2 = Yes] 

[Type: single answer (i.e. Tick only one)] 

Maximum contracted download speed means the maximum theoretical speed — according to the 
ISP’s contractual obligations — at which data can be downloaded. The five options offered are 
measured in Mbit/s (Mb/s or Mbps) or Gbit/s (Gb/s or Gbits). These stand for megabits per sec-
ond or gigabits per second and are a measure of bandwidth (the total information flow over a 
given time) on a telecommunications medium. Mbps is not to be confused with MBps (megabytes 
per second). Often the problem is that speed tests and ISP’s use ‘bits per second’ while down-
load agents/programs use ‘bytes per second’. Note that 1 Byte = 8 bits. 

The five options offered are: a) less than 30 Mbit/s; b) at least 30 but less than 100 Mbit/s; c) at 
least 100 but less than 500 Mbit/s; d) at least 500 but less than 1 Gbit/s; and e) at least 1 Gbit/s. 

Additional categories can be added at national level, if needed. 

Note that the actual bandwidth and download speeds experienced are dependent on a 

5 IEEE is the acronym for Institute of Electrical Electronics Engineers. 

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combination of factors including the ISP, the equipment and the software used, the internet traffic 
and the destination server. Hence, it may differ from the contracted download speed which is 
requested in this question. Respondents are not requested to run a speed test on their equipment 
in order to respond to the question. 

Enterprises (respondents) can obtain the information about the maximum contracted download 
speed of the fastest fixed line internet connection from their monthly invoices for 
telecommunication services (internet) or in the contract with the telecommunication services 
(internet) providers. 

Question A4: Is the speed of your fixed line connection(s) to the internet usually sufficient for 
the actual needs of the enterprise? 

[Scope: enterprises with fixed broadband connection to the internet, i.e. A2 = Yes]  

[Type: single answer (i.e. Tick only one); binary (Yes/No)]  
The objective for enterprises to fully satisfy their needs in terms of adequate internet connection 
is a key focus of Commission policies. Investment is necessary for improving the infrastructure for 
high-speed broadband connections. The purpose of the question is to ‘count’ how many 
enterprises consider that their connection speed to the internet adequately serves their needs. 
Essentially question A4 aims at measuring the degree of demand for improved connectivity to the 
internet as well as the enterprise’s overall appreciation of their current fixed internet connection. 
Responses to question A4 will be combined with respondents’ choices in A3 on contracted 
download speed. Since some enterprises may have premises in various geographic locations, 
and in addition, the speed of the connection to the internet may fluctuate (e.g. depending on the 
day or the time of the day) it is proposed to use the formulation ‘usually sufficient’. This would 
mean that the Yes/No possible responses would imply ‘Yes, in most of the cases speed is 
sufficient’ or ‘No, in most of the cases speed is not sufficient’. Although the question is linked to 
Commission policies, the results cannot be isolated and used for taking any kind of decision on 
investment which might be needed for improving the infrastructure for providing high-speed 
broadband connections. 

Use of a mobile connection to the internet for business purposes 

(Scope: enterprises with access to the internet, i.e. if A1 >0)  

The following question refers to the usage of portable devices connecting to the internet through 
mobile telephone networks for business purposes. This includes the use of devices where the 
enterprise provides the devices and pays for the subscription and the costs of use fully or at least 
up to a limit.  

In principle, these are portable devices that are provided allowing mobile connection to the 
internet via mobile telephone networks. In the scope of the survey are enterprises that provide 
portable devices as part of the employee’s equipment, provided that the conditions in the 
introduction of the module (payment of subscription and usage costs) are fulfilled and the 
portable devices allow mobile connection to the internet via mobile telephone networks. 
Additionally, ‘business use’ means that the purpose of the use is related to work.  

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Mobile and wireless connection to the internet: Question A5 clearly refers to ‘mobile6 connection 
to the internet’ through mobile telephone networks (either within or outside the premises of the 
enterprise) which should not be confused with ‘wireless connection to the internet’ (e.g. Wi-Fi, 
Bluetooth, to a certain extent WiMAX). The exclusive use of wireless connection to the internet is 
excluded either within the premises of the enterprise or within the range of a hotspot or hot-zone 
outside the premises of the enterprise.  

From a practical point of view, portable devices can switch between mobile (telephone networks) 
and wireless connections (Wi-Fi, Bluetooth, and WiMax). However, the intention is to measure 
enterprise’s take up of ‘mobile connection of the internet’ when portable devices connect to the 
internet over mobile telephone networks, regardless of the fact that devices may switch/ connect 
to wireless networks and regardless of the technology used (4G or more advanced, but also less 
than 3G in the absence of adequate signal) for accessing the mobile telephone networks.  

Portable devices: The portable devices that allow a mobile connection to the internet refer to 
laptops, tablet computers or smartphones.  

‘Smartphones’ qualify as computers and were developed to provide functionalities beyond those 
of cellular phones (cell phones or mobile phones) which served only one purpose: voice 
communications and exchange of SMS/ MMS. Smartphones and similar devices are equipped 
with customised software, increased bandwidth efficiency, internet access, digital cameras, 
portable music players, global positioning system (GPS) functions and many more — business or 
entertainment — options despite their relatively small size.  

However, growth in demand for portable devices requiring processors that are more powerful, 
abundant memory and larger screens has placed low-power portable devices at the centre of 
interest. Netbooks, notebooks, and tablet computers equipped with built-in card modem (SIM-
card) or external dongles (USB modem), have considerably extended the family of ‘mobile 
connectivity’ devices to compact size ‘portable computers’.  

The use of handsets exclusively for voice/SMS/MMS communications (e.g. enterprises requiring 
that persons employed are available to take business calls at all times and anywhere) are not in 
the scope of questions A5. Additionally, M2M (machine-to-machine) communications should not 
be considered in the scope of the mobile use of the internet.  

In certain cases, a portable computer (e.g. laptop or tablet computer) can be connected to a 
handset (e.g. via Bluetooth, cable) and the handset can be connected to the internet through a 
mobile connection (via the mobile telephone network). The portable computer is considered the 
device connected to the internet. In this case, the handset is the equivalent to a USB modem or a 
card modem. Moreover, the portable computer is the device used for accessing the business 
software application (or any application) via the internet and not the handset. 

Question A5: How many persons employed use a portable device provided by the enterprise, 

6 The network type referring to the ‘Mobile technology’ is the wireless wide area network (WWAN). It provides high-speed internet ac-
cess through portable devices and coverage of the national territory in each country. It is considered to provide full telephone network 
coverage. The exclusive use of the Wireless Local Area Networks is not in the scope of questions A5. 

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that allows connection to the internet via mobile telephone networks, for business purposes? 

(e.g. portable computers or other portable devices such as smartphones) 

If you can’t provide this value, 

please indicate an estimate of the percentage of the total number of persons employed who 
use a portable device provided by the enterprise, that allows connection to the internet via 
mobile telephone networks, for business purposes (e.g. portable computers or other portable 
devices like smartphones). 

[Scope: enterprises with access to the internet, i.e. A1 > 0; optional question]  

[Type: numerical, absolute or percentage values]  

The question refers to enterprises providing portable devices that allow (through a 
contract/subscription) mobile connection to the internet. Portable computers using mobile 
telephone networks may refer to notebooks, netbooks, laptops, tablet computers, etc. while other 
portable devices may refer to smartphones using mobile telephone networks. Mobile connection 
to the internet refers to connection over mobile telephone networks.  

Technically, a connection via high capacity ‘speed’ mobile telephone networks refers to 
accessing the internet using a long range wireless transmission of the 3rd / 4th or even 5th 
generation (3G, 4G, 5G) mobile network technologies based on the code division multiple access 
(‘CDMA’) as UMTS (universal mobile telephone system — Wideband W-CDMA); CDMA2000x; 
CDMA 2000 1xEV-DO; CDMA 2000 1xEV-DV).  

•  3G (or 3-G) is short for third-generation mobile telephone technology. The services 

associated with 3G provide the ability to transfer both voice data (a telephone call) and non-
voice data (such as downloading information, exchanging e-mail and instant messaging). It 
includes high-speed mobile networks (e.g. CDMA2000 1X, W-CDMA, CDMA2000 1xEV-DO).  

•  4G is the fourth generation of cellular wireless standards (mobile telephone technology). It is 
a successor to the 3G and 2G families of standards. The International Telecommunication 
Union (ITU) Radiocommunication Sector specified the international mobile 
telecommunications advanced requirements for 4G standards, setting peak speed 
requirements for 4G service at 100 Mbit/s for high mobility communication (such as from 
trains and cars) and 1 Gbit/s for low mobility communication (such as pedestrians and 
stationary users).  

•  5G is the fifth generation technology standard for broadband cellular networks, which cellular 
phone companies began deploying worldwide in 2019, and is the successor to the 4G. The 
main advantage of the new networks is that they will have greater bandwidth, giving higher 
download speeds, eventually up to 10 gigabits per second (Gbit/s).  

•  UMTS is one of the 3G mobile phone technologies. It uses W-CDMA as the underlying 

standard. It is standardised by the 3GPP, and represents the European answer to the ITU 
IMT-2000 requirements for 3G cellular radio systems. It presently delivers packet switched 
data transmission speeds up to 384 Kbit/s and up to 2 Mbit/s when fully implemented.  

•  CDMA2000 1x is an IMT-2000 3G mobile network technology, based on CDMA that delivers 

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packet switched data transmission speeds of up to 144 kbps. It is also referred to as 1XRTT.  

•  CDMA2000 1xEV-DO is an IMT-2000 3G mobile network technology, based on CDMA that 

delivers packet switched data transmission speeds of up to 2.4 Mbps.  

The concept of ‘persons employed’ to be used in this variable is the one described in the 
explanatory note of variable X2 (‘Average Number of number of employees and self-employed 
persons (Persons Employed)’).  

This variable can be collected in absolute or in percentage values. Eurostat recommends using a 
combination of both, giving the respondent the opportunity to choose either to answer in absolute 
values or percentage values. The absolute value might be more appropriate for smaller 
enterprises and the percentage for bigger ones. When the number of persons employed is small, 
it is easier for respondents to identify which ones use a portable device provided by the 
enterprise, that allows internet connection via mobile telephone networks, for business purposes, 
instead of having to calculate the percentages afterwards. Collection in percentage terms may 
introduce measurement errors, because of the rounding that is necessary to provide an answer in 
percentages made of integer numbers between 1 and 100. Collection in percentage terms may 
also introduce other measurement errors as it leads the respondent to make an educated guess 
of its value introducing the bias usually associated with this type of answer. On the other hand, 
for bigger enterprises, with high numbers of persons employed, the identification of every worker 
using a portable device provided by the enterprise, that allows internet connection via mobile 
telephone networks, for business purposes can be burdensome or not possible and result in non-
response. In this case, the collection of this information in percentage terms may be preferable. 

Independently of how this variable is collected, the background variable ‘number of employees 
and self-employed’ is needed for grossing up the overall percentage of persons employed who 
use portable devices provided by the enterprise, that allows internet connection via mobile 
telephone networks, for business purposes. In order to compute the overall percentage, both the 
total number of employees and self-employed persons and the total number of persons employed 
using a portable device provided by the enterprise that allows internet connection via mobile 
telephone networks, for business purposes need to be grossed up. If collected in percentage 
terms, the corresponding absolute value needs to be calculated afterwards multiplying the 
answer with the background variable to gross up the number of persons employed using a 
portable device provided by the enterprise that allows internet connection via mobile telephone 
networks, for business purposes.  

This question refers to persons employed using portable devices (within or outside the premises) 
that allow internet access via mobile telephone networks for business purposes, provided by the 
enterprise permanently or temporary (e.g. when needed or upon request). The question refers to 
persons (not to devices), hence it is suggested to request an estimate of persons provided with 
devices. Portable devices provided when needed or upon request e.g. meetings away from office 
premises, business trips, participation at conferences, provision of maintenance and on-site 
support to customers, are in the scope of the question. Cases in which persons employed use 
portable devices without accessing the internet (but devices allow for it) are in scope too.  

A mobile internet connection for the enterprise owner(s) should be included in A5. 

Remote access and meetings via the internet 

(Scope: enterprises with access to the internet, i.e. if A1 >0) 

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Remote access 

This set of questions aims to measure enterprises’ readiness, capacity or willingness to make it 
possible for their employees to work remotely by allowing them a remote access to enterprise’s 
resources (e.g. remote e-mail access, remote access to documents and ICT systems of the en-
terprise). 

The policy interest for indicators relating to the technological readiness of the enterprises to apply 
teleworking and/or use virtual meetings originates from the experience due to the Covid-19 
pandemic. However, remote working and virtual meetings are not temporary phenomena. The 
recent experience has proven that the ever-increasing capabilities of ICT have made in many 
cases teleworking (full or partial) as efficient as working in the office even in businesses where 
this was not considered before. Therefore, the voices highlighting the economic (less costs for 
enterprises), environmental (less commuting) and social (work-life balance) benefits of 
teleworking have increased substantially. Therefore, there is a need to understand the remote 
working readiness of European enterprises (in terms of facilities provided to their employees), the 
percentage of employees that could potentially telework and the uptake of this new way of 
working in Europe. 

Question A6: Do any of the persons employed have remote access to the following? (via 
computers or portable devices such as smartphones) 

[Scope: enterprises with access to the internet, i.e. A1 > 0]  

[Type: single answer per item (i.e. Tick only one); binary (Yes/No); multiple items may be 
expected]  

Question A6 measures the technological readiness of the enterprises to apply teleworking by 
providing their employees with remote access to the enterprise’s resources.  

The question does not ask about the use, but about the access, so the possibility to remotely 
connect to enterprise’s resources.  

The access may be via any device, desktop computer or any portable device, including 
smartphones. There is no limitation in the scope concerning the ownership of the device, it covers 
access through a private device (owned by the employee) as well as a corporate device (pro-
vided by the enterprise).  

a)  E-mail system of the enterprise 

Enterprises, which provide a remote access to enterprise’s e-mail system for their employees, 
should answer ‘Yes’ to this answer option. If the official e-mail system of the enterprise is for in-
stance Gmail (or other), which can be accessed anywhere, then the enterprise should tick ‘Yes’ 
to question A6a).  

b)  Documents of the enterprise (e.g. files, spreadsheets, presentations, charts, photos) 

This answer option covers a remote access to enterprise’s documents, including spreadsheets, 
presentations, or any other files. This item should also be ticked ‘Yes’ if only a limited number of 
documents can be accessed remotely. The item does not include in scope cases of accessing 
the documents (e.g. files, spreadsheets, presentations, charts, photos) via e-mail mailbox (saved 
in a sent or received email).  

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c)  Business applications or software of the enterprise (e.g. access to accounting, sales, 

orders, CRM)  

Please exclude applications used for internal communication, e.g. Skype, Teams, Viva 
Engage (old Yammer).  

Answer option c) includes remote access to enterprise’s business applications or software used 
by the enterprise, such as application or software related to accounting, sales or other business 
software. This item should also be ticked ‘Yes’ if only a limited number of business applications or 
software can be accessed remotely. The use of applications for internal communication, such as 
Skype, MS Teams, Viva Engage (old Yammer) are excluded from the scope of answer option c). 

Meetings via the internet 

The question aims to collect information about conducting meetings via the internet (remote 
meetings) by enterprises.  

Telework and in particular virtual meetings are very relevant topics, also when looking at the 
Covid-19 pandemic which has forced many enterprises globally to turn to teleworking and/or vir-
tual meetings, in order to maintain their businesses while respecting confinement and social dis-
tancing measures imposed by national governments.  

This indicator is also relevant in the context of the use of digital technologies to build a smarter 
and more sustainable economy. 

Question A7: Does your enterprise conduct remote meetings (via e.g. Skype, Zoom, MS 
Teams, WebEx)? 

[Scope: enterprises with access to the internet, i.e. A1 > 0]  

[Type: single answer (i.e. Tick only one); binary (Yes/No)]  

Question A7 aims measuring whether enterprises conduct remote (online) meetings via internet 
using tools such as Zoom, Skype, MS Teams or others. The remote meeting can be internal 
(between persons in the enterprise, whether they are located in the office or outside) or external 
(with persons from outside the enterprise e.g. business partners). Remote meetings can be 
conducted either via video, via audio only or via both, video and audio. Both, remote meetings 
conducted via dedicated apps or via browsers are included. The video meetings may be 
conducted via any device, desktop or any portable device, including smartphones. 

Other use of the internet 

Question A8: Does your enterprise pay to advertise on the internet? (e.g. adverts on search 
engines, on social media, on other websites or apps) 

[Scope: enterprises with access to the internet, i.e. A1 = Yes] 

[Type: single answer per item (i.e. Tick only one); binary (Yes/No); filter question; optional 
question] 

The question refers to enterprises using paid adverts (ads) on the internet to advertise their 

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goods or services. For example, enterprises may pay for their ads to appear at the top of the 
search results of various search engines, on social media banners, etc. when relevant search 
terms were entered. 

Question A9: Does your enterprise pay to advertise on the internet using any of the following 
targeted advertising methods? 

[Scope: enterprises that pay to advertise on the internet, i.e. A8 = Yes] 

[Type: single answer per item (i.e. Tick only one); binary (Yes/No); optional question] 

The following responses refer to targeted advertisement methods. In general, advertisement 
methods that are used over the internet offer enterprises more possibilities to reach their target 
audience. It is possible that enterprises reply to all four answer options with “no”, meaning that an 
enterprise pays for ads (A8=Yes) without using any of the proposed targeting methods. 

a) Based on content or keywords searched by internet users 

Response a) refers to the use of technologies that adapt adverts to the contents of the displayed 
pages (contextual advertising). Internet users are presented with adverts that automated systems 
have selected for them according to the content of the webpage or based on keywords that users 
have searched on the internet. For example, enterprises may pay for having their ads at the top 
of results for relevant search terms of various search engines, on social media banners, etc. 
Adverts based on the context of the webpage may be "push" type advertisements or "pull" 
advertisements. For the former, adverts are shown regardless of the need of the user while for 
the latter the user was actively seeking marketing content (e.g. through keywords) and thus the 
advert is shown.  

b) Based on the tracking of internet users' past activities or profile 

Response b) refers to the use of technologies for collecting information about the online 
(browsing) activity of internet users (behavioural advertising, web browsing habits). Tracking 
cookies (files stored on internet users’ computers when visiting a website) are used to help 
determine the users' interests based on the web pages that were visited, the content that users 
browsed and other actions that were taken online. The aim is to deliver adverts tailored to the 
user's interests and preferences. 

c) Based on the geolocation of internet users 

In the context of using the internet for ads, advertising based on the geolocation of internet users, 
refers to the use of technology (geo targeting) that regardless of the device (portable or not) and 
the connection to the internet (fixed incl. wireless or mobile) may be used in order to deliver ad-
vertisements based on the geographical location (e.g. hotels in the proximity). Determination of 
geolocation of the user is used by classical Google advertisement and Facebook advertisement. 
Geolocation is particularly used in cases of "push" advertisement via apps on mobile devices. 
However, it is reminded that the current scope is broader than mobile advertisement that may be 
implied by the use of apps on mobile devices according to the terminology that is used in the 
questionnaire. Technically, the important issue would be the proper identification of the internet 
protocol (IP) address of the connection to the internet through the closest tower antenna or 
router. The use of geo targeting technologies for advertising can be combined with the use of 
cookies (behavioural advertising, web browsing habits) in order to precise the need of a potential 
customer. 

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d) Any other method of targeted advertising on the internet not specified above 

Other methods of internet advertising refer to the use of paid ads for delivering marketing 
messages to consumers that are not covered by the above mentioned dynamic methods (such as 
advertisements targeting specific websites that have different demographic characteristics – e.g. 
general population, younger or older persons, men or women or retargeting – targeting audiences 
who have visited website or social media profiles, paid advertising articles or videos, paid 
cooperation with You Tubers or well-known personalities (influencers)).  

A combined use of the above mentioned technologies is possible. 

2.3.2.3. Module B: e-Commerce sales 
This module covers e-commerce sales (received orders) which are conducted via a website or 
apps or as EDI (electronic data interchange)-type sales. Since the 2011 survey, the 
measurement of e-commerce sales is done as a split between web sales and EDI-type sales 
which are separate submodules and comprise mandatory reporting variables. 

One important difference in this module, compared with most other variables in the questionnaire, 
is that systematically the reference period is the previous calendar year, instead of the current 
situation. Flow economic variables like turnover and purchases – the main variables to measure 
in e-commerce – need to be measured for a longer period instead of at just one point in time. In 
order to keep comparability with the main business statistics, the calendar year is taken as 
reference period. 

Definition of e-commerce  

In order to ensure international comparability of the ICT usage in enterprises statistics, the OECD 
definition of e-commerce is used as a basis throughout this module but referring merely to sales: 

The 2009 definition of e-commerce 

OECD definition of e-commerce 

Guidelines for the interpretation 

Include: orders made in web pages, extranet 
or EDI. The type is defined by the method of 
making the order.  

Exclude: orders made by telephone calls, 
facsimile, or manually typed e-mail. 

An e-commerce transaction is the sale or 
purchase of goods or services, conducted 
over computer networks by methods 
specifically designed for the purpose of 
receiving or placing of orders. The goods or 
services are ordered by those methods, but 
the payment and the ultimate delivery of the 
goods or services do not have to be 
conducted online. An e-commerce 
transaction can be between enterprises, 
households, individuals, governments, and 
other public or private organisations. 

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The framework for measurement 

Type 

Definition of the type of e-commerce 

Web e-commerce 

Orders made at an online store (web shop) or via web forms on the 
Internet or extranet regardless of how the web is accessed (computer, 
laptop, mobile phone, etc.) 

EDI e-commerce 

Orders initiated with EDI. EDI is an e-business tool for exchanging 
different kinds of business messages. EDI is here used as a generic 
term for sending or receiving business information in an agreed format 
which allows its automatic processing (e.g. EDIFACT, XML, etc.) and 
without the individual message being manually typed. “EDI e-
commerce” is limited to EDI messages placing an order. 

E-commerce sales may also include renewal orders (standing orders/recurrent 
payments/subscriptions) received in the reference period, which are associated with the initial 
order made via e-commerce channels before the reference year. It has been agreed that such 
standing orders are within the scope of the e-commerce module. However, experiences provided 
by statistical compilers show that measuring this would be challenging, because many 
enterprises cannot track the information needed to distinguish if such standing orders are linked 
to an initial order made via an e-commerce channel. Therefore, it has been decided not to add 
any explicit clarification on that matter in the questionnaire (neither model questionnaire, nor 
national questionnaires). If a respondent asks whether they should include such order/payments 
in the reference period, which are resulting from an initial order made before the reference period, 
they should be advised that standing orders/recurrent payments/subscriptions received in the 
reference period should be reported by them within the e-commerce module. 

Defining e-commerce sales in the questionnaire  

The e-commerce definition is implemented as follows: 

In e-commerce sales of goods or services, the order is placed via web sites, apps or 
EDI-type messages by methods specifically designed for the purpose of receiving orders.  

The payment may be done online or offline.  

e-Commerce does not include orders written in e-mail. 

Defining web sales in the questionnaire  

Web sales cover the following: 

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•  Online store (web shop) is the most obvious and clear example of web-based e-commerce. 
It is a separate site in the web or a separate part of a website where products are presented 
and typically ordered via shopping cart functionality. 

•  Web forms are simple forms integrated in the enterprises’ website where goods and services 
can be ordered. In these forms, the order can be typed or clicked/selected and then sent it by 
clicking the “send” button on the web site. 

•  Extranet is a closed environment for agreed partners or customers where other information     
between those parties can be accessed or exchanged. If there are sales done in extranet, it is 
counted as web sales. In extranet, the actual shopping can be done either in a web shop or 
via web forms, as explained above. 

•  Bookings and binding/fulfilled reservations are considered similar to orders; for some        
economic sectors (e.g. NACE Rev. 2, sector 55 Accommodation), these terms are more        
commonly used to describe the order. The questions in this module are about sales (and         
generated turnover), therefore the scope is by default limited to the fulfilled sales, orders, and  
reservations. Reservations and orders that are not fulfilled are outside the scope of this     
module. 

•  Sales via apps for mobile devices or computers are also counted as web sales. App is a 

short name for web application. There are two types: the browser-based and the client-based 
web applications. The former are web applications accessible over a webpage (html and Ja-
vaScript) and run within the web browser. The latter are installed on a device (e.g. 
smartphone), run without going through a browser but using web protocols. 

•  Sales via external e-commerce marketplace websites or apps are also counted as enter-
prise web sales. E-Commerce marketplaces are external websites or apps used by several 
enterprises for trading (selling) their goods or services to customers. 

Defining EDI-type sales in the questionnaire 

In the beginning of the submodule on EDI-type sales, the concept of EDI-type sales is explained 
for the respondents. 

EDI can be defined as the transfer of structured data, by agreed message standards, from one 
computer system to another without human intervention. EDI provides a technical basis for auto-
mated commercial ‘conversations’ between two entities, either internal or external. The term EDI 
encompasses the entire electronic data interchange process, including the transmission, mes-
sage flow, document format, and software used to interpret the documents. 

Demand-driven orders concern situations where, for example, a certain minimum level of inven-
tory has been specified in the system of the buying enterprise; when the inventory drops below 
the specified minimum level, the system transmits an EDI message to the selling enterprise for 
more goods to be delivered. 

This is an example of an advanced, but in certain activities common, way of business automation 
through the automated integration of systems from two trading partners. 

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Questions on web sales of goods or services 

Web sales cover orders, bookings and reservations placed by your customers via 

•  your enterprise’s websites or apps:  

•  online store (web shop)  
•  web forms 
•  extranet (web shop or web forms) 
•  booking/reservation applications for services 
•  apps for mobile devices or computers  

•  e-commerce marketplace websites or apps (used by several enterprises for trading goods 

or services). 

Orders written in e-mail are not counted as web sales. 

Question B1: During 2023, did your enterprise have web sales of goods or services via: 

[Scope: enterprises with access to the internet, i.e. A1 > 0] 

[Type: single answer per item (i.e. Tick only one); binary (Yes/No); multiple items may be 
expected] 

a)  your enterprise's websites or apps? (including extranets) 

b)  e-commerce marketplace websites or apps used by several enterprises for trading 
goods or services? (e.g. e-Bookers, Booking, hotels.com, eBay, Amazon, Amazon 
Business, Alibaba, Rakuten, TimoCom) 

[Please add national examples of e-commerce marketplaces including government mar-
ketplaces] 

The question aims to quantify the use of electronic marketplaces by enterprises (intermediary e-
commerce websites or apps) for receiving orders via various web platforms in the context of en-
terprise web sales. The question is relevant for developing policy initiatives addressing market 
competition issues, dominant positions of e-commerce players that are active in marketplaces, 
and (discriminatory) business models in various e-commerce marketplaces. 

The term ‘e-commerce marketplace’ refers to websites or apps used by several enterprises for 
trading products (e.g. e-Bookers, Booking, hotels.com, eBay, Amazon, Amazon Business, 
Alibaba, Rakuten, TimoCom, Glovo, Wolt). Amazon Business is a marketplace on Amazon.com 
that is addressed to business-to-business (B2B) commercial transactions. 

TimoCom runs the largest transport platform in Europe (it operates in 44 European countries). 
Services on this platform include: transport orders (to request orders digitally and manage 
transport orders); freight exchange (to avoid empty runs); vehicle offers; fixed contracts (transport 
tenders platform); and warehousing exchange. 

Amadeus is not an example of an e-commerce marketplace. It is a platform that connects travel 
providers and travel sellers all over the globe, offering search, pricing, booking, ticketing, transac-
tion and servicing capabilities. It mainly provides services designed for enterprises within the 
travel sector and it builds the technology that enables travel agents and websites to book airline, 
rail, cruise and ferry tickets, hotel rooms, car hire, tour packages and more. Travel agencies can 
build their services on top of Amadeus, e.g. their own platforms. Amadeus is not a marketplace, 
but rather an infrastructure. 

Note that e-commerce marketplaces are different from e-commerce platforms, e.g. Shopify, 
WooCommerce, Magento, Bigcommerce, that provide scalable, self-made online solutions for 

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business that would like to set up their own e-commerce website. 

The following should not be considered as e-commerce marketplaces: 

a)  a website or app of an enterprise selling the enterprise's own products; 

b)  a website or app of one seller acting as a distributor, selling other enterprises' specific prod-

ucts; 

c)  a website that provides e-commerce solutions for other enterprises to install for the enter-

prises’ own e-commerce functionality; and 

d)  a website that focuses on non-trading activities like collaborative design. 

The issue of translating accurately the term ‘e-commerce marketplaces’ in other languages is im-
portant. Other alternatives might work better (e.g. internet-based trade platforms) in other lan-
guages. 

Question B2: What was the value of your web sales? (please refer to the provided definition 
of web sales) 

[Scope: enterprises which had web sales of goods or services, i.e. B1a = Yes or B1b = 
Yes] 

[Type: numerical, absolute or percentage values] 

Please answer to a) OR b) 

Please answer to a) OR b) 

a)  What was the value of your web sales of goods or             

excluding VAT) 

(National currency,                

services, in 2023? 

OR 

b)  What percentage of total turnover was generated by 

web sales of goods or services, in 2023? 

If you cannot provide the exact percentage an approximation 
will suffice. 

________________ 

└┘ └┘ └┘ , └┘ % 

For turnover, the structural business statistics (SBS) definition is to be used. See background var-
iable X3 in this document. 

Collecting electronic orders in percentage values has the advantage of allowing to obtain an esti-
mate from respondents who don’t have any records in the enterprise that can provide this value. 
Therefore, unless specific records of all these transactions are kept in a centralised form, it may 
be difficult for the enterprise to provide the value of orders received that were placed via a web-
site or app. Another way of coping with this problem is to allow enterprises to estimate the web 
sales turnover in monetary terms by indicating that ‘an approximation will suffice’. 

For enterprises with a very small share of e-commerce in turnover, there is evidence that re-
spondents round this significantly to around 1%. Therefore, when collected in this way as a 

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percentage figure, a relatively large proportion of the values collected are 1%. Another challenge 
of this method is that, for big enterprises, a value of less than 1% can still be a significant amount 
in value terms. When answers from big enterprises are involved, this can bring a significant insta-
bility to the results. 

In addition, larger percentage shares are often also reported rounded to multiples of 10% or 5%. 

For these reasons the answer should be provided in decimals. 

Due to challenges in collecting data in percentage figures, the preferred way to collect value of 
sales via a website or app is in absolute values. Unfortunately, for many enterprises, this value is 
not available. For this reason, the recommended method is to ask the enterprise to answer in pre-
cise absolute values if it has the information. Or as an alternative, if such information is not avail-
able, an estimate could be asked, either in monetary terms or as a percentage of the total turno-
ver. 

Currently some countries ask for the absolute value while others ask for the percentage value. 
The current formulation aims to provide an alternative in order to avoid the bias that might exist 
from asking only one of the two ways, i.e. underestimation in the case of the absolute value and 
overestimation in the case of percentage values. It may be preferable to give the option to en-
terprises for using the one for which they can provide the more accurate answers. 

Eurostat’ preferred practice is to get the grossed-up value of the percentage, i.e. the percentage 
of turnover resulting from orders received that were placed via a website or apps. 

In a situation where an enterprise (e.g. airline) gets commissions from sales (e.g. car rental) 
made from another enterprise (e.g. car rental enterprise) via its (air enterprise) website or app, 
the commissions should be counted as e-commerce. 

Question B3: What was the percentage breakdown of the value of web sales in 2023 for the 
following: (Please refer to value of web sales you reported in B2) 

If you cannot provide the exact percentages an approximation will suffice. 

a)  via your enterprise's websites or apps? (including extranets) 

b)  via e-commerce marketplace websites or apps used by several enterprises for trading 
goods or services? (e.g. e-Bookers, Booking, hotels.com, eBay, Amazon, Amazon 
Business, Alibaba, Rakuten, TimoCom) 

[Please add national examples of e-commerce marketplaces including government 
marketplaces.] 

[Scope: enterprises which had web sales of goods or services via own websites or apps 
and via e-commerce marketplaces, i.e. B1a = Yes and B1b = Yes] 

[Type: numerical, percentage values that add up to 100%] 

In the case of a web questionnaire, it is recommended that B1 serves as a ‘smart filter’. B3 
should only be answered if both B1 a) and B1 b) have been answered with ‘Yes’. If only one of 
the two has been answered with ‘Yes’, then the respective value for B3 should be stored as 100 
% and the respondent should go to B4. 

The sum of answer options a) and b) should always result in 100%. 

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Question B4: What was the percentage breakdown of the value of web sales in 2023 by type 
of customer: (Please refer to value of web sales you reported in B2) 

If you cannot provide the exact percentages, an approximation will suffice. 

a) Sales to private consumers (B2C) 

b) Sales to other enterprises (B2B) and Sales to public sector (B2G) 

Total: 100% 

[Scope: enterprises which had web sales of goods or services, i.e. B1a = Yes or B1b = 
Yes] 

[Type: numerical, percentage values that add up to 100%] 

For respondents having received orders that were placed via a website or app, a percentage 
breakdown of the turnover by type of customer is requested. The two most important types of e-
commerce occur in business-to-consumer (B2C) and business-to-business (B2B) markets. 

a)  Sales to private consumers (B2C) 

The term B2C stands for business-to-consumer and refers to electronic commerce transactions 
between enterprises and the individuals as the end consumer. Business-to-consumer electronic 
commerce typically takes the form of websites or apps that offer the possibility to individuals to 
place orders for products. 

b)  Sales to other enterprises (B2B) and Sales to public sector (B2G) 

The term B2B stands for business-to-business and refers to electronic commerce transactions 
between enterprises (different from transactions between enterprises and other groups, like con-
sumers (individuals) and the government). B2B refers to commercial transactions between the 
responding enterprise and other enterprises (e.g. manufacturer and a wholesaler, a wholesaler 
and a retailer). Business-to-business electronic commerce typically takes the form of processes 
between trading partners and is performed in higher volumes than business-to-consumer applica-
tions (e.g. use of e-marketplaces or via the respondent’s websites using login/password proce-
dures). B2B e-marketplaces connect buyers and sellers through a hub where online transactions 
can be executed. The distinction of e-commerce between B2B and B2C is important because 
B2B transactions have advantages for enterprises, e.g. reduction of product cycle times, lowering 
stock levels and increasing trade volumes. 

B2G stands for business-to-government and includes the electronic commercial transactions be-
tween the responding enterprise and public authorities that are conducted via a website or apps. 

Question B5: During 2023, did your enterprise have web sales to customers located in the 
following geographic areas? 

a)  Own country  

b)  Other EU countries  

c)  Rest of the world  

[Scope: enterprises which have received orders that were placed via a website or apps, i.e. 
B1 = Yes]  

[Type: one single answer per item needed, i.e. Tick only one; binary (Yes/No); multiple 
items may be expected]  

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In this question, the respondent enterprise that had web sales (via enterprises websites or apps 
and/or e-commerce marketplace websites or apps used by several enterprises for trading goods 
or services), during 2023, is asked about the location of their customers. Three answer options 
are provided, namely:  

a)  Own country: the customer is located in the same country as the enterprise;  

b)  Other EU countries: the customer is located in one of the other EU countries (any of the 27 

EU countries other than the country of the enterprise);  

c)  Rest of the world: the customer is located outside the EU27 member states.  

The respondents shall tick all options that apply. 

Question B6: What was the percentage breakdown of the value of web sales in 2023 to 
customers located in the following geographic areas? 

(Please refer to value of web sales you reported in B2)  

If you cannot provide the exact percentages an approximation will suffice.  

[Scope: enterprises received orders placed via a website or apps, i.e. B1 = Yes; optional 
question]  

[Type: numerical, percentage values that add up to 100%] 

 Own country 

 Other EU countries 

 Rest of the world 

└┘ └┘ └┘ , └┘ % 

└┘ └┘ └┘ , └┘ % 

└┘ └┘ └┘ , └┘ % 

For respondents that had web sales (via enterprises websites or apps and/or e-commerce mar-
ketplace websites or apps used by several enterprises for trading goods or services), during 
2023, a percentage breakdown of the turnover by location of customer is requested. The same 
three location categories as in question B5 are used, namely: a) Own country, b) Other EU coun-
tries and c) Rest of the world.  

The respondent is requested to answer to this question with an approximation, if the exact per-
centages are not available.  

In case of implementing a web questionnaire, it is recommended that B5 serves as a ‘smart filter’. 
B6 should only be answered if at least two of the possible responses in question B5, a), b) or c) 
have been answered with ‘Yes’, otherwise question B7 should be answered. 

Questions on EDI-type sales 

EDI-type sales cover orders placed by your customers via EDI-type messages, meaning: 

• 

in an agreed or standard format suitable for automated processing; 

•  EDI-type order message created from the business system of the customer; 

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• 

• 

• 

including orders transmitted via EDI-service provider; 

including automatic system-generated demand-driven orders; 

including orders received directly into enterprise resource planning (ERP) system. 

Examples of EDI: EDIFACT, XML/EDI (e.g. UBL, Rosettanet, [please add national examples]) 

Question B7: During 2023, did your enterprise have EDI-type sales of goods or services? 

[Scope: enterprises with access to the internet, i.e. A1 > 0] 

[Type: single answer (i.e. Tick only one); binary (Yes/No); filter question] 

Question B8: What was the value of your EDI-type sales? (please refer to the provided 
definition of EDI-type sales) 

[Scope: enterprises which had EDI-type sales of goods or services, i.e. B7 = Yes] 

[Type: numerical, absolute or percentage values] 

Please answer to a) OR b) 

a)  What was the value of your EDI-type sales of goods or services, in 2023? (National cur-

rency, excluding VAT) 

b)  What percentage of total turnover was generated by EDI-type sales of goods or ser-

vices, in 2023? 

If you cannot provide the exact percentage, an approximation will suffice. 

For turnover, the SBS definition is to be used. See background variable X3 in this document. 

Please refer to the definition of EDI in the beginning of Chapter 2.3.2.3. and the guidelines below. 

Guidelines for the distinction of e-commerce, web or EDI type in specific cases 

1.  Distinction between EDI-type sales and web sales in a situation where both technologies are 

involved 

Case/situation: 

The responding enterprise has a website or app with sales functionalities. The customer chooses 
the product and the transaction is completed by pressing the Send button. The website might cre-
ate an EDI-type message that is sent: to the sales department to prepare electronically the trans-
portation documents and the goods; to the accounting department to prepare the electronic in-
voice; and to the department responsible for dispatching to prepare the planning of the dispatch 
of the goods. The same case can be replicated if the customer is not using the enterprise’s web-
site or app but the website or app of an online shop which produces the same EDI messages. 

Classification/explanation: 

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According to the e-commerce definition7, this is referred to as web sales because the order was 
placed via the enterprise’s website or app (or a website of a third party selling on behalf of the en-
terprise – online store) even if the enterprise received it as an EDI-type message. The reporting 
enterprise should avoid double counting the sales. 

2.  Selling credit online via apps, e.g. on mobile phones. 

This concerns: 

a)  Reporting enterprises whose principle economic activity is selling credit: the fact that 
they sell credit over the internet should be broadly considered as e-commerce. It should be 
considered as a commercial transaction, similar to selling vouchers (with/without specifying 
product) and it would be initially registered as liability towards other enterprises (e.g. retailers, 
wholesalers, service providers). The ‘service fee’ should be registered as turnover when the 
final client ultimately uses the credit/voucher and orders the products. It may be that, for taxa-
tion purposes, there is a ‘timing issue’ for registering the ‘service fee’ before the final client 
uses the credit/voucher. 

b)  Reporting enterprises that sell credit/vouchers and do not refer to any of their specific 
products (e.g. gift cards with a certain preloaded amount): From the accounting perspec-
tive, selling credit over the internet is not e-commerce (no specific product is ordered), so it 
should be registered as advance payment and not as turnover. It should be registered as 
commercial transaction and turnover only when the customer orders specific products via the 
internet (e-commerce) or otherwise (brick-and-mortar business). 

c)  Reporting enterprises that sell credit/vouchers and implicitly refer to single/specific 

products (e.g. set of tickets for the cinema): In principle this should be the same as above. 
However, in some countries, in accordance with national tax law, it may be registered as com-
mercial transaction and turnover because the specific product and its respective price is spec-
ified on the credit/voucher. 

In the context of the survey, the most important issue is to avoid double counting (when selling 
credit and when receiving orders) and to enable enterprises to respond as accurately as possible, 
depending on how e-commerce has been implemented into their accounting system. 

3.  Clarification on sales over an extranet: web sales or EDI-sales 

Enterprises (respondents) may receive orders placed over an extranet and these usually concern 
business-to-business transactions. It may not always be sufficiently clear for respondents 
whether the orders received over an extranet should be considered as web sales or EDI-type 
sales. If it is unclear, the level of automation for placing the order should be considered and clari-
fications may be required from the respondents. In fact, respondents should know how their cus-
tomers place orders besides just EDI-type messages. 

The OECD definition of e-commerce is based on the way the order is placed. If the order is 
placed via forms/web forms via extranet, it is clearly web sales, regardless of the fact that an ex-
change of EDI-type messages follows as explained in case 1) above. The issue to be clarified 
with the enterprise (respondent) should be exactly on the operation of their partner when placing 

7 The type of e-commerce transaction is defined by the method of making the order. This approach should mitigate the interpretation 
problems where both types, EDI and web, are used in the process. An example is a situation where an order is made by the customer 
through a web application but the information is transmitted to the seller as an EDI-message. Here the type of selling application is, 
however, web; EDI is only a business application to transmit information. 

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the order. 

Only the description of the actual operation for placing the order would provide the necessary in-
formation for web or EDI-type sales and, in certain cases, the NSI must take a decision. How-
ever, it is suggested that ‘web sales’ are completely excluded before declaring the sales as ‘EDI-
sales’. 

4.  Frame agreements 

Goods or services that are agreed to be sold in a frame agreement, but are ordered later via web 
or EDI-type orders are also included in e-commerce sales. This means a situation where enter-
prises reach an agreement for an agreed number/amount/volume/limit of products – not neces-
sarily online – for an agreed time frame, but the products are ordered electronically later when 
they are needed (it could be multiple smaller orders summing up to the frame agreement). In this 
case, the initial sale (frame agreement) should not be included as e-commerce, but the order of 
the products via web or EDI message should be (and only this turnover generated by the orders 
for the specific reference year). 

5.  Examples of special cases 

Examples on what IS e-commerce: 

a) e-commerce website or app that offers the possibility to fill in a web form (online) hence the or-
der is placed using a website or app. 

The is the usual case of an enterprise having web sales. 

b) e-commerce website or app that offers the possibility to fill in a web form (online), hence the 
order is placed using a website or app and the responding enterprise enters the website and re-
trieves online the order in any format. 

This refers to the use of ‘methods specifically designed for the purpose of receiving orders’ over a 
website even though the selling enterprise (responding enterprise) retrieves (downloads) the or-
der on its own initiative. 

Examples on what is NOT e-commerce: 

The following cases are not considered as e-commerce web sales because the web form or the 
PDF order form is used as a Word document, text document or an email. The website does not 
provide the means to ‘place the order’ but in principle to ‘construct the order’ that is eventually 
placed differently (email, other electronic message via the website or post). In these cases, the 
orders are not made necessarily on web pages, i.e. they can be made by printing a PDF docu-
ment which is then ‘reintroduced’ into a computer system and, because of this break, there is no 
e-commerce because there is a human intervention in the process, and a ‘break’ in the automa-
tion. 

a) e-commerce website or app that offers the possibility to fill in a web form (online). The form is 
‘printed’ in PDF and sent by the customer to the responding enterprise as an email attachment. 

b) e-commerce website or app that offers the possibility to fill in a web form (online). The form is 
‘printed’ in PDF and sent by the customer to the responding enterprise by post. 

c) e-commerce website or app that offers the possibility to fill in a PDF order form (not necessarily 
online). The form is ‘saved’ in PDF, completed and sent by the customer to the responding enter-
prise as an email attachment. 

d) e-commerce website or app that offers the possibility to fill in a PDF order form (not neces-
sarily online). The form is ‘saved’ in PDF, completed and sent by the customer to the responding 
enterprise via the same website as an attachment to a message. 

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e) e-commerce website that offers the possibility to fill in a PDF order form (not necessarily 
online). The form is ‘saved’ in PDF, completed and sent by the customer to the responding enter-
prise by post. 

2.3.2.4. Module C: ICT specialists and skills 

Question C1: Does your enterprise employ ICT specialists? 

ICT specialists are employees for whom ICT is the main job. For example, to develop, operate or 
maintain ICT systems or applications.  

[Scope: all enterprises]  

[Type: single answer (i.e. Tick only one); binary (Yes/No)]  

The purpose of this question is to measure if enterprises employ ICT specialists, and hence indi-
rectly the incidence of ICT specialist skills. Skills of ICT specialists refer to skills for developing, 
operating, maintaining ICT systems or applications.  

Additionally, ICT specialists have the relevant skills to specify, design, install, support, manage, 
evaluate or perform research activities. Use of the term ICT specialists: In general, ‘specialists’ 
are considered professionals in an ICT field e.g. database specialists, ICT support specialists, 
etc. Alternatively, the term ‘ICT professionals’ could be used. It would strictly correspond to the 
international standard classification of occupations ISCO 08 (25 Information and communications 
technology professionals, broken down to 251 Software and applications developers and ana-
lysts, and 252 Database and network professionals).  

However, the scope needs to be wider than the strict definition of ISCO 08 ‘ICT professionals’. 
Therefore, in the context of the survey the clarification ‘ICT is their main job’ is important. In par-
ticular because respondents would not have the specific classification in mind when answering 
the questions. The term ‘ICT specialists’ is used and includes the following ISCO 08 codes and 
groups (occupations)8:  

     133 Information and communications technology service managers  

     2152 Electronics engineers  

     2153 Telecommunications engineers  

     2166 Graphic and multimedia designers 2356 Information technology trainers  

     2434 Information and communications technology sales professionals  

     25 Information and communications technology professionals  

     251 Software and applications developers and analysts  

     252 Database and network professionals  

     35 Information and communications technicians  

8 For more information on ISCO 08 codes and groups: International Standard Classification of Occupations (ISCO) - ILOSTAT. 

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     351 Information and communications technology operations and user support technicians  

     352 Telecommunications and broadcasting technicians  

     7422 Information and communications technology installers and servicers  

In the questions on ICT specialists and skills (C1, C2, C3, C4 and C5), reference is made to per-
sons employed by the responding enterprise, excluding those who provide their services as ICT 
specialists to the responding enterprise on behalf of other enterprise(s), and are either employed 
by the other enterprise(s) or are self-employed (outsourcing).  

Question C2: Did your enterprise provide any type of training to develop ICT related skills of 
the persons employed during 2023? 

[Scope: all enterprises]  

[Type: single answer per item (i.e. Tick only one); binary (Yes/No); multiple items may be 
expected]  

a)  Training for ICT specialists  

Tick No if your enterprise didn’t employ ICT specialists during 2023. 

b)  Training for other persons employed 

The purpose of this question is to identify enterprises that provide professional ICT training to 
their persons employed either internally or using external trainers. Training in reply option a) re-
fers exclusively to ICT specialist skills (if the enterprise didn’t employ ICT specialists during 2023 
respondents should tick ‘No’). Reply option b) refers to ICT professional training aiming at devel-
oping or upgrading ICT related skills of other persons employed than ICT specialists (skills for the 
using specific work-related applications (including specialised software tools) or generic software 
tools).  

The intention of the question is to measure the provision of training to develop ICT related skills 
which does not mean that employees and self-employed persons actually attended the training. 

ICT skills refer to skills related for instance to tasks such as: management of online marketing or 
e-commerce; management of enterprise’s social media profile; programming languages; design 
or management of websites or apps; management of databases or data analysis; maintenance of 
computer networks, servers, etc.; IT-security or privacy management; use or design of specific 
software applications; management of telecommunications systems and networks etc. The upper 
mentioned list of ICT skills refers merely to some prominent examples and is not exhaustive; 
other ICT related skills depending on the functions and the needs of the enterprise could be in-
cluded. 

Question C3: Did your enterprise recruit or try to recruit ICT specialists during 2023? 

[Scope: all enterprises]  

[Type: single answer (i.e. Tick only one); binary (Yes/No); filter question]  

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The aim of the current and the next question is to identify mismatches or shortages9 of ICT spe-
cialists in the labour market, in case where the enterprise recruited or tried to recruit personnel for 
jobs requiring specific ICT specialist skills. If C3 is ‘No’ respondents should continue with ques-
tion C6. 

Question C4: During 2023, did your enterprise have vacancies for ICT specialists that were 
difficult to fill? 

[Scope: enterprises that recruited or tried to recruit ICT specialists during 2023, i.e. C3 = 
Yes]  

[Type: single answer (i.e. Tick only one); binary (Yes/No)]  

The aim of this question is to identify mismatches or shortages in the labour market concerning 
ICT specialist skills when the response to question C3 is ‘Yes’. Question C4 refers only to difficul-
ties due to external factors, e.g. general lack of applicants, lack of applicants with skills or experi-
ence relevant for the post. The question does not refer to situations when the post could not have 
been filled due to some internal organisational difficulties during the recruitment.  

Question C5: Did your enterprise have any of the following difficulties to recruit ICT 
specialists during 2023? 

[Scope: enterprises that recruited or tried to recruit ICT specialists during 2023 and those 
vacancies were hard to fill, i.e. C3 = Yes and C4 = Yes; optional question]  

[Type: single answer per item (i.e. Tick only one); binary (Yes/No); multiple items may be 
expected]  

For enterprises that have had difficulties filling vacancies for jobs requiring ICT specialist skills, 
the following reasons are collected:  

a)  Lack of applications  

Enterprises tried to recruit personnel with ICT specialist skills and had difficulties because there 
were not enough candidates/ applicants responding to the vacancy announcement. The differ-
ence between a) and b) is that the former refers to quantity (no one applied) and the latter refers 
rather to quality of applications (there were people applying, but the qualifications related to their 
education/training were not appropriate).  

b)  Applicants’ lack of relevant ICT related qualifications from education and/or training  

Enterprises tried to recruit personnel with ICT specialist skills and had difficulties to find candi-
dates with adequate knowledge in the specific ICT field that the enterprise required (qualifications 
of candidates were not appropriate for the enterprise). One important element in this item is that it 
refers to formal education. The difference between a) and b) is that the former refers to quantity 
(not having people applying) and the latter refers to quality (there were people applying, but the 
qualifications were not appropriate). This item does not include the work experience as part of the 

9 Mismatch between the job seekers’ competencies and those required or expected by the employers. Shortages indicate an insuffi-
cient number of skilled people in the labour market due to not enough job seekers, too low pay rates offered for them, low unemploy-
ment. 

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skill of the candidates.  

c)  Applicants’ lack of relevant work experience  

Enterprises had difficulties in recruiting a person for a job vacancy because there were not 
enough candidates with the required work experience needed for the vacant position. 

d)  Applicants’ salary expectations too high  

Enterprises found difficulties in recruiting personnel with ICT specialist skills, because the remu-
neration expected by the candidates was more than what the enterprise was willing to offer for 
the specific vacant position. 

As the list does not include all reasons in having difficulties when recruiting ICT specialists, it is 
possible that none of the items a) to d) is ticked ‘Yes’. 

Question C6: Who performed your enterprise’s ICT functions in 2023? (e.g. maintenance of 
ICT infrastructure; support for office software; development or support of business 
management software/systems and/or web solutions; security and data protection) 

[Scope: all enterprises]  

[Type: single answer per item (i.e. Tick only one); binary (Yes/No); multiple items may be 
expected] 

a)  own employees (incl. those employed in parent or affiliate enterprises)  

b)  external suppliers  

The aim of this question is to obtain information who performed ICT functions in the enterprise. 

Note that ‘external supplier’10 refers to any other enterprise that performed the ICT functions like 
the examples mentioned in brackets under the question. Affiliates and parent enterprises are not 
considered as external suppliers.  

ICT functions refer to:  

•  Maintenance of ICT infrastructure – software components that are necessary to operate the 
hardware (e.g. operating system software, drivers for peripherals, network software), as well 
as updating and upgrading system related software. Maintenance functions also include the 
repair of broken equipment, and the extension of existing infrastructure in terms of hardware 
and subsequently of the necessary software.  

•  Providing support for office software – software dedicated to producing documents, presenta-
tions, worksheets, graphs, charts, etc. Support in the context of office automation software 
might for example refer to installation of office software, training on the effective use of the 
software, granting access to users for databases.  

•  Development of business management software/systems – e.g. ERP — enterprise resource 
planning used to manage resources by sharing information among different functional areas 

10 This would be consistent with the global value chain methodology - Business Statistics Framework for Global Value Chains - 2024 
edition (europa.eu). 

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such as accounting, planning, production, marketing; CRM software application for managing 
information about customers; human resources information management. Development in-
cludes providing tailor-made, customised solutions for the responding enterprises as well as 
customisation of purchased, packaged, off-the-shelf software (e.g. SAP) or software that is 
provided as a service over the cloud. In addition, all types of maintenance (corrective, adap-
tive, perfective and preventive) of software solutions due to business evolution — e.g. correc-
tion of bugs, updating and extension of software functionalities, customisation and adjustment 
is part of development. The development/purchase of custom-made (ad hoc solutions) ‘busi-
ness management software/systems’ is also in the scope as ‘developed by external supplier’.  

•  Support for business management software/systems – e.g. ERP, CRM, HR, databases. Sup-
port refers to providing advice for solving problems, for improving the effectiveness of the use 
of the solutions, training, etc.  

•  Development of web solutions – e.g. development of own enterprise’s website, apps, e-com-
merce solutions, etc. Ad hoc programming or introducing links to ‘social media on an enter-
prise’s website should be considered as development, extending the existing functionalities of 
the website.  

•  Development of web applications is a process which may include: defining goals and audi-
ence scope, technology selection, developing of technical architecture and structure, third 
party vendors analysis and selection (e.g. SSL certificate, payment gateway) designing layout 
and interface, quality, security and usability testing.  

There are two types of web applications: the browser based and the client-based web appli-
cations. The former are web applications accessible over a webpage (html and Javascript) 
and run within the web browser. The latter are installed on a device (e.g. smartphone), run 
without going through a browser but they use web protocols. Having such apps an enterprise 
may for example manage its database, use it to scale and automate business growth for 
sales, marketing, operations (business automation). Using such apps allows a customer to 
place an order or buy credit (e-commerce) for example. 

•  Support for web solutions – e.g. support of own enterprise’s websites, apps, e-commerce so-
lutions, excluding hosting your enterprise’s website. Support for ‘web solutions’ includes as-
sistance to update e.g. lists of products, prices and other information of the enterprise, but not 
extending functionalities of the web solution. Providing support in the context of e-commerce 
marketplaces could be considered in the cases of training, provision of instructions and guide-
lines, etc. Hosting an enterprise’s website (i.e. provision of servers, computers or computer 
space, and software tools) or providing the infrastructure by an e-commerce platform is not 
considered to be on its own merits ‘support for web solutions’. ICT security and data protec-
tion – e.g. security testing, training on security, resolving ICT security incidents, excluding up-
grades of pre-packaged software.  

Note: Due to the different observation periods, the case where C1 is ticked “No” and C6a is ticked 
“Yes” or C1 is ticked “Yes” and C6a is ticked “No” should not be treated as an error without verifi-
cation.  

In cases where no one carries out the mentioned functions, the enterprise should tick “No” to both 
questions. Answering “Yes” to both questions is possible, in case that the ICT functions are car-
ried out by both own employees and by external suppliers. 

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2.3.2.5. Module D: ICT Security 
[Scope: enterprises with access to the internet, i.e. if A1>0] 

ICT security means measures, controls and procedures applied on enterprise’s ICT systems to 
ensure integrity, authenticity, availability and confidentiality of enterprise’s data and systems. Se-
curity risks, measures implemented and incidents experienced can be classified by the following 
conceptual approaches:  

•  Confidentiality, Integrity, and Availability (CIA) Triad with the attributes 

•  Confidentiality (limited access, restrictions of access to information to prevent the disclo-

sure of information to unauthorized individuals or systems).  

• 

Integrity (the assurance of the accuracy and consistency of systems and data). Integrity 
is a pre-requisite for ensuring confidentiality. Without it, encryption is useless.  

•  Availability (making sure that the computing systems, the security controls, and the com-
munication channels are functioning correct) and newly added non-repudiation or authen-
ticity11.  

• 

“3 A” basic security tools: Authentication, Authorisation, Auditing.  

•  Maturity scale: enterprises which are conscious of security risks; enterprises which have im-
plemented some security measures; enterprises which ensure regular maintenance and up-
dates of security policy and measures. 

Question D1: Does your enterprise apply any of the following ICT security measures on its 
ICT systems? 

[Scope: enterprises with access to the internet, i.e. if A1>0]  

[Type: single answer per item, i.e. Tick only one; binary (Yes/No); multiple items may be 
expected]  

The intention of the questions is to find out what security measures enterprises apply on their 
own ICT systems in order to ensure integrity, authenticity, availability and confidentiality of data 
and information systems. This includes also security measures on operational technologies, 
meaning the use of IT (hardware and software) to monitor and control physical processes, de-
vices and infrastructure (including purchased and provided by external suppliers). 

The answer options reflect the information system-related security facilities: identification, authori-
sation and authentication.  

Identification is the ability to identify uniquely a user of a system or an application that is running 
in the system. It is the process of associating a user with something that has occurred on a 
server, on a network, or with some other resource. This information is almost always logged. 
Usually, it is a username or some type of very unique identifier assigned to that particular func-
tion.  

Authentication is the process that determines whether a client really is who they say they are. 

11 Information security 

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Authentication can be done with the help of passwords (authentication by knowledge), or with ad-
ditional devices, such as smart cards, hardware tokens or identity cards (authentication by owner-
ship). The last possibility would be authentication by characteristics, i.e. using biometrical authen-
tication, such as fingerprints scanner or retina patterns. A strong identification is defined by at 
least a combination of two authentication methods, e.g. passwords and smart cards. The authen-
tication methods can be classified in: a) Static passwords (These do not changed unless they ex-
pire or user changes them); b) One-time password (OTP) such as Personal Identification Num-
bers (PINs) delivered through SMS texts or otherwise c) Digital certificates (e.g. x.509 and such); 
d) Biometric credential.  

Authorization is the process that determines what an authenticated client can and cannot do on a 
network. In general, identification and authentication of users are used in the context of authorisa-
tion.  

a)  Authentication via strong password (e.g. minimum length, use of numbers and special 

characters, changed periodically)  

The strong password may have one or several requirements concerning for instance a minimum 
length (specified minimum number of characters), characters to be a mix of uppercase, lower-
case alphanumeric and special characters, forcing users to change passwords periodically, pass-
words to be transmitted and stored in encrypted form. The described conditions follow the ISO 
norm 9594-1. 

Note: In scope is the usage of the authentication via strong password for authentication to any 
ICT system or software of the enterprise, including remote access (e.g. the enterprise’s network, 
business software, any other applications).  

b)  Authentication via biometric methods used to access the enterprise’s ICT system (e.g. 

authentication based on fingerprints, voice, face)  

Authentication via biometric methods involves using some part of a person’s physical or behav-
ioural characteristics to authenticate them. The biometric authentication can be done based on 
for instance a fingerprint, an iris scan, a retina scan, voice recognition, face recognition or some 
other physical characteristic. A single characteristic or multiple characteristics could be used, de-
pending on the infrastructure and the level of security desired. With biometric authentication, the 
physical characteristic being examined is usually mapped to a username. This username is used 
to make decisions after the person has been authenticated. In some cases, the user must enter 
the username when attempting to authenticate; in others, a lookup is done on the biometric sam-
ple in order to determine the username.  

Biometric authentication is performed by doing a comparison of the physical aspect a person pre-
sents for authentication against a copy that has been stored. For example, one would place their 
finger on a fingerprint scanner for comparison against the stored sample. If the fingerprint 
matches the stored sample, then the authentication is considered to be successful.12 

Note: Usage of biometric methods for purpose not related with ICT systems and hardware protec-
tion (e.g. access for enterprises premises not related to IT) is not in the scope. 

12 Biometric Authentication - an overview | ScienceDirect Topics 

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In scope is the usage of the authentication via biometric methods to access the enterprise’s ICT 
system or software, including remote access (e.g. the enterprise’s network, business software, 
any other applications).  

c)  Authentication based on a combination of at least two authentication mechanisms (i.e. 
combination of e.g. user-defined password, one-time password (OTP), code generated 
via a security token or received via a smartphone, biometric method (e.g. based on fin-
gerprints, voice, face))  

This answer option covers authentication methods where identification is defined by a combina-
tion of at least two authentication methods.  

Note: In scope is the usage of authentication based on a combination of at least two authentica-
tion mechanisms to access any ICT system or software of the enterprise, including remote ac-
cess (e.g. network, business software, any other applications).  

d)  Encryption of data, documents or e-mails 

Encryption is the process of encoding messages or information in a way that only authorized per-
sons can access it. Encryption does not of itself prevent interference but denies the message 
content to the interceptor. In an encryption scheme, the intended information or message, re-
ferred to as plaintext, is encrypted using an encryption algorithm, generating cipher text that can 
be read only after decryption. An authorized recipient can easily decrypt the message with the 
key provided by the originator to recipients, but not to unauthorized interceptors. The purpose of 
encryption is to ensure that only somebody who is authorized to access data (e.g. a text message 
or a file), will be able to read it, using the decryption key. Somebody who is not authorized can be 
excluded, because he or she does not have the required key, without which it is impossible to 
read the encrypted information.  

e)  Data backup to a separate location (including backup to the cloud)  

Offsite data backup is part of the off-site data protection strategy of sending critical data from the 
main site to another location by means of removable storage media, e.g. magnetic type, external 
hard-disks, or electronically via remote backup services. Offsite data protection is normally part of 
a contingency plan that describes disaster recovery actions in case of security incidents. Included 
is the backup of data in the cloud (enterprises purchasing cloud computing services).  

f)  Network access control (management of user rights in enterprise’s network)  

Access control to enterprise network enables access and enforces security policy based on the 
state of the computer and the identity of the user.  

Network access control (NAC), also called network admission control, is a method of bolstering 
the security of a proprietary network by restricting the availability of network resources to end-
point devices that comply with a defined security policy13. For example, if a connecting system 

13 Explanation | CyberSecurity Training | www.cybertraining365.com 

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doesn’t have the standard corporate anti-virus package, the user should get a different access 
control policy than if everything is installed and all the signatures are up-to-date14.  

Note: User rights outside of the enterprise’s IT system are out of scope. 

g)  VPN (Virtual Private Network extends a private network across a public network to ena-

ble secure exchange of data over public network)  

A virtual private network (VPN) extends a private network across a public network, such as the 
Internet. It enables users to send and receive data across shared or public networks as if their 
computing devices were directly connected to the private network. Applications running across 
the VPN may therefore benefit from the functionality, security, and management of the private 
network. VPNs may allow employees to securely access a corporate intranet while located out-
side the office. They are used to securely connect geographically separated offices of an organi-
zation, creating one cohesive network.  

h)  ICT security monitoring system used to detect suspicious activity (e.g. intrusion detec-

tion or prevention system that monitors users’ or devices’ behaviour, network traffic) 

Please exclude antivirus software and default firewall solution included in the operat-
ing system of personal computers and routers. 

The answer option covers ICT security monitoring system used to detect suspicious activity, ex-
cept antivirus software and default firewall solution included in the operating system of personal 
computers and routers. The answer option covers using e.g. ICT intrusion detection methods 
such as Next Generation Intrusion Prevention System (NGIPS), Next Generation Firewall 
(NGFW), or Intrusion detection system (IDS).  

Example of the Next Generation Intrusion Prevention System (NGIPS) can be the NSFOCUS 
Next Generation Intrusion Prevention System provides advanced multi-stage AI analysis for de-
tection and mitigation of tomorrow and beyond unknown and zero-day advanced persistent 
threats (APTs). NSFOCUS goes beyond signature and behaviour-based detection, using cutting 
edge Intelligent Detection advanced intelligence heuristics learning technology for network and 
application threat detection. NGIPS also combines AI with state-of-the-art threat intelligence to 
detect malicious sites and botnets. An optional virtual sandboxing capability can be added to the 
NGIPS system using the NSFOCUS Threat Analysis System. The TAS uses multiple innovative 
detection engines to identify known and zero-day APTs, including IP reputation engines, anti-vi-
rus engines, static and dynamic analysis engines and virtual sandbox execution mimicking live 
hardware environments.15  

A next-generation firewall (NGFW) is a part of the third generation of firewall technology, com-
bining a traditional firewall with other network device filtering functions, such as an application 
firewall using in-line deep packet inspection (DPI), an intrusion prevention system (IPS). Other 
techniques might also be employed, such as TLS/SSL encrypted traffic inspection, website filter-
ing, QoS/bandwidth management, antivirus inspection and third-party identity management inte-
gration (i.e. LDAP, RADIUS, Active Directory). NGFWs include the typical functions of traditional 

14 What is NAC anyway? | Network World 
15 Next Generation Intrusion Prevention (NGIPS) - NSFOCUS, Inc., a global network and cyber security leader, protects enterprises 
and carriers from advanced cyber attacks. (nsfocusglobal.com) 

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firewalls such as packet filtering, network- and port-address translation (NAT), stateful inspection, 
and virtual private network (VPN) support. The goal of next-generation firewalls is to include more 
layers of the OSI model, improving filtering of network traffic that is dependent on the packet con-
tents.  

NGFWs perform deeper inspection compared to stateful inspection performed by the first- and 
second-generation firewalls. NGFWs use a more thorough inspection style, checking packet pay-
loads and matching signatures for harmful activities such as exploitable attacks and malware.16 

An intrusion detection system (IDS; also intrusion protection system or IPS) is a device or 
software application that monitors a network or systems for malicious activity or policy violations. 
Any intrusion activity or violation is typically reported either to an administrator or collected cen-
trally using a security information and event management (SIEM) system. A SIEM system com-
bines outputs from multiple sources and uses alarm filtering techniques to distinguish malicious 
activity from false alarms.  

IDS types range in scope from single computers to large networks. The most common classifica-
tions are network intrusion detection systems (NIDS) and host-based intrusion detection systems 
(HIDS). A system that monitors important operating system files is an example of an HIDS, while 
a system that analyses incoming network traffic is an example of a NIDS. It is also possible to 
classify IDS by detection approach. The most well-known variants are signature-based detection 
(recognizing bad patterns, such as malware) and anomaly-based detection (detecting deviations 
from a model of “good” traffic, which often relies on machine learning). Another common variant 
is reputation-based detection (recognizing the potential threat according to the reputation scores). 
Some IDS products have the ability to respond to detected intrusions. Systems with response ca-
pabilities are typically referred to as an intrusion prevention system. Intrusion detection systems 
can also serve specific purposes by augmenting them with custom tools, such as using a honey-
pot to attract and characterize malicious traffic.17 

i)  Maintaining log files that enable analysis after ICT security incidents 

Information system application activities or user activities can be logged and used for analysis in 
case of security incidents in order to take appropriate actions to prevent these kinds of incidents 
in future or to quantify occurred damage.  

Note: Activities relate to computer activities. 

j) 

ICT risk assessment, i.e. periodical assessment of probability and consequences of 
ICT security incidents  

Traditional risk assessment includes general ICT-related issues such as accidental outages, 
hardware failures, and uptime. There are basically three risk management components:  

•  Evaluation and assessment, to identify assets and evaluate their properties and characteris-

tics.  

•  Risk assessment, to discover threats and vulnerabilities that pose risk to assets.  

16 Next-generation firewall - Wikipedia 
17 Intrusion detection system - Wikipedia 

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•  Risk mitigation, to address risk by transferring, eliminating or accepting it. ICT risk assess-

ments are performed to allow enterprises to assess, identify and modify their overall ICT se-
curity.  

k)  ICT security tests (e.g. performing penetration tests, testing security alert system, re-

view of security measures, testing of backup systems)  

The intention of ICT security tests it to test established ICT security measures. Penetration tests 
for example can help determine whether a system is vulnerable to attack, if the defences were 
sufficient, and which defences (if any) the test defeated. 

Question D2: Does your enterprise make persons employed aware of their obligations in ICT 
security related issues in the following ways? 

[Scope: enterprises with access to the internet, i.e. if A1>0]  

[Type: single answer per item, i.e. Tick only one; binary (Yes/No); multiple items may be 
expected]  

This question should provide information on methods applied by enterprises to raise awareness 
on ICT security issues among their persons employed. The reply options distinguish between ob-
ligatory and non-obligatory methods of awareness raising. In addition, there is a distinction be-
tween legally binding and non-binding commitment. Enterprises might apply different levels of ob-
ligations, e.g. between staff working in IT departments and other staff. In this case, the options 
should refer to all staff.  

a)  Voluntary training or internally available information (e.g. information on the intranet)  

This option covers information offers that can be followed or retrieved by persons employed vol-
untarily, e.g. ICT security information on the intranet of an enterprise or as information leaflets. 
Information can be also provided by enterprise ICT department. 

b)  Compulsory training courses or viewing compulsory material 

This option covers compulsory trainings or presentations on the information security policy, pre-
cautions or principles. The difference between training and compulsory material (which can be a 
presentation) lies in the degree of interactivity or active involvement of the trainees. The option 
includes eLearning techniques with control of participation. Positive replies would indicate a 
higher level of awareness of the enterprise compared to training on staff initiative.  

c)  By contract (e.g. contract of employment)  

This option focuses on the legal aspects of information security policy. Persons employed could 
be informed and bound to the information security policy by contract or letter of appointment. 
Usually, this would be accompanied by complementary actions such as handover of an infor-
mation document or participation in training activities. The purpose of the formulation of this item 
is to point to the contract of employment as primary source of making staff aware via contracts. At 
the same time, other types of contracts, which e.g. might specifically aim at ICT security obliga-
tions, should not be excluded from the replies of the enterprises. 

Question D3: Does your enterprise have document(s) on measures, practices or procedures 
on ICT security? 

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(Documents on ICT security and confidentiality of data cover employee training in ICT use, 
ICT security measures, the evaluation of ICT security measures, plans for updating ICT 
security documents, etc.)  

[Scope: enterprises with access to the internet, i.e. if A1>0]  

[Type: one single answer needed, i.e. Tick only one; binary (Yes/No); filter question]  

ICT security document(s) refer to document(s) describing how an enterprise plans to maintain se-
curity and confidentiality of data. Documents can describe how employees will be trained in the 
safe usage of ICT, which security measures will be implemented and carried out, procedures for 
evaluating the effectiveness of ICT security measures, plans for updating the ICT security docu-
ment(s), etc. Enterprises can have one or more documents regarding ICT security. The 
measures, practices or procedures should be enforced in the enterprise. The document(s) can be 
in a paper or electronic form.  

The existence of document(s) on measures, practices or procedures on ICT security (an ICT se-
curity policy) in an enterprise means that the enterprise is aware of the importance of its ICT and 
the risks related to them. The enterprise has made the effort to formulate a document(s) by identi-
fying the systems, their risks and how to deal with these risks. Focus is on mentioned measures, 
practices or procedures on ICT security that are actually applied. A proxy for such kind of docu-
ment(s) is the term “policy formally defined”, i.e. policy that is documented and adopted by the 
enterprise. Normally, such document(s) on ICT security contains the objectives and aims of 
measures, practices or procedures. The document should contain or make reference to defini-
tions of the relevant terms related to ICT security. In addition, it should contain documentation or 
make reference to documentation of the enterprise information systems. A main component 
would be the assessment of security risks in terms of likelihood of occurrence of incidents and 
their possible impact on the operations of the enterprise. Ideally, the document should distinguish 
between different groups of actors and their responsibilities and functions in relation to infor-
mation system operations and incident handling. Finally, the document(s) should describe secu-
rity controls and measures as well as contingency planning in case of ICT security incidents.  

The degree of documentation might depend on the size of the enterprise and on the results of an 
ICT security risk assessment. In addition, the document(s) on ICT security might not cover all the 
above mentioned aspects. It may be possible that enterprises (e.g. SMEs) don’t employ ICT spe-
cialists and thus outsource various ICT functions. These external service providers may have 
such documents or ‘formally defined ICT security policy’ that addresses different risks and which 
goal is to protect ICT infrastructure of their business partners.  

Concerning “outsourcing” (e.g. in the case of using cloud computing services): an enterprise 
should report having document(s) on ICT security when a) it is the ICT security policy, formally 
defined in document(s) on ICT security by the enterprise, or b) the enterprise has a service provi-
sion contract or a formal agreement that foresees issues related to the enterprise’s ICT security 
concerning the risks to be addressed and the standards to be followed. In the latter - case (b) - 
the ICT security policy of the external supplier of ICT services (e.g. provider of cloud computing 
services, subcontractor for ICT functions related to maintenance, development, or support) is di-
rectly applied in order to protect the enterprise against ICT security risks according to certain 
standards. In these cases, enterprises that benefit from the security policy of their service pro-
vider should answer with ‘Yes’ to D3 and the following question.  

The existence of such documents in an enterprise means that the enterprise is aware of the 

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importance of its ICT and the risks related to them. It is assumed that the existence of documents 
on ICT security and the frequency of reviewing these documents is positively correlated to the 
readiness of the enterprises to report ICT security incidents.  

Question D4: When was the document(s) on measures, practices or procedures on ICT 
security defined or most recently reviewed? 

(Documents on ICT security and confidentiality of data cover employee training in ICT use, 
ICT security measures, the evaluation of ICT security measures, plans for updating ICT 
security documents, etc.)  

[Scope: enterprises which have document(s) on measures, practices or procedures on ICT 
security, i.e. D3 = Yes]  

[Type: one single answer needed, i.e. Tick only one; optional question]  

This question should provide information on the latest period in time that the document(s) on ICT 
security was reviewed/defined according to the following responses: 

a)  within the last 12 months,  

b)  more than 12 months and up to 24 months ago and 

c)   more than 24 months ago.  

Question D5: During 2023, did your enterprise experience any ICT related security incident 
leading to the following consequences?  

[Scope: enterprises with access to the internet, i.e. if A1>0]  

[Type: single answer per item, i.e. Tick only one; binary (Yes/No); multiple items may be 
expected]  

This question concerns information communication system security related incidents. Enterprises 
might be reluctant to report on these types of incidents as this is likely to be considered as sensi-
tive information. Therefore it would be important to include a strong statement on data confidenti-
ality when submitting this questionnaire to the enterprise. Moreover, users consider statistics on 
security related incidents as very important information for policy purposes. The number of ques-
tions on this sensitive issue is kept to the absolute minimum and no quantitative information is 
asked. The options are built around the general elements of information security: availability, in-
tegrity and confidentiality.  

a)  Unavailability of ICT services due to hardware or software failures  

This answer option covers cases when the ICT services of the enterprise are unavailable due to 
failure of the hardware or software.  

b)  Unavailability of ICT services due to attack from outside, e.g. ransomware attacks, De-

nial of Service attacks  

The distinction of this answer option from item a) is the cause of the unavailability of the ICT ser-
vices, which in this instance is caused by malicious attacks from outside.  

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Attacks from outside the enterprise can lead to unavailability of services. A typical example would 
be a distributed denial of service (DDoS) attack that attempts to make an information system re-
source unavailable to its intended users. A common method for DDoS attacks involves saturating 
the target information system with external communication requests so that it cannot respond ad-
equately to legitimate requests. 

c)  Destruction or corruption of data due to hardware or software failures  

This option covers issues of data integrity related to unintended incidents caused by hardware or 
software failures. Unintended incidents could be crashes of servers or hard disks due to hard-
ware failures or crashes of servers due to software failures, e.g. erroneous updates.  

d)  Destruction or corruption of data due to infection of malicious software or unauthor-

ised intrusion  

This answer option covers issues of data integrity related to attacks with a malicious purpose. In 
addition, this option comprises as well failures of data integrity due to unauthorised access (inter-
nal or external intrusion) to the ICT system of an enterprise. An intrusion is an attempt to bypass 
security controls on an information system. Means of intrusion can be eavesdropping, viruses, 
worms, Trojan horses, logic or time bombs, brute force attacks, etc. 

e)  Disclosure of confidential data due to intrusion, pharming, phishing attack, intentional 

actions by own employees  

This answer option refers only to cases of disclosure of confidential data due to malicious actions.  

The data can be information on persons, staff or clients, intellectual property in terms of trade se-
crets, information or other confidential information such as e.g. business numbers or information 
on managerial decisions. A possible way for getting access to confidential data could be brute 
force attacks for disclosing passwords or phishing and pharming techniques.  

Phishing is a criminally fraudulent attempt to acquire sensitive information such as usernames, 
passwords and credit card details by masquerading as a trustworthy entity in an electronic com-
munication. The term “pharming” connotes an attack to redirect the traffic of a website to another, 
bogus website in order to acquire sensitive information.  

Intellectual property is defined as “intangible property that is the result of creativity”. These assets 
can be artistic or commercial. Intellectual property can be protected by law. Types of legal protec-
tion include copyrights, patents and trademarks. Under intellectual property law, owners are 
granted certain exclusive rights to intangible assets. In this case, intellectual property is accessi-
ble by third parties but only the owner may exploit the assets. In case of unauthorised exploita-
tion, the right holder can initiate legal actions.  

Trade secrets are intellectual properties that are not legally protected. “A trade secret is a for-
mula, practice, process, design, instrument, pattern, or compilation of information which is not 
generally known or reasonably ascertainable, by which a business can obtain an economic ad-
vantage over competitors or customers. In some jurisdictions, such secrets are referred to as 
‘confidential information’ or ‘classified information’”. The option only refers to intellectual property 
in terms of trade secrets as they are not protected by law and their disclosure would have nega-
tive effects on the economic performance of an enterprise.  

f)  Disclosure of confidential data due to unintentional actions by own employees  

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This answer option refers only to cases of disclosure of confidential data was caused by uninten-
tional actions of enterprise’s own employees.  

Note: ‘No’ to all items is possible in cases where none of the mentioned consequences were ex-
perienced due to any ICT related security incident. 

2.3.2.6. Module E: Artificial Intelligence 

[Scope: enterprises with access to the internet, i.e. A1 > 0] 

Artificial intelligence refers to systems that use technologies, such as text mining, computer vi-
sion, speech recognition, natural language generation, machine learning, and deep learn-
ing, to gather and/or use data to predict, recommend or decide, with varying levels of autonomy, 
the best action to achieve specific goals. 

Artificial intelligence systems can be purely software-based, e.g.: 

•  chatbots and business virtual assistants based on natural language processing; 

• 

face recognition systems based on computer vision or speech recognition systems; 

•  machine translation software; 

•  data analysis based on machine learning; 

or embedded in devices, e.g.: 

•  autonomous robots for warehouse automation or production assembly works; 

•  autonomous drones for production surveillance or parcel handling. 

Question E1: Does your enterprise use any of the following artificial intelligence 
technologies?  

Scope: enterprises with access to the internet, i.e. if A1>0] 

[Type: single answer per item (i.e. Tick only one); binary (Yes/No); multiple items may be 
expected; filter question] 

The aim of this question is to provide information on the use of artificial intelligence technologies 
used by enterprises, e.g. text mining, speech recognition, natural language generation. The an-
swer options a) to g) may not be an exhaustive list of artificial intelligence technologies but con-
stitute the most prevalent AI technologies that enterprises use nowadays. 

The question is about use. So, if an enterprise creates AI solutions for others to use, i.e. 
‘for sale’, then it should not answer ‘Yes’ to E1. However, if an enterprise creates AI solu-
tions and uses them for their business, then it should answer ‘Yes’ to the relevant answer 
options in E1. 

The responding enterprise is asked to choose among the following AI technologies; multiple an-
swers are allowed:  

a)  AI technologies performing analysis of written language (e.g. text mining) 

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This answer option refers to the use of AI technologies performing analysis of written language, 
also referred to as text mining. 

Text mining, also known as text analysis, is the process of transforming unstructured text data 
into meaningful and actionable information, identifying facts, relationships and assertions that 
would otherwise remain buried in the mass of unstructured or low-structured textual big data. 
Once extracted, this information is converted into a structured form that can be further analysed, 
or presented directly using clustered HTML tables, mind maps, charts, etc. Text mining employs 
a variety of methodologies to process the text, one of the most important of these being Natural 
Language Processing (NLP)18. 

Natural language processing – a subfield of computer science, information engineering and artifi-
cial intelligence concerned with the interactions between computers and human (natural) lan-
guages, in particular how to program computers to process and analyse large amounts of natural 
language data.  

Examples for the usage of technologies performing analysis of written language (text mining): 

•  Social media: Text mining software packages are available for analysing social media appli-

cations to monitor and analyse the online plain text from internet news, blogs, email, etc. Text 
mining tools help to identify and analyse number of posts, likes and followers on the social 
media. This kind of analysis show people’s reaction to different posts, news and how they 
spread around. It shows the behaviour of people belonging to speciﬁc age group or communi-
ties having similarity and variation in views about the same post. 

•  Business intelligence: Text mining plays a signiﬁcant role in business intelligence that helps 

organizations and enterprises analyse their customers and competitors in order to take better 
decisions. It provides a deeper insight about business and gives information how to improve 
the customer satisfaction and gain competitive advantages. By bringing text mining into the 
equation, business intelligence (BI) tools are able to leverage unstructured data in addition to 
structured data and expand the data sets which model uses to generate business insight. The 
text mining tools like IBM text analytics, Rapid miner, GATE help take decisions about the or-
ganization that generate alerts about good and bad performance, market changeover that 
help to take remedial actions. It also helps in telecommunication industry, business and com-
merce applications and customer chain management system.19 BI can uses AI or not. How-
ever, only BI using AI is in the scope. 

•  Risk Management: No matter the industry, insufficient risk analysis is often a leading cause of 
failure. Adoption of risk management software based on text mining technology can dramati-
cally increase the ability to mitigate risk. This provides enterprises with easy access to thou-
sands of sources (and petabytes) of text documents, enabling them to link together infor-
mation and access the right information at the right time. 

•  Knowledge Management: When managing large volumes of text documents, ability to find im-
portant information quickly is essential. Text mining solutions can help enterprises locate 

18 NIST Cloud Computing Security Reference Architecture 
19 Text Mining Techniques Applications and Issues 

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information quickly and accurately, enabling them to unlock deeper insights and deliver prod-
ucts to the market faster. 

•  Cybercrime Prevention: The anonymous nature of the internet and the many communication 
features operated through it contribute to the increased risk of internet-based crimes. Text 
mining is making cybercrime prevention easier for enterprises by establishing more context 
around the intelligence they are being fed. This enables them to pinpoint real threats and limit 
the number of false positives created by keywords taken out of context. 

•  Enhanced Customer Service: Text mining and natural language processing have proven im-
mensely helpful for customer care teams. They are commonly adopted to improve the cus-
tomer experience by leveraging valuable information sources such as surveys, trouble tickets 
and customer call notes to improve the quality, effectiveness and speed of problem resolu-
tion. Text analysis is also central to chatbots as it captures the information necessary to pro-
vide a rapid, automated response to the customer. As a result, enterprises can dramatically 
reduce their reliance on call centre operators to solve customer problems. 

•  Contextual Advertising: Digital advertising is a moderately new and growing field of applica-
tion for text analytics. Compared to the traditional cookie-based approach, contextual adver-
tising analyzes the text on a webpage to understand the content on a deeper level. For exam-
ple, someone reading an article about the top new book releases may be served an ad for 
Kindle due to its relevance. This is not only beneficial in terms of serving more accurate and 
targeted advertisements, but it completely preserves user privacy. 

•  Spam Filtering: Email is an effective, fast and reasonably cheap way to communicate, but it 
comes with a dark side: spam. Today, spam is a major issue for internet service providers, 
increasing their costs for service management and hardware software updating. For users, 
spam is both an entry point for viruses and a detriment to productivity. Text mining techniques 
can be implemented to improve the effectiveness of statistical filtering methods by leveraging 
established prior knowledge. This not only makes for more efficient email management, but it 
improves the user experience considerably.20 

b)  AI technologies converting spoken language into machine-readable format (speech 

recognition) 

This answer option refers to the use of AI technologies converting spoken language into ma-
chine-readable format, also referred to as speech recognition. 

Speech recognition is an interdisciplinary subfield of computer science and computational linguis-
tics that develops methodologies and technologies that enable the recognition and translation of 
spoken language into text by computers. It is also known as automatic speech recognition (ASR), 
computer speech recognition or speech to text (STT). It incorporates knowledge and research in 
the computer science, linguistics and computer engineering fields. 

Natural language processing – is a subfield of computer science, information engineering and ar-
tificial intelligence concerned with the interactions between computers and human (natural) 

20 Text Mining Examples to Leverage Right Now 

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languages, in particular how to program computers to process and analyse large amounts of nat-
ural language data.  

Although the science of speech recognition dates many decades back it has benefited substan-
tially by AI. AI based speech recognition employs the latest machine learning advancements. 
These are high-learnability neural network algorithms and rich lexicons that enable fine recogni-
tion accuracy. 

Examples for the usage of technologies converting spoken language into machine-readable for-
mat (speech recognition): 

•  Speech recognition is used in call centre, where it handles incoming customer calls. 

•  Digital assistants use speech recognition: Google Voice, Amazon Alexa, Microsoft Cortana, 

and Apple’s Siri. 

c)  AI technologies generating written or spoken language (natural language generation, 

speech synthesis) 

This answer option refers to the use of AI technologies generating written or spoken language, 
also referred to as natural language generation. 

Natural language generation – is the ability of a computer program to convert structured data into 
natural language representation. It can be used to produce long documents that summarize or 
explain the contents of computer databases, for example generating technical manuals, generat-
ing product descriptions for a large e-commerce site, making news reports (automated journal-
ism) or summarizing medical records. It can also be used to generate short blurbs of text in inter-
active conversations (a chatbot) which might even be read out loud by a text-to-speech system 
(artificial production of human speech). 

Examples of the usage of Technologies generating written or spoken language (natural language 
generation): 

•  Chatbot: A chatbot is an AI software that can simulate a conversation (or a chat) with a user 

in natural language through messaging applications, websites, mobile apps or through the tel-
ephone. Formulating responses to questions in natural language is one of the most typical ex-
amples of Natural Language Processing applied in various enterprises’ end-use applications.  

d)  AI technologies identifying objects or persons based on images or videos (image 

recognition, image processing) 

This answer option refers to the use of AI technologies identifying objects based on images, also 
referred to as image recognition or image processing. Image processing and image recognition 
are examples of computer vision application.  

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Computer vision21 is an interdisciplinary scientific field that deals with how computers can gain 
high-level understanding from digital images or videos. From the perspective of engineering, it 
seeks to understand and automate tasks that the human visual system can do. Computer vision 
tasks include methods for acquiring, processing, analysing and understanding digital images, and 
extraction of high-dimensional data from the real world in order to produce numerical or symbolic 
information, e.g. in the forms of decisions. The scientific discipline of computer vision is con-
cerned with the theory behind artificial systems that extract information from images. The image 
data can take many forms, such as video sequences, views from multiple cameras, multi-dimen-
sional data from a 3D scanner or medical scanning device. Sub-domains of computer vision in-
clude scene reconstruction, event detection, video tracking, object recognition, face recognition, 
motion estimation, and image restoration.  

Examples of the usage of Technologies identifying objects or persons based on images (image 
recognition, image processing): 

•  User identification and authentication via biometric methods implemented by the enterprise 

(e.g. based on fingerprints, faces).  

•  Drones equipped with image recognition capabilities can provide vision-based automatic 

monitoring, inspection and control of the assets located in remote areas. 

• 

In manufacturing – inspecting production lines, evaluating critical points on a regular basis 
within the premises. Monitoring the quality of final products to reduce defects. Assessing the 
condition of workers can help manufacturing industries to have a complete control of different 
activities in the systems. Examples of usage are: predictive maintenance, defect detection, 
safety (manufacturers are using computer vision to protect their employees by providing the 
safest working environment they can), assembling products and components (computer vi-
sion can assist in assembling products accurately and reducing the amount of time it takes to 
complete a product build), reading barcodes (computer vision can read barcodes to quickly 
and easily track components and packages at all stages of development, through a manufac-
turing facility to departure and beyond. Many manufacturers have begun incorporating bar-
code into their systems to better route products through the proper assembly lines).22 

•  Autonomous vehicles with image recognition can identify activity on the road and take neces-
sary actions. Mini robots can help logistics industries to locate and transfer objects from one 
place to another. It also maintains the database of the product movement history to prevent 
the product from being misplaced or stolen. 

e)  Machine learning (e.g. deep learning) for data analysis 

This answer option refers to the use of AI technologies based on machine learning for data analy-
sis, for instance deep learning. 

21 Computer visio 
22 Computer vision use cases for manufacturing 

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Machine learning involves ‘training’ a computer model to better perform an automated task, e.g. 
data analytics. Machine learning uses algorithms whose performance improves as they are ex-
posed to more data over time. 

Deep learning is a subset of machine learning in which multi-layered neural networks learn from 
vast amounts of data.  

Neural networks (Artificial neural networks (ANN) or connectionist systems) are computing sys-
tems vaguely inspired by the biological neural networks. The neural network itself is not an algo-
rithm, but rather a framework for many different machine learning algorithms to work together and 
process complex data inputs. Such systems "learn" to perform tasks by considering examples, 
generally without being programmed with any task-specific rules.  

Although neural networks are not explicitly mentioned in this answer option, they are in scope of 
this item. 

Examples of the usage of Machine learning (e.g. deep learning) for data analysis: 

•  Recommender systems based on machine learning – they are utilized in a variety of areas 
and are most commonly recognized as playlist generators for video and music services like 
Netflix, YouTube and Spotify, product recommenders for services such as Amazon, or con-
tent recommenders for social media platforms such as Facebook and Twitter. 

•  Dynamic pricing also referred to as surge pricing, demand pricing, or time-based pricing is a 
pricing strategy in which businesses set flexible prices for products or services based on cur-
rent market demands. Enterprises are able to change prices based on algorithms that take 
into account competitor pricing, supply and demand, and other external factors in the market 

•  Cyber threat detection – machine learning has become a vital technology for cybersecurity. 
Machine learning pre-emptively stamps out cyber threats and bolsters security infrastructure 
through pattern detection, real-time cybercrime mapping and thorough penetration testing. Mi-
crosoft uses its own cybersecurity platform, Windows Defender Advanced Threat Protection 
(ATP), for preventative protection, breach detection, automated investigation and response. 
Windows Defender ATP IS built into Windows 10 devices, automatically updates and employs 
cloud AI and multiple levels of machine learning algorithms to spot threats.  

f)  AI technologies automating different workflows or assisting in decision-making (e.g. 

AI-based software robotic process automation) 

This answer option refers to the use of AI technologies automating different workflows or assist-
ing in decision making, also referred to as Artificial Intelligence based software robotic process 
automation. 

AI based software robotic process automation is process automation based on AI technologies. 
Robotic process automation (RPA) is a fast-emerging process automation approach that uses 
software robots to replicate human tasks. After recording a process workflow, a virtual bot mimics 

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the actions performed by humans in the application’s graphical user interface and automates their 
execution23. 

While process automation is not a new term and could be done without AI, i.e. rules-based auto-
mation, this answer option refers only to the cases where AI is used in order to boost the benefits 
of traditional process automation. So as RPA gets paired with AI disciplines such as natural lan-
guage processing or computer vision, the possibilities for effective automation grow considerably. 

Converging AI with RPA enables businesses to automate more complex, end-to-end processes 
than ever before, and integrate predictive modelling and insights into these processes (which 
could for instance even identify new processes to automate24 to help humans work smarter and 
faster.25  

Examples of the usage of technologies automating different workflows or assisting in decision-
making (AI-based software robotic process automation): 

Automated bots are excellent at carrying out repetitive jobs much faster and with much more pre-
cision than human counterparts. A software bot is a programme designed to automate tasks. 
Typically, these tasks are simple, repetitive and routine. So, a software bot can perform them 
quicker and more efficiently than a human could. Software bots can take several different forms. 
For example, one of the most well-known types of software bot today is a chatbot. Other types of 
bot include web crawler bots and rule-based automation bots. 

RPA that is used to work in conjunction with people by automating repetitive processes (at-
tended automation) is not within the scope of the question. 

g)  AI technologies enabling physical movement of machines via autonomous decisions 
based on observation of surroundings (autonomous robots, self-driving vehicles, au-
tonomous drones) 

This answer option refers to the use of AI technologies enabling physical movement of machines 
via autonomous decisions based on observation of surroundings, such as autonomous robots, 
self-driving vehicles, autonomous drones. 

Only systems that have hardware parts (machine-like) and use artificial intelligence to learn and 
perform their tasks should be included in this answer option. Pure software robots or robots that 
automate the production process through a repetitive action without any use of AI technologies 
are excluded from the scope. 

Examples of the usage of technologies enabling physical movement of machines via autonomous 
decisions based on observation of surroundings (autonomous robots, self-driving vehicles, auton-
omous drones): 

This answer may refer for instance to robots that use machine learning in order to learn how to 
better perform a task, or drones that are able to choose the best route for example for parcel 

23  Process Mining and Robotic Process Automation: A Perfect Match.pdf 
24 Robotic Process Automation  
25 Robotic Process Automation vs. AI 

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delivery based on machine learning, or a self-driving vehicle that uses a combination of machine 
learning and computer vision for driving safely. 

Question E2: Does your enterprise use artificial intelligence software or systems for any of 
the following purposes? 

[Scope: enterprises that use AI technologies, i.e. E1a) = yes or E1b) = yes or E1c) = yes or 
E1d) = yes or E1e) = yes or E1f) = yes or E1g) = yes] 

[Type: single answer per item (i.e. Tick only one); binary (Yes/No); multiple items may be 
expected] 

Question E2 is relevant for enterprises that use one of the AI technologies listed in question E1, 
i.e. enterprises that ticked yes in at least one of the answer options in question E1. The question 
aims at identifying the purpose of the use of AI technologies in the enterprises. More specifically, 
the enterprises are asked to reply whether they use AI technologies for the specific purposes by 
business function, namely marketing or sales, production process, organisation of business 
administration processes, for management of enterprises, for logistics, for ICT security, for 
human resources management or recruiting. 

Note: As the list of purposes for using AI technologies is not exhaustive, it may occur that each 
answer category in E2 is ticked ‘No’. 

a)  Use of AI for marketing or sales 

some of the examples may be: 

•  customer profiling, price optimisation, personalised marketing offers, market 

analysis based on machine learning 

•  chatbots based on natural language processing for customer support 

•  autonomous robots for order processing 

This answer option is related to the use of AI technologies for marketing or sales purposes. 

They may refer to chatbots that are able to analyse written language based on natural language 
processing and provide real-time customer support, e.g. by answering to customer questions and 
needs as a real person (preprogrammed chatbots that are not able to learn and only respond to 
specific predefined requests should be excluded from the scope). Other examples that can be 
included in this category are software programmes that can perform customer profiling, or 
recommender systems that can propose personalised marketing offers and the knowledge they 
have acquired about the enterprises’ target market based on neural networks (e.g. through online 
advertisement or even phone notifications when the customer approaches a specific shop) or 
artificial intelligence software programmes that are able to perform market analysis in order to 
guide and steer the marketing decisions of the enterprises. 

b)  Use of AI for production or service processes 

some of examples may be: 

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•  predictive maintenance or process optimisation based on machine learning 

• 

tools to classify products or find defects in products based on computer vision 

•  autonomous drones for production surveillance, security or inspection tasks 

•  assembly works performed by autonomous robots 

This answer option is related to the use of AI technologies for production or service purposes. 

AI can boost the automation of the production line by integrating industrial robots into the 
workflow and teaching them to perform labour-intensive, dangerous or mundane tasks, thereby 
improving productivity while maintaining quality and safety. Examples include autonomous robots 
that can perform assembly works or autonomous drones that perform inspection and surveillance 
tasks thanks to computer vision and machine learning, or systems that are able to classify 
products or find defects thanks to computer vision, or to ensure predictive maintenance for 
assessing the state of machinery and/or prevention of equipment failure thanks to algorithms 
based on machine learning and neural networks. 

Predictive maintenance uses condition-monitoring equipment to evaluate an asset’s performance 
in real time. A key element in this process is the internet of things (IoT). IoT allows for different 
assets and systems to connect, work together, and share, analyse and action data. IoT relies on 
predictive maintenance sensors to capture information, make sense of it and identify any areas 
that need attention. 

Some examples of using predictive maintenance and predictive maintenance sensors include 
vibration analysis, oil analysis, thermal imaging, and equipment observation. 

c)  Use of AI for organisation of business administration processes or management 

some of the examples may be: 

•  business virtual assistants based on machine learning and/or natural language 

processing, e.g. for document drafting 

•  data analysis or strategic decision making based on machine learning, e.g. risk 

assessment, based on machine learning 

•  planning or business forecasting based on machine learning 

•  human resources management based on machine learning or natural language 
processing, e.g. candidates pre-selection screening, employee profiling or 
performance analysis 

This answer option is related to the use of AI technologies for any business administration 
processes or management of enterprises, including human resources management. 

They may refer to business virtual assistants that are able to perform secretarial and assistance 
tasks based on machine learning and natural language processing and generation. They also 
may refer to voice-to-text conversion systems which are based on speech recognition and can 
produce written records (e.g. meeting minutes). Other systems may include automated planning 
or scheduling based on machine learning and any other intelligent automation of workflows within 
the enterprises that is based on AI software-based process automation. 

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They may refer to business software or systems based on AI algorithms (e.g. neural networks) 
that are able to analyse data and help board members to take investment and other corporate 
decisions in the boards of enterprises. The business software or system may also be able to 
facilitate the distribution of information to shareholders via AI software robotic process automation 
or even ensure representation and voting by shareholders in general meetings of the enterprise 
(through natural language generation). Other examples include intelligent systems that are used 
for sales and/or business forecasting or risk assessment based on machine learning. 

The human resources management based on machine learning or natural language processing 
may refer to systems that are able to do candidate preselection screening and automation of 
recruiting based on text mining and machine learning, or to automate the recruiting and/or 
onboarding process based on AI software robotics process automation. Onboarding is the 
process of integrating a new employee into the organisation and its culture. Tactics used in this 
process include formal meetings, lectures, videos, printed materials, or computer-based 
orientations to introduce newcomers to their new jobs and organisations. 

Enterprises may use machine learning methods to predict employee absences from work. 
Employees are one of the enterprise’s most important resources. Their absence negatively 
affects productivity. Analyses may uncover reasons of absences that can be addressed by the 
enterprise e.g. improve the mental health of staff, invest in safety practices to prevent accidents 
at work, and improve workplace morale. 

Other examples include personalised training offers (online course and digital classroom, best 
time frame for new courses and schedules to fit personal preferences) based on AI-powered staff 
profiling or human resources virtual assistants (HR bots), and electronic employee helpdesks that 
are able to handle staff requests based on natural language processing and generation. 
Moreover, they can use machine learning for employee performance analysis, and computer 
vision for sentiment and group analysis. 

d)  Use of AI for logistics 

some of the examples may be: 

•  autonomous robots for pick-and-pack solutions in warehouses for parcel shipping, 

tracing, distribution and sorting 

• 

route optimisation based on machine learning 

This answer option is related to the use of AI technologies for logistics. 

Logistics is the process of planning and executing the efficient transportation and storage of 
goods from the point of origin to the point of consumption. The goal of logistics is to meet 
customer requirements in a timely, cost-effective manner. 

Examples of usage include AI that refers to autonomous robots for pick-and-pack solutions in 
warehouses, or for parcel handling (shipping, tracing, distribution and sorting), or parcel delivery 
and route optimisation that increase safety and efficiency, based on computer vision and machine 
learning. Other examples may include computer vision inventory management and execution. AI-
powered technology is already able to extract characteristics of products such as price tags, shelf 
condition, and brand to provide real-time warehouse inventory management. 

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Visual inspection powered by AI is identifying damage, classifying the damage type, and 
determining the appropriate corrective action faster than ever before. 

Robotic process automation – the combination of robotic process automation and AI can 
automate and streamline routine business tasks to cut corners significantly (collecting and 
processing data files; shipment scheduling and tracking; order processing and sending 
confirmation emails; capturing, researching, and closing out loads).26 

e)  Use of AI for ICT security 

some of the examples may be: 

• 

face recognition based on computer vision for authentication of ICT users 

•  detection and prevention of cyberattacks based on machine learning 

This answer option is related to the use of AI technologies for the enterprise’s ICT security. 

They may refer to biometric authentication systems (fingerprints, face, iris, voice) based on 
computer vision and/or natural language processing. Other examples may include intelligent AI-
powered antivirus software, spam filter applications (SpamAssassin), fraud detection, botnet 
detection, hacking incident forecasting or cyberattack detection and prevention systems (network 
intrusion detection and prevention) that protect the enterprise networks and systems from 
intrusion and other ICT security incidents. 

Ordinary antivirus or spam filter applications that do not use any artificial intelligence technologies 
are out of the scope of this question. 

f)  Use of AI for accounting, controlling or finance management 

some of the examples may be: 

•  machine learning to analyse data that helps to make financial decisions 

• 

invoice processing based on machine learning 

•  machine learning or natural language processing for bookkeeping documents 

This answer option is related to the use of AI technologies for accounting, controlling or finance 
management. 

They may refer to intelligent systems that are used for accounting and bookkeeping services (e.g. 
for validation transaction activities, automatic suggestions or completion of accounting codes), 
and for invoice processing (e.g. to read invoices, extract the needed data, code the invoices, 
track outstanding invoices and automate the follow-up collection process). They may also refer to 
machine learning algorithms that analyse data to identify potential fraud issues and flag them for 
review to avoid loss of revenue. Another example may be analysis of large volumes of 

26 Artificial Intelligence in Logistics: Exploring Applications and Opportunities 

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transactions to detect hidden errors or trends. In procurement, invoicing or billings, the AI can 
detect potential issues with future purchases, such as late payments and/or deliveries. AI-
enabled systems can support auditing, and compliance with corporate, state, and federal 
regulations, by monitoring the pertinent documents and raising alerts where necessary. 

Machine learning or natural language processing for bookkeeping documents refers to the usage 
of AI to automate and simplify bookkeeping tasks, e.g. AI can quickly and accurately record 
transactions in ledgers, and automate processes by extracting data from invoices, receipts, and 
other documents using optical character recognition. The example can also be translated as 
usage of machine learning or natural language processing for bookkeeping tasks. 

g)  Use of AI for research and development (R&D) or innovation activity (excluding 

research on AI) 

some of the examples may be: 

•  analysis of data for conducting research, solving research problems, developing a 

new or significantly improved product/service based on machine learning 

This answer option is related to the use of artificial intelligence technologies for research and 
development (R&D) or innovation activity. AI technologies that are not used for the R&D and 
innovation activity, but which are used to be embedded in the enterprise’s new or improved 
product or service, are not within the scope of this answer option, nor is R&D activity on AI. 

They may refer to machine learning to analyse data to obtain useful insights, categorise, predict, 
and make evidence-based decisions for solving research problems or developing new or 
significantly improved products/services. AI might be used in the planning or implementation. 

R&D27 activity is creative and systematic work undertaken in order to increase the stock of 
knowledge - including knowledge of humankind, culture and society - and to devise new 
applications of available knowledge. For an activity to be an R&D activity, it must satisfy five core 
criteria (at least in principle), namely the activity must be novel, creative, uncertain, systematic 
and transferable. The term R&D covers three types of activity: basic research, applied research 
and experimental development. 

An innovation28 is a new or improved product or process (or combination thereof) that differs 
significantly from the unit’s previous products or processes and which has been made available 
to potential users (product) or brought into use by the unit (process). 

The minimum requirement for an innovation to occur is that the product or process (marketing or 
organisational) method must be new or significantly improved in the enterprise. This includes not 
only products, processes and methods that enterprises are the first to develop but also those that 
have been adopted from other enterprises or organisations. 

27 OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, 
The Measurement of Scientific, Technological and Innovation Activities, OECD Publishing, Paris. 
28 OECD/Eurostat (2018), Oslo Manual 2018: Guidelines for Collecting, Reporting and Using Data on Innovation, 4th Edition, The 
Measurement of Scientific, Technological and Innovation Activities, OECD Publishing, Paris/Eurostat, Luxembourg. 

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Question E3: How did your enterprise acquire the artificial intelligence software or systems 
that it uses? 

[Scope: enterprises that use AI technologies, i.e. E1a) = yes or E1b) = yes or E1c) = yes or 
E1d) = yes or E1e) = yes or E1f) = yes or E1g) = yes; optional question] 

[Type: single answer per item (i.e. Tick only one); binary (Yes/No); multiple items may be 
expected] 

Question E3 is optional and should be answered by enterprises that use one of the AI 
technologies listed in question E1, i.e. enterprises that ticked ‘yes’ in at least one of the answer 
options in question E1. The question aims at identifying how the enterprises acquired the AI 
technologies that they are using. Five ways of acquisition, with varying levels of involvement 
on the part of the enterprise, have been identified; namely the AI technologies were developed 
by own employees (including those employed in the parent or affiliate enterprises); commercial 
software or systems were modified by own employees (including those employed in the parent or 
affiliate enterprises); open-source software or systems were modified by own employees 
(including those employed in the parent or affiliate enterprises); commercial software or systems 
ready to use were purchased (including examples where it was already incorporated in a 
purchased item or system); and external providers were contracted to develop or modify AI 
technologies. 

a)  They were developed by own employees (including those employed in the parent or 

affiliate enterprise) 

This answer option covers the cases where the enterprise uses AI software or a system, as listed 
in question E1, that was fully developed by its own employees (including those employed in 
parent or affiliate enterprises as affiliates and parent enterprises are not considered as external 
suppliers according to the current practices in global value chain statistics). 

For instance, the enterprise’s employees developed from scratch a machine learning algorithm 
that makes sales or business forecasting. 

b)  Commercial software or systems were modified by own employees (including those 

employed in parent or affiliate enterprise) 

This answer option covers the cases where the enterprise purchased a commercial AI software 
or system as listed in question E1. The commercial software or system was then modified by its 
own employees (including those employed in parent or affiliate enterprises) before being used by 
the enterprise (e.g. the software was adapted to the enterprise’s need). 

For instance, the enterprise bought a commercial AI software that does customer profiling and 
the enterprise’s employees further developed and customised the software for their own market 
needs and/or adapted it to better integrate into their own systems. 

c)  Open-source software or systems were modified by own employees (including those 

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employed in parent or affiliate enterprise) 

This answer option covers the cases where the enterprise acquired an open-source AI software 
or system as listed in question E1. The open-source software or system was modified by its own 
employees (including those employed in parent or affiliate enterprises) before being used by the 
enterprise (e.g. the software was adapted to the enterprise’s need). 

For instance, the enterprise acquired an open-source AI software that does customer profiling 
and the enterprise’s employees further developed and customised the software for their own 
market needs and/or adapted it to better integrate into their own systems. 

d)  Commercial software or systems ready to use were purchased (including examples 

where it was already incorporated in a purchased item or system) 

This answer option covers the cases where the enterprise purchased a commercial AI software 
or system as listed in question E1. The commercial software or system was used directly by the 
enterprise without any further development. This answer option includes cases where the AI 
technology is only part of a bigger system. 

For instance, the respondent enterprise bought a ready-to-use software for detection and 
prevention of cyberattacks based on machine learning. The software was directly installed on the 
enterprise’s network without any further customisation. 

e)  External providers were contracted to develop or modify them 

This answer option covers the cases where the enterprise contracted external service providers 
to develop for the respondent enterprise a customised AI software or system as listed in question 
E1. 

For instance, the respondent enterprise contracted an external provider to provide them with a 
chatbot based on natural language processing specially developed for their customer support. 
The external provider may have developed the chatbot entirely from scratch or modified an 
already existing chatbot to meet the customer enterprise’s needs. The ready-made systems that 
are not customised are outside the scope of this answer option and fall under answer options d). 

Note that ‘external supplier’ refers to any other enterprise that developed the AI software or 
system. Affiliates and parent enterprises are not considered as external suppliers and are 
therefore covered under answer option a). This complies with the current practices in global value 
chain statistics. 

According to the definitions in the international sourcing survey, there are four types of sourcing 
based on ‘location’ and ‘control’. Accordingly, outsourcing (i.e. external suppliers) includes 
sourcing to an enterprise that is not affiliated, otherwise it is considered as insourcing (i.e. own 
staff), hence the proposed clarification would be consistent with global value chains. 

Note: If one of the questions in E1 was ticked ‘yes’, then the enterprise had to acquire or 
purchase AI software or systems from somewhere. In cases where one of the E1 questions is 
ticked ‘yes’ and all of the questions in E3 are ticked ‘no’, it should be considered, if feasible, to 
contact the enterprise and verify their response (e.g. information obtained, and from whom they 
obtained or purchased the software or system). 

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Question E4: Does your enterprise process data on individuals using AI technologies? 

Some of examples of data on individuals (e.g. employees, job applicants or customers) 
are: sex, age, racial or ethnic origin, disability, religion or belief, sexual orientation, facial 
images, record of purchases, occupation or address. 

[Scope: enterprises that use AI technologies, i.e. E1a) = yes or E1b) = yes or E1c) = yes or 
E1d) = yes or E1e) = yes or E1f) = yes or E1g) = yes; optional question] 

[Type: single answer (i.e. Tick only one); binary (Yes/No)] 

The intention of the question is to find out if an enterprise processes data on individuals (e.g. 
employees, job applicants or customers) using AI technologies. Some examples of data on 
individuals are data on sex, age, racial or ethnic origin, disability, religion or belief, sexual 
orientation, facial images, purchase records, occupation or address. 

An enterprise might process data on individuals using AI technologies when, for example, doing 
customer  profiling  or  pre-selection  screening  of  job  candidates,  or  when  analysing  employee 
performance. 

Question E5. Does your enterprise have any measures to check the results generated by AI 
technologies for possible biases towards individuals based on sex, age, racial or ethnic origin, 
disability, religion or belief, sexual orientation? 

Some of examples of measures to check for possible bias:  

•  analysing the results of various machine learning models  

•  examining the dataset that was used to train the machine learning model 

•  data augmentation (which involves techniques to artificially generate additional 

data points from existing data, i.e. synthetic data) 

[Scope: enterprises that have any measures to check the results generated by AI 
technologies for possible biases towards individuals based on sex, age, racial or ethnic 
origin, disability, religion or belief, sexual orientation, i.e. E4 = yes; optional question] 

[Type: single answer (i.e. Tick only one); binary (Yes/No)] 

AI has many benefits, such as improving healthcare, education and decision-making, increasing 
the efficiency of production, boosting security, and improving customer service. However, it also 
has risks (e.g. non-transparent decision-making, gender-based or other kinds of discrimination). 
Bias  refers  to  the  inherent  risks  of  any  societal  or  economic  activity.  AI  bias  can  affect  and 
discriminate against people, if left unchecked by the social control mechanisms that govern human 
behaviour. Given the potential risks of using AI, it is necessary to monitor awareness of them and 
take measures to ensure that the subsequent use of AI systems does not lead to biases. 

The aim is to find out if an enterprise has any measures in place to check the results generated 
by AI technologies for possible biases towards individuals based on sex, age, racial or ethnic 
origin, disability, religion or belief, or sexual orientation. 

AI bias is ‘an anomaly in the output of machine learning algorithms, due to prejudiced assumptions 

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made during the algorithm development process or prejudices in the training data’29. AI systems 
contain biases due to: 

•  unconscious errors in thinking that affect individuals’ judgments and decisions and 

•  a  lack  of  complete  data  (incomplete  data  may  not  be  representative  and  may  therefore 

include bias.). 

An AI system can be as good as the quality of its input data, so it is important to minimise biases 
in AI. To avoid results generated by AI technologies that are biased towards individuals based on 
sex, age, racial or ethnic origin, disability, religion or belief, or sexual orientation, an enterprise can 
use different measures. Some measures to check for possible biases are:  

•  analysing the results of various machine learning models;  

•  examining the dataset used to train machine learning models (to see if it is representative 

and large enough to prevent common biases such as sampling biases;  

•  monitoring models over time for biases; 

•  augmenting  data  (using  techniques  to  artificially  generate  additional  data  points  from 

existing data, i.e. synthetic data); 

•  diversifying the organisation (diversification in the AI community makes it easier to identify 
biases,  with  people  who  first  notice  biases  generally  being  users  from  the  community 
affected by them).30 

Question E6: Has your enterprise ever considered using any of the artificial intelligence 
technologies listed in question E1? 

[Scope: enterprises that had not used any of the AI technologies listed in question E1, i.e. 
E1a) = no and E1b) = no and E1c) = no and E1d) = no and E1e) = no and E1f) = no and E1g) 
= no; optional question] 

[Type: single answer (i.e. Tick only one); binary (Yes/No)] 

Question E6 is asked to those enterprises that have not used any of the AI technologies listed in 
question E1, answer options a) to g). 

The question is aiming to filter out those enterprises that have considered using AI technologies 
but have not yet used them. This question is a filter for the following question E7. 

Question E7: What are the reasons for your enterprise not to use any of the Artificial 
Intelligence technologies listed in question E1? 

[Scope: enterprises that had not used any of the AI technologies listed in question E1, but 

29  Bias in AI: What it is, Types, Examples & 6 Ways to Fix it in 2023 (aimultiple.com) 
30 See footnote 27. 

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have considered using them, i.e. E6 = Yes; optional question] 

[Type: single answer per item (i.e. Tick only one); binary (Yes/ No); multiple items may be 
expected] 

Question E7 is aiming to collect information on the possible reasons (e.g. associated costs, lack 
of expertise, ethical concerns) why an enterprise has not used any of the AI technologies listed in 
question E1, although they have considered it (they have replied yes to question E6). 

Multiple reasons for not using AI technologies may be valid for one enterprise. 

a)  The costs seemed too high 

This answer option includes cases where the enterprise estimated that the costs related to using 
the AI technologies listed in question E1 were higher than the expected benefits. Therefore, the 
enterprise decided finally not to use them. The costs related to AI technologies use may include: 
costs of acquiring the AI systems; staff costs (costs of hiring a specialist, training, etc.); 
hardware/software costs due to incompatibility with existing equipment/software/systems; costs 
for purchasing training data for the AI systems; or other unexpected costs due to uncertainty 
factors e.g. due to legal consequences or violation of data privacy. 

b)  There is a lack of relevant expertise in the enterprise 

This item covers cases where enterprises have decided not to use any AI because they did not 
have enough relevant expertise in the enterprise, namely sufficient human resources, knowledge 
or skills, e.g. the required specialists are insufficiently available within the enterprise, or it is 
difficult to hire them. AI is an advanced and complex ICT technology that may require specialised 
knowledge to be managed and maintained even when acquired as a ready-to-use system. 
Therefore, some enterprises may be reluctant to use any AI systems because they do not have, 
or find it difficult to employ, specialised personnel with the required expertise. 

c)  Incompatibility with existing equipment, software or systems 

This item covers cases where enterprises have decided not to use any AI technologies because 
of incompatibility with existing equipment, software or systems. The introduction of AI 
technologies in the enterprise may not necessarily be compatible with the existing systems in use 
in the enterprise. It may therefore bring a certain uncertainty and require a resource investment in 
terms of the integration of the new system into the existing systems. An enterprise might 
therefore choose not to use AI technology because this might require a complete change in the 
technical infrastructure that the enterprise is already using, which could bring about additional 
acquisition costs, need for specialised personnel, need for additional training for the enterprise’s 
employees, or even the introduction of a new system for customers or suppliers of the enterprise. 

d)  Difficulties with availability or quality of the necessary data 

This answer option covers cases where the responding enterprise has not used any AI 
technologies due to difficulties with the availability or quality of the necessary data from within or 
from outside the responding enterprise. Data are essential for AI systems to be trained and 
operate efficiently. Difficulties with the availability or quality of the necessary data refers to cases 

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where there is no available data within the enterprise or it is difficult and costly to access them 
from external sources, or the available data is not detailed enough or do not include the 
necessary information to enable the training of the AI systems for the specific purpose of the 
enterprise (e.g. the enterprise may not be able to gain access to data regarding consumer 
behaviour in their customer segment for training an AI software for marketing or sales). 

e)  Concerns regarding violation of data protection and privacy 

This answer option refers to cases where the respondent enterprise has not used any AI 
technologies due to concerns regarding violation of data protection and privacy. 

One of the key elements of trustworthy AI technologies is the privacy and data governance, along 
with other requirements such as technical robustness, transparency, and diversity. AI systems 
must guarantee privacy and data protection throughout a system’s entire life cycle. This includes 
the information initially provided by the user, as well as the information generated about the user 
over the course of their interaction with the system (e.g. outputs that the AI system generated for 
specific users or how users responded to particular recommendations). Digital records of human 
behaviour may allow AI systems to infer not only individual preferences, but also their sexual 
orientation, age, gender, and religious or political views. To allow individuals to trust the data 
gathering process, it must be ensured that data collected about them will not be used to 
unlawfully or unfairly discriminate against them31. 

f)  Lack of clarity regarding the legal consequences (e.g. liability in the case of damage 

caused by the use of artificial intelligence) 

This answer option refers to cases where the respondent enterprise has not used any AI 
technologies due to lack of clarity regarding the legal consequences. 

Lawful AI is particularly important for the European Commission in the context of trustworthy AI32. 
AI systems do not operate in a lawless world. Several of the legally binding rules at European, 
national and international level already apply or are relevant to the development, deployment and 
use of AI systems today. Legal sources include, but are not limited to: EU primary law (the 
Treaties of the European Union and its Charter of Fundamental Rights); EU secondary law (such 
as the General Data Protection Regulation, the Product Liability Directive, the Regulation on the 
Free Flow of Non-Personal Data, anti-discrimination Directives, consumer law, and Safety and 
Health at Work Directives); the UN Human Rights treaties and the Council of Europe conventions 
(such as the European Convention on Human Rights); and numerous EU Member State laws. 
Besides generally applicable rules, various domain-specific rules exist that apply to particular AI 
applications (such as the Medical Device Regulation in the healthcare sector). 

The law provides both positive and negative obligations, which means that it should not only be 
interpreted with reference to what cannot be done, but also with reference to what should be 

31 Ethics guidelines for trustworthy AI 
32 See a footnote 29. 

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done and what may be done. The law not only prohibits certain actions but also enables others. 
In this regard, it can be noted that the EU Charter contains articles on the ‘freedom to conduct a 
business’ and the ’freedom of the arts and sciences’, alongside articles addressing areas to 
ensure AI’s trustworthiness, such as for instance data protection and non-discrimination. 

g)  Ethical considerations 

This answer option refers to cases where the respondent enterprise has not used any AI 
technologies because of ethical considerations. This is a particularly important topic for the EU 
(as well as internationally), as it tries to regulate the use of AI in order to tackle the associated 
risks. The EU is considered a front runner with regard to establishing a framework on ethical rules 
for AI. In this context, the European Commission has issued ethical guidelines33 for trustworthy AI 
that includes the ethical dimension, i.e. ensuring adherence to ethical principles and values of 
respect for human autonomy, prevention of harm, fairness and explicability. Trustworthy AI 
should be: (i) lawful - respecting all applicable laws and regulations; (ii) ethical - respecting ethical 
principles and values; and (iii) robust - both from a technical and social perspective since, even 
with good intentions, AI systems can cause unintentional harm34. 

Achieving trustworthy AI requires not only compliance with the law, which is but one of its three 
components. Laws are not always up to speed with technological developments, can at times be 
out of step with ethical norms, or may simply not be well suited to addressing certain issues. For 
AI systems to be trustworthy, they should therefore also be ethical, ensuring alignment with 
ethical norms. 

h)  Artificial intelligence technologies are not useful for the enterprise 

This answer option refers to enterprises that have not used any AI technologies because they 
were not considered useful for the enterprise, for instance not needed because of the activity of 
the enterprise. 

2.3.2.7. Module X: Background information 

The background variables have several purposes. Firstly, they are used for breakdowns. That is 
the case for the ‘Main economic activity of the enterprise’ and ‘Average number of employees 
and self-employed persons (persons employed)’. 

Secondly, they are needed to weight the percentages of turnover from e-commerce. The 
background variable ‘Total turnover’ is used for that purpose. The variable ‘Average number of 
employees and self-employed persons (persons employed)’ is similarly used to weight the 
percentage of persons employed using computers, the percentage of persons employed using 
computers with access to the internet, etc. The number of employees and self-employed persons 

33 See a footnote 29. 
34 Ethics guidelines for trustworthy AI | Shaping Europe’s digital future (europa.eu) 

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(persons employed) is also used to weight the qualitative variables. 

Thirdly, the background variables are used in the sampling design. Namely, ‘Main economic 
activity’ and ‘Average number of employees and self-employed persons (persons employed)’ are 
used to stratify the sample. 

The background variables described so far may be collected through the ICT survey 
questionnaire or obtained from alternative sources. The alternative sources are mainly the 
registers and one main business survey, usually used to produce the structural business 
statistics. It is very important that the background information is at least consistent with the 
structural business statistics. 

Question X1: Main economic activity of the enterprise 

[Scope: all enterprises] 

[Type: categorical] 

The main (or principal) economic activity is identified as the activity that contributes most to the 
total value added of the enterprise. The principal activity so identified does not necessarily 
account for 50% or more of the enterprise’s total value added. The classification of principal 
activity is determined by reference to NACE, first at the highest level of classification and then at 
more detailed levels (‘top-down’ method). 

The nomenclature NACE Rev. 2 is available on: https://ec.europa.eu/eurostat/web/nace 

The main economic activity of the enterprise should be classified by NACE Rev. 2 at its highest 
level of detail (4 digits). Nevertheless, only the following level of detail which is used in the 
breakdown is strictly necessary. 

NACE Rev. 2 groupings 

Aggregates for calculation of national NACE Rev. 2 aggregates 

1 

2 

3 

10 - 33 + 35 - 39 
+ 41 - 43 + 45 - 
47 + 49 - 53 + 55 
- 56 + 58 - 63 + 
68 - 75 + 77 - 82 
+ 95.1 

10 - 33  Manufacturing 

10 - 18 

Manufacture of products based on food, beverages, 
tobacco, textile, leather, wood, pulp and paper; publishing 
and printing 

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4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

19 - 23 

Manufacture of coke, refined petroleum products, chemical 
products, basic pharmaceutical products, rubber and 
plastics, other non-metallic mineral products 

24 - 25 

Manufacture of basic metals and fabricated metal products 
excluding machines and equipment 

26 - 33 

Manufacture of computers, electric and optical products, 
electrical equipment, machinery and equipment n.e.c., 
motor vehicles, other transport equipment, furniture, other 
manufacturing, repair and installation of machinery and 
equipment 

35 - 39 

Production and distribution of electricity, gas, steam and air 
conditioning; water supply, sewerage, waste management 
and remediation activities 

41 - 43  Construction 

45 - 47  Wholesale and retail trade; repair of motor vehicles and 

motorcycles 

47  Retail trade 

49 - 53  Transport and storage 

55  Accommodation 

55 - 56  Accommodation and food service activities 

58 - 63 

Information and communication 

68  Real estate activities 

69 - 75  Professional, scientific and technical activities 

77 - 82  Administrative and support service activities 

26.1 - 26.4 + 26.8 
+ 46.5 + 58.2 + 
61 + 62.01 + 
62.02 + 62.03 + 
62.09 + 63.1 + 
95.1 

Manufacture of electronic components and boards, 
consumer electronics, magnetic and optical media; 
wholesale of information and communication equipment; 
software publishing; telecommunications; computer 
programming, consultancy and facilities management 
activities, other information technology and computer service 
activities; data processing, hosting and related activities, web 

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portals; repair of computers and communication equipment 
or ICT sector 

Aggregates for calculation of European NACE Rev. 2 aggregates 

3a  

3b 

3c  

4a  

4b  

4c  

4d  

6a  

6b  

6c  

6d  

6e  

7a  

7b  

9a  

10 - 12  Manufacture of beverages, food and tobacco products 

13 - 15  Manufacture of textiles, wearing apparel, leather and related 

products 

16 - 18   Manufacture of wood and products of wood and cork, except 

furniture; articles of straw and plaiting materials; paper and 
paper products; printing and reproduction of recorded media 

19   Manufacture of coke and refined petroleum product 

20   Manufacture of chemicals and chemical products 

21   Manufacture of basic pharmaceutical products and 

pharmaceutical preparations 

22 - 23   Manufacture of rubber and plastic products; other non-

metallic mineral products 

26   Manufacture of computer, electronic and optical products 

27   Manufacture of electrical equipment, machinery and 

equipment n.e.c. 

28   Manufacture of machinery and equipment n.e.c. 

29 - 30   Manufacture of motor vehicles, trailers and semi-trailers; 

other transport equipment 

31 - 33   Manufacture of furniture; other manufacturing; repair and 

installation of machinery and equipment 

35   Electricity, gas, steam and air conditioning supply 

36 - 39   Water collection, treatment and supply; sewerage; waste 

collection, treatment and disposal activities; materials 
recovery; remediation activities and other waste 
management services 

45   Wholesale and retail trade and repair of motor vehicles and 

motorcycles 

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Aggregates for calculation of European NACE Rev. 2 aggregates 

9b  

14a  

14b  

14c  

16a  

16b  

16c  

46   Wholesale trade, except of motor vehicles and motorcycles 

58 - 60   Publishing activities; motion picture, video and television 

programme production, sound recording and music 
publishing activities; programming and broadcasting activities 

61   Telecommunications 

62 - 63   Computer programming, consultancy and related activities; 

information service activities 

69 - 71   Legal and accounting activities; activities of head offices; 

management consultancy activities; architectural and 
engineering activities; technical testing and analysis 

72   Scientific research and development 

73 - 75   Advertising and market research; other professional, 
scientific and technical activities; veterinary activities 

17a  

77 - 78 + 80-82   Activities for: rental and leasing; employment; security and 

investigation; services to buildings and landscape; office 
administrative, office support and other business support 

17b  

18a  

79   Travel agency, tour operator and other reservation service 

and related activities 

95.1   Repair of computers and communication equipment  

The NACE Rev. 2 categories are grouped together for dissemination purposes into several 
aggregates organised in five hierarchal levels. At the first level, there are two categories 
distinguishing ‘Manufacturing, Energy and Construction’ and ‘Non-financial services’. At the 
second level, activities are grouped at the Section level of NACE, making 11 categories. The 
content of these groupings is described above.  

X2: Average number of employees and self-employed persons (persons employed), during 
2023 

[Scope: all enterprises] 

[Type: categorical] 

With the introduction of the Regulation (EU) 2019/2152, the variable ‘persons employed’ is 
replaced by the variable ‘employees and self-employed persons’. This change in the 
denomination of the variable does not imply any change in the scope. The two variables 

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represent exactly the same concept. For the sake of user friendliness, the term ‘employees and 
self-employed persons’ is only used in the introductory part of the questionnaire and in Module X; 
in the rest of the questionnaire, the term ‘persons employed’ is used. 

For the purpose of general harmonisation of enterprise ICT usage statistics and the more general 
field of business statistics, the concept of employees and self-employed persons used here is 
taken from Regulation (EU) 2020/1197 of 30 July 2020 implementing Regulation 2020/2152 on 
European Business Statistics (p. 92, Variable 120101: Number of employees and self-employed 
persons). 

The number of employees and self-employed persons is the sum of the number of 
employees and number of self-employed persons. 

The number of employees represents the average number of persons who were, at some time 
during the reference period, employees of the statistical unit. 

Explanatory note: 

While the employment relationship, which qualifies the parties (into employee and employer), is 
defined in specific legislation or contracts, the term ‘employee’ usually means a person hired by 
the statistical unit to provide services to it on a regular basis, in exchange for benefits, and where 
the services provided are not part of an independent business. For the sake of clarity, 
apprentices, if hired under such conditions, are considered employees. 

The average should be calculated as the arithmetic mean of the number of employees over the 
shortest time periods of equal length fitting into the reference period, for which regular 
observations are practicable (e.g. daily, weekly, monthly, quarterly). 

The number of self-employed persons is the average number of persons who were at some 
time during the reference period the sole owners or joint owners of the statistical unit in which 
they work. Family workers and outworkers whose income is a function of the value of the outputs 
of the statistical unit are also included. 

Note: In order to check the comparability of data, it is necessary to indicate whether voluntary 
workers have been included under this heading or not. 

The number of employees and self-employed persons should be measured as the yearly average 
during the previous calendar year. For comparability reasons, the number of employees and self-
employed persons should not be confused with the number of employees (that excludes unpaid 
workers) or the number of employees in FTE units. 

The average number of employees and self-employed persons is coded into five size categories, 
of which three are compulsory and the other two are optional. 

Size categories (according to the number of employees and self-employed persons) 

Compulsory 

1  10 to 49  

Small enterprises 

2  50 to 249  

Medium enterprises 

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3  250 or more  

Large enterprises 

Optional 

4  Less than 2 

Small micro-enterprises 

5  2 to 9 

Big micro-enterprises 

Question X3: Total turnover (in value terms, excluding VAT), for 2023 

[Scope: all enterprises] 

[Type: numerical] 

This background variable is needed to weight the percentage of turnover resulting from orders 
received via computer networks (value of web sales and value of EDI-type sales in Module B: e-
Commerce sales). 

For the purpose of general harmonisation of enterprise ICT usage statistics and the more general 
field of business statistics, the concept of turnover used here is taken from Regulation (EU) 
2020/1197 of 30 July 2020 (p. 104, Variable 140301: Net turnover) implementing Regulation (EU) 
2019/2152: 

For all activities except for NACE 64, 65 and some activities of NACE 66 net turnover consists of 
all income arising during the reference period in the course of ordinary activities of the statistical 
unit, and is presented net of all price reductions, discounts and rebates granted by it. 

Income is defined as increases in economic benefits during the reference period in the form of 
inflows or enhancements of assets or decreases of liabilities that result in increases in equity, 
other than those relating to contributions from equity participants. 

The inflows referred to are arising from contracts with customers and are realised through the 
satisfaction by the statistical unit of performance obligations as provided for in said contracts. 
Usually, a performance obligation is represented by the sale (transfer) of goods or the rendering 
of services, however, the gross inflows can also contain revenues obtained as a yield on the use 
by others of the statistical unit’s assets. 

Excluded from net turnover are: 

•  all taxes, duties or levies linked directly to revenue; 

•  any amounts collected on behalf of any principal, if the statistical unit is acting as an agent in 

its relationship with said principal; 

•  all income not arising in the course of ordinary activities of the statistical unit. Usually, these 
types of income are classified as ‘Other (operating) income’, ‘Financial income’, ‘Extra-
ordinary income’ or under a similar heading, depending on the respective set of generally 
accepted accounting standards used to prepare the financial statements. 

Infra-annual statistics may not be able to take into account aspects such as annual price 
reductions, subsidies, rebates and discounts.

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3 

Data transmission 

Once the data have been collected by the NSIs, they must be transmitted to Eurostat. This chap-
ter provides guidance on how to compute aggregates and transmit the data. 

3.1. General description 
Data files on ICT usage and e-commerce in enterprises transmitted to Eurostat include statistical 
data elements laid down in the Implementing Act. The Implementing Act lists 65 mandatory 
variables and 30 optional variables.  

Metadata and quality reports complement those data. 

3.2. Codification, indicators and breakdown 
aggregates 
NSIs have to codify microdata, compute aggregates and break them down into different 
combinations depending on the enterprises’ activity, sector and size (number of employees and 
self-employed persons). Those steps are necessary prerequisites for transmitting data to 
Eurostat. 

3.2.1. Codification of microdata 

The following table provides guidance on how NSIs can codify microdata gathered using the 
model questionnaire in order to develop the indicators that need to be sent to Eurostat.  

The use of the following codification is not mandatory but recommended as the scope of the 
indicators to be transmitted to Eurostat are based on that codification. 

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Filter/ 
Remarks 
(Standard 
codification) 

All enterprises 

Variable name 

Code 

Description 

Module A: Access and use of the 
internet 

Access to the Internet 

A1: How many persons employed have 
access to the internet for business 
purposes? 

(including fixed line, fixed wireless and 
mobile telephone network connection) 

EMPIUSEVAL 

Nnnnn 

Absolute number 

Blank 

No answer 

EMPIUSEPCT 

Nnn 

Percentage (0-100, round to the nearest 
integer) 

Blank 

No answer 

Derived value 

EMPIUSE 

Nnnnn 

Reconstructed absolute number:  

Blank 

IF EMPIUSEVAL<> Blank THEN 
EMPIUSEVAL  

ELSEIF EMPIUSEPCT<>Blank AND EMP 
<> Blank 

THEN EMPIUSEPCT * EMP / 100 

ELSE Blank 

Use of a fixed line connection to the 
internet for business purposes 

A2: Does your enterprise use any type 
of fixed connection to the internet? 
(e.g. ADSL, SDSL, VDSL, fiber optics 
technology (FTTP), cable technology, 
fixed wireless) 

Enterprises 
where 
EMPIUSE > 0 

FIXBB 

1 

0 

Yes 

No 

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Variable name 

Code 

Description 

Blank 

No answer 

3 

Data transmission 

Filter/ 
Remarks 
(Standard 
codification) 

ISPDF1 

ISPDFOK 

9 

1 

2 

3 

4 

5 

Not applicable (EMPIUSE = Blank or 
EMPIUSE = 0) 

A3: What is the maximum contracted 
download speed of the fastest fixed 
internet connection of your enterprise? 

Enterprises 
where 
FIXBB=1 

a) less than 30 Mbit/s 

b) at least 30 but less than 100 Mbit/s 

c) at least 100 but less than 500 Mbit/s 

d) at least 500 but less than 1 Gbit/s 

e) at least 1 Gbit/s 

Blank 

No answer 

9 

1 

0 

Not applicable (FIXBB=Blank or 
FIXBB<>1) 

A4: Is the speed of your fixed line 
connection(s) to the internet usually 
sufficient for the actual needs of the 
enterprise? 

Enterprises 
where 
FIXBB=1 

Yes 

No 

Blank 

No answer 

9 

Not applicable (FIXBB=Blank or 
FIXBB<>1) 

Use of a mobile connection to the 
internet for business purposes 

A5: How many persons employed use 
a portable device provided by the 
enterprise, that allows connection to 
the internet via mobile telephone 
networks, for business purposes? 

Enterprises 
where 
EMPIUSE > 0 

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Filter/ 
Remarks 
(Standard 
codification) 

3 

Data transmission 

Variable name 

Code 

Description 

EMPMD2VAL 

Nnnnn 

(e.g. portable computers, or other 
portable devices such as smartphones) 

Absolute number (use as many digits as 
necessary) 

(optional) 

Blank 

No answer or option not included 

-1 

Not applicable (EMPIUSE = Blank or 
EMPIUSE = 0) 

EMPMD2PCT 

Nnn 

Percentage (0-100, round to the nearest 
integer) 

(optional) 

Blank 

No answer or option not included 

-1 

Not applicable (EMPIUSE = Blank or 
EMPIUSE = 0) 

Derived value 

EMPMD2 

Nnnnn 

Reconstructed absolute number:  

(optional) 

Blank 

IF EMPIUSE=Blank or EMPIUSE=0 
THEN -1 

-1 

ELSEIF EMPMD2VAL<> Blank THEN 
EMPMD2VAL 

ELSEIF EMPMD2PCT<>Blank AND EMP 
<> Blank 

THEN EMPMD2PCT * EMP / 100 

ELSE Blank 

Remote access and meetings via the 
internet 

A6: Do any of the persons employed 
have remote access to the following? 
(via computers or portable devices such 
as smartphones) 

Enterprises 
where 
EMPIUSE > 0 

RA_M 

a) E-mail system of the enterprise 

1 

0 

Yes 

No 

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Variable name 

Code 

Description 

3 

Data transmission 

Filter/ 
Remarks 
(Standard 
codification) 

RA_D 

RA_S 

RM 

Blank 

No answer 

9 

1 

0 

Not applicable (EMPIUSE = Blank or 
EMPIUSE = 0) 

b) Documents of the enterprise (e.g. files, 
spreadsheets, presentations, charts, 
photos) 

Yes 

No 

Blank 

No answer 

9 

1 

0 

Not applicable (EMPIUSE = Blank or 
EMPIUSE = 0) 

c) Business applications or software of 
the enterprise (e.g. access to accounting, 
sales, orders, CRM) 
Please exclude applications used for 
internal communication, e.g. Skype, MS 
Teams, Yammer. 

Yes 

No 

Blank 

No answer 

9 

1 

0 

Not applicable (EMPIUSE = Blank or 
EMPIUSE = 0) 

A7: Does your enterprise conduct 
remote meetings (via e.g. Skype, 
Zoom, MS Teams, WebEx)? 

Enterprises 
where 
EMPIUSE > 0 

Yes 

No 

Blank 

No answer 

9 

Not applicable (EMPIUSE = Blank or 
EMPIUSE = 0) 

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3 

Data transmission 

Variable name 

Code 

Description 

Filter/ 
Remarks 
(Standard 
codification) 

ADS   
(optional) 

(optional) 

ADS_KW 

Other use of the internet 

A8: Does your enterprise pay to 
advertise on the internet? 
(e.g. adverts on search engines, on social 
media, on other websites or apps) 

Enterprises 
where 
EMPIUSE > 0 

1 

0 

Yes 

No 

Blank 

No answer 

9 

1 

0 

Not applicable (EMPIUSE = Blank or 
EMPIUSE = 0) 

A9: Does your enterprise pay to 
advertise on the internet using any of 
the following targeted advertising 
methods? 

a) Based on content or keywords 
searched by internet users  

Enterprises 
where ADS=1 

Yes 

No 

Blank 

No answer 

9 

Not applicable (ADS=Blank or ADS<>1) 

ADS_TRK 

b) Based on the tracking of internet users' 
past activities or profile 

1 

0 

Yes 

No 

Blank 

No answer 

9 

Not applicable (ADS=Blank or ADS<>1) 

ADS_LOC 

c) Based on the geolocation of internet 
users 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

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3 

Data transmission 

Filter/ 
Remarks 
(Standard 
codification) 

Variable name 

Code 

Description 

1 

0 

Yes 

No 

Blank 

No answer 

9 

Not applicable (ADS=Blank or ADS<>1) 

ADS_OTH 

d) Any other method of targeted 
advertising on the internet not specified 
above 

1 

0 

Yes 

No 

Blank 

No answer 

9 

Not applicable (ADS=Blank or ADS<>1) 

Module B: e-Commerce sales 

Web sales of goods or services 

B1: During 2023, did your enterprise 
have web sales of goods or services 
via: 

Enterprises 
where 
EMPIUSE > 0 

AWS_COWN 

a) your enterprise's websites or apps? 
(including extranets) 

1 

0 

Yes 

No 

Blank 

No answer 

9 

Not applicable (EMPIUSE = Blank or 
EMPIUSE = 0) 

b) e-commerce marketplace websites or 
apps used by several enterprises for 
trading goods or services? (e.g. e-
Bookers, Booking, hotels.com, eBay, 
Amazon, Amazon Business, Alibaba, 
Rakuten, TimoCom) 

1 

Yes 

AWS_CMP 

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Filter/ 
Remarks 
(Standard 
codification) 

Enterprises 
where 
AWS_COWN
=1 or 
AWS_CMP=1 

3 

Data transmission 

Variable name 

Code 

Description 

0 

No 

Blank 

No answer 

9 

Not applicable (EMPIUSE = Blank or 
EMPIUSE = 0) 

B2: What was the value of your web 
sales? 

AWSVALVAL 

Nnnnn 

a) What was the value of your web sales 
of goods or services, in 2023? 

Blank 

No answer 

-1 

Not applicable ((AWS_COWN=Blank or 
AWS_COWN<>1) and (AWS_CMP=Blank 
or AWS_CMP<>1)) 

AWSVALPCT 

Nnn 

b) What percentage of the turnover was 
generated by web sales of goods or 
services, in 2023? (0-100, please provide 
at least one decimal) 

Blank 

No answer 

-1 

Not applicable ((AWS_COWN=Blank or 
AWS_COWN<>1) and (AWS_CMP=Blank 
or AWS_CMP<>1)) 

AWSVAL 

Nnnnn 

Reconstructed absolute number:  

Derived values 

Blank 

-1 

IF (AWS_COWN=Blank or 
AWS_COWN<>1) and (AWS_CMP=Blank 
or AWS_CMP<>1) THEN -1 

ELSEIF AWSVALVAL <> Blank THEN 
AWSVALVAL 

ELSEIF AWSVALPCT <>Blank AND 
TOVT <> Blank 

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3 

Data transmission 

Filter/ 
Remarks 
(Standard 
codification) 

Enterprises 
where 
AWS_COWN
=1 and 
AWS_CMP=1 

Variable name 

Code 

Description 

THEN AWSVALPCT * TOVT / 100 

ELSE Blank 

B3: What was the percentage 
breakdown of the value of the web 
sales in 2023 for the following: 

AWS_COWNP
CT 

a) via your enterprise's websites or apps? 
(including extranets) 

AWS_CMPPC
T 

Nnn 

Percentage (0-100) 

Blank 

No answer 

-1 

Not applicable ((AWS_COWN=Blank or 
AWS_COWN<>1) or (AWS_CMP=Blank 
or AWS_CMP<>1)) 

b) via e-commerce marketplace websites 
or apps used by several enterprises for 
trading goods or services? (e.g. e-
Bookers, Booking, hotels.com, eBay, 
Amazon, Amazon Business, Alibaba, 
Rakuten, TimoCom) 

Nnn 

Percentage (0-100) 

Blank 

No answer 

-1 

Not applicable ((AWS_COWN=Blank or 
AWS_COWN<>1) or (AWS_CMP=Blank 
or AWS_CMP<>1)) 

Derived values 

The following two variables reconstruct 
the turnover resulting from the orders 
received via a website or apps (in 
monetary terms, excluding VAT) in 2023, 
via the respective channels (enterprise's 
website or e-commerce marketplace), 
based on the replies to questions B1 and 

Enterprises 
where 
AWS_COWN
=1 or 
AWS_CMP=1 

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3 

Data transmission 

Filter/ 
Remarks 
(Standard 
codification) 

Variable name 

Code 

Description 

B3. The formulae below indicate how they 
could be calculated: 

AWSVAL_CO
WN 

Nnnnn 

Reconstructed absolute number:  

Blank 

IF AWS_COWN=1 THEN 

-1 

IF AWS_CMP=Blank or AWS_CMP=0 

THEN AWSVAL  

ELSE 

IF AWS_COWNPCT=Blank 

THEN Blank 

ELSE AWSVAL * AWS_COWNPCT / 100 

ELSEIF AWS_COWN=0 THEN 0 

ELSEIF AWS_COWN=Blank THEN Blank 

ELSE -1 

AWSVAL_CMP  Nnnnn 

Reconstructed absolute number:  

Blank 

IF AWS_CMP=1 THEN 

-1 

IF AWS_COWN=Blank or AWS_COWN=0 

THEN AWSVAL  

ELSE 

IF AWS_CMPPCT=Blank 

THEN Blank 

ELSE AWSVAL * AWS_CMPPCT / 100 

ELSEIF AWS_CMP=0 THEN 0 

ELSEIF AWS_CMP=Blank THEN Blank 

ELSE -1 

B4: What was the percentage 
breakdown of the value of the web 
sales in 2023 by type of customer: 

Enterprises 
where 
AWS_COWN

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3 

Data transmission 

Filter/ 
Remarks 
(Standard 
codification) 

=1 or 
AWS_CMP=1 

Variable name 

Code 

Description 

AWSVALCPCT 

a) Sales to private consumers (B2C) 

Nnn 

Percentage (0-100) 

Blank 

No answer 

-1 

Not applicable ((AWS_COWN=Blank or 
AWS_COWN<>1) and (AWS_CMP=Blank 
or AWS_CMP<>1)) 

b) Sales to other enterprises (B2B) and 
Sales to public sector (B2G) 

AWSVALBGPC
T 

Nnn 

Percentage (0-100) 

Blank 

No answer 

-1 

Not applicable ((AWS_COWN=Blank or 
AWS_COWN<>1) and (AWS_CMP=Blank 
or AWS_CMP<>1)) 

AWSVALC 

Nnnnn 

Reconstructed absolute number:  

Derived values 

Blank 

-1 

IF (AWS_COWN=Blank or 
AWS_COWN<>1) and (AWS_CMP=Blank 
or AWS_CMP<>1) THEN -1  

ELSEIF AWSVALCPCT<>Blank AND 
AWSVAL <> Blank 

THEN AWSVALCPCT * AWSVAL / 100 

ELSE Blank 

AWSVALBG 

Nnnnn 

Reconstructed absolute number:  

Blank 

-1 

IF (AWS_COWN=Blank or 
AWS_COWN<>1) and (AWS_CMP=Blank 
or AWS_CMP<>1) THEN -1  

ELSEIF AWSVALBGPCT<>Blank AND 
AWSVAL <> Blank 

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Filter/ 
Remarks 
(Standard 
codification) 

Enterprises 
where 
AWS_COWN
=1 or 
AWS_CMP=1 

3 

Data transmission 

Variable name 

Code 

Description 

THEN AWSVALBGPCT * AWSVAL / 100 

ELSE Blank 

B5: During 2023, did your enterprise 
have web sales to customers located 
in the following geographic areas? 

AWSHM 

a) Own country 

1 

0 

Yes 

No 

Blank 

No answer 

AWSEU 

AWSWW 

9 

1 

0 

Not applicable ((AWS_COWN=Blank or 
AWS_COWN<>1) and (AWS_CMP=Blank 
or AWS_CMP<>1)) 

b) Other EU countries 

Yes 

No 

Blank 

No answer 

9 

1 

0 

Not applicable ((AWS_COWN=Blank or 
AWS_COWN<>1) and (AWS_CMP=Blank 
or AWS_CMP<>1)) 

c) Rest of the world 

Yes 

No 

Blank 

No answer 

9 

Not applicable ((AWS_COWN=Blank or 
AWS_COWN<>1) and (AWS_CMP=Blank 
or AWS_CMP<>1)) 

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3 

Data transmission 

Filter/ 
Remarks 
(Standard 
codification) 

Enterprises 
where at least 
2 of AWSHM, 
AWSEU and 
AWSWW are 
1 

Variable name 

Code 

Description 

(optional) 

B6: What was the percentage 
breakdown of the value of web sales in 
2023 to customers located in the 
following geographic areas? 
(Please refer to value of web sales you 
reported in B2) 

AWSHM_PCT 

a) Own country 

Nnn 

Percentage (0-100) 

Blank 

No answer 

-1 

Not applicable (at most 1 of AWSHM, 
AWSEU and AWSWW is 1, or AWSHM, 
AWSEU and AWSWW are blank or not 
applicable) 

AWSEU_PCT 

b) Other EU countries 

Nnn 

Percentage (0-100) 

Blank 

No answer 

-1 

Not applicable (at most 1 of AWSHM, 
AWSEU and AWSWW is 1, or AWSHM, 
AWSEU and AWSWW are blank or not 
applicable) 

AWSWW_PCT 

c) Rest of the world 

Nnn 

Percentage (0-100) 

Blank 

No answer 

-1 

Not applicable (at most 1 of AWSHM, 
AWSEU and AWSWW is 1, or AWSHM, 
AWSEU and AWSWW are blank or not 
applicable) 

Derived values 

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3 

Data transmission 

Variable name 

Code 

Description 

The following three variables reconstruct 
the turnover resulting from the orders 
received via a website or apps (in 
monetary terms, excluding VAT) in 2023, 
broken down by the customer location 
(home country, other EU country, other 
non-EU country), based on the replies to 
questions B5 and B6. The formulae below 
indicate how they could be calculated: 

AWSVAL_HM 

Nnnnn 

Reconstructed absolute number:  

Filter/ 
Remarks 
(Standard 
codification) 

Enterprises 
where 
AWS_COWN
=1 or 
AWS_CMP=1 

Blank 

IF AWSHM=1 THEN 

-1 

IF (AWSEU=Blank or AWSEU=0) and 
(AWSWW=Blank or AWSWW=0) 

THEN AWSVAL  

ELSE 

IF AWSHM_PCT=Blank 

THEN Blank 

ELSE AWSVAL * AWSHM_PCT / 100 

ELSEIF AWSHM=0 THEN 0 

ELSEIF AWSHM=Blank THEN Blank 

ELSE -1 

AWSVAL_EU 

Nnnnn 

Reconstructed absolute number:  

Blank 

IF AWSEU=1 THEN 

-1 

IF (AWSHM=Blank or AWSHM=0) and 
(AWSWW=Blank or AWSWW=0) 

THEN AWSVAL  

ELSE 

IF AWSEU_PCT=Blank 

THEN Blank 

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3 

Data transmission 

Filter/ 
Remarks 
(Standard 
codification) 

Variable name 

Code 

Description 

ELSE AWSVAL * AWSEU_PCT / 100 

ELSEIF AWSEU=0 THEN 0 

ELSEIF AWSEU=Blank THEN Blank 

ELSE -1 

AWSVAL_WW 

Nnnnn 

Reconstructed absolute number:  

Blank 

IF AWSWW=1 THEN 

-1 

IF (AWSHM=Blank or AWSHM=0) and 
(AWSEU=Blank or AWSEU=0) 

THEN AWSVAL  

ELSE 

IF AWSWW_PCT=Blank 

THEN Blank 

ELSE AWSVAL * AWSWW_PCT / 100 

ELSEIF AWSWW=0 THEN 0 

ELSEIF AWSWW=Blank THEN Blank 

ELSE -1 

EDI-type sales 

B7: During 2023, did your enterprise 
have EDI-type sales of goods or 
services? 

Enterprises 
where 
EMPIUSE > 0 

AXSELL 

1 

0 

Yes 

No 

Blank 

No answer 

9 

Not applicable (EMPIUSE = Blank or 
EMPIUSE = 0) 

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Filter/ 
Remarks 
(Standard 
codification) 

Enterprises 
where 
AXSELL=1 

3 

Data transmission 

Variable name 

Code 

Description 

B8: What was the value of your EDI-
type sales? 

AXSVALVAL 

Nnnnn 

a) What was the value of your EDI-type 
sales of goods or services, in 2023? 

Blank 

No answer 

-1 

Not applicable (AXSELL=Blank or 
AXSELL<>1) 

AXSVALPCT 

Nnn 

b) What percentage of total turnover was 
generated by EDI-type sales of goods or 
services, in 2023? 

Blank 

No answer 

-1 

Not applicable (AXSELL=Blank or 
AXSELL<>1) 

AXSVAL 

Nnnnn 

Reconstructed absolute number:  

Derived value 

Blank 

IF AXSELL<>1 THEN -1  

-1 

ELSEIF AXSVALVAL <> Blank THEN 
AXSVALVAL 

ELSEIF AXSVALPCT <>Blank AND TOVT 
<> Blank 

THEN AXSVALPCT * TOVT / 100 

ELSE Blank 

Module C: ICT specialists and skills 

C1: Does your enterprise employ ICT 
specialists? 
ICT specialists are employees for whom 
ICT is the main job. For example, to 
develop, operate or maintain ICT systems 
or applications. 

All enterprises 

ITSP2 

1 

Yes 

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3 

Data transmission 

Filter/ 
Remarks 
(Standard 
codification) 

All enterprises 

Variable name 

Code 

Description 

0 

No 

Blank 

No answer 

C2: Did your enterprise provide any 
type of training to develop ICT related 
skills of the persons employed, during 
2023? 

a) Training for ICT specialists 
Tick No if your enterprise didn't employ 
any ICT specialists during 2023 

ITSPT2 

ITUST2 

1 

0 

Yes 

No 

Blank 

No answer 

b) Training for other persons employed 

1 

0 

Yes 

No 

Blank 

No answer 

C3: Did your enterprise recruit or try to 
recruit ICT specialists, during 2023? 

All enterprises 

ITSPRCR2 

1 

0 

Yes 

No 

Blank 

No answer 

C4: During 2023, did your enterprise 
have vacancies for ICT specialists that 
were difficult to fill? 

Enterprises 
where 
ITSPRCR2=1 

ITSPVAC2 

1 

0 

Yes 

No 

Blank 

No answer 

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3 

Data transmission 

Variable name 

Code 

Description 

Filter/ 
Remarks 
(Standard 
codification) 

(optional) 

ITSPDLA 

ITSPDLET 

ITSPDLWE 

9 

1 

0 

Not applicable (ITSPRCR2=Blank or 
ITSPRCR2<>1) 

C5: Did your enterprise have any of the 
following difficulties to recruit ICT 
specialists during 2023? 

Enterprises 
where 
ITSPVAC2=1 

a) Lack of applications 

Yes 

No 

Blank 

No answer 

9 

1 

0 

Not applicable (ITSPVAC2=Blank or 
ITSPVAC2<>1) 

b) Applicants' lack of relevant ICT related 
qualifications from education and/or 
training 

Yes 

No 

Blank 

No answer 

9 

1 

0 

Not applicable (ITSPVAC2=Blank or 
ITSPVAC2<>1) 

c) Applicants' lack of relevant work 
experience 

Yes 

No 

Blank 

No answer 

9 

Not applicable (ITSPVAC2=Blank or 
ITSPVAC2<>1) 

ITSPDSAL 

1 

Yes 

d) Applicants' salary expectations too high 

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3 

Data transmission 

Filter/ 
Remarks 
(Standard 
codification) 

Variable name 

Code 

Description 

0 

No 

Blank 

No answer 

9 

Not applicable (ITSPVAC2=Blank or 
ITSPVAC2<>1) 

C6: Who performed your enterprise's 
ICT functions in 2023? 
(e.g. maintenance of ICT infrastructure; 
support for office software; development 
or support of business management 
software/systems and/or web solutions; 
security and data protection) 

a) own employees (incl. those employed 
in parent or affiliate enterprises) 

All enterprises 

IT_OWN 

IT_EXT 

1 

0 

Yes 

No 

Blank 

No answer 

b) external suppliers 

1 

0 

Yes 

No 

Blank 

No answer 

Module D: ICT security 

D1: Does your enterprise use any of 
the following ICT security measures? 

Enterprises 
where 
EMPIUSE > 0 

SECMSPSW 

a) Authentication via strong password 
(e.g. minimum length, use of numbers 
and special characters, changed 
periodically) 

1 

0 

Yes 

No 

Blank 

No answer 

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3 

Data transmission 

Variable name 

Code 

Description 

Filter/ 
Remarks 
(Standard 
codification) 

SECMUIBM 

SECMDUO 

SECMDENC 

9 

1 

0 

Not applicable (EMPIUSE = Blank or 
EMPIUSE = 0) 

b) Authentication via biometric methods 
used to access the enterprise's ICT 
system (e.g. authentication based on 
fingerprints, voice, face) 

Yes 

No 

Blank 

No answer 

9 

Not applicable (EMPIUSE = Blank or 
EMPIUSE = 0) 

c) Authentication based on a combination 
of at least two authentication mechanisms 
(i.e. combination of e.g. user-defined 
password, one-time password (OTP), 
code generated via a security token or 
received via a smartphone, biometric 
method (e.g. based on fingerprints, voice, 
face)) 

1 

0 

Yes 

No 

Blank 

No answer 

9 

1 

0 

Not applicable (EMPIUSE = Blank or 
EMPIUSE = 0) 

d) Encryption of data, documents or e-
mails 

Yes 

No 

Blank 

No answer 

9 

Not applicable (EMPIUSE = Blank or 
EMPIUSE = 0) 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

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3 

Data transmission 

Filter/ 
Remarks 
(Standard 
codification) 

Variable name 

Code 

Description 

SECMOSBU 

e) Data backup to a separate location 
(including backup to the cloud) 

SECMNAC 

SECMVPN 

1 

0 

Yes 

No 

Blank 

No answer 

9 

1 

0 

Not applicable (EMPIUSE = Blank or 
EMPIUSE = 0) 

f) Network access control (management 
of user rights in the enterprise's network) 

Yes 

No 

Blank 

No answer 

9 

1 

0 

Not applicable (EMPIUSE = Blank or 
EMPIUSE = 0) 

g) VPN (Virtual Private Network extends a 
private network across a public network to 
enable secure exchange of data over 
public network) 

Yes 

No 

Blank 

No answer 

9 

Not applicable (EMPIUSE = Blank or 
EMPIUSE = 0) 

SECMSMS1 

h) ICT security monitoring system used to 
detect suspicious activity (e.g. intrusion 
detection or prevention systems that 
monitors users’ or devices’ behaviour, 
network traffic) 
Please exclude antivirus software and 
default firewall solution included in the 

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3 

Data transmission 

Variable name 

Code 

Description 

Filter/ 
Remarks 
(Standard 
codification) 

operating system of personal computers 
and routers. 

1 

0 

Yes 

No 

Blank 

No answer 

9 

1 

0 

Not applicable (EMPIUSE = Blank or 
EMPIUSE = 0) 

i) Maintaining log files that enable 
analysis after ICT security incidents 

Yes 

No 

Blank 

No answer 

9 

1 

0 

Not applicable (EMPIUSE = Blank or 
EMPIUSE = 0) 

j) ICT risk assessment, i.e. periodical 
assessment of probability and 
consequences of ICT security incidents 

Yes 

No 

Blank 

No answer 

9 

1 

0 

Not applicable (EMPIUSE = Blank or 
EMPIUSE = 0) 

k) ICT security tests (e.g. performing 
penetration tests, testing security alert 
system, review of security measures, 
testing of backup systems) 

Yes 

No 

Blank 

No answer 

SECMLOG 

SECMRASS 

SECMTST 

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3 

Data transmission 

Filter/ 
Remarks 
(Standard 
codification) 

Enterprises 
where 
EMPIUSE > 0 

Variable name 

Code 

Description 

9 

Not applicable (EMPIUSE = Blank or 
EMPIUSE = 0) 

SECM_CNT 

Derived value: ICT security measures 

0-11 

Give one point for each of the following 11 
conditions, if true: 

SECMSPSW=1, SECMUIBM=1, 
SECMDUO=1, SECMDENC=1, 
SECMOSBU=1, SECMNAC=1, 
SECMVPN=1, SECMSMS=1, 
SECMLOG=1, SECMRASS=1, 
SECMTST=1 

-1 

Not applicable (EMPIUSE = Blank or 
EMPIUSE = 0) 

D2: Does your enterprise make 
persons employed aware of their 
obligations in ICT security related 
issues in the following ways? 

Enterprises 
where 
EMPIUSE > 0 

SECAWVTGI 

a) Voluntary training or internally available 
information (e.g. information on the 
intranet) 

1 

0 

Yes 

No 

Blank 

No answer 

9 

Not applicable (EMPIUSE = Blank or 
EMPIUSE = 0) 

SECAWCTP 

b) Compulsory training courses or viewing 
compulsory material 

1 

Yes 

106 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
3 

Data transmission 

Variable name 

Code 

Description 

Filter/ 
Remarks 
(Standard 
codification) 

0 

No 

Blank 

No answer 

9 

1 

0 

Not applicable (EMPIUSE = Blank or 
EMPIUSE = 0) 

c) By contract (e.g. contract of 
employment) 

Yes 

No 

Blank 

No answer 

9 

Not applicable (EMPIUSE = Blank or 
EMPIUSE = 0) 

D3: Does your enterprise have 
document(s) on measures, practices or 
procedures on ICT security? 
Documents on ICT security and 
confidentiality of data cover employee 
training in ICT use, ICT security 
measures, the evaluation of ICT security 
measures, plans for updating ICT security 
documents, etc. 

Enterprises 
where 
EMPIUSE > 0 

1 

0 

Yes 

No 

Blank 

No answer 

9 

Not applicable (EMPIUSE = Blank or 
EMPIUSE = 0) 

D4: When were your enterprise's 
document(s) on measures, practices or 
procedures on ICT security, defined or 
most recently reviewed? 
Documents on ICT security and 
confidentiality of data cover employee 
training in ICT use, ICT security 
measures, the evaluation of ICT security 

Enterprises 
where 
SECPOL2=1 

SECAWCONT 

SECPOL2 

SECPREV 

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3 

Data transmission 

Filter/ 
Remarks 
(Standard 
codification) 

Variable name 

Code 

Description 

(optional) 

measures, plans for updating ICT security 
documents, etc. 

1 

2 

3 

a) within the last 12 months 

b) more than 12 months and up to 24 
months ago 

c) more than 24 months ago 

Blank 

No answer 

SEC2IUSVF 

SEC2IUSVA 

9 

1 

0 

Not applicable (SECPOL2=Blank or 
SECPOL2<>1) 

D5: During 2023, did your enterprise 
experience any ICT related security 
incident leading to the following 
consequences? 

a) Unavailability of ICT services due to 
hardware or software failures 

Enterprises 
where 
EMPIUSE > 0 

Yes 

No 

Blank 

No answer 

9 

1 

0 

Not applicable (EMPIUSE = Blank or 
EMPIUSE = 0) 

b) Unavailability of ICT services due to 
attack from outside, e.g. ransomware 
attacks, Denial of Service attacks 

Yes 

No 

Blank 

No answer 

9 

Not applicable (EMPIUSE = Blank or 
EMPIUSE = 0) 

SEC2IDCDF 

c) Destruction or corruption of data due to 
hardware or software failures 

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3 

Data transmission 

Variable name 

Code 

Description 

Filter/ 
Remarks 
(Standard 
codification) 

SEC2IDCDA 

SEC2ICNFA 

SEC2ICNFF 

1 

0 

Yes 

No 

Blank 

No answer 

9 

1 

0 

Not applicable (EMPIUSE = Blank or 
EMPIUSE = 0) 

d) Destruction or corruption of data due to 
infection of malicious software or 
unauthorised intrusion 

Yes 

No 

Blank 

No answer 

9 

1 

0 

Not applicable (EMPIUSE = Blank or 
EMPIUSE = 0) 

e) Disclosure of confidential data due to 
intrusion, pharming, phishing attack, 
intentional actions by own employees 

Yes 

No 

Blank 

No answer 

9 

1 

0 

Not applicable (EMPIUSE = Blank or 
EMPIUSE = 0) 

f) Disclosure of confidential data due to 
unintentional actions by own employees 

Yes 

No 

Blank 

No answer 

9 

Not applicable (EMPIUSE = Blank or 
EMPIUSE = 0) 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

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Variable name 

Code 

Description 

3 

Data transmission 

Filter/ 
Remarks 
(Standard 
codification) 

Module E: Artificial Intelligence 

E1: Does your enterprise use any of 
the following Artificial Intelligence (AI) 
technologies? 

Enterprises 
where 
EMPIUSE > 0 

AI_TTM 

a) AI technologies performing analysis of 
written language (e.g. text mining) 

AI_TSR 

AI_TNLG 

1 

0 

Yes 

No 

Blank 

No answer 

9 

1 

0 

Not applicable (EMPIUSE = Blank or 
EMPIUSE = 0) 

b) AI Technologies converting spoken 
language into machine-readable format 
(speech recognition) 

Yes 

No 

Blank 

No answer 

9 

1 

0 

Not applicable (EMPIUSE = Blank or 
EMPIUSE = 0) 

c) AI Technologies generating written or 
spoken language (natural language 
generation, speech synthesis) 

Yes 

No 

Blank 

No answer 

9 

Not applicable (EMPIUSE = Blank or 
EMPIUSE = 0) 

AI_TIR 

d) AI Technologies identifying objects or 
persons based on images or videos 
(image recognition, image processing) 

1 

Yes 

110 

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3 

Data transmission 

Variable name 

Code 

Description 

Filter/ 
Remarks 
(Standard 
codification) 

AI_TML 

AI_TPA 

AI_TAR 

0 

No 

Blank 

No answer 

9 

1 

0 

Not applicable (EMPIUSE = Blank or 
EMPIUSE = 0) 

e) Machine learning (e.g. deep learning) 
for data analysis 

Yes 

No 

Blank 

No answer 

9 

1 

0 

Not applicable (EMPIUSE = Blank or 
EMPIUSE = 0) 

f) AI Technologies automating different 
workflows or assisting in decision making 
(e.g. AI based software robotic process 
automation) 

Yes 

No 

Blank 

No answer 

9 

1 

0 

Not applicable (EMPIUSE = Blank or 
EMPIUSE = 0) 

g) AI Technologies enabling physical 
movement of machines via autonomous 
decisions based on observation of 
surroundings (autonomous robots, self-
driving vehicles, autonomous drones) 

Yes 

No 

Blank 

No answer 

9 

Not applicable (EMPIUSE = Blank or 
EMPIUSE = 0) 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

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3 

Data transmission 

Filter/ 
Remarks 
(Standard 
codification) 

Enterprises 
where 
AI_TTM=1 or 
AI_TSR=1 or 
AI_TNLG=1 
or AI_TIR=1 
or AI_TML=1 
or AI_TPA=1 
or AI_TAR=1 

Variable name 

Code 

Description 

E2: Does your enterprise use Artificial 
Intelligence software or systems for 
any of the following purposes? 

AI_PMS 

a) Use of AI for marketing or sales 

AI_PPP 

1 

0 

Yes 

No 

Blank 

No answer 

Not applicable ((AI_TTM=Blank or 
AI_TTM<>1) and (AI_TSR=Blank or 
AI_TSR<>1) and (AI_TNLG=Blank or 
AI_TNLG<>1) and (AI_TIR=Blank or 
AI_TIR<>1) and (AI_TML=Blank or 
AI_TML<>1) and (AI_TPA=Blank or 
AI_TPA<>1) and (AI_TAR=Blank or 
AI_TAR<>1)) 

b) Use of AI for production or service 
processes 

Yes 

No 

9 

1 

0 

Blank 

No answer 

9 

Not applicable ((AI_TTM=Blank or 
AI_TTM<>1) and (AI_TSR=Blank or 
AI_TSR<>1) and (AI_TNLG=Blank or 
AI_TNLG<>1) and (AI_TIR=Blank or 
AI_TIR<>1) and (AI_TML=Blank or 
AI_TML<>1) and (AI_TPA=Blank or 
AI_TPA<>1) and (AI_TAR=Blank or 
AI_TAR<>1)) 

112 

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3 

Data transmission 

Variable name 

Code 

Description 

AI_PBAM 

c) Use of AI for organisation of business 
administration processes or management 

Filter/ 
Remarks 
(Standard 
codification) 

1 

0 

Yes 

No 

Blank 

No answer 

Not applicable ((AI_TTM=Blank or 
AI_TTM<>1) and (AI_TSR=Blank or 
AI_TSR<>1) and (AI_TNLG=Blank or 
AI_TNLG<>1) and (AI_TIR=Blank or 
AI_TIR<>1) and (AI_TML=Blank or 
AI_TML<>1) and (AI_TPA=Blank or 
AI_TPA<>1) and (AI_TAR=Blank or 
AI_TAR<>1)) 

d) Use of AI for logistics 

Yes 

No 

9 

1 

0 

Blank 

No answer 

Not applicable ((AI_TTM=Blank or 
AI_TTM<>1) and (AI_TSR=Blank or 
AI_TSR<>1) and (AI_TNLG=Blank or 
AI_TNLG<>1) and (AI_TIR=Blank or 
AI_TIR<>1) and (AI_TML=Blank or 
AI_TML<>1) and (AI_TPA=Blank or 
AI_TPA<>1) and (AI_TAR=Blank or 
AI_TAR<>1)) 

e) Use of AI for ICT security 

Yes 

No 

9 

1 

0 

Blank 

No answer 

9 

Not applicable ((AI_TTM=Blank or 
AI_TTM<>1) and (AI_TSR=Blank or 
AI_TSR<>1) and (AI_TNLG=Blank or 
AI_TNLG<>1) and (AI_TIR=Blank or 
AI_TIR<>1) and (AI_TML=Blank or 

AI_PLOG 

AI_PITS 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

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3 

Data transmission 

Filter/ 
Remarks 
(Standard 
codification) 

Variable name 

Code 

Description 

AI_TML<>1) and (AI_TPA=Blank or 
AI_TPA<>1) and (AI_TAR=Blank or 
AI_TAR<>1)) 

AI_PFIN 

f) Use of AI for accounting, controlling or 
finance management 

AI_PRDI 

1 

0 

Yes 

No 

Blank 

No answer 

Not applicable ((AI_TTM=Blank or 
AI_TTM<>1) and (AI_TSR=Blank or 
AI_TSR<>1) and (AI_TNLG=Blank or 
AI_TNLG<>1) and (AI_TIR=Blank or 
AI_TIR<>1) and (AI_TML=Blank or 
AI_TML<>1) and (AI_TPA=Blank or 
AI_TPA<>1) and (AI_TAR=Blank or 
AI_TAR<>1)) 

g) Use of AI for research and 
development (R&D) or innovation activity 
(excluding research on AI) 

Yes 

No 

9 

1 

0 

Blank 

No answer 

9 

Not applicable ((AI_TTM=Blank or 
AI_TTM<>1) and (AI_TSR=Blank or 
AI_TSR<>1) and (AI_TNLG=Blank or 
AI_TNLG<>1) and (AI_TIR=Blank or 
AI_TIR<>1) and (AI_TML=Blank or 
AI_TML<>1) and (AI_TPA=Blank or 
AI_TPA<>1) and (AI_TAR=Blank or 
AI_TAR<>1)) 

(optional) 

E3: How did your enterprise acquire 
the Artificial Intelligence software or 
systems that it uses? 

Enterprises 
where 
AI_TTM=1 or 
AI_TSR=1 or 

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Filter/ 
Remarks 
(Standard 
codification) 

AI_TNLG=1 
or AI_TIR=1 
or AI_TML=1 
or AI_TPA=1 
or AI_TAR=1 

3 

Data transmission 

Variable name 

Code 

Description 

AI_ADOWN 

a) They were developed by own 
employees (including those employed in 
parent or affiliate enterprise) 

1 

0 

Yes 

No 

Blank 

No answer or option not included 

AI_AMOWN 

Not applicable ((AI_TTM=Blank or 
AI_TTM<>1) and (AI_TSR=Blank or 
AI_TSR<>1) and (AI_TNLG=Blank or 
AI_TNLG<>1) and (AI_TIR=Blank or 
AI_TIR<>1) and (AI_TML=Blank or 
AI_TML<>1) and (AI_TPA=Blank or 
AI_TPA<>1) and (AI_TAR=Blank or 
AI_TAR<>1)) 

b) Commercial software or systems were 
modified by own employees (including 
those employed in parent or affiliate 
enterprise) 

Yes 

No 

9 

1 

0 

Blank 

No answer or option not included 

9 

Not applicable ((AI_TTM=Blank or 
AI_TTM<>1) and (AI_TSR=Blank or 
AI_TSR<>1) and (AI_TNLG=Blank or 
AI_TNLG<>1) and (AI_TIR=Blank or 
AI_TIR<>1) and (AI_TML=Blank or 
AI_TML<>1) and (AI_TPA=Blank or 
AI_TPA<>1) and (AI_TAR=Blank or 
AI_TAR<>1)) 

AI_AOS 

c) Open-source software or systems were 
modified by own employees (including 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

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3 

Data transmission 

Filter/ 
Remarks 
(Standard 
codification) 

Variable name 

Code 

Description 

those employed in parent or affiliate 
enterprise) 

1 

0 

Yes 

No 

Blank 

No answer or option not included 

Not applicable ((AI_TTM=Blank or 
AI_TTM<>1) and (AI_TSR=Blank or 
AI_TSR<>1) and (AI_TNLG=Blank or 
AI_TNLG<>1) and (AI_TIR=Blank or 
AI_TIR<>1) and (AI_TML=Blank or 
AI_TML<>1) and (AI_TPA=Blank or 
AI_TPA<>1) and (AI_TAR=Blank or 
AI_TAR<>1)) 

d) Commercial software or systems ready 
to use were purchased (including 
examples where it was already 
incorporated in a purchased item or 
system) 

Yes 

No 

9 

1 

0 

Blank 

No answer or option not included 

Not applicable ((AI_TTM=Blank or 
AI_TTM<>1) and (AI_TSR=Blank or 
AI_TSR<>1) and (AI_TNLG=Blank or 
AI_TNLG<>1) and (AI_TIR=Blank or 
AI_TIR<>1) and (AI_TML=Blank or 
AI_TML<>1) and (AI_TPA=Blank or 
AI_TPA<>1) and (AI_TAR=Blank or 
AI_TAR<>1)) 

e) External providers were contracted to 
develop or modify them 

Yes 

No 

9 

1 

0 

Blank 

No answer or option not included 

AI_ARDY 

AI_AEXT 

116 

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3 

Data transmission 

Variable name 

Code 

Description 

9 

Not applicable ((AI_TTM=Blank or 
AI_TTM<>1) and (AI_TSR=Blank or 
AI_TSR<>1) and (AI_TNLG=Blank or 
AI_TNLG<>1) and (AI_TIR=Blank or 
AI_TIR<>1) and (AI_TML=Blank or 
AI_TML<>1) and (AI_TPA=Blank or 
AI_TPA<>1) and (AI_TAR=Blank or 
AI_TAR<>1)) 

AI_PDI 
(optional) 

E4: Does your enterprise process data 
on individuals using AI technologies? 

Filter/ 
Remarks 
(Standard 
codification) 

Enterprises 
where 
AI_TTM=1 or 
AI_TSR=1 or 
AI_TNLG=1 
or AI_TIR=1 
or AI_TML=1 
or AI_TPA=1 
or AI_TAR=1 

1 

0 

Yes 

No 

Blank 

No answer or option not included 

9 

Not applicable ((AI_TTM=Blank or 
AI_TTM<>1) and (AI_TSR=Blank or 
AI_TSR<>1) and (AI_TNLG=Blank or 
AI_TNLG<>1) and (AI_TIR=Blank or 
AI_TIR<>1) and (AI_TML=Blank or 
AI_TML<>1) and (AI_TPA=Blank or 
AI_TPA<>1) and (AI_TAR=Blank or 
AI_TAR<>1)) 

AI_BIAS 
(optional) 

E5: Does your enterprise have any 
measures to check the results 
generated by AI technologies for 
possible biases towards individuals 
based on sex, age, racial or ethnic 
origin, disability, religion or belief, 
sexual orientation? 

Enterprises 
where 
AI_PDI=1 

1 

0 

Yes 

No 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

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3 

Data transmission 

Filter/ 
Remarks 
(Standard 
codification) 

Enterprises 
where 
(AI_TTM=Bla
nk or 
AI_TTM=0) 
and 
(AI_TSR=Blan
k or 
AI_TSR=0) 
and 
(AI_TNLG=Bl
ank or 
AI_TNLG=0) 
and 
(AI_TIR=Blan
k or 
AI_TIR=0) 
and 
(AI_TML=Blan
k or 
AI_TML=0) 
and 
(AI_TPA=Blan
k or 
AI_TPA=0) 
and 
(AI_TAR=Blan
k or 
AI_TAR=0) 

Variable name 

Code 

Description 

Blank 

No answer or option not included 

9 

Not applicable (AI_PDI=Blank or 
AI_PDI<>1) 

AI_EC 
(optional) 

E6: Has your enterprise ever 
considered using any of the Artificial 
Intelligence technologies listed in 
question E1? 

1 

0 

Yes 

No 

Blank 

No answer or option not included 

9 

Not applicable (AI_TTM<>0 or 
AI_TSR<>0 or AI_TNLG<>0 or 
AI_TIR<>0 or AI_TML<>0 or AI_TPA<>0 
or AI_TAR<>0) 

118 

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3 

Data transmission 

Variable name 

Code 

Description 

Filter/ 
Remarks 
(Standard 
codification) 

(optional) 

E7: What are the reasons for your 
enterprise not to use any of the 
Artificial Intelligence technologies 
listed in question E1? 

Enterprises 
where 
AI_EC=1 

AI_BCST 

a) The costs seem too high 

1 

0 

Yes 

No 

Blank 

No answer or option not included 

9 

1 

0 

Not applicable (AI_EC=Blank or 
AI_EC<>1) 

b) There is a lack of relevant expertise in 
the enterprise 

Yes 

No 

Blank 

No answer or option not included 

9 

1 

0 

Not applicable (AI_EC=Blank or 
AI_EC<>1) 

c) Incompatibility with existing equipment, 
software or systems 

Yes 

No 

Blank 

No answer or option not included 

9 

1 

0 

Not applicable (AI_EC=Blank or 
AI_EC<>1) 

d) Difficulties with availability or quality of 
the necessary data 

Yes 

No 

AI_BLE 

AI_BINC 

AI_BDDT 

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3 

Data transmission 

Filter/ 
Remarks 
(Standard 
codification) 

Variable name 

Code 

Description 

AI_BCDP 

AI_BLEG 

AI_BEC 

Blank 

No answer or option not included 

9 

1 

0 

Not applicable (AI_EC=Blank or 
AI_EC<>1) 

e) Concerns regarding violation of data 
protection and privacy 

Yes 

No 

Blank 

No answer or option not included 

9 

1 

0 

Not applicable (AI_EC=Blank or 
AI_EC<>1) 

f) Lack of clarity about the legal 
consequences (e.g. liability in case of 
damage caused by the use of Artificial 
Intelligence) 

Yes 

No 

Blank 

No answer or option not included 

9 

1 

0 

Not applicable (AI_EC=Blank or 
AI_EC<>1) 

g) Ethical considerations 

Yes 

No 

Blank 

No answer or option not included 

9 

Not applicable (AI_EC=Blank or 
AI_EC<>1) 

AI_BNU 

h) Artificial Intelligence technologies are 
not useful for the enterprise 

1 

Yes 

120 

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3 

Data transmission 

Variable name 

Code 

Description 

0 

No 

Blank 

No answer or option not included 

9 

Not applicable (AI_EC=Blank or 
AI_EC<>1) 

Filter/ 
Remarks 
(Standard 
codification) 

Module X: Background Information 

X1: Main economic activity of the 
enterprise, during 2023 (Nace rev 2.0) 

All enterprises 

NACE2 

xx.xx 

Nace code of the enterprise, 4 digits 

EMPL 

Nnnnnn 

X2: Average number of persons 
employed, during 2023 

Number of employed persons (use as 
many digits as necessary) 

All enterprises 

X3: Total turnover (in monetary terms, 
excluding VAT), for 2023 

All enterprises 

TOVT 

Nnnnnn 

Total turnover (use as many digits as 
necessary) 

Y1: Enterprise ID 

All enterprises 

ENT_ID 

XxNnnn
nnn 

Unique id of the enterprise (2 letters for 
country code, then 7 digits) 

Y2: Enterprise Weight 

All enterprises 

ENT_WGHT 

Nnnnnn.
nnnnnn 

Grossing up factor of the enterprise (6 
digits, decimal point, 6 digits) 

DI4_INDEX 

0-12 

Derived value: Digital Intensity index, 
v4 

Give one point for each of the following 12 
conditions, if true: 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

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Variable name 

Code 

Description 

3 

Data transmission 

Filter/ 
Remarks 
(Standard 
codification) 

EMPIUSE<>Blank and EMPL<>Blank 
and EMPIUSE > 0.50 * EMPL 

ITSP2=1 

ISPDF1=2 or ISPDF1=3 or ISPDF1=4 or 
ISPDF1=5 

RM=1 

SECAWVTGI=1 or SECAWCTP=1 or 
SECAWCONT=1 

ITSPT2=1 or ITUST2=1 

SECM_CNT between 3 and 11 

SECPOL2=1 

RA_M=1 or RA_D=1 or RA_S=1 

AI_TTM=1 or AI_TSR=1 or AI_TNLG=1 or 
AI_TIR=1 or AI_TML=1 or AI_TPA=1 or 
AI_TAR=1 

(AWSVAL<>Blank and 
AWSVAL>=TOVT*0.01) or 
(AXSVAL<>Blank and 
AXSVAL>=TOVT*0.01) or 
(AWSVAL<>Blank and AWSVAL<>-1 and 
AXSVAL<>Blank and AXSVAL<>-1 and 
(AWSVAL+AXSVAL)>=TOVT*0.01) 

TOVT<>Blank and AWSVAL<>Blank and 
AWSVALC<>Blank AND AWSVALC>0 
AND (AWSVAL>0.01 * TOVT) and 
AWSVALC>0.1 * AWSVAL 

AI_CNT 

0-7 

Derived value: Artificial intelligence 
technologies counter 
Give one point for each of the following 7 
conditions, if true: 

AI_TTM=1 

AI_TSR=1 

AI_TNLG=1 

AI_TIR=1 

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3 

Data transmission 

Variable name 

Code 

Description 

Filter/ 
Remarks 
(Standard 
codification) 

AI_TML=1 

AI_TPA=1 

AI_TAR=1 

9 

Not applicable (EMPIUSE = Blank or 
EMPIUSE = 0) 

3.2.2. Digital intensity index 

The last variable of the table above is the digital intensity index (codified as DI3_INDEX). It 
measures the use of different digital technologies at enterprise level. The digital intensity index 
score of an enterprise is determined by how many of the selected digital technologies it uses and 
is between 0 and 12.  

The index is used to calculate certain aggregates such as the number of enterprises that use ICT 
security measures according to their digital intensity index.  

The table below provides guidance on how to calculate it.  

DI4_INDEX 

0-12 

Derived value: Digital Intensity 
index 

Description (references to MQ 
2024 see section 3) 

Give one point for each of the 
following 12 conditions, if true: 

EMPIUSE<>Blank and 
EMPL<>Blank and EMPIUSE > 
0.50 * EMPL 

Enterprises where more than 50% 
of the persons employed had 
access to the internet for business 
purposes 

ITSP2=1 

Employ ICT specialists 

ISPDF1=2 or ISPDF1=3 or 
ISPDF1=4 or ISPDF1=5 

RM=1 

SECAWVTGI=1 or SECAWCTP=1 
or SECAWCONT=1 

The maximum contracted download 
speed of the fastest fixed line 
internet connection is at least 30 
Mb/s 

Enterprises which conducted 
remote meetings (via e.g. Skype, 
Zoom, MS Teams, WebEx, etc.) 

Enterprises make persons 
employed aware of their obligations 
in ICT security related issues 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

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3 

Data transmission 

DI4_INDEX 

0-12 

Derived value: Digital Intensity 
index 

Description (references to MQ 
2024 see section 3) 

ITSPT2=1 or ITUST2=1 

SECM_CNT between 3 and 11 

SECPOL2=1 

RA_M=1 or RA_D=1 or RA_S=1 

AI_TTM=1 or AI_TSR=1 or 
AI_TNLG=1 or AI_TIR=1 or 
AI_TML=1 or AI_TPA=1 or 
AI_TAR=1 

(AWSVAL<>Blank and 
AWSVAL>=TOVT*0.01) or 
(AXSVAL<>Blank and 
AXSVAL>=TOVT*0.01) or 
(AWSVAL<>Blank and 
AWSVAL<>-1 and 
AXSVAL<>Blank and AXSVAL<>-1 
and 
(AWSVAL+AXSVAL)>=TOVT*0.01
) 

TOVT<>Blank and 
AWSVAL<>Blank and 
AWSVALC<>Blank and 
AWSVALC>0 AND (AWSVAL>0.01 
* TOVT) and AWSVALC>0.1 * 
AWSVAL 

Any type of training provided to 
develop ICT related skills of the 
persons employed, during 2023 

Use at least 3 ICT security 
measures 

Enterprise with document(s) on 
measures, practices or procedures 
on ICT security 

Any of the persons employed 
having remote access to any of the 
following: e-mail, documents, 
business apps 

Use any AI technology 

Used any computer networks for 
sales (at least 1%) 

Enterprises where web sales are 
more than 1% of the total turnover 
and B2C web sales more than 10% 
of the web sales 

3.2.3. Indicator coding and scope 

Indicators have to be computed by NSIs using the microdata gathered. The indicators are 
aggregates of microdata. The table below presents, among other indicators (column Variable 
codes), their description and the questions of the model questionnaire they are derived from. The 
indicators’ scope can be computed using the codification of microdata presented above in 
Section 3.2.1.  

Some indicators computed by Eurostat are also presented in the table for information. 

124 

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3 

Data transmission 

Model 
questionnaire 
2024 

back 

Variable 
codes 
(CAPITAL 
LETTERS) 

ENT_SAMP
LE 

back 

ENT 

back 

EMPL 

back 

TOVT 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Count 
(ENT_ID) 
unraised 

FALSE 

2023 

Count 
(ENT_ID) 

FALSE 

2023 

∑ (EMPL) 

FALSE 

2023 

∑ (TOVT) 

FALSE 

2023 

Number of 
enterprises 
in final (net) 
sample - 
non-raised 
figures 

Number of 
enterprises 
in population 
(surveyed 
size and 
industry 
groups 
correspondin
g to raised 
figures) 

Average 
number of 
employed 
persons in 
population 
(surveyed 
size and 
industry 
groups 
correspondin
g to raised 
figures) 

Total 
turnover in 
population, 
in value 
terms, 
excluding 
VAT 
(surveyed 
size and 
industry 
groups 
correspondin
g to raised 
figures) 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

125 

 
3 

Data transmission 

Model 
questionnaire 
2024 

Derived from 
A1 

Variable 
codes 
(CAPITAL 
LETTERS) 

E_IUSE 

Derived from 
A1 

E_IUSE_GT
10 

Derived from 
A1 

E_IUSE_GT
50 

Derived from 
A1 

E_IUSE_GE
10A 

A2 

E_FIXBB 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Enterprises 
where 
persons 
employed 
have access 
to the 
internet for 
business 
purposes 

Enterprises 
where more 
than 10% of 
the persons 
employed 
have access 
to the 
internet for 
business 
purposes 

Enterprises 
where more 
than 50% of 
the persons 
employed 
have access 
to the 
internet for 
business 
purposes 

Enterprises 
where at 
least 10 
persons 
employed 
have access 
to the 
internet for 
business 
purposes 

Use any 
type of fixed 
connection 

Count 
(ENT_ID) 
where 
EMPIUSE<>
Blank and 
EMPIUSE>0 

Count 
(ENT_ID) 
where 
EMPIUSE<>
Blank and 
EMPL<>Bla
nk and 
EMPIUSE > 
0.10 * EMPL 

Count 
(ENT_ID) 
where 
EMPIUSE<>
Blank and 
EMPL<>Bla
nk and 
EMPIUSE > 
0.50 * EMPL 

Count 
(ENT_ID) 
where 
EMPIUSE<>
Blank and 
EMPIUSE 
>= 10 

Count 
(ENT_ID) 
where 
FIXBB=1 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

A2 

E_FIXBBX 

A2 

E_FIXBBZ 

A3a 

E_ISPDF_L
T30 

A3b 

E_ISPDF1_
30_100 

A3c 

E_ISPDF_1
00_500 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

to the 
internet 

Don't use 
any type of 
fixed 
connection 
to the 
internet 

Don't know if 
they use any 
type of fixed 
connection 
to the 
internet 

The 
maximum 
contracted 
download 
speed of the 
fastest fixed 
internet 
connection 
is less than 
30 Mb/s 

The 
maximum 
contracted 
download 
speed of the 
fastest fixed 
internet 
connection 
is at least 30 
Mb/s but 
less than 
100 Mb/s 

The 
maximum 
contracted 
download 
speed of the 
fastest fixed 

FALSE 

2023 

TRUE 

2023 

FALSE 

2023 

Count 
(ENT_ID) 
where 
FIXBB=0 

Count 
(ENT_ID) 
where 
FIXBB=Blan
k 

Count 
(ENT_ID) 
where 
ISPDF1=1 

FALSE 

2023 

Count 
(ENT_ID) 
where 
ISPDF1=2 

FALSE 

2023 

Count 
(ENT_ID) 
where 
ISPDF1=3 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

internet 
connection 
is at least 
100 Mb/s but 
less than 
500 Mb/s 

The 
maximum 
contracted 
download 
speed of the 
fastest fixed 
internet 
connection 
is at least 
500 Mb/s but 
less than 1 
Gb/s 

The 
maximum 
contracted 
download 
speed of the 
fastest fixed 
internet 
connection 
is at least 1 
Gb/s 

The 
maximum 
contracted 
download 
speed of the 
fastest fixed 
internet 
connection 
is at least 30 
Mb/s 

The 
maximum 
contracted 
download 

FALSE 

2023 

Count 
(ENT_ID) 
where 
ISPDF1=4 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

Count 
(ENT_ID) 
where 
ISPDF1=5 

Count 
(ENT_ID) 
where 
ISPDF1=2 
or 
ISPDF1=3 
or 
ISPDF1=4 
or 
ISPDF1=5 

Count 
(ENT_ID) 
where 
ISPDF1=3 

A3d 

E_ISPDF_5
00_1G 

A3e 

E_ISPDF_G
E1G 

Derived from 
A3b to A3e 

E_ISPDF1_
GE30 

Derived from 
A3c to A3e 

E_ISPDF1_
GE100 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

speed of the 
fastest fixed 
internet 
connection 
is at least 
100 Mb/s 

The speed 
of the fixed 
line 
connection(s
) to the 
internet is 
sufficient for 
the actual 
needs of the 
enterprise 

The speed 
of the fixed 
line 
connection(s
) to the 
internet is 
not sufficient 
for the actual 
needs of the 
enterprise 

The speed 
of the fixed 
line 
connection(s
) to the 
internet (at 
least 30 
Mb/s) is 
sufficient for 
the actual 
needs of the 
enterprise 

The speed 
of the fixed 
line 
connection(s

or 
ISPDF1=4 
or 
ISPDF1=5 

Count 
(ENT_ID) 
where 
ISPDFOK=1 

FALSE 

2020 

FALSE 

2020 

Count 
(ENT_ID) 
where 
ISPDFOK=0 

FALSE 

2020 

FALSE 

2020 

Count 
(ENT_ID) 
where 
(ISPDF1=2 
or 
ISPDF1=3 
or 
ISPDF1=4 
or 
ISPDF1=5) 
and 
ISPDFOK=1 

Count 
(ENT_ID) 
where 
ISPDF1=1 

A4 

E_ISPDF1O
K 

A4 

E_ISPDF1O
KX 

Derived from 
A3b to A3e and 
A4 

E_ISPDF1O
K_GE30 

Derived from 
A3a and A4 

E_ISPDFOK
X_LT30 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Derived from 
A3b and A4 

E_ISPDF1O
KX_30_100 

Derived from 
A3c and A4 

E_ISPDFOK
X_100_500 

Derived from 
A3d and A4 

E_ISPDFOK
X_500_1G 

and 
ISPDFOK=0 

Count 
(ENT_ID) 
where 
ISPDF1=2 
and 
ISPDFOK=0 

Count 
(ENT_ID) 
where 
ISPDF1=3 
and 
ISPDFOK=0 

Count 
(ENT_ID) 
where 
ISPDF1=4 
and 
ISPDFOK=0 

) to the 
internet (less 
than 
30Mb/s) is 
not sufficient 
for the actual 
needs of the 
enterprise 

The speed 
of the fixed 
line 
connection(s
) to the 
internet ([3-
100[ Mb/s) is 
not sufficient 
for the actual 
needs of the 
enterprise 

The speed 
of the fixed 
line 
connection(s
) to the 
internet 
([100-500[ 
Mb/s) is not 
sufficient for 
the actual 
needs of the 
enterprise 

The speed 
of the fixed 
line 
connection(s
) to the 
internet 
([500-1000[ 
Mb/s) is not 
sufficient for 
the actual 
needs of the 
enterprise 

FALSE 

2020 

FALSE 

2020 

FALSE 

2020 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Derived from 
A3e and A4 

E_ISPDFOK
X_GE1G 

Derived from 
A3b to A3e and 
A4 

E_ISPDF1O
KX_GE30 

Derived from 
A3c to A3e and 
A4 

E_ISPDF1O
KX_GE100 

Derived from 
A5 

E_EMPMD2
_GT0 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

FALSE 

2020 

FALSE 

2020 

FALSE 

2020 

TRUE 

2022 

The speed 
of the fixed 
line 
connection(s
) to the 
internet (at 
least 1 Gb/s) 
is not 
sufficient for 
the actual 
needs of the 
enterprise 

The speed 
of the fixed 
line 
connection(s
) to the 
internet (at 
least 30 
Mb/s) is not 
sufficient for 
the actual 
needs of the 
enterprise 

The speed 
of the fixed 
line 
connection(s
) to the 
internet (at 
least 100 
Mb/s) is not 
sufficient for 
the actual 
needs of the 
enterprise 

Enterprises 
where some 
employed 
persons use 
a portable 
device 
provided by 

Count 
(ENT_ID) 
where 
ISPDF1=5 
and 
ISPDFOK=0 

Count 
(ENT_ID) 
where 
(ISPDF1=2 
or 
ISPDF1=3 
or 
ISPDF1=4 
or 
ISPDF1=5) 
and 
ISPDFOK=0 

Count 
(ENT_ID) 
where 
(ISPDF1=3 
or 
ISPDF1=4 
or 
ISPDF1=5) 
and 
ISPDFOK=0 

Count 
(ENT_ID) 
where 
EMPMD2<>
Blank and 
EMPMD2>0 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Derived from 
A5 

E_EMPMD2
_GT10 

Derived from 
A5 

E_EMPMD2
_GT20 

the 
enterprise, 
that allows 
internet 
connection 
via mobile 
telephone 
networks, for 
business 
purposes 

Enterprises 
where more 
than 10% of 
the 
employed 
persons use 
a portable 
device 
provided by 
the 
enterprise, 
that allows 
internet 
connection 
via mobile 
telephone 
networks, for 
business 
purposes 

Enterprises 
where more 
than 20% of 
the 
employed 
persons use 
a portable 
device 
provided by 
the 
enterprise, 
that allows 
internet 
connection 
via mobile 

Count 
(ENT_ID) 
where 
EMPMD2<>
Blank and 
EMPL<>Bla
nk and 
(EMPMD2>
EMPL * 0.1) 

Count 
(ENT_ID) 
where 
EMPMD2<>
Blank and 
EMPL<>Bla
nk and 
(EMPMD2>
EMPL * 0.2) 

TRUE 

2022 

TRUE 

2022 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Derived from 
A5 

E_EMPMD2
_GT50 

Derived from 
A5 

E_PMD 

Derived from 
A5 

E_PMDX 

telephone 
networks, for 
business 
purposes 

Enterprises 
where more 
than 50% of 
the 
employed 
persons use 
a portable 
device 
provided by 
the 
enterprise, 
that allows 
internet 
connection 
via mobile 
telephone 
networks, for 
business 
purposes 

Provide 
portable 
devices that 
allow a 
mobile 
connection 
to the 
internet 
using mobile 
telephone 
networks, for 
business 
purposes 

Does not 
provide 
portable 
devices that 
allow a 
mobile 
connection 

TRUE 

2022 

Count 
(ENT_ID) 
where 
EMPMD2<>
Blank and 
EMPL<>Bla
nk and 
(EMPMD2>
EMPL * 0.5) 

TRUE 

2022 

Count 
(ENT_ID) 
where 
EMPMD2>0 

TRUE 

2022 

Count 
(ENT_ID) 
where 
EMPMD2=0 
or 
EMPMD2=Bl
ank 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Derived from 
A2 and A5 

E_BROAD3 

Derived from 
A2 and A5 

E_BROAD3
X 

A6a 

E_RA_M 

A6a 

E_RA_MX 

to the 
internet 
using mobile 
telephone 
networks, for 
business 
purposes 

Connect to 
the Internet 
via fixed or 
mobile 
broadband 
(as of 2018) 

Connect to 
the Internet 
but not via 
fixed or 
mobile 
broadband 
(as of 2018) 

Enterprises 
with 
employed 
persons with 
remote 
access to 
the email 
system of 
the 
enterprise 

Enterprises 
with 
employed 
persons with 
no remote 
access to 
the email 
system of 

TRUE 

2022 

TRUE 

2022 

FALSE 

2022 

Count 
(ENT_ID) 
where 
FIXBB=1 or 
EMPMD2>0 

Count 
(ENT_ID) 
where 
(FIXBB=0 or 
FIXBB=Blan
k) and 
(EMPMD2=0 
or 
EMPMD2=Bl
ank) 

Count 
(ENT_ID) 
where 
RA_M=1 

FALSE 

2022 

Count 
(ENT_ID) 
where 
RA_M=0 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

A6b 

E_RA_D 

A6b 

E_RA_DX 

A6c 

E_RA_S 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

FALSE 

2022 

Count 
(ENT_ID) 
where 
RA_D=1 

FALSE 

2022 

Count 
(ENT_ID) 
where 
RA_D=0 

FALSE 

2022 

Count 
(ENT_ID) 
where 
RA_S=1 

the 
enterprise 

Enterprises 
with 
employed 
persons with 
remote 
access to 
the 
documents 
of the 
enterprise 
(e.g. files, 
spreadsheet
s, 
presentation
s, charts, 
photos) 

Enterprises 
with 
employed 
persons with 
no remote 
access to 
the 
documents 
of the 
enterprise 
(e.g. files, 
spreadsheet
s, 
presentation
s, charts, 
photos) 

Enterprises 
with 
employed 
persons with 
remote 
access to 
the business 
applications 
or software 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

of the 
enterprise 
(e.g. access 
to 
accounting, 
sales, 
orders, 
CRM) 

Enterprises 
with 
employed 
persons with 
no remote 
access to 
the business 
applications 
or software 
of the 
enterprise 
(e.g. access 
to 
accounting, 
sales, 
orders, 
CRM) 

Enterprises 
with 
employed 
persons with 
remote 
access to 
the email 
system or 
documents, 
business 
applications 
or software 
of the 
enterprise 

Enterprises 
with 
employed 
persons with 

FALSE 

2022 

Count 
(ENT_ID) 
where 
RA_S=0 

FALSE 

2022 

Count 
(ENT_ID) 
where 
RA_M=1 or 
RA_D=1 or 
RA_S=1 

FALSE 

2022 

Count 
(ENT_ID) 
where 
RA_M=1 

A6c 

E_RA_SX 

Derived A6a to 
A6c 

E_RA 

Derived A6a to 
A6c 

E_RA_ALL 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

and RA_D=1 
and RA_S=1 

FALSE 

2022 

Count 
(ENT_ID) 
where 
RA_D=1 or 
RA_S=1 

Count 
(ENT_ID) 
where RM=1 

FALSE 

2022 

Count 
(ENT_ID) 
where RM=0 

FALSE 

2022 

remote 
access to 
the email 
system and 
documents 
and 
business 
applications 
or software 
of the 
enterprise 

Enterprises 
with 
employed 
persons with 
remote 
access to 
the 
documents, 
business 
applications 
or software 
of the 
enterprise 

Enterprises 
which 
conducted 
remote 
meetings 
(via e.g. 
Skype, 
Zoom, MS 
Teams, 
WebEx, etc.) 

Enterprises 
which did 
not conduct 
remote 
meetings 
(via e.g. 
Skype, 
Zoom, MS 

Derived A6b to 
A6c 

E_RA_DS 

A7 

E_RM 

A7 

E_RMX 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

A8 

A8 

E_ADS 

E_ADSX 

A9a 

E_ADS_KW 

A9a 

E_ADS_KW
X 

A9b 

E_ADS_TR
K 

A9b 

E_ADS_TR
KX 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Teams, 
WebEx, etc.) 

Pay to 
advertise on 
the Internet 

Don't pay to 
advertise on 
the Internet 

Pay to 
advertise on 
the internet, 
based on 
content or 
keywords 
searched by 
internet 
users 

Pay to 
advertise on 
the internet, 
but not 
based on 
content or 
keywords 
searched by 
internet 
users 

Pay to 
advertise on 
the internet, 
based on the 
tracking of 
internet 
users' past 
activities or 
profile 

Pay to 
advertise on 
the internet, 

TRUE 

2023 

TRUE 

2023 

TRUE 

2023 

Count 
(ENT_ID) 
where 
ADS=1 

Count 
(ENT_ID) 
where 
ADS=0 

Count 
(ENT_ID) 
where 
ADS_KW=1 

TRUE 

2023 

Count 
(ENT_ID) 
where 
ADS_KW=0 

TRUE 

2023 

Count 
(ENT_ID) 
where 
ADS_TRK=1 

Count 
(ENT_ID) 

TRUE 

2023 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

but not 
based on the 
tracking of 
internet 
users' past 
activities or 
profile 

Pay to 
advertise on 
the internet, 
based on the 
geolocation 
of internet 
users 

Pay to 
advertise on 
the internet, 
but not 
based on the 
geolocation 
of internet 
users 

Pay to 
advertise on 
the internet, 
based on 
any other 
method of 
target 
advertising 

Pay to 
advertise on 
the internet, 
but not 
based on 
any other 
method of 
target 
advertising 

Pay to 
advertise on 
the internet, 

where 
ADS_TRK=0 

Count 
(ENT_ID) 
where 
ADS_LOC=
1 

Count 
(ENT_ID) 
where 
ADS_LOC=
0 

Count 
(ENT_ID) 
where 
ADS_OTH=
1 

Count 
(ENT_ID) 
where 
ADS_OTH=
0 

TRUE 

2023 

TRUE 

2023 

TRUE 

2023 

TRUE 

2023 

Count 
(ENT_ID) 
where 

TRUE 

2023 

A9c 

A9c 

E_ADS_LO
C 

E_ADS_LO
CX 

A9d 

E_ADS_OT
H 

A9d 

E_ADS_OT
HX 

A9a to A9c 

E_ADS3 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

B1a 

B1a 

B1b 

B1b 

E_AWS_CO
WN 

E_AWS_CO
WNX 

E_AWS_CM
P 

E_AWS_CM
PX 

Derived from 
B1a and B1b 

E_AWS_CB
OTH 

based on 
content, 
keywords, 
internet 
users' past 
activities or 
profile or the 
geolocation 

Enterprises 
which sold 
via their own 
websites or 
apps 

Enterprises 
which did 
not sell via 
their own 
websites or 
apps 

Enterprises 
which sold 
via an e-
commerce 
marketplace 

Enterprises 
which did 
not sell via 
an e-
commerce 
marketplace 

Enterprises 
which sold 
via their own 
websites or 
apps and via 
an e-
commerce 
marketplace 

ADS_KW=1 
or 
ADS_TRK=1 
or 
ADS_LOC=
1 

Count 
(ENT_ID) 
where 
AWS_COW
N=1 

Count 
(ENT_ID) 
where 
AWS_COW
N=0 

Count 
(ENT_ID) 
where 
AWS_CMP=
1 

Count 
(ENT_ID) 
where 
AWS_CMP=
0 

Count 
(ENT_ID) 
where 
AWS_COW
N=1 and 
AWS_CMP=
1 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

Derived from 
B1a and B1b 

E_AWSELL 

Have 
received 
orders via 

Count 
(ENT_ID) 
where 

FALSE 

2023 

140 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Derived from 
B1a and B1b 

E_AWSELL
X 

Derived from 
B1a and B1b 

E_AWS_CM
PQ 

B2 

B2 

E_AWSVAL
S 

E_AWSVAL
B 

B2 

E_AWSVAL 

websites or 
apps (web 
sales) 

Have not 
received 
orders via 
websites or 
apps 

Enterprises 
which sold 
via an e-
commerce 
marketplace 
but not via 
their own 
websites or 
apps 

Sales via 
websites or 
apps, 
excluding 
VAT (<1% of 
turnover) 

Sales via 
websites or 
apps, 
excluding 
VAT (>= 1% 
of turnover) 

Total sales 
via websites 
or apps, 
excluding 
VAT 

(AWS_COW
N=1 or 
AWS_CMP=
1) 

Count 
(ENT_ID) 
where 
(AWS_COW
N=0 and 
AWS_CMP=
0) 

Count 
(ENT_ID) 
where 
AWS_COW
N=0 and 
AWS_CMP=
1 

∑ (AWSVAL) 
where 
AWSVAL <> 
Blank and 
AWSVAL <> 
-1 and 
AWSVAL < 
TOVT * 1% 

∑ (AWSVAL) 
where 
AWSVAL <> 
Blank and 
AWSVAL <> 
-1 and 
AWSVAL >= 
TOVT * 1% 

∑ (AWSVAL) 
where 
AWSVAL <> 
Blank and 
AWSVAL <> 
-1 

FALSE 

2023 

FALSE 

NEW 
derived 
variable 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

B2 

E_WSEL0 

B2 

E_WSEL1 

B2 

E_WSEL2 

B2 

E_WSEL5 

B2 

E_WSEL10 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

Total sales 
via websites 
or apps 
equal to or 
above 0 

Total sales 
via websites 
or apps 
equal to or 
above 1% of 
turnover 

Total sales 
via websites 
or apps 
equal to or 
above 2% of 
turnover 

Total sales 
via websites 
or apps 
equal to or 
above 5% of 
turnover 

Total sales 
via websites 
or apps 
equal to or 
above 10% 
of turnover 

Count 
(ENT_ID) 
where 
AWSVAL <> 
Blank and 
AWSVAL <> 
-1 and 
AWSVAL>=0 

Count 
(ENT_ID) 
where 
AWSVAL <> 
Blank and 
AWSVAL <> 
-1 and 
AWSVAL>=
TOVT * 1% 

Count 
(ENT_ID) 
where 
AWSVAL <> 
Blank and 
AWSVAL <> 
-1 and 
AWSVAL>=
TOVT * 2% 

Count 
(ENT_ID) 
where 
AWSVAL <> 
Blank and 
AWSVAL <> 
-1 and 
AWSVAL>=
TOVT * 5% 

Count 
(ENT_ID) 
where 
AWSVAL <> 
Blank and 
AWSVAL <> 
-1 and 

142 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

B2 

E_WSEL25 

B2 

E_WSEL50 

B2 

E_WSELZ 

Derived from 
B1 and B2 

E_AWSVAL
_CMPQ 

B3a 

E_AWSVAL
_COWN 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Total sales 
via websites 
or apps 
equal to or 
above 25% 
of turnover 

Total sales 
via websites 
or apps 
equal to or 
above 50% 
of turnover 

Don't know 
the % of 
sales via 
websites or 
apps 

Web sales 
by 
enterprises 
which only 
sell through 
an e-
commerce 
marketplace 

Sales via 
their own 
websites or 
apps 

AWSVAL>=
TOVT * 10% 

Count 
(ENT_ID) 
where 
AWSVAL <> 
Blank and 
AWSVAL <> 
-1 and 
AWSVAL>=
TOVT * 25% 

Count 
(ENT_ID) 
where 
AWSVAL <> 
Blank and 
AWSVAL <> 
-1 and 
AWSVAL>=
TOVT * 50% 

Count 
(ENT_ID) 
where 
AWSVAL=Bl
ank 

∑ (AWSVAL) 
where 
AWSVAL <> 
Blank and 
AWSVAL <> 
-1 and 
AWS_COW
N=0 and 
AWS_CMP=
1 

∑(AWSVAL_
COWN) 
where 
AWSVAL_C
OWN <> 
Blank and 

FALSE 

2023 

FALSE 

2023 

TRUE 

2023 

FALSE 

NEW 
derived 
variable 

FALSE 

2023 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

B3b 

E_AWSVAL
_CMP 

Sales via an 
e-commerce 
marketplace 

Derived from 
B1b, B2 and 
B3b 

E_AWS_CM
P_GE20 

Derived from 
B1b, B2 and 
B3b 

E_AWS_CM
P_GE50 

Derived from 
B1b, B2 and 
B3b 

T_AWS_CM
P_GE20 

Derived from 
B1b, B2 and 
B3b 

T_AWS_CM
P_GE50 

Enterprises 
which sold 
via an e-
commerce 
marketplace 
for at least 
20% of the 
web sales 

Enterprises 
which sold 
via an e-
commerce 
marketplace 
for at least 
50% of the 
web sales 

Turnover of 
the 
enterprises 
where web 
sales via an 
e-commerce 
marketplace 
were at least 
20% of the 
web sales 

Turnover of 
the 
enterprises 
where web 
sales via an 

AWSVAL_C
OWN <> -1 

∑(AWSVAL_
CMP) where 
AWSVAL_C
MP <> Blank 
and 
AWSVAL_C
MP <> -1 

Count 
(ENT_ID) 
where 
AWS_CMP=
1 and 
(AWSVAL_C
MP >= 
AWSVAL * 
0.20) 

Count 
(ENT_ID) 
where 
AWS_CMP=
1 and 
(AWSVAL_C
MP >= 
AWSVAL * 
0.50) 

∑ (TOVT) 
where 
AWS_CMP=
1 and 
(AWSVAL_C
MP >= 
AWSVAL * 
0.20) 

∑ (TOVT) 
where 
AWS_CMP=
1 and 
(AWSVAL_C

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

144 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

e-commerce 
marketplace 
were at least 
50% of the 
web sales 

Sales via 
websites or 
apps (B2C) 

E_AWSVAL
_B2C 

E_AWSVAL
_B2BG 

Sales via 
websites or 
apps (B2B) 
and (B2G) 

B4a 

B4b 

Derived from 
B4a 

E_AWS_B2
C 

Derived from 
B4b 

E_AWS_B2
BG 

Derived from 
B1b and B4a 

E_AWS_B2
C_CMP 

Enterprises 
which sold 
via websites 
or apps - 
B2C 

Enterprises 
which sold 
via websites 
or apps - 
B2B and 
B2G 

Enterprises 
which sold 
via websites 
or apps - 
B2C and via 
an e-
commerce 
marketplace 

MP >= 
AWSVAL * 
0.50) 

∑ 
(AWSVALC) 
where 
AWSVALC<
>Blank and 
AWSVALC<
>-1 

∑ 
(AWSVALB
G) where 
AWSVALBG
<>Blank and 
AWSVALBG
<>-1 

Count 
(ENT_ID) 
where 
AWSVALC<
>Blank and 
AWSVALC>
0 

Count 
(ENT_ID) 
where 
AWSVALBG
<>Blank and 
AWSVALBG
>0 

Count 
(ENT_ID) 
where 
(AWSVALC<
>Blank and 
AWSVALC>
0) and 
AWS_CMP=
1 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

145 

 
Model 
questionnaire 
2024 

Derived from 
B2 and B4a 

Variable 
codes 
(CAPITAL 
LETTERS) 

E_AWSVAL
_B2C_GE10
WS 

Derived from 
B2 and B4a 

E_AWSVAL
_B2C_GE5
WS 

Derived from 
B2 and B4a 

E_AWS_B2
C_GT1WS 

3 

Data transmission 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

Enterprises 
where B2C 
sales via 
websites or 
apps were 
10% or more 
of the total 
web sales 

Enterprises 
where B2C 
sales via 
websites or 
apps were 
5% or more 
of the total 
web sales 

Enterprises 
where B2C 
sales via 
websites or 
apps were 
more than 
1% of the 
total web 
sales 

Count 
(ENT_ID) 
where 
AWSVALC<
>Blank AND 
AWSVALC>
0 and 
AWSVAL<>
Blank and 
AWSVAL<>-
1 and 
AWSVALC>
=0.1 * 
AWSVAL 

Count 
(ENT_ID) 
where 
AWSVALC<
>Blank AND 
AWSVALC>
0 and 
AWSVAL<>
Blank and 
AWSVAL<>-
1 and 
AWSVALC>
=0.05 * 
AWSVAL 

Count 
(ENT_ID) 
where 
AWSVALC<
>Blank AND 
AWSVALC>
0 and 
AWSVAL<>
Blank and 
AWSVAL<>-
1 and 
AWSVALC>
0.01 * 
AWSVAL 

146 

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3 

Data transmission 

Model 
questionnaire 
2024 

Derived from 
B2 and B4a 

Variable 
codes 
(CAPITAL 
LETTERS) 

E_AWSVAL
_GT1_B2C_
GT10WS 

Derived from 
B2 and B4a 

E_AWS_GT
1_B2C_GT1
0WS 

Derived from 
B2 and B4a 

T_AWS_GT
1_B2C_GT1
0WS 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

B2C web 
sales of 
enterprises 
where web 
sales > 1% 
of total 
turnover and 
B2C web 
sales > 10% 
of the web 
sales 

Enterprises 
where web 
sales were 
more than 
1% of the 
total 
turnover and 
B2C web 
sales more 
than 10% of 
the web 
sales 

Turnover of 
the 
enterprises 
where web 
sales were 
more than 
1% of the 
total 
turnover and 
B2C web 
sales more 
than 10% of 

∑ 
(AWSVALC) 
where 
TOVT<>Bla
nk and 
AWSVAL<>
Blank and 
AWSVALC<
>Blank AND 
(AWSVAL>0
.01 * TOVT) 
and 
AWSVALC>
0.1 * 
AWSVAL 

Count 
(ENT_ID) 
where 
TOVT<>Bla
nk and 
AWSVAL<>
Blank and 
AWSVALC<
>Blank AND 
AWSVALC>
0 AND 
(AWSVAL>0
.01 * TOVT) 
and 
AWSVALC>
0.1 * 
AWSVAL 

∑ (TOVT) 
where 
TOVT<>Bla
nk and 
AWSVAL<>
Blank and 
AWSVALC<
>Blank AND 
AWSVALC>
0 AND 
(AWSVAL>0
.01 * TOVT) 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

147 

 
3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Derived from 
B1b, B2 and 
B4a 

E_AWSVAL
_B2C_GE10
WS_CMP 

B5a 

E_AWSHM 

B5b 

E_AWSEU 

B5c 

E_AWSWW 

the web 
sales 

Enterprises 
where B2C 
sales via 
websites or 
apps were 
10% or more 
of the total 
web sales 
and which 
sold via an 
e-commerce 
marketplace 

Received 
orders 
placed via a 
website or 
apps from 
customers in 
own country 

Received 
orders 
placed via a 
website or 
apps from 
customers in 
other EU 
countries 

Received 
orders 
placed via a 
website or 
apps from 
customers in 

and 
AWSVALC>
0.1 * 
AWSVAL 

Count 
(ENT_ID) 
where 
AWSVALC<
>Blank AND 
AWSVALC>
0 and 
AWSVAL<>
Blank and 
AWSVAL<>-
1 and 
AWSVALC>
=0.1 * 
AWSVAL 
and 
AWS_CMP=
1 

Count 
(ENT_ID) 
where 
AWSHM=1 

Count 
(ENT_ID) 
where 
AWSEU=1 

Count 
(ENT_ID) 
where 
AWSWW=1 

FALSE 

2023 

FALSE 

2022 

FALSE 

2022 

FALSE 

2022 

148 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

the rest of 
the world 

E_AWSFOR  Received 

Derived from 
B5b and B5c 

Derived from 
B3a and B5a 

E_AWSVAL
_HM_COW
N 

Derived from 
B3b and B5a 

E_AWSVAL
_HM_CMP 

Derived from 
B3a and B5b 

E_AWSVAL
_EU_COWN 

orders 
placed via a 
website or 
apps from 
customers in 
foreign 
countries 
(EU or rest 
of the world) 

Sales via 
their own 
websites or 
apps from 
customers in 
the own 
country 

Sales via an 
e-commerce 
marketplace 
from 
customers in 
the own 
country 

Sales via 
their own 
websites or 
apps from 
customers in 
other EU 
countries 

FALSE 

2022 

FALSE 

2022 

FALSE 

2022 

FALSE 

2022 

Count 
(ENT_ID) 
where 
AWSEU=1 
or 
AWSWW=1 

∑(AWSVAL_
COWN) 
where 
AWSVAL_C
OWN <> 
Blank and 
AWSVAL_C
OWN <> -1 
and 
AWSHM=1 

∑(AWSVAL_
CMP) where 
AWSVAL_C
MP <> Blank 
and 
AWSVAL_C
MP <> -1 
and 
AWSHM=1 

∑(AWSVAL_
COWN) 
where 
AWSVAL_C
OWN <> 
Blank and 
AWSVAL_C
OWN <> -1 
and 
AWSEU=1 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Derived from 
B3b and B5b 

E_AWSVAL
_EU_CMP 

Derived from 
B3a and B5c 

E_AWSVAL
_WW_COW
N 

Derived from 
B3b and B5c 

E_AWSVAL
_WW_CMP 

Derived from 
B1, B2 and B5a 

E_AWSVAL
_HM_CMPQ 

Derived from 
B1, B2 and B5b 

E_AWSVAL
_EU_CMPQ 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Sales via an 
e-commerce 
marketplace 
from 
customers in 
other EU 
countries 

Sales via 
their own 
websites or 
apps from 
customers 
located in 
the rest of 
the world 

Sales via an 
e-commerce 
marketplace 
from 
customers 
located in 
the rest of 
the world 

Web sales 
by 
enterprises 
which only 
sell through 
an e-
commerce 
marketplace 
from 
customers in 
the own 
country 

Web sales 
by 
enterprises 

∑(AWSVAL_
CMP) where 
AWSVAL_C
MP <> Blank 
and 
AWSVAL_C
MP <> -1 
and 
AWSEU=1 

∑(AWSVAL_
COWN) 
where 
AWSVAL_C
OWN <> 
Blank and 
AWSVAL_C
OWN <> -1 
and 
AWSWW=1 

∑(AWSVAL_
CMP) where 
AWSVAL_C
MP <> Blank 
and 
AWSVAL_C
MP <> -1 
and 
AWSWW=1 

∑ (AWSVAL) 
where 
AWSVAL <> 
Blank and 
AWSVAL <> 
-1 and 
AWSHM=1 
and 
AWS_COW
N=0 and 
AWS_CMP=
1 

∑ (AWSVAL) 
where 
AWSVAL <> 

FALSE 

2022 

FALSE 

2022 

FALSE 

2022 

FALSE 

NEW 
derived 
variable 

FALSE 

NEW 
derived 
variable 

150 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

which only 
sell through 
an e-
commerce 
marketplace 
from 
customers in 
other EU 
countries 

Web sales 
by 
enterprises 
which only 
sell through 
an e-
commerce 
marketplace 
from 
customers 
located in 
the rest of 
the world 

Sales via a 
website or 
apps - from 
customers 
located in 
the own 
country 

Sales via a 
website or 
apps - from 
customers 
located in 
other EU 
countries 

Sales via a 
website or 
apps - from 
customers 
located in 

Blank and 
AWSVAL <> 
-1 and 
AWSEU=1 
and 
AWS_COW
N=0 and 
AWS_CMP=
1 

∑ (AWSVAL) 
where 
AWSVAL <> 
Blank and 
AWSVAL <> 
-1 and 
AWSWW=1 
and 
AWS_COW
N=0 and 
AWS_CMP=
1 

∑(AWSVAL_
HM) where 
AWSVAL_H
M <> Blank 
and 
AWSVAL_H
M <> -1 

∑(AWSVAL_
EU) where 
AWSVAL_E
U <> Blank 
and 
AWSVAL_E
U <> -1 

∑(AWSVAL_
WW) where 
AWSVAL_W
W <> Blank 
and 

FALSE 

NEW 
derived 
variable 

TRUE 

2022 

TRUE 

2022 

TRUE 

2022 

Derived from 
B1, B2 and B5c 

E_AWSVAL
_WW_CMP
Q 

B6a 

B6b 

B6c 

E_AWSVAL
_HM 

E_AWSVAL
_EU 

E_AWSVAL
_WW 

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Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Derived from 
B2, B5b, B5c, 
B6b and B6c 

E_AWSFOR
_GE20 

Derived from 
B2, B5b, B5c, 
B6b and B6c 

E_AWSFOR
_GE50 

Derived from 
B1, B2, B5b, 
B5c, B6b and 
B6c 

E_AWSFOR
_GE20_CM
P 

3 

Data transmission 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

TRUE 

2022 

TRUE 

2022 

TRUE 

2022 

the rest of 
the world 

AWSVAL_W
W <> -1 

Received 
orders 
placed via a 
website or 
apps from 
customers in 
foreign 
countries, 
with an 
export 
turnover at 
least 20% of 
the web 
sales 

Received 
orders 
placed via a 
website or 
apps from 
customers in 
foreign 
countries, 
with an 
export 
turnover at 
least 50% of 
the web 
sales 

Sold via an 
e-commerce 
marketplace 
and received 
orders 
placed via a 
website or 
apps from 
customers in 
foreign 
countries, 
with an 
export 

Count 
(ENT_ID) 
where 
(AWSEU=1 
or 
AWSWW=1) 
and 
(AWSVAL_E
U + 
AWSVAL_W
W >= 0.20 * 
AWSVAL) 

Count 
(ENT_ID) 
where 
(AWSEU=1 
or 
AWSWW=1) 
and 
(AWSVAL_E
U + 
AWSVAL_W
W >= 0.50 * 
AWSVAL) 

Count 
(ENT_ID) 
where 
AWS_CMP=
1 and 
(AWSEU=1 
or 
AWSWW=1) 
and 
(AWSVAL_E
U + 
AWSVAL_W

152 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Derived from 
B1, B2, B5b, 
B5c, B6b and 
B6c 

E_AWSFOR
_GE50_CM
P 

B7 

E_AXSELL 

turnover at 
least 20% of 
the web 
sales 

Sold via an 
e-commerce 
marketplace 
and received 
orders 
placed via a 
website or 
apps from 
customers in 
foreign 
countries, 
with an 
export 
turnover at 
least 50% of 
the web 
sales 

Have 
received 
orders via 
EDI-type 
messages 

B7 

E_AXSELLX  Have not 
received 
orders via 
EDI-type 
messages 

B7 

E_AXSELLZ  Do not know 

Derived from 
B1 and B7 

E_AESELL 

if have 
received 
orders via 
EDI-type 
messages 

Have 
received 
orders via 
computer 
networks 

W >= 0.20 * 
AWSVAL) 

Count 
(ENT_ID) 
where 
AWS_CMP=
1 and 
(AWSEU=1 
or 
AWSWW=1) 
and 
(AWSVAL_E
U + 
AWSVAL_W
W >= 0.50 * 
AWSVAL) 

Count 
(ENT_ID) 
where 
AXSELL=1 

Count 
(ENT_ID) 
where 
AXSELL=0 

Count 
(ENT_ID) 
where 
AXSELL=Bl
ank 

Count 
(ENT_ID) 
where 
(AWS_COW
N=1 or 

TRUE 

2022 

FALSE 

2023 

FALSE 

2023 

TRUE 

2023 

FALSE 

2023 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Derived from 
B2 and B7 

E_WSEL1Q 

Enterprises 
with web 
sales via 
websites or 
apps equal 
to or above 
1% of 
turnover, 
and no 
orders 
received via 
EDI-type 
messages 

B8 

E_AXSVALS  Total sales 

via EDI type 
messages, 
excluding 
VAT(<1% of 
turnover) 

B8 

E_AXSVALB  Total sales 

B8 

E_AXSVAL 

via EDI type 
messages, 
excluding 
VAT(>=1% 
of turnover) 

Total sales 
via EDI type 
messages, 
excluding 
VAT 

AWS_CMP=
1) or 
AXSELL=1 

Count 
(ENT_ID) 
where 
AWSVAL <> 
Blank and 
AWSVAL <> 
-1 and 
AWSVAL>=
TOVT * 1% 
and 
(AXSELL=0 
or 
AXSELL=Bl
ank) 

∑ (AXSVAL) 
where 
AXSVAL <> 
Blank and 
AXSVAL <> 
-1 and 
AXSVAL<TO
VT * 1% 

∑ (AXSVAL) 
where 
AXSVAL <> 
Blank and 
AXSVAL <> 
-1 and 
AXSVAL>=T
OVT * 1% 

∑ (AXSVAL]) 
where 
AXSVAL <> 
Blank and 
AXSVAL <> 
-1 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

B8 

E_XSEL0 

Enterprises 
with sales 
via EDI-type 

Count 
(ENT_ID) 
where 

FALSE 

2023 

154 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

B8 

E_XSEL1 

B8 

E_XSEL2 

B8 

E_XSEL5 

B8 

E_XSEL10 

messages 
equal to or 
above 0 

Enterprises 
with sales 
via EDI-type 
messages 
equal to or 
above 1% of 
turnover 

Enterprises 
with sales 
via EDI-type 
messages 
equal to or 
above 2% of 
turnover 

Enterprises 
with sales 
via EDI-type 
messages 
equal to or 
above 5% of 
turnover 

Enterprises 
with sales 
via EDI-type 
messages 
equal to or 
above 10% 
of turnover 

AXSVAL <> 
Blank and 
AXSVAL <> 
-1 and 
AXSVAL>=0 

Count 
(ENT_ID) 
where 
AXSVAL <> 
Blank and 
AXSVAL <> 
-1 and 
AXSVAL>=T
OVT * 1% 

Count 
(ENT_ID) 
where 
AXSVAL <> 
Blank and 
AXSVAL <> 
-1 and 
AXSVAL>=T
OVT * 2% 

Count 
(ENT_ID) 
where 
AXSVAL <> 
Blank and 
AXSVAL <> 
-1 and 
AXSVAL>=T
OVT * 5% 

Count 
(ENT_ID) 
where 
AXSVAL <> 
Blank and 
AXSVAL <> 
-1 and 
AXSVAL>=T
OVT * 10% 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

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Model 
questionnaire 
2024 

B8 

Variable 
codes 
(CAPITAL 
LETTERS) 

E_XSEL25 

B8 

E_XSEL50 

B8 

E_XSELZ 

Derived from 
B2 and B8 

E_ESELL 

3 

Data transmission 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

FALSE 

2023 

FALSE 

2023 

TRUE 

2023 

FALSE 

2023 

Enterprises 
with sales 
via EDI-type 
messages 
equal to or 
above 25% 
of turnover 

Enterprises 
with sales 
via EDI-type 
messages 
equal to or 
above 50% 
of turnover 

Don't know 
the % of 
sales via 
EDI-type 
messages 

Enterprises 
which used 
any 
computer 
networks for 
sales (at 
least 1%) 

Count 
(ENT_ID) 
where 
AXSVAL <> 
Blank and 
AXSVAL <> 
-1 and 
AXSVAL>=T
OVT * 25% 

Count 
(ENT_ID) 
where 
AXSVAL <> 
Blank and 
AXSVAL <> 
-1 and 
AXSVAL>=T
OVT * 50% 

Count 
(ENT_ID) 
where 
AXSVAL=Bl
ank 

Count 
(ENT_ID) 
where 
(AWSVAL<>
Blank and 
AWSVAL>=
TOVT*1%) 
or 
(AXSVAL<>
Blank and 
AXSVAL>=T
OVT*1%) or 
(AWSVAL<>
Blank and 
AWSVAL<>-
1 and 
AXSVAL<>B
lank and 
AXSVAL<>-
1 and 

156 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Derived from 
B2 and B8 

E_ETURN 

Total 
electronic 
sales, 
excluding 
VAT 

Derived from 
B2, B4a and B8 

E_AWSVAL
_B2C_GE10
EC 

Enterprises 
where B2C 
sales via 
websites or 
apps were 
10% or more 
of the e-
commerce 
turnover 

C1 

E_ITSP2 

Employ ICT 
specialists 

(AWSVAL+A
XSVAL)>=T
OVT*1%) 

∑ (AWSVAL) 
where 
AWSVAL <> 
Blank and 
AWSVAL <> 
-1 + ∑ 
(AXSVAL) 
where 
AXSVAL <> 
Blank and 
AXSVAL <> 
-1 

Count 
(ENT_ID) 
where 
AWSVALC<
>Blank AND 
AWSVALC>
0 and 
AWSVAL<>
Blank and 
AXSVAL<>B
lank and 
((AXSVAL = 
-1 and 
AWSVALC>
=0.1 * 
AWSVAL) or 
(AXSVAL <> 
-1 and 
AWSVALC>
=0.1 * 
(AWSVAL+A
XSVAL))) 

Count(ENT_
ID) where 
ITSP2=1 

FALSE 

2023 

FALSE 

2023 

FALSE 

2022 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

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3 

Data transmission 

Model 
questionnaire 
2024 

C1 

Variable 
codes 
(CAPITAL 
LETTERS) 

E_ITSP2X 

C2a 

E_ITSPT2 

C2a 

E_ITSPT2X 

C2b 

E_ITUST2 

C2b 

E_ITUST2X 

Derived from 
C2a and C2b 

E_ITT2 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Don't 
employ ICT 
specialists 

Have 
provided 
training to 
develop ICT 
skills of 
personnel: 
for ICT 
specialists 

Have not 
provided 
training to 
develop ICT 
skills of 
personnel: 
for ICT 
specialists 

Have 
provided 
training to 
develop ICT 
skills of 
personnel: 
for other 
persons 
employed 

Have not 
provided 
training to 
develop ICT 
skills of 
personnel: 
for other 
persons 
employed 

Have 
provided 
training to 
develop ICT 

Count(ENT_
ID) where 
ITSP2=0 

Count(ENT_
ID) where 
ITSPT2=1 

FALSE 

2022 

FALSE 

2022 

Count(ENT_
ID) where 
ITSPT2=0 

FALSE 

2022 

Count(ENT_
ID) where 
ITUST2=1 

FALSE 

2022 

Count(ENT_
ID) where 
ITUST2=0 

FALSE 

2022 

FALSE 

2022 

Count(ENT_
ID) where 
ITSPT2=1 or 
ITUST2=1 

158 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Derived from 
C1 and C2a 

E_ITSP2_IT
T2 

Derived from 
C1 and C2b 

E_ITSP2X_I
TT2 

C3 

C3 

E_ITSPRCR
2 

E_ITSPRCR
2X 

Derived from 
C1 and C3 

E_ITSP2_O
R_RCR2 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

skills of 
personnel 

Employ ICT 
specialists 
and have 
provided 
training to 
develop ICT 
skills of 
personnel 

Don't 
employ ICT 
specialists, 
but have 
provided 
training to 
develop ICT 
skills of 
personnel 

Have 
recruited/trie
d to recruit 
ICT 
specialists 

Have not 
recruited/trie
d to recruit 
ICT 
specialists 

Employ ICT 
specialists or 
have 
recruited/trie
d to recruit 
ICT 
specialists 

Count(ENT_
ID) where 
ITSP2=1 
and 
(ITSPT2=1 
or 
ITUST2=1) 

Count(ENT_
ID) where 
ITSP2=0 
and 
ITUST2=1 

Count(ENT_
ID) where 
ITSPRCR2=
1 

Count(ENT_
ID) where 
ITSPRCR2=
0 

Count(ENT_
ID) where 
ITSP2=1 or 
ITSPRCR2=
1 

FALSE 

2022 

FALSE 

2022 

FALSE 

2022 

FALSE 

2022 

FALSE 

2022 

C4 

E_ITSPVAC
2 

Had hard-to-
fill vacancies 
for ICT 
specialists 

Count(ENT_
ID) where 
ITSPVAC2=
1 

FALSE 

2022 

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Model 
questionnaire 
2024 

C4 

Variable 
codes 
(CAPITAL 
LETTERS) 

E_ITSPVAC
2X 

Derived from 
C1 and C4 

E_ITSP2_V
AC2 

C5a 

E_ITSPDLA 

C5a 

E_ITSPDLA
X 

C5b 

E_ITSPDLE
T 

3 

Data transmission 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Count(ENT_
ID) where 
ITSPVAC2=
0 

Count(ENT_
ID) where 
ITSP2=1 
and 
ITSPVAC2=
1 

Count(ENT_
ID) where 
ITSPDLA=1 

FALSE 

2022 

FALSE 

2022 

TRUE 

2022 

Count(ENT_
ID) where 
ITSPDLA=0 

TRUE 

2022 

TRUE 

2022 

Count(ENT_
ID) where 
ITSPDLET=
1 

Had no 
hard-to-fill 
vacancies 
for ICT 
specialists 

Employ ICT 
specialists 
and had 
hard-to-fill 
vacancies 
for ICT 
specialists 

Enterprises 
had the 
following 
difficulty to 
recruit ICT 
specialists: 
Lack of 
applications 

Enterprises 
did not have 
the following 
difficulty to 
recruit ICT 
specialists: 
Lack of 
applications 

Enterprises 
had the 
following 
difficulty to 
recruit ICT 
specialists: 
Applicants' 
lack of 
relevant ICT 
qualifications 
from 
education 
and/or 
training 

160 

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3 

Data transmission 

Model 
questionnaire 
2024 

C5b 

Variable 
codes 
(CAPITAL 
LETTERS) 

E_ITSPDLE
TX 

C5c 

E_ITSPDLW
E 

C5c 

E_ITSPDLW
EX 

C5d 

E_ITSPDSA
L 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

TRUE 

2022 

Count(ENT_
ID) where 
ITSPDLET=
0 

TRUE 

2022 

Count(ENT_
ID) where 
ITSPDLWE=
1 

TRUE 

2022 

Count(ENT_
ID) where 
ITSPDLWE=
0 

TRUE 

2022 

Count(ENT_
ID) where 
ITSPDSAL=
1 

Enterprises 
did not have 
the following 
difficulty to 
recruit ICT 
specialists: 
Applicants' 
lack of 
relevant ICT 
qualifications 
from 
education 
and/or 
training 

Enterprises 
had the 
following 
difficulty to 
recruit ICT 
specialists: 
Applicants' 
lack of 
relevant 
work 
experience 

Enterprises 
did not have 
the following 
difficulty to 
recruit ICT 
specialists: 
Applicants' 
lack of 
relevant 
work 
experience 

Enterprises 
had the 
following 
difficulty to 
recruit ICT 
specialists: 
Applicants' 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

C5d 

E_ITSPDSA
LX 

C6a 

E_IT_OWN 

C6a 

E_IT_OWNX 

C6b 

E_IT_EXT 

salary 
expectation 
too high 

Enterprises 
did not have 
the following 
difficulty to 
recruit ICT 
specialists: 
Applicants' 
salary 
expectation 
too high 

ICT 
functions 
were 
performed 
by own 
employees 
(incl. those 
employed in 
parent or 
affiliate 
enterprises) 

ICT 
functions 
were not 
performed 
by own 
employees 
(incl. those 
employed in 
parent or 
affiliate 
enterprises) 

ICT 
functions 
were 
performed 
by external 
suppliers 

TRUE 

2022 

Count(ENT_
ID) where 
ITSPDSAL=
0 

Count(ENT_
ID) where 
IT_OWN=1 

FALSE 

2022 

Count(ENT_
ID) where 
IT_OWN=0 

FALSE 

2022 

Count(ENT_
ID) where 
IT_EXT=1 

FALSE 

2022 

162 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

C6b 

E_IT_EXTX 

Derived from 
C6a and C6b 

E_IT_OWN
Q 

Derived from 
C6a and C6b 

E_IT_EXTQ 

Derived from 
C1 and C6a 

E_IT_OWN_
ITSP2 

Derived from 
C1 and C6b 

E_IT_EXT_I
TSP2 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

ICT 
functions 
were not 
performed 
by external 
suppliers 

ICT 
functions 
were entirely 
performed 
by own 
employees 
(incl. those 
employed in 
parent or 
affiliate 
enterprises) 

ICT 
functions 
were entirely 
performed 
by external 
suppliers 

Employ ICT 
specialists 
and ICT 
functions 
were 
performed 
by own 
employees 
(incl. those 
employed in 
parent or 
affiliate 
enterprises) 

Employ ICT 
specialists 
and ICT 
functions 
were 
performed 

FALSE 

2022 

FALSE 

2022 

FALSE 

2022 

FALSE 

2022 

Count(ENT_
ID) where 
IT_EXT=0 

Count(ENT_
ID) where 
IT_OWN=1 
and 
IT_EXT=0 

Count(ENT_
ID) where 
IT_OWN=0 
and 
IT_EXT=1 

Count(ENT_
ID) where 
IT_OWN=1 
AND 
ITSP2=1 

FALSE 

2022 

Count(ENT_
ID) where 
IT_EXT=1 
AND 
ITSP2=1 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

163 

 
3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Derived from 
C2a and C2b, 
C6b 

E_IT_EXT_I
TT2 

D1a 

E_SECMSP
SW 

D1a 

E_SECMSP
SWX 

D1b 

E_SECMUIB
M 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

by external 
suppliers 

Provided 
training to 
develop ICT 
skills of 
personnel 
and ICT 
functions 
were 
performed 
by external 
suppliers 

ICT security 
measure 
used: strong 
password 
authenticatio
n, i.e. 
minimum 
length of 8 
mixed 
characters, 
periodical 
change 

ICT security 
measure not 
used: strong 
password 
authenticatio
n, i.e. 
minimum 
length of 8 
mixed 
characters, 
periodical 
change 

ICT security 
measure 
used: user 
identification 
and 

FALSE 

2022 

FALSE 

2022 

Count(ENT_
ID) where 
IT_EXT=1 
AND 
(ITSPT2=1 
or 
ITUST2=1) 

Count(ENT_
ID) where 
SECMSPS
W=1 

FALSE 

2022 

Count(ENT_
ID) where 
SECMSPS
W=0 

FALSE 

2022 

Count(ENT_
ID) where 
SECMUIBM
=1 

164 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

D1b 

E_SECMUIB
MX 

D1c 

E_SECMDU
O 

authenticatio
n via 
biometric 
methods 
implemented 
by the 
enterprise 
(e.g. based 
on 
fingerprints, 
voice, faces) 

ICT security 
measure not 
used: user 
identification 
and 
authenticatio
n via 
biometric 
methods 
implemented 
by the 
enterprise 
(e.g. based 
on 
fingerprints, 
voice, faces) 

ICT security 
measure 
used: at 
least two 
authenticatio
n 
mechanisms 
(i.e. 
combination 
of e.g. user-
defined 
password, 
one-time 
password 
(OTP), code 
generated 
via a 

FALSE 

2022 

Count(ENT_
ID) where 
SECMUIBM
=0 

FALSE 

2022 

Count(ENT_
ID) where 
SECMDUO=
1 

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165 

 
3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

D1c 

E_SECMDU
OX 

D1d 

E_SECMDE
NC 

security 
token or 
received via 
a 
smartphone, 
biometric 
method 
faces) 

ICT security 
measure not 
used: at 
least two 
authenticatio
n 
mechanisms 
(i.e. 
combination 
of e.g. user-
defined 
password, 
one-time 
password 
(OTP), code 
generated 
via a 
security 
token or 
received via 
a 
smartphone, 
biometric 
method (e.g. 
based on 
fingerprints, 

ICT security 
measure 
used: 
encryption 
techniques 
for data, 
documents 
or e-mails 

FALSE 

2022 

Count(ENT_
ID) where 
SECMDUO=
0 

FALSE 

2022 

Count(ENT_
ID) where 
SECMDENC
=1 

166 

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3 

Data transmission 

Model 
questionnaire 
2024 

D1d 

Variable 
codes 
(CAPITAL 
LETTERS) 

E_SECMDE
NCX 

D1e 

E_SECMOS
BU 

D1e 

E_SECMOS
BUX 

D1f 

E_SECMNA
C 

D1f 

E_SECMNA
CX 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

ICT security 
measure not 
used: 
encryption 
techniques 
for data, 
documents 
or e-mails 

ICT security 
measure 
used: data 
backup to a 
separate 
location 
(including 
backup to 
the cloud) 

ICT security 
measure not 
used: data 
backup to a 
separate 
location 
(including 
backup to 
the cloud) 

ICT security 
measure 
used: 
network 
access 
control 
(manageme
nt of access 
by devices 
and users to 
the 
enterprise's 
network) 

ICT security 
measure not 
used: 
network 

FALSE 

2022 

FALSE 

2022 

Count(ENT_
ID) where 
SECMDENC
=0 

Count(ENT_
ID) where 
SECMOSBU
=1 

FALSE 

2022 

Count(ENT_
ID) where 
SECMOSBU
=0 

FALSE 

2022 

Count(ENT_
ID) where 
SECMNAC=
1 

FALSE 

2022 

Count(ENT_
ID) where 
SECMNAC=
0 

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167 

 
3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

D1g 

E_SECMVP
N 

D1g 

E_SECMVP
NX 

FALSE 

2022 

Count(ENT_
ID) where 
SECMVPN=
1 

FALSE 

2022 

Count(ENT_
ID) where 
SECMVPN=
0 

access 
control 
(manageme
nt of access 
by devices 
and users to 
the 
enterprise's 
network) 

ICT security 
measure 
used: VPN 
(Virtual 
Private 
Network 
extends a 
private 
network 
across a 
public 
network to 
enable 
secure 
exchange of 
data over 
public 
network) 

ICT security 
measure not 
used: VPN 
(Virtual 
Private 
Network 
extends a 
private 
network 
across a 
public 
network to 
enable 
secure 
exchange of 
data over 

168 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

D1h 

E_SECMSM
S1 

D1h 

E_SECMSM
S1X 

D1i 

E_SECMLO
G 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

public 
network) 

ICT security 
measure 
used: 
monitoring 
system that 
allows to 
detect 
suspicious 
activity in the 
ICT systems 
and alerts 
the 
enterprises 
about it, 
other than 
standalone 
anti-virus 
software 

ICT security 
measure not 
used: 
monitoring 
system that 
allows to 
detect 
suspicious 
activity in the 
ICT systems 
and alerts 
the 
enterprises 
about it, 
other than 
standalone 
anti-virus 
software 

ICT security 
measure 
used: 
maintaining 
log files for 

FALSE 

NEW 

Count(ENT_
ID) where 
SECMSMS1
=1 

FALSE 

NEW 

Count(ENT_
ID) where 
SECMSMS1
=0 

FALSE 

2022 

Count(ENT_
ID) where 
SECMLOG=
1 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

D1i 

D1j 

E_SECMLO
GX 

E_SECMRA
SS 

D1j 

E_SECMRA
SSX 

D1k 

E_SECMTS
T 

analysis 
after security 
incidents 

ICT security 
measure not 
used: 
maintaining 
log files for 
analysis 
after security 
incidents 

ICT security 
measure 
used: ICT 
risk 
assessment, 
i.e. 
periodically 
assessment 
of probability 
and 
consequenc
es of ICT 
security 
incidents 

ICT security 
measure not 
used: ICT 
risk 
assessment, 
i.e. 
periodically 
assessment 
of probability 
and 
consequenc
es of ICT 
security 
incidents 

ICT security 
measure 
used: ICT 

FALSE 

2022 

FALSE 

2022 

Count(ENT_
ID) where 
SECMLOG=
0 

Count(ENT_
ID) where 
SECMRASS
=1 

FALSE 

2022 

Count(ENT_
ID) where 
SECMRASS
=0 

Count(ENT_
ID) where 

FALSE 

2022 

170 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

security 
tests (e.g. 
performing 
penetration 
tests, testing 
security alert 
system, 
review of 
security 
measures, 
testing of 
backup 
systems) 

ICT security 
measure not 
used: ICT 
security 
tests (e.g. 
performing 
penetration 
tests, testing 
security alert 
system, 
review of 
security 
measures, 
testing of 
backup 
systems) 

Use any ICT 
security 
measure 

Use at least 
3 ICT 
security 
measures 

Use at least 
5 ICT 
security 
measures 

SECMTST=
1 

FALSE 

2022 

Count(ENT_
ID) where 
SECMTST=
0 

FALSE 

2022 

FALSE 

2022 

FALSE 

2022 

Count(ENT_
ID) where 
SECM_CNT
>=1 

Count(ENT_
ID) where 
SECM_CNT
>=3 

Count(ENT_
ID) where 
SECM_CNT
>=5 

D1k 

E_SECMTS
TX 

Derived from 
D1a to D1k 

E_SECMGE
1 

Derived from 
D1a to D1k 

E_SECMGE
3 

Derived from 
D1a to D1k 

E_SECMGE
5 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

171 

 
Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Derived from 
D1a to D1k 

E_SECMGE
7 

Derived from 
D1a to D1k 

E_SECMAL
L 

D2a 

E_SECAWV
TGI 

D2a 

E_SECAWV
TGIX 

3 

Data transmission 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

FALSE 

2022 

FALSE 

2022 

FALSE 

2022 

Count(ENT_
ID) where 
SECM_CNT
>=7 

Count(ENT_
ID) where 
SECM_CNT
=11 

Count(ENT_
ID) where 
SECAWVTG
I=1 

FALSE 

2022 

Count(ENT_
ID) where 
SECAWVTG
I=0 

Use at least 
7 ICT 
security 
measures 

Use all ICT 
security 
measures 

Make 
persons 
employed 
aware of 
their 
obligations 
in ICT 
security 
related 
issues by 
voluntary 
training or 
internally 
available 
information 
(e.g. 
information 
on the 
intranet) 

Don't make 
persons 
employed 
aware of 
their 
obligations 
in ICT 
security 
related 
issues by 
voluntary 
training or 
internally 
available 
information 
(e.g. 

172 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

D2b 

E_SECAWC
TP 

D2b 

E_SECAWC
TPX 

D2c 

E_SECAWC
ONT 

information 
on the 
intranet) 

Make 
persons 
employed 
aware of 
their 
obligations 
in ICT 
security 
related 
issues by 
compulsory 
training 
courses or 
viewing 
compulsory 
material 

Don't make 
persons 
employed 
aware of 
their 
obligations 
in ICT 
security 
related 
issues by 
compulsory 
training 
courses or 
viewing 
compulsory 
material 

Make 
persons 
employed 
aware of 
their 
obligations 
in ICT 
security 

FALSE 

2022 

Count(ENT_
ID) where 
SECAWCTP
=1 

FALSE 

2022 

Count(ENT_
ID) where 
SECAWCTP
=0 

FALSE 

2022 

Count(ENT_
ID) where 
SECAWCO
NT=1 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

D2c 

E_SECAWC
ONTX 

Derived from 
D2a to D2c 

E_SECAWA
NY 

Derived from 
D2a to D2c 

E_SECAWN
ONE 

related 
issues by 
contract 
(e.g. 
contract of 
employment) 

Don't make 
persons 
employed 
aware of 
their 
obligations 
in ICT 
security 
related 
issues by 
contract 
(e.g. 
contract of 
employment) 

Make 
persons 
employed 
aware of 
their 
obligations 
in ICT 
security 
related 
issues 

Don't make 
persons 
employed 
aware of 
their 
obligations 
in ICT 
security 
related 
issues 

FALSE 

2022 

Count(ENT_
ID) where 
SECAWCO
NT=0 

FALSE 

2022 

FALSE 

2022 

Count(ENT_
ID) where 
SECAWVTG
I=1 or 
SECAWCTP
=1 or 
SECAWCO
NT=1 

Count(ENT_
ID) where 
SECAWVTG
I=0 and 
SECAWCTP
=0 and 
SECAWCO
NT=0 

Derived from 
C2a and D2a 

E_SECAWV
TGI_ITSPT2 

Make 
persons 

Count(ENT_
ID) where 

FALSE 

2022 

174 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Derived from 
C2b and D2a 

E_SECAWV
TGI_ITUST2 

SECAWVTG
I=1 and 
ITSPT2=1 

FALSE 

2022 

Count(ENT_
ID) where 
SECAWVTG
I=1 and 
ITUST2=1 

employed 
aware of 
their 
obligations 
in ICT 
security 
related 
issues by 
voluntary 
training or 
internally 
available 
information 
and have 
provided 
training to 
develop ICT 
skills for ICT 
specialists 

Make 
persons 
employed 
aware of 
their 
obligations 
in ICT 
security 
related 
issues by 
voluntary 
training or 
internally 
available 
information 
and have 
provided 
training to 
develop ICT 
skills for 
other 
persons 
employed 

Derived from 
C2a and D2b 

E_SECAWC
TP_ITSPT2 

Make 
persons 

Count(ENT_
ID) where 

FALSE 

2022 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Derived from 
C2b and D2b 

E_SECAWC
TP_ITUST2 

SECAWCTP
=1 and 
ITSPT2=1 

FALSE 

2022 

Count(ENT_
ID) where 
SECAWCTP
=1 and 
ITUST2=1 

employed 
aware of 
their 
obligations 
in ICT 
security 
related 
issues by 
compulsory 
training 
courses or 
viewing 
compulsory 
material and 
have 
provided 
training to 
develop ICT 
skills for ICT 
specialists 

Make 
persons 
employed 
aware of 
their 
obligations 
in ICT 
security 
related 
issues by 
compulsory 
training 
courses or 
viewing 
compulsory 
material and 
have 
provided 
training to 
develop ICT 
skills for 
other 
persons 
employed 

176 

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3 

Data transmission 

Model 
questionnaire 
2024 

Derived from 
C2a and D2c 

Variable 
codes 
(CAPITAL 
LETTERS) 

E_SECAWC
ONT_ITSPT
2 

Derived from 
C2b and D2c 

E_SECAWC
ONT_ITUST
2 

Derived from 
C2a and D2 

E_SECAWA
NY_ITSPT2 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Make 
persons 
employed 
aware of 
their 
obligations 
in ICT 
security 
related 
issues by 
contract and 
have 
provided 
training to 
develop ICT 
skills for ICT 
specialists 

Make 
persons 
employed 
aware of 
their 
obligations 
in ICT 
security 
related 
issues by 
contract and 
have 
provided 
training to 
develop ICT 
skills for 
other 
persons 
employed 

Make 
persons 
employed 
aware of 
their 
obligations 
in ICT 
security 

FALSE 

2022 

Count(ENT_
ID) where 
SECAWCO
NT=1 and 
ITSPT2=1 

FALSE 

2022 

Count(ENT_
ID) where 
SECAWCO
NT=1 and 
ITUST2=1 

FALSE 

2022 

Count(ENT_
ID) where 
(SECAWVT
GI=1 or 
SECAWCTP
=1 or 
SECAWCO

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Derived from 
C2b and D2 

E_SECAWA
NY_ITUST2 

D3 

D3 

E_SECPOL
2 

E_SECPOL
2X 

NT=1) and 
ITSPT2=1 

Count(ENT_
ID) where 
(SECAWVT
GI=1 or 
SECAWCTP
=1 or 
SECAWCO
NT=1) and 
ITUST2=1 

FALSE 

2022 

Count(ENT_
ID) where 
SECPOL2=1 

FALSE 

2022 

Count(ENT_
ID) where 
SECPOL2=0 

FALSE 

2022 

related 
issues and 
have 
provided 
training to 
develop ICT 
skills for ICT 
specialists 

Make 
persons 
employed 
aware of 
their 
obligations 
in ICT 
security 
related 
issues and 
have 
provided 
training to 
develop ICT 
skills for 
other 
persons 
employed 

Have 
document(s) 
on 
measures, 
practices or 
procedures 
on ICT 
security 

Have no 
document(s) 
on 
measures, 
practices or 
procedures 
on ICT 
security 

178 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Derived from 
C1 and D3 

E_SECPOL
2_ITSP2 

Derived from 
D2a to D2c and 
D3 

E_SECAWA
NY_POL2 

D4a 

E_SECPRE
V_CY 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

FALSE 

2022 

FALSE 

2022 

Count(ENT_
ID) where 
ITSP2=1 
and 
SECPOL2=1 

Count(ENT_
ID) where 
(SECAWVT
GI=1 or 
SECAWCTP
=1 or 
SECAWCO
NT=1) and 
SECPOL2=1 

TRUE 

2022 

Count(ENT_
ID) where 
SECPREV=
1 

Employ ICT 
specialists 
and have 
document(s) 
on 
measures, 
practices or 
procedures 
on ICT 
security 

Make 
persons 
employed 
aware of 
their 
obligations 
in ICT 
security 
related 
issues and 
have 
document(s) 
on 
measures, 
practices or 
procedures 
on ICT 
security 

The 
document(s) 
on 
measures, 
practices or 
procedures 
were defined 
or most 
recently 
reviewed: 
within the 
last 12 
months 

D4b 

E_SECPRE
V_1_2 

The 
document(s) 

Count(ENT_
ID) where 

TRUE 

2022 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

on 
measures, 
practices or 
procedures 
were defined 
or most 
recently 
reviewed: 
more than 
12 months 
and up to 24 
months ago 

The 
document(s) 
on 
measures, 
practices or 
procedures 
were defined 
or most 
recently 
reviewed: 
more than 
24 months 
ago 

The 
document(s) 
on 
measures, 
practices or 
procedures 
were defined 
or most 
recently 
reviewed: 
within the 
last 24 
months 

Have 
experienced 
at least once 
problems 
due to ICT 

SECPREV=
2 

TRUE 

2022 

Count(ENT_
ID) where 
SECPREV=
3 

TRUE 

2022 

Count(ENT_
ID) where 
SECPREV=
1 or 
SECPREV=
2 

FALSE 

2022 

Count(ENT_
ID) where 
SEC2IUSVF
=1 

D4c 

E_SECPRE
V_MT2 

Derived from 
D4a and D4b 

E_SECPRE
V_LE2 

D5a 

E_SEC2IUS
VF 

180 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

security 
incident: 
unavailability 
of ICT 
services due 
to hardware 
or software 
failures 

Have not 
experienced 
at least once 
problems 
due to ICT 
security 
incident: 
unavailability 
of ICT 
services due 
to hardware 
or software 
failures 

Have 
experienced 
at least once 
problems 
due to ICT 
security 
incident: 
unavailability 
of ICT 
services 
(e.g. 
Ransomwar
e attacks, 
Denial of 
Service 
attacks) 

Have not 
experienced 
at least once 
problems 
due to ICT 
security 

FALSE 

2022 

Count(ENT_
ID) where 
SEC2IUSVF
=0 

FALSE 

2022 

Count(ENT_
ID) where 
SEC2IUSVA
=1 

FALSE 

2022 

Count(ENT_
ID) where 
SEC2IUSVA
=0 

D5a 

E_SEC2IUS
VFX 

D5b 

E_SEC2IUS
VA 

D5b 

E_SEC2IUS
VAX 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

181 

 
3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Derived from 
D5a and D5b 

E_SEC2IUS
V 

D5c 

E_SEC2IDC
DF 

incident: 
unavailability 
of ICT 
services 
(e.g. 
Ransomwar
e attacks, 
Denial of 
Service 
attacks) 

Have 
experienced 
at least once 
problems 
due to ICT 
security 
incident: 
unavailability 
of ICT 
services 
(e.g. Denial 
of Service 
attacks, 
ransomware 
attacks, 
hardware or 
software 
failures - 
excluding 
mechanical 
failure, theft) 

Have 
experienced 
at least once 
problems 
due to ICT 
security 
incident: 
destruction 
or corruption 
of data due 
to hardware 
or software 
failures 

FALSE 

2022 

Count(ENT_
ID) where 
SEC2IUSVF
=1 or 
SEC2IUSVA
=1 

FALSE 

2022 

Count(ENT_
ID) where 
SEC2IDCDF
=1 

182 

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3 

Data transmission 

Model 
questionnaire 
2024 

D5c 

Variable 
codes 
(CAPITAL 
LETTERS) 

E_SEC2IDC
DFX 

D5d 

E_SEC2IDC
DA 

D5d 

E_SEC2IDC
DAX 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

FALSE 

2022 

Count(ENT_
ID) where 
SEC2IDCDF
=0 

FALSE 

2022 

Count(ENT_
ID) where 
SEC2IDCDA
=1 

FALSE 

2022 

Count(ENT_
ID) where 
SEC2IDCDA
=0 

Have not 
experienced 
at least once 
problems 
due to ICT 
security 
incident: 
destruction 
or corruption 
of data due 
to hardware 
or software 
failures 

Have 
experienced 
at least once 
problems 
due to ICT 
security 
incident: 
destruction 
or corruption 
of data due 
to infection 
of malicious 
software or 
unauthorised 
intrusion 

Have not 
experienced 
at least once 
problems 
due to ICT 
security 
incident: 
destruction 
or corruption 
of data due 
to infection 
of malicious 
software or 
unauthorised 
intrusion 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

183 

 
Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Derived from 
D5c and D5d 

E_SEC2IDC
D 

D5e 

E_SEC2ICN
FA 

D5e 

E_SEC2ICN
FAX 

3 

Data transmission 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

FALSE 

2022 

Count(ENT_
ID) where 
SEC2IDCDF
=1 or 
SEC2IDCDA
=1 

FALSE 

2022 

Count(ENT_
ID) where 
SEC2ICNFA
=1 

FALSE 

2022 

Count(ENT_
ID) where 
SEC2ICNFA
=0 

Have 
experienced 
at least once 
problems 
due to ICT 
security 
incident: 
destruction 
or corruption 
of data (e.g. 
due to 
infection of 
malicious 
software or 
unauthorised 
intrusion, 
hardware or 
software 
failures) 

Have 
experienced 
at least once 
problems 
due to ICT 
security 
incident: 
disclosure of 
confidential 
data due to 
intrusion, 
pharming, 
phishing 
attack, 
intentional 
actions by 
own 
employees 

Have not 
experienced 
at least once 
problems 
due to ICT 
security 
incident: 

184 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

disclosure of 
confidential 
data due to 
intrusion, 
pharming, 
phishing 
attack, 
intentional 
actions by 
own 
employees 

Have 
experienced 
at least once 
problems 
due to ICT 
security 
incident: 
disclosure of 
confidential 
data due to 
unintentional 
actions by 
own 
employees 

Have not 
experienced 
at least once 
problems 
due to ICT 
security 
incident: 
disclosure of 
confidential 
data due to 
unintentional 
actions by 
own 
employees 

Have 
experienced 
at least once 
problems 

FALSE 

2022 

Count(ENT_
ID) where 
SEC2ICNFF
=1 

FALSE 

2022 

Count(ENT_
ID) where 
SEC2ICNFF
=0 

FALSE 

2022 

Count(ENT_
ID) where 
SEC2ICNFA
=1 or 

D5f 

E_SEC2ICN
FF 

D5f 

E_SEC2ICN
FFX 

Derived from 
D5e and D5f 

E_SEC2ICN
F 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

185 

 
3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

due to ICT 
security 
incident: 
disclosure of 
confidential 
data (e.g. 
due to 
intrusion, 
pharming, 
phishing 
attack, 
actions by 
own 
employees 
(intentionally 
or 
unintentional
ly)) 

Have 
experienced 
at least once 
any problem 
due to ICT 
security 
incident: 
unavailibility 
of ICT 
services, 
destruction 
or corruption 
of data, 
disclosure of 
confidential 
data (for any 
reason) 

Have 
experienced 
at least once 
3 or more 
problems 
due to ICT 
security 
incident: 
unavailibility 

SEC2ICNFF
=1 

Count(ENT_
ID) where 
SEC2IUSVF
=1 or 
SEC2IUSVA
=1 or 
SEC2IDCDF
=1 or 
SEC2IDCDA
=1 or 
SEC2ICNFA
=1 or 
SEC2ICNFF
=1 

Count(ENT_
ID) where 
SEC2IUSVF
+SEC2IUSV
A+SEC2IDC
DF+SEC2ID
CDA+SEC2I
CNFA+SEC
2ICNFF 

FALSE 

2022 

FALSE 

2022 

Derived from 
D5a to D5f 

E_SEC2IAN
Y 

Derived from 
D5a to D5f 

E_SEC2IGE
3 

186 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

E1a 

E_AI_TTM 

E1a 

E_AI_TTMX 

E1b 

E_AI_TSR 

E1b 

E_AI_TSRX 

between 3 
and 6 

Count(ENT_
ID) where 
AI_TTM=1 

FALSE 

2023 

Count(ENT_
ID) where 
AI_TTM=0 

FALSE 

2023 

Count(ENT_
ID) where 
AI_TSR=1 

FALSE 

2023 

Count(ENT_
ID) where 
AI_TSR=0 

FALSE 

2023 

of ICT 
services, 
destruction 
or corruption 
of data, 
disclosure of 
confidential 
data (for any 
reason) 

Enterprises 
use AI 
technologies 
performing 
analysis of 
written 
language 

Enterprises 
do not use 
AI 
technologies 
performing 
analysis of 
written 
language 

Enterprises 
use AI 
technologies 
converting 
spoken 
language 
into 
machine-
readable 
format 

Enterprises 
do not use 
AI 
technologies 
converting 
spoken 
language 
into 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

187 

 
3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

E1c 

E_AI_TNLG 

E1c 

E_AI_TNLG
X 

E1d 

E_AI_TIR 

E1d 

E_AI_TIRX 

E1e 

E_AI_TML 

machine-
readable 
format 

Enterprises 
use AI 
technologies 
generating 
written or 
spoken 
language 

Enterprises 
do not use 
AI 
technologies 
generating 
written or 
spoken 
language 

Enterprises 
use AI 
technologies 
identifying 
objects or 
persons 
based on 
images 

Enterprises 
do not use 
AI 
technologies 
identifying 
objects or 
persons 
based on 
images 

Enterprises 
use machine 
learning 
(e.g. deep 
learning) for 
data 
analysis 

Count(ENT_
ID) where 
AI_TNLG=1 

FALSE 

2023 

Count(ENT_
ID) where 
AI_TNLG=0 

FALSE 

2023 

Count(ENT_
ID) where 
AI_TIR=1 

FALSE 

2023 

Count(ENT_
ID) where 
AI_TIR=0 

FALSE 

2023 

Count(ENT_
ID) where 
AI_TML=1 

FALSE 

2023 

188 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

E1e 

E_AI_TMLX 

E1f 

E_AI_TPA 

E1f 

E_AI_TPAX 

E1g 

E_AI_TAR 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Count(ENT_
ID) where 
AI_TML=0 

FALSE 

2023 

Count(ENT_
ID) where 
AI_TPA=1 

FALSE 

2023 

Count(ENT_
ID) where 
AI_TPA=0 

FALSE 

2023 

Count(ENT_
ID) where 
AI_TAR=1 

FALSE 

2023 

Enterprises 
do not use 
machine 
learning 
(e.g. deep 
learning) for 
data 
analysis 

Enterprises 
use AI 
technologies 
automating 
different 
workflows or 
assisting in 
decision 
making 

Enterprises 
do not use 
AI 
technologies 
automating 
different 
workflows or 
assisting in 
decision 
making 

Enterprises 
use AI 
technologies 
enabling 
physical 
movement of 
machines 
via 
autonomous 
decisions 
based on 
observation 
of 
surroundings 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

189 

 
Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

E1g 

E_AI_TARX 

Derived from 
E1 

E_AI_TANY 

Derived from 
E1 

E_AI_TX 

3 

Data transmission 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Enterprises 
do not use 
AI 
technologies 
enabling 
physical 
movement of 
machines 
via 
autonomous 
decisions 
based on 
observation 
of 
surroundings 

Enterprises 
use at least 
1 of the 
listed 
artificial 
intelligence 
technologies
: (AI_TTM, 
AI_TSR, 
AI_TNLG, 
AI_TIR, 
AI_TML, 
AI_TPA, 
AI_TAR) 

Enterprises 
doesn't use 
artificial 
intelligence 
technologies
. 

Count(ENT_
ID) where 
AI_TAR=0 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

Count 
(ENT_ID) 
where 
(AI_TTM+AI
_TSR+AI_T
NLG+AI_TIR
+AI_TML+AI
_TPA+AI_TA
R) between 
1 and 7 

Count 
(ENT_ID) 
where 
AI_TTM=0 
and 
AI_TSR=0 
and 
AI_TNLG=0 
and 
AI_TIR=0 
and 
AI_TML=0 
and 
AI_TPA=0 

190 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Derived from 
E1 

E_AI_TGE2 

Derived from 
E1 

E_AI_TGE3 

E2a 

E_AI_PMS 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

FALSE 

2023 

FALSE 

2023 

and 
AI_TAR=0 

Count 
(ENT_ID) 
where 
(AI_TTM+AI
_TSR+AI_T
NLG+AI_TIR
+AI_TML+AI
_TPA+AI_TA
R) between 
2 and 7 

Count 
(ENT_ID) 
where 
(AI_TTM+AI
_TSR+AI_T
NLG+AI_TIR
+AI_TML+AI
_TPA+AI_TA
R) between 
3 and 7 

Count(ENT_
ID) where 
AI_PMS=1 

FALSE 

2023 

Enterprises 
use at least 
2 of the 
listed 
artificial 
intelligence 
technologies
: (AI_TTM, 
AI_TSR, 
AI_TNLG, 
AI_TIR, 
AI_TML, 
AI_TPA, 
AI_TAR) 

Enterprises 
use at least 
3 of the 
listed 
artificial 
intelligence 
technologies
: (AI_TTM, 
AI_TSR, 
AI_TNLG, 
AI_TIR, 
AI_TML, 
AI_TPA, 
AI_TAR) 

Enterprises 
use AI 
technologies 
for 
marketing or 
sales 

E2a 

E_AI_PMSX  Enterprises 
do not use 
AI 
technologies 
for 

Count(ENT_
ID) where 
AI_PMS=0 

FALSE 

2023 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

191 

 
3 

Data transmission 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

E2b 

E_AI_PPP 

E2b 

E_AI_PPPX 

marketing or 
sales 

Enterprises 
use AI 
technologies 
for 
production 
or service 
processes 

Enterprises 
do not use 
AI 
technologies 
for 
production 
or service 
processes 

E2c 

E_AI_PBAM  Enterprises 

E2c 

E_AI_PBAM
X 

use AI 
technologies 
for 
organisation 
of business 
administratio
n processes 
or 
managemen
t 

Enterprises 
do not use 
AI 
technologies 
for 
organisation 
of business 
administratio
n processes 
or 
managemen
t 

Count(ENT_
ID) where 
AI_PPP=1 

FALSE 

2023 

Count(ENT_
ID) where 
AI_PPP=0 

FALSE 

2023 

Count(ENT_
ID) where 
AI_PBAM=1 

FALSE 

2023 

Count(ENT_
ID) where 
AI_PBAM=0 

FALSE 

2023 

192 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Count(ENT_
ID) where 
AI_PLOG=1 

Count(ENT_
ID) where 
AI_PLOG=0 

Count(ENT_
ID) where 
AI_PITS=1 

Count(ENT_
ID) where 
AI_PITS=0 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

Count(ENT_
ID) where 
AI_PFIN=1 

FALSE 

2023 

Count(ENT_
ID) where 
AI_PFIN=0 

FALSE 

2023 

E2e 

E_AI_PLOG  Enterprises 

E2e 

E_AI_PLOG
X 

E2f 

E_AI_PITS 

use AI 
technologies 
for logistics 

Enterprises 
do not use 
AI 
technologies 
for logistics 

Enterprises 
use AI 
technologies 
for ICT 
security 

E2f 

E_AI_PITSX  Enterprises 
do not use 
AI 
technologies 
for ICT 
security 

E2g 

E_AI_PFIN 

Enterprises 
use AI 
technologies 
for 
accounting, 
controlling or 
finance 
managemen
t 

E2g 

E_AI_PFINX  Enterprises 
do not use 
AI 
technologies 
for 
accounting, 
controlling or 
finance 
managemen
t 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

193 

 
3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

E2h 

E_AI_PRDI 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Count(ENT_
ID) where 
AI_PRDI=1 

FALSE 

2023 

Enterprises 
use AI 
technologies 
for research 
and 
development 
(R&D) or 
innovation 
activity 

Count(ENT_
ID) where 
AI_PRDI=0 

FALSE 

2023 

E2h 

E_AI_PRDIX  Enterprises 
do not use 
AI 
technologies 
for research 
and 
development 
(R&D) or 
innovation 
activity 

Derived from 
E2 

E_AI_P1AN
Y 

Derived from 
E2 

E_AI_P1GE
2 

FALSE 

2023 

FALSE 

2023 

Enterprises 
use artificial 
intelligence 
for at least 1 
of the listed 
purposes: 
(AI_PMS, 
AI_PPP, 
AI_PBAM, 
AI_PLOG, 
AI_PITS, 
AI_PFIN, 
AI_PRDI) 

Enterprises 
use artificial 
intelligence 
for at least 2 
of the listed 
purposes: 
(AI_PMS, 
AI_PPP, 
AI_PBAM, 
AI_PLOG, 
AI_PITS, 

Count 
(ENT_ID) 
where 
(AI_PMS+AI
_PPP+AI_P
BAM+AI_PL
OG+AI_PIT
S+AI_PFIN+
AI_PRDI) 
between 1 
and 7 

Count 
(ENT_ID) 
where 
(AI_PMS+AI
_PPP+AI_P
BAM+AI_PL
OG+AI_PIT
S+AI_PFIN+
AI_PRDI) 
between 2 
and 7 

194 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Derived from 
E2 

E_AI_P1GE
3 

E3a 

E_AI_ADO
WN 

E3a 

E_AI_ADO
WNX 

E3b 

E_AI_AMO
WN 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

FALSE 

2023 

TRUE 

2023 

TRUE 

2023 

TRUE 

2023 

Count 
(ENT_ID) 
where 
(AI_PMS+AI
_PPP+AI_P
BAM+AI_PL
OG+AI_PIT
S+AI_PFIN+
AI_PRDI) 
between 3 
and 7 

Count(ENT_
ID) where 
AI_ADOWN
=1 

Count(ENT_
ID) where 
AI_ADOWN
=0 

Count(ENT_
ID) where 
AI_AMOWN
=1 

AI_PFIN, 
AI_PRDI) 

Enterprises 
use artificial 
intelligence 
for at least 3 
of the listed 
purposes: 
(AI_PMS, 
AI_PPP, 
AI_PBAM, 
AI_PLOG, 
AI_PITS, 
AI_PFIN, 
AI_PRDI) 

The 
enterprises' 
AI 
technologies 
were 
developed 
by own 
employees 

The 
enterprises' 
AI 
technologies 
were not 
developed 
by own 
employees 

The 
enterprises' 
AI 
technologies 
were 
commercial 
software or 
systems 
modified by 
own 
employees 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

195 

 
3 

Data transmission 

Model 
questionnaire 
2024 

E3b 

Variable 
codes 
(CAPITAL 
LETTERS) 

E_AI_AMO
WNX 

E3c 

E_AI_AOS 

E3c 

E_AI_AOSX 

E3d 

E_AI_ARDY 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

TRUE 

2023 

Count(ENT_
ID) where 
AI_AMOWN
=0 

Count(ENT_
ID) where 
AI_AOS=1 

TRUE 

2023 

Count(ENT_
ID) where 
AI_AOS=0 

TRUE 

2023 

Count(ENT_
ID) where 
AI_ARDY=1 

TRUE 

2023 

The 
enterprises' 
AI 
technologies 
were not 
commercial 
software or 
systems 
modified by 
own 
employees 

The 
enterprises' 
AI 
technologies 
were open-
source 
software or 
systems 
modified by 
own 
employees 

The 
enterprises' 
AI 
technologies 
were not 
open-source 
software or 
systems 
modified by 
own 
employees 

The 
enterprises' 
AI 
technologies 
were 
purchased 
commercial 
software or 
systems 
ready to use 

196 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

3 

Data transmission 

Model 
questionnaire 
2024 

E3d 

Variable 
codes 
(CAPITAL 
LETTERS) 

E_AI_ARDY
X 

E3e 

E_AI_AEXT 

E3e 

E_AI_AEXT
X 

E4 

E_AI_PDI 

E4 

E_AI_PDIX 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Count(ENT_
ID) where 
AI_ARDY=0 

TRUE 

2023 

Count(ENT_
ID) where 
AI_AEXT=1 

TRUE 

2023 

Count(ENT_
ID) where 
AI_AEXT=0 

TRUE 

2023 

Count(ENT_
ID) where 
AI_PDI=1 

TRUE 

NEW 

Count(ENT_
ID) where 
AI_PDI=0 

TRUE 

NEW 

The 
enterprises' 
AI 
technologies 
were not 
purchased 
commercial 
software or 
systems 
ready to use 

The 
enterprises' 
AI 
technologies 
were 
developed or 
modified by 
external 
providers 

The 
enterprises' 
AI 
technologies 
were not 
developed or 
modified by 
external 
providers 

Enterprises 
which 
process data 
on 
individuals 
using AI 
technologies 

Enterprises 
which do not 
process data 
on 
individuals 
using AI 
technologies 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

197 

 
3 

Data transmission 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

E5 

E_AI_BIAS 

Enterprises 
which have 
measures to 
check the 
results 
generated 
by AI 
technologies 
for possible 
biases 
towards 
individuals 

Count(ENT_
ID) where 
AI_BIAS=1 

TRUE 

NEW 

Count(ENT_
ID) where 
AI_BIAS=0 

TRUE 

NEW 

TRUE 

NEW 

Count(ENT_
ID) where 
AI_BIAS=1 
and 
AI_ADOWN
=1 

E5 

E_AI_BIASX  Enterprises 

Derived from 
E5, E3a 

E_AI_BIAS_
ADOWN 

which do not 
have 
measures to 
check the 
results 
generated 
by AI 
technologies 
for possible 
biases 
towards 
individuals 

Enterprises 
which have 
measures to 
check the 
results 
generated 
by AI for 
possible 
biases 
towards 
individuals, 
and their AI 
technologies 
were 
developed 
by own 
employees 

198 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Derived from 
E5, E3b 

E_AI_BIAS_
AMOWN 

Derived from 
E5, E3c 

E_AI_BIAS_
AOS 

Derived from 
E5, E3d 

E_AI_BIAS_
ARDY 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Enterprises 
which have 
measures to 
check the 
results 
generated 
by AI for 
possible 
biases 
towards 
individuals, 
and their AI 
technologies 
were  
commercial 
software or 
systems 
modified by 
own 
employees 

Enterprises 
which have 
measures to 
check the 
results 
generated 
by AI for 
possible 
biases 
towards 
individuals, 
and their AI 
technologies 
were open-
source 
software or 
systems 
modified by 
own 
employees 

Enterprises 
which have 
measures to 
check the 

TRUE 

NEW 

Count(ENT_
ID) where 
AI_BIAS=1 
and 
AI_AMOWN
=1 

TRUE 

NEW 

Count(ENT_
ID) where 
AI_BIAS=1 
and 
AI_AOS=1 

Count(ENT_
ID) where 
AI_BIAS=1 

TRUE 

NEW 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

199 

 
3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

results 
generated 
by AI for 
possible 
biases 
towards 
individuals, 
and their AI 
technologies 
were 
purchased 
commercial 
software or 
systems 
ready to use 

Enterprises 
which have 
measures to 
check the 
results 
generated 
by AI for 
possible 
biases 
towards 
individuals, 
and their AI 
technologies 
were 
developed or 
modified by 
external 
providers 

Enterprises 
have ever 
considered 
to use AI 
technologies 

Enterprises 
have never 
considered 
to use AI 
technologies 

and 
AI_ARDY=1 

TRUE 

NEW 

Count(ENT_
ID) where 
AI_BIAS=1 
and 
AI_AEXT=1 

Count(ENT_
ID) where 
AI_EC=1 

Count(ENT_
ID) where 
AI_EC=0 

TRUE 

2023 

TRUE 

2023 

Derived from 
E5, E3e 

E_AI_BIAS_
AEXT 

E6 

E_AI_EC 

E6 

E_AI_ECX 

200 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

E7a 

E_AI_BCST 

E7a 

E_AI_BCST
X 

E7b 

E_AI_BLE 

E7b 

E_AI_BLEX 

E7c 

E_AI_BINC 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Count(ENT_
ID) where 
AI_BCST=1 

TRUE 

2023 

Count(ENT_
ID) where 
AI_BCST=0 

TRUE 

2023 

Count(ENT_
ID) where 
AI_BLE=1 

TRUE 

2023 

Count(ENT_
ID) where 
AI_BLE=0 

TRUE 

2023 

Count(ENT_
ID) where 
AI_BINC=1 

TRUE 

2023 

Enterprises 
do not use 
AI 
technologies
, because 
the costs 
seem too 
high 

Enterprises 
do not use 
AI 
technologies
, but not 
because the 
costs seem 
too high 

Enterprises 
do not use 
AI 
technologies
, because of 
a lack of 
relevant 
expertise 

Enterprises 
do not use 
AI 
technologies
, but not 
because of a 
lack of 
relevant 
expertise 

Enterprises 
do not use 
AI 
technologies
, because of 
incompatibilit
y with 
existing 
equipment, 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

201 

 
3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

E7c 

software or 
systems 

E_AI_BINCX  Enterprises 
do not use 
AI 
technologies
, but not 
because of 
incompatibilit
y with 
existing 
equipment, 
software or 
systems 

E7d 

E_AI_BDDT 

E7d 

E_AI_BDDT
X 

Enterprises 
do not use 
AI 
technologies
, because of 
difficulties 
with 
availability or 
quality of the 
necessary 
data 

Enterprises 
do not use 
AI 
technologies
, but not 
because of 
difficulties 
with 
availability or 
quality of the 
necessary 
data 

Count(ENT_
ID) where 
AI_BINC=0 

TRUE 

2023 

Count(ENT_
ID) where 
AI_BDDT=1 

TRUE 

2023 

Count(ENT_
ID) where 
AI_BDDT=0 

TRUE 

2023 

E7e 

E_AI_BCDP  Enterprises 
do not use 
AI 
technologies
, because od 

Count(ENT_
ID) where 
AI_BCDP=1 

TRUE 

2023 

202 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

E7e 

E_AI_BCDP
X 

E7f 

E_AI_BLEG 

E7f 

E_AI_BLEG
X 

concerns 
regarding 
violation of 
data 
protection 
and privacy 

Enterprises 
do not use 
AI 
technologies
, but not 
because od 
concerns 
regarding 
violation of 
data 
protection 
and privacy 

Enterprises 
do not use 
AI 
technologies
, because of 
a lack of 
clarity about 
the legal 
consequenc
es 

Enterprises 
do not use 
AI 
technologies
, but not 
because of a 
lack of clarity 
about the 
legal 
consequenc
es 

Count(ENT_
ID) where 
AI_BCDP=0 

TRUE 

2023 

Count(ENT_
ID) where 
AI_BLEG=1 

TRUE 

2023 

Count(ENT_
ID) where 
AI_BLEG=0 

TRUE 

2023 

E7g 

E_AI_BEC 

Enterprises 
do not use 
AI 

Count(ENT_
ID) where 
AI_BEC=1 

TRUE 

2023 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

203 

 
3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

E7g 

E_AI_BECX 

E7h 

E_AI_BNU 

technologies
, because of 
ethical 
consideratio
ns 

Enterprises 
do not use 
AI 
technologies
, but not 
because of 
ethical 
consideratio
ns 

Enterprises 
do not use 
AI 
technologies
, because 
artificial 
Intelligence 
technologies 
are not 
useful for the 
enterprise 

E7h 

E_AI_BNUX  Enterprises 
do not use 
AI 
technologies
, but not 
because 
artificial 
Intelligence 
technologies 
are not 
useful for the 
enterprise 

Count(ENT_
ID) where 
AI_BEC=0 

TRUE 

2023 

Count(ENT_
ID) where 
AI_BNU=1 

TRUE 

2023 

Count(ENT_
ID) where 
AI_BNU=0 

TRUE 

2023 

A1 

P_IUSE 

Persons 
employed 
have access 
to the 
internet for 

∑ 
(EMPIUSE) 
where 
EMPIUSE<>
Blank and 

FALSE 

2023 

204 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Derived from 
A1 (Eurostat 
computed) 1 

P_IUSEX 

Derived from 
B1 

P_AWSELL 

Derived from 
B7 

P_AXSELL 

Derived from 
B1 and B7 

P_AESELL 

Derived, see 
section 5 and 
Annex 3 

E_DI4_VLO 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

business 
purposes 

Persons 
employed 
who do not 
have access 
to the 
internet for 
business 
purposes 

Persons 
employed by 
enterprises 
which have 
received 
orders via 
websites or 
apps 

Persons 
employed by 
enterprises 
which have 
received 
orders via 
EDI-type 
messages 

Persons 
employed by 
enterprises 
which have 
received 
orders via 
computer 
networks 

Enterprises 
with very low 
digital 
intensity 
index 

EMPIUSE<>
-1 

∑ (EMPL) - 
∑ 
(EMPIUSE) 
where 
EMPIUSE<>
Blank and 
EMPIUSE<>
-1 

∑ (EMPL) 
where 
(AWS_COW
N=1 or 
AWS_CMP=
1) 

∑ (EMPL) 
where 
AXSELL=1 

∑ (EMPL) 
where 
(AWS_COW
N=1 or 
AWS_CMP=
1) or 
AXSELL=1 

Count 
(ENT_ID) 
where 
DI4_INDEX>
=0 and 
DI4_INDEX<
=3 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

FALSE 

2022 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

205 

 
3 

Data transmission 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Variable 
codes 
(CAPITAL 
LETTERS) 

E_DI4_LO 

Model 
questionnaire 
2024 

Derived, see 
section 5 and 
Annex 3 

Derived, see 
section 5 and 
Annex 3 

E_DI4_HI 

Derived, see 
section 5 and 
Annex 3 

E_DI4_VHI 

E_DI4_GEL
O 

Derived, see 
section 5 and 
Annex 3  
(Eurostat 
computed) 2 

Enterprises 
with low 
digital 
intensity 
index 

Enterprises 
with high 
digital 
intensity 
index 

Enterprises 
with very 
high digital 
intensity 
index 

Enterprises 
with at least 
low (basic) 
digital 
intensity 
index 

Derived, see 
section 5 and 
E1 

E_DI4_VLO
_AI_TANY 

Enterprises 
with very low 
digital 
intensity 

FALSE 

2022 

FALSE 

2022 

FALSE 

2022 

TRUE 

2022 

FALSE 

2024 

Count 
(ENT_ID) 
where 
DI4_INDEX>
=4 and 
DI4_INDEX<
=6 

Count 
(ENT_ID) 
where 
DI4_INDEX>
=7 and 
DI4_INDEX<
=9 

Count 
(ENT_ID) 
where 
DI4_INDEX>
=10 and 
DI4_INDEX<
=12 

[Count(ENT
_ID) where 
DI4_INDEX>
=4 and 
DI4_INDEX<
=6] + [Count 
(ENT_ID) 
where 
DI4_INDEX>
=7 and 
DI4_INDEX<
=9] + [Count 
(ENT_ID) 
where 
DI4_INDEX>
=10 and 
DI4_INDEX<
=12] 

Count 
(ENT_ID) 
where 
DI4_INDEX>

206 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Derived, see 
section 5 and 
E1 

E_DI4_LO_
AI_TANY 

Derived, see 
section 5 and 
E1 

E_DI4_HI_A
I_TANY 

index and 
use any 
artificial 
intelligence 
technology 

Enterprises 
with low 
digital 
intensity 
index and 
use any 
artificial 
intelligence 
technology 

Enterprises 
with high 
digital 
intensity 
index and 
use any 
artificial 
intelligence 
technology 

=0 and 
DI4_INDEX<
=3 and 
(AI_TTM=1 
or AI_TSR=1 
or 
AI_TNLG=1 
or AI_TIR=1 
or AI_TML=1 
or AI_TPA=1 
or 
AI_TAR=1) 

Count 
(ENT_ID) 
where 
DI4_INDEX>
=4 and 
DI4_INDEX<
=6 and 
(AI_TTM=1 
or AI_TSR=1 
or 
AI_TNLG=1 
or AI_TIR=1 
or AI_TML=1 
or AI_TPA=1 
or 
AI_TAR=1) 

Count 
(ENT_ID) 
where 
DI4_INDEX>
=7 and 
DI4_INDEX<
=9 and 
(AI_TTM=1 
or AI_TSR=1 
or 
AI_TNLG=1 
or AI_TIR=1 
or AI_TML=1 
or AI_TPA=1 
or 
AI_TAR=1) 

FALSE 

2024 

FALSE 

2024 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

207 

 
3 

Data transmission 

Model 
questionnaire 
2024 

Derived, see 
section 5 and 
E1 

Variable 
codes 
(CAPITAL 
LETTERS) 

E_DI4_VHI_
AI_TANY 

Derived, see 
section 5 and 
Annex 3 

E_DI4_VLO
_SECMGE1 

Derived, see 
section 5 and 
Annex 3 

E_DI4_LO_
SECMGE1 

Derived, see 
section 5 and 
Annex 3 

E_DI4_HI_S
ECMGE1 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

FALSE 

2024 

FALSE 

2024 

FALSE 

2024 

FALSE 

2024 

Enterprises 
with very 
high digital 
intensity 
index that 
use any 
artificial 
intelligence 
technology 

Enterprises 
with very low 
digital 
intensity 
index, which 
use any ICT 
security 
measure 

Enterprises 
with low 
digital 
intensity 
index, which 
use any ICT 
security 
measure 

Enterprises 
with high 
digital 
intensity 
index, which 
use any ICT 
security 
measure 

Count 
(ENT_ID) 
where 
DI4_INDEX>
=10 and 
DI4_INDEX<
=12 and 
(AI_TTM=1 
or AI_TSR=1 
or 
AI_TNLG=1 
or AI_TIR=1 
or AI_TML=1 
or AI_TPA=1 
or 
AI_TAR=1) 

Count 
(ENT_ID) 
where 
SECM_CNT
>=1 and 
DI4_INDEX>
=0 and 
DI4_INDEX<
=3 

Count 
(ENT_ID) 
where 
SECM_CNT
>=1 and 
DI4_INDEX>
=4 and 
DI4_INDEX<
=6 

Count 
(ENT_ID) 
where 
SECM_CNT
>=1 and 
DI4_INDEX>
=7 and 
DI4_INDEX<
=9 

208 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

3 

Data transmission 

Model 
questionnaire 
2024 

Derived, see 
section 5 and 
Annex 3 

Variable 
codes 
(CAPITAL 
LETTERS) 

E_DI4_VHI_
SECMGE1 

Derived, see 
section 5 and 
Annex 3 

E_DI4_VLO
_SECMGE3 

Derived, see 
section 5 and 
Annex 3 

E_DI4_LO_
SECMGE3 

Derived, see 
section 5 and 
Annex 3 

E_DI4_HI_S
ECMGE3 

Derived, see 
section 5 and 
Annex 3 

E_DI4_VHI_
SECMGE3 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

FALSE 

2024 

FALSE 

2024 

FALSE 

2024 

FALSE 

2024 

FALSE 

2024 

Enterprises 
with very 
high digital 
intensity 
index, which 
use any ICT 
security 
measure 

Enterprises 
with very low 
digital 
intensity 
index, which 
use 3 or 
more ICT 
security 
measures 

Enterprises 
with low 
digital 
intensity 
index, which 
use 3 or 
more ICT 
security 
measures 

Enterprises 
with high 
digital 
intensity 
index, which 
use 3 or 
more ICT 
security 
measures 

Enterprises 
with very 
high digital 
intensity 
index, which 
use 3 or 
more ICT 

Count 
(ENT_ID) 
where 
SECM_CNT
>=1 and 
DI4_INDEX>
=10 and 
DI4_INDEX<
=12 

Count 
(ENT_ID) 
where 
SECM_CNT
>=3 and 
DI4_INDEX>
=0 and 
DI4_INDEX<
=3 

Count 
(ENT_ID) 
where 
SECM_CNT
>=3 and 
DI4_INDEX>
=4 and 
DI4_INDEX<
=6 

Count 
(ENT_ID) 
where 
SECM_CNT
>=3 and 
DI4_INDEX>
=7 and 
DI4_INDEX<
=9 

Count 
(ENT_ID) 
where 
SECM_CNT
>=3 and 
DI4_INDEX>
=10 and 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

209 

 
3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Derived, see 
section 5 and 
Annex 3 

E_DI4_VLO
_SECMGE5 

Derived, see 
section 5 and 
Annex 3 

E_DI4_LO_
SECMGE5 

Derived, see 
section 5 and 
Annex 3 

E_DI4_HI_S
ECMGE5 

Derived, see 
section 5 and 
Annex 3 

E_DI4_VHI_
SECMGE5 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

FALSE 

2024 

FALSE 

2024 

FALSE 

2024 

FALSE 

2024 

security 
measures 

Enterprises 
with very low 
digital 
intensity 
index, which 
use 5 or 
more ICT 
security 
measures 

Enterprises 
with low 
digital 
intensity 
index, which 
use 5 or 
more ICT 
security 
measures 

Enterprises 
with high 
digital 
intensity 
index, which 
use 5 or 
more ICT 
security 
measures 

Enterprises 
with very 
high digital 
intensity 
index, which 
use 5 or 
more ICT 
security 
measures 

DI4_INDEX<
=12 

Count 
(ENT_ID) 
where 
SECM_CNT
>=5 and 
DI4_INDEX>
=0 and 
DI4_INDEX<
=3 

Count 
(ENT_ID) 
where 
SECM_CNT
>=5 and 
DI4_INDEX>
=4 and 
DI4_INDEX<
=6 

Count 
(ENT_ID) 
where 
SECM_CNT
>=5 and 
DI4_INDEX>
=7 and 
DI4_INDEX<
=9 

Count 
(ENT_ID) 
where 
SECM_CNT
>=5 and 
DI4_INDEX>
=10 and 
DI4_INDEX<
=12 

Derived, see 
section 5 and 
Annex 3 

E_DI4_VLO
_SECMGE7 

Enterprises 
with very low 
digital 
intensity 

Count 
(ENT_ID) 
where 
SECM_CNT

FALSE 

2024 

210 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Derived, see 
section 5 and 
Annex 3 

E_DI4_LO_
SECMGE7 

Derived, see 
section 5 and 
Annex 3 

E_DI4_HI_S
ECMGE7 

Derived, see 
section 5 and 
Annex 3 

E_DI4_VHI_
SECMGE7 

Derived, see 
section 5 and 
Annex 3 

E_DI4_VLO
_SECAWAN
Y 

index, which 
use 7 or 
more ICT 
security 
measures 

Enterprises 
with low 
digital 
intensity 
index, which 
use 7 or 
more ICT 
security 
measures 

Enterprises 
with high 
digital 
intensity 
index, which 
use 7 or 
more ICT 
security 
measures 

Enterprises 
with very 
high digital 
intensity 
index, which 
use 7 or 
more ICT 
security 
measures 

Enterprises 
with very low 
digital 
intensity 
index, which 
make 
persons 
employed 
aware of 
their 

>=7 and 
DI4_INDEX>
=0 and 
DI4_INDEX<
=3 

Count 
(ENT_ID) 
where 
SECM_CNT
>=7 and 
DI4_INDEX>
=4 and 
DI4_INDEX<
=6 

Count 
(ENT_ID) 
where 
SECM_CNT
>=7 and 
DI4_INDEX>
=7 and 
DI4_INDEX<
=9 

Count 
(ENT_ID) 
where 
SECM_CNT
>=7 and 
DI4_INDEX>
=10 and 
DI4_INDEX<
=12 

Count 
(ENT_ID) 
where 
(SECAWVT
GI=1 or 
SECAWCTP
=1 or 
SECAWCO
NT=1) and 
DI4_INDEX>

FALSE 

2024 

FALSE 

2024 

FALSE 

2024 

FALSE 

2024 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

211 

 
3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Derived, see 
section 5 and 
Annex 3 

E_DI4_LO_
SECAWANY 

Derived, see 
section 5 and 
Annex 3 

E_DI4_HI_S
ECAWANY 

Derived, see 
section 5 and 
Annex 3 

E_DI4_VHI_
SECAWANY 

obligations 
in ICT 
security 
related 
issues 

Enterprises 
with low 
digital 
intensity 
index, which 
make 
persons 
employed 
aware of 
their 
obligations 
in ICT 
security 
related 
issues 

Enterprises 
with high 
digital 
intensity 
index, which 
make 
persons 
employed 
aware of 
their 
obligations 
in ICT 
security 
related 
issues 

Enterprises 
with very 
high digital 
intensity 
index, which 
make 
persons 
employed 

=0 and 
DI4_INDEX<
=3 

Count 
(ENT_ID) 
where 
(SECAWVT
GI=1 or 
SECAWCTP
=1 or 
SECAWCO
NT=1) and 
DI4_INDEX>
=4 and 
DI4_INDEX<
=6 

Count 
(ENT_ID) 
where 
(SECAWVT
GI=1 or 
SECAWCTP
=1 or 
SECAWCO
NT=1) and 
DI4_INDEX>
=7 and 
DI4_INDEX<
=9 

Count 
(ENT_ID) 
where 
(SECAWVT
GI=1 or 
SECAWCTP
=1 or 
SECAWCO

FALSE 

2024 

FALSE 

2024 

FALSE 

2024 

212 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Derived, see 
section 5 and 
Annex 3 

E_DI4_VLO
_SECPOL2 

Derived, see 
section 5 and 
Annex 3 

E_DI4_LO_
SECPOL2 

Derived, see 
section 5 and 
Annex 3 

E_DI4_HI_S
ECPOL2 

aware of 
their 
obligations 
in ICT 
security 
related 
issues 

Enterprises 
with very low 
digital 
intensity 
index, in 
which have 
document(s) 
on 
measures, 
practices or 
procedures 
on ICT 
security 

Enterprises 
with low 
digital 
intensity 
index, in 
which have 
document(s) 
on 
measures, 
practices or 
procedures 
on ICT 
security 

Enterprises 
with high 
digital 
intensity 
index, in 
which have 
document(s) 
on 
measures, 
practices or 

NT=1) and 
DI4_INDEX>
=10 and 
DI4_INDEX<
=12 

Count 
(ENT_ID) 
where 
SECPOL2=1 
and 
DI4_INDEX>
=0 and 
DI4_INDEX<
=3 

Count 
(ENT_ID) 
where 
SECPOL2=1 
and 
DI4_INDEX>
=4 and 
DI4_INDEX<
=6 

Count 
(ENT_ID) 
where 
SECPOL2=1 
and 
DI4_INDEX>
=7 and 
DI4_INDEX<
=9 

FALSE 

2024 

FALSE 

2024 

FALSE 

2024 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

213 

 
3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Derived, see 
section 5 and 
Annex 3 

E_DI4_VHI_
SECPOL2 

Derived, see 
section 5 and 
Annex 3 

E_DI4_VLO
_SECPREV
_CY 

Derived, see 
section 5 and 
Annex 3 

E_DI4_LO_
SECPREV_
CY 

FALSE 

2024 

TRUE 

2024 

TRUE 

2024 

procedures 
on ICT 
security 

Enterprises 
with very 
high digital 
intensity 
index, in 
which have 
document(s) 
on 
measures, 
practices or 
procedures 
on ICT 
security 

Enterprises 
with very low 
digital 
intensity 
index, in 
which the 
document(s) 
on 
measures, 
practices or 
procedures 
were defined 
or most 
recently 
reviewed 
within the 
last 12 
months 

Enterprises 
with low 
digital 
intensity 
index, in 
which the 
document(s) 
on 
measures, 

Count 
(ENT_ID) 
where 
SECPOL2=1 
and 
DI4_INDEX>
=10 and 
DI4_INDEX<
=12 

Count 
(ENT_ID) 
where 
SECPREV=
1 and 
DI4_INDEX>
=0 and 
DI4_INDEX<
=3 

Count 
(ENT_ID) 
where 
SECPREV=
1 and 
DI4_INDEX>
=4 and 
DI4_INDEX<
=6 

214 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Derived, see 
section 5 and 
Annex 3 

E_DI4_HI_S
ECPREV_C
Y 

Derived, see 
section 5 and 
Annex 3 

E_DI4_VHI_
SECPREV_
CY 

TRUE 

2024 

TRUE 

2024 

practices or 
procedures 
were defined 
or most 
recently 
reviewed 
within the 
last 12 
months 

Enterprises 
with high 
digital 
intensity 
index, in 
which the 
document(s) 
on 
measures, 
practices or 
procedures 
were defined 
or most 
recently 
reviewed 
within the 
last 12 
months 

Enterprises 
with very 
high digital 
intensity 
index, in 
which the 
document(s) 
on 
measures, 
practices or 
procedures 
were defined 
or most 
recently 
reviewed 
within the 

Count 
(ENT_ID) 
where 
SECPREV=
1 and 
DI4_INDEX>
=7 and 
DI4_INDEX<
=9 

Count 
(ENT_ID) 
where 
SECPREV=
1 and 
DI4_INDEX>
=10 and 
DI4_INDEX<
=12 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

215 

 
Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

back 

ENT_SAMP
LE 

back 

ENT 

back 

EMPL 

back 

TOVT 

3 

Data transmission 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Count 
(ENT_ID) 
unraised 

FALSE 

2023 

Count 
(ENT_ID) 

FALSE 

2023 

∑ (EMPL) 

FALSE 

2023 

∑ (TOVT) 

FALSE 

2023 

last 12 
months 

Number of 
enterprises 
in final (net) 
sample - 
non-raised 
figures 

Number of 
enterprises 
in population 
(surveyed 
size and 
industry 
groups 
correspondin
g to raised 
figures) 

Average 
number of 
employed 
persons in 
population 
(surveyed 
size and 
industry 
groups 
correspondin
g to raised 
figures) 

Total 
turnover in 
population, 
in value 
terms, 
excluding 
VAT 
(surveyed 
size and 
industry 
groups 
correspondin

216 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Derived from 
A1 

E_IUSE 

Derived from 
A1 

E_IUSE_GT
10 

Derived from 
A1 

E_IUSE_GT
50 

Derived from 
A1 

E_IUSE_GE
10A 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

g to raised 
figures) 

Enterprises 
where 
persons 
employed 
have access 
to the 
internet for 
business 
purposes 

Enterprises 
where more 
than 10% of 
the persons 
employed 
have access 
to the 
internet for 
business 
purposes 

Enterprises 
where more 
than 50% of 
the persons 
employed 
have access 
to the 
internet for 
business 
purposes 

Enterprises 
where at 
least 10 
persons 
employed 
have access 
to the 
internet for 
business 
purposes 

Count 
(ENT_ID) 
where 
EMPIUSE<>
Blank and 
EMPIUSE>0 

Count 
(ENT_ID) 
where 
EMPIUSE<>
Blank and 
EMPL<>Bla
nk and 
EMPIUSE > 
0.10 * EMPL 

Count 
(ENT_ID) 
where 
EMPIUSE<>
Blank and 
EMPL<>Bla
nk and 
EMPIUSE > 
0.50 * EMPL 

Count 
(ENT_ID) 
where 
EMPIUSE<>
Blank and 
EMPIUSE 
>= 10 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

217 

 
3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

A2 

E_FIXBB 

A2 

E_FIXBBX 

A2 

E_FIXBBZ 

A3a 

E_ISPDF_L
T30 

A3b 

E_ISPDF1_
30_100 

A3c 

E_ISPDF_1
00_500 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Use any 
type of fixed 
connection 
to the 
internet 

Don't use 
any type of 
fixed 
connection 
to the 
internet 

Don't know if 
they use any 
type of fixed 
connection 
to the 
internet 

The 
maximum 
contracted 
download 
speed of the 
fastest fixed 
internet 
connection 
is less than 
30 Mb/s 

The 
maximum 
contracted 
download 
speed of the 
fastest fixed 
internet 
connection 
is at least 30 
Mb/s but 
less than 
100 Mb/s 

The 
maximum 
contracted 

FALSE 

2023 

FALSE 

2023 

TRUE 

2023 

FALSE 

2023 

Count 
(ENT_ID) 
where 
FIXBB=1 

Count 
(ENT_ID) 
where 
FIXBB=0 

Count 
(ENT_ID) 
where 
FIXBB=Blan
k 

Count 
(ENT_ID) 
where 
ISPDF1=1 

FALSE 

2023 

Count 
(ENT_ID) 
where 
ISPDF1=2 

Count 
(ENT_ID) 

FALSE 

2023 

218 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

A3d 

E_ISPDF_5
00_1G 

A3e 

E_ISPDF_G
E1G 

Derived from 
A3b to A3e 

E_ISPDF1_
GE30 

download 
speed of the 
fastest fixed 
internet 
connection 
is at least 
100 Mb/s but 
less than 
500 Mb/s 

The 
maximum 
contracted 
download 
speed of the 
fastest fixed 
internet 
connection 
is at least 
500 Mb/s but 
less than 1 
Gb/s 

The 
maximum 
contracted 
download 
speed of the 
fastest fixed 
internet 
connection 
is at least 1 
Gb/s 

The 
maximum 
contracted 
download 
speed of the 
fastest fixed 
internet 
connection 
is at least 30 
Mb/s 

where 
ISPDF1=3 

FALSE 

2023 

Count 
(ENT_ID) 
where 
ISPDF1=4 

FALSE 

2023 

FALSE 

2023 

Count 
(ENT_ID) 
where 
ISPDF1=5 

Count 
(ENT_ID) 
where 
ISPDF1=2 
or 
ISPDF1=3 
or 
ISPDF1=4 
or 
ISPDF1=5 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

219 

 
3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Derived from 
A3c to A3e 

E_ISPDF1_
GE100 

A4 

E_ISPDF1O
K 

A4 

E_ISPDF1O
KX 

Derived from 
A3b to A3e and 
A4 

E_ISPDF1O
K_GE30 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

The 
maximum 
contracted 
download 
speed of the 
fastest fixed 
internet 
connection 
is at least 
100 Mb/s 

The speed 
of the fixed 
line 
connection(s
) to the 
internet is 
sufficient for 
the actual 
needs of the 
enterprise 

The speed 
of the fixed 
line 
connection(s
) to the 
internet is 
not sufficient 
for the actual 
needs of the 
enterprise 

The speed 
of the fixed 
line 
connection(s
) to the 
internet (at 
least 30 
Mb/s) is 
sufficient for 
the actual 
needs of the 
enterprise 

FALSE 

2023 

FALSE 

2020 

Count 
(ENT_ID) 
where 
ISPDF1=3 
or 
ISPDF1=4 
or 
ISPDF1=5 

Count 
(ENT_ID) 
where 
ISPDFOK=1 

FALSE 

2020 

Count 
(ENT_ID) 
where 
ISPDFOK=0 

FALSE 

2020 

Count 
(ENT_ID) 
where 
(ISPDF1=2 
or 
ISPDF1=3 
or 
ISPDF1=4 
or 
ISPDF1=5) 
and 
ISPDFOK=1 

220 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Derived from 
A3a and A4 

E_ISPDFOK
X_LT30 

Derived from 
A3b and A4 

E_ISPDF1O
KX_30_100 

Derived from 
A3c and A4 

E_ISPDFOK
X_100_500 

Derived from 
A3d and A4 

E_ISPDFOK
X_500_1G 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

FALSE 

2020 

FALSE 

2020 

FALSE 

2020 

FALSE 

2020 

The speed 
of the fixed 
line 
connection(s
) to the 
internet (less 
than 
30Mb/s) is 
not sufficient 
for the actual 
needs of the 
enterprise 

The speed 
of the fixed 
line 
connection(s
) to the 
internet ([3-
100[ Mb/s) is 
not sufficient 
for the actual 
needs of the 
enterprise 

The speed 
of the fixed 
line 
connection(s
) to the 
internet 
([100-500[ 
Mb/s) is not 
sufficient for 
the actual 
needs of the 
enterprise 

The speed 
of the fixed 
line 
connection(s
) to the 
internet 
([500-1000[ 
Mb/s) is not 

Count 
(ENT_ID) 
where 
ISPDF1=1 
and 
ISPDFOK=0 

Count 
(ENT_ID) 
where 
ISPDF1=2 
and 
ISPDFOK=0 

Count 
(ENT_ID) 
where 
ISPDF1=3 
and 
ISPDFOK=0 

Count 
(ENT_ID) 
where 
ISPDF1=4 
and 
ISPDFOK=0 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Derived from 
A3e and A4 

E_ISPDFOK
X_GE1G 

Derived from 
A3b to A3e and 
A4 

E_ISPDF1O
KX_GE30 

Derived from 
A3c to A3e and 
A4 

E_ISPDF1O
KX_GE100 

FALSE 

2020 

FALSE 

2020 

FALSE 

2020 

sufficient for 
the actual 
needs of the 
enterprise 

The speed 
of the fixed 
line 
connection(s
) to the 
internet (at 
least 1 Gb/s) 
is not 
sufficient for 
the actual 
needs of the 
enterprise 

The speed 
of the fixed 
line 
connection(s
) to the 
internet (at 
least 30 
Mb/s) is not 
sufficient for 
the actual 
needs of the 
enterprise 

The speed 
of the fixed 
line 
connection(s
) to the 
internet (at 
least 100 
Mb/s) is not 
sufficient for 
the actual 
needs of the 
enterprise 

Count 
(ENT_ID) 
where 
ISPDF1=5 
and 
ISPDFOK=0 

Count 
(ENT_ID) 
where 
(ISPDF1=2 
or 
ISPDF1=3 
or 
ISPDF1=4 
or 
ISPDF1=5) 
and 
ISPDFOK=0 

Count 
(ENT_ID) 
where 
(ISPDF1=3 
or 
ISPDF1=4 
or 
ISPDF1=5) 
and 
ISPDFOK=0 

Derived from 
A5 

E_EMPMD2
_GT0 

Enterprises 
where some 

Count 
(ENT_ID) 

TRUE 

2022 

222 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

employed 
persons use 
a portable 
device 
provided by 
the 
enterprise, 
that allows 
internet 
connection 
via mobile 
telephone 
networks, for 
business 
purposes 

Enterprises 
where more 
than 10% of 
the 
employed 
persons use 
a portable 
device 
provided by 
the 
enterprise, 
that allows 
internet 
connection 
via mobile 
telephone 
networks, for 
business 
purposes 

Enterprises 
where more 
than 20% of 
the 
employed 
persons use 
a portable 
device 
provided by 
the 

where 
EMPMD2<>
Blank and 
EMPMD2>0 

Count 
(ENT_ID) 
where 
EMPMD2<>
Blank and 
EMPL<>Bla
nk and 
(EMPMD2>
EMPL * 0.1) 

Count 
(ENT_ID) 
where 
EMPMD2<>
Blank and 
EMPL<>Bla
nk and 
(EMPMD2>
EMPL * 0.2) 

TRUE 

2022 

TRUE 

2022 

Derived from 
A5 

E_EMPMD2
_GT10 

Derived from 
A5 

E_EMPMD2
_GT20 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Derived from 
A5 

E_EMPMD2
_GT50 

Derived from 
A5 

E_PMD 

enterprise, 
that allows 
internet 
connection 
via mobile 
telephone 
networks, for 
business 
purposes 

Enterprises 
where more 
than 50% of 
the 
employed 
persons use 
a portable 
device 
provided by 
the 
enterprise, 
that allows 
internet 
connection 
via mobile 
telephone 
networks, for 
business 
purposes 

Provide 
portable 
devices that 
allow a 
mobile 
connection 
to the 
internet 
using mobile 
telephone 
networks, for 
business 
purposes 

TRUE 

2022 

Count 
(ENT_ID) 
where 
EMPMD2<>
Blank and 
EMPL<>Bla
nk and 
(EMPMD2>
EMPL * 0.5) 

TRUE 

2022 

Count 
(ENT_ID) 
where 
EMPMD2>0 

Derived from 
A5 

E_PMDX 

Does not 
provide 

Count 
(ENT_ID) 

TRUE 

2022 

224 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

portable 
devices that 
allow a 
mobile 
connection 
to the 
internet 
using mobile 
telephone 
networks, for 
business 
purposes 

Connect to 
the Internet 
via fixed or 
mobile 
broadband 
(as of 2018) 

Connect to 
the Internet 
but not via 
fixed or 
mobile 
broadband 
(as of 2018) 

Enterprises 
with 
employed 
persons with 
remote 
access to 
the email 
system of 
the 
enterprise 

Enterprises 
with 
employed 
persons with 

where 
EMPMD2=0 
or 
EMPMD2=Bl
ank 

Count 
(ENT_ID) 
where 
FIXBB=1 or 
EMPMD2>0 

Count 
(ENT_ID) 
where 
(FIXBB=0 or 
FIXBB=Blan
k) and 
(EMPMD2=0 
or 
EMPMD2=Bl
ank) 

Count 
(ENT_ID) 
where 
RA_M=1 

TRUE 

2022 

TRUE 

2022 

FALSE 

2022 

FALSE 

2022 

Count 
(ENT_ID) 
where 
RA_M=0 

Derived from 
A2 and A5 

E_BROAD3 

Derived from 
A2 and A5 

E_BROAD3
X 

A6a 

E_RA_M 

A6a 

E_RA_MX 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

A6b 

E_RA_D 

A6b 

E_RA_DX 

A6c 

E_RA_S 

no remote 
access to 
the email 
system of 
the 
enterprise 

Enterprises 
with 
employed 
persons with 
remote 
access to 
the 
documents 
of the 
enterprise 
(e.g. files, 
spreadsheet
s, 
presentation
s, charts, 
photos) 

Enterprises 
with 
employed 
persons with 
no remote 
access to 
the 
documents 
of the 
enterprise 
(e.g. files, 
spreadsheet
s, 
presentation
s, charts, 
photos) 

Enterprises 
with 
employed 
persons with 
remote 

FALSE 

2022 

Count 
(ENT_ID) 
where 
RA_D=1 

FALSE 

2022 

Count 
(ENT_ID) 
where 
RA_D=0 

FALSE 

2022 

Count 
(ENT_ID) 
where 
RA_S=1 

226 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

A6c 

E_RA_SX 

Derived A6a to 
A6c 

E_RA 

access to 
the business 
applications 
or software 
of the 
enterprise 
(e.g. access 
to 
accounting, 
sales, 
orders, 
CRM) 

Enterprises 
with 
employed 
persons with 
no remote 
access to 
the business 
applications 
or software 
of the 
enterprise 
(e.g. access 
to 
accounting, 
sales, 
orders, 
CRM) 

Enterprises 
with 
employed 
persons with 
remote 
access to 
the email 
system or 
documents, 
business 
applications 
or software 
of the 
enterprise 

FALSE 

2022 

Count 
(ENT_ID) 
where 
RA_S=0 

FALSE 

2022 

Count 
(ENT_ID) 
where 
RA_M=1 or 
RA_D=1 or 
RA_S=1 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

227 

 
3 

Data transmission 

Model 
questionnaire 
2024 

Derived A6a to 
A6c 

Variable 
codes 
(CAPITAL 
LETTERS) 

E_RA_ALL 

Derived A6b to 
A6c 

E_RA_DS 

A7 

E_RM 

A7 

E_RMX 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Enterprises 
with 
employed 
persons with 
remote 
access to 
the email 
system and 
documents 
and 
business 
applications 
or software 
of the 
enterprise 

Enterprises 
with 
employed 
persons with 
remote 
access to 
the 
documents, 
business 
applications 
or software 
of the 
enterprise 

Enterprises 
which 
conducted 
remote 
meetings 
(via e.g. 
Skype, 
Zoom, MS 
Teams, 
WebEx, etc.) 

Enterprises 
which did 
not conduct 
remote 
meetings 

FALSE 

2022 

Count 
(ENT_ID) 
where 
RA_M=1 
and RA_D=1 
and RA_S=1 

FALSE 

2022 

Count 
(ENT_ID) 
where 
RA_D=1 or 
RA_S=1 

Count 
(ENT_ID) 
where RM=1 

FALSE 

2022 

Count 
(ENT_ID) 
where RM=0 

FALSE 

2022 

228 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

A8 

A8 

E_ADS 

E_ADSX 

A9a 

E_ADS_KW 

A9a 

E_ADS_KW
X 

A9b 

E_ADS_TR
K 

(via e.g. 
Skype, 
Zoom, MS 
Teams, 
WebEx, etc.) 

Pay to 
advertise on 
the Internet 

Don't pay to 
advertise on 
the Internet 

Pay to 
advertise on 
the internet, 
based on 
content or 
keywords 
searched by 
internet 
users 

Pay to 
advertise on 
the internet, 
but not 
based on 
content or 
keywords 
searched by 
internet 
users 

Pay to 
advertise on 
the internet, 
based on the 
tracking of 
internet 
users' past 
activities or 
profile 

TRUE 

2023 

TRUE 

2023 

TRUE 

2023 

Count 
(ENT_ID) 
where 
ADS=1 

Count 
(ENT_ID) 
where 
ADS=0 

Count 
(ENT_ID) 
where 
ADS_KW=1 

TRUE 

2023 

Count 
(ENT_ID) 
where 
ADS_KW=0 

TRUE 

2023 

Count 
(ENT_ID) 
where 
ADS_TRK=1 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

229 

 
3 

Data transmission 

Model 
questionnaire 
2024 

A9b 

Variable 
codes 
(CAPITAL 
LETTERS) 

E_ADS_TR
KX 

A9c 

A9c 

E_ADS_LO
C 

E_ADS_LO
CX 

A9d 

E_ADS_OT
H 

A9d 

E_ADS_OT
HX 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

TRUE 

2023 

Count 
(ENT_ID) 
where 
ADS_TRK=0 

TRUE 

2023 

TRUE 

2023 

TRUE 

2023 

TRUE 

2023 

Count 
(ENT_ID) 
where 
ADS_LOC=
1 

Count 
(ENT_ID) 
where 
ADS_LOC=
0 

Count 
(ENT_ID) 
where 
ADS_OTH=
1 

Count 
(ENT_ID) 
where 
ADS_OTH=
0 

Pay to 
advertise on 
the internet, 
but not 
based on the 
tracking of 
internet 
users' past 
activities or 
profile 

Pay to 
advertise on 
the internet, 
based on the 
geolocation 
of internet 
users 

Pay to 
advertise on 
the internet, 
but not 
based on the 
geolocation 
of internet 
users 

Pay to 
advertise on 
the internet, 
based on 
any other 
method of 
target 
advertising 

Pay to 
advertise on 
the internet, 
but not 
based on 
any other 
method of 
target 
advertising 

230 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

A9a to A9c 

E_ADS3 

B1a 

B1a 

B1b 

B1b 

E_AWS_CO
WN 

E_AWS_CO
WNX 

E_AWS_CM
P 

E_AWS_CM
PX 

Derived from 
B1a and B1b 

E_AWS_CB
OTH 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

TRUE 

2023 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

Pay to 
advertise on 
the internet, 
based on 
content, 
keywords, 
internet 
users' past 
activities or 
profile or the 
geolocation 

Enterprises 
which sold 
via their own 
websites or 
apps 

Enterprises 
which did 
not sell via 
their own 
websites or 
apps 

Enterprises 
which sold 
via an e-
commerce 
marketplace 

Enterprises 
which did 
not sell via 
an e-
commerce 
marketplace 

Enterprises 
which sold 
via their own 
websites or 
apps and via 
an e-
commerce 
marketplace 

Count 
(ENT_ID) 
where 
ADS_KW=1 
or 
ADS_TRK=1 
or 
ADS_LOC=
1 

Count 
(ENT_ID) 
where 
AWS_COW
N=1 

Count 
(ENT_ID) 
where 
AWS_COW
N=0 

Count 
(ENT_ID) 
where 
AWS_CMP=
1 

Count 
(ENT_ID) 
where 
AWS_CMP=
0 

Count 
(ENT_ID) 
where 
AWS_COW
N=1 and 
AWS_CMP=
1 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

231 

 
3 

Data transmission 

Model 
questionnaire 
2024 

Derived from 
B1a and B1b 

Variable 
codes 
(CAPITAL 
LETTERS) 

E_AWSELL 

Derived from 
B1a and B1b 

E_AWSELL
X 

Derived from 
B1a and B1b 

E_AWS_CM
PQ 

B2 

B2 

E_AWSVAL
S 

E_AWSVAL
B 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

FALSE 

2023 

FALSE 

2023 

FALSE 

NEW 
derived 
variable 

FALSE 

2023 

FALSE 

2023 

Have 
received 
orders via 
websites or 
apps (web 
sales) 

Have not 
received 
orders via 
websites or 
apps 

Enterprises 
which sold 
via an e-
commerce 
marketplace 
but not via 
their own 
websites or 
apps 

Sales via 
websites or 
apps, 
excluding 
VAT (<1% of 
turnover) 

Sales via 
websites or 
apps, 
excluding 
VAT (>= 1% 
of turnover) 

Count 
(ENT_ID) 
where 
(AWS_COW
N=1 or 
AWS_CMP=
1) 

Count 
(ENT_ID) 
where 
(AWS_COW
N=0 and 
AWS_CMP=
0) 

Count 
(ENT_ID) 
where 
AWS_COW
N=0 and 
AWS_CMP=
1 

∑ (AWSVAL) 
where 
AWSVAL <> 
Blank and 
AWSVAL <> 
-1 and 
AWSVAL < 
TOVT * 1% 

∑ (AWSVAL) 
where 
AWSVAL <> 
Blank and 
AWSVAL <> 
-1 and 
AWSVAL >= 
TOVT * 1% 

B2 

E_AWSVAL 

Total sales 
via websites 
or apps, 

∑ (AWSVAL) 
where 
AWSVAL <> 

FALSE 

2023 

232 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

B2 

E_WSEL0 

B2 

E_WSEL1 

B2 

E_WSEL2 

B2 

E_WSEL5 

B2 

E_WSEL10 

excluding 
VAT 

Total sales 
via websites 
or apps 
equal to or 
above 0 

Total sales 
via websites 
or apps 
equal to or 
above 1% of 
turnover 

Total sales 
via websites 
or apps 
equal to or 
above 2% of 
turnover 

Total sales 
via websites 
or apps 
equal to or 
above 5% of 
turnover 

Total sales 
via websites 
or apps 
equal to or 

Blank and 
AWSVAL <> 
-1 

Count 
(ENT_ID) 
where 
AWSVAL <> 
Blank and 
AWSVAL <> 
-1 and 
AWSVAL>=0 

Count 
(ENT_ID) 
where 
AWSVAL <> 
Blank and 
AWSVAL <> 
-1 and 
AWSVAL>=
TOVT * 1% 

Count 
(ENT_ID) 
where 
AWSVAL <> 
Blank and 
AWSVAL <> 
-1 and 
AWSVAL>=
TOVT * 2% 

Count 
(ENT_ID) 
where 
AWSVAL <> 
Blank and 
AWSVAL <> 
-1 and 
AWSVAL>=
TOVT * 5% 

Count 
(ENT_ID) 
where 
AWSVAL <> 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

233 

 
3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

B2 

E_WSEL25 

B2 

E_WSEL50 

B2 

E_WSELZ 

Derived from 
B1 and B2 

E_AWSVAL
_CMPQ 

B3a 

E_AWSVAL
_COWN 

above 10% 
of turnover 

Total sales 
via websites 
or apps 
equal to or 
above 25% 
of turnover 

Total sales 
via websites 
or apps 
equal to or 
above 50% 
of turnover 

Don't know 
the % of 
sales via 
websites or 
apps 

Web sales 
by 
enterprises 
which only 
sell through 
an e-
commerce 
marketplace 

Sales via 
their own 
websites or 
apps 

Blank and 
AWSVAL <> 
-1 and 
AWSVAL>=
TOVT * 10% 

Count 
(ENT_ID) 
where 
AWSVAL <> 
Blank and 
AWSVAL <> 
-1 and 
AWSVAL>=
TOVT * 25% 

Count 
(ENT_ID) 
where 
AWSVAL <> 
Blank and 
AWSVAL <> 
-1 and 
AWSVAL>=
TOVT * 50% 

Count 
(ENT_ID) 
where 
AWSVAL=Bl
ank 

∑ (AWSVAL) 
where 
AWSVAL <> 
Blank and 
AWSVAL <> 
-1 and 
AWS_COW
N=0 and 
AWS_CMP=
1 

∑(AWSVAL_
COWN) 
where 
AWSVAL_C

FALSE 

2023 

FALSE 

2023 

TRUE 

2023 

FALSE 

NEW 
derived 
variable 

FALSE 

2023 

234 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

OWN <> 
Blank and 
AWSVAL_C
OWN <> -1 

∑(AWSVAL_
CMP) where 
AWSVAL_C
MP <> Blank 
and 
AWSVAL_C
MP <> -1 

Count 
(ENT_ID) 
where 
AWS_CMP=
1 and 
(AWSVAL_C
MP >= 
AWSVAL * 
0.20) 

Count 
(ENT_ID) 
where 
AWS_CMP=
1 and 
(AWSVAL_C
MP >= 
AWSVAL * 
0.50) 

∑ (TOVT) 
where 
AWS_CMP=
1 and 
(AWSVAL_C
MP >= 
AWSVAL * 
0.20) 

B3b 

E_AWSVAL
_CMP 

Sales via an 
e-commerce 
marketplace 

Derived from 
B1b, B2 and 
B3b 

E_AWS_CM
P_GE20 

Derived from 
B1b, B2 and 
B3b 

E_AWS_CM
P_GE50 

Derived from 
B1b, B2 and 
B3b 

T_AWS_CM
P_GE20 

Enterprises 
which sold 
via an e-
commerce 
marketplace 
for at least 
20% of the 
web sales 

Enterprises 
which sold 
via an e-
commerce 
marketplace 
for at least 
50% of the 
web sales 

Turnover of 
the 
enterprises 
where web 
sales via an 
e-commerce 
marketplace 
were at least 
20% of the 
web sales 

Derived from 
B1b, B2 and 
B3b 

T_AWS_CM
P_GE50 

Turnover of 
the 
enterprises 

∑ (TOVT) 
where 
AWS_CMP=

FALSE 

2023 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

235 

 
3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

where web 
sales via an 
e-commerce 
marketplace 
were at least 
50% of the 
web sales 

Sales via 
websites or 
apps (B2C) 

E_AWSVAL
_B2C 

E_AWSVAL
_B2BG 

Sales via 
websites or 
apps (B2B) 
and (B2G) 

B4a 

B4b 

Derived from 
B4a 

E_AWS_B2
C 

Derived from 
B4b 

E_AWS_B2
BG 

Derived from 
B1b and B4a 

E_AWS_B2
C_CMP 

Enterprises 
which sold 
via websites 
or apps - 
B2C 

Enterprises 
which sold 
via websites 
or apps - 
B2B and 
B2G 

Enterprises 
which sold 
via websites 
or apps - 
B2C and via 
an e-

1 and 
(AWSVAL_C
MP >= 
AWSVAL * 
0.50) 

∑ 
(AWSVALC) 
where 
AWSVALC<
>Blank and 
AWSVALC<
>-1 

∑ 
(AWSVALB
G) where 
AWSVALBG
<>Blank and 
AWSVALBG
<>-1 

Count 
(ENT_ID) 
where 
AWSVALC<
>Blank and 
AWSVALC>
0 

Count 
(ENT_ID) 
where 
AWSVALBG
<>Blank and 
AWSVALBG
>0 

Count 
(ENT_ID) 
where 
(AWSVALC<
>Blank and 
AWSVALC>
0) and 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

236 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Derived from 
B2 and B4a 

E_AWSVAL
_B2C_GE10
WS 

Derived from 
B2 and B4a 

E_AWSVAL
_B2C_GE5
WS 

Derived from 
B2 and B4a 

E_AWS_B2
C_GT1WS 

Description  Scope 

Optional 

commerce 
marketplace 

AWS_CMP=
1 

Notes / 
Source 
(if not 
previous 
year) 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

Enterprises 
where B2C 
sales via 
websites or 
apps were 
10% or more 
of the total 
web sales 

Enterprises 
where B2C 
sales via 
websites or 
apps were 
5% or more 
of the total 
web sales 

Enterprises 
where B2C 
sales via 
websites or 
apps were 
more than 
1% of the 
total web 
sales 

Count 
(ENT_ID) 
where 
AWSVALC<
>Blank AND 
AWSVALC>
0 and 
AWSVAL<>
Blank and 
AWSVAL<>-
1 and 
AWSVALC>
=0.1 * 
AWSVAL 

Count 
(ENT_ID) 
where 
AWSVALC<
>Blank AND 
AWSVALC>
0 and 
AWSVAL<>
Blank and 
AWSVAL<>-
1 and 
AWSVALC>
=0.05 * 
AWSVAL 

Count 
(ENT_ID) 
where 
AWSVALC<
>Blank AND 
AWSVALC>
0 and 
AWSVAL<>
Blank and 
AWSVAL<>-
1 and 
AWSVALC>

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Derived from 
B2 and B4a 

E_AWSVAL
_GT1_B2C_
GT10WS 

Derived from 
B2 and B4a 

E_AWS_GT
1_B2C_GT1
0WS 

Derived from 
B2 and B4a 

T_AWS_GT
1_B2C_GT1
0WS 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

0.01 * 
AWSVAL 

∑ 
(AWSVALC) 
where 
TOVT<>Bla
nk and 
AWSVAL<>
Blank and 
AWSVALC<
>Blank AND 
(AWSVAL>0
.01 * TOVT) 
and 
AWSVALC>
0.1 * 
AWSVAL 

Count 
(ENT_ID) 
where 
TOVT<>Bla
nk and 
AWSVAL<>
Blank and 
AWSVALC<
>Blank AND 
AWSVALC>
0 AND 
(AWSVAL>0
.01 * TOVT) 
and 
AWSVALC>
0.1 * 
AWSVAL 

∑ (TOVT) 
where 
TOVT<>Bla
nk and 
AWSVAL<>
Blank and 
AWSVALC<
>Blank AND 
AWSVALC>

B2C web 
sales of 
enterprises 
where web 
sales > 1% 
of total 
turnover and 
B2C web 
sales > 10% 
of the web 
sales 

Enterprises 
where web 
sales were 
more than 
1% of the 
total 
turnover and 
B2C web 
sales more 
than 10% of 
the web 
sales 

Turnover of 
the 
enterprises 
where web 
sales were 
more than 
1% of the 
total 
turnover and 

238 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

B2C web 
sales more 
than 10% of 
the web 
sales 

Enterprises 
where B2C 
sales via 
websites or 
apps were 
10% or more 
of the total 
web sales 
and which 
sold via an 
e-commerce 
marketplace 

Received 
orders 
placed via a 
website or 
apps from 
customers in 
own country 

Received 
orders 
placed via a 
website or 
apps from 
customers in 
other EU 
countries 

Received 
orders 
placed via a 

0 AND 
(AWSVAL>0
.01 * TOVT) 
and 
AWSVALC>
0.1 * 
AWSVAL 

Count 
(ENT_ID) 
where 
AWSVALC<
>Blank AND 
AWSVALC>
0 and 
AWSVAL<>
Blank and 
AWSVAL<>-
1 and 
AWSVALC>
=0.1 * 
AWSVAL 
and 
AWS_CMP=
1 

Count 
(ENT_ID) 
where 
AWSHM=1 

Count 
(ENT_ID) 
where 
AWSEU=1 

FALSE 

2023 

FALSE 

2022 

FALSE 

2022 

Count 
(ENT_ID) 

FALSE 

2022 

Derived from 
B1b, B2 and 
B4a 

E_AWSVAL
_B2C_GE10
WS_CMP 

B5a 

E_AWSHM 

B5b 

E_AWSEU 

B5c 

E_AWSWW 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

website or 
apps from 
customers in 
the rest of 
the world 

E_AWSFOR  Received 

Derived from 
B5b and B5c 

where 
AWSWW=1 

Count 
(ENT_ID) 
where 
AWSEU=1 
or 
AWSWW=1 

∑(AWSVAL_
COWN) 
where 
AWSVAL_C
OWN <> 
Blank and 
AWSVAL_C
OWN <> -1 
and 
AWSHM=1 

∑(AWSVAL_
CMP) where 
AWSVAL_C
MP <> Blank 
and 
AWSVAL_C
MP <> -1 
and 
AWSHM=1 

∑(AWSVAL_
COWN) 
where 
AWSVAL_C
OWN <> 
Blank and 
AWSVAL_C
OWN <> -1 

FALSE 

2022 

FALSE 

2022 

FALSE 

2022 

FALSE 

2022 

orders 
placed via a 
website or 
apps from 
customers in 
foreign 
countries 
(EU or rest 
of the world) 

Sales via 
their own 
websites or 
apps from 
customers in 
the own 
country 

Sales via an 
e-commerce 
marketplace 
from 
customers in 
the own 
country 

Sales via 
their own 
websites or 
apps from 
customers in 
other EU 
countries 

Derived from 
B3a and B5a 

E_AWSVAL
_HM_COW
N 

Derived from 
B3b and B5a 

E_AWSVAL
_HM_CMP 

Derived from 
B3a and B5b 

E_AWSVAL
_EU_COWN 

240 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Derived from 
B3b and B5b 

E_AWSVAL
_EU_CMP 

Derived from 
B3a and B5c 

E_AWSVAL
_WW_COW
N 

Derived from 
B3b and B5c 

E_AWSVAL
_WW_CMP 

Derived from 
B1, B2 and B5a 

E_AWSVAL
_HM_CMPQ 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

FALSE 

2022 

FALSE 

2022 

FALSE 

2022 

FALSE 

NEW 
derived 
variable 

and 
AWSEU=1 

∑(AWSVAL_
CMP) where 
AWSVAL_C
MP <> Blank 
and 
AWSVAL_C
MP <> -1 
and 
AWSEU=1 

∑(AWSVAL_
COWN) 
where 
AWSVAL_C
OWN <> 
Blank and 
AWSVAL_C
OWN <> -1 
and 
AWSWW=1 

∑(AWSVAL_
CMP) where 
AWSVAL_C
MP <> Blank 
and 
AWSVAL_C
MP <> -1 
and 
AWSWW=1 

∑ (AWSVAL) 
where 
AWSVAL <> 
Blank and 
AWSVAL <> 
-1 and 
AWSHM=1 
and 
AWS_COW
N=0 and 
AWS_CMP=
1 

Sales via an 
e-commerce 
marketplace 
from 
customers in 
other EU 
countries 

Sales via 
their own 
websites or 
apps from 
customers 
located in 
the rest of 
the world 

Sales via an 
e-commerce 
marketplace 
from 
customers 
located in 
the rest of 
the world 

Web sales 
by 
enterprises 
which only 
sell through 
an e-
commerce 
marketplace 
from 
customers in 
the own 
country 

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241 

 
Description  Scope 

Optional 

3 

Data transmission 

Notes / 
Source 
(if not 
previous 
year) 

NEW 
derived 
variable 

FALSE 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Derived from 
B1, B2 and B5b 

E_AWSVAL
_EU_CMPQ 

Derived from 
B1, B2 and B5c 

E_AWSVAL
_WW_CMP
Q 

B6a 

B6b 

B6c 

E_AWSVAL
_HM 

E_AWSVAL
_EU 

E_AWSVAL
_WW 

Web sales 
by 
enterprises 
which only 
sell through 
an e-
commerce 
marketplace 
from 
customers in 
other EU 
countries 

Web sales 
by 
enterprises 
which only 
sell through 
an e-
commerce 
marketplace 
from 
customers 
located in 
the rest of 
the world 

Sales via a 
website or 
apps - from 
customers 
located in 
the own 
country 

Sales via a 
website or 
apps - from 
customers 
located in 
other EU 
countries 

Sales via a 
website or 
apps - from 
customers 

∑ (AWSVAL) 
where 
AWSVAL <> 
Blank and 
AWSVAL <> 
-1 and 
AWSEU=1 
and 
AWS_COW
N=0 and 
AWS_CMP=
1 

∑ (AWSVAL) 
where 
AWSVAL <> 
Blank and 
AWSVAL <> 
-1 and 
AWSWW=1 
and 
AWS_COW
N=0 and 
AWS_CMP=
1 

∑(AWSVAL_
HM) where 
AWSVAL_H
M <> Blank 
and 
AWSVAL_H
M <> -1 

∑(AWSVAL_
EU) where 
AWSVAL_E
U <> Blank 
and 
AWSVAL_E
U <> -1 

∑(AWSVAL_
WW) where 
AWSVAL_W
W <> Blank 

FALSE 

NEW 
derived 
variable 

TRUE 

2022 

TRUE 

2022 

TRUE 

2022 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Derived from 
B2, B5b, B5c, 
B6b and B6c 

E_AWSFOR
_GE20 

Derived from 
B2, B5b, B5c, 
B6b and B6c 

E_AWSFOR
_GE50 

Derived from 
B1, B2, B5b, 
B5c, B6b and 
B6c 

E_AWSFOR
_GE20_CM
P 

TRUE 

2022 

TRUE 

2022 

TRUE 

2022 

located in 
the rest of 
the world 

Received 
orders 
placed via a 
website or 
apps from 
customers in 
foreign 
countries, 
with an 
export 
turnover at 
least 20% of 
the web 
sales 

Received 
orders 
placed via a 
website or 
apps from 
customers in 
foreign 
countries, 
with an 
export 
turnover at 
least 50% of 
the web 
sales 

Sold via an 
e-commerce 
marketplace 
and received 
orders 
placed via a 
website or 
apps from 
customers in 
foreign 
countries, 
with an 

and 
AWSVAL_W
W <> -1 

Count 
(ENT_ID) 
where 
(AWSEU=1 
or 
AWSWW=1) 
and 
(AWSVAL_E
U + 
AWSVAL_W
W >= 0.20 * 
AWSVAL) 

Count 
(ENT_ID) 
where 
(AWSEU=1 
or 
AWSWW=1) 
and 
(AWSVAL_E
U + 
AWSVAL_W
W >= 0.50 * 
AWSVAL) 

Count 
(ENT_ID) 
where 
AWS_CMP=
1 and 
(AWSEU=1 
or 
AWSWW=1) 
and 
(AWSVAL_E
U + 
AWSVAL_W

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Derived from 
B1, B2, B5b, 
B5c, B6b and 
B6c 

E_AWSFOR
_GE50_CM
P 

B7 

E_AXSELL 

export 
turnover at 
least 20% of 
the web 
sales 

Sold via an 
e-commerce 
marketplace 
and received 
orders 
placed via a 
website or 
apps from 
customers in 
foreign 
countries, 
with an 
export 
turnover at 
least 50% of 
the web 
sales 

Have 
received 
orders via 
EDI-type 
messages 

B7 

E_AXSELLX  Have not 
received 
orders via 
EDI-type 
messages 

B7 

E_AXSELLZ  Do not know 

if have 
received 
orders via 
EDI-type 
messages 

Have 
received 
orders via 

Derived from 
B1 and B7 

E_AESELL 

W >= 0.20 * 
AWSVAL) 

Count 
(ENT_ID) 
where 
AWS_CMP=
1 and 
(AWSEU=1 
or 
AWSWW=1) 
and 
(AWSVAL_E
U + 
AWSVAL_W
W >= 0.50 * 
AWSVAL) 

Count 
(ENT_ID) 
where 
AXSELL=1 

Count 
(ENT_ID) 
where 
AXSELL=0 

Count 
(ENT_ID) 
where 
AXSELL=Bl
ank 

Count 
(ENT_ID) 
where 
(AWS_COW

TRUE 

2022 

FALSE 

2023 

FALSE 

2023 

TRUE 

2023 

FALSE 

2023 

244 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Derived from 
B2 and B7 

E_WSEL1Q 

computer 
networks 

Enterprises 
with web 
sales via 
websites or 
apps equal 
to or above 
1% of 
turnover, 
and no 
orders 
received via 
EDI-type 
messages 

B8 

E_AXSVALS  Total sales 

via EDI type 
messages, 
excluding 
VAT(<1% of 
turnover) 

B8 

E_AXSVALB  Total sales 

B8 

E_AXSVAL 

via EDI type 
messages, 
excluding 
VAT(>=1% 
of turnover) 

Total sales 
via EDI type 
messages, 
excluding 
VAT 

N=1 or 
AWS_CMP=
1) or 
AXSELL=1 

Count 
(ENT_ID) 
where 
AWSVAL <> 
Blank and 
AWSVAL <> 
-1 and 
AWSVAL>=
TOVT * 1% 
and 
(AXSELL=0 
or 
AXSELL=Bl
ank) 

∑ (AXSVAL) 
where 
AXSVAL <> 
Blank and 
AXSVAL <> 
-1 and 
AXSVAL<TO
VT * 1% 

∑ (AXSVAL) 
where 
AXSVAL <> 
Blank and 
AXSVAL <> 
-1 and 
AXSVAL>=T
OVT * 1% 

∑ (AXSVAL]) 
where 
AXSVAL <> 
Blank and 
AXSVAL <> 
-1 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

B8 

E_XSEL0 

Enterprises 
with sales 

Count 
(ENT_ID) 

FALSE 

2023 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

245 

 
3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

B8 

E_XSEL1 

B8 

E_XSEL2 

B8 

E_XSEL5 

B8 

E_XSEL10 

via EDI-type 
messages 
equal to or 
above 0 

Enterprises 
with sales 
via EDI-type 
messages 
equal to or 
above 1% of 
turnover 

Enterprises 
with sales 
via EDI-type 
messages 
equal to or 
above 2% of 
turnover 

Enterprises 
with sales 
via EDI-type 
messages 
equal to or 
above 5% of 
turnover 

Enterprises 
with sales 
via EDI-type 
messages 
equal to or 
above 10% 
of turnover 

where 
AXSVAL <> 
Blank and 
AXSVAL <> 
-1 and 
AXSVAL>=0 

Count 
(ENT_ID) 
where 
AXSVAL <> 
Blank and 
AXSVAL <> 
-1 and 
AXSVAL>=T
OVT * 1% 

Count 
(ENT_ID) 
where 
AXSVAL <> 
Blank and 
AXSVAL <> 
-1 and 
AXSVAL>=T
OVT * 2% 

Count 
(ENT_ID) 
where 
AXSVAL <> 
Blank and 
AXSVAL <> 
-1 and 
AXSVAL>=T
OVT * 5% 

Count 
(ENT_ID) 
where 
AXSVAL <> 
Blank and 
AXSVAL <> 
-1 and 
AXSVAL>=T
OVT * 10% 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

246 

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3 

Data transmission 

Model 
questionnaire 
2024 

B8 

Variable 
codes 
(CAPITAL 
LETTERS) 

E_XSEL25 

B8 

E_XSEL50 

B8 

E_XSELZ 

Derived from 
B2 and B8 

E_ESELL 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

FALSE 

2023 

FALSE 

2023 

TRUE 

2023 

FALSE 

2023 

Enterprises 
with sales 
via EDI-type 
messages 
equal to or 
above 25% 
of turnover 

Enterprises 
with sales 
via EDI-type 
messages 
equal to or 
above 50% 
of turnover 

Don't know 
the % of 
sales via 
EDI-type 
messages 

Enterprises 
which used 
any 
computer 
networks for 
sales (at 
least 1%) 

Count 
(ENT_ID) 
where 
AXSVAL <> 
Blank and 
AXSVAL <> 
-1 and 
AXSVAL>=T
OVT * 25% 

Count 
(ENT_ID) 
where 
AXSVAL <> 
Blank and 
AXSVAL <> 
-1 and 
AXSVAL>=T
OVT * 50% 

Count 
(ENT_ID) 
where 
AXSVAL=Bl
ank 

Count 
(ENT_ID) 
where 
(AWSVAL<>
Blank and 
AWSVAL>=
TOVT*1%) 
or 
(AXSVAL<>
Blank and 
AXSVAL>=T
OVT*1%) or 
(AWSVAL<>
Blank and 
AWSVAL<>-
1 and 
AXSVAL<>B
lank and 
AXSVAL<>-
1 and 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Derived from 
B2 and B8 

E_ETURN 

Total 
electronic 
sales, 
excluding 
VAT 

Derived from 
B2, B4a and B8 

E_AWSVAL
_B2C_GE10
EC 

Enterprises 
where B2C 
sales via 
websites or 
apps were 
10% or more 
of the e-
commerce 
turnover 

C1 

E_ITSP2 

Employ ICT 
specialists 

(AWSVAL+A
XSVAL)>=T
OVT*1%) 

∑ (AWSVAL) 
where 
AWSVAL <> 
Blank and 
AWSVAL <> 
-1 + ∑ 
(AXSVAL) 
where 
AXSVAL <> 
Blank and 
AXSVAL <> 
-1 

Count 
(ENT_ID) 
where 
AWSVALC<
>Blank AND 
AWSVALC>
0 and 
AWSVAL<>
Blank and 
AXSVAL<>B
lank and 
((AXSVAL = 
-1 and 
AWSVALC>
=0.1 * 
AWSVAL) or 
(AXSVAL <> 
-1 and 
AWSVALC>
=0.1 * 
(AWSVAL+A
XSVAL))) 

Count(ENT_
ID) where 
ITSP2=1 

FALSE 

2023 

FALSE 

2023 

FALSE 

2022 

248 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

C1 

E_ITSP2X 

C2a 

E_ITSPT2 

C2a 

E_ITSPT2X 

C2b 

E_ITUST2 

C2b 

E_ITUST2X 

Derived from 
C2a and C2b 

E_ITT2 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Don't 
employ ICT 
specialists 

Have 
provided 
training to 
develop ICT 
skills of 
personnel: 
for ICT 
specialists 

Have not 
provided 
training to 
develop ICT 
skills of 
personnel: 
for ICT 
specialists 

Have 
provided 
training to 
develop ICT 
skills of 
personnel: 
for other 
persons 
employed 

Have not 
provided 
training to 
develop ICT 
skills of 
personnel: 
for other 
persons 
employed 

Have 
provided 
training to 
develop ICT 

Count(ENT_
ID) where 
ITSP2=0 

Count(ENT_
ID) where 
ITSPT2=1 

FALSE 

2022 

FALSE 

2022 

Count(ENT_
ID) where 
ITSPT2=0 

FALSE 

2022 

Count(ENT_
ID) where 
ITUST2=1 

FALSE 

2022 

Count(ENT_
ID) where 
ITUST2=0 

FALSE 

2022 

FALSE 

2022 

Count(ENT_
ID) where 
ITSPT2=1 or 
ITUST2=1 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Derived from 
C1 and C2a 

E_ITSP2_IT
T2 

Derived from 
C1 and C2b 

E_ITSP2X_I
TT2 

C3 

C3 

E_ITSPRCR
2 

E_ITSPRCR
2X 

Derived from 
C1 and C3 

E_ITSP2_O
R_RCR2 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

skills of 
personnel 

Employ ICT 
specialists 
and have 
provided 
training to 
develop ICT 
skills of 
personnel 

Don't 
employ ICT 
specialists, 
but have 
provided 
training to 
develop ICT 
skills of 
personnel 

Have 
recruited/trie
d to recruit 
ICT 
specialists 

Have not 
recruited/trie
d to recruit 
ICT 
specialists 

Employ ICT 
specialists or 
have 
recruited/trie
d to recruit 
ICT 
specialists 

Count(ENT_
ID) where 
ITSP2=1 
and 
(ITSPT2=1 
or 
ITUST2=1) 

Count(ENT_
ID) where 
ITSP2=0 
and 
ITUST2=1 

Count(ENT_
ID) where 
ITSPRCR2=
1 

Count(ENT_
ID) where 
ITSPRCR2=
0 

Count(ENT_
ID) where 
ITSP2=1 or 
ITSPRCR2=
1 

FALSE 

2022 

FALSE 

2022 

FALSE 

2022 

FALSE 

2022 

FALSE 

2022 

C4 

E_ITSPVAC
2 

Had hard-to-
fill vacancies 
for ICT 
specialists 

Count(ENT_
ID) where 
ITSPVAC2=
1 

FALSE 

2022 

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3 

Data transmission 

Model 
questionnaire 
2024 

C4 

Variable 
codes 
(CAPITAL 
LETTERS) 

E_ITSPVAC
2X 

Derived from 
C1 and C4 

E_ITSP2_V
AC2 

C5a 

E_ITSPDLA 

C5a 

E_ITSPDLA
X 

C5b 

E_ITSPDLE
T 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Count(ENT_
ID) where 
ITSPVAC2=
0 

Count(ENT_
ID) where 
ITSP2=1 
and 
ITSPVAC2=
1 

Count(ENT_
ID) where 
ITSPDLA=1 

FALSE 

2022 

FALSE 

2022 

TRUE 

2022 

Count(ENT_
ID) where 
ITSPDLA=0 

TRUE 

2022 

TRUE 

2022 

Count(ENT_
ID) where 
ITSPDLET=
1 

Had no 
hard-to-fill 
vacancies 
for ICT 
specialists 

Employ ICT 
specialists 
and had 
hard-to-fill 
vacancies 
for ICT 
specialists 

Enterprises 
had the 
following 
difficulty to 
recruit ICT 
specialists: 
Lack of 
applications 

Enterprises 
did not have 
the following 
difficulty to 
recruit ICT 
specialists: 
Lack of 
applications 

Enterprises 
had the 
following 
difficulty to 
recruit ICT 
specialists: 
Applicants' 
lack of 
relevant ICT 
qualifications 
from 
education 
and/or 
training 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

251 

 
Model 
questionnaire 
2024 

C5b 

Variable 
codes 
(CAPITAL 
LETTERS) 

E_ITSPDLE
TX 

C5c 

E_ITSPDLW
E 

C5c 

E_ITSPDLW
EX 

C5d 

E_ITSPDSA
L 

3 

Data transmission 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

TRUE 

2022 

Count(ENT_
ID) where 
ITSPDLET=
0 

TRUE 

2022 

Count(ENT_
ID) where 
ITSPDLWE=
1 

TRUE 

2022 

Count(ENT_
ID) where 
ITSPDLWE=
0 

TRUE 

2022 

Count(ENT_
ID) where 
ITSPDSAL=
1 

Enterprises 
did not have 
the following 
difficulty to 
recruit ICT 
specialists: 
Applicants' 
lack of 
relevant ICT 
qualifications 
from 
education 
and/or 
training 

Enterprises 
had the 
following 
difficulty to 
recruit ICT 
specialists: 
Applicants' 
lack of 
relevant 
work 
experience 

Enterprises 
did not have 
the following 
difficulty to 
recruit ICT 
specialists: 
Applicants' 
lack of 
relevant 
work 
experience 

Enterprises 
had the 
following 
difficulty to 
recruit ICT 
specialists: 
Applicants' 

252 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

C5d 

E_ITSPDSA
LX 

C6a 

E_IT_OWN 

C6a 

E_IT_OWNX 

C6b 

E_IT_EXT 

salary 
expectation 
too high 

Enterprises 
did not have 
the following 
difficulty to 
recruit ICT 
specialists: 
Applicants' 
salary 
expectation 
too high 

ICT 
functions 
were 
performed 
by own 
employees 
(incl. those 
employed in 
parent or 
affiliate 
enterprises) 

ICT 
functions 
were not 
performed 
by own 
employees 
(incl. those 
employed in 
parent or 
affiliate 
enterprises) 

ICT 
functions 
were 
performed 
by external 
suppliers 

TRUE 

2022 

Count(ENT_
ID) where 
ITSPDSAL=
0 

Count(ENT_
ID) where 
IT_OWN=1 

FALSE 

2022 

Count(ENT_
ID) where 
IT_OWN=0 

FALSE 

2022 

Count(ENT_
ID) where 
IT_EXT=1 

FALSE 

2022 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

C6b 

E_IT_EXTX 

Derived from 
C6a and C6b 

E_IT_OWN
Q 

Derived from 
C6a and C6b 

E_IT_EXTQ 

Derived from 
C1 and C6a 

E_IT_OWN_
ITSP2 

Derived from 
C1 and C6b 

E_IT_EXT_I
TSP2 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

ICT 
functions 
were not 
performed 
by external 
suppliers 

ICT 
functions 
were entirely 
performed 
by own 
employees 
(incl. those 
employed in 
parent or 
affiliate 
enterprises) 

ICT 
functions 
were entirely 
performed 
by external 
suppliers 

Employ ICT 
specialists 
and ICT 
functions 
were 
performed 
by own 
employees 
(incl. those 
employed in 
parent or 
affiliate 
enterprises) 

Employ ICT 
specialists 
and ICT 
functions 
were 
performed 

FALSE 

2022 

FALSE 

2022 

FALSE 

2022 

FALSE 

2022 

Count(ENT_
ID) where 
IT_EXT=0 

Count(ENT_
ID) where 
IT_OWN=1 
and 
IT_EXT=0 

Count(ENT_
ID) where 
IT_OWN=0 
and 
IT_EXT=1 

Count(ENT_
ID) where 
IT_OWN=1 
AND 
ITSP2=1 

FALSE 

2022 

Count(ENT_
ID) where 
IT_EXT=1 
AND 
ITSP2=1 

254 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Derived from 
C2a and C2b, 
C6b 

E_IT_EXT_I
TT2 

D1a 

E_SECMSP
SW 

D1a 

E_SECMSP
SWX 

D1b 

E_SECMUIB
M 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

by external 
suppliers 

Provided 
training to 
develop ICT 
skills of 
personnel 
and ICT 
functions 
were 
performed 
by external 
suppliers 

ICT security 
measure 
used: strong 
password 
authenticatio
n, i.e. 
minimum 
length of 8 
mixed 
characters, 
periodical 
change 

ICT security 
measure not 
used: strong 
password 
authenticatio
n, i.e. 
minimum 
length of 8 
mixed 
characters, 
periodical 
change 

ICT security 
measure 
used: user 
identification 
and 

FALSE 

2022 

FALSE 

2022 

Count(ENT_
ID) where 
IT_EXT=1 
AND 
(ITSPT2=1 
or 
ITUST2=1) 

Count(ENT_
ID) where 
SECMSPS
W=1 

FALSE 

2022 

Count(ENT_
ID) where 
SECMSPS
W=0 

FALSE 

2022 

Count(ENT_
ID) where 
SECMUIBM
=1 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

D1b 

E_SECMUIB
MX 

D1c 

E_SECMDU
O 

authenticatio
n via 
biometric 
methods 
implemented 
by the 
enterprise 
(e.g. based 
on 
fingerprints, 
voice, faces) 

ICT security 
measure not 
used: user 
identification 
and 
authenticatio
n via 
biometric 
methods 
implemented 
by the 
enterprise 
(e.g. based 
on 
fingerprints, 
voice, faces) 

ICT security 
measure 
used: at 
least two 
authenticatio
n 
mechanisms 
(i.e. 
combination 
of e.g. user-
defined 
password, 
one-time 
password 
(OTP), code 
generated 
via a 

FALSE 

2022 

Count(ENT_
ID) where 
SECMUIBM
=0 

FALSE 

2022 

Count(ENT_
ID) where 
SECMDUO=
1 

256 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

D1c 

E_SECMDU
OX 

D1d 

E_SECMDE
NC 

security 
token or 
received via 
a 
smartphone, 
biometric 
method 
faces) 

ICT security 
measure not 
used: at 
least two 
authenticatio
n 
mechanisms 
(i.e. 
combination 
of e.g. user-
defined 
password, 
one-time 
password 
(OTP), code 
generated 
via a 
security 
token or 
received via 
a 
smartphone, 
biometric 
method (e.g. 
based on 
fingerprints, 

ICT security 
measure 
used: 
encryption 
techniques 
for data, 
documents 
or e-mails 

FALSE 

2022 

Count(ENT_
ID) where 
SECMDUO=
0 

FALSE 

2022 

Count(ENT_
ID) where 
SECMDENC
=1 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

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3 

Data transmission 

Model 
questionnaire 
2024 

D1d 

Variable 
codes 
(CAPITAL 
LETTERS) 

E_SECMDE
NCX 

D1e 

E_SECMOS
BU 

D1e 

E_SECMOS
BUX 

D1f 

E_SECMNA
C 

D1f 

E_SECMNA
CX 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

ICT security 
measure not 
used: 
encryption 
techniques 
for data, 
documents 
or e-mails 

ICT security 
measure 
used: data 
backup to a 
separate 
location 
(including 
backup to 
the cloud) 

ICT security 
measure not 
used: data 
backup to a 
separate 
location 
(including 
backup to 
the cloud) 

ICT security 
measure 
used: 
network 
access 
control 
(manageme
nt of access 
by devices 
and users to 
the 
enterprise's 
network) 

ICT security 
measure not 
used: 
network 

FALSE 

2022 

FALSE 

2022 

Count(ENT_
ID) where 
SECMDENC
=0 

Count(ENT_
ID) where 
SECMOSBU
=1 

FALSE 

2022 

Count(ENT_
ID) where 
SECMOSBU
=0 

FALSE 

2022 

Count(ENT_
ID) where 
SECMNAC=
1 

FALSE 

2022 

Count(ENT_
ID) where 
SECMNAC=
0 

258 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

D1g 

E_SECMVP
N 

D1g 

E_SECMVP
NX 

FALSE 

2022 

Count(ENT_
ID) where 
SECMVPN=
1 

FALSE 

2022 

Count(ENT_
ID) where 
SECMVPN=
0 

access 
control 
(manageme
nt of access 
by devices 
and users to 
the 
enterprise's 
network) 

ICT security 
measure 
used: VPN 
(Virtual 
Private 
Network 
extends a 
private 
network 
across a 
public 
network to 
enable 
secure 
exchange of 
data over 
public 
network) 

ICT security 
measure not 
used: VPN 
(Virtual 
Private 
Network 
extends a 
private 
network 
across a 
public 
network to 
enable 
secure 
exchange of 
data over 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

259 

 
Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

D1h 

E_SECMSM
S1 

D1h 

E_SECMSM
S1X 

D1i 

E_SECMLO
G 

3 

Data transmission 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

public 
network) 

ICT security 
measure 
used: 
monitoring 
system that 
allows to 
detect 
suspicious 
activity in the 
ICT systems 
and alerts 
the 
enterprises 
about it, 
other than 
standalone 
anti-virus 
software 

ICT security 
measure not 
used: 
monitoring 
system that 
allows to 
detect 
suspicious 
activity in the 
ICT systems 
and alerts 
the 
enterprises 
about it, 
other than 
standalone 
anti-virus 
software 

ICT security 
measure 
used: 
maintaining 
log files for 

FALSE 

NEW 

Count(ENT_
ID) where 
SECMSMS1
=1 

FALSE 

NEW 

Count(ENT_
ID) where 
SECMSMS1
=0 

FALSE 

2022 

Count(ENT_
ID) where 
SECMLOG=
1 

260 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

D1i 

D1j 

E_SECMLO
GX 

E_SECMRA
SS 

D1j 

E_SECMRA
SSX 

D1k 

E_SECMTS
T 

analysis 
after security 
incidents 

ICT security 
measure not 
used: 
maintaining 
log files for 
analysis 
after security 
incidents 

ICT security 
measure 
used: ICT 
risk 
assessment, 
i.e. 
periodically 
assessment 
of probability 
and 
consequenc
es of ICT 
security 
incidents 

ICT security 
measure not 
used: ICT 
risk 
assessment, 
i.e. 
periodically 
assessment 
of probability 
and 
consequenc
es of ICT 
security 
incidents 

ICT security 
measure 
used: ICT 

FALSE 

2022 

FALSE 

2022 

Count(ENT_
ID) where 
SECMLOG=
0 

Count(ENT_
ID) where 
SECMRASS
=1 

FALSE 

2022 

Count(ENT_
ID) where 
SECMRASS
=0 

Count(ENT_
ID) where 

FALSE 

2022 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

security 
tests (e.g. 
performing 
penetration 
tests, testing 
security alert 
system, 
review of 
security 
measures, 
testing of 
backup 
systems) 

ICT security 
measure not 
used: ICT 
security 
tests (e.g. 
performing 
penetration 
tests, testing 
security alert 
system, 
review of 
security 
measures, 
testing of 
backup 
systems) 

Use any ICT 
security 
measure 

Use at least 
3 ICT 
security 
measures 

Use at least 
5 ICT 
security 
measures 

SECMTST=
1 

FALSE 

2022 

Count(ENT_
ID) where 
SECMTST=
0 

FALSE 

2022 

FALSE 

2022 

FALSE 

2022 

Count(ENT_
ID) where 
SECM_CNT
>=1 

Count(ENT_
ID) where 
SECM_CNT
>=3 

Count(ENT_
ID) where 
SECM_CNT
>=5 

D1k 

E_SECMTS
TX 

Derived from 
D1a to D1k 

E_SECMGE
1 

Derived from 
D1a to D1k 

E_SECMGE
3 

Derived from 
D1a to D1k 

E_SECMGE
5 

262 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Derived from 
D1a to D1k 

E_SECMGE
7 

Derived from 
D1a to D1k 

E_SECMAL
L 

D2a 

E_SECAWV
TGI 

D2a 

E_SECAWV
TGIX 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

FALSE 

2022 

FALSE 

2022 

FALSE 

2022 

Count(ENT_
ID) where 
SECM_CNT
>=7 

Count(ENT_
ID) where 
SECM_CNT
=11 

Count(ENT_
ID) where 
SECAWVTG
I=1 

FALSE 

2022 

Count(ENT_
ID) where 
SECAWVTG
I=0 

Use at least 
7 ICT 
security 
measures 

Use all ICT 
security 
measures 

Make 
persons 
employed 
aware of 
their 
obligations 
in ICT 
security 
related 
issues by 
voluntary 
training or 
internally 
available 
information 
(e.g. 
information 
on the 
intranet) 

Don't make 
persons 
employed 
aware of 
their 
obligations 
in ICT 
security 
related 
issues by 
voluntary 
training or 
internally 
available 
information 
(e.g. 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

263 

 
3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

D2b 

E_SECAWC
TP 

D2b 

E_SECAWC
TPX 

D2c 

E_SECAWC
ONT 

information 
on the 
intranet) 

Make 
persons 
employed 
aware of 
their 
obligations 
in ICT 
security 
related 
issues by 
compulsory 
training 
courses or 
viewing 
compulsory 
material 

Don't make 
persons 
employed 
aware of 
their 
obligations 
in ICT 
security 
related 
issues by 
compulsory 
training 
courses or 
viewing 
compulsory 
material 

Make 
persons 
employed 
aware of 
their 
obligations 
in ICT 
security 

FALSE 

2022 

Count(ENT_
ID) where 
SECAWCTP
=1 

FALSE 

2022 

Count(ENT_
ID) where 
SECAWCTP
=0 

FALSE 

2022 

Count(ENT_
ID) where 
SECAWCO
NT=1 

264 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

D2c 

E_SECAWC
ONTX 

Derived from 
D2a to D2c 

E_SECAWA
NY 

Derived from 
D2a to D2c 

E_SECAWN
ONE 

related 
issues by 
contract 
(e.g. 
contract of 
employment) 

Don't make 
persons 
employed 
aware of 
their 
obligations 
in ICT 
security 
related 
issues by 
contract 
(e.g. 
contract of 
employment) 

Make 
persons 
employed 
aware of 
their 
obligations 
in ICT 
security 
related 
issues 

Don't make 
persons 
employed 
aware of 
their 
obligations 
in ICT 
security 
related 
issues 

FALSE 

2022 

Count(ENT_
ID) where 
SECAWCO
NT=0 

FALSE 

2022 

FALSE 

2022 

Count(ENT_
ID) where 
SECAWVTG
I=1 or 
SECAWCTP
=1 or 
SECAWCO
NT=1 

Count(ENT_
ID) where 
SECAWVTG
I=0 and 
SECAWCTP
=0 and 
SECAWCO
NT=0 

Derived from 
C2a and D2a 

E_SECAWV
TGI_ITSPT2 

Make 
persons 

Count(ENT_
ID) where 

FALSE 

2022 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

265 

 
3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Derived from 
C2b and D2a 

E_SECAWV
TGI_ITUST2 

SECAWVTG
I=1 and 
ITSPT2=1 

FALSE 

2022 

Count(ENT_
ID) where 
SECAWVTG
I=1 and 
ITUST2=1 

employed 
aware of 
their 
obligations 
in ICT 
security 
related 
issues by 
voluntary 
training or 
internally 
available 
information 
and have 
provided 
training to 
develop ICT 
skills for ICT 
specialists 

Make 
persons 
employed 
aware of 
their 
obligations 
in ICT 
security 
related 
issues by 
voluntary 
training or 
internally 
available 
information 
and have 
provided 
training to 
develop ICT 
skills for 
other 
persons 
employed 

Derived from 
C2a and D2b 

E_SECAWC
TP_ITSPT2 

Make 
persons 

Count(ENT_
ID) where 

FALSE 

2022 

266 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Derived from 
C2b and D2b 

E_SECAWC
TP_ITUST2 

SECAWCTP
=1 and 
ITSPT2=1 

FALSE 

2022 

Count(ENT_
ID) where 
SECAWCTP
=1 and 
ITUST2=1 

employed 
aware of 
their 
obligations 
in ICT 
security 
related 
issues by 
compulsory 
training 
courses or 
viewing 
compulsory 
material and 
have 
provided 
training to 
develop ICT 
skills for ICT 
specialists 

Make 
persons 
employed 
aware of 
their 
obligations 
in ICT 
security 
related 
issues by 
compulsory 
training 
courses or 
viewing 
compulsory 
material and 
have 
provided 
training to 
develop ICT 
skills for 
other 
persons 
employed 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

267 

 
Model 
questionnaire 
2024 

Derived from 
C2a and D2c 

Variable 
codes 
(CAPITAL 
LETTERS) 

E_SECAWC
ONT_ITSPT
2 

Derived from 
C2b and D2c 

E_SECAWC
ONT_ITUST
2 

Derived from 
C2a and D2 

E_SECAWA
NY_ITSPT2 

3 

Data transmission 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Make 
persons 
employed 
aware of 
their 
obligations 
in ICT 
security 
related 
issues by 
contract and 
have 
provided 
training to 
develop ICT 
skills for ICT 
specialists 

Make 
persons 
employed 
aware of 
their 
obligations 
in ICT 
security 
related 
issues by 
contract and 
have 
provided 
training to 
develop ICT 
skills for 
other 
persons 
employed 

Make 
persons 
employed 
aware of 
their 
obligations 
in ICT 
security 

FALSE 

2022 

Count(ENT_
ID) where 
SECAWCO
NT=1 and 
ITSPT2=1 

FALSE 

2022 

Count(ENT_
ID) where 
SECAWCO
NT=1 and 
ITUST2=1 

FALSE 

2022 

Count(ENT_
ID) where 
(SECAWVT
GI=1 or 
SECAWCTP
=1 or 
SECAWCO

268 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Derived from 
C2b and D2 

E_SECAWA
NY_ITUST2 

D3 

D3 

E_SECPOL
2 

E_SECPOL
2X 

NT=1) and 
ITSPT2=1 

Count(ENT_
ID) where 
(SECAWVT
GI=1 or 
SECAWCTP
=1 or 
SECAWCO
NT=1) and 
ITUST2=1 

FALSE 

2022 

Count(ENT_
ID) where 
SECPOL2=1 

FALSE 

2022 

Count(ENT_
ID) where 
SECPOL2=0 

FALSE 

2022 

related 
issues and 
have 
provided 
training to 
develop ICT 
skills for ICT 
specialists 

Make 
persons 
employed 
aware of 
their 
obligations 
in ICT 
security 
related 
issues and 
have 
provided 
training to 
develop ICT 
skills for 
other 
persons 
employed 

Have 
document(s) 
on 
measures, 
practices or 
procedures 
on ICT 
security 

Have no 
document(s) 
on 
measures, 
practices or 
procedures 
on ICT 
security 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

269 

 
Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Derived from 
C1 and D3 

E_SECPOL
2_ITSP2 

Derived from 
D2a to D2c and 
D3 

E_SECAWA
NY_POL2 

D4a 

E_SECPRE
V_CY 

3 

Data transmission 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

FALSE 

2022 

FALSE 

2022 

Count(ENT_
ID) where 
ITSP2=1 
and 
SECPOL2=1 

Count(ENT_
ID) where 
(SECAWVT
GI=1 or 
SECAWCTP
=1 or 
SECAWCO
NT=1) and 
SECPOL2=1 

TRUE 

2022 

Count(ENT_
ID) where 
SECPREV=
1 

Employ ICT 
specialists 
and have 
document(s) 
on 
measures, 
practices or 
procedures 
on ICT 
security 

Make 
persons 
employed 
aware of 
their 
obligations 
in ICT 
security 
related 
issues and 
have 
document(s) 
on 
measures, 
practices or 
procedures 
on ICT 
security 

The 
document(s) 
on 
measures, 
practices or 
procedures 
were defined 
or most 
recently 
reviewed: 
within the 
last 12 
months 

D4b 

E_SECPRE
V_1_2 

The 
document(s) 

Count(ENT_
ID) where 

TRUE 

2022 

270 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

on 
measures, 
practices or 
procedures 
were defined 
or most 
recently 
reviewed: 
more than 
12 months 
and up to 24 
months ago 

The 
document(s) 
on 
measures, 
practices or 
procedures 
were defined 
or most 
recently 
reviewed: 
more than 
24 months 
ago 

The 
document(s) 
on 
measures, 
practices or 
procedures 
were defined 
or most 
recently 
reviewed: 
within the 
last 24 
months 

Have 
experienced 
at least once 
problems 
due to ICT 

SECPREV=
2 

TRUE 

2022 

Count(ENT_
ID) where 
SECPREV=
3 

TRUE 

2022 

Count(ENT_
ID) where 
SECPREV=
1 or 
SECPREV=
2 

FALSE 

2022 

Count(ENT_
ID) where 
SEC2IUSVF
=1 

D4c 

E_SECPRE
V_MT2 

Derived from 
D4a and D4b 

E_SECPRE
V_LE2 

D5a 

E_SEC2IUS
VF 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

271 

 
3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

security 
incident: 
unavailability 
of ICT 
services due 
to hardware 
or software 
failures 

Have not 
experienced 
at least once 
problems 
due to ICT 
security 
incident: 
unavailability 
of ICT 
services due 
to hardware 
or software 
failures 

Have 
experienced 
at least once 
problems 
due to ICT 
security 
incident: 
unavailability 
of ICT 
services 
(e.g. 
Ransomwar
e attacks, 
Denial of 
Service 
attacks) 

Have not 
experienced 
at least once 
problems 
due to ICT 
security 

FALSE 

2022 

Count(ENT_
ID) where 
SEC2IUSVF
=0 

FALSE 

2022 

Count(ENT_
ID) where 
SEC2IUSVA
=1 

FALSE 

2022 

Count(ENT_
ID) where 
SEC2IUSVA
=0 

D5a 

E_SEC2IUS
VFX 

D5b 

E_SEC2IUS
VA 

D5b 

E_SEC2IUS
VAX 

272 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Derived from 
D5a and D5b 

E_SEC2IUS
V 

D5c 

E_SEC2IDC
DF 

incident: 
unavailability 
of ICT 
services 
(e.g. 
Ransomwar
e attacks, 
Denial of 
Service 
attacks) 

Have 
experienced 
at least once 
problems 
due to ICT 
security 
incident: 
unavailability 
of ICT 
services 
(e.g. Denial 
of Service 
attacks, 
ransomware 
attacks, 
hardware or 
software 
failures - 
excluding 
mechanical 
failure, theft) 

Have 
experienced 
at least once 
problems 
due to ICT 
security 
incident: 
destruction 
or corruption 
of data due 
to hardware 
or software 
failures 

FALSE 

2022 

Count(ENT_
ID) where 
SEC2IUSVF
=1 or 
SEC2IUSVA
=1 

FALSE 

2022 

Count(ENT_
ID) where 
SEC2IDCDF
=1 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

273 

 
Model 
questionnaire 
2024 

D5c 

Variable 
codes 
(CAPITAL 
LETTERS) 

E_SEC2IDC
DFX 

D5d 

E_SEC2IDC
DA 

D5d 

E_SEC2IDC
DAX 

3 

Data transmission 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

FALSE 

2022 

Count(ENT_
ID) where 
SEC2IDCDF
=0 

FALSE 

2022 

Count(ENT_
ID) where 
SEC2IDCDA
=1 

FALSE 

2022 

Count(ENT_
ID) where 
SEC2IDCDA
=0 

Have not 
experienced 
at least once 
problems 
due to ICT 
security 
incident: 
destruction 
or corruption 
of data due 
to hardware 
or software 
failures 

Have 
experienced 
at least once 
problems 
due to ICT 
security 
incident: 
destruction 
or corruption 
of data due 
to infection 
of malicious 
software or 
unauthorised 
intrusion 

Have not 
experienced 
at least once 
problems 
due to ICT 
security 
incident: 
destruction 
or corruption 
of data due 
to infection 
of malicious 
software or 
unauthorised 
intrusion 

274 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Derived from 
D5c and D5d 

E_SEC2IDC
D 

D5e 

E_SEC2ICN
FA 

D5e 

E_SEC2ICN
FAX 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

FALSE 

2022 

Count(ENT_
ID) where 
SEC2IDCDF
=1 or 
SEC2IDCDA
=1 

FALSE 

2022 

Count(ENT_
ID) where 
SEC2ICNFA
=1 

FALSE 

2022 

Count(ENT_
ID) where 
SEC2ICNFA
=0 

Have 
experienced 
at least once 
problems 
due to ICT 
security 
incident: 
destruction 
or corruption 
of data (e.g. 
due to 
infection of 
malicious 
software or 
unauthorised 
intrusion, 
hardware or 
software 
failures) 

Have 
experienced 
at least once 
problems 
due to ICT 
security 
incident: 
disclosure of 
confidential 
data due to 
intrusion, 
pharming, 
phishing 
attack, 
intentional 
actions by 
own 
employees 

Have not 
experienced 
at least once 
problems 
due to ICT 
security 
incident: 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

275 

 
3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

disclosure of 
confidential 
data due to 
intrusion, 
pharming, 
phishing 
attack, 
intentional 
actions by 
own 
employees 

Have 
experienced 
at least once 
problems 
due to ICT 
security 
incident: 
disclosure of 
confidential 
data due to 
unintentional 
actions by 
own 
employees 

Have not 
experienced 
at least once 
problems 
due to ICT 
security 
incident: 
disclosure of 
confidential 
data due to 
unintentional 
actions by 
own 
employees 

Have 
experienced 
at least once 
problems 

FALSE 

2022 

Count(ENT_
ID) where 
SEC2ICNFF
=1 

FALSE 

2022 

Count(ENT_
ID) where 
SEC2ICNFF
=0 

FALSE 

2022 

Count(ENT_
ID) where 
SEC2ICNFA
=1 or 

D5f 

E_SEC2ICN
FF 

D5f 

E_SEC2ICN
FFX 

Derived from 
D5e and D5f 

E_SEC2ICN
F 

276 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

due to ICT 
security 
incident: 
disclosure of 
confidential 
data (e.g. 
due to 
intrusion, 
pharming, 
phishing 
attack, 
actions by 
own 
employees 
(intentionally 
or 
unintentional
ly)) 

Have 
experienced 
at least once 
any problem 
due to ICT 
security 
incident: 
unavailibility 
of ICT 
services, 
destruction 
or corruption 
of data, 
disclosure of 
confidential 
data (for any 
reason) 

Have 
experienced 
at least once 
3 or more 
problems 
due to ICT 
security 
incident: 
unavailibility 

SEC2ICNFF
=1 

Count(ENT_
ID) where 
SEC2IUSVF
=1 or 
SEC2IUSVA
=1 or 
SEC2IDCDF
=1 or 
SEC2IDCDA
=1 or 
SEC2ICNFA
=1 or 
SEC2ICNFF
=1 

Count(ENT_
ID) where 
SEC2IUSVF
+SEC2IUSV
A+SEC2IDC
DF+SEC2ID
CDA+SEC2I
CNFA+SEC
2ICNFF 

FALSE 

2022 

FALSE 

2022 

Derived from 
D5a to D5f 

E_SEC2IAN
Y 

Derived from 
D5a to D5f 

E_SEC2IGE
3 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

277 

 
3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

E1a 

E_AI_TTM 

E1a 

E_AI_TTMX 

E1b 

E_AI_TSR 

E1b 

E_AI_TSRX 

between 3 
and 6 

Count(ENT_
ID) where 
AI_TTM=1 

FALSE 

2023 

Count(ENT_
ID) where 
AI_TTM=0 

FALSE 

2023 

Count(ENT_
ID) where 
AI_TSR=1 

FALSE 

2023 

Count(ENT_
ID) where 
AI_TSR=0 

FALSE 

2023 

of ICT 
services, 
destruction 
or corruption 
of data, 
disclosure of 
confidential 
data (for any 
reason) 

Enterprises 
use AI 
technologies 
performing 
analysis of 
written 
language 

Enterprises 
do not use 
AI 
technologies 
performing 
analysis of 
written 
language 

Enterprises 
use AI 
technologies 
converting 
spoken 
language 
into 
machine-
readable 
format 

Enterprises 
do not use 
AI 
technologies 
converting 
spoken 
language 
into 

278 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

E1c 

E_AI_TNLG 

E1c 

E_AI_TNLG
X 

E1d 

E_AI_TIR 

E1d 

E_AI_TIRX 

E1e 

E_AI_TML 

machine-
readable 
format 

Enterprises 
use AI 
technologies 
generating 
written or 
spoken 
language 

Enterprises 
do not use 
AI 
technologies 
generating 
written or 
spoken 
language 

Enterprises 
use AI 
technologies 
identifying 
objects or 
persons 
based on 
images 

Enterprises 
do not use 
AI 
technologies 
identifying 
objects or 
persons 
based on 
images 

Enterprises 
use machine 
learning 
(e.g. deep 
learning) for 
data 
analysis 

Count(ENT_
ID) where 
AI_TNLG=1 

FALSE 

2023 

Count(ENT_
ID) where 
AI_TNLG=0 

FALSE 

2023 

Count(ENT_
ID) where 
AI_TIR=1 

FALSE 

2023 

Count(ENT_
ID) where 
AI_TIR=0 

FALSE 

2023 

Count(ENT_
ID) where 
AI_TML=1 

FALSE 

2023 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

279 

 
Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

E1e 

E_AI_TMLX 

E1f 

E_AI_TPA 

E1f 

E_AI_TPAX 

E1g 

E_AI_TAR 

3 

Data transmission 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Count(ENT_
ID) where 
AI_TML=0 

FALSE 

2023 

Count(ENT_
ID) where 
AI_TPA=1 

FALSE 

2023 

Count(ENT_
ID) where 
AI_TPA=0 

FALSE 

2023 

Count(ENT_
ID) where 
AI_TAR=1 

FALSE 

2023 

Enterprises 
do not use 
machine 
learning 
(e.g. deep 
learning) for 
data 
analysis 

Enterprises 
use AI 
technologies 
automating 
different 
workflows or 
assisting in 
decision 
making 

Enterprises 
do not use 
AI 
technologies 
automating 
different 
workflows or 
assisting in 
decision 
making 

Enterprises 
use AI 
technologies 
enabling 
physical 
movement of 
machines 
via 
autonomous 
decisions 
based on 
observation 
of 
surroundings 

280 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

E1g 

E_AI_TARX 

Derived from 
E1 

E_AI_TANY 

Derived from 
E1 

E_AI_TX 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Enterprises 
do not use 
AI 
technologies 
enabling 
physical 
movement of 
machines 
via 
autonomous 
decisions 
based on 
observation 
of 
surroundings 

Enterprises 
use at least 
1 of the 
listed 
artificial 
intelligence 
technologies
: (AI_TTM, 
AI_TSR, 
AI_TNLG, 
AI_TIR, 
AI_TML, 
AI_TPA, 
AI_TAR) 

Enterprises 
doesn't use 
artificial 
intelligence 
technologies
. 

Count(ENT_
ID) where 
AI_TAR=0 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

Count 
(ENT_ID) 
where 
(AI_TTM+AI
_TSR+AI_T
NLG+AI_TIR
+AI_TML+AI
_TPA+AI_TA
R) between 
1 and 7 

Count 
(ENT_ID) 
where 
AI_TTM=0 
and 
AI_TSR=0 
and 
AI_TNLG=0 
and 
AI_TIR=0 
and 
AI_TML=0 
and 
AI_TPA=0 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

281 

 
3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Derived from 
E1 

E_AI_TGE2 

Derived from 
E1 

E_AI_TGE3 

E2a 

E_AI_PMS 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

FALSE 

2023 

FALSE 

2023 

and 
AI_TAR=0 

Count 
(ENT_ID) 
where 
(AI_TTM+AI
_TSR+AI_T
NLG+AI_TIR
+AI_TML+AI
_TPA+AI_TA
R) between 
2 and 7 

Count 
(ENT_ID) 
where 
(AI_TTM+AI
_TSR+AI_T
NLG+AI_TIR
+AI_TML+AI
_TPA+AI_TA
R) between 
3 and 7 

Count(ENT_
ID) where 
AI_PMS=1 

FALSE 

2023 

Enterprises 
use at least 
2 of the 
listed 
artificial 
intelligence 
technologies
: (AI_TTM, 
AI_TSR, 
AI_TNLG, 
AI_TIR, 
AI_TML, 
AI_TPA, 
AI_TAR) 

Enterprises 
use at least 
3 of the 
listed 
artificial 
intelligence 
technologies
: (AI_TTM, 
AI_TSR, 
AI_TNLG, 
AI_TIR, 
AI_TML, 
AI_TPA, 
AI_TAR) 

Enterprises 
use AI 
technologies 
for 
marketing or 
sales 

E2a 

E_AI_PMSX  Enterprises 
do not use 
AI 
technologies 
for 

Count(ENT_
ID) where 
AI_PMS=0 

FALSE 

2023 

282 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

E2b 

E_AI_PPP 

E2b 

E_AI_PPPX 

E2c 

E_AI_PBAM  Enterprises 

E2c 

E_AI_PBAM
X 

use AI 
technologies 
for 
organisation 
of business 
administratio
n processes 
or 
managemen
t 

Enterprises 
do not use 
AI 
technologies 
for 
organisation 
of business 
administratio
n processes 
or 
managemen
t 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

marketing or 
sales 

Enterprises 
use AI 
technologies 
for 
production 
or service 
processes 

Enterprises 
do not use 
AI 
technologies 
for 
production 
or service 
processes 

Count(ENT_
ID) where 
AI_PPP=1 

FALSE 

2023 

Count(ENT_
ID) where 
AI_PPP=0 

FALSE 

2023 

Count(ENT_
ID) where 
AI_PBAM=1 

FALSE 

2023 

Count(ENT_
ID) where 
AI_PBAM=0 

FALSE 

2023 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

283 

 
3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Count(ENT_
ID) where 
AI_PLOG=1 

Count(ENT_
ID) where 
AI_PLOG=0 

Count(ENT_
ID) where 
AI_PITS=1 

Count(ENT_
ID) where 
AI_PITS=0 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

FALSE 

2023 

Count(ENT_
ID) where 
AI_PFIN=1 

FALSE 

2023 

Count(ENT_
ID) where 
AI_PFIN=0 

FALSE 

2023 

E2e 

E_AI_PLOG  Enterprises 

E2e 

E_AI_PLOG
X 

E2f 

E_AI_PITS 

use AI 
technologies 
for logistics 

Enterprises 
do not use 
AI 
technologies 
for logistics 

Enterprises 
use AI 
technologies 
for ICT 
security 

E2f 

E_AI_PITSX  Enterprises 
do not use 
AI 
technologies 
for ICT 
security 

E2g 

E_AI_PFIN 

Enterprises 
use AI 
technologies 
for 
accounting, 
controlling or 
finance 
managemen
t 

E2g 

E_AI_PFINX  Enterprises 
do not use 
AI 
technologies 
for 
accounting, 
controlling or 
finance 
managemen
t 

284 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

E2h 

E_AI_PRDI 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Count(ENT_
ID) where 
AI_PRDI=1 

FALSE 

2023 

Enterprises 
use AI 
technologies 
for research 
and 
development 
(R&D) or 
innovation 
activity 

Count(ENT_
ID) where 
AI_PRDI=0 

FALSE 

2023 

E2h 

E_AI_PRDIX  Enterprises 
do not use 
AI 
technologies 
for research 
and 
development 
(R&D) or 
innovation 
activity 

Derived from 
E2 

E_AI_P1AN
Y 

Derived from 
E2 

E_AI_P1GE
2 

FALSE 

2023 

FALSE 

2023 

Enterprises 
use artificial 
intelligence 
for at least 1 
of the listed 
purposes: 
(AI_PMS, 
AI_PPP, 
AI_PBAM, 
AI_PLOG, 
AI_PITS, 
AI_PFIN, 
AI_PRDI) 

Enterprises 
use artificial 
intelligence 
for at least 2 
of the listed 
purposes: 
(AI_PMS, 
AI_PPP, 
AI_PBAM, 
AI_PLOG, 
AI_PITS, 

Count 
(ENT_ID) 
where 
(AI_PMS+AI
_PPP+AI_P
BAM+AI_PL
OG+AI_PIT
S+AI_PFIN+
AI_PRDI) 
between 1 
and 7 

Count 
(ENT_ID) 
where 
(AI_PMS+AI
_PPP+AI_P
BAM+AI_PL
OG+AI_PIT
S+AI_PFIN+
AI_PRDI) 
between 2 
and 7 

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285 

 
Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Derived from 
E2 

E_AI_P1GE
3 

E3a 

E_AI_ADO
WN 

E3a 

E_AI_ADO
WNX 

E3b 

E_AI_AMO
WN 

3 

Data transmission 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

FALSE 

2023 

TRUE 

2023 

TRUE 

2023 

TRUE 

2023 

Count 
(ENT_ID) 
where 
(AI_PMS+AI
_PPP+AI_P
BAM+AI_PL
OG+AI_PIT
S+AI_PFIN+
AI_PRDI) 
between 3 
and 7 

Count(ENT_
ID) where 
AI_ADOWN
=1 

Count(ENT_
ID) where 
AI_ADOWN
=0 

Count(ENT_
ID) where 
AI_AMOWN
=1 

AI_PFIN, 
AI_PRDI) 

Enterprises 
use artificial 
intelligence 
for at least 3 
of the listed 
purposes: 
(AI_PMS, 
AI_PPP, 
AI_PBAM, 
AI_PLOG, 
AI_PITS, 
AI_PFIN, 
AI_PRDI) 

The 
enterprises' 
AI 
technologies 
were 
developed 
by own 
employees 

The 
enterprises' 
AI 
technologies 
were not 
developed 
by own 
employees 

The 
enterprises' 
AI 
technologies 
were 
commercial 
software or 
systems 
modified by 
own 
employees 

286 

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3 

Data transmission 

Model 
questionnaire 
2024 

E3b 

Variable 
codes 
(CAPITAL 
LETTERS) 

E_AI_AMO
WNX 

E3c 

E_AI_AOS 

E3c 

E_AI_AOSX 

E3d 

E_AI_ARDY 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

TRUE 

2023 

Count(ENT_
ID) where 
AI_AMOWN
=0 

Count(ENT_
ID) where 
AI_AOS=1 

TRUE 

2023 

Count(ENT_
ID) where 
AI_AOS=0 

TRUE 

2023 

Count(ENT_
ID) where 
AI_ARDY=1 

TRUE 

2023 

The 
enterprises' 
AI 
technologies 
were not 
commercial 
software or 
systems 
modified by 
own 
employees 

The 
enterprises' 
AI 
technologies 
were open-
source 
software or 
systems 
modified by 
own 
employees 

The 
enterprises' 
AI 
technologies 
were not 
open-source 
software or 
systems 
modified by 
own 
employees 

The 
enterprises' 
AI 
technologies 
were 
purchased 
commercial 
software or 
systems 
ready to use 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

287 

 
3 

Data transmission 

Model 
questionnaire 
2024 

E3d 

Variable 
codes 
(CAPITAL 
LETTERS) 

E_AI_ARDY
X 

E3e 

E_AI_AEXT 

E3e 

E_AI_AEXT
X 

E4 

E_AI_PDI 

E4 

E_AI_PDIX 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Count(ENT_
ID) where 
AI_ARDY=0 

TRUE 

2023 

Count(ENT_
ID) where 
AI_AEXT=1 

TRUE 

2023 

Count(ENT_
ID) where 
AI_AEXT=0 

TRUE 

2023 

Count(ENT_
ID) where 
AI_PDI=1 

TRUE 

NEW 

Count(ENT_
ID) where 
AI_PDI=0 

TRUE 

NEW 

The 
enterprises' 
AI 
technologies 
were not 
purchased 
commercial 
software or 
systems 
ready to use 

The 
enterprises' 
AI 
technologies 
were 
developed or 
modified by 
external 
providers 

The 
enterprises' 
AI 
technologies 
were not 
developed or 
modified by 
external 
providers 

Enterprises 
which 
process data 
on 
individuals 
using AI 
technologies 

Enterprises 
which do not 
process data 
on 
individuals 
using AI 
technologies 

288 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

E5 

E_AI_BIAS 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Enterprises 
which have 
measures to 
check the 
results 
generated 
by AI 
technologies 
for possible 
biases 
towards 
individuals 

Count(ENT_
ID) where 
AI_BIAS=1 

TRUE 

NEW 

Count(ENT_
ID) where 
AI_BIAS=0 

TRUE 

NEW 

TRUE 

NEW 

Count(ENT_
ID) where 
AI_BIAS=1 
and 
AI_ADOWN
=1 

E5 

E_AI_BIASX  Enterprises 

Derived from 
E5, E3a 

E_AI_BIAS_
ADOWN 

which do not 
have 
measures to 
check the 
results 
generated 
by AI 
technologies 
for possible 
biases 
towards 
individuals 

Enterprises 
which have 
measures to 
check the 
results 
generated 
by AI for 
possible 
biases 
towards 
individuals, 
and their AI 
technologies 
were 
developed 
by own 
employees 

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3 

Data transmission 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Enterprises 
which have 
measures to 
check the 
results 
generated 
by AI for 
possible 
biases 
towards 
individuals, 
and their AI 
technologies 
were  
commercial 
software or 
systems 
modified by 
own 
employees 

Enterprises 
which have 
measures to 
check the 
results 
generated 
by AI for 
possible 
biases 
towards 
individuals, 
and their AI 
technologies 
were open-
source 
software or 
systems 
modified by 
own 
employees 

Enterprises 
which have 
measures to 
check the 

TRUE 

NEW 

Count(ENT_
ID) where 
AI_BIAS=1 
and 
AI_AMOWN
=1 

TRUE 

NEW 

Count(ENT_
ID) where 
AI_BIAS=1 
and 
AI_AOS=1 

Count(ENT_
ID) where 
AI_BIAS=1 

TRUE 

NEW 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Derived from 
E5, E3b 

E_AI_BIAS_
AMOWN 

Derived from 
E5, E3c 

E_AI_BIAS_
AOS 

Derived from 
E5, E3d 

E_AI_BIAS_
ARDY 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

results 
generated 
by AI for 
possible 
biases 
towards 
individuals, 
and their AI 
technologies 
were 
purchased 
commercial 
software or 
systems 
ready to use 

Enterprises 
which have 
measures to 
check the 
results 
generated 
by AI for 
possible 
biases 
towards 
individuals, 
and their AI 
technologies 
were 
developed or 
modified by 
external 
providers 

Enterprises 
have ever 
considered 
to use AI 
technologies 

Enterprises 
have never 
considered 
to use AI 
technologies 

and 
AI_ARDY=1 

TRUE 

NEW 

Count(ENT_
ID) where 
AI_BIAS=1 
and 
AI_AEXT=1 

Count(ENT_
ID) where 
AI_EC=1 

Count(ENT_
ID) where 
AI_EC=0 

TRUE 

2023 

TRUE 

2023 

Derived from 
E5, E3e 

E_AI_BIAS_
AEXT 

E6 

E_AI_EC 

E6 

E_AI_ECX 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

E7a 

E_AI_BCST 

E7a 

E_AI_BCST
X 

E7b 

E_AI_BLE 

E7b 

E_AI_BLEX 

E7c 

E_AI_BINC 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Count(ENT_
ID) where 
AI_BCST=1 

TRUE 

2023 

Count(ENT_
ID) where 
AI_BCST=0 

TRUE 

2023 

Count(ENT_
ID) where 
AI_BLE=1 

TRUE 

2023 

Count(ENT_
ID) where 
AI_BLE=0 

TRUE 

2023 

Count(ENT_
ID) where 
AI_BINC=1 

TRUE 

2023 

Enterprises 
do not use 
AI 
technologies
, because 
the costs 
seem too 
high 

Enterprises 
do not use 
AI 
technologies
, but not 
because the 
costs seem 
too high 

Enterprises 
do not use 
AI 
technologies
, because of 
a lack of 
relevant 
expertise 

Enterprises 
do not use 
AI 
technologies
, but not 
because of a 
lack of 
relevant 
expertise 

Enterprises 
do not use 
AI 
technologies
, because of 
incompatibilit
y with 
existing 
equipment, 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

E7c 

software or 
systems 

E_AI_BINCX  Enterprises 
do not use 
AI 
technologies
, but not 
because of 
incompatibilit
y with 
existing 
equipment, 
software or 
systems 

E7d 

E_AI_BDDT 

E7d 

E_AI_BDDT
X 

Enterprises 
do not use 
AI 
technologies
, because of 
difficulties 
with 
availability or 
quality of the 
necessary 
data 

Enterprises 
do not use 
AI 
technologies
, but not 
because of 
difficulties 
with 
availability or 
quality of the 
necessary 
data 

Count(ENT_
ID) where 
AI_BINC=0 

TRUE 

2023 

Count(ENT_
ID) where 
AI_BDDT=1 

TRUE 

2023 

Count(ENT_
ID) where 
AI_BDDT=0 

TRUE 

2023 

E7e 

E_AI_BCDP  Enterprises 
do not use 
AI 
technologies
, because od 

Count(ENT_
ID) where 
AI_BCDP=1 

TRUE 

2023 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

E7e 

E_AI_BCDP
X 

E7f 

E_AI_BLEG 

E7f 

E_AI_BLEG
X 

concerns 
regarding 
violation of 
data 
protection 
and privacy 

Enterprises 
do not use 
AI 
technologies
, but not 
because od 
concerns 
regarding 
violation of 
data 
protection 
and privacy 

Enterprises 
do not use 
AI 
technologies
, because of 
a lack of 
clarity about 
the legal 
consequenc
es 

Enterprises 
do not use 
AI 
technologies
, but not 
because of a 
lack of clarity 
about the 
legal 
consequenc
es 

Count(ENT_
ID) where 
AI_BCDP=0 

TRUE 

2023 

Count(ENT_
ID) where 
AI_BLEG=1 

TRUE 

2023 

Count(ENT_
ID) where 
AI_BLEG=0 

TRUE 

2023 

E7g 

E_AI_BEC 

Enterprises 
do not use 
AI 

Count(ENT_
ID) where 
AI_BEC=1 

TRUE 

2023 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

E7g 

E_AI_BECX 

E7h 

E_AI_BNU 

technologies
, because of 
ethical 
consideratio
ns 

Enterprises 
do not use 
AI 
technologies
, but not 
because of 
ethical 
consideratio
ns 

Enterprises 
do not use 
AI 
technologies
, because 
artificial 
Intelligence 
technologies 
are not 
useful for the 
enterprise 

E7h 

E_AI_BNUX  Enterprises 
do not use 
AI 
technologies
, but not 
because 
artificial 
Intelligence 
technologies 
are not 
useful for the 
enterprise 

Count(ENT_
ID) where 
AI_BEC=0 

TRUE 

2023 

Count(ENT_
ID) where 
AI_BNU=1 

TRUE 

2023 

Count(ENT_
ID) where 
AI_BNU=0 

TRUE 

2023 

A1 

P_IUSE 

Persons 
employed 
have access 
to the 
internet for 

∑ 
(EMPIUSE) 
where 
EMPIUSE<>
Blank and 

FALSE 

2023 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

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Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Derived from 
A1 (Eurostat 
computed)1 

P_IUSEX 

Derived from 
A5 

P_EMPMD2  Persons 

Derived from 
A5 

P_PMD 

3 

Data transmission 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

TRUE 

2023 

TRUE 

2022 

EMPIUSE<>
-1 

∑ (EMPL) - 
∑ 
(EMPIUSE) 
where 
EMPIUSE<>
Blank and 
EMPIUSE<>
-1 

∑ 
(EMPMD2) 
where 
EMPMD2<>
Blank and 
EMPMD2<>-
1  

∑ (EMPL) 
where 
EMPMD2>0 

TRUE 

2022 

business 
purposes 

Persons 
employed 
who do not 
have access 
to the 
internet for 
business 
purposes 

employed 
who use a 
portable 
device 
provided by 
the 
enterprise 
that allows 
internet 
connection 
via mobile 
telephone 
networks, for 
business 
purposes 

Persons 
employed by 
enterprises 
which 
provide 
portable 
devices that 
allow a 
mobile 
connection 
to the 
internet 
using mobile 
telephone 
networks, for 
business 
purposes 

296 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Derived from 
A5 

P_PMDX 

Derived from 
A5 

P_EMPMD2
X 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

TRUE 

2022 

∑ (EMPL) 
where 
EMPMD2=0 
or 
EMPMD2=Bl
ank 

TRUE 

2022 

∑ (EMPL) 
where 
EMPMD2>0 
- ∑ 
(EMPMD2) 
where 
EMPMD2<>
Blank and 
EMPMD2>0 

Persons 
employed by 
enterprises 
which do not 
provide 
portable 
devices that 
allow a 
mobile 
connection 
to the 
internet 
using mobile 
telephone 
networks, for 
business 
use 

Persons 
employed 
who don't 
use a 
portable 
device 
provided by 
the 
enterprise 
that allows 
internet 
connection 
via mobile 
telephone 
networks, for 
business 
purposes 

Derived from 
B1 

P_AWSELL 

FALSE 

2023 

Persons 
employed by 
enterprises 
which have 
received 
orders via 
websites or 
apps 

∑ (EMPL) 
where 
(AWS_COW
N=1 or 
AWS_CMP=
1) 

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3 

Data transmission 

Model 
questionnaire 
2024 

Derived from 
B7 

Variable 
codes 
(CAPITAL 
LETTERS) 

P_AXSELL 

Derived from 
B1 and B7 

P_AESELL 

Derived, see 
section 5 and 
Annex 3 

E_DI4_VLO 

Derived, see 
section 5 and 
Annex 3 

E_DI4_LO 

Derived, see 
section 5 and 
Annex 3 

E_DI4_HI 

Derived, see 
section 5 and 
Annex 3 

E_DI4_VHI 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Persons 
employed by 
enterprises 
which have 
received 
orders via 
EDI-type 
messages 

Persons 
employed by 
enterprises 
which have 
received 
orders via 
computer 
networks 

Enterprises 
with very low 
digital 
intensity 
index 

Enterprises 
with low 
digital 
intensity 
index 

Enterprises 
with high 
digital 
intensity 
index 

Enterprises 
with very 
high digital 
intensity 
index 

∑ (EMPL) 
where 
AXSELL=1 

FALSE 

2023 

FALSE 

2023 

FALSE 

2022 

FALSE 

2022 

FALSE 

2022 

FALSE 

2022 

∑ (EMPL) 
where 
(AWS_COW
N=1 or 
AWS_CMP=
1) or 
AXSELL=1 

Count 
(ENT_ID) 
where 
DI4_INDEX>
=0 and 
DI4_INDEX<
=3 

Count 
(ENT_ID) 
where 
DI4_INDEX>
=4 and 
DI4_INDEX<
=6 

Count 
(ENT_ID) 
where 
DI4_INDEX>
=7 and 
DI4_INDEX<
=9 

Count 
(ENT_ID) 
where 
DI4_INDEX>
=10 and 

298 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

E_DI4_GEL
O 

Derived, see 
section 5 and 
Annex 3 
(Eurostat 
computed)2 

Enterprises 
with at least 
low (basic) 
digital 
intensity 
index 

Derived, see 
section 5 and 
E1 

E_DI4_VLO
_AI_TANY 

Derived, see 
section 5 and 
E1 

E_DI4_LO_
AI_TANY 

Enterprises 
with very low 
digital 
intensity 
index and 
use any 
artificial 
intelligence 
technology 

Enterprises 
with low 
digital 
intensity 
index and 
use any 
artificial 

TRUE 

2022 

FALSE 

2024 

FALSE 

2024 

DI4_INDEX<
=12 

[Count(ENT
_ID) where 
DI4_INDEX>
=4 and 
DI4_INDEX<
=6] + [Count 
(ENT_ID) 
where 
DI4_INDEX>
=7 and 
DI4_INDEX<
=9] + [Count 
(ENT_ID) 
where 
DI4_INDEX>
=10 and 
DI4_INDEX<
=12] 

Count 
(ENT_ID) 
where 
DI4_INDEX>
=0 and 
DI4_INDEX<
=3 and 
(AI_TTM=1 
or AI_TSR=1 
or 
AI_TNLG=1 
or AI_TIR=1 
or AI_TML=1 
or AI_TPA=1 
or 
AI_TAR=1) 

Count 
(ENT_ID) 
where 
DI4_INDEX>
=4 and 
DI4_INDEX<
=6 and 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Derived, see 
section 5 and 
E1 

E_DI4_HI_A
I_TANY 

Derived, see 
section 5 and 
E1 

E_DI4_VHI_
AI_TANY 

FALSE 

2024 

FALSE 

2024 

intelligence 
technology 

Enterprises 
with high 
digital 
intensity 
index and 
use any 
artificial 
intelligence 
technology 

Enterprises 
with very 
high digital 
intensity 
index that 
use any 
artificial 
intelligence 
technology 

(AI_TTM=1 
or AI_TSR=1 
or 
AI_TNLG=1 
or AI_TIR=1 
or AI_TML=1 
or AI_TPA=1 
or 
AI_TAR=1) 

Count 
(ENT_ID) 
where 
DI4_INDEX>
=7 and 
DI4_INDEX<
=9 and 
(AI_TTM=1 
or AI_TSR=1 
or 
AI_TNLG=1 
or AI_TIR=1 
or AI_TML=1 
or AI_TPA=1 
or 
AI_TAR=1) 

Count 
(ENT_ID) 
where 
DI4_INDEX>
=10 and 
DI4_INDEX<
=12 and 
(AI_TTM=1 
or AI_TSR=1 
or 
AI_TNLG=1 
or AI_TIR=1 
or AI_TML=1 
or AI_TPA=1 
or 
AI_TAR=1) 

300 

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3 

Data transmission 

Model 
questionnaire 
2024 

Derived, see 
section 5 and 
Annex 3 

Variable 
codes 
(CAPITAL 
LETTERS) 

E_DI4_VLO
_SECMGE1 

Derived, see 
section 5 and 
Annex 3 

E_DI4_LO_
SECMGE1 

Derived, see 
section 5 and 
Annex 3 

E_DI4_HI_S
ECMGE1 

Derived, see 
section 5 and 
Annex 3 

E_DI4_VHI_
SECMGE1 

Derived, see 
section 5 and 
Annex 3 

E_DI4_VLO
_SECMGE3 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

FALSE 

2024 

FALSE 

2024 

FALSE 

2024 

FALSE 

2024 

FALSE 

2024 

Enterprises 
with very low 
digital 
intensity 
index, which 
use any ICT 
security 
measure 

Enterprises 
with low 
digital 
intensity 
index, which 
use any ICT 
security 
measure 

Enterprises 
with high 
digital 
intensity 
index, which 
use any ICT 
security 
measure 

Enterprises 
with very 
high digital 
intensity 
index, which 
use any ICT 
security 
measure 

Enterprises 
with very low 
digital 
intensity 
index, which 
use 3 or 
more ICT 

Count 
(ENT_ID) 
where 
SECM_CNT
>=1 and 
DI4_INDEX>
=0 and 
DI4_INDEX<
=3 

Count 
(ENT_ID) 
where 
SECM_CNT
>=1 and 
DI4_INDEX>
=4 and 
DI4_INDEX<
=6 

Count 
(ENT_ID) 
where 
SECM_CNT
>=1 and 
DI4_INDEX>
=7 and 
DI4_INDEX<
=9 

Count 
(ENT_ID) 
where 
SECM_CNT
>=1 and 
DI4_INDEX>
=10 and 
DI4_INDEX<
=12 

Count 
(ENT_ID) 
where 
SECM_CNT
>=3 and 
DI4_INDEX>
=0 and 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Derived, see 
section 5 and 
Annex 3 

E_DI4_LO_
SECMGE3 

Derived, see 
section 5 and 
Annex 3 

E_DI4_HI_S
ECMGE3 

Derived, see 
section 5 and 
Annex 3 

E_DI4_VHI_
SECMGE3 

Derived, see 
section 5 and 
Annex 3 

E_DI4_VLO
_SECMGE5 

Derived, see 
section 5 and 
Annex 3 

E_DI4_LO_
SECMGE5 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

security 
measures 

Enterprises 
with low 
digital 
intensity 
index, which 
use 3 or 
more ICT 
security 
measures 

Enterprises 
with high 
digital 
intensity 
index, which 
use 3 or 
more ICT 
security 
measures 

Enterprises 
with very 
high digital 
intensity 
index, which 
use 3 or 
more ICT 
security 
measures 

Enterprises 
with very low 
digital 
intensity 
index, which 
use 5 or 
more ICT 
security 
measures 

Enterprises 
with low 
digital 
intensity 

DI4_INDEX<
=3 

Count 
(ENT_ID) 
where 
SECM_CNT
>=3 and 
DI4_INDEX>
=4 and 
DI4_INDEX<
=6 

Count 
(ENT_ID) 
where 
SECM_CNT
>=3 and 
DI4_INDEX>
=7 and 
DI4_INDEX<
=9 

Count 
(ENT_ID) 
where 
SECM_CNT
>=3 and 
DI4_INDEX>
=10 and 
DI4_INDEX<
=12 

Count 
(ENT_ID) 
where 
SECM_CNT
>=5 and 
DI4_INDEX>
=0 and 
DI4_INDEX<
=3 

Count 
(ENT_ID) 
where 
SECM_CNT

FALSE 

2024 

FALSE 

2024 

FALSE 

2024 

FALSE 

2024 

FALSE 

2024 

302 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Derived, see 
section 5 and 
Annex 3 

E_DI4_HI_S
ECMGE5 

Derived, see 
section 5 and 
Annex 3 

E_DI4_VHI_
SECMGE5 

Derived, see 
section 5 and 
Annex 3 

E_DI4_VLO
_SECMGE7 

Derived, see 
section 5 and 
Annex 3 

E_DI4_LO_
SECMGE7 

index, which 
use 5 or 
more ICT 
security 
measures 

Enterprises 
with high 
digital 
intensity 
index, which 
use 5 or 
more ICT 
security 
measures 

Enterprises 
with very 
high digital 
intensity 
index, which 
use 5 or 
more ICT 
security 
measures 

Enterprises 
with very low 
digital 
intensity 
index, which 
use 7 or 
more ICT 
security 
measures 

Enterprises 
with low 
digital 
intensity 
index, which 
use 7 or 
more ICT 
security 
measures 

>=5 and 
DI4_INDEX>
=4 and 
DI4_INDEX<
=6 

Count 
(ENT_ID) 
where 
SECM_CNT
>=5 and 
DI4_INDEX>
=7 and 
DI4_INDEX<
=9 

Count 
(ENT_ID) 
where 
SECM_CNT
>=5 and 
DI4_INDEX>
=10 and 
DI4_INDEX<
=12 

Count 
(ENT_ID) 
where 
SECM_CNT
>=7 and 
DI4_INDEX>
=0 and 
DI4_INDEX<
=3 

Count 
(ENT_ID) 
where 
SECM_CNT
>=7 and 
DI4_INDEX>
=4 and 
DI4_INDEX<
=6 

FALSE 

2024 

FALSE 

2024 

FALSE 

2024 

FALSE 

2024 

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3 

Data transmission 

Model 
questionnaire 
2024 

Derived, see 
section 5 and 
Annex 3 

Variable 
codes 
(CAPITAL 
LETTERS) 

E_DI4_HI_S
ECMGE7 

Derived, see 
section 5 and 
Annex 3 

E_DI4_VHI_
SECMGE7 

Derived, see 
section 5 and 
Annex 3 

E_DI4_VLO
_SECAWAN
Y 

Derived, see 
section 5 and 
Annex 3 

E_DI4_LO_
SECAWANY 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

FALSE 

2024 

FALSE 

2024 

FALSE 

2024 

FALSE 

2024 

Enterprises 
with high 
digital 
intensity 
index, which 
use 7 or 
more ICT 
security 
measures 

Enterprises 
with very 
high digital 
intensity 
index, which 
use 7 or 
more ICT 
security 
measures 

Enterprises 
with very low 
digital 
intensity 
index, which 
make 
persons 
employed 
aware of 
their 
obligations 
in ICT 
security 
related 
issues 

Enterprises 
with low 
digital 
intensity 
index, which 
make 
persons 
employed 
aware of 
their 

Count 
(ENT_ID) 
where 
SECM_CNT
>=7 and 
DI4_INDEX>
=7 and 
DI4_INDEX<
=9 

Count 
(ENT_ID) 
where 
SECM_CNT
>=7 and 
DI4_INDEX>
=10 and 
DI4_INDEX<
=12 

Count 
(ENT_ID) 
where 
(SECAWVT
GI=1 or 
SECAWCTP
=1 or 
SECAWCO
NT=1) and 
DI4_INDEX>
=0 and 
DI4_INDEX<
=3 

Count 
(ENT_ID) 
where 
(SECAWVT
GI=1 or 
SECAWCTP
=1 or 
SECAWCO
NT=1) and 
DI4_INDEX>

304 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Derived, see 
section 5 and 
Annex 3 

E_DI4_HI_S
ECAWANY 

Derived, see 
section 5 and 
Annex 3 

E_DI4_VHI_
SECAWANY 

Derived, see 
section 5 and 
Annex 3 

E_DI4_VLO
_SECPOL2 

obligations 
in ICT 
security 
related 
issues 

Enterprises 
with high 
digital 
intensity 
index, which 
make 
persons 
employed 
aware of 
their 
obligations 
in ICT 
security 
related 
issues 

Enterprises 
with very 
high digital 
intensity 
index, which 
make 
persons 
employed 
aware of 
their 
obligations 
in ICT 
security 
related 
issues 

Enterprises 
with very low 
digital 
intensity 
index, in 
which have 
document(s) 
on 

=4 and 
DI4_INDEX<
=6 

Count 
(ENT_ID) 
where 
(SECAWVT
GI=1 or 
SECAWCTP
=1 or 
SECAWCO
NT=1) and 
DI4_INDEX>
=7 and 
DI4_INDEX<
=9 

Count 
(ENT_ID) 
where 
(SECAWVT
GI=1 or 
SECAWCTP
=1 or 
SECAWCO
NT=1) and 
DI4_INDEX>
=10 and 
DI4_INDEX<
=12 

Count 
(ENT_ID) 
where 
SECPOL2=1 
and 
DI4_INDEX>
=0 and 

FALSE 

2024 

FALSE 

2024 

FALSE 

2024 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Derived, see 
section 5 and 
Annex 3 

E_DI4_LO_
SECPOL2 

Derived, see 
section 5 and 
Annex 3 

E_DI4_HI_S
ECPOL2 

Derived, see 
section 5 and 
Annex 3 

E_DI4_VHI_
SECPOL2 

FALSE 

2024 

FALSE 

2024 

FALSE 

2024 

measures, 
practices or 
procedures 
on ICT 
security 

Enterprises 
with low 
digital 
intensity 
index, in 
which have 
document(s) 
on 
measures, 
practices or 
procedures 
on ICT 
security 

Enterprises 
with high 
digital 
intensity 
index, in 
which have 
document(s) 
on 
measures, 
practices or 
procedures 
on ICT 
security 

Enterprises 
with very 
high digital 
intensity 
index, in 
which have 
document(s) 
on 
measures, 
practices or 
procedures 

DI4_INDEX<
=3 

Count 
(ENT_ID) 
where 
SECPOL2=1 
and 
DI4_INDEX>
=4 and 
DI4_INDEX<
=6 

Count 
(ENT_ID) 
where 
SECPOL2=1 
and 
DI4_INDEX>
=7 and 
DI4_INDEX<
=9 

Count 
(ENT_ID) 
where 
SECPOL2=1 
and 
DI4_INDEX>
=10 and 
DI4_INDEX<
=12 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Derived, see 
section 5 and 
Annex 3 

E_DI4_VLO
_SECPREV
_CY 

Derived, see 
section 5 and 
Annex 3 

E_DI4_LO_
SECPREV_
CY 

Derived, see 
section 5 and 
Annex 3 

E_DI4_HI_S
ECPREV_C
Y 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

on ICT 
security 

Enterprises 
with very low 
digital 
intensity 
index, in 
which the 
document(s) 
on 
measures, 
practices or 
procedures 
were defined 
or most 
recently 
reviewed 
within the 
last 12 
months 

Enterprises 
with low 
digital 
intensity 
index, in 
which the 
document(s) 
on 
measures, 
practices or 
procedures 
were defined 
or most 
recently 
reviewed 
within the 
last 12 
months 

Enterprises 
with high 
digital 
intensity 
index, in 

TRUE 

2024 

TRUE 

2024 

Count 
(ENT_ID) 
where 
SECPREV=
1 and 
DI4_INDEX>
=0 and 
DI4_INDEX<
=3 

Count 
(ENT_ID) 
where 
SECPREV=
1 and 
DI4_INDEX>
=4 and 
DI4_INDEX<
=6 

TRUE 

2024 

Count 
(ENT_ID) 
where 
SECPREV=
1 and 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

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3 

Data transmission 

Model 
questionnaire 
2024 

Variable 
codes 
(CAPITAL 
LETTERS) 

Description  Scope 

Optional 

Notes / 
Source 
(if not 
previous 
year) 

Derived, see 
section 5 and 
Annex 3 

E_DI4_VHI_
SECPREV_
CY 

DI4_INDEX>
=7 and 
DI4_INDEX<
=9 

Count 
(ENT_ID) 
where 
SECPREV=
1 and 
DI4_INDEX>
=10 and 
DI4_INDEX<
=12 

which the 
document(s) 
on 
measures, 
practices or 
procedures 
were defined 
or most 
recently 
reviewed 
within the 
last 12 
months 

Enterprises 
with very 
high digital 
intensity 
index, in 
which the 
document(s) 
on 
measures, 
practices or 
procedures 
were defined 
or most 
recently 
reviewed 
within the 
last 12 
months 

TRUE 

2024 

3.2.4. Activity and employee number codes 

Once the indicators have been computed, they must be broken down by economic activity, sector 
and size using the codes below.  

It is mandatory to transmit the data according to the breakdowns in Sections 3.2.4.1 to 3.2.4.3. 
However, the breakdowns in Sections 3.2.4.4. to 3.2.4.8. are optional. 

Some breakdowns computed by Eurostat are also presented in the tables below for information. 

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Data transmission 

3.2.4.1. Economic activity breakdown 
The economic activities to be covered are the following: 

NACE Rev. 2 category 

Description 

Section C 

Section D, E 

Section F 

Section G 

Section H 

Section I 

Section J 

Section L 

Section M 

Section N 

Manufacturing 

Electricity, gas and steam, water supply, sewerage 
and waste management 

Construction 

Wholesale and retail trade; repair of motor vehicles 
and motorcycles 

Transportation and storage 

Accommodation and food service activities 

Information and communication 

Real estate activities 

Professional, scientific and technical activities 

Administrative and support activities 

Group 95.1 

Repair of computers and communication equipment 

The following activity and number_employees codes should be applied to all indicators. 

For the possible calculation of national aggregates the economic activities are to be subdivided 
into the following sections using the given codes for enterprises with 10 or more employees 
and self-employed persons: 

ACTIVITY 

NUMBER_   
EMPLOYEES 

Explanation 

C10T18 

EGE10 

C19T23 

EGE10 

C24_25 

EGE10 

C26T33 

EGE10 

10 or more employees and self-employed persons in NACE 
section C10T18 

10 or more employees and self-employed persons in NACE 
section C19T23 

10 or more employees and self-employed persons in NACE 
section C24_25 

10 or more employees and self-employed persons in NACE 
section C26T33 

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Data transmission 

ACTIVITY 

NUMBER_   
EMPLOYEES 

Explanation 

C10T33 

EGE10 

[C10T18]+[C19T23]+[C24_25]+[C26T33] 

D35TE39 

EGE10 

10 or more employees and self-employed persons in NACE 
section D35_E39 

C10TE39 
Eurostat 
computed 

EGE10 

[C10T18]+[C19T23]+[C24_25]+[C26T33]+[D35TE39]  

F41T43 

EGE10 

G45T47 

EGE10 

G47 

EGE10 

H49T53 

EGE10 

I55 

EGE10 

I55_56 

EGE10 

J58T63 

EGE10 

L68 

EGE10 

M69T75 

EGE10 

N77T82 

EGE10 

ICT_T 

EGE10 

10 or more employees and self-employed persons in NACE 
section F41T43 

10 or more employees and self-employed persons in NACE 
section G45T47 

10 or more employees and self-employed persons in NACE 
section G47 

10 or more employees and self-employed persons in NACE 
section H49T53 

10 or more employees and self-employed persons in NACE 
section I55 

10 or more employees and self-employed persons in NACE 
section I55_56 

10 or more employees and self-employed persons in NACE 
section J58T63 

10 or more employees and self-employed persons in NACE 
section L68 

10 or more employees and self-employed persons in NACE 
section M69T75 

10 or more employees and self-employed persons in NACE 
section N77T82 (including N79) 

10 or more employees and self-employed persons in NACE 
groups  
26.1-26.4, 26.8, 46.5, 58.2, 61, 62, 63.1, 95.1 

For the possible calculation of European aggregates the economic activities are to be subdivided 
into the following sections using the given codes for enterprises with 10 or more employees 
and self-employed persons. 

ACTIVITY 

NUMBER_ 
EMPLOYEES 

Explanation 

C10T12 

EGE10 

10 or more employees and self-employed persons in NACE 
section C10T12 

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Data transmission 

ACTIVITY 

NUMBER_ 
EMPLOYEES 

Explanation 

C13T15 

EGE10 

C16T18 

EGE10 

C19 

C20 

C21 

EGE10 

EGE10 

EGE10 

10 or more employees and self-employed persons in NACE 
section C13T15 

10 or more employees and self-employed persons in NACE 
section C16T18 

10 or more employees and self-employed persons in NACE 
section C19 

10 or more employees and self-employed persons in NACE 
section C20 

10 or more employees and self-employed persons in NACE 
section C21 

C22_23 

EGE10 

10 or more employees and self-employed persons in NACE 
section C22_23 

C26 

C27 

C28 

C27_28 
Eurostat 
computed 

EGE10 

EGE10 

EGE10 

EGE10 

C29_30 

EGE10 

C31T33 

EGE10 

D35 

EGE10 

E36T39 

EGE10 

G45 

G46 

EGE10 

EGE10 

J58T60 

EGE10 

J61 

EGE10 

J62_63 

EGE10 

10 or more employees and self-employed persons in NACE 
section C26 

10 or more employees and self-employed persons in NACE 
section C27 

10 or more employees and self-employed persons in NACE 
section C28 

10 or more employees and self-employed persons in NACE 
section C27_28 

10 or more employees and self-employed persons in NACE 
section C29_30 

10 or more employees and self-employed persons in NACE 
section C31T33 

10 or more employees and self-employed persons in NACE 
section D35 

10 or more employees and self-employed persons in NACE 
section E36T39 

10 or more employees and self-employed persons in NACE 
section G45 

10 or more employees and self-employed persons in NACE 
section G46 

10 or more employees and self-employed persons in NACE 
section J58T60 

10 or more employees and self-employed persons in NACE 
section J61 

10 or more employees and self-employed persons in NACE 
section J62_63 

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Data transmission 

ACTIVITY 

NUMBER_ 
EMPLOYEES 

Explanation 

M69T71 

EGE10 

M72 

EGE10 

M73T75 

EGE10 

10 or more employees and self-employed persons in NACE 
section M69T71 

10 or more employees and self-employed persons in NACE 
section M72 

10 or more employees and self-employed persons in NACE 
section M73T75 

N77T82X7
9 

EGE10 

10 or more employees and self-employed persons in NACE 
section N77T82_X79 

N79 

EGE10 

S951 

EGE10 

10 or more employees and self-employed persons in NACE 
section N79 

10 or more employees and self-employed persons in NACE 
group S951 

3.2.4.2. Enterprise size class 
These activity and number_employees codes should be applied to all variables. 

ACTIVITY 

NUMBER_ 
EMPLOYEES 

Explanation 

10 or more employees and self-employed persons in 
NACE sections 
[C10TE39]+[F41T43]+[G45T47]+[H49T53]+[I55_56]+[J58T
63]+[L68]+ [M69T75]+[N77T82]+[S951] 

10 - 49 employees and self-employed persons (small 
enterprises) in given NACE sections 

50 - 249 employees and self-employed persons (medium 
enterprises) in given NACE sections 

250 or more employees and self-employed persons (large 
enterprises) in given NACE sections 

E10T249 

C10TS951XK [E10T49] + C10TS951XK [E50T249] 

C10TS951X
K 

EGE10 

E10T49 

E50T249 

EGE250 

C10TS951X
K 

C10TS951X
K 

C10TS951X
K 

C10TS951X
K 
Eurostat 
computed 

3.2.4.3. Sector enterprise size class 
These activities and number_employees codes should be applied to all variables: 

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Data transmission 

ACTIVITY 

NUMBER_ 
EMPLOYEES 

Explanation 

C10TF43 

EGE10 

G45TS951XK  EGE10 

C10TF43 

E10T49 

G45TS951XK  E10T49 

C10TF43 

E50T249 

G45TS951XK  E50T249 

C10TF43 

EGE250 

G45TS951XK  EGE250 

C10TF43  
Eurostat 
computed  

G45TS951XK 
Eurostat 
computed 

E10T249 

E10T249 

10 or more employees and self-employed persons in 
NACE sections C to F 

10 or more employees and self-employed persons in 
NACE sections G to N, S951, except K 

10 – 49 employees and self-employed persons (small 
enterprises) in NACE sections C to F 

10 – 49 employees and self-employed persons (small 
enterprises) in NACE sections G to N, S951, except K 

50 – 249 employees and self-employed persons (medium 
enterprises) in NACE sections C to F 

50 – 249 employees and self-employed persons (medium 
enterprises) in NACE sections G to N, S951, except K 

250 or more employees and self-employed persons (large 
enterprises) in NACE sections C to F 

250 or more employees and self-employed persons (large 
enterprises) in NACE sections G to N, S951, except K 

10 – 249 employees and self-employed persons (small 
and medium enterprises) in NACE sections C to F  
C10TF43 [E10T49] + C10TF43 [E50T249] 

10 – 249 employees and self-employed persons (small 
and medium enterprises) in NACE sections G to N, S951, 
except K  
G45TS951XK [E10T49]+ G45TS951XK [E50T249] 

3.2.4.4. Economic activity breakdowns for micro-enterprises 
The results of the statistics compiled for microenterprises are to be broken down in accordance 
with the NACE Rev. 2. sections, divisions and group for the following size class breakdowns: 0-9 
employees and self-employed persons, 2-9 and 0-1 employees and self-employed persons, or 
only 2-9 employees and self-employed persons.  

The economic activities are to be subdivided into the following sections using the codes for 
enterprises with 0-9 employees and self-employed persons.  

These activities and number_employees codes should be used for all variables. 

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3 

Data transmission 

ACTIVITY 

NUMBER_ 
EMPLOYEES 

Explanation 

C10TS951XK  E0T9 

C10T18 

E0T9 

C19T23 

E0T9 

C24_25 

E0T9 

C26T33 

E0T9 

0-9 employees and self-employed persons in NACE 
sections 10-33, 35-39, 41-43, 45-47, 49-53, 55-56, 58-63, 
68-75, 77-82, 95.1  

0-9 employees and self-employed persons in NACE section 
C10T18 

0-9 employees and self-employed persons in NACE section 
C19T23 

0-9 employees and self-employed persons in NACE section 
C24_25 

0-9 employees and self-employed persons in NACE section 
C26T33 

C10T33 

E0T9 

[C10T18]+[C19T23]+[C24_25]+[C26T33] 

D35TE39 

E0T9 

0-9 employees and self-employed persons in NACE section 
D35TE39 

C10TE39 
Eurostat 
computed 

E0T9 

[C10T18]+[ C19T23]+[C24_25]+[C26T33]+[D35TE39] 

F41T43 

E0T9 

G45T47 

E0T9 

G47 

E0T9 

H49T53 

E0T9 

I55 

E0T9 

I55_56 

E0T9 

J58T63 

E0T9 

L68 

E0T9 

L68TM75 

E0T9 

M69T75 

E0T9 

N77T82 

E0T9 

0-9 employees and self-employed persons in NACE section 
F41T43 

0-9 employees and self-employed persons in NACE section 
G45T47 

0-9 employees and self-employed persons in NACE section 
G47 

0-9 employees and self-employed persons in NACE section 
H49T53 

0-9 employees and self-employed persons in NACE section 
I55 

0-9 employees and self-employed persons in NACE section 
I55_56 

0-9 employees and self-employed persons in NACE section 
J58T63 

0-9 employees and self-employed persons in NACE section 
L68 

0-9 employees and self-employed persons in NACE section 
L68TM75 

0-9 employees and self-employed persons in NACE section 
M69T75 

0-9 employees and self-employed persons in NACE section 
N77T82 (including N79) 

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Data transmission 

ACTIVITY 

NUMBER_ 
EMPLOYEES 

Explanation 

S951 

E0T9 

ICT_T 

E0T9 

0-9 employees and self-employed persons in NACE group 
S951 

0-9 employees and self-employed persons in NACE 26.1-
26.4, 26.8, 46.5, 58.2, 61, 62, 63.1, 95.1 

3.2.4.5. Sector enterprise size classes for enterprises with 0-9 
employees and self-employed persons 
These activities and number_employees codes should be used for all variables. The breakdowns 
for enterprises with 0-9 employees and self-employed persons are for NSIs providing the 
breakdown. 

ACTIVITY 

NUMBER_ 
EMPLOYEES 

Explanation 

C10TF43 

E0T9 

G45TS951XK  E0T9 

0-9 employees and self-employed persons (very small 
enterprises) in NACE sections C to F 

0-9 employees and self-employed persons (very small 
enterprises) in NACE sections G to N, S951, except K 

3.2.4.6. Optional sector breakdowns by size class for enter-
prises with 0-1 and 2-9 employees and self-employed persons 
These activity and number_employees codes should be used for all variables.  

The breakdowns for enterprises with 0-1 and 2-9 employees and self-employed persons are for 
NSIs providing the breakdown. 

ACTIVITY 

NUMBER_ 
EMPLOYEES 

Explanation 

C10TS951XK 

E0T1 

C10TS951XK 

E2T9 

C10TF43 

E0T1 

C10TF43 

E2T9 

0-1 employees and self-employed persons (micro-
enterprises) in given NACE sections 

2-9 employees and self-employed persons (mini-
enterprises) in given NACE sections 

0-1 employees and self-employed persons (micro-
enterprises) in NACE sections C to F 

2-9 employees and self-employed persons (mini-
enterprises) in NACE sections C to F 

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Data transmission 

G45TS951XK 

E0T1 

G45TS951XK 

E2T9 

0-1 employees and self-employed persons (micro-
enterprises) in NACE sections G to N, S951, except K 

2-9 employees and self-employed persons (mini-
enterprises) in NACE sections G to N, S951, except K 

3.2.4.7. Optional breakdowns by economic activity for enter-
prises with 0-1 employees and self-employed persons 
These activity and number_employees codes should be used for all variables.  

The breakdowns for enterprises with 0-1 employees and self-employed persons are for NSIs 
providing the breakdown. 

ACTIVITY 

NUMBER_ 
EMPLOYEES 

Explanation 

C10TS951XK  E0T1 

C10T18 

E0T1 

C19T23 

E0T1 

C24_25 

E0T1 

C26T33 

E0T1 

0-1 employees and self-employed persons in NACE 
sections 10-33, 35-39, 41-43, 45-47, 49-53, 55-56, 58-63, 
68-75, 77-82, 95.1  

0-1 employees and self-employed persons in NACE section 
C10T18 

0-1 employees and self-employed persons in NACE section 
C19T23 

0-1 employees and self-employed persons in NACE section 
C24_25 

0-1 employees and self-employed persons in NACE section 
C26T33 

C10T33 

E0T1 

[C10T18]+[C19T23]+[C24_25]+[C26T33] 

D35TE39 

E0T1 

0-1 employees and self-employed persons in NACE section 
D35TE39 

C10TE39 
Eurostat 
computed 

E0T1 

[C10T18]+[C19T23]+[C24_25]+[C26T33]+[D35TE39] 

F41T43 

E0T1 

G45T47 

E0T1 

G47 

E0T1 

H49T53 

E0T1 

I55 

E0T1 

0-1 employees and self-employed persons in NACE section 
F41T43 

0-1 employees and self-employed persons in NACE section 
G45T47 

0-1 employees and self-employed persons in NACE section 
G47 

0-1 employees and self-employed persons in NACE section 
H49T53 

0-1 employees and self-employed persons in NACE section 
I55 

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Data transmission 

ACTIVITY 

NUMBER_ 
EMPLOYEES 

Explanation 

I55_56 

E0T1 

J58T63 

E0T1 

L68 

E0T1 

L68TM75 

E0T1 

M69T75 

E0T1 

N77T82 

E0T1 

S951 

E0T1 

ICT_T 

E0T1 

0-1 employees and self-employed persons in NACE section 
I55_56 

0-1 employees and self-employed persons in NACE section 
J58T63 

0-1 employees and self-employed persons in NACE section 
L68 

0-1 employees and self-employed persons in NACE section 
L68TM75 

0-1 employees and self-employed persons in NACE section 
M69T75 

0-1 employees and self-employed persons in NACE section 
N77T82 (including N79) 

0-1 employees and self-employed persons in NACE group 
S951 

0-1 employees and self-employed persons in NACE 26.1-
26.4, 26.8, 46.5, 58.2, 61, 62, 63.1, 95.1 

3.2.4.8. Optional breakdowns by economic activity for enter-
prises with 2-9 employees and self-employed persons 
These activity and number_employees codes should be used for all variables.  

The breakdowns for enterprises with 2-9 employees and self-employed persons are for NSIs 
providing the breakdown. 

ACTIVITY 

NUMBER_ 
EMPLOYEES 

Explanation 

C10TS951XK  E2T9 

C10T18 

E2T9 

C19T23 

E2T9 

C24_25 

E2T9 

C26T33 

E2T9 

2-9 employees and self-employed persons in NACE 
sections 10-33, 35-39, 41-43, 45-47, 49-53, 55-56, 58-63, 
68-75, 77-82, 95.1  

2-9 employees and self-employed persons in NACE 
section C10T18 

2-9 employees and self-employed persons in NACE 
section C19T23 

2-9 employees and self-employed persons in NACE 
section C24_25 

2-9 employees and self-employed persons in NACE 
section C26T33 

C10T33 

E2T9 

[C10T18]+[C19T23]+[C24_25]+[C26T33] 

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Data transmission 

ACTIVITY 

NUMBER_ 
EMPLOYEES 

Explanation 

D35TE39 

E2T9 

2-9 employees and self-employed persons in NACE 
section D35TE39 

C10TE39 
Eurostat 
computed 

E2T9 

[C10T18]+[C19T23]+[C24_25]+[C26T33]+[D35TE39] 

F41T43 

E2T9 

G45T47 

E2T9 

G47 

E2T9 

H49T53 

E2T9 

I55 

E2T9 

I55_56 

E2T9 

J58T63 

E2T9 

L68 

E2T9 

L68TM75 

E2T9 

M69T75 

E2T9 

N77T82 

E2T9 

S951 

E2T9 

ICT_T 

E2T9 

2-9 employees and self-employed persons in NACE 
section F41T43 

2-9 employees and self-employed persons in NACE 
section G45T47 

2-9 employees and self-employed persons in NACE 
section G47 

2-9 employees and self-employed persons in NACE 
section H49T53 

2-9 employees and self-employed persons in NACE 
section I55 

2-9 employees and self-employed persons in NACE 
section I55_56 

2-9 employees and self-employed persons in NACE 
section J58T63 

2-9 employees and self-employed persons in NACE 
section L68 

2-9 employees and self-employed persons in NACE 
section L68TM75 

2-9 employees and self-employed persons in NACE 
section M69T75 

2-9 employees and self-employed persons in NACE 
section N77T82 (including N79) 

2-9 employees and self-employed persons in NACE group 
S951 

2-9 employees and self-employed persons in NACE 26.1-
26.4, 26.8, 46.5, 58.2, 61, 62, 63.1, 95.1 

3.2.5. Regional data 

Regional data is collected for a selection of indicators and activities. The data should refer to 
enterprises with 10+ employees.  

Indicators for regional data.  

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The indicators which should be provided relate to questions A1, A3, B2, D1, F1 (first priority) as 
well as C1, C3 and E1 (second priority).  

Enterprise size and Economic activities for regional data.  

The economic activities to be covered are the following: 

NACE category 

Explanation 

Section C 

Manufacturing 

Section D, E 

Section F 

Section G 

Section H 

Section I 

Section J 

Section L 

Electricity, gas and steam, water supply, sewerage 
and waste management 

Construction 

Wholesale and retail trade; repair of motor vehicles 
and motorcycles 

Transportation and storage 

Accommodation and food service activities 

Information and communication 

Real estate activities 

Section M, N 

Professional, scientific, technical, administrative and support 
activities 

The following activity and number_employees codes should be used: 

ACTIVITY 

NUMBER_   
EMPLOYEES 

Explanation 

C10T33 

EGE10 

D35TE39 

EGE10 

10 or more employees and self-employed persons in 
NACE section C10T33 

10 or more employees and self-employed persons in 
NACE section D35TE39 

C10TE39 
Eurostat 
computed 

EGE10 

[C10T33]+[ D35TE39]  

F41T43 

EGE10 

G45T47 

EGE10 

H49T53 

EGE10 

I55_56 

EGE10 

10 or more employees and self-employed persons in 
NACE section F41T43 

10 or more employees and self-employed persons in 
NACE section G45T47 

10 or more employees and self-employed persons in 
NACE section H49T53 

10 or more employees and self-employed persons in 
NACE section I55_56 

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ACTIVITY 

NUMBER_   
EMPLOYEES 

Explanation 

J58T63 

EGE10 

L68TM75 

EGE10 

N77T82 

EGE10 

C10TS951XK 

EGE10 

C10TF43 

EGE10 

G45TS951XK 

EGE10 

10 or more employees and self-employed persons in 
NACE section J58T63 

10 or more employees and self-employed persons in 
NACE section L68TM75 

10 or more employees and self-employed persons in 
NACE section N77T82 (including N79) 

10 or more employees and self-employed persons in 
NACE sections 
[C10TE39]+[F41T43]+[G45T47]+[H49T53]+[I55_56]+ 
[J58T63]+[L68]+ [M69T75]+[N77T82]+[S951] 

10 or more employees and self-employed persons in 
NACE sections C to F 

10 or more employees and self-employed persons in 
NACE sections G to N, S951, except K 

For regional data, the column REGIONAL in the data file in SDMX format should not contain _Z, 
but the respective NUTS35 1 and NUTS 2 region codes (see 12).  

For countries providing regional data, where only one NUTS 1 region representing the national 
total exists, the variables and breakdowns requested for regional data should be included twice in 
the data set: first with _Z in the column REGIONAL and a second time with the respective NUTS 
1 code in the column REGIONAL. 

35 NUTS stands for Nomenclature Of Territorial Units For Statistics. 

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Region codes 

Region 

Region Name 

Region 

Region Name 

AT1 

Ostösterreich 

AT11 

Burgenland (AT) 

AT12 

Niederösterreich 

AT13 

Wien 

AT2 

Südösterreich 

AT21 

Kärnten 

AT22 

Steiermark 

AT3 

Westösterreich 

AT31 

Oberösterreich 

AT32 

Salzburg 

AT33 

Tirol 

AT34 

Vorarlberg 

BE1 

BE10 

Région de Bruxelles-
Capitale/Brussels 
Hoofdstedelijk Gewest 

Région de Bruxelles-
Capitale/Brussels 
Hoofdstedelijk Gewest 

BE2 

Vlaams Gewest 

BE21 

Prov. Antwerpen 

BE22 

Prov. Limburg (BE) 

BE23 

Prov. Oost-Vlaanderen 

BE24 

Prov. Vlaams-Brabant 

BE25 

Prov. West-Vlaanderen 

BE3 

Région wallonne 

BE31 

Prov. Brabant wallon 

BE32 

Prov. Hainaut 

BE33 

Prov. Liège 

BE34 

Prov. Luxembourg (BE) 

BE35 

Prov. Namur 

BG3 

Severna i yugoiztochna 
Bulgaria 

BG31 

Severozapaden 

BG32 

Severen tsentralen 

BG33 

Severoiztochen 

BG34 

Yugoiztochen 

BG4 

Yugozapadna i yuzhna 
tsentralna Bulgaria 

BG41 

Yugozapaden 

BG42 

Yuzhen tsentralen 

CY0 

Kypros 

CY00 

Kypros 

CZ0 

Cesko 

CZ01 

Praha 

CZ02 

Strední Cechy 

CZ03 

Jihozápad 

CZ04 

Severozápad 

CZ05 

Severovýchod 

CZ06 

Jihovýchod 

CZ07 

Strední Morava 

CZ08 

Moravskoslezsko 

DE1 

Baden-Württemberg 

DE11 

Stuttgart 

DE12 

Karlsruhe 

DE13 

Freiburg 

DE14 

Tübingen 

DE2 

Bayern 

DE21 

Oberbayern 

DE22 

Niederbayern 

DE23 

Oberpfalz 

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Region 

Region Name 

Region 

Region Name 

DE24 

Oberfranken 

DE25 

Mittelfranken 

DE26 

Unterfranken 

DE27 

Schwaben 

DE3 

Berlin 

DE30 

Berlin 

DE4 

Brandenburg 

DE40 

Brandenburg 

DE5 

Bremen 

DE50 

Bremen 

DE6 

Hamburg 

DE60 

Hamburg 

DE7 

Hessen 

DE71 

Darmstadt 

DE72 

Gießen 

DE73 

Kassel 

DE8 

DE80 

Mecklenburg-
Vorpommern 

Mecklenburg-
Vorpommern 

DE9 

Niedersachsen 

DE91 

Braunschweig 

DE92 

Hannover 

DE93 

Lüneburg 

DE94 

Weser-Ems 

DEA 

Nordrhein-Westfalen 

DEA1 

Düsseldorf 

DEA2 

Köln 

DEA3 

Münster 

DEA4 

Detmold 

DEA5 

Arnsberg 

DEB 

Rheinland-Pfalz 

DEB1 

Koblenz 

DEB2 

Trier 

DEB3 

Rheinhessen-Pfalz 

DEC 

Saarland 

DEC0 

Saarland 

DED 

Sachsen 

DED2 

Dresden 

DED4 

Chemnitz 

DED5 

Leipzig 

DEE 

Sachsen-Anhalt 

DEE0 

Sachsen-Anhalt 

DEF 

Schleswig-Holstein 

DEF0 

Schleswig-Holstein 

DEG 

Thüringen 

DEG0 

Thüringen 

DK0 

Danmark 

DK01 

Hovedstaden 

DK02 

Sjælland 

DK03 

Syddanmark 

DK04 

Midtjylland 

DK05 

Nordjylland 

EE0 

Eesti 

EE00 

Eesti 

EL3 

Attiki 

EL30 

Attiki 

EL4 

Nisia Aigaiou, Kriti 

EL41 

Voreio Aigaio 

EL42 

Notio Aigaio 

EL43 

Kriti 

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Region 

Region Name 

Region 

Region Name 

EL5 

Voreia Ellada 

EL51 

Anatoliki Makedonia, 
Thraki 

EL52 

Kentriki Makedonia 

EL53 

Dytiki Makedonia 

EL54 

Ipeiros 

EL6 

Kentriki Ellada 

EL61 

Thessalia 

EL62 

Ionia Nisia 

EL63 

Dytiki Ellada 

EL64 

Sterea Ellada 

EL65 

Peloponnisos 

ES1 

Noroeste (ES) 

ES11 

Galicia 

ES12 

Principado de Asturias 

ES13 

Cantabria 

ES2 

Noreste (ES) 

ES21 

País Vasco 

ES22 

Comunidad Foral de 
Navarra 

ES23 

La Rioja 

ES24 

Aragón 

ES3 

Comunidad de Madrid 

ES30 

Comunidad de Madrid 

ES4 

Centro (ES) 

ES41 

Castilla y León 

ES42 

Castilla-la Mancha 

ES43 

Extremadura 

ES5 

Este (ES) 

ES51 

Cataluña 

ES52 

Comunitat Valenciana 

ES53 

Illes Balears 

ES6 

Sur (ES) 

ES61 

Andalucía 

ES62 

Región de Murcia 

ES63 

Ciudad de Ceuta 

ES64 

Ciudad de Melilla 

ES7 

Canarias 

ES70 

Canarias 

FI1 

FI19 

FI1B 

FI1C 

FI1D 

FI2 

FI20 

FR1 

Manner-Suomi 

Länsi-Suomi 

Helsinki-Uusimaa 

Etelä-Suomi 

Pohjois- ja Itä-Suomi 

Åland 

Åland 

Île de France 

FR10 

Île de France 

FRB 

Centre - Val de Loire 

FRB0 

Centre - Val de Loire 

FRC 

Bourgogne - Franche-
Comté 

FRC1 

Bourgogne 

FRC2 

Franche-Comté 

FRD 

Normandie 

FRD1 

Basse-Normandie 

FRD2 

Haute-Normandie 

FRE 

Hauts-de-France 

FRE1 

Nord-Pas-de-Calais 

FRE2 

Picardie 

FRF 

Grand Est 

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Region 

Region Name 

Region 

Region Name 

FRF1 

Alsace 

HR02 

Panonska Hrvatska 

FRF2 

Champagne-Ardenne 

HR03 

Jadranska Hrvatska 

FRF3 

Lorraine 

FRG 

Pays-de-la-Loire 

FRG0 

Pays-de-la-Loire 

FRH 

Bretagne 

FRH0 

Bretagne 

FRI 

FRI1 

FRI2 

FRI3 

FRJ 

Nouvelle-Aquitaine 

Aquitaine 

Limousin 

Poitou-Charentes 

Occitanie 

HR05 

Grad Zagreb 

HR06 

Sjeverna Hrvatska 

HU1 

Közép-Magyarország 

HU11 

Budapest 

HU12 

Pest 

HU2 

Dunántúl 

HU21 

Közép-Dunántúl 

HU22 

Nyugat-Dunántúl 

HU23 

Dél-Dunántúl 

HU3 

Alföld és Észak 

FRJ1 

Languedoc-Roussillon 

HU31 

Észak-Magyarország 

FRJ2 

Midi-Pyrénées 

HU32 

Észak-Alföld 

FRK 

Auvergne - Rhône-Alpes 

HU33 

Dél-Alföld 

FRK1 

Auvergne 

FRK2 

Rhône-Alpes 

FRL 

FRL0 

Provence-Alpes-Côte 
d'Azur 

Provence-Alpes-Côte 
d'Azur 

FRM 

Corse 

FRM0 

Corse 

FRY 

RUP FR - Régions 
ultrapériphériques 
françaises 

FRY1 

Guadeloupe 

FRY2 

Martinique 

FRY3 

Guyane 

FRY4 

La Réunion 

FRY5 

Mayotte 

HR0 

Hrvatska 

IE0 

IE04 

IE05 

IE06 

IS0 

Éire/Ireland 

Northern and Western 

Southern 

Eastern and Midland 

Ísland 

IS00 

Ísland 

ITC 

Nord-Ovest 

ITC1 

Piemonte 

ITC2 

ITC3 

ITC4 

ITF 

ITF1 

ITF2 

Valle d'Aosta/Vallée 
d'Aoste 

Liguria 

Lombardia 

Sud 

Abruzzo 

Molise 

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Region 

Region Name 

Region 

Region Name 

ITF3 

ITF4 

ITF5 

ITF6 

ITG 

Campania 

Puglia 

Basilicata 

Calabria 

Isole 

ITG1 

Sicilia 

ITG2 

Sardegna 

ITH 

Nord-Est 

ITH1 

ITH2 

ITH3 

ITH4 

ITH5 

ITI 

ITI1 

ITI2 

ITI3 

ITI4 

LT0 

Provincia Autonoma di 
Bolzano/Bozen 

Provincia Autonoma di 
Trento 

Veneto 

Friuli-Venezia Giulia 

Emilia-Romagna 

Centro (IT) 

Toscana 

Umbria 

Marche 

Lazio 

Lietuva 

NL11 

Groningen 

NL12 

Friesland (NL) 

NL13 

Drenthe 

NL2 

Oost-Nederland 

NL21 

Overijssel 

NL22 

Gelderland 

NL23 

Flevoland 

NL3 

West-Nederland 

NL33 

Zuid-Holland 

NL34 

Zeeland 

NL35 

Utrecht 

NL36 

Zuid-Holland 

NL4 

Zuid-Nederland 

NL41 

Noord-Brabant 

NL42 

Limburg (NL) 

NO0 

Norge 

NO02 

Innlandet 

NO06 

Trøndelag/Trööndelage 

NO07 

Nord-Norge 

NO08 

Oslo og Viken 

LT01 

Sostines regionas 

NO09 

Agder og Sør-Østlandet 

LT02 

Vidurio ir vakaru Lietuvos 
regionas 

LU0 

Luxembourg 

LU00 

Luxembourg 

LV0 

Latvija 

LV00 

Latvija 

MT0 

Malta 

MT00 

Malta 

NO0A 

Vestlandet 

NO0B 

Svalbard and Jan Mayen 

PL2 

Makroregion Poludniowy 

PL21 

Malopolskie 

PL22 

Slaskie 

PL4 

Makroregion Pólnocno-
Zachodni 

PL41 

Wielkopolskie 

NL1 

Noord-Nederland 

PL42 

Zachodniopomorskie 

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Region 

Region Name 

Region 

Region Name 

PL43 

Lubuskie 

PL5 

Makroregion 
Poludniowo-Zachodni 

PL51 

Dolnoslaskie 

PL52 

Opolskie 

PL6 

Makroregion Pólnocny 

PL61 

Kujawsko-Pomorskie 

PL62 

Warminsko-Mazurskie 

PL63 

Pomorskie 

PL7 

Makroregion Centralny 

PL71 

Lódzkie 

PL72 

Swietokrzyskie 

PL8 

Makroregion Wschodni 

PL81 

Lubelskie 

PL82 

Podkarpackie 

PL84 

Podlaskie 

PL9 

Makroregion 
Województwo 
Mazowieckie 

PL91 

Warszawski stoleczny 

PL92 

Mazowiecki regionalny 

PT1 

Continente 

PT11 

Norte 

PT15 

Algarve 

PT17 

Área Metropolitana de 
Lisboa 

PT18 

Alentejo 

PT19 

Centro (PT) 

PT2 

PT20 

Região Autónoma dos 
Açores (PT) 

Região Autónoma dos 
Açores (PT) 

PT3 

PT30 

Região Autónoma da 
Madeira (PT) 

Região Autónoma da 
Madeira (PT) 

RO1 

Macroregiunea unu 

RO11 

Nord-Vest 

RO12 

Centru 

RO2 

Macroregiunea doi 

RO21 

Nord-Est 

RO22 

Sud-Est 

RO3 

Macroregiunea trei 

RO31 

Sud - Muntenia 

RO32 

Bucuresti - Ilfov 

RO4 

Macroregiunea patru 

RO41 

Sud-Vest Oltenia 

RO42 

Vest 

SE1 

Östra Sverige 

SE11 

Stockholm 

SE12 

Östra Mellansverige 

SE2 

Södra Sverige 

SE21 

Småland med öarna 

SE22 

Sydsverige 

SE23 

Västsverige 

SE3 

Norra Sverige 

SE31 

Norra Mellansverige 

SE32 

Mellersta Norrland 

SE33 

Övre Norrland 

SI0 

SI03 

SI04 

SK0 

Slovenija 

Vzhodna Slovenija 

Zahodna Slovenija 

Slovensko 

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Region 

Region Name 

Region 

Region Name 

SK01 

Bratislavský kraj 

SK03 

Stredné Slovensko 

SK02 

Západné Slovensko 

SK04 

Východné Slovensko 

3.3. Transmission format for the ICT usage and e-
commerce in enterprises survey 
Once microdata have been collected and aggregates computed, the data can be transmitted to 
Eurostat.  

The purpose of this section is to describe how the ICT enterprise survey data should be compiled 
and sent to Eurostat. The 2024 results must be transmitted to Eurostat as a single txt (tab-
delimited) file following the Statistical Data and Metadata eXchange (SDMX) CSV format.  

Each data transmission is assumed to be a full transmission. New transmissions will replace 
previous transmissions.  

For the content validation tool and Eurostat to be able to process the data, they must be 
transmitted in the format described below. Extra columns or codes will not be recognised. 
Comments fields should be used to indicate clearly any deviations Eurostat should be aware of – 
this is critical for ensuring the fullest comparability of the data for Eurostat’s users. Non-
comparable data will not be published.  

The examples given in this section are laid out as they are for ease of reading only. 

3.3.1. SDMX  

SDMX is an international initiative aimed at standardising and modernising (‘industrialising’) the 
mechanisms and processes for exchanging statistical data and metadata among international 
organisations and their member countries.  

SDMX is sponsored by seven international organisations including the Bank for International 
Settlements (BIS), the European Central Bank (ECB), Eurostat (Statistical Office of the European 
Union), the International Monetary Fund (IMF), the Organisation for Economic Co-operation and 
Development (OECD), the United Nations Statistical Division (UNSD), and the World Bank.  

SDMX has three key components.  

•  A model — the information model — to describe data and metadata  

The SDMX information model describes statistics in a standard way. It identifies objects and their 
relationships. A description is necessary to represent data, in order to make them meaningful. 
The descriptors are modelled according to whether they are dimensions (identifying and 
describing data), attributes (providing additional information about the data) or measures 
(representing the phenomenon to be measured). The structural descriptors are brought together 

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in the Data Structure Definition (DSD). This identifies the dimensions, attributes and measures of 
a data set, associating them with common code lists and concepts. The DSD provides all the 
information necessary to fully describe the data transmitted36.  

• A standard for automated communication called Content-oriented Guidelines  

The Content-oriented Guidelines (COGs) are a set of recommendations within the scope of the 
SDMX standard that are designed to maximise interoperability. They are intended to be 
applicable to all statistical domains. The COGs focus on harmonising specific concepts and 
terminology that are common to a large number of statistical domains. Such harmonisation helps 
achieving an even more efficient exchange of comparable data and metadata, and builds on 
existing experience from implementation. 

The COGs cover cross-domain concepts, code lists, subject-matter domains, a glossary, and 
implementation-specific guidelines37.  

•  An IT architecture and set of tools for data and metadata exchange  

To support more automated and efficient data and metadata exchange, standard tools and an IT 
architecture are required. In practice, this means that SDMX promotes the use of standard 
SDMX-compliant formats. It provides the necessary tools to support the Model, create 
SDMXcompliant files, store SDMX-related artefacts, map and transcode from existing databases, 
and validate the structure (and in future the content) of data files. It also provides the necessary 
architecture to connect IT systems to the SDMX world, enabling data to be shared more easily38.  

The SDMX standard thus provides essential support for statisticians, in that it: maximises the 
amount of information made available to users; enables the process to be automated; and allows 
web-service queries. 

3.3.2. SDMX format for the ICT usage and e-commerce 
in enterprises survey 

The first line contains the column headers. They must be in uppercase characters, and separated 
by a tab.  

Column 1 - DATAFLOW  

The dataflow column should contain ESTAT:INFOSOC_ ENT_A(1.0). This value should be 
repeated for each line of the file.  

Column 2 - FREQ  

The freq column should contain A, as the survey is annual.  

36 More details on the SDMX information model can be found in the Technical specifications. 
37 For more information see the recommended practices offered by the SDMX Content-Oriented Guidelines. 
38 For more information see SDMX explained - Eurostat (europa.eu). 

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Column 3 – TIME_PERIOD  

Put the reference year of the survey, with four digits, e.g. 2024.  

Column 4 – REF_AREA  

Put here your country code. This code should follow the two alpha ISO code.  

Codes to be used for the Member States, Candidate Countries and other participating countries 
are as follows: BE, BG, CZ, DK, DE, EE, IE, EL, ES, FR, HR, IT, CY, LV, LT, LU, HU, MT, NL, 
AT, PL, PT, RO, SI, SK, FI, SE, IS, NO, ME, MK, AL, RS, TR, BA.  

Column 5 – TABLE_IDENTIFIER  

Please use the following code  

ENT2 – Enterprise/ecommerce ICT survey  

Column 6 – EMBARGO_TIME  

YYYY-MM-DD - example: 2024-10-31. This would mean that data must not be published before 
the 31st October 2024. Please leave empty if there is no embargo date. This value should be 
repeated for each line of the file.  

Column 7 – INDICATOR – The variables, their codes and descriptions  

Variable codes are the codes, which Eurostat uses for its database and publications. Data should 
be delivered to Eurostat using these codes in upper case. There is no given order necessary for 
the variables. If a variable is not collected, it should be removed from the dataset or the value 
entry should be left blank. In this case, please add a note of explanation in a separate file. 

Scope of the observation variables by module  

This shows the observation variables. Their exact scope has been defined based on section 3 
"Codification of microdata" where the filters to be applied are given.  

Column 8 – UNIT_MEASURE and column 18 – UNIT_MULT  

Please use the following codes 

UNIT_MEASURE 

UNIT_MULT 

Explanation 

PS 

PN 

EUR 

XDC 

0 

0 

6 

6 

Persons – use these codes when referring to 
employed persons (employees or self-employed 
persons) 

Pure Number – use these codes when referring to 
enterprises 

Million Euros – use these codes to report amounts, in 
million euros. Use these codes if your country belongs 
to the Eurozone. 

Million National Currency – use these codes to report 
amounts, in million national currency. Use these 
codes if your country does not belong to the 
Eurozone. 

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Column 9 – ACTIVITY  

Please use the following codes 

ACTIVITY 

Explanation 

C10T12 

C10T18 

C10T33 

C10TE39 

C10TF43 

Manufacture of beverages, food and tobacco products 

Manufacture of products based on: food, beverages, tobacco, textile, 
leather, wood, pulp and paper; publishing and printing 

Manufacturing 

Manufacturing, electricity, gas, steam and air conditioning; water 
supply, sewerage, waste management and remediation activities 

Manufacturing, electricity, gas, steam and air conditioning supply; 
water supply and construction 

C10TS951XK 

All enterprises, without financial sector 

C13T15 

Manufacture of textiles, wearing apparel, leather and related products 

C16T18 

Manufacture of wood & products of wood & cork, except furniture; 
articles of straw & plaiting materials; paper & paper products; printing 
& reproduction of recorded media 

C19 

Manufacture of coke and refined petroleum products 

C19T23 

C20 

C21 

C22_23 

C24_25 

C26 

C26T33 

C27 

C27_28 

C28 

C29_30 

Manufacture of coke, refined petroleum, chemical & basic 
pharmaceutical products, rubber & plastics, other non-metallic mineral 
products 

Manufacture of chemicals and chemical products 

Manufacture of basic pharmaceutical products and pharmaceutical 
preparations 

Manufacture of rubber and plastics products, and other non-metallic 
mineral products 

Manufacture of basic metals & fabricated metal products excluding 
machines & equipment 

Manufacture of computer, electronic and optical products 

Manufacture of computers, electric & optical products, electrical 
equipment, machinery & equipment n.e.c., motor vehicles, oth. 
transport equipment, furniture, oth. manufacturing, repair & installation 
of machinery & equipment 

Manufacture of electrical equipment 

Manufacture of electrical equipment, machinery and equipment n.e.c. 

Manufacture of machinery and equipment n.e.c. 

Manufacture of motor vehicles, trailers and semi-trailers, other 
transport equipment 

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ACTIVITY 

Explanation 

C31T33 

D35 

D35TE39 

E36T39 

F41T43 

G45 

G45T47 

Manufacture of furniture and other manufacturing; repair and 
installation of machinery and equipment 

Electricity, gas, steam and air conditioning supply 

Electricity, gas, steam and air conditioning; water supply, sewerage, 
waste management and remediation activities 

Water supply, sewerage, waste management and remediation 

Construction 

Trade of motor vehicles and motorcycles 

Wholesale and retail trade; repair of motor vehicles and motorcycles 

G45TS951XK 

Services, without financial sector 

G46 

G47 

H49T53 

I55 

I55_56 

ICT_T 

J58T60 

J58T63 

J61 

J62_63 

L68 

L68TM75 

M69T71 

M69T75 

M72 

M73T75 

N77T82 

Wholesale trade, except of motor vehicles and motorcycles 

Retail trade, except of motor vehicles and motorcycles 

Transport and storage 

Accommodation 

Accommodation and food service activities 

NACE 26.1-26.4, 26.8, 46.5, 58.2, 61, 62, 63.1, 95.1 

Publishing activities; motion picture, video & television programme 
production, sound recording & music publishing; programming & 
broadcasting 

Information and communication 

Telecommunications 

Computer programming, consultancy and related activities, 
information service activities 

Real estate activities 

Real estate; professional, scientific and technical service activities  
(only for micro-enterprises and regional data: Eurostat computed for 
national totals) 

Legal, accounting, management, architecture, engineering, technical 
testing and analysis  
activities 

Professional, scientific and technical service activities 

Scientific research and development 

Other professional, scientific and technical activities 

Administrative and support service activities 

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ACTIVITY 

Explanation 

N77T82X79 

N79 

S951 

Activities for rental and leasing, employment, security & investigation , 
services to buildings &landscape, office administrative, office support 
& other business support 

Travel agency; tour operator reservation service and related activities 

Repair of computers and communication equipment 

Column 10 – NUMBER_EMPLOYEES  

Please use the following codes 

NUMBER_EMPLOYEES 

Explanation 

E0T1 

E0T9 

E10T249 

E10T49 

E2T9 

E50T249 

EGE10 

EGE250 

From 0 to 1 employee or self-employed 

From 0 to 9 employees or self-employed 

From 10 to 249 employees or self-employed 

From 10 to 49 employees or self-employed 

From 2 to 9 employees or self-employed 

From 50 to 249 employees or self-employed 

10 or more employees or self-employed 

250 or more employees or self-employed 

Annex 2 provides an overview of the requested activity and number_employees codes 
combinations. 

Column 11 - REGIONAL  

The regional column should contain _Z for national totals.  

For regional data the column should contain the respective region code.  

Column 12 – CUST_BREAKDOWN  

The cust_breakdown column should contain _T.  

Column 13 – OBS_VALUE  

All variables should be grossed up to the enterprise population, except for  

•  variables starting with P_ which should be grossed up to the population of employees and 

self-employed persons  

•  currency or monetary variables: grossed up to turnover (sales variables)  

•  sample values (remain non-raised figures). 

For the values in this txt file a dot (“.”) should be used as decimal point. Thousand separators 
should not be used. In order to avoid small differences which occur in the crosschecking phase, 
values must not be rounded to integer figures but decimal figures should be reported instead. 

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The value can have maximum of 10 digits for the whole part and maximum of 7 decimal 
digits. 

Any non-numerical cell entry will be interpreted as a missing value. If a variable is not collected 
and is not removed from the dataset, the value entry should be left blank and an explanation 
provided in a separate file.  

Column 14 – OBS_STATUS and column 15 – OBS_ STATUS_1  

These columns allow to flag the data. Each column can contain only one flag. Put the first flag, if 
any, in OBS_STATUS. Then put the second flag, if any, in OBS_ STATUS_1. The codes to be 
used are as follows: 

OBS_STATUS 

Explanation 

A 

B 

U 

Normal value (no 
flag) 

Break in series 

Low reliability 

Column 16 – CONF_STATUS  

These columns allow to define the confidentiality status. The codes to be used are as follows:  

CONF_STATUS 

Explanation 

F 

C 

Free for publication 

Confidential 

Column 17 – COMMENT_OBS  

This column allows to put a footnote. Actually, it may contain several footnotes, separated by 
a ‘@’ character. A footnote that applies to many cells must be repeated for each cell. Right now, 
the maximum length of the combined footnotes is 255 characters.  

Column 18 – UNIT_MULT  

This column is a unit multiplier. Use 0 if the UNIT is PS or PN. Use 6 if the unit is EUR or XDC. 

Column 19 – DECIMALS  

This column is foreseen to allow to indicate how many of the OBS_VALUE decimals are 
significant. Right now, this column is not used. It should be set to 0. 

3.3.3. Example 

This is an example of a file that could be sent. The data is made up. 

DATAFLOW 

FREQ 

TIME_PERIOD  REF_AREA 

TABLE_    
IDENTIFIER 

EBARGO_ 
TIME 

ESTAT:INFOSOC_ 
ENT_A(1.0) 

A 

2024 

XY 

ENT2 

2024-10-11 

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ESTAT:INFOSOC_ 
ENT_A(1.0) 

ESTAT:INFOSOC_ 
ENT_A(1.0) 

A 

A 

2024 

2024 

XY 

XY 

ENT2 

2024-10-11 

ENT2 

2024-10-11 

INDICATOR 

UNIT_ 
MEASURE 

ACTIVITY 

NUMBER_           

EMPLOYEES 

REGIONAL 

CUST_              

BREAKDOWN 

EMPL 

EMPL 

EMPL 

PS 

PS 

PS 

C10T12 

EGE10 

C10T18 

EGE10 

C10T33 

EGE10 

_Z 

_Z 

_Z 

_T 

_T 

_T 

OBS_ 
VALUE 

OBS_ 
STATUS 

OBS_      
STATUS_1 

CONF_ 
STATUS 

COMMENT_OBS 

UNIT_ 
MULT 

DECIMALS 

1 

2 

3 

U 

B 

U 

A 

A 

B 

F 

C 

F 

Note 1 

Note 1@Note 2 

Note 2 

0 

0 

0 

0 

0 

0 

3.4. Data confidentiality 
Confidentiality is a fundamental principle of European statistics, as defined by Regulation (EC) 
223/2009, Commission Regulation (EU) No 557/2013 and the European Statistics Code of 
Practice (ESCoP).  

Recital 24 of Regulation (EC) 223/2009 on European statistics makes provision for the 
establishment of common principles and guidelines ensuring the confidentiality of data used for 
the production of European statistics and access to those confidential data, taking due account of 
technical developments and the requirements of users in a democratic society. 

For that purpose, Article 20(4) of Regulation (EC) 223/2009 requires the following.  

‘Within their respective spheres of competence, the NSIs and other national authorities and the 
Commission (Eurostat) shall take all necessary regulatory, administrative, technical and 
organisational measures to ensure the physical and logical protection of confidential data 
(statistical disclosure control).’ 

‘Confidential data’ means:  

‘data which allow statistical units to be identified, either directly or indirectly, thereby disclosing 
individual information. Confidentiality aims at protecting data from unauthorised disclosure that 
could be prejudicial or harmful to the interest of the source or other relevant parties. To determine 
whether a statistical unit is identifiable, account shall be taken of all relevant means that might 
reasonably be used by a third party to identify the statistical unit’ (Article 3(4) of Regulation (EC) 
223/2009). 

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In practice, for Eurostat not to publish confidential data, compilers need to indicate during data 
transmission which data are confidential, using the c-flag.  

It is recommended to use flags wisely and sparingly. Most cells should not be flagged.  

Depending on the status of national confidentiality, there are several implications for how 
European aggregates are to be published.  

Data used for the production of statistics by national and EU authorities are considered 
confidential if statistical units can be identified, directly or indirectly, and if information about 
individuals or businesses can be disclosed as a result.  

•  Direct identification means identification of the respondent (statistical unit) from their formal 

identifiers (e.g. name, address, identification number).  

• 

Indirect identification means inferring a respondent's identity through a combination of 
variables or characteristics (e.g. age, gender, education).  

Statistical disclosure control can be ensured through physical protection and statistical disclosure 
control (SDC). For further details, refer to the Eurostat website.  

Confidentiality rules are based on the number of enterprises. Two criteria, relevant for confidential 
disclosure, are proposed at national level:  

•  criterion A – data refer to less than three statistical units;  

•  criterion B – one or a few enterprises contribute to over 85 % of the total volume of 

aggregated data.  

Data compilers should explain the national confidentiality rules in the metadata and quality 
reports. 

3.5. Transmission deadlines 
The Implementing Act states that:  

• 

• 

• 

the annual metadata report for survey year 2024 must be transmitted to Eurostat by 31 
May 2024.  

the transmission deadline for the data relating to ICT usage and e-commerce in 
enterprises survey is 5 October of the survey year, i.e. 5 October 2024 for survey year 
2024.  

the annual quality report for survey year 2024 must be transmitted to Eurostat by 5 
November 2024. 

3.6. Transmission channels 

The transmission and delivery of data sets is managed by EDAMIS (Electronic Data Files 
Administration and Management Information System), adopted as the unique entry point for 
the transmission of data to Eurostat.  

The EDAMIS portal is accessible via the following link: https://webgate.ec.europa.eu/edamis4  

EDAMIS is made available through different networks: the internet and secure European 

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networks like TESTA (Trans European Services for Telematics between Administrations) and 
CCN (Common Communication Network).  

Information on networks, comparison between the different transmission methods and step-
by-step instructions for submitting files are provided in EDAMIS short and extensive user 
guides developed by Eurostat to which access is given on request. 

3.7. Data revisions 

Revisions are broadly defined as any change to the value of a statistic released to the public. 
They can occur when new observations (one additional month or quarter) become available 
and some past values are modified or when the current and/or some past values are 
modified. Data are generally revised to incorporate new, improved information. Revisions are 
therefore inevitable whenever statistics are produced that report promptly on economic 
developments despite the fact that some relevant information is still missing. 

Further guidance on data revision principles can be found in the European Statistics Code of 
Practice. 

3.8. Support of data providers 

Specific support is available here: ESTAT-ICT-SURVEYS@ ec.europa.eu.

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4 

Data quality 

Assessing data quality is a crucial step in providing users with high-quality statistics.  

Consequently, Eurostat has a quality framework in place to assess data on ICT usage and e-
commerce in enterprises. It carries out a number of checks on the data submitted to Eurostat 
based on validation rules. NSIs are required to send quality and metadata reports to assess the 
overall quality of their data. 

4.1. Quality framework 

4.1.1. European business statistics quality framework 

Efforts were made to standardise the quality framework for business and trade statistics under 
the EBS Regulation. As shown below, the core EBS quality framework involves different 
measures that ensure high-quality statistics and allow users to understand the quality issues for 
the statistics concerned.  

The core set of measures are written in black and ensure the quality of business and trade 
statistics. These should eventually be in place for all business and trade statistics. Measures 
written in blue are considered optional depending on the needs of the individual domains. 

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4.1.2. Implementing the framework for data on ICT 
usage and e-commerce in enterprises 

The table below explains how the EBS quality framework is applied when collecting data on ICT 
usage and e-commerce in enterprises. 

REQUIREMENT SYSTEM 

The requirements system comprises all legal requirements, standards and rules agreed 
at working-group level. 

European business statistics legal 
acts 

Quality items to be documented in 
metadata reports 

European statistical system (ESS) 
handbooks, particularly the 
handbook quality and metadata 
reporting 

See Section 2.1 – Legislative background 

12.3.1. Data completeness – rate (rates are not 
requested but deviation from question / item in model 
questionnaire have to be provided) 14.2.1. Punctuality – 
delivery (Number of days between the delivery date of 
data and the target date on which they were scheduled 
for delivery) 

ESS handbook for quality and metadata reports 

Recommendations 

See Chapter 2 – Data compilation 

Rules for submitting data 

See Chapter 3 – Data transmission 

Rules for validating data 

See Annex 2 – Validation rules 

GUIDANCE SYSTEM 

The guidance system provides further advice to help compilers improve and ensure the 
quality of data. 

European business statistics 
manuals and domain-specific 
manuals, including best practices 
and methods 

ESS manual 

Expert groups 

Information Society Statistics Working Group 
Information Society Statistics Task Force 

European statistical training 
programme courses 

Not applicable 

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User support 

Questions to be addressed to ESTAT-ICT-
SURVEYS@ec.europa.eu. 

REPORTING SYSTEM 

The reporting system is used by data providers to report on the quality of the European 
business and trade statistics sent to Eurostat. 

Metadata for countries and EU 
according to the single integrated 
metadata structure reporting 
standards 

MONITORING SYSTEM 

Metadata are available via the following link: ESS  

Metadata Handler EU metadata are available via the 
following link: Eurostat metadata 

The monitoring system comprises all measures Eurostat uses to keep track of the qual-
ity of European business and trade statistics. 

Validation reports 

Countries contacted in case of issues. 

National metadata reports 
complemented with national 
quality reports if necessary 

Plausibility checks 

ASSESSMENT SYSTEM 

Metadata collected according to the single integrated 
metadata structure. 

Checks for consistency within data sets, cross-country 
data and time series analyses. 

The assessment system describes measurements Eurostat uses to analyse how Euro-
pean statistical system countries comply with legal requirements. It also analyses the 
quality of national data submitted to Eurostat. 

Compliance assessment (legal 
requirements) 

Quality assessment 

 Annual compliance assessment. 

Annual quality reviews before publication, 
recommendations issued in this manual, experience of 
NSIs taken into account when preparing the model 
questionnaire. 

User feedback / user satisfaction 
surveys 

Feedback collected from regular users and via 
questions sent out by user support 

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4.2. Metadata and quality reports 
Metadata, also described as «data about data», provide information about data and are essential 
for their understanding. They allow users to make comparisons between data and assess the 
quality of data. Metadata can be expressed as text (for example descriptions), values (for 
example percentage rates) and codes (from controlled vocabularies such as code lists).  

There are two types of metadata: structural metadata and reference metadata.  

Structural metadata cover the information that is used to identify and describe the data. Some 
examples of types of structural metadata are: variable names; variable codes; classifications 
used; technical descriptions of a data set (e.g. data formats, time dimensions, etc.); and data set 
locations. Structural metadata need to be linked with data so that they can be searched for and 
identified. 

Reference metadata describe the contents and quality of statistical data. According to the latest 
version of the European statistical system handbook for quality and metadata reports, metadata 
are subdivided into:  

•  conceptual metadata – these explain the concepts used;  

•  methodological metadata – these refer to methods used in preparing the statistical data; 

•  quality metadata – these refer to and explain quality dimensions of the statistical outputs.  

Several tools have been developed to produce high-quality and standardised metadata within the 
ESS. The single integrated metadata structure (SIMS) was created to support quality reporting on 
European statistics. It provides a standard, integrated and comprehensible framework for 
metadata and quality reporting in the ESS. It was formed by integrating and standardising two 
reporting structures. In particular, the Euro-SDMX metadata structure and the ESS standard for 

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quality reports structure39. It is a template for the ESS reference metadata report structure and 
quality report, which contain information about quality concepts at different levels of detail. 

4.2.1. Metadata and quality report on ICT usage and e-
commerce in enterprises 

For the ICT usage and e-commerce survey, the EBS Regulation asks reporting countries to 
submit quality and metadata reports to Eurostat each year. The information needed for data 
treatment should be included in the metadata report and all remaining information should be 
included in the quality report. The content of those reports is then combined into one national 
reference metadata report for each country.  

The deadlines to submit the quality and metadata reports are laid down in the Implementing Act. 
The deadline to send Eurostat the metadata report is before the quality report.  

The current structure of the reference metadata report is the following:  

Contact: information about the organisation, contact name and details (address, email, telephone 
number) of the person in charge in the organisation.  

Metadata update: dates when the metadata have been certified, posted and updated.  

Statistical presentation: description of the data and classifications used (e.g. NACE Rev. 2); 
statistical concepts and definitions; coverage of the statistical domain; statistical population and 
statistical units data refer to; time coverage and reference area.  

Unit of measure: for the survey on ICT usage and e-commerce in enterprises, the percentage of 
enterprises, of turnover, and of employees and self-employed persons is used.  

Reference period: the reference period is usually the survey year, but for certain variables it is 
the year before the survey year.  

Institutional mandate: information about complementary national legislation constituting the 
legal basis for the survey.  

Confidentiality: this includes confidentiality policy – provisions concerning confidentiality in legal 
acts; and data treatment – rules applied to keep confidential data undisclosed.  

Release policy: this covers the data release schedule/ calendar and where this calendar can be 
found. The release policy refers to several principles as laid down in the Regulation on European 
statistics and in the European statistics code of practice. For example, objectivity, impartiality, 
confidentiality and accessibility.  

Frequency of dissemination: data on ICT usage and e-commerce in enterprises are 
disseminated annually. Accessibility and clarity: this refers to various formats used in the 

39 More information and a visualisation of this structure is available in the European Statistical System (ESS) hand-
book for quality and metadata reports — 2020 edition, p 238-241. 

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dissemination data on ICT usage and e-commerce in enterprises at national level. For example, 
news releases, publications and online databases.  

Quality management: quality assurance and quality assessment describe the systems and 
frameworks in place to manage the quality of surveys and processes on ICT usage and e-
commerce in enterprises.  

Relevance: the main national users and their needs are taken into account when developing the 
survey on ICT usage and e-commerce in enterprises. User satisfaction is also taken into account 
either through a survey or other methods.  

Accuracy: accuracy of data is the closeness of computations or estimates to the exact or true 
values that the statistics were intended to measure. It is assessed based on overall comments on 
accuracy and the amount of sampling and non-sampling errors. These include: coverage errors; 
measurement errors; non-response errors; processing errors; and model assumption errors.  

Timeliness and punctuality: Timeliness refers to the time elapsed between the event or 
phenomenon the data describe taking place and the time at which the data becomes available. 
Punctuality refers to the time lag between the actual delivery of the data and the target date that 
they should have been delivered. For example, 5 October 2024.  

Coherence and comparability: European statistics should be coherent, maintaining internal 
consistency over time. It should also be possible to compare statistics between regions and 
countries, as well as combine and make joint use of related data from different sources. 
Information on geographical and intertemporal comparability, and cross-domain coherence are 
provided in the metadata report.  

Cost and burden: provides information on the cost of collecting and producing data on ICT 
usage and e-commerce in enterprises, and the burden on respondents.  

Data revision: data revision is any change in a value of a statistic released to the public. This 
includes data revision policy applicable to data output and how it can be implemented practically.  

Statistical processing: information about: data on ICT usage and e-commerce in enterprises 
source data (e.g. frame population and sampling); data collection frequency; type of data 
collection (e.g. paper, web, electronic); data validation; and data compilation.  

Comments: contains problems encountered, lessons learnt, any other comments, and the list of 
annexes.  

Related metadata: not applicable for this survey.  

Annexes: links to annexes. 

4.2.2. European Statistical System metadata handler 

Reporting countries should deliver the metadata and quality report using the MH. The ESS 
Metadata Handler (ESS-MH) is a web-based application that supports the production, exchange 
and dissemination of reference metadata in the ESS. This MH accommodates SDMX-compliant 
files based on SIMS. It supports standardising reference metadata and quality reports in the ESS. 
The diagram below presents the high-level business process to report SDMX-compliant reference 
metadata. It also presents use of the MH. 

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Eurostat and NSIs use this application to produce metadata files. The application is accessible 
through a password system40.  

Information about the metadata file on ICT usage and e-commerce in enterprises is provided 
below:  

File name: INFOSOC_ETNSI_ A_CC_YEAR_0000  

Domain: INFOSOC  

Metadata flow: INFOSOC_ETNSI_ A  

Typology: simsie - Sims structure for INFOSOC enterprises  

Country: CC (country code)  

Organisation – provider: name of the organisation providing the metadata file  

Reference year: YEAR  

Period: A0  

Status: Draft; Ready for validation; Validated; Ready for publication, Published  

Modified on: dd/mm/yyyy hh:mm:ss  

By: username  

40 For any issues, contact: ESTAT-DATA-METADATA-SERVICES@ec.europa.eu 

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Published on: dd/mm/yyyy hh:mm:ss  

Owner: username  

The main application features of the metadata file for users are:  

copy an existing file to create a new one;  

recall the file to make changes;  

download;  

view history;  

preview;  

print.  

After the changes have been made, the file is sent to the domain manager at Eurostat for 
validation. The domain manager will approve the file if all information is clear. If not, they can ask 
for clarifications. Reporting countries are asked to correct or add missing information. The file is 
then submitted again for approval.  

The template of the national reference metadata report is provided in Annex 3 – metadata 
reporting template. 

4.3. Data validation by Eurostat 
Although reporting countries are responsible for the quality of the data provided, Eurostat carries 
out a series of checks to ensure the accuracy of data submission format and the absence of 
errors. The validation process is currently structured according to the validation levels 
classification established by the ESS.  

Validation level 0: consistency with expected IT structural requirements;  

Validation level 1: consistency within the data set;  

Validation level 2: consistency with other data sets within the same domain and the same data 
source;  

Validation level 3: consistency within the same domain between different data sources;  

Validation level 4: consistency between separate domains in the same data provider;  

Validation level 5: consistency with data of other data providers;  

Only levels 0 to 3 are currently used to validate data on ICT usage and e-commerce in 
enterprises. Checks with the highest priority include: format checks; checks on the completeness 
of the file and uniqueness of records (level 0); and checks on data consistency (level 1). Failure 
to pass these levels will result in the file being rejected. The other checks may result in a list of 
warnings for which the reporting country is asked either to send revised data or to confirm the 
data correctness according to the type of warning spotted. A description of the different data 
checks is provided below. The rules applied are included in Annex 2 – validation rules.  

Validation level 0: consistency with expected IT structural requirements  

The first step is to check whether the file complies with the structure and format required by the 

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submission format presented in Section 3.3.2. The checks performed at this stage refer to the:  

validity of format: data are expected to be sent in the sole SDMX-TXT format as defined in 
Chapter 3. The number of columns in the file should be in line with the submission format 
presented in Section 3.3.2.  

validity of codes: these checks are performed on each dimension and attribute at record level. 
Their aim is to make sure that each reported code belongs to the code list related to that 
particular dimension or attribute.  

Complying with the submission format is the highest priority. Failing to pass checks at this level 
will result in the file being rejected.  

Validation level 1: consistency within the data set  

The next step in the validation process is to analyse the content of the file. The checks are 
divided into the three categories below: 

•  completeness of the file: this check verifies that the number of records in the file is equal to 

the total number expected for this data set.   

• 

inter-record consistency checks: these verify the consistency between the observation 
value of two or more records. These records can be linked by an equality or an inequality. 
The link is described in a consistency rule. Typically, the consistency of the total with the sum 
of details will be verified through these types of checks.  

•  consistency checks on ratios: ICT data are shared on the Eurostat website as percentages. 
Checks are run on these percentages to assess their plausibility by comparing them with 
a configurable range defined for every unit. 

Validation level 2: consistency with other data sets within the same domain and the same 
data source  

In this step, two series of checks are performed:  

•  variation over previous reference years: data provided for a new reference year are compared 

with those from previous years. The variation should fall within a pre-defined range.  

•  consistency checks on reference data and flags are carried out and compared with the 

information in the quality report provided by the NSIs.  

If the variation of the data falls outside the pre-defined range when compared with previous 
reference years, Eurostat will contact the country concerned and ask them to clarify and confirm 
the accuracy of the data. Eurostat will also contact countries if there is a discrepancy in the 
reference data.  

Validation level 3: consistency within the same domain and a different data source  

Data on ICT usage and e-commerce in enterprises are compared with similar data reported by 
other countries. The comparison is carried out at the level of main indicators, enterprise sizes and 
NACE activities, and is based on the most recent detailed data submitted to Eurostat.  

If there is significant inconsistency between the two sources, Eurostat will contact the reporting 
countries to ask them to provide clarifications and possible corrections.

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5 

Data dissemination 

Once Eurostat has received and validated the data transferred by the NSIs, the European aggre-
gates are calculated and the complete database with the results from the surveys is published 
online. 

5.1. Data description 
Eurostat publishes the ‘ICT usage and e-commerce in enterprises’ data gathered by the NSIs on 
the basis of the model questionnaire along with EU metadata. The results of the surveys are 
published as ratios (e.g. the percentage of companies with e-commerce sales that represent at 
least 1% of turnover).  

A ratio is the result of the division of a numerator and a denominator. Numerators and 
denominators are aggregated variables.  

For example, a ratio is computed by dividing the aggregated variable E_IUSE (enterprises where 
persons employed have access to the internet) by the aggregated variable ENT (total 
enterprises), and the result is E_IUSE with unit PC_ENT (percentage of all enterprises).  

Similarly, EU ratios are computed by dividing the sum (over all Member States) of the aggregated 
variable E_IUSE by the sum of the aggregated variable ENT.  

Given that data published in this domain are based on model questionnaires that change every 
year, Eurostat provides, on a dedicated webpage, a table that lists the available ratios, enterprise 
sizes and activities (NACE Rev. 2) per year. 

5.2. Confidentiality and flags 
The aggregated data received from the NSIs also contain some flags to indicate confidentiality, 
statistical reliability, and break in series. Then, the ratios are computed and they inherit the flags. 
Data flagged as unreliable are not published at national level, but are taken into account for 
calculating the EU aggregates. Data flagged as confidential are neither published at national level 
nor taken into account for EU aggregates. 

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5.3. Dissemination channels 
ICT usage and e-commerce in enterprises data are accessible on Eurostat’s website through 
different paths: the data navigation tree, the ‘Statistics Explained’ articles, the news items and the 
dedicated webpage.  

Eurostat navigation tree  

ICT usage and e-commerce in enterprises data are published on Database - Eurostat 
(europa.eu) under the ‘ICT usage in enterprises’ sub-branch of the ‘science, technology, digital 
society’ heading. 

Statistics Explained  

‘Statistics Explained’ is an official Eurostat website presenting statistical topics in an easily 
understandable way. The Digital economy and society page contains the links to the main 
statistical articles related to ICT data for enterprises, household and individuals. 

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News Items  

News items are displayed in reverse chronological order on the News page (accessible by 
clicking on ‘News’ in the drop-down menu anywhere in the Eurostat portal and then on News 
articles). They provide short texts and visuals on our data, including first data releases. From the 
menu on the left, the theme of interest can be selected and articles on ICT in enterprises (among 
other things) can be viewed. 

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Dedicated webpage  

In addition, a dedicated website on the digital economy and society provides information on the 
data, links to the database and a comprehensive database covering all data collected, 
publications, visualisations, methodological information and legislation. 

The subpage on the Comprehensive database provides information on the variables and 
breakdowns collected over time, a description of how to use the database, model questionnaires, 
country-specific notes, and other related information. 

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References 

References 

Commission Implementing Regulation (EU) 2020/1197 of 30 July 2020 laying down technical 
specifications and arrangements pursuant to Regulation (EU) 2019/2152 of the European 
Parliament and of the Council on European business statistics, repealing 10 legal acts in the field 
of business statistics (Text with EEA relevance), OJ L271, 18.8.2020, p. 1-170, 
http://data.europa.eu/eli/reg_impl/2020/1197/oj   

Commission Implementing Regulation (EU) 2023/1507 of 20 July 2023 laying down the technical 
specifications of data requirements and the deadlines for submission of metadata and quality re-
ports for the topic ICT usage and e-commerce for the reference year 2024, pursuant to Regula-
tion (EU) 2019/2152 of the European Parliament and of the Council (Text with EEA relevance), 
OJ L 184, 21.7.2023, p. 8-16, http://data.europa.eu/eli/reg_impl/2023/1507/oj  

Contacts EDAMIS Help Centre: (How to request support on EDAMIS | Eurostat CROS).  

Council Regulation (EEC) No 696/93 of 15 March 1993 on the statistical units for the observation 
and analysis of the production system in the Community, OJ L 76, p. 1-11, http://data.eu-
ropa.eu/eli/reg/1993/696/oj 

Regulation (EC) No 223/2009 of the European Parliament and of the Council of 11 March 2009 
on European statistics and repealing Regulation (EC, Euratom) No 1101/2008 of the European 
Parliament and of the Council on the transmission of data subject to statistical confidentiality to 
the Statistical Office of the European Communities, Council Regulation (EC) No 322/97 on 
Community Statistics, and Council Decision 89/382/EEC, Euratom establishing a Committee on 
the Statistical Programmes of the European Communities (Text with relevance for the EEA and 
for Switzerland), OJ L 87, 31.3.3009, p. 164-173; http://data.europa.eu/eli/reg/2009/223/oj  

European Commission (2021), Communication from the Commission ‘2030 Digital Compass: the 
European way for the Digital decade’, COM/2021/118 final (https://eur-lex.europa.eu/legal-
content/en/TXT/?uri=CELEX:52021DC0118)  

European Commission Political Guidelines 2019-2024 (https://op.europa.eu/en/publication-
detail/-/publication/62e534f4-62c1-11ea-b735-01aa75ed71a1)  

Quality Assurance Framework of the European Statistical System (ESS-QAF-V2.0-final.pdf).  

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

351 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
References 

European Statistical System Committee (2017), European Statistics Code of Practice — revised 
edition 2017 (European Statistics Code of Practice - Eurostat).  

Eurostat - ESS Reference Metadata reporting standards 
(https://ec.europa.eu/eurostat/web/metadata/reference-metadata-reporting-standards).  

Eurostat (2015), Single Integrated Metadata Structure 2.0 (SIMS v2.0) and its underlying 
reporting structures. The ESS Quality and reference metadata reporting standards ESMS 2.0 and 
ESQRS 2. (https://ec.europa.eu/eurostat/documents/64157/4373903/SIMS-2-0-Revised-
standards-November-2015-ESSC-final.pdf).  

Eurostat (2020), European Statistical System handbook for quality and metadata reports 
(https://ec.europa.eu/eurostat/documents/3859598/10501168/KS-GQ-19-006-EN-N.pdf).  

Eurostat (2021), European Business Statistics Manual 
(https://ec.europa.eu/eurostat/web/products-manuals-and-guidelines/-/ks-gq-21-001).  

Eurostat (2021), European business statistics methodological manual for statistical business 
registers (https://ec.europa.eu/eurostat/web/products-manuals-and-guidelines/-/ks-gq-20-006).  

Eurostat Data Transmission Helpdesk: ESTAT-SUPPORT-EDAMIS@ec.europa.eu  

NACE Rev. 2 - Statistical classification of economic activities 
(https://ec.europa.eu/eurostat/web/products-manuals-and-guidelines/-/ks-ra-07-015) 

Regulation (EU) 2019/2152 of the European Parliament and of the Council of 27 November 2019 
on European business statistics, repealing 10 legal acts in the field of business statistics (Text 
with EEA relevance), OJ L 327, 17.12.2019, p. 1–35,  http://data.europa.eu/eli/reg/2019/2152/oj 

Statistical Data and Metadata eXchange (SDMX) website (https://sdmx.org).  

UNECE – Generic Statistical Business Process Model (GSBPM) 
(https://statswiki.unece.org/display/GSBPM/GSBPM+v5.1).  

UNECE – High-Level Group for the Modernisation of Official Statistics (Modernstats) 
(https://unece.org/statistics/modernstats).  

Validation and Transformation Language (VTL) on the SDMX website) (https://sdmx.org/)

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A1 

Model questionnaire 

Annexes 

Annex 1 – Model questionnaire 

Annex 2 – Validation rules 

Annex 3 – Metadata reporting template 

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Model questionnaire 

A1 

Annex 1 – Model questionnaire 

EU Survey on ICT Usage and e-commerce in Enterprises 

2024 

General outline of the survey 

Sampling unit: 

Enterprise 

Scope / Target                
Population: 

Economic activity: 

Enterprises classified in the following categories of NACE Rev. 2: 

- Section C –  

“Manufacturing”; 

- Section D, E –   “Electricity, gas, steam and air conditioning supply” 

“Water supply, sewerage, waste management and 
remediation activities”; 

- Section F –  

“Construction”; 

- Section G –  

“Wholesale and retail trade; repair of motor vehicles and 
motorcycles”; 

- Section H –  

“Transportation and storage”; 

- Section I –  

“Accommodation and food service activities”; 

- Section J –  

“Information and communication”; 

- Section L –  

“Real estate activities”; 

- Section M – 

“Professional, scientific and technical activities”; 

- Section N – 

"Administrative and support service activities"; 

- Group 95.1 –  

“Repair of computers and communication equipment” 

Enterprise size: 

Enterprises with 10 or more employees and self-employed persons41. 
Optional: enterprises with number of employees and self-employed 
persons  between 0 and 9. 

Geographic scope: 

Enterprises located in any part of the territory of the country. 

41 With the introduction of the Framework Regulation on European Business Statistics the variable “persons employed” is replaced 
by the variable “employees and self-employed persons”. This change in the denomination of the variable does not imply any change 
in the scope. The two variables represent exactly the same concept. For the sake of user friendliness, the term “employees and self-
employed persons” is only used in the introductory part of the questionnaire and in Module X, while in the rest of the questionnaire 
the term “persons employed” is used.  

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Model questionnaire 

Sampling unit: 

Reference  
period: 

Enterprise 

Where not specified respondents should consider as reference their 
current situation (survey period in 2024). Year 2023 for the value or % of 
sales data and where specified. 

Recommended     
survey  period:  First quarter 2024. 

Questionnaire:  The layout of the national questionnaire should be defined by the country. 
However, countries should follow the order of the list of variables enclosed, 
if possible. The background information (Module X) should be placed at 
the end of the questionnaire. This information can be obtained in 3 
different ways: from national registers, from Structural Business Statistics 
or collected directly with the ICT usage survey. Every effort should be 
made to obtain them from the most recent SBS survey. Countries can 
include additional questions. 

Target            
respondent: 

A decision maker with major responsibility for ICT-related issues in the 
enterprise (the ICT manager or a senior professional in the ICT 
department). In smaller enterprises, the respondent should be someone at 
the level of managing director or the owner. In any case the respondent 
should not be someone with responsibilities only in accounting. 

Sample size, 
stratification: 

The sampling design and the resulting sample size should be appropriate 
for obtaining accurate, reliable and representative results on the variables 
and items in the model questionnaire. 

This objective should be achieved for the overall proportions as well as for 
the proportions for the different breakdowns of the population defined 
below: NACE and size class. NACE breakdown and enterprise size class 
breakdown are not required to be cross-tabulated. 

This requirement aims at ensuring the collection of a complete dataset – 
without empty, confidential or unreliable cells – for these indicators – with 
an exception for those broken down by economic activity for the calculation 
of European NACE aggregates.  

NACE 
breakdown: 

(To be applied to: all variables; enterprises with 10 or more 
employees and self-employed persons; whole territory of the 
country.) 

Data should be broken down by the following NACE Rev. 2 aggregates for 
possible calculation of national NACE Rev. 2 aggregates: 

1      10-33, 35-39, 41-43, 45-47, 49-53, 55-56, 58-63, 68-75, 77-82, 95.1 

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Model questionnaire 

A1 

Sampling unit: 

Enterprise 

2     10-33 

3  10-18 

4  19-23 

5  24-25 

6  26-33 

7  35-39 

8  41-43 

9  45-47 

10  47 

11  49-53 

12  55  

13    55-56 

14  58-63 

15  68 

16  69-75 

17  77-82 

18  26.1-26.4, 26.8, 46.5, 58.2, 61, 62, 63.1, 95.1 

Breakdowns for which national data should be provided with the purpose 
of possible calculation of European NACE aggregates. 

The production and transmission of these aggregates with an accuracy 
that allows the release at national level is optional. The production and 
transmission of these aggregates with an accuracy that may not allow the 
release at national level (use of flag u: unreliable) but are accurate enough 
to be combined with other countries' aggregates to be released at 
European level is mandatory.)  

3a  10-12 

3b  13-15 

3c  16-18 

4a  19 

4b  20 

4c  21 

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Sampling unit: 

Enterprise 

4d  22-23 

6a  26 

6b  27  

6c  28 

6d  29-30 

6e  31-33 

7a  35 

7b  36-39  

9a  45 

9b  46 

14a 58-60 

14b 61 

14c 62-63 

16a     69-71 

16b     72 

16c     73-75 

17a 77-78 + 80-82 

17b    79 

18a 95.1 

Size class 
breakdown: 

(To be applied to: all variables; aggregate of all mandatory NACE 
aggregates [1 to 18 defined above]; whole territory of the country.) 

Data should be broken down by the following size classes according to the 
number of employees and self-employed persons: 

1  10 or more 

2  10-49 (small enterprises) 

3  50-249 (medium enterprises) 

4  250 or more (large enterprises) 

Optional: 

5  0-9 

6  0-1 

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Model questionnaire 

A1 

Sampling unit: 

Enterprise 

7  2-9 

Weighting of 
results: 

Treatment of 
non-
response/'Do 
not know': 

Results should in general be weighted by number of enterprises. 

Turnover weighting should be used for sales related questions. 
Quantitative variables in the e-Commerce module related to sales should 
be weighted by total turnover. 

Weighting by the number of employees and self-employed persons should 
be applied for variables related to questions A1, A5, and for other variables 
e.g. % sending orders via a website or EDI-type messages., as specified in 
the transmission format document. 

Unit non-response: 

The non-respondent units should be assumed to resemble those who have 
responded to the survey and be treated as non-selected units. For this, the 
weighting or the grossing up factors should be adjusted: the design weight 
Nh / nh is replaced by Nh / mh where Nh is the size of stratum h, nh is the 
sample size in stratum h and mh is the number of respondents in stratum 
h. 

Item non-response: 

Logical corrections should be made, when information can be deduced 
from other variables, and priority given to further contacts with enterprises 
to collect the missing information. 

For the categorical variables (e.g. the YES/NO questions), respondents 
with item non response or 'do not know' should not be imputed with values 
from respondents who answered the question. 

Numerical variables shouldn’t be imputed (see also Methodological 
Manual). 

Tabulation of 
results: 

For the categorical variables, estimates should be made for the total 
number of enterprises for each response category, tabulated using the 
breakdowns specified above. 

For the quantitative variables (turnover, sales and number of employees 
and self-employed persons), when collected in absolute or percentage 
terms (and not in percentage classes), estimates should be made for the 
total values in absolute terms, tabulated using breakdowns as specified in 
the transmission format document. 

Data           
transmission: 

Results are to be sent to Eurostat following the transmission format 
described in a forthcoming Eurostat document. 

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Model questionnaire 

Disclaimer: References to third-party brands, products and trademarks are for the sake of clarifi-
cation and are not intended to promote the use of such products. 

ICT-ENT 2024 - Model Questionnaire V1.4  – Response burden and numbering 

Module 

Description 

A 

Access and use of the internet  

Access to the internet 

Use of a fixed line connection to the internet 
for business purposes 

Use of a mobile connection to the internet for 
business purposes 

Remote access and meetings via the internet 

Other use of internet 

B 

e-Commerce sales 

C 

D 

E 

X 

Web sales of goods or services 

EDI-type sales 

ICT specialists and skills 

ICT security 

Artificial Intelligence 

Total number of questions/responses 

Background characteristics 

Total number of questions/responses with 
background characteristics 

Mandatory 
variables 

Optional 
 variables 

8 

1 

3 

0 

4 

0 

12 

10 

2 

7 

21 

14 

62 

3 

65 

6 

0 

0 

1 

0 

5 

3 

3 

0 

4 

1 

16 

30 

0 

30 

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Model questionnaire 

A1 

EU SURVEY ON ICT USAGE AND E-COMMERCE IN ENTERPRISES 
2024 
MODEL QUESTIONNAIRE VERSION 1.4 

Module A: Access and use of the internet 

(Scope: all enterprises) 

A1 

How many persons employed have access to the inter-
net for business purposes? 

(including fixed line, fixed wireless and mobile telephone net-
work connection)  

(Filter question) 

 (Number) 

_________ 

If you can't provide this value, 

please indicate an estimate of the percentage of the total 
number of persons employed who have access to the in-
ternet for business purposes 

└┘└┘└┘ ,└┘%  

└┘└┘└┘ ,└┘% 

If the value=0, go to C1 

Use of a fixed line connection to the internet for business purposes 

A2.   Does your enterprise use any type of fixed connection to 

the internet? (e.g. ADSL, SDSL, VDSL, fiber optics technology 
(FTTP), cable technology, fixed wireless)  

(Add national examples) 

(Filter question) 

Yes  

No  

->go to A5 (if 
optional  in-
cluded), 

else go to A6 

A3. 

What is the maximum contracted download speed of the 
fastest fixed internet connection of your enterprise? 

(additional categories at national level can be added, if 
needed) 

(Tick only one) 

a) less than 30 Mbit/s 

b) at least 30 but less than 100 Mbit/s 

c) at least 100 Mbit/s but less than 500 Mbit/s 

d) at least 500 Mbit/s but less than 1 Gbit/s 

e) at least 1 Gbit/s 

 

 

 

 

 

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A4. 

Is the speed of your fixed connection(s) to the internet usu-
ally sufficient for the actual needs of the enterprise? 

Yes  

   No  

Use of a mobile connection to the internet for business purposes 

The following question refers to the usage of portable devices connecting to the internet 
through mobile telephone networks for business purposes. This includes the use of devices 
where the enterprise provides the devices and pays for the subscription and the costs of use 
fully or at least up to a limit. 

A5. 

How many persons employed use a portable device provided 
by the enterprise, that allows connection to the internet via 
mobile telephone networks, for business purposes?   

 (Number) 

_________ 

(e.g. portable computers or other portable devices such as 
smartphones) 

If you can't provide this value, 

please indicate an estimate of the percentage of the total 
number of persons employed who use a portable device pro-
vided by the enterprise, that allows connection to the internet 
via mobile telephone networks, for business purposes 

 (e.g. portable computers, or other portable devices such as 
smartphones) 

└┘ └┘ └┘ % 

– Optional 

Remote access and meetings via the internet                                                                           
Scope: enterprises with access to the internet, i.e. if A1>0) 

A6. 

Do any of the persons employed have remote access to the following?  
(via computers or portable devices such as smartphones) 

a) E-mail system of the enterprise 

b) Documents of the enterprise (e.g. files, spreadsheets, presentations, 
charts, photos) 

     c) Business applications or software of the enterprise (e.g. access to  
        accounting, sales, orders, CRM) 
       Please exclude applications used for internal communication, e.g.   
       Skype, MS Teams, Yammer. 

Yes 

 

 

 

No 

 

 

 

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361 

 
 
 
 
 
 
 
 
 
 
  
  
 
 
 
  
 
 
 
 
 
 
 
 
 
A7.  Does your enterprise conduct remote meetings (via e.g. 

Skype, Zoom, MS Teams, WebEx)? 

Model questionnaire 

A1 

Yes  

No  

->go to A8 (if 
optional  in-
cluded) 

else go to B1 

Other use of the internet 

– Optional 

A8.  

Does your enterprise pay to advertise on the internet? 
(e.g. adverts on search engines, on social media, on other web-
sites or apps) 

Yes  

No  
->go to B1 

(Filter question) 

A9.  

Does your enterprise pay to advertise on the internet using 
any of the following targeted advertising methods? 

Yes 

No 

a) Based on content or keywords searched by internet users  

b) Based on the tracking of internet users' past activities or profile  

c) Based on the geolocation of internet users 

d) Any other method of targeted advertising on the internet not 
specified above 

 

 

 

 

 

 

 

 

Module B: e-Commerce sales 

(Scope: enterprises with access to the internet, i.e. if A1>0) 

In e-commerce sales of goods or services, the order is placed via web sites, apps or EDI-type messages (EDI: Elec-
tronic Data interchange) by methods specifically designed for the purpose of receiving orders.  

The payment may be done online or offline.  

e-Commerce does not include orders written in e-mail.  

Please report web and EDI-type sales separately. They are defined by the method of placing the order:  

•  WEB sales: the customer places the order on a website or through an app; 

•  EDI type sales: an EDI-type order message is created from the business system of the customer. 

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Web sales of goods or services 

Web sales cover orders, bookings and reservations placed by your customers via 

• 

your enterprise’s websites or apps: 

online store (web shop); 

o 
o  web forms; 
o 
o 
o 

extranet (web shop or web forms) ; 
booking/reservation applications for services; 
apps for mobile devices or computers; 

• 

e-commerce marketplace websites or apps (used by several enterprises for trading goods or services). 

Orders written in e-mail are not counted as web sales. 

B1. 

During 2023, did your enterprise have web sales of goods or services via: 

a) your enterprise's websites or apps?  

(including extranets) 

Yes 

No 

 

 

b) e-commerce marketplace websites or apps used by several enterprises 
for trading goods or services? 
 (e.g. e-Bookers, Booking, hotels.com, eBay, Amazon, Amazon Business, Alibaba, 
Rakuten, TimoCom) 

 

 

[Please add national examples of e-commerce marketplaces incl. government 
marketplaces] 

If both B1 a) and B1 b) = “No” then go to B7  

What was the value of your web sales? 

(please refer to the provided definition of web sales) 

B2. 

Please answer to a) OR b) 

a) What was the value of your web sales of goods or services, in 2023? 

(National currency, ex-
cluding VAT) 

________________ 

OR 

b) What percentage of total turnover was generated by web sales of goods or 

services, in 2023? 

└┘ └┘ └┘ , └┘ % 

If you cannot provide the exact percentage an approximation will suffice. 

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Model questionnaire 

A1 

Question B3 should be answered only if both B1 a) and B1 b) = "Yes" 

What was the percentage breakdown of the value of web sales in 2023 for the 
following: 

B3. 

(Please refer to value of web sales you reported in B2) 

If you cannot provide the exact percentages an approximation will suffice.  

a) via your enterprise's websites or apps?  

(including extranets) 

b) via e-commerce marketplace websites or apps used by several enterprises 
for trading goods or services?  
(e.g. e-Bookers, Booking, hotels.com, eBay, Amazon, Amazon Business, Alibaba, 
Rakuten, TimoCom) 

[Please add national examples of e-commerce marketplaces incl. government mar-
ketplaces] 

TOTAL 

└┘ └┘ └┘ % 

└┘ └┘ └┘ % 

1  0  0 % 

What was the percentage breakdown of the value of web sales in 2023 by 
type of customer: 

B4. 

(Please refer to value of web sales you reported in B2) 

If you cannot provide the exact percentages an approximation will suffice. 

a) Sales to private consumers (B2C) 

└┘ └┘ └┘ % 

b) Sales to other enterprises (B2B) and Sales to public sector (B2G) 

└┘ └┘ └┘ % 

TOTAL  

1  0  0 % 

B5. 

During 2023, did your enterprise have web sales to customers located in 
the following geographic areas?  

a) Own country 

b) Other EU countries 

c) Rest of the world 

Yes 

 

 

 

No 

 

 

 

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The following question (B6) should only be answered if at least two of the above possible re-
sponses in question B5  a) , b) or c) are answered with "Yes", otherwise go to B7 

What was the percentage breakdown of the value of web sales in 
2023 to customers located in the following geographic areas?  

B6. 

– Optional 

(Please refer to value of web sales you reported in B2)  

If you cannot provide the exact percentages an approximation will suffice.  

a) Own country  

b) Other EU countries  

c) Rest of the world 

TOTAL 

EDI-type sales 

└┘ └┘ └┘ % 

└┘ └┘ └┘ % 

└┘ └┘ └┘ % 

1  0  0 % 

EDI-type sales cover orders placed by customers via EDI-type messages (EDI: Electronic Data inter-
change) meaning: 

in an agreed or standard format suitable for automated processing; 

• 
•  EDI-type order message created from the business system of the customer; 
• 
• 
• 

including orders transmitted via EDI-service provider; 
including automatic system generated demand driven orders; 
including orders received directly into your ERP system. 

Examples of EDI: EDIFACT, XML/EDI (e.g. UBL, Rosettanet, [please add national examples]). 

B7. 

During 2023, did your enterprise have EDI-type sales of goods or ser-
vices? 

(Filter question) 

Yes  

No   
-> go to C1 

What was the value of your EDI-type sales? 

B8. 

(please refer to the provided definition of EDI-type sales) 

Please answer to a) OR b) 

a) What was the value of your EDI-type sales of goods or services, in 2023? 

(National currency, ex-
cluding VAT) 

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Model questionnaire 

A1 

________________ 

OR 

b) What percentage of total turnover was generated by EDI-type sales of 

└┘ └┘ └┘,└┘ % 

goods or services, in 2023? 

If you cannot provide the exact percentage an approximation will suffice. 

Module C: ICT specialists and skills 

(Scope: all enterprises) 

C1. 

C2. 

C3. 

C4. 

C5. 

Does your enterprise employ ICT specialists? 
ICT specialists are persons employed for whom ICT is the main job. For example, to de-
velop, operate or maintain ICT systems or applications. 

Yes  

No  

Did your enterprise provide any type of training to develop ICT related 
skills of the persons employed, during 2023? 

Yes 

No 

a) Training for ICT specialists  
Tick “No” if your enterprise didn't employ ICT specialists during 2023. 

b) Training for other persons employed  

 

 

 

 

Did your enterprise recruit or try to recruit ICT specialists during 2023? 
(Filter question) 

Yes  

During 2023, did your enterprise have vacancies for ICT specialists that 
were difficult to fill? 

Yes  

No   
->go to C6 

No  
->go to C6 

Did your enterprise have any of the following difficulties to recruit ICT 
specialists during 2023? – Optional 

Yes  

No 

a) Lack of applications 

b) Applicants' lack of relevant ICT related qualifications from education and/or 
training;  

c) Applicants' lack of relevant work experience  

d) Applicants' salary expectations too high 

 

 

 

 

 

 

 

 

C6. 

Who performed your enterprise's ICT functions in 2023 (e.g. maintenance 
of ICT infrastructure, support for office software, development or support of 
business management software/systems and/or web solutions, security and 
data protection)? 

Yes 

    No 

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a) own employees (incl. those employed in parent or affiliate enterprises) 

b) external suppliers 

Module D: ICT Security  

 

 

 

 

(Scope: enterprises with access to the internet, i.e. if A1>0) 

ICT security means measures, controls and procedures applied on enterprise’s ICT systems to ensure integ-
rity, authenticity, availability and confidentiality of enterprise’s data and systems. 

D1. 

Does your enterprise apply any of the following ICT security measures 
on its ICT systems? 

Yes 

No 

a) authentication via strong password  
(e.g. minimum length, use of numbers and special characters, changed peri-
odically) 

 

b) authentication via biometric methods used to access the enterprise’s ICT 
system (e.g. authentication based on fingerprints, voice, face) 

 

c)  authentication based on a combination of at least two authentication 
mechanisms  
(i.e. combination of e.g. user-defined password, one-time password (OTP), 
code generated via a security token or received via a smartphone, biometric 
method (e.g. based on fingerprints, voice, face)) 

d)  Encryption of data, documents or e-mails  

e) Data backup to a separate location (including backup to the cloud) 

f)  Network access control (management of user rights in enterprise’s net-

work) 

g) VPN  
(Virtual Private Network extends a private network across a public network to 
enable secure exchange of data over public network) 

h) ICT security monitoring system used to detect suspicious activity (e.g. in-
trusion detection or prevention systems that monitors users’ or devices’ be-
haviour, network traffic) 

* Please exclude antivirus software and default firewall solution included in 
the operating system of personal computers and routers. 

i) Maintaining log files that enable analysis after ICT security incidents  

j) ICT risk assessment, i.e. periodical assessment of probability and conse-
quences of ICT security incidents  

k) ICT security tests (e.g. performing penetration tests, testing security alert 
system, review of security measures, testing of backup systems)  

 

  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

D2. 

Does your enterprise make persons employed aware of their obliga-
tions in ICT security related issues in the following ways? 

Yes 

No 

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a) Voluntary training or internally available information (e.g. information on 

the intranet) 

b)  Compulsory training courses or viewing compulsory material 

c)  By contract (e.g. contract of employment) 

 

 

 

 

 

 

D3.  

Does your enterprise have document(s) on measures, practices or pro-
cedures on ICT security?  
(Filter question)  
(Documents on ICT security and confidentiality of data cover employee train-
ing in ICT use, ICT security measures, the evaluation of ICT security 
measures, plans for updating ICT security documents, etc.) 

Yes 

No 
->go to D5 

D4. 

When were your enterprise’s document(s) on measures, practices or 
procedures on ICT security, defined or most recently reviewed?  

(Documents on ICT security and confidentiality of data cover employee train-
ing in ICT use, ICT security measures, the evaluation of ICT security 
measures, plans for updating ICT security documents, etc.) 

(Tick only one) – Optional 
*42a) within the last 12 months 
b) more than 12 months and up to 24 months ago 

c) more than 24 months ago 

 
 
 

D5.  

During 2023, did your enterprise experience any ICT related security in-
cident leading to the following consequences? 

Yes 

No 

a) Unavailability of ICT services due to hardware or software failures 

b) Unavailability of ICT services due to attack from outside, e.g. ransomware 
attacks, Denial of Service attacks 

c) Destruction or corruption of data due to hardware or software failures 

d) Destruction or corruption of data due to infection of malicious software or 
unauthorised intrusion 

e) Disclosure of confidential data due to intrusion, pharming, phishing attack, 
intentional actions by own employees 

f) Disclosure of confidential data due to unintentional actions by own employ-
ees 

 

 

 

 

 

 

 

 

 

 

 

 

42 Formulation in the national questionnaires should correspond to: a) <= 12 months; b) > 12 months and <= 24 months; and c) more than 24 
months  

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Module E: Artificial Intelligence  

(Scope: enterprises with access to the internet, i.e. if A1>0) 

Artificial intelligence refers to systems that use technologies such as: text mining, computer vision, 
speech recognition, natural language generation, machine learning, deep learning to gather 
and/or use data to predict, recommend or decide, with varying levels of autonomy, the best action to 
achieve specific goals. 

Artificial intelligence systems can be purely software based, e.g.: 

chatbots and business virtual assistants based on natural language processing; 
face recognition systems based on computer vision or speech recognition systems; 

• 
• 
•  machine translation software; 
• 

data analysis based on machine learning; 

or embedded in devices, e.g.: 

• 
• 

autonomous robots for warehouse automation or production assembly works; 
autonomous drones for production surveillance or parcel handling. 

E1.   Does your enterprise use any of the following Artificial Intelligence (AI) tech-

nologies? 

Yes 

No 

a) AI technologies performing analysis of written language (e.g. text mining) 

b) AI Technologies converting spoken language into machine-readable format 
(speech recognition) 

c) AI Technologies generating written or spoken language (natural language gener-
ation, speech synthesis) 

d) AI Technologies identifying objects or persons based on images or videos (image 
recognition, image processing) 

e) Machine learning (e.g. deep learning) for data analysis 

f) AI Technologies automating different workflows or assisting in decision making 
(e.g. AI based software robotic process automation) 

g) AI Technologies enabling physical movement of machines via autonomous deci-
sions based on observation of surroundings (autonomous robots, self-driving vehi-
cles, autonomous drones) 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

E1 a) to g) = “No” then go to E6 (if optional included) else go to X1 

Does your enterprise use Artificial Intelligence software or systems for any of 
the following purposes? 

E2.  

Yes 

No 

a) Use of  AI for marketing or sales 

some of the examples may be: 

•  customer profiling, price optimisation, personalised marketing offers, market anal-

ysis based on machine learning 

•  chatbots based on natural language processing for customer support 
•  autonomous robots for orders processing 

 

 

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b) Use of AI for production or service processes  

some of the examples may be: 

•  predictive maintenance or process optimization based on machine learning 
•  tools to classify products or find defects in products based on computer vision 
•  autonomous drones for production surveillance, security or inspection tasks 
•  assembly works performed by autonomous robots 

 

 

c) Use of AI for organisation of business administration processes or manage-
ment  

some of the examples may be: 

•  business virtual assistants based on machine learning and/or natural language 

processing, e.g. for document drafting 

•  data analysis or strategic decision making based on machine learning, e.g. risk 

 

 

assessment based on machine learning 

•  planning or business forecasting based on machine learning 
•  human resources management based on machine learning or natural language 
processing, e.g. candidates pre-selection screening, employee profiling or perfor-
mance analysis 

d) Use of AI for logistics 

some of the examples may be: 

•  autonomous robots for pick-and-pack solutions in warehouses for parcel ship-

 

 

ping, tracing, distribution or sorting 

•  route optimization based on machine learning 

e) Use of AI for ICT security 

some of the examples may be: 

•  face recognition based on computer vision for authentication of ICT users 
•  detection and prevention of cyber-attacks based on machine learning 

 

 

f)  Use of AI for accounting, controlling or finance management 

some of the examples may be: 

•  machine learning to analyse data that helps to make financial decisions 
•  invoice processing based on machine learning 
•  machine learning or natural language processing used for bookkeeping tasks 

 

 

g) Use of AI for research and development (R&D) or innovation activity (ex-
cluding research on AI) 

some of the examples may be: 

•  analysis of data for conducting research, solving research problems, developing a 

new or significantly improved product/service based on machine learning 

 

 

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How did you enterprise acquire the Artificial Intelligence software or systems 
that it uses? 

Yes 

No 

E3.  

– Optional 

a) They were developed by own employees (including those employed in parent or 
affiliate enterprise) 
b) Commercial software or systems were modified by own employees (including 
those employed in parent or affiliate enterprise) 

c) Open-source software or systems were modified by own employees (including 
those employed in parent or affiliate enterprise) 

d) Commercial software or systems ready to use were purchased (including exam-
ples where it was already incorporated in a purchased item or system) 

e) External providers were contracted to develop or modify them 

 

 

 

 

 

Does your enterprise process data on individuals using AI technologies? 

Some of examples of data on individuals (e.g. employees, job applicants or cus-
tomers) are: sex, age, racial or ethnic origin, disability, religion or belief, sexual 
orientation, facial images, record of purchases, occupation or address. 

Yes  

– Optional 

 

 

 

 

 

No   
-> go to 
E6 (if op-
tional  in-
cluded) 

else go 
to X1 

Does your enterprise have any measures to check the results generated 
by AI technologies for possible biases towards individuals based on sex, 
age, racial or ethnic origin, disability, religion or belief, sexual orientation? 
Some of examples of measures to check for possible bias:  
•  analysing the results of various machine learning models  
•  examining the dataset that was used to train the machine learning model 
•  data augmentation (which involves techniques to artificially generate addi-

tional data points from existing data, i.e. synthetic data) 

– Optional 

Yes  

No   

E4. 

E5. 

Questions E6 is presented only to respondents who answered ‘No’ to E1a)-g) i.e. enterprises that did not 
use any of the Artificial Intelligence technologies listed in question E1. 

Has your enterprise ever considered using any of the Artificial Intelligence 
technologies listed in question E1? – Optional 

E6.  

(Filter question) 

Yes  

No  

-> go to 
X1 

E7. 

What are the reasons for your enterprise not to use any of the Artificial Intelli-
gence technologies listed in question E1? – Optional 

Yes 

No 

a) The costs seem too high 

b) There is a lack of relevant expertise in the enterprise 

 

 

 

 

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c) Incompatibility with existing equipment, software or systems 

d) Difficulties with availability or quality of the necessary data 

e) Concerns regarding violation of data protection and privacy 

f) Lack of clarity about the legal consequences (e.g. liability in case of damage 
caused by the use of Artificial Intelligence) 

g) Ethical considerations  

h) Artificial Intelligence technologies are not useful for the enterprise 

 

 

 

 

 

 

 

 

 

 

 

 

Module X: Background information 

(X1-X3) available in some countries from SBS, the business register or administra-
tive data and thus not to be included; latest available information should be pro-
vided 

X1.  Main economic activity of the enterprise, during 2023 

X2.  Average number of employees and self-employed persons (persons 

employed), during 2023 

X3.  Total turnover (in monetary terms, excluding VAT), for 2023 

 … 

 … 

 … 

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EU Survey on ICT Usage and e-Commerce in Enterprises 

Glossary  

App(s) 

A mobile app, short for mobile application or just app, is application soft-
ware designed for a specific purpose (e.g. entertainment, shopping), down-
loaded and used on computers depending on their operating system. (e.g. 
portable devices such as tablets, Smartphones)  

Further information: http://en.wikipedia.org/wiki/Mobile_app; 
http://www.techopedia.com/definition/2953/mobile-application-mobile-app 

Authentication 
methods  

Authentication is a way to ascertain that a user is who they claim to be. 
This is usually performed by presenting one or more challenges to the 
user. There are three broad categories of challenges: 

Biometric au-
thentication 

1) Something the user knows. The user is asked for a secret, known only 
to her. Typical examples are passwords and PINs, but can also take the 
form of security questions. 

2) Something the user has. The user is in possession of a unique token, 
like a key. In the case of computer tokens, this can take the form of an NFC 
tag, or a device. 

3) Something the user is. Aka biometrics. The user is asked to present a 
part of her body that forms unique and repeatable patterns, like finger-
prints, voice, or face recognition. 

Source:https://www.enisa.europa.eu/topics/csirts-in-europe/glossary/au-
thentication-methods 

Biometric authentication is a security process that relies on the unique bio-
logical characteristics of an individual to verify that he is who is says he 
is. Biometric authentication systems compare a biometric data capture to 
stored, confirmed authentic data in a database. If both samples of the bio-
metric data match, authentication is confirmed. Typically, biometric authen-
tication is used to manage access to physical and digital resources such as 
buildings, rooms and computing devices. Types of biometric authentication 
technologies: 
Retina scans produce an image of the blood vessel pattern in the light-sen-
sitive surface lining the individual's inner eye.  

Iris recognition is used to identify individuals based on unique patterns 
within the ring-shaped region surrounding the pupil of the eye.  

Fingerscanning, the digital version of the ink-and-paper fingerprinting pro-
cess, works with details in the pattern of raised areas and branches in a 
human finger image. 

Finger vein ID is based on the unique vascular pattern in an individual's fin-
ger.  

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Facial recognition systems work with numeric codes called faceprints, 
which identify 80 nodal points on a human face. 

Voice identification systems rely on characteristics created by the shape of 
the speaker's mouth and throat, rather than more variable conditions. 

Source: https://searchsecurity.techtarget.com/definition/biometric-authenti-
cation  

Business pro-
cess  

A business process or business method is a collection of related, struc-
tured activities or tasks that produce a specific service or product (serve a 
particular goal) for a particular customer or customers. Business processes 
can be of three types: Management processes (e.g. corporate governance, 
strategic management), Operational processes (e.g. purchasing, manufac-
turing, marketing and sale) and Supporting processes (e.g. accounting, re-
cruitment, technical support). 

Source: http://en.wikipedia.org/wiki/Business_process 

Chatbots or Vir-
tual agent 

A chatbot or virtual agent is a computer generated, animated, artificial intel-
ligence virtual character that serves as an online customer service repre-
sentative. 

CRM 

Customer Relationship Management (CRM) is a management methodol-
ogy which places the customer at the centre of the business activity, based 
in an intensive use of information technologies to collect, integrate, process 
and analyse information related to the customers.  

Denial of ser-
vice attack 

One can distinguish between: 

1. Operational CRM – Integration of the front office business processes 
that are in contact with the customer. 

2. Analytical CRM – Analysis, through data mining, of the information avail-
able in the enterprise on its customers. This aims to gather in depth 
knowledge of the customer and how to answer to its needs. 

A denial-of-service attack (DoS attack) or distributed denial-of-service at-
tack (DDoS attack) is an attempt to make a computer resource unavailable 
to its intended users. Although the means to carry out, motives for, and tar-
gets of a DoS attack may vary, it generally consists of the concerted efforts 
of a person or persons to prevent an internet site or service from function-
ing efficiently or at all, temporarily or indefinitely. Perpetrators of DoS at-
tacks typically target sites or services hosted on high-profile web servers 
such as banks, credit card payment gateways, and even root name serv-
ers. 

One common method of attack involves saturating the target (victim) ma-
chine with external communications requests, such that it cannot respond 
to legitimate traffic, or responds so slowly as to be rendered effectively un-
available. In general terms, DoS attacks are implemented by either forcing 
the targeted computer(s) to reset, or consuming its resources so that it can 
no longer provide its intended service or obstructing the communication 

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media between the intended users and the victim so that they can no 
longer communicate adequately. 

DSL 

EDI, EDI-type  

Digital Subscriber Line (DSL) is a family of technologies that provides digi-
tal data transmission over the wires of a local telephone network. DSL is 
widely understood to mean Asymmetric Digital Subscriber Line (ADSL), the 
most commonly installed technical varieties of DSL. DSL service is deliv-
ered simultaneously with regular telephone on the same telephone line as 
it uses a higher frequency band that is separated by filtering. 

Source: http://en.wikipedia.org/wiki/DSL 

Electronic Data Interchange (EDI) refers to the structured transmission of 
data or documents between organizations or enterprises by electronic 
means. It also refers specifically to a family of standards (EDI-type) and 
EDI-type messages suitable for automated processing.  

Source: http://en.wikipedia.org/wiki/Electronic_Data_Interchange 

EDI e-commerce  Orders initiated with EDI-type messages. EDI (electronic data interchange) 
is an e-business tool for exchanging different kinds of business messages. 
EDI is here used as a generic term for sending or receiving business infor-
mation in an agreed format suitable for automated processing (e.g. 
EDIFACT, XML) and without the individual message being manually typed. 
“EDI e-commerce” is limited to EDI messages placing an order. 

Electronic com-
merce  

(e-Commerce) 

E-mail 

ERP 

Source: OECD, DSTI/ICCP/IIS(2009)5/FINAL 

An e-commerce transaction is the sale or purchase of goods or services, 
conducted over computer networks by methods specifically designed for 
the purpose of receiving or placing of orders. The goods or services are or-
dered by those methods, but the payment and the ultimate delivery of the 
goods or services do not have to be conducted online. An e-commerce 
transaction can be between enterprises, households, individuals, govern-
ments, and other public or private organisations. e-Commerce comprises 
orders made in Web pages or apps, extranet or EDI and excludes orders 
made by telephone calls, facsimile, or manually typed e-mail. The type is 
defined by the method of making the order. 

Source: OECD, DSTI/ICCP/IIS(2009)5/FINAL 

Electronic transmission of messages, including text and attachments, from 
one computer to another located within or outside of the organisation. This 
includes electronic mail by internet or other computer networks.  

Enterprise Resource Planning (ERP) consists of one or of a set of software 
applications that integrate information and processes across the several 
business functions of the enterprise. Typically ERP integrates planning, 
procurement, sales, marketing, customer relationship, finance and human 
resources. 

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ERP software can be customised or package software. These latter are 
single-vendor, enterprise wide, software packages, but they are built in a 
modular way allowing enterprises to customise the system to their specific 
activity implementing only some of those modules. 

ERP systems typically have the following characteristics: 

1.    are designed for client server environment (traditional or web-based); 

2.    integrate the majority of a business’s processes; 

3.    process a large majority of an organization’s transactions; 

4.    use enterprise-wide database that stores each piece of data only once; 

5.    allow access to the data in real time. 

A closed network that uses internet protocols to securely share enterprise's 
information with suppliers, vendors, customers or other businesses part-
ners. It can take the form of a secure extension of an Intranet that allows 
external users to access some parts of the enterprise's Intranet. It can also 
be a private part of the enterprise's website, where business partners can 
navigate after being authenticated in a login page. 

The Global Positioning System (GPS) is a satellite-based radionavigation 
system. Is one of the global network of satellites that enable satellite navi-
gation through GPS signals. GPS receiver anywhere on or near the Earth 
where there is an unobstructed line of sight to four or more GPS satellites 
can determine location, time, and velocity using this information. 

Source: https://en.wikipedia.org/wiki/Global_Positioning_System, 
https://en.wikipedia.org/wiki/GPS_signals 

The internet is a global system of interconnected computer networks that 
use the standard internet Protocol Suite (TCP/IP) to serve billions of users 
worldwide. It is a network of networks that consists of millions of private, 
public, academic, business, and government networks of local to global 
scope that are linked by a broad array of electronic and optical networking 
technologies. The internet carries a vast array of information resources and 
services, most notably the inter-linked hypertext documents of the World 
Wide Web (WWW) and the infrastructure to support electronic mail.   

Source: http://en.wikipedia.org/wiki/internet 

Relates to internet Protocol based networks: www, Extranet over the inter-
net, EDI over the internet, internet-enabled mobile phones. 

An intrusion is an attempt to bypass security controls on a information sys-
tem. Means of intrusion can be eavesdropping, viruses, worms, trojan 
horses, logic or time bombs, brute force attacks, etc. 

Intrusion detection is a process with the purpose of detecting intrusions or 
attempts of intrusions into a computer or network to compromise the confi-
dentiality, integrity or availability by observation of system, application and 

Extranet 

GPS 

Internet 

Intrusion 

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user activity as well as network traffic. Intrusion detection systems take 
preventive actions against intrusions without direct human intervention. 

Malicious soft-
ware 

Malicious software, also known as "malware" is any piece of software that 
performs undesirable operations such as data theft or some other type of 
computer compromise. 

Source:https://www.enisa.europa.eu/topics/csirts-in-europe/glossary/mal-
ware  

The term "e-commerce marketplaces" refers to websites or apps used by 
several enterprises for trading products e.g. Booking, eBay, Amazon, Ama-
zon Business, Alibaba, Rakuten). e-Commerce marketplaces are different 
from e-commerce platforms. The latter provide scalable, self-made online 
solutions for business that would like to set up their own e-commerce web-
site. 

Machine learning (e.g. deep learning) involves ‘training’ a computer model 
to better perform an automated task, e.g. pattern recognition. 

Natural language generation is the ability for a computer program to con-
vert data into natural language representation. 

Natural language processing is the ability for a computer program to under-
stand human language as it is spoken. 

Marketplace(s)  

(e-Commerce 
marketplaces) 

Machine learn-
ing (incl. deep 
learning) 

Natural lan-
guage genera-
tion (NLG) 

Natural lan-
guage pro-
cessing (NLP) 

Online payment  An online payment is an integrated ordering-payment transaction 

Pharming 

Phishing 

Ransomware 

The term “pharming” connotes an attack to redirect the traffic of a website 
to another, bogus website in order to acquire sensitive information. 

Phishing is a criminally fraudulent attempt to acquire sensitive information 
such as usernames, passwords and credit card details by masquerading 
as a trustworthy entity in an electronic communication. 

Ransomware is a type of malware (like Viruses, Trojans, etc.) that infect 
the computer systems of users and manipulates the infected system in a 
way, that the victim can not (partially or fully) use it and the data stored on 
it. The victim usually shortly after receives a blackmail note by pop-up, 
pressing the victim to pay a ransom (hence the name) to regain full access 
to system and files. 

Source:https://www.enisa.europa.eu/topics/csirts-in-europe/glossary/ran-
somware 

Robotic process 
automation (Ar-
tificial Intelli-
gence based) 

Artificial Intelligence based robotic process automation refers to software 
that automates business processes (e.g. workflows automation) based on 
Artificial Intelligence technologies. 

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Sales via web-
site (web sales) 

Web sales are sales made via an online store (web shop), via web forms 
on a website or extranet, or apps. Web sales are distinguished from EDI 
sales. In particular, the type of e-commerce transaction is defined by the 
method of making the order. This approach should mitigate the interpreta-
tion problems where both types, EDI and Web, are used in the process. An 
example is a situation where an order is made by the customer through a 
web application but the information is transmitted to the seller as an EDI-
type message. Here the type of selling application is however web; EDI is 
only a business application to transmit information about the sale. Web 
sales can be done by mobile phones using an internet browser. 

Source: OECD, DSTI/ICCP/IIS(2009)5/FINAL 

Speech recogni-
tion 

Speech recognition is the ability of a machine or program to identify words 
and phrases in spoken language and convert them to a machine-readable 
format. 

VPN 

A virtual private network (VPN) extends a private network across a public 
network, and enables users to send and receive data across shared or 
public networks as if their computing devices were directly connected to 
the private network. Applications running on a computing device, e.g., a 
laptop, desktop, smartphone, across a VPN may therefore benefit from the 
functionality, security, and management of the private network. Encryption 
is a common, though not an inherent, part of a VPN connection. 

Source: https://en.wikipedia.org/wiki/Virtual_private_network 

Web e-com-
merce 

Web (e-commerce) sales are sales made via an online store (web shop), 
via web forms on a website or extranet, or apps regardless of how the web 
is accessed (computer, laptop, mobile phone etc.) 

Website 

Wi-Fi 

Source: OECD, DSTI/ICCP/IIS(2009)5/FINAL 

Location on the World Wide Web identified by a Web address. Collection of 
Web files on a particular subject that includes a beginning file called a 
home page. Information is encoded with specific languages (Hypertext 
mark-up language (HTML), XML, Java) readable with a Web browser, like 
Netscape's Navigator or Microsoft's internet Explorer. 

Wi-Fi (or Wi-fi, WiFi, Wifi, wifi), short for ‘Wireless Fidelity’, is a set of ether-
net standards for wireless local area networks (WLAN) currently based on 
the IEEE 802.11 specifications. New standards beyond the 802.11 specifi-
cations, such as 802.16 have been developed. Wi-Fi was intended to be 
used for wireless devices and LANs, but is now often used for internet ac-
cess (one of the main international standards for wireless broadband inter-
net access and networking, with widespread use in business, homes and 
public spaces). It is based on radio signals with a frequency of 2.4 GHz 
and theoretically capable of speeds of over 54 Mbit/s. It enables a person 
with a wireless-enabled computer or personal digital assistant to connect to 
the internet when close to an access point called a hotspot.  

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xDSL 

XML 

Digital Subscriber Line. DSL technologies are designed to increase band-
width available over standard copper telephone wires. Includes IDSL, 
HDSL, SDSL, ADSL, RADSL, VDSL, DSL-Lite. 

The Extensible Markup Language is a markup language for documents 
containing structured information. Structured information contains both 
content (words, pictures, etc.) and some indication of what role that content 
plays (for example, content in a section heading has a different meaning 
from content in a footnote, which means something different than content 
in a figure caption or content in a database table, etc.). Almost all docu-
ments have some structure. A markup language is a mechanism to identify 
structures in a document. The XML specification defines a standard way to 
add markup to documents. 

Source: http://www.xml.com/ 

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A2 

Validation rules 

Annex 2 – Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

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Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

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Sdmxe 

Sdmxe 

Sdmxe 

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Sdmxe 

Sdmxe 

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Sdmxe 

* 

* 

* 

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* 

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* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

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* 

* 

* 

* 

* 

* 

* 

* 

ACTIVITY 

byNaceEU 

C10T12 <= C10T18 

ACTIVITY 

byNaceEU 

C10T12 <= C10TS951XK 

ACTIVITY 

byNaceEU 

C10T12 >= 0 

ACTIVITY 

byNaceEU 

C13T15 <= C10T18 

ACTIVITY 

byNaceEU 

C13T15 <= C10TS951XK 

ACTIVITY 

byNaceEU 

C13T15 >= 0 

ACTIVITY 

byNaceEU 

C16T18 <= C10T18 

ACTIVITY 

byNaceEU 

C16T18 <= C10TS951XK 

ACTIVITY 

byNaceEU 

C16T18 >= 0 

ACTIVITY 

byNaceEU 

C19 <= C10TS951XK 

ACTIVITY 

byNaceEU 

C19 <= C19T23 

ACTIVITY 

byNaceEU 

C19 >= 0 

ACTIVITY 

byNaceEU 

C19T23 = C19 + C20 + C21 
+ C22_23 

ACTIVITY 

byNaceEU 

C20 <= C10TS951XK 

ACTIVITY 

byNaceEU 

C20 <= C19T23 

ACTIVITY 

byNaceEU 

C20 >= 0 

ACTIVITY 

byNaceEU 

C21 <= C10TS951XK 

ACTIVITY 

byNaceEU 

C21 <= C19T23 

ACTIVITY 

byNaceEU 

C21 >= 0 

ACTIVITY 

byNaceEU 

C22_23 <= C10TS951XK 

ACTIVITY 

byNaceEU 

C22_23 <= C19T23 

ACTIVITY 

byNaceEU 

C22_23 >= 0 

ACTIVITY 

byNaceEU 

C26 <= C10TS951XK 

ACTIVITY 

byNaceEU 

C26 <= C26T33 

ACTIVITY 

byNaceEU 

C26 >= 0 

ACTIVITY 

byNaceEU 

C27 <= C10TS951XK 

ACTIVITY 

byNaceEU 

C27 <= C27_28 

380 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

ACTIVITY 

byNaceEU 

C27 >= 0 

ACTIVITY 

byNaceEU 

C27_28 <= C10TS951XK 

ACTIVITY 

byNaceEU 

C27_28 <= C26T33 

ACTIVITY 

byNaceEU 

C27_28 = C27 + C28 

ACTIVITY 

byNaceEU 

C27_28 >= 0 

ACTIVITY 

byNaceEU 

C28 <= C10TS951XK 

ACTIVITY 

byNaceEU 

C28 <= C27_28 

ACTIVITY 

byNaceEU 

C28 >= 0 

ACTIVITY 

byNaceEU 

C29_30 <= C10TS951XK 

ACTIVITY 

byNaceEU 

C29_30 <= C26T33 

ACTIVITY 

byNaceEU 

C29_30 >= 0 

ACTIVITY 

byNaceEU 

C31T33 <= C10TS951XK 

ACTIVITY 

byNaceEU 

C31T33 <= C26T33 

ACTIVITY 

byNaceEU 

C31T33 >= 0 

ACTIVITY 

byNaceEU 

D35 <= C10TS951XK 

ACTIVITY 

byNaceEU 

D35 <= D35TE39 

ACTIVITY 

byNaceEU 

D35 >= 0 

ACTIVITY 

byNaceEU 

D35TE39 = D35 + E36T39 

ACTIVITY 

byNaceEU 

E36T39 <= C10TS951XK 

ACTIVITY 

byNaceEU 

E36T39 <= D35TE39 

ACTIVITY 

byNaceEU 

E36T39 >= 0 

ACTIVITY 

byNaceEU 

G45 <= C10TS951XK 

ACTIVITY 

byNaceEU 

G45 <= G45T47 

ACTIVITY 

byNaceEU 

G45 >= 0 

ACTIVITY 

byNaceEU 

G46 <= C10TS951XK 

ACTIVITY 

byNaceEU 

G46 <= G45T47 

ACTIVITY 

byNaceEU 

G46 >= 0 

ACTIVITY 

byNaceEU 

G47 <= C10TS951XK 

ACTIVITY 

byNaceEU 

G47 <= G45T47 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

381 

 
A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

ACTIVITY 

byNaceEU 

G47 >= 0 

ACTIVITY 

byNaceEU 

J58T60 <= C10TS951XK 

ACTIVITY 

byNaceEU 

J58T60 <= J58T63 

ACTIVITY 

byNaceEU 

J58T60 >= 0 

ACTIVITY 

byNaceEU 

J61 <= C10TS951XK 

ACTIVITY 

byNaceEU 

J61 <= J58T63 

ACTIVITY 

byNaceEU 

J61 >= 0 

ACTIVITY 

byNaceEU 

J62_63 <= C10TS951XK 

ACTIVITY 

byNaceEU 

J62_63 <= J58T63 

ACTIVITY 

byNaceEU 

J62_63 >= 0 

ACTIVITY 

byNaceEU 

M69T71 <= C10TS951XK 

ACTIVITY 

byNaceEU 

M69T71 <= M69T75 

ACTIVITY 

byNaceEU 

M69T71 >= 0 

ACTIVITY 

byNaceEU 

M69T75 = M69T71 + M72 + 
M73T75 

ACTIVITY 

byNaceEU 

M72 <= C10TS951XK 

ACTIVITY 

byNaceEU 

M72 <= M69T75 

ACTIVITY 

byNaceEU 

M72 >= 0 

ACTIVITY 

byNaceEU 

M73T75 <= C10TS951XK 

ACTIVITY 

byNaceEU 

M73T75 <= M69T75 

ACTIVITY 

byNaceEU 

ACTIVITY 

byNaceEU 

M73T75 >= 0 

N77T82X79 <= 
C10TS951XK 

ACTIVITY 

byNaceEU 

N77T82X79 <= N77T82 

ACTIVITY 

byNaceEU 

N77T82X79 >= 0 

ACTIVITY 

byNaceNat 

C10T18 <= C10T33 

ACTIVITY 

byNaceNat 

C10T18 <= C10TS951XK 

ACTIVITY 

byNaceNat 

C10T18 = C10T12 + 
C13T15 + C16T18 

ACTIVITY 

byNaceNat 

C10T18 >= 0 

ACTIVITY 

byNaceNat 

C10T33 <= C10TF43 

382 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

ACTIVITY 

byNaceNat 

C10T33 <= C10TS951XK 

ACTIVITY 

byNaceNat 

C10T33 = C10T18 + 
C19T23 + C24_25 + 
C26T33 

ACTIVITY 

byNaceNat 

C10T33 >= 0 

ACTIVITY 

byNaceNat 

C10TE39 <= C10TS951XK 

ACTIVITY 

byNaceNat 

C10TE39 = C10T33 + 
D35TE39 

ACTIVITY 

byNaceNat 

C10TE39 >= 0 

ACTIVITY 

byNaceNat 

C10TF43 <= C10TS951XK 

ACTIVITY 

byNaceNat 

C10TF43 = C10T33 + 
D35TE39 + F41T43 

ACTIVITY 

byNaceNat 

C10TF43 >= 0 

ACTIVITY 

byNaceNat 

C10TS951XK >= 0 

ACTIVITY 

byNaceNat 

C19T23 <= C10T33 

ACTIVITY 

byNaceNat 

C19T23 <= C10TS951XK 

ACTIVITY 

byNaceNat 

C19T23 >= 0 

ACTIVITY 

byNaceNat 

C24_25 <= C10T33 

ACTIVITY 

byNaceNat 

C24_25 <= C10TS951XK 

ACTIVITY 

byNaceNat 

C24_25 >= 0 

ACTIVITY 

byNaceNat 

C26T33 <= C10T33 

ACTIVITY 

byNaceNat 

C26T33 <= C10TS951XK 

ACTIVITY 

byNaceNat 

C26T33 = C26 + C27_28 + 
C29_30 + C31T33 

ACTIVITY 

byNaceNat 

C26T33 >= 0 

ACTIVITY 

byNaceNat 

D35TE39 <= C10TF43 

ACTIVITY 

byNaceNat 

D35TE39 <= C10TS951XK 

ACTIVITY 

byNaceNat 

D35TE39 >= 0 

ACTIVITY 

byNaceNat 

F41T43 <= C10TF43 

ACTIVITY 

byNaceNat 

F41T43 <= C10TS951XK 

ACTIVITY 

byNaceNat 

F41T43 >= 0 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

383 

 
A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

ACTIVITY 

byNaceNat 

G45T47 <= C10TS951XK 

ACTIVITY 

byNaceNat 

G45T47 <= G45TS951XK 

ACTIVITY 

byNaceNat  G45T47 = G45 + G46 + G47 

ACTIVITY 

byNaceNat 

G45T47 >= 0 

ACTIVITY 

byNaceNat 

H49T53 <= C10TS951XK 

ACTIVITY 

byNaceNat 

H49T53 <= G45TS951XK 

ACTIVITY 

byNaceNat 

H49T53 >= 0 

ACTIVITY 

byNaceNat 

I55 <= C10TS951XK 

ACTIVITY 

byNaceNat 

I55 <= G45TS951XK 

ACTIVITY 

byNaceNat 

I55 <= I55_56 

ACTIVITY 

byNaceNat 

I55 >= 0 

ACTIVITY 

byNaceNat 

I55_56 <= C10TS951XK 

ACTIVITY 

byNaceNat 

I55_56 <= G45TS951XK 

ACTIVITY 

byNaceNat 

I55_56 >= 0 

ACTIVITY 

byNaceNat 

ICT_T <= C10TS951XK 

ACTIVITY 

byNaceNat 

ICT_T <= C26T33 + 
G45T47 + J58T63 + S951 

ACTIVITY 

byNaceNat 

ICT_T >= 0 

ACTIVITY 

byNaceNat 

J58T63 <= C10TS951XK 

ACTIVITY 

byNaceNat 

J58T63 <= G45TS951XK 

ACTIVITY 

byNaceNat 

J58T63 = J58T60 + J61 + 
J62_63 

ACTIVITY 

byNaceNat 

J58T63 >= 0 

ACTIVITY 

byNaceNat 

L68 <= C10TS951XK 

ACTIVITY 

byNaceNat 

L68 <= G45TS951XK 

ACTIVITY 

byNaceNat 

L68 >= 0 

ACTIVITY 

byNaceNat 

L68TM75 <= C10TS951XK 

ACTIVITY 

byNaceNat 

L68TM75 <= G45TS951XK 

ACTIVITY 

byNaceNat 

L68TM75 = L68 + M69T75 

ACTIVITY 

byNaceNat 

L68TM75 >= 0 

384 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

ACTIVITY 

byNaceNat 

M69T75 <= C10TS951XK 

ACTIVITY 

byNaceNat 

M69T75 <= G45TS951XK 

ACTIVITY 

byNaceNat 

M69T75 >= 0 

ACTIVITY 

byNaceNat 

N77T82 <= C10TS951XK 

ACTIVITY 

byNaceNat 

N77T82 <= G45TS951XK 

ACTIVITY 

byNaceNat 

N77T82 = N77T82X79 + 
N79 

ACTIVITY 

byNaceNat 

N77T82 >= 0 

ACTIVITY 

byNaceNat 

N79 <= N77T82 

ACTIVITY 

byNaceNat 

N79 >= 0 

ACTIVITY 

byNaceNat 

S951 <= C10TS951XK 

ACTIVITY 

byNaceNat 

S951 <= G45TS951XK 

ACTIVITY 

byNaceNat 

S951 >= 0 

ACTIVITY 

Total 

ACTIVITY 

Total 

ACTIVITY 

Total 

ACTIVITY 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

Total 

 back 

 back 

 back 

 back 

a1 

a1 

a1 

C10TS951XK = C10T18 + 
C19T23 + C24_25 + 
C26T33 + D35TE39 + 
F41T43 + G45T47 + 
H49T53 + I55_56 + J58T63 
+ L68 + M69T75 + N77T82 
+ S951 

C10TS951XK = C10TF43 + 
G45TS951XK 

G45TS951XK = G45T47 + 
H49T53 + I55_56 + J58T63 
+ L68 + M69T75 + N77T82 
+ S951 

G45TS951XK >= 0 

empl <= 25000000 

ent <= 5000000 

ent_sample <= ent 

tovt <= 10000000 

e_iuse <= ent 

e_iuse_ge10a <= e_iuse 

e_iuse_gt10 <= e_iuse 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

385 

 
A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

a1 

e_iuse_gt50 <= e_iuse_gt10 

a2 

a2 

a2 

a2 

a2 

a3 

e_fixbb <= e_iuse 

e_fixbbx <= e_iuse 

e_fixbbz <= e_iuse 

e_iuse = e_fixbb + e_fixbbx 
+ e_fixbbz 

e_iuse >= e_fixbb + e_fixbbx 
+ e_fixbbz 

e_fixbb = e_ispdf_lt30 + 
e_ispdf1_30_100 + 
e_ispdf_100_500 + 
e_ispdf_500_1G + 
e_ispdf_ge1G 

e_fixbb >= e_ispdf_lt30 + 
e_ispdf1_30_100 + 
e_ispdf_100_500 + 
e_ispdf_500_1G + 
e_ispdf_ge1G 

INDICATOR 

a3 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

a3 

e_ispdf_100_500 <= e_fixbb 

a3 

a3 

a3 

e_ispdf_500_1G <= e_fixbb 

e_ispdf_ge1G <= e_fixbb 

e_ispdf_lt30 <= e_fixbb 

a3 

e_ispdf1_30_100 <= e_fixbb 

a3 

e_ispdf1_ge100 <= e_fixbb 

INDICATOR 

a3 

e_ispdf1_ge100 = 
e_ispdf_100_500 + 
e_ispdf_500_1G + 
e_ispdf_ge1G 

INDICATOR 

a3 

e_ispdf1_ge30 <= e_fixbb 

INDICATOR 

INDICATOR 

a3 

a4 

e_ispdf1_ge30 = 
e_ispdf1_30_100 + 
e_ispdf_100_500 + 
e_ispdf_500_1G + 
e_ispdf_ge1G 

e_fixbb = e_ispdf1ok + 
e_ispdf1okx 

386 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

INDICATOR 

INDICATOR 

INDICATOR 

a4 

a4 

a4 

INDICATOR 

a4a3 

INDICATOR 

a4a3 

INDICATOR 

a4a3 

INDICATOR 

a4a3 

INDICATOR 

a4a3 

INDICATOR 

a4a3 

INDICATOR 

a4a3 

INDICATOR 

a4a3 

INDICATOR 

a4a3 

INDICATOR 

a4a3 

INDICATOR 

a4a3 

INDICATOR 

a4a3 

INDICATOR 

a4a3 

INDICATOR 

a4a3 

INDICATOR 

a4a3 

INDICATOR 

a4a3 

e_fixbb >= e_ispdf1ok + 
e_ispdf1okx 

e_ispdf1ok <= e_fixbb 

e_ispdf1okx <= e_fixbb 

e_ispdf1ok_ge30 <= 
e_ispdf1_ge30 

e_ispdf1ok_ge30 <= 
e_ispdf1ok 

e_ispdf1okx_30_100 <= 
e_ispdf1_30_100 

e_ispdf1okx_30_100 <= 
e_ispdf1okx 

e_ispdf1okx_ge100 <= 
e_ispdf1_ge100 

e_ispdf1okx_ge100 <= 
e_ispdf1okx 

e_ispdf1okx_ge30 <= 
e_ispdf1_ge30 

e_ispdf1okx_ge30 <= 
e_ispdf1okx 

e_ispdfokx_100_500 <= 
e_ispdf_100_500 

e_ispdfokx_100_500 <= 
e_ispdf1okx 

e_ispdfokx_500_1G <= 
e_ispdf_500_1G 

e_ispdfokx_500_1G <= 
e_ispdf1okx 

e_ispdfokx_ge1G <= 
e_ispdf_ge1G 

e_ispdfokx_ge1G <= 
e_ispdf1okx 

e_ispdfokx_lt30 <= 
e_ispdf_lt30 

e_ispdfokx_lt30 <= 
e_ispdf1okx 

INDICATOR 

a5 

e_empmd2_gt0 <= e_iuse 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

387 

 
A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

INDICATOR 

INDICATOR 

INDICATOR 

a5 

a5 

a5 

e_empmd2_gt0 <= e_pmd 

e_empmd2_gt0 = e_pmd 

e_empmd2_gt10 <= 
e_empmd2_gt0 

INDICATOR 

a5 

e_empmd2_gt10 <= e_iuse 

INDICATOR 

a5 

e_empmd2_gt20 <= 
e_empmd2_gt10 

INDICATOR 

a5 

e_empmd2_gt20 <= e_iuse 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

a5 

a5 

a5 

a5 

a5 

a5 

a5a2 

a5a2 

a5a2 

a5a2 

a5a2 

a5a2 

a5a2 

a6 

a6 

a6 

e_empmd2_gt50 <= 
e_empmd2_gt20 

e_empmd2_gt50 <= e_iuse 

e_iuse = e_pmd + e_pmdx 

e_iuse >= e_pmd + e_pmdx 

e_pmd <= e_iuse 

e_pmdx <= e_iuse 

e_broad3 <= e_fixbb + 
e_pmd 

e_broad3 <= e_iuse 

e_broad3x <= e_iuse 

e_fixbb <= e_broad3 

e_iuse = e_broad3 + 
e_broad3x 

e_iuse >= e_broad3 + 
e_broad3x 

e_pmd <= e_broad3 

e_iuse = e_ra_d + e_ra_dx 

e_iuse = e_ra_m + e_ra_mx 

e_iuse = e_ra_s + e_ra_sx 

a6 

e_iuse >= e_ra_d + e_ra_dx 

a6 

a6 

a6 

e_iuse >= e_ra_m + 
e_ra_mx 

e_iuse >= e_ra_s + e_ra_sx 

e_ra <= e_iuse 

388 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

a6 

a6 

a6 

a6 

a6 

a6 

a6 

a6 

a6 

e_ra <= e_ra_m + e_ra_d + 
e_ra_s 

e_ra_all <= e_iuse 

e_ra_all <= e_ra_d 

e_ra_all <= e_ra_m 

e_ra_all <= e_ra_s 

e_ra_d <= e_iuse 

e_ra_d <= e_ra 

e_ra_d <= e_ra_ds 

e_ra_ds <= e_iuse 

a6 

e_ra_ds <= e_ra_d + e_ra_s 

a6 

a6 

a6 

a6 

a6 

a6 

a6 

a6 

a7 

a7 

a7 

a7 

a8 

a8 

a8 

a8 

a9 

a9 

e_ra_dx <= e_iuse 

e_ra_m <= e_iuse 

e_ra_m <= e_ra 

e_ra_mx <= e_iuse 

e_ra_s <= e_iuse 

e_ra_s <= e_ra 

e_ra_s <= e_ra_ds 

e_ra_sx <= e_iuse 

e_iuse = e_rm + e_rmx 

e_iuse >= e_rm + e_rmx 

e_rm <= e_iuse 

e_rmx <= e_iuse 

e_ads <= e_iuse 

e_adsx <= e_iuse 

e_iuse = e_ads + e_adsx 

e_iuse >= e_ads + e_adsx 

e_ads = e_ads_kw + 
e_ads_kwx 

e_ads = e_ads_loc + 
e_ads_locx 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

389 

 
A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

a9 

a9 

a9 

a9 

a9 

a9 

a9 

a9 

a9 

a9 

a9 

a9 

a9 

a9 

a9 

a9 

a9 

a9 

a9 

b1 

b1 

b1 

b1 

b1 

e_ads = e_ads_oth + 
e_ads_othx 

e_ads = e_ads_trk + 
e_ads_trkx 

e_ads >= e_ads_kw + 
e_ads_kwx 

e_ads >= e_ads_loc + 
e_ads_locx 

e_ads >= e_ads_oth + 
e_ads_othx 

e_ads >= e_ads_trk + 
e_ads_trkx 

e_ads_kw <= e_ads 

e_ads_kw <= e_ads3 

e_ads_kwx <= e_ads 

e_ads_loc <= e_ads 

e_ads_loc <= e_ads3 

e_ads_locx <= e_ads 

e_ads_oth <= e_ads 

e_ads_othx <= e_ads 

e_ads_trk <= e_ads 

e_ads_trk <= e_ads3 

e_ads_trkx <= e_ads 

e_ads3 <= e_ads 

e_ads3 <= e_ads_kw + 
e_ads_trk + e_ads_loc 

e_aws_cboth <= 
e_aws_cmp 

e_aws_cboth <= 
e_aws_cown 

e_aws_cboth <= e_iuse 

e_aws_cmp <= e_awsell 

e_aws_cmp <= e_iuse 

390 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

b1 

b1 

b1 

b1 

b1 

b1 

b1 

b1 

b1 

b1 

b1 

b1 

b1 

b1 

b1 

b1 

b1 

b2 

b2 

b2 

b2 

b2 

e_aws_cmpq <= 
e_aws_cmp 

e_aws_cmpq <= 
e_aws_cownx 

e_aws_cmpq <= e_iuse 

e_aws_cmpx <= e_iuse 

e_aws_cown <= e_awsell 

e_aws_cown <= e_iuse 

e_aws_cownx <= e_iuse 

e_awsell <= e_aws_cmp + 
e_aws_cown 

e_awsell <= e_iuse 

e_awsell = e_aws_cmp + 
e_aws_cown - e_aws_cboth 

e_awsellx <= e_iuse 

e_iuse = e_aws_cmp + 
e_aws_cmpx 

e_iuse = e_aws_cown + 
e_aws_cownx 

e_iuse = e_awsell + 
e_awsellx 

e_iuse >= e_aws_cmp + 
e_aws_cmpx 

e_iuse >= e_aws_cown + 
e_aws_cownx 

e_iuse >= e_awsell + 
e_awsellx 

e_awsell = e_wsel0 + 
e_wselz 

e_awsell >= e_wsel0 + 
e_wselz 

e_awsval <= tovt 

e_awsval = e_awsvalb + 
e_awsvals 

e_awsvalb <= tovt 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

391 

 
A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

b2 

b2 

b2 

b2 

b2 

b2 

b2 

b2 

b2 

INDICATOR 

b2b1 

e_awsvals <= tovt 

e_wsel0 <= e_awsell 

e_wsel1 <= e_wsel0 

e_wsel10 <= e_wsel5 

e_wsel2 <= e_wsel1 

e_wsel25 <= e_wsel10 

e_wsel5 <= e_wsel2 

e_wsel50 <= e_wsel25 

e_wselz <= e_awsell 

e_awsval_cmpq <= 
e_awsval 

INDICATOR 

INDICATOR 

b3 

b3 

e_awsval = e_awsval_cown 
+ e_awsval_cmp 

e_awsval >= 
e_awsval_cown + 
e_awsval_cmp 

INDICATOR 

b3 

e_awsval_cmp <= e_awsval 

INDICATOR 

b3 

INDICATOR 

b3b2b1 

INDICATOR 

b3b2b1 

INDICATOR 

b3b2b1 

INDICATOR 

b3b2b1 

e_awsval_cown <= 
e_awsval 

e_aws_cmp_ge20 <= 
e_aws_cmp 

e_aws_cmp_ge50 <= 
e_aws_cmp 

e_aws_cmp_ge50 <= 
e_aws_cmp_ge20 

e_awsval_cmpq <= 
e_awsval_cmp 

INDICATOR 

b3b2b1 

t_aws_cmp_ge20 <= tovt 

INDICATOR 

b3b2b1 

t_aws_cmp_ge50 <= 
t_aws_cmp_ge20 

INDICATOR 

b3b2b1 

t_aws_cmp_ge50 <= tovt 

INDICATOR 

INDICATOR 

b4 

b4 

e_aws_b2bg <= e_awsell 

e_aws_b2c <= e_awsell 

392 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

b4 

b4 

b4 

b4 

b4 

b4 

b4 

b4 

b4 

b4 

INDICATOR 

b4b1 

INDICATOR 

b4b1 

INDICATOR 

b4b2 

INDICATOR 

b4b2 

INDICATOR 

b4b2 

INDICATOR 

b4b2 

INDICATOR 

b4b2 

INDICATOR 

b4b2 

INDICATOR 

b4b2 

INDICATOR 

b4b2 

INDICATOR 

b4b2 

e_awsell <= e_aws_b2c + 
e_aws_b2bg 

e_awsell >= ( e_aws_b2c + 
e_aws_b2bg ) / 2 

e_awsval = e_awsval_b2c + 
e_awsval_b2bg 

e_awsval >= e_awsval_b2c 
+ e_awsval_b2bg 

e_awsval_b2bg <= 
e_awsval 

e_awsval_b2bg <= e_eturn 

e_awsval_b2bg <= tovt 

e_awsval_b2c <= e_awsval 

e_awsval_b2c <= e_eturn 

e_awsval_b2c <= tovt 

e_aws_b2c_cmp <= 
e_aws_b2c 

e_aws_b2c_cmp <= 
e_aws_cmp 

e_aws_b2c_gt1ws <= 
e_aws_b2c 

e_aws_b2c_gt1ws <= 
e_awsell 

e_aws_gt1_b2c_gt10ws <= 
e_aws_b2c 

e_aws_gt1_b2c_gt10ws <= 
e_awsval_b2c_ge10ws 

e_awsval_b2c_ge10ws <= 
e_aws_b2c 

e_awsval_b2c_ge10ws <= 
e_awsell 

e_awsval_b2c_ge10ws <= 
e_awsval_b2c_ge5ws 

e_awsval_b2c_ge5ws <= 
e_aws_b2c 

e_awsval_b2c_ge5ws <= 
e_aws_b2c_gt1ws 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

393 

 
A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

INDICATOR 

b4b2 

INDICATOR 

b4b2 

INDICATOR 

b4b2 

INDICATOR 

b4b2 

INDICATOR 

b4b2b1 

INDICATOR 

b4b2b1 

e_awsval_b2c_ge5ws <= 
e_awsell 

e_awsval_gt1_b2c_gt10ws 
<= e_awsval_b2c 

e_awsval_gt1_b2c_gt10ws 
<= t_aws_gt1_b2c_gt10ws 

t_aws_gt1_b2c_gt10ws <= 
tovt 

e_awsval_b2c_ge10ws_cmp 
<= e_aws_cmp 

e_awsval_b2c_ge10ws_cmp 
<= e_awsval_b2c_ge10ws 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

b5 

b5 

b5 

b5 

b5 

b5 

INDICATOR 

b5b2b1 

INDICATOR 

b5b2b1 

INDICATOR 

b5b2b1 

INDICATOR 

b5b3 

INDICATOR 

b5b3 

INDICATOR 

b5b3 

INDICATOR 

b5b3 

INDICATOR 

b5b3 

INDICATOR 

b5b3 

e_awseu <= e_awsell 

e_awseu <= e_awsfor 

e_awsfor <= e_awseu + 
e_awsww 

e_awshm <= e_awsell 

e_awsww <= e_awsell 

e_awsww <= e_awsfor 

e_awsval_eu_cmpq <= 
e_awsval_eu_cmp 

e_awsval_hm_cmpq <= 
e_awsval_hm_cmp 

e_awsval_ww_cmpq <= 
e_awsval_ww_cmp 

e_awsval_eu_cmp <= 
e_awsval_cmp 

e_awsval_eu_cown <= 
e_awsval_cown 

e_awsval_hm_cmp <= 
e_awsval_cmp 

e_awsval_hm_cown <= 
e_awsval_cown 

e_awsval_ww_cmp <= 
e_awsval_cmp 

e_awsval_ww_cown <= 
e_awsval_cown 

394 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

e_awsval = e_awsval_hm + 
e_awsval_eu + 
e_awsval_ww 

e_awsval >= e_awsval_hm 
+ e_awsval_eu + 
e_awsval_ww 

e_awsval_eu <= e_awsval 

e_awsval_hm <= e_awsval 

e_awsval_ww <= e_awsval 

b6 

b6 

b6 

b6 

b6 

INDICATOR 

b6b5b2 

e_awsfor_ge20 <= e_awsfor 

INDICATOR 

b6b5b2 

e_awsfor_ge50 <= e_awsfor 

INDICATOR 

b6b5b2 

INDICATOR 

b6b5b2b1 

INDICATOR 

b6b5b2b1 

INDICATOR 

b6b5b2b1 

INDICATOR 

b6b5b2b1 

e_awsfor_ge50 <= 
e_awsfor_ge20 

e_awsfor_ge20_cmp <= 
e_aws_cmp 

e_awsfor_ge20_cmp <= 
e_awsfor_ge20 

e_awsfor_ge50_cmp <= 
e_aws_cmp 

e_awsfor_ge50_cmp <= 
e_awsfor_ge50 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

b7 

e_axsell <= e_iuse 

b7  e_axsell = e_xsel0 + e_xselz 

b7 

b7 

b7 

b7 

b7 

b7b1 

b7b1 

b7b1 

e_axsell >= e_xsel0 + 
e_xselz 

e_axsellx <= e_iuse 

e_axsellz <= e_iuse 

e_iuse = e_axsell + 
e_axsellx + e_axsellz 

e_iuse >= e_axsell + 
e_axsellx + e_axsellz 

e_aesell <= e_awsell + 
e_axsell 

e_aesell <= e_iuse 

e_awsell <= e_aesell 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

395 

 
A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

b7b1 

b7b2 

b7b2 

b8 

b8 

b8 

b8 

b8 

b8 

b8 

b8 

b8 

b8 

b8 

b8 

b8 

b8 

b8b2 

b8b2 

b8b2 

b8b2 

b8b2 

b8b2 

b8b2 

b8b2 

b8b2 

e_axsell <= e_aesell 

e_wsel1q <= e_axsellx 

e_wsel1q <= e_wsel1 

e_axsval <= tovt 

e_axsval = e_axsvalb + 
e_axsvals 

e_axsvalb <= e_axsval 

e_axsvalb <= tovt 

e_axsvals <= e_axsval 

e_axsvals <= tovt 

e_xsel0 <= e_axsell 

e_xsel1 <= e_xsel0 

e_xsel10 <= e_xsel5 

e_xsel2 <= e_xsel1 

e_xsel25 <= e_xsel10 

e_xsel5 <= e_xsel2 

e_xsel50 <= e_xsel25 

e_xselz <= e_axsell 

e_awsval <= e_eturn 

e_awsvalb <= e_eturn 

e_awsvals <= e_eturn 

e_axsval <= e_eturn 

e_axsvalb <= e_eturn 

e_axsvals <= e_eturn 

e_esell <= e_aesell 

e_eturn <= tovt 

e_eturn = e_awsval + 
e_axsval 

INDICATOR 

b8b2 

e_eturn = e_awsvals + 
e_awsvalb + e_axsvals + 
e_axsvalb 

396 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

INDICATOR 

b8b4b2 

e_awsval_b2c_ge10ec <= 
e_aws_b2c 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

c1 

c1 

c1 

c1 

c2 

c2 

c2 

c2 

c2 

c2 

c2 

c2 

c2 

c2 

c2 

c2 

c2c1 

c2c1 

c2c1 

c2c1 

c2c1 

c2c1 

c2c1 

c3 

c3 

c3 

c3 

e_itsp2 <= ent 

e_itsp2x <= ent 

ent = e_itsp2 + e_itsp2x 

ent >= e_itsp2 + e_itsp2x 

e_itspt2 <= e_itt2 

e_itspt2 <= ent 

e_itspt2x <= ent 

e_itt2 <= e_itspt2 + e_itust2 

e_itt2 <= ent 

e_itust2 <= e_itt2 

e_itust2 <= ent 

e_itust2x <= ent 

ent = e_itspt2 + e_itspt2x 

ent = e_itust2 + e_itust2x 

ent >= e_itspt2 + e_itspt2x 

ent >= e_itust2 + e_itust2x 

e_itsp2_itt2 <= e_itsp2 

e_itsp2_itt2 <= e_itt2 

e_itsp2_itt2 <= ent 

e_itsp2x_itt2 <= e_itsp2x 

e_itsp2x_itt2 <= e_itust2 

e_itsp2x_itt2 <= ent 

e_itspt2 <= e_itsp2 

e_itsprcr2 <= ent 

e_itsprcr2x <= ent 

ent = e_itsprcr2 + 
e_itsprcr2x 

ent >= e_itsprcr2 + 
e_itsprcr2x 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

397 

 
A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

INDICATOR 

c3c1 

e_itsp2 <= e_itsp2_or_rcr2 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

c3c1 

c3c1 

c4 

c4 

c4 

c4 

e_itsp2_or_rcr2 <= e_itsp2 + 
e_itsprcr2 

e_itsprcr2 <= 
e_itsp2_or_rcr2 

e_itsprcr2 = e_itspvac2 + 
e_itspvac2x 

e_itsprcr2 >= e_itspvac2 + 
e_itspvac2x 

e_itspvac2 <= e_itsprcr2 

e_itspvac2x <= e_itsprcr2 

INDICATOR 

c4c1 

e_itsp2_vac2 <= e_itsp2 

INDICATOR 

c4c1 

e_itsp2_vac2 <= e_itsprcr2 

INDICATOR 

c4c1 

e_itsp2_vac2 <= e_itspvac2 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

c5 

c5 

c5 

c5 

c5 

c5 

c5 

c5 

c5 

c5 

c5 

c5 

c5 

c5 

e_itspdla <= e_itspvac2 

e_itspdlax <= e_itspvac2 

e_itspdlet <= e_itspvac2 

e_itspdletx <= e_itspvac2 

e_itspdlwe <= e_itspvac2 

e_itspdlwex <= e_itspvac2 

e_itspdsal <= e_itspvac2 

e_itspdsalx <= e_itspvac2 

e_itspvac2 = e_itspdla + 
e_itspdlax 

e_itspvac2 = e_itspdlet + 
e_itspdletx 

e_itspvac2 = e_itspdlwe + 
e_itspdlwex 

e_itspvac2 = e_itspdsal + 
e_itspdsalx 

e_itspvac2 >= e_itspdla + 
e_itspdlax 

e_itspvac2 >= e_itspdlet + 
e_itspdletx 

398 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

c5 

c5 

c6 

c6 

c6 

c6 

c6 

c6 

c6 

c6 

c6 

c6 

e_itspvac2 >= e_itspdlwe + 
e_itspdlwex 

e_itspvac2 >= e_itspdsal + 
e_itspdsalx 

e_it_ext <= ent 

e_it_extq <= ent 

e_it_extx <= ent 

e_it_own <= ent 

e_it_ownq <= ent 

e_it_ownx <= ent 

ent = e_it_ext + e_it_extx 

ent = e_it_own + e_it_ownx 

ent >= e_it_ext + e_it_extx 

ent >= e_it_extq + e_it_own 

c6  ent >= e_it_own + e_it_ownx 

c6 

ent >= e_it_ownq + e_it_ext 

c6c1 

c6c1 

c6c1 

e_it_ext_itsp2 <= e_it_ext 

e_it_ext_itsp2 <= e_itsp2 

e_it_ext_itsp2 <= ent 

INDICATOR 

c6c1 

e_it_own_itsp2 <= e_it_own 

INDICATOR 

c6c1 

e_it_own_itsp2 <= e_itsp2 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

c6c1 

c6c2 

c6c2 

c6c2 

d1 

d1 

d1 

e_it_own_itsp2 <= ent 

e_it_ext_itt2 <= e_it_ext 

e_it_ext_itt2 <= e_itt2 

e_it_ext_itt2 <= ent 

e_iuse = e_secmdenc + 
e_secmdencx 

e_iuse = e_secmduo + 
e_secmduox 

e_iuse = e_secmlog + 
e_secmlogx 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

399 

 
A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

d1 

d1 

d1 

d1 

d1 

d1 

d1 

d1 

d1 

d1 

d1 

d1 

d1 

d1 

d1 

d1 

d1 

d1 

d1 

e_iuse = e_secmnac + 
e_secmnacx 

e_iuse = e_secmosbu + 
e_secmosbux 

e_iuse = e_secmrass + 
e_secmrassx 

e_iuse = e_secmsms1 + 
e_secmsms1x 

e_iuse = e_secmspsw + 
e_secmspswx 

e_iuse = e_secmtst + 
e_secmtstx 

e_iuse = e_secmuibm + 
e_secmuibmx 

e_iuse = e_secmvpn + 
e_secmvpnx 

e_iuse >= e_secmdenc + 
e_secmdencx 

e_iuse >= e_secmduo + 
e_secmduox 

e_iuse >= e_secmlog + 
e_secmlogx 

e_iuse >= e_secmnac + 
e_secmnacx 

e_iuse >= e_secmosbu + 
e_secmosbux 

e_iuse >= e_secmrass + 
e_secmrassx 

e_iuse >= e_secmsms1 + 
e_secmsms1x 

e_iuse >= e_secmspsw + 
e_secmspswx 

e_iuse >= e_secmtst + 
e_secmtstx 

e_iuse >= e_secmuibm + 
e_secmuibmx 

e_iuse >= e_secmvpn + 
e_secmvpnx 

400 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

INDICATOR 

d1 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

d1 

d1 

d1 

e_secmall <= e_iuse 

e_secmall <= e_secmge7 

e_secmdenc <= e_iuse 

d1 

e_secmdenc <= e_secmge1 

d1 

d1 

d1 

d1 

d1 

d1 

d1 

d1 

d1 

d1 

d1 

d1 

d1 

d1 

d1 

d1 

d1 

e_secmdencx <= e_iuse 

e_secmduo <= e_iuse 

e_secmduo <= e_secmge1 

e_secmduox <= e_iuse 

e_secmge1 <= e_secmspsw 
+ e_secmuibm + 
e_secmduo + e_secmdenc 
+ e_secmosbu + e_secmnac 
+ e_secmvpn + 
e_secmsms1 + e_secmlog + 
e_secmrass + e_secmtst 

e_secmge3 <= e_iuse 

e_secmge3 <= e_secmge1 

e_secmge5 <= e_iuse 

e_secmge5 <= e_secmge3 

e_secmge7 <= e_iuse 

e_secmge7 <= e_secmge5 

e_secmlog <= e_iuse 

e_secmlog <= e_secmge1 

e_secmlogx <= e_iuse 

e_secmnac <= e_iuse 

e_secmnac <= e_secmge1 

e_secmnacx <= e_iuse 

e_secmosbu <= e_iuse 

d1 

e_secmosbu <= e_secmge1 

d1 

d1 

d1 

e_secmosbux <= e_iuse 

e_secmrass <= e_iuse 

e_secmrass <= e_secmge1 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

401 

 
A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

d1 

d1 

e_secmrassx <= e_iuse 

e_secmsms1 <= e_iuse 

d1  e_secmsms1 <= e_secmge1 

d1 

d1 

e_secmsms1x <= e_iuse 

e_secmspsw <= e_iuse 

d1 

e_secmspsw <= e_secmge1 

d1 

d1 

d1 

d1 

d1 

e_secmspswx <= e_iuse 

e_secmtst <= e_iuse 

e_secmtst <= e_secmge1 

e_secmtstx <= e_iuse 

e_secmuibm <= e_iuse 

d1 

e_secmuibm <= e_secmge1 

d1 

d1 

d1 

d1 

d2 

d2 

d2 

d2 

d2 

d2 

d2 

d2 

d2 

e_secmuibmx <= e_iuse 

e_secmvpn <= e_iuse 

e_secmvpn <= e_secmge1 

e_secmvpnx <= e_iuse 

e_iuse = e_secawany + 
e_secawnone 

e_iuse = e_secawcont + 
e_secawcontx 

e_iuse = e_secawctp + 
e_secawctpx 

e_iuse = e_secawvtgi + 
e_secawvtgix 

e_iuse >= e_secawany + 
e_secawnone 

e_iuse >= e_secawcont + 
e_secawcontx 

e_iuse >= e_secawcont + 
e_secawnone 

e_iuse >= e_secawctp + 
e_secawctpx 

e_iuse >= e_secawctp + 
e_secawnone 

402 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

d2 

d2 

d2 

d2 

d2 

d2 

d2 

d2 

e_iuse >= e_secawvtgi + 
e_secawnone 

e_iuse >= e_secawvtgi + 
e_secawvtgix 

e_secawany <= e_iuse 

e_secawany <= e_secawvtgi 
+ e_secawctp + 
e_secawcont 

e_secawcont <= e_iuse 

e_secawcont <= 
e_secawany 

e_secawcontx <= e_iuse 

e_secawctp <= e_iuse 

d2 

e_secawctp <= e_secawany 

d2 

d2 

d2 

e_secawctpx <= e_iuse 

e_secawnone <= e_iuse 

e_secawvtgi <= e_iuse 

d2  e_secawvtgi <= e_secawany 

d2 

e_secawvtgix <= e_iuse 

d2c2 

d2c2 

d2c2 

d2c2 

d2c2 

d2c2 

d2c2 

d2c2 

e_secawany_itspt2 <= 
e_itspt2 

e_secawany_itspt2 <= 
e_iuse 

e_secawany_itspt2 <= 
e_secawany 

e_secawany_itust2 <= 
e_itust2 

e_secawany_itust2 <= 
e_iuse 

e_secawany_itust2 <= 
e_secawany 

e_secawcont_itspt2 <= 
e_itspt2 

e_secawcont_itspt2 <= 
e_iuse 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

403 

 
A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

d2c2 

d2c2 

d2c2 

d2c2 

d2c2 

d2c2 

d2c2 

d2c2 

d2c2 

d2c2 

d2c2 

d2c2 

d2c2 

d2c2 

d2c2 

d2c2 

d3 

d3 

d3 

d3 

e_secawcont_itspt2 <= 
e_secawcont 

e_secawcont_itust2 <= 
e_itust2 

e_secawcont_itust2 <= 
e_iuse 

e_secawcont_itust2 <= 
e_secawcont 

e_secawctp_itspt2 <= 
e_itspt2 

e_secawctp_itspt2 <= 
e_iuse 

e_secawctp_itspt2 <= 
e_secawctp 

e_secawctp_itust2 <= 
e_itust2 

e_secawctp_itust2 <= 
e_iuse 

e_secawctp_itust2 <= 
e_secawctp 

e_secawvtgi_itspt2 <= 
e_itspt2 

e_secawvtgi_itspt2 <= 
e_iuse 

e_secawvtgi_itspt2 <= 
e_secawvtgi 

e_secawvtgi_itust2 <= 
e_itust2 

e_secawvtgi_itust2 <= 
e_iuse 

e_secawvtgi_itust2 <= 
e_secawvtgi 

e_iuse = e_secpol2 + 
e_secpol2x 

e_iuse >= e_secpol2 + 
e_secpol2x 

e_secpol2 <= e_iuse 

e_secpol2x <= e_iuse 

404 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

INDICATOR 

d3c1 

e_secpol2_itsp2 <= e_itsp2 

INDICATOR 

d3c1 

e_secpol2_itsp2 <= e_iuse 

INDICATOR 

d3c1 

e_secpol2_itsp2 <= 
e_secpol2 

INDICATOR 

d3d2 

e_secawany_pol2 <= e_iuse 

INDICATOR 

d3d2 

INDICATOR 

d3d2 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

d4 

d4 

d4 

d4 

e_secawany_pol2 <= 
e_secawany 

e_secawany_pol2 <= 
e_secpol2 

e_secpol2 = e_secprev_cy + 
e_secprev_1_2 + 
e_secprev_mt2 

e_secpol2 >= e_secprev_cy 
+ e_secprev_1_2 + 
e_secprev_mt2 

e_secprev_1_2 <= 
e_secpol2 

e_secprev_1_2 <= 
e_secprev_le2 

INDICATOR 

d4 

e_secprev_cy <= e_secpol2 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

d4 

d4 

d4 

d4 

d5 

d5 

d5 

d5 

e_secprev_cy <= 
e_secprev_le2 

e_secprev_le2 <= 
e_secpol2 

e_secprev_le2 = 
e_secprev_cy + 
e_secprev_1_2 

e_secprev_mt2 <= 
e_secpol2 

e_iuse = e_sec2icnfa + 
e_sec2icnfax 

e_iuse = e_sec2icnff + 
e_sec2icnffx 

e_iuse = e_sec2idcda + 
e_sec2idcdax 

e_iuse = e_sec2idcdf + 
e_sec2idcdfx 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

405 

 
A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

d5 

d5 

d5 

d5 

d5 

d5 

d5 

d5 

d5 

d5 

d5 

d5 

d5 

d5 

d5 

d5 

d5 

d5 

d5 

d5 

d5 

d5 

e_iuse = e_sec2iusva + 
e_sec2iusvax 

e_iuse = e_sec2iusvf + 
e_sec2iusvfx 

e_iuse >= e_sec2icnfa + 
e_sec2icnfax 

e_iuse >= e_sec2icnff + 
e_sec2icnffx 

e_iuse >= e_sec2idcda + 
e_sec2idcdax 

e_iuse >= e_sec2idcdf + 
e_sec2idcdfx 

e_iuse >= e_sec2iusva + 
e_sec2iusvax 

e_iuse >= e_sec2iusvf + 
e_sec2iusvfx 

e_sec2iany <= e_iuse 

e_sec2iany <= e_sec2icnfa 
+ e_sec2icnff + e_sec2idcda 
+ e_sec2idcdf + 
e_sec2iusva + e_sec2iusvf 

e_sec2icnf <= e_iuse 

e_sec2icnf <= e_sec2icnff + 
e_sec2icnfa 

e_sec2icnfa <= e_iuse 

e_sec2icnfa <= e_sec2iany 

e_sec2icnfa <= e_sec2icnf 

e_sec2icnfax <= e_iuse 

e_sec2icnff <= e_iuse 

e_sec2icnff <= e_sec2iany 

e_sec2icnff <= e_sec2icnf 

e_sec2icnffx <= e_iuse 

e_sec2idcd <= e_iuse 

e_sec2idcd <= e_sec2idcdf 
+ e_sec2idcda 

406 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

d5 

d5 

d5 

d5 

d5 

d5 

d5 

d5 

d5 

d5 

d5 

d5 

d5 

d5 

d5 

d5 

d5 

d5 

d5 

d5 

e1 

e1 

e1 

e1 

e1 

e1 

e1 

e_sec2idcda <= e_iuse 

e_sec2idcda <= e_sec2iany 

e_sec2idcda <= e_sec2idcd 

e_sec2idcdax <= e_iuse 

e_sec2idcdf <= e_iuse 

e_sec2idcdf <= e_sec2iany 

e_sec2idcdf <= e_sec2idcd 

e_sec2idcdfx <= e_iuse 

e_sec2ige3 <= e_iuse 

e_sec2ige3 <= e_sec2iany 

e_sec2iusv <= e_iuse 

e_sec2iusv <= e_sec2iusvf 
+ e_sec2iusva 

e_sec2iusva <= e_iuse 

e_sec2iusva <= e_sec2iany 

e_sec2iusva <= e_sec2iusv 

e_sec2iusvax <= e_iuse 

e_sec2iusvf <= e_iuse 

e_sec2iusvf <= e_sec2iany 

e_sec2iusvf <= e_sec2iusv 

e_sec2iusvfx <= e_iuse 

e_ai_tany <= e_ai_ttm + 
e_ai_tsr + e_ai_tnlg + 
e_ai_tir + e_ai_tml + 
e_ai_tpa + e_ai_tar 

e_ai_tany <= e_iuse 

e_ai_tar <= e_ai_tany 

e_ai_tar <= e_iuse 

e_ai_tarx <= e_iuse 

e_ai_tge2 <= e_ai_tany 

e_ai_tge2 <= e_iuse 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

407 

 
A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

e1 

e1 

e1 

e1 

e1 

e1 

e1 

e1 

e1 

e1 

e1 

e1 

e1 

e1 

e1 

e1 

e1 

e1 

e1 

e1 

e1 

e1 

e1 

e_ai_tge3 <= e_ai_tge2 

e_ai_tge3 <= e_iuse 

e_ai_tir <= e_ai_tany 

e_ai_tir <= e_iuse 

e_ai_tirx <= e_iuse 

e_ai_tml <= e_ai_tany 

e_ai_tml <= e_iuse 

e_ai_tmlx <= e_iuse 

e_ai_tnlg <= e_ai_tany 

e_ai_tnlg <= e_iuse 

e_ai_tnlgx <= e_iuse 

e_ai_tpa <= e_ai_tany 

e_ai_tpa <= e_iuse 

e_ai_tpax <= e_iuse 

e_ai_tsr <= e_ai_tany 

e_ai_tsr <= e_iuse 

e_ai_tsrx <= e_iuse 

e_ai_ttm <= e_ai_tany 

e_ai_ttm <= e_iuse 

e_ai_ttmx <= e_iuse 

e_ai_tx <= e_iuse 

e_iuse = e_ai_tany + 
e_ai_tx 

e_iuse = e_ai_tar + 
e_ai_tarx 

INDICATOR 

e1 

e_iuse = e_ai_tir + e_ai_tirx 

INDICATOR 

INDICATOR 

INDICATOR 

e1 

e1 

e1 

e_iuse = e_ai_tml + 
e_ai_tmlx 

e_iuse = e_ai_tnlg + 
e_ai_tnlgx 

e_iuse = e_ai_tpa + 
e_ai_tpax 

408 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

e1 

e1 

e1 

e1 

e1 

e1 

e1 

e1 

e1 

e1 

e2 

e2 

e2 

e2 

e2 

e2 

e2 

e2 

e2 

e2 

e2 

e2 

e_iuse = e_ai_tsr + 
e_ai_tsrx 

e_iuse = e_ai_ttm + 
e_ai_ttmx 

e_iuse >= e_ai_tany + 
e_ai_tx 

e_iuse >= e_ai_tar + 
e_ai_tarx 

e_iuse >= e_ai_tir + 
e_ai_tirx 

e_iuse >= e_ai_tml + 
e_ai_tmlx 

e_iuse >= e_ai_tnlg + 
e_ai_tnlgx 

e_iuse >= e_ai_tpa + 
e_ai_tpax 

e_iuse >= e_ai_tsr + 
e_ai_tsrx 

e_iuse >= e_ai_ttm + 
e_ai_ttmx 

e_ai_p1any <= e_ai_pms + 
e_ai_ppp + e_ai_pbam + 
e_ai_plog + e_ai_pits + 
e_ai_pfin + e_ai_prdi 

e_ai_p1any <= e_ai_tany 

e_ai_p1ge2 <= e_ai_p1any 

e_ai_p1ge2 <= e_ai_tany 

e_ai_p1ge3 <= e_ai_p1ge2 

e_ai_p1ge3 <= e_ai_tany 

e_ai_pbam <= e_ai_p1any 

e_ai_pbam <= e_ai_tany 

e_ai_pbamx <= e_ai_tany 

e_ai_pfin <= e_ai_p1any 

e_ai_pfin <= e_ai_tany 

e_ai_pfinx <= e_ai_tany 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

409 

 
A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

e2 

e2 

e2 

e2 

e2 

e2 

e2 

e2 

e2 

e2 

e2 

e2 

e2 

e2 

e2 

e2 

e2 

e2 

e2 

e2 

e2 

e2 

e2 

e2 

e_ai_pits <= e_ai_p1any 

e_ai_pits <= e_ai_tany 

e_ai_pitsx <= e_ai_tany 

e_ai_plog <= e_ai_p1any 

e_ai_plog <= e_ai_tany 

e_ai_plogx <= e_ai_tany 

e_ai_pms <= e_ai_p1any 

e_ai_pms <= e_ai_tany 

e_ai_pmsx <= e_ai_tany 

e_ai_ppp <= e_ai_p1any 

e_ai_ppp <= e_ai_tany 

e_ai_pppx <= e_ai_tany 

e_ai_prdi <= e_ai_p1any 

e_ai_prdi <= e_ai_tany 

e_ai_prdix <= e_ai_tany 

e_ai_tany = e_ai_pbam + 
e_ai_pbamx 

e_ai_tany = e_ai_pfin + 
e_ai_pfinx 

e_ai_tany = e_ai_pits + 
e_ai_pitsx 

e_ai_tany = e_ai_plog + 
e_ai_plogx 

e_ai_tany = e_ai_pms + 
e_ai_pmsx 

e_ai_tany = e_ai_ppp + 
e_ai_pppx 

e_ai_tany = e_ai_prdi + 
e_ai_prdix 

e_ai_tany >= e_ai_pbam + 
e_ai_pbamx 

e_ai_tany >= e_ai_pfin + 
e_ai_pfinx 

410 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

e2 

e2 

e2 

e2 

e2 

e3 

e3 

e3 

e3 

e3 

e3 

e3 

e3 

e3 

e3 

e3 

e3 

e3 

e3 

e3 

e3 

e3 

e_ai_tany >= e_ai_pits + 
e_ai_pitsx 

e_ai_tany >= e_ai_plog + 
e_ai_plogx 

e_ai_tany >= e_ai_pms + 
e_ai_pmsx 

e_ai_tany >= e_ai_ppp + 
e_ai_pppx 

e_ai_tany >= e_ai_prdi + 
e_ai_prdix 

e_ai_adown <= e_ai_tany 

e_ai_adownx <= e_ai_tany 

e_ai_aext <= e_ai_tany 

e_ai_aextx <= e_ai_tany 

e_ai_amown <= e_ai_tany 

e_ai_amownx <= e_ai_tany 

e_ai_aos <= e_ai_tany 

e_ai_aosx <= e_ai_tany 

e_ai_ardy <= e_ai_tany 

e_ai_ardyx <= e_ai_tany 

e_ai_tany = e_ai_adown + 
e_ai_adownx 

e_ai_tany = e_ai_aext + 
e_ai_aextx 

e_ai_tany = e_ai_amown + 
e_ai_amownx 

e_ai_tany = e_ai_aos + 
e_ai_aosx 

e_ai_tany = e_ai_ardy + 
e_ai_ardyx 

e_ai_tany >= e_ai_adown + 
e_ai_adownx 

e_ai_tany >= e_ai_aext + 
e_ai_aextx 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

411 

 
A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

e3 

e3 

e3 

e4 

e4 

e4 

e4 

e5 

e5 

e5 

e5 

INDICATOR 

e5e3 

INDICATOR 

e5e3 

INDICATOR 

e5e3 

INDICATOR 

e5e3 

e_ai_tany >= e_ai_amown + 
e_ai_amownx 

e_ai_tany >= e_ai_aos + 
e_ai_aosx 

e_ai_tany >= e_ai_ardy + 
e_ai_ardyx 

e_ai_pdi <= e_ai_tany 

e_ai_pdix <= e_ai_tany 

e_ai_tany = 
e_ai_pdi+e_ai_pdix 

e_ai_tany >= 
e_ai_pdi+e_ai_pdix 

e_ai_bias <= e_ai_pdi 

e_ai_biasx <= e_ai_pdi 

e_ai_pdi = 
e_ai_bias+e_ai_biasx 

e_ai_pdi >= 
e_ai_bias+e_ai_biasx 

e_ai_bias_adown <= 
e_ai_adown 

e_ai_bias_adown <= 
e_ai_bias 

e_ai_bias_adown <= 
e_ai_pdi 

e_ai_bias_aext <= 
e_ai_aext 

INDICATOR 

e5e3  e_ai_bias_aext <= e_ai_bias 

INDICATOR 

e5e3 

e_ai_bias_aext <= e_ai_pdi 

INDICATOR 

e5e3 

INDICATOR 

e5e3 

INDICATOR 

e5e3 

e_ai_bias_amown <= 
e_ai_amown 

e_ai_bias_amown <= 
e_ai_bias 

e_ai_bias_amown <= 
e_ai_pdi 

INDICATOR 

e5e3 

e_ai_bias_aos <= e_ai_aos 

INDICATOR 

e5e3 

e_ai_bias_aos <= e_ai_bias 

412 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

INDICATOR 

e5e3 

e_ai_bias_aos <= e_ai_pdi 

INDICATOR 

e5e3 

INDICATOR 

e5e3 

e_ai_bias_ardy <= 
e_ai_ardy 

e_ai_bias_ardy <= 
e_ai_bias 

INDICATOR 

e5e3 

e_ai_bias_ardy <= e_ai_pdi 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

e6 

e6 

e6 

e6 

e7 

e7 

e7 

e7 

e7 

e7 

e7 

e7 

e7 

e7 

e7 

e7 

e7 

e7 

e7 

e7 

e7 

e7 

e_ai_ec <= e_ai_tx 

e_ai_ecx <= e_ai_tx 

e_ai_tx = e_ai_ec + 
e_ai_ecx 

e_ai_tx >= e_ai_ec + 
e_ai_ecx 

e_ai_bcdp <= e_ai_ec 

e_ai_bcdpx <= e_ai_ec 

e_ai_bcst <= e_ai_ec 

e_ai_bcstx <= e_ai_ec 

e_ai_bddt <= e_ai_ec 

e_ai_bddtx <= e_ai_ec 

e_ai_bec <= e_ai_ec 

e_ai_becx <= e_ai_ec 

e_ai_binc <= e_ai_ec 

e_ai_bincx <= e_ai_ec 

e_ai_ble <= e_ai_ec 

e_ai_bleg <= e_ai_ec 

e_ai_blegx <= e_ai_ec 

e_ai_blex <= e_ai_ec 

e_ai_bnu <= e_ai_ec 

e_ai_bnux <= e_ai_ec 

e_ai_ec = e_ai_bcdp + 
e_ai_bcdpx 

e_ai_ec = e_ai_bcst + 
e_ai_bcstx 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

413 

 
A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

e7 

e7 

e7 

e7 

e7 

e7 

e7 

e7 

e7 

e7 

e7 

e7 

e7 

e7 

e_ai_ec = e_ai_bddt + 
e_ai_bddtx 

e_ai_ec = e_ai_bec + 
e_ai_becx 

e_ai_ec = e_ai_binc + 
e_ai_bincx 

e_ai_ec = e_ai_ble + 
e_ai_blex 

e_ai_ec = e_ai_bleg + 
e_ai_blegx 

e_ai_ec = e_ai_bnu + 
e_ai_bnux 

e_ai_ec >= e_ai_bcdp + 
e_ai_bcdpx 

e_ai_ec >= e_ai_bcst + 
e_ai_bcstx 

e_ai_ec >= e_ai_bddt + 
e_ai_bddtx 

e_ai_ec >= e_ai_bec + 
e_ai_becx 

e_ai_ec >= e_ai_binc + 
e_ai_bincx 

e_ai_ec >= e_ai_ble + 
e_ai_blex 

e_ai_ec >= e_ai_bleg + 
e_ai_blegx 

e_ai_ec >= e_ai_bnu + 
e_ai_bnux 

p_a1 

p_a1 

p_a1 

p_a1 

p_b1 

p_b7 

empl = p_iuse + p_iusex 

empl >= p_iuse + p_iusex 

p_iuse <= empl 

p_iusex <= empl 

p_awsell <= empl 

p_axsell <= empl 

INDICATOR 

p_b7b1 

p_aesell <= empl 

414 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

INDICATOR 

p_b7b1 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

p_b7b1 

p_b7b1 

xc3 

xc3 

xc3 

xc3 

xc3 

xc3 

xc3 

xc3 

xc3 

xc3 

xc3 

xc3 

xc3 

xc3 

xc3 

xc3 

xc3 

p_aesell <= p_awsell + 
p_axsell 

p_awsell <= p_aesell 

p_axsell <= p_aesell 

e_daown <= e_iuse 

e_daown = e_dascrm + 
e_dascrmx 

e_daown = e_daserp + 
e_daserpx 

e_daown = e_dasgov + 
e_dasgovx 

e_daown = e_dasloc + 
e_daslocx 

e_daown = e_dassat + 
e_dassatx 

e_daown = e_dassds + 
e_dassdsx 

e_daown = e_dassm + 
e_dassmx 

e_daown = e_dasweb + 
e_daswebx 

e_daown >= e_dascrm + 
e_dascrmx 

e_daown >= e_daserp + 
e_daserpx 

e_daown >= e_dasgov + 
e_dasgovx 

e_daown >= e_dasloc + 
e_daslocx 

e_daown >= e_dassat + 
e_dassatx 

e_daown >= e_dassds + 
e_dassdsx 

e_daown >= e_dassm + 
e_dassmx 

e_daown >= e_dasweb + 
e_daswebx 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

415 

 
A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

INDICATOR 

INDICATOR 

INDICATOR 

xc3 

xc3 

xc3 

INDICATOR 

xc4 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

xc4 

xc4 

xc4 

xc4 

xc4 

xc4 

xc4 

xc4 

xc4 

xc4 

xc4 

xc4 

xc4 

xc4 

xc4 

xc4 

xc4 

xc4 

xc4 

xc4 

xc4 

e_daownx <= e_iuse 

e_iuse = e_daown + 
e_daownx 

e_iuse >= e_daown + 
e_daownx 

e_dasany <= e_daserp + 
e_dascrm + e_dassm + 
e_dasweb + e_dasloc + 
e_dassds + e_dasgov + 
e_dassat 

e_dasany <= e_iuse 

e_dasany2 <= e_daown 

e_dasany2 <= e_dasany 

e_dasany2 <= e_dasweb + 
e_dasloc + e_dassds + 
e_dasgov + e_dassat 

e_dasany2 <= e_iuse 

e_dascrm <= e_daown 

e_dascrm <= e_dasany 

e_dascrm <= e_iuse 

e_dascrmx <= e_daown 

e_dascrmx <= e_iuse 

e_daserp <= e_daown 

e_daserp <= e_dasany 

e_daserp <= e_iuse 

e_daserpx <= e_daown 

e_daserpx <= e_iuse 

e_dasge3 <= e_daown 

e_dasge3 <= e_dasany 

e_dasge3 <= e_iuse 

e_dasgov <= e_daown 

e_dasgov <= e_dasany 

e_dasgov <= e_dasany2 

416 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

xc4 

xc4 

xc4 

xc4 

xc4 

xc4 

xc4 

xc4 

xc4 

xc4 

xc4 

xc4 

xc4 

xc4 

xc4 

xc4 

xc4 

xc4 

xc4 

xc4 

xc4 

xc4 

xc4 

xc4 

xc4 

xc4 

xc4 

xc4 

xc4 

e_dasgov <= e_iuse 

e_dasgovx <= e_daown 

e_dasgovx <= e_iuse 

e_dasloc <= e_daown 

e_dasloc <= e_dasany 

e_dasloc <= e_dasany2 

e_dasloc <= e_iuse 

e_daslocx <= e_daown 

e_daslocx <= e_iuse 

e_dassat <= e_daown 

e_dassat <= e_dasany 

e_dassat <= e_dasany2 

e_dassat <= e_iuse 

e_dassatx <= e_daown 

e_dassatx <= e_iuse 

e_dassds <= e_daown 

e_dassds <= e_dasany 

e_dassds <= e_dasany2 

e_dassds <= e_iuse 

e_dassdsx <= e_daown 

e_dassdsx <= e_iuse 

e_dassm <= e_daown 

e_dassm <= e_dasany 

e_dassm <= e_iuse 

e_dassmx <= e_daown 

e_dassmx <= e_iuse 

e_dasweb <= e_daown 

e_dasweb <= e_dasany 

e_dasweb <= e_dasany2 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

417 

 
A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

INDICATOR 

INDICATOR 

INDICATOR 

xc4 

xc4 

xc4 

e_dasweb <= e_iuse 

e_daswebx <= e_daown 

e_daswebx <= e_iuse 

INDICATOR 

xc4a8 

e_dasany_ads <= e_ads 

INDICATOR 

xc4a8 

e_dasany_ads <= e_dasany 

INDICATOR 

xc4a8 

e_dasany_ads <= e_iuse 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

xc5 

xc5 

xc5 

xc5 

e_daext <= e_iuse 

e_daextx <= e_iuse 

e_iuse = e_daext + 
e_daextx 

e_iuse >= e_daext + 
e_daextx 

INDICATOR 

xc5xc3  e_da <= e_daown + e_daext 

INDICATOR 

INDICATOR 

xc5xc3 

xc5xc3 

e_da >= e_daext 

e_da >= e_daown 

INDICATOR 

xc5xc3e1 

e_ai_da <= e_ai_tany 

INDICATOR 

xc5xc3e1 

e_ai_da <= e_da 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

xd1 

xd1 

xd1 

xd1 

xd1e1 

xd1e1 

e_cc <= e_iuse 

e_ccx <= e_iuse 

e_iuse = e_cc + e_ccx 

e_iuse >= e_cc + e_ccx 

e_ai_cc <= e_ai_tany 

e_ai_cc <= e_cc 

INDICATOR 

xd1xc5xc3 

e_cc_da <= e_cc 

INDICATOR 

xd1xc5xc3 

e_cc_da <= e_da 

INDICATOR 

INDICATOR 

INDICATOR 

xd2 

xd2 

xd2 

e_cc = e_cc_pcpu + 
e_cc_pcpux 

e_cc = e_cc_pcrm + 
e_cc_pcrmx 

e_cc = e_cc_pdb + 
e_cc_pdbx 

418 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

xd2 

xd2 

xd2 

xd2 

e_cc = e_cc_pdev + 
e_cc_pdevx 

e_cc = e_cc_pem + 
e_cc_pemx 

e_cc = e_cc_perp + 
e_cc_perpx 

e_cc = e_cc_pfacc + 
e_cc_pfaccx 

INDICATOR 

xd2  e_cc = e_cc_pfil + e_cc_pfilx 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

e_cc = e_cc_psec + 
e_cc_psecx 

e_cc = e_cc_psoft + 
e_cc_psoftx 

e_cc = e_cc1_pany + 
e_cc1_pnone 

e_cc >= e_cc_pcpu + 
e_cc_pcpux 

e_cc >= e_cc_pcrm + 
e_cc_pcrmx 

e_cc >= e_cc_pdb + 
e_cc_pdbx 

e_cc >= e_cc_pdev + 
e_cc_pdevx 

e_cc >= e_cc_pem + 
e_cc_pemx 

e_cc >= e_cc_perp + 
e_cc_perpx 

e_cc >= e_cc_pfacc + 
e_cc_pfaccx 

e_cc >= e_cc_pfil + 
e_cc_pfilx 

e_cc >= e_cc_psec + 
e_cc_psecx 

e_cc >= e_cc_psoft + 
e_cc_psoftx 

e_cc >= e_cc1_pany + 
e_cc1_pnone 

e_cc_pcpu <= e_cc 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

419 

 
A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

e_cc_pcpu <= e_cc_phw 

e_cc_pcpu <= e_cc1_pany 

e_cc_pcpux <= e_cc 

e_cc_pcrm <= e_cc 

e_cc_pcrm <= e_cc1_pany 

e_cc_pcrm <= e_cc1_si 

e_cc_pcrmx <= e_cc 

e_cc_pdb <= e_cc 

e_cc_pdb <= e_cc_pdbfil 

e_cc_pdb <= e_cc_phw 

e_cc_pdb <= e_cc1_pany 

e_cc_pdb <= e_cc1_s 

e_cc_pdb <= e_cc1_si 

e_cc_pdbfil <= e_cc 

e_cc_pdbfil <= e_cc_pdb + 
e_cc_pfil 

e_cc_pdbx <= e_cc 

e_cc_pdev <= e_cc 

e_cc_pdev <= e_cc1_pany 

e_cc_pdev <= e_cc1_s 

e_cc_pdev <= e_cc1_si 

e_cc_pdevx <= e_cc 

e_cc_pem <= e_cc 

e_cc_pem <= e_cc1_pany 

e_cc_pemx <= e_cc 

e_cc_perp <= e_cc 

e_cc_perp <= e_cc1_pany 

e_cc_perp <= e_cc1_si 

e_cc_perpx <= e_cc 

e_cc_pfacc <= e_cc 

420 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

xd2 

e_cc_pfacc <= e_cc1_pany 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

e_cc_pfacc <= e_cc1_si 

e_cc_pfaccx <= e_cc 

e_cc_pfil <= e_cc 

e_cc_pfil <= e_cc_pdbfil 

e_cc_pfil <= e_cc_phw 

e_cc_pfil <= e_cc1_pany 

e_cc_pfilx <= e_cc 

e_cc_phw <= e_cc 

e_cc_phw <= e_cc_pdb + 
e_cc_pfil + e_cc_pcpu 

e_cc_psec <= e_cc 

e_cc_psec <= e_cc1_pany 

e_cc_psec <= e_cc1_s 

e_cc_psec <= e_cc1_si 

e_cc_psecx <= e_cc 

e_cc_psoft <= e_cc 

e_cc_psoft <= e_cc1_pany 

e_cc_psoftx <= e_cc 

e_cc1_b <= e_cc 

e_cc1_b <= e_cc_pcrmx 

e_cc1_b <= e_cc_pdbx 

e_cc1_b <= e_cc_pdevx 

e_cc1_b <= e_cc_pem + 
e_cc_psoft + e_cc_pfil + 
e_cc_pcpu 

e_cc1_b <= e_cc_perpx 

e_cc1_b <= e_cc_pfaccx 

e_cc1_b <= e_cc_psecx 

e_cc1_bi <= e_cc 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

421 

 
A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

INDICATOR 

xd2 

INDICATOR 

INDICATOR 

INDICATOR 

xd2 

xd2 

xd2 

e_cc1_bi <= e_cc_pdbx 

e_cc1_bi <= e_cc_pdevx 

e_cc1_bi <= e_cc_pem + 
e_cc_psoft + e_cc_pfil + 
e_cc_pcpu + e_cc_pfacc + 
e_cc_perp + e_cc_pcrm 

e_cc1_bi <= e_cc_psecx 

e_cc1_bi = e_cc1_b + 
e_cc1_i 

e_cc1_i <= e_cc 

e_cc1_i <= e_cc_pdbx 

e_cc1_i <= e_cc_pdevx 

e_cc1_i <= e_cc_pfacc + 
e_cc_perp + e_cc_pcrm 

e_cc1_i <= e_cc_psecx 

e_cc1_pany <= e_cc 

e_cc1_pany <= e_cc_pem + 
e_cc_psoft + e_cc_pfacc + 
e_cc_perp + e_cc_pcrm + 
e_cc_psec + e_cc_pdb + 
e_cc_pfil + e_cc_pcpu + 
e_cc_pdev 

e_cc1_pnone <= e_cc 

e_cc1_pnone <= 
e_cc_pcpux 

e_cc1_pnone <= 
e_cc_pcrmx 

INDICATOR 

xd2 

e_cc1_pnone <= e_cc_pdbx 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

xd2 

xd2 

xd2 

xd2 

e_cc1_pnone <= 
e_cc_pdevx 

e_cc1_pnone <= 
e_cc_pemx 

e_cc1_pnone <= 
e_cc_perpx 

e_cc1_pnone <= 
e_cc_pfaccx 

422 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

INDICATOR 

xd2 

e_cc1_pnone <= e_cc_pfilx 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

xd2 

INDICATOR 

xd2 

INDICATOR 

xd2xc4e1 

INDICATOR 

xd2xc4e1 

INDICATOR 

xd2xc4e1 

INDICATOR 

xd2xc4e1 

INDICATOR 

xd2xc4e1 

INDICATOR 

xd2xc4e1 

INDICATOR 

xd2xc4e1 

INDICATOR 

xd2xc4e1 

INDICATOR 

xd2xc4e1 

e_cc1_pnone <= 
e_cc_psecx 

e_cc1_pnone <= 
e_cc_psoftx 

e_cc1_ps = e_cc_pdev 

e_cc1_s <= e_cc 

e_cc1_s <= e_cc_psec + 
e_cc_pdb + e_cc_pdev 

e_cc1_si <= e_cc 

e_cc1_si <= e_cc_pfacc + 
e_cc_perp + e_cc_pcrm + 
e_cc_psec + e_cc_pdb + 
e_cc_pdev 

e_cc1_si = e_cc1_s + 
e_cc1_i 

e_ai_cc1si_dasany <= 
e_ai_tany + e_cc1_si + 
e_dasany 

e_ai_cc1si_dasany2 <= 
e_ai_cc1si_dasany 

e_ai_cc1si_dasany2 <= 
e_ai_tany + e_cc1_si + 
e_dasany2 

e_ai_cc1si_dasge3 <= 
e_ai_cc1si_dasany 

e_ai_cc1si_dasge3 <= 
e_ai_tany + e_cc1_si + 
e_dasge3 

e_ai_tany <= 
e_ai_cc1si_dasany 

e_ai_tany <= 
e_ai_cc1si_dasany2 

e_ai_tany <= 
e_ai_cc1si_dasge3 

e_cc1_si <= 
e_ai_cc1si_dasany 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

423 

 
A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

INDICATOR 

xd2xc4e1 

INDICATOR 

xd2xc4e1 

INDICATOR 

xd2xc4e1 

INDICATOR 

xd2xc4e1 

INDICATOR 

xd2xc4e1 

e_cc1_si <= 
e_ai_cc1si_dasany2 

e_cc1_si <= 
e_ai_cc1si_dasge3 

e_dasany <= 
e_ai_cc1si_dasany 

e_dasany2 <= 
e_ai_cc1si_dasany2 

e_dasge3 <= 
e_ai_cc1si_dasge3 

INDICATOR 

xd2xc5xc3e1 

e_ai_cc1si_da <= e_ai_tany 

INDICATOR 

xd2xc5xc3e1 

e_ai_cc1si_da <= e_cc1_si 

INDICATOR 

xd2xc5xc3e1 

e_ai_cc1si_da <= e_da 

INDICATOR 

xd2xc5xc3e1 

INDICATOR 

xd2xc5xc3e1 

INDICATOR 

xd2xc5xc3e1 

e_ai_cc1si_da_any <= 
e_ai_tany + e_cc1_si + 
e_da 

e_ai_cc1si_da_any = 
e_ai_cc1si_da + 
e_aix_cc1si_da + 
e_ai_cc1six_da + 
e_ai_cc1si_dax + 
e_aix_cc1six_da + 
e_aix_cc1si_dax + 
e_ai_cc1six_dax 

e_ai_cc1si_dax <= 
e_ai_tany 

INDICATOR 

xd2xc5xc3e1 

e_ai_cc1si_dax <= e_cc1_si 

INDICATOR 

xd2xc5xc3e1 

INDICATOR 

xd2xc5xc3e1 

e_ai_cc1si_dax <= e_iuse - 
e_da 

e_ai_cc1six_da <= 
e_ai_tany 

INDICATOR 

xd2xc5xc3e1 

e_ai_cc1six_da <= e_da 

INDICATOR 

xd2xc5xc3e1 

INDICATOR 

xd2xc5xc3e1 

INDICATOR 

xd2xc5xc3e1 

e_ai_cc1six_da <= e_iuse - 
e_cc1_si 

e_ai_cc1six_dax <= 
e_ai_tany 

e_ai_cc1six_dax <= e_iuse - 
e_cc1_si 

424 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

INDICATOR 

xd2xc5xc3e1 

e_ai_cc1six_dax <= e_iuse - 
e_da 

INDICATOR 

xd2xc5xc3e1 

INDICATOR 

xd2xc5xc3e1 

e_ai_tany <= 
e_ai_cc1si_da_any 

e_ai_tany = e_ai_cc1si_da + 
e_ai_cc1six_da + 
e_ai_cc1si_dax + 
e_ai_cc1six_dax 

INDICATOR 

xd2xc5xc3e1 

e_aix_cc1si_da <= e_ai_tx 

INDICATOR 

xd2xc5xc3e1 

e_aix_cc1si_da <= e_cc1_si 

INDICATOR 

xd2xc5xc3e1 

e_aix_cc1si_da <= e_da 

INDICATOR 

xd2xc5xc3e1 

e_aix_cc1si_dax <= e_ai_tx 

INDICATOR 

xd2xc5xc3e1 

e_aix_cc1si_dax <= 
e_cc1_si 

INDICATOR 

xd2xc5xc3e1 

e_aix_cc1si_dax <= e_iuse - 
e_da 

INDICATOR 

xd2xc5xc3e1 

e_aix_cc1six_da <= e_ai_tx 

INDICATOR 

xd2xc5xc3e1 

e_aix_cc1six_da <= e_da 

INDICATOR 

xd2xc5xc3e1 

e_aix_cc1six_da <= e_iuse - 
e_cc1_si 

INDICATOR 

xd2xc5xc3e1 

INDICATOR 

xd2xc5xc3e1 

e_cc1_si <= 
e_ai_cc1si_da_any 

e_cc1_si = e_ai_cc1si_da + 
e_aix_cc1si_da + 
e_ai_cc1si_dax + 
e_aix_cc1si_dax 

INDICATOR 

xd2xc5xc3e1 

e_da <= e_ai_cc1si_da_any 

INDICATOR 

xd2xc5xc3e1 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

zdi 

zdi 

zdi 

zdi 

e_da = e_ai_cc1si_da + 
e_aix_cc1si_da + 
e_ai_cc1six_da + 
e_aix_cc1six_da 

e_di4_gelo <= ent 

e_di4_gelo = e_di4_lo + 
e_di4_hi + e_di4_vhi 

e_di4_gelo >= e_di4_lo + 
e_di4_hi + e_di4_vhi 

e_di4_hi <= e_di4_gelo 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

425 

 
A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

INDICATOR 

zdi 

zdi 

zdi 

zdi 

zdi 

zdi 

zdi 

zdi 

e_di4_hi <= ent 

e_di4_lo <= e_di4_gelo 

e_di4_lo <= ent 

e_di4_vhi <= e_di4_gelo 

e_di4_vhi <= ent 

e_di4_vlo <= ent 

ent = e_di4_vlo + e_di4_lo + 
e_di4_hi + e_di4_vhi 

ent >= e_di4_vlo + e_di4_lo 
+ e_di4_hi + e_di4_vhi 

INDICATOR 

zdid1 

INDICATOR 

zdid1 

INDICATOR 

zdid1 

INDICATOR 

zdid1 

INDICATOR 

zdid1 

INDICATOR 

zdid1 

INDICATOR 

zdid1 

INDICATOR 

zdid1 

INDICATOR 

zdid1 

INDICATOR 

zdid1 

INDICATOR 

zdid1 

INDICATOR 

zdid1 

INDICATOR 

zdid1 

e_di4_hi_secmge1 <= 
e_di4_hi 

e_di4_hi_secmge1 <= 
e_secmge1 

e_di4_hi_secmge3 <= 
e_di4_hi 

e_di4_hi_secmge3 <= 
e_secmge3 

e_di4_hi_secmge5 <= 
e_di4_hi 

e_di4_hi_secmge5 <= 
e_secmge5 

e_di4_hi_secmge7 <= 
e_di4_hi 

e_di4_hi_secmge7 <= 
e_secmge7 

e_di4_lo_secmge1 <= 
e_di4_lo 

e_di4_lo_secmge1 <= 
e_secmge1 

e_di4_lo_secmge3 <= 
e_di4_lo 

e_di4_lo_secmge3 <= 
e_secmge3 

e_di4_lo_secmge5 <= 
e_di4_lo 

426 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

INDICATOR 

zdid1 

INDICATOR 

zdid1 

INDICATOR 

zdid1 

INDICATOR 

zdid1 

INDICATOR 

zdid1 

INDICATOR 

zdid1 

INDICATOR 

zdid1 

INDICATOR 

zdid1 

INDICATOR 

zdid1 

INDICATOR 

zdid1 

INDICATOR 

zdid1 

INDICATOR 

zdid1 

INDICATOR 

zdid1 

INDICATOR 

zdid1 

INDICATOR 

zdid1 

INDICATOR 

zdid1 

INDICATOR 

zdid1 

INDICATOR 

zdid1 

INDICATOR 

zdid1 

e_di4_lo_secmge5 <= 
e_secmge5 

e_di4_lo_secmge7 <= 
e_di4_lo 

e_di4_lo_secmge7 <= 
e_secmge7 

e_di4_vhi_secmge1 <= 
e_di4_vhi 

e_di4_vhi_secmge1 <= 
e_secmge1 

e_di4_vhi_secmge3 <= 
e_di4_vhi 

e_di4_vhi_secmge3 <= 
e_secmge3 

e_di4_vhi_secmge5 <= 
e_di4_vhi 

e_di4_vhi_secmge5 <= 
e_secmge5 

e_di4_vhi_secmge7 <= 
e_di4_vhi 

e_di4_vhi_secmge7 <= 
e_secmge7 

e_di4_vlo_secmge1 <= 
e_di4_vlo 

e_di4_vlo_secmge1 <= 
e_secmge1 

e_di4_vlo_secmge3 <= 
e_di4_vlo 

e_di4_vlo_secmge3 <= 
e_secmge3 

e_di4_vlo_secmge5 <= 
e_di4_vlo 

e_di4_vlo_secmge5 <= 
e_secmge5 

e_di4_vlo_secmge7 <= 
e_di4_vlo 

e_di4_vlo_secmge7 <= 
e_secmge7 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

427 

 
A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

INDICATOR 

zdid2 

INDICATOR 

zdid2 

INDICATOR 

zdid2 

INDICATOR 

zdid2 

INDICATOR 

zdid2 

INDICATOR 

zdid2 

INDICATOR 

zdid2 

INDICATOR 

zdid2 

INDICATOR 

zdid3 

INDICATOR 

zdid3 

INDICATOR 

zdid3 

INDICATOR 

zdid3 

INDICATOR 

zdid3 

INDICATOR 

zdid3 

INDICATOR 

zdid3 

INDICATOR 

zdid3 

INDICATOR 

zdid4 

INDICATOR 

zdid4 

INDICATOR 

zdid4 

e_di4_hi_secawany <= 
e_di4_hi 

e_di4_hi_secawany <= 
e_secawany 

e_di4_lo_secawany <= 
e_di4_lo 

e_di4_lo_secawany <= 
e_secawany 

e_di4_vhi_secawany <= 
e_di4_vhi 

e_di4_vhi_secawany <= 
e_secawany 

e_di4_vlo_secawany <= 
e_di4_vlo 

e_di4_vlo_secawany <= 
e_secawany 

e_di4_hi_secpol2 <= 
e_di4_hi 

e_di4_hi_secpol2 <= 
e_secpol2 

e_di4_lo_secpol2 <= 
e_di4_lo 

e_di4_lo_secpol2 <= 
e_secpol2 

e_di4_vhi_secpol2 <= 
e_di4_vhi 

e_di4_vhi_secpol2 <= 
e_secpol2 

e_di4_vlo_secpol2 <= 
e_di4_vlo 

e_di4_vlo_secpol2 <= 
e_secpol2 

e_di4_hi_secprev_cy <= 
e_di4_hi 

e_di4_hi_secprev_cy <= 
e_secprev_cy 

e_di4_lo_secprev_cy <= 
e_di4_lo 

428 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

INDICATOR 

zdid4 

INDICATOR 

zdid4 

INDICATOR 

zdid4 

INDICATOR 

zdid4 

INDICATOR 

zdid4 

INDICATOR 

zdie1 

INDICATOR 

zdie1 

INDICATOR 

zdie1 

INDICATOR 

zdie1 

INDICATOR 

zdie1 

INDICATOR 

zdie1 

INDICATOR 

zdie1 

INDICATOR 

zdie1 

INDICATOR 

zdie1 

INDICATOR 

zdie1 

NUMBER_EMPLOYEES 

NUMBER_EMPLOYEES 

NUMBER_EMPLOYEES 

bySize 

bySize 

bySize 

e_di4_lo_secprev_cy <= 
e_secprev_cy 

e_di4_vhi_secprev_cy <= 
e_di4_vhi 

e_di4_vhi_secprev_cy <= 
e_secprev_cy 

e_di4_vlo_secprev_cy <= 
e_di4_vlo 

e_di4_vlo_secprev_cy <= 
e_secprev_cy 

e_ai_tany = 
e_di4_vlo_ai_tany + 
e_di4_lo_ai_tany + 
e_di4_hi_ai_tany + 
e_di4_vhi_ai_tany 

e_ai_tany >= 
e_di4_vlo_ai_tany + 
e_di4_lo_ai_tany + 
e_di4_hi_ai_tany + 
e_di4_vhi_ai_tany 

e_di4_hi_ai_tany <= 
e_ai_tany 

e_di4_hi_ai_tany <= 
e_di4_hi 

e_di4_lo_ai_tany <= 
e_ai_tany 

e_di4_lo_ai_tany <= 
e_di4_lo 

e_di4_vhi_ai_tany <= 
e_ai_tany 

e_di4_vhi_ai_tany <= 
e_di4_vhi 

e_di4_vlo_ai_tany <= 
e_ai_tany 

e_di4_vlo_ai_tany <= 
e_di4_vlo 

E0T1 <= E0T9 

E0T1 >= 0 

E0T9 = E0T1 + E2T9 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

429 

 
A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

AT 

AT 

AT 

AT 

AT 

AT 

AT 

AT 

AT 

AT 

AT 

AT 

NUMBER_EMPLOYEES 

NUMBER_EMPLOYEES 

bySize 

bySize 

NUMBER_EMPLOYEES 

bySize 

NUMBER_EMPLOYEES 

NUMBER_EMPLOYEES 

NUMBER_EMPLOYEES 

NUMBER_EMPLOYEES 

NUMBER_EMPLOYEES 

NUMBER_EMPLOYEES 

NUMBER_EMPLOYEES 

NUMBER_EMPLOYEES 

NUMBER_EMPLOYEES 

bySize 

bySize 

bySize 

bySize 

bySize 

bySize 

bySize 

bySize 

bySize 

NUMBER_EMPLOYEES 

bySize 

NUMBER_EMPLOYEES 

NUMBER_EMPLOYEES 

NUMBER_EMPLOYEES 

bySize 

bySize 

bySize 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

AT 

AT 

AT 

AT 

AT 

AT 

AT 

AT 

AT 

AT 

AT 

AT 

E0T9 >= 0 

E10T249 <= EGE10 

E10T249 = E10T49 + 
E50T249 

E10T249 >= 0 

E10T49 <= E10T249 

E10T49 <= EGE10 

E10T49 >= 0 

E2T9 <= E0T9 

E2T9 >= 0 

E50T249 <= E10T249 

E50T249 <= EGE10 

E50T249 >= 0 

EGE10 = E10T49 + 
E50T249 + EGE250 

EGE10 >= 0 

EGE250 <= EGE10 

EGE250 >= 0 

_Z = AT1 + AT2 + AT3 

AT1 <= _Z 

AT1 = AT11 + AT12 + AT13 

AT11 <= AT1 

AT12 <= AT1 

AT13 <= AT1 

AT2 <= _Z 

AT2 = AT21 + AT22 

AT21 <= AT2 

AT22 <= AT2 

AT3 <= _Z 

AT3 = AT31 + AT32 + AT33 
+ AT34 

430 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

AT 

AT 

AT 

AT 

BE 

BE 

BE 

BE 

BE 

BE 

BE 

BE 

BE 

BE 

BE 

BE 

BE 

BE 

BE 

BE 

BE 

BE 

BG 

BG 

BG 

BG 

BG 

BG 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

AT 

AT 

AT 

AT 

BE 

BE 

BE 

BE 

BE 

BE 

BE 

BE 

BE 

BE 

BE 

BE 

BE 

BE 

BE 

BE 

BE 

BE 

BG 

BG 

BG 

BG 

BG 

BG 

AT31 <= AT3 

AT32 <= AT3 

AT33 <= AT3 

AT34 <= AT3 

_Z = BE1 + BE2 + BE3 

BE1 <= _Z 

BE1 = BE10 

BE10 <= BE1 

BE2 <= _Z 

BE2 = BE21 + BE22 + BE23 
+ BE24 + BE25 

BE21 <= BE2 

BE22 <= BE2 

BE23 <= BE2 

BE24 <= BE2 

BE25 <= BE2 

BE3 <= _Z 

BE3 = BE31 + BE32 + BE33 
+ BE34 + BE35 

BE31 <= BE3 

BE32 <= BE3 

BE33 <= BE3 

BE34 <= BE3 

BE35 <= BE3 

_Z = BG3 + BG4 

BG3 <= _Z 

BG3 = BG31 + BG32 + 
BG33 + BG34 

BG31 <= BG3 

BG32 <= BG3 

BG33 <= BG3 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

431 

 
A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

BG 

BG 

BG 

BG 

BG 

CY 

CY 

CZ 

CZ 

CZ 

CZ 

CZ 

CZ 

CZ 

CZ 

CZ 

CZ 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

Sdmxe 

DE 

REGIONAL 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

BG 

BG 

BG 

BG 

BG 

CY 

CY 

CZ 

CZ 

CZ 

CZ 

CZ 

CZ 

CZ 

CZ 

CZ 

CZ 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

BG34 <= BG3 

BG4 <= _Z 

BG4 = BG41 + BG42 

BG41 <= BG4 

BG42 <= BG4 

CY0 = _Z 

CY00 = CY0 

CZ0 = _Z 

CZ0 = CZ01 + CZ02 + CZ03 
+ CZ04 + CZ05 + CZ06 + 
CZ07 + CZ08 

CZ01 <= CZ0 

CZ02 <= CZ0 

CZ03 <= CZ0 

CZ04 <= CZ0 

CZ05 <= CZ0 

CZ06 <= CZ0 

CZ07 <= CZ0 

CZ08 <= CZ0 

_Z = DE1 + DE2 + DE3 + 
DE4 + DE5 + DE6 + DE7 + 
DE8 + DE9 + DEA + DEB + 
DEC + DED + DEE + DEF + 
DEG 

DE1 <= _Z 

DE1 = DE11 + DE12 + 
DE13 + DE14 

DE11 <= DE1 

DE12 <= DE1 

DE13 <= DE1 

DE14 <= DE1 

DE2 <= _Z 

432 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE2 = DE21 + DE22 + 
DE23 + DE24 + DE25 + 
DE26 + DE27 

DE21 <= DE2 

DE22 <= DE2 

DE23 <= DE2 

DE24 <= DE2 

DE25 <= DE2 

DE26 <= DE2 

DE27 <= DE2 

DE3 <= _Z 

DE3 = DE30 

DE30 <= DE3 

DE4 <= _Z 

DE4 = DE40 

DE40 <= DE4 

DE5 <= _Z 

DE5 = DE50 

DE50 <= DE5 

DE6 <= _Z 

DE6 = DE60 

DE60 <= DE6 

DE7 <= _Z 

DE7 = DE71 + DE72 + 
DE73 

DE71 <= DE7 

DE72 <= DE7 

DE73 <= DE7 

DE8 <= _Z 

DE8 = DE80 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

433 

 
A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE80 <= DE8 

DE9 <= _Z 

DE9 = DE91 + DE92 + 
DE93 + DE94 

DE91 <= DE9 

DE92 <= DE9 

DE93 <= DE9 

DE94 <= DE9 

DEA <= _Z 

DEA = DEA1 + DEA2 + 
DEA3 + DEA4 + DEA5 

DEA1 <= DEA 

DEA2 <= DEA 

DEA3 <= DEA 

DEA4 <= DEA 

DEA5 <= DEA 

DEB <= _Z 

DEB = DEB1 + DEB2 + 
DEB3 

DEB1 <= DEB 

DEB2 <= DEB 

DEB3 <= DEB 

DEC <= _Z 

DEC = DEC0 

DEC0 <= DEC 

DED <= _Z 

DED = DED2 + DED4 + 
DED5 

DED2 <= DED 

DED4 <= DED 

DED5 <= DED 

434 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DK 

DK 

DK 

DK 

DK 

DK 

DK 

EE 

EE 

EL 

EL 

EL 

EL 

EL 

EL 

EL 

EL 

EL 

EL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DE 

DK 

DK 

DK 

DK 

DK 

DK 

DK 

EE 

EE 

EL 

EL 

EL 

EL 

EL 

EL 

EL 

EL 

EL 

EL 

DEE <= _Z 

DEE = DEE0 

DEE0 <= DEE 

DEF <= _Z 

DEF = DEF0 

DEF0 <= DEF 

DEG <= _Z 

DEG = DEG0 

DEG0 <= DEG 

DK0 = _Z 

DK0 = DK01 + DK02 + 
DK03 + DK04 + DK05 

DK01 <= DK0 

DK02 <= DK0 

DK03 <= DK0 

DK04 <= DK0 

DK05 <= DK0 

EE0 = _Z 

EE00 = EE0 

_Z = EL3 + EL4 + EL5 + 
EL6 

EL3 <= _Z 

EL3 = EL30 

EL30 <= EL3 

EL4 <= _Z 

EL4 = EL41 + EL42 + EL43 

EL41 <= EL4 

EL42 <= EL4 

EL43 <= EL4 

EL5 <= _Z 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

435 

 
A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

EL 

EL 

EL 

EL 

EL 

EL 

EL 

EL 

EL 

EL 

EL 

EL 

ES 

ES 

ES 

ES 

ES 

ES 

ES 

ES 

ES 

ES 

ES 

ES 

ES 

ES 

ES 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

EL 

EL 

EL 

EL 

EL 

EL 

EL 

EL 

EL 

EL 

EL 

EL 

ES 

ES 

EL5 = EL51 + EL52 + EL53 
+ EL54 

EL51 <= EL5 

EL52 <= EL5 

EL53 <= EL5 

EL54 <= EL5 

EL6 <= _Z 

EL6 = EL61 + EL62 + EL63 
+ EL64 + EL65 

EL61 <= EL6 

EL62 <= EL6 

EL63 <= EL6 

EL64 <= EL6 

EL65 <= EL6 

_Z = ES1 + ES2 + ES3 + 
ES4 + ES5 + ES6 + ES7 

ES1 <= _Z 

ES  ES1 = ES11 + ES12 + ES13 

ES 

ES 

ES 

ES 

ES 

ES 

ES 

ES 

ES 

ES 

ES 

ES 

ES11 <= ES1 

ES12 <= ES1 

ES13 <= ES1 

ES2 <= _Z 

ES2 = ES21 + ES22 + ES23 
+ ES24 

ES21 <= ES2 

ES22 <= ES2 

ES23 <= ES2 

ES24 <= ES2 

ES3 <= _Z 

ES3 = ES30 

ES30 <= ES3 

436 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

ES 

ES 

ES 

ES 

ES 

ES 

ES 

ES 

ES 

ES 

ES 

ES 

ES 

ES 

ES 

ES 

ES 

ES 

ES 

FI 

FI 

FI 

FI 

FI 

FI 

FI 

FI 

FI 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

ES 

ES4 <= _Z 

ES  ES4 = ES41 + ES42 + ES43 

ES 

ES 

ES 

ES 

ES41 <= ES4 

ES42 <= ES4 

ES43 <= ES4 

ES5 <= _Z 

ES  ES5 = ES51 + ES52 + ES53 

ES 

ES 

ES 

ES 

ES 

ES 

ES 

ES 

ES 

ES 

ES 

ES 

FI 

FI 

FI 

FI 

FI 

FI 

FI 

FI 

FI 

ES51 <= ES5 

ES52 <= ES5 

ES53 <= ES5 

ES6 <= _Z 

ES6 = ES61 + ES62 + ES63 
+ ES64 

ES61 <= ES6 

ES62 <= ES6 

ES63 <= ES6 

ES64 <= ES6 

ES7 <= _Z 

ES7 = ES70 

ES70 <= ES7 

_Z = FI1 + FI2 

FI1 <= _Z 

FI1 = FI19 + FI1B + FI1C + 
FI1D 

FI19 <= FI1 

FI1B <= FI1 

FI1C <= FI1 

FI1D <= FI1 

FI2 <= _Z 

FI2 = FI20 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

437 

 
A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

FI 

REGIONAL 

Sdmxe 

FR 

REGIONAL 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

FI 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FI20 <= FI2 

_Z = FR1 + FRB + FRC + 
FRD + FRE + FRF + FRG + 
FRH + FRI + FRJ + FRK + 
FRL + FRM + FRY 

FR1 <= _Z 

FR1 = FR10 

FR10 <= FR1 

FRB <= _Z 

FRB = FRB0 

FRB0 <= FRB 

FRC <= _Z 

FRC = FRC1 + FRC2 

FRC1 <= FRC 

FRC2 <= FRC 

FRD <= _Z 

FRD = FRD1 + FRD2 

FRD1 <= FRD 

FRD2 <= FRD 

FRE <= _Z 

FRE = FRE1 + FRE2 

FRE1 <= FRE 

FRE2 <= FRE 

FRF <= _Z 

FRF = FRF1 + FRF2 + 
FRF3 

FRF1 <= FRF 

FRF2 <= FRF 

FRF3 <= FRF 

FRG <= _Z 

FRG = FRG0 

438 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FR 

FRG0 <= FRG 

FRH <= _Z 

FRH = FRH0 

FRH0 <= FRH 

FRI <= _Z 

FRI = FRI1 + FRI2 + FRI3 

FRI1 <= FRI 

FRI2 <= FRI 

FRI3 <= FRI 

FRJ <= _Z 

FRJ = FRJ1 + FRJ2 

FRJ1 <= FRJ 

FRJ2 <= FRJ 

FRK <= _Z 

FRK = FRK1 + FRK2 

FRK1 <= FRK 

FRK2 <= FRK 

FRL <= _Z 

FRL = FRL0 

FRL0 <= FRL 

FRM <= _Z 

FRM = FRM0 

FRM0 <= FRM 

FRY <= _Z 

FRY = FRY1 + FRY2 + 
FRY3 + FRY4 + FRY5 

FRY1 <= FRY 

FRY2 <= FRY 

FRY3 <= FRY 

FRY4 <= FRY 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

439 

 
A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

FR 

HR 

HR 

HR 

HR 

HR 

HR 

HU 

HU 

HU 

HU 

HU 

HU 

HU 

HU 

HU 

HU 

HU 

HU 

HU 

HU 

HU 

IE 

IE 

IE 

IE 

IE 

IS 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

FR 

HR 

HR 

HR 

HR 

HR 

HR 

HU 

HU 

HU 

HU 

HU 

HU 

HU 

HU 

HU 

HU 

HU 

HU 

HU 

HU 

HU 

IE 

IE 

IE 

IE 

IE 

IS 

FRY5 <= FRY 

HR0 = _Z 

HR0 = HR02 + HR03 + 
HR05 + HR06 

HR02 <= HR0 

HR03 <= HR0 

HR05 <= HR0 

HR06 <= HR0 

_Z = HU1 + HU2 + HU3 

HU1 <= _Z 

HU1 = HU11 + HU12 

HU11 <= HU1 

HU12 <= HU1 

HU2 <= _Z 

HU2 = HU21 + HU22 + 
HU23 

HU21 <= HU2 

HU22 <= HU2 

HU23 <= HU2 

HU3 <= _Z 

HU3 = HU31 + HU32 + 
HU33 

HU31 <= HU3 

HU32 <= HU3 

HU33 <= HU3 

IE0 = _Z 

IE0 = IE04 + IE05 + IE06 

IE04 <= IE0 

IE05 <= IE0 

IE06 <= IE0 

IS0 = _Z 

440 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

IS 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

IT 

IT 

IT 

IT 

IT 

IT 

IT 

IT 

IT 

IT 

IT 

IT 

IT 

IT 

IT 

IT 

IT 

IT 

IT 

IT 

IT 

IT 

IT 

IT 

IT 

IT 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

IS 

IT 

IT 

IT 

IT 

IT 

IT 

IT 

IT 

IT 

IT 

IT 

IT 

IT 

IT 

IT 

IT 

IT 

IT 

IT 

IT 

IT 

IT 

IT 

IT 

IT 

IT 

IS00 = IS0 

_Z = ITC + ITF + ITG + ITH 
+ ITI 

ITC <= _Z 

ITC = ITC1 + ITC2 + ITC3 + 
ITC4 

ITC1 <= ITC 

ITC2 <= ITC 

ITC3 <= ITC 

ITC4 <= ITC 

ITF <= _Z 

ITF = ITF1 + ITF2 + ITF3 + 
ITF4 + ITF5 + ITF6 

ITF1 <= ITF 

ITF2 <= ITF 

ITF3 <= ITF 

ITF4 <= ITF 

ITF5 <= ITF 

ITF6 <= ITF 

ITG <= _Z 

ITG = ITG1 + ITG2 

ITG1 <= ITG 

ITG2 <= ITG 

ITH <= _Z 

ITH = ITH1 + ITH2 + ITH3 + 
ITH4 + ITH5 

ITH1 <= ITH 

ITH2 <= ITH 

ITH3 <= ITH 

ITH4 <= ITH 

ITH5 <= ITH 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

441 

 
A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

IT 

IT 

IT 

IT 

IT 

IT 

LT 

LT 

LT 

LT 

LU 

LU 

LV 

LV 

MT 

MT 

NL 

NL 

NL 

NL 

NL 

NL 

NL 

NL 

NL 

NL 

NL 

NL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

IT 

IT 

IT 

IT 

IT 

IT 

LT 

LT 

LT 

LT 

LU 

LU 

LV 

LV 

MT 

MT 

NL 

NL 

ITI <= _Z 

ITI = ITI1 + ITI2 + ITI3 + ITI4 

ITI1 <= ITI 

ITI2 <= ITI 

ITI3 <= ITI 

ITI4 <= ITI 

LT0 = _Z 

LT0 = LT01 + LT02 

LT01 <= LT0 

LT02 <= LT0 

LU0 = _Z 

LU00 = LU0 

LV0 = _Z 

LV00 = LV0 

MT0 = _Z 

MT00 = MT0 

_Z = NL1 + NL2 + NL3 + 
NL4 

NL1 <= _Z 

NL  NL1 = NL11 + NL12 + NL13 

NL 

NL 

NL 

NL 

NL11 <= NL1 

NL12 <= NL1 

NL13 <= NL1 

NL2 <= _Z 

NL  NL2 = NL21 + NL22 + NL23 

NL 

NL 

NL 

NL 

NL21 <= NL2 

NL22 <= NL2 

NL23 <= NL2 

NL3 <= _Z 

442 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

NL 

NL 

NL 

NL 

NL 

NL 

NL 

NL 

NL 

NO 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

Sdmxe 

NO 

REGIONAL 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

NO 

NO 

NO 

NO 

NO 

NO 

NO 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

NL 

NL 

NL 

NL 

NL 

NL 

NL 

NL 

NL 

NO 

NO 

NO 

NO 

NO 

NO 

NO 

NO 

NO 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

NL3 = NL31 + NL32 + NL33 
+ NL34 

NL31 <= NL3 

NL32 <= NL3 

NL33 <= NL3 

NL34 <= NL3 

NL4 <= _Z 

NL4 = NL41 + NL42 

NL41 <= NL4 

NL42 <= NL4 

NO0 = _Z 

NO0 = NO02 + NO06 + 
NO07 + NO08 + NO09 + 
NO0A + NO0B 

NO02 <= NO0 

NO06 <= NO0 

NO07 <= NO0 

NO08 <= NO0 

NO09 <= NO0 

NO0A <= NO0 

NO0B <= NO0 

_Z = PL2 + PL4 + PL5 + 
PL6 + PL7 + PL8 + PL9 

PL2 <= _Z 

PL2 = PL21 + PL22 

PL21 <= PL2 

PL22 <= PL2 

PL4 <= _Z 

PL4 = PL41 + PL42 + PL43 

PL41 <= PL4 

PL42 <= PL4 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

443 

 
A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PT 

PT 

PT 

PT 

PT 

PT 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PL 

PT 

PT 

PT 

PT 

PT 

PT 

PL43 <= PL4 

PL5 <= _Z 

PL5 = PL51 + PL52 

PL51 <= PL5 

PL52 <= PL5 

PL6 <= _Z 

PL6 = PL61 + PL62 + PL63 

PL61 <= PL6 

PL62 <= PL6 

PL63 <= PL6 

PL7 <= _Z 

PL7 = PL71 + PL72 

PL71 <= PL7 

PL72 <= PL7 

PL8 <= _Z 

PL8 = PL81 + PL82 + PL84 

PL81 <= PL8 

PL82 <= PL8 

PL84 <= PL8 

PL9 <= _Z 

PL9 = PL91 + PL92 

PL91 <= PL9 

PL92 <= PL9 

_Z = PT1 + PT2 + PT3 

PT1 <= _Z 

PT1 = PT11 + PT15 + PT16 
+ PT17 + PT18 

PT11 <= PT1 

PT15 <= PT1 

PT16 <= PT1 

444 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

PT 

PT 

PT 

PT 

PT 

PT 

PT 

PT 

RO 

RO 

RO 

RO 

RO 

RO 

RO 

RO 

RO 

RO 

RO 

RO 

RO 

RO 

RO 

RO 

RO 

SE 

SE 

SE 

SE 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

PT 

PT 

PT 

PT 

PT 

PT 

PT 

PT 

RO 

RO 

RO 

RO 

RO 

RO 

RO 

RO 

RO 

RO 

RO 

RO 

RO 

RO 

RO 

RO 

RO 

SE 

SE 

SE 

SE 

PT17 <= PT1 

PT18 <= PT1 

PT2 <= _Z 

PT2 = PT20 

PT20 <= PT2 

PT3 <= _Z 

PT3 = PT30 

PT30 <= PT3 

_Z = RO1 + RO2 + RO3 + 
RO4 

RO1 <= _Z 

RO1 = RO11 + RO12 

RO11 <= RO1 

RO12 <= RO1 

RO2 <= _Z 

RO2 = RO21 + RO22 

RO21 <= RO2 

RO22 <= RO2 

RO3 <= _Z 

RO3 = RO31 + RO32 

RO31 <= RO3 

RO32 <= RO3 

RO4 <= _Z 

RO4 = RO41 + RO42 

RO41 <= RO4 

RO42 <= RO4 

_Z = SE1 + SE2 + SE3 

SE1 <= _Z 

SE1 = SE11 + SE12 

SE11 <= SE1 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

445 

 
A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

SE 

SE 

SE 

SE 

SE 

SE 

SE 

SE 

SE 

SE 

SE 

SI 

SI 

SI 

SI 

SK 

SK 

SK 

SK 

SK 

SK 

BA 

BG 

CZ 

DK 

HR 

HU 

IS 

MK 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

REGIONAL 

UNIT_MEASURE 

UNIT_MEASURE 

UNIT_MEASURE 

UNIT_MEASURE 

UNIT_MEASURE 

UNIT_MEASURE 

UNIT_MEASURE 

UNIT_MEASURE 

SE 

SE 

SE12 <= SE1 

SE2 <= _Z 

SE  SE2 = SE21 + SE22 + SE23 

SE 

SE 

SE 

SE 

SE21 <= SE2 

SE22 <= SE2 

SE23 <= SE2 

SE3 <= _Z 

SE  SE3 = SE31 + SE32 + SE33 

SE 

SE 

SE 

SI 

SI 

SI 

SI 

SK 

SK 

SK 

SK 

SK 

SK 

Cur 

Cur 

Cur 

Cur 

Cur 

Cur 

Cur 

Cur 

SE31 <= SE3 

SE32 <= SE3 

SE33 <= SE3 

SI0 = _Z 

SI0 = SI03 + SI04 

SI03 <= SI0 

SI04 <= SI0 

SK0 = _Z 

SK0 = SK01 + SK02 + SK03 
+ SK04 

SK01 <= SK0 

SK02 <= SK0 

SK03 <= SK0 

SK04 <= SK0 

EUR = XDC / 1.95583 

EUR = XDC / 1.9558 

EUR = XDC / 24.004 

EUR = XDC / 7.4509 

EUR = XDC / 7.5345 

EUR = XDC / 381.85 

EUR = XDC / 149.13 

EUR = XDC / 61.5587 

446 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

A2 

Validation rules 

Survey 

Country 

KeyName 

KeyGroup 

Message 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

Sdmxe 

NO 

PL 

RO 

RS 

SE 

TR 

UK 

UNIT_MEASURE 

UNIT_MEASURE 

UNIT_MEASURE 

UNIT_MEASURE 

UNIT_MEASURE 

UNIT_MEASURE 

UNIT_MEASURE 

Cur 

Cur 

Cur 

Cur 

Cur 

Cur 

Cur 

EUR = XDC / 11.4248 

EUR = XDC / 4.5420 

EUR = XDC / 4.9467 

EUR = XDC / 117.2513 

EUR = XDC / 11.4788 

EUR = XDC / 25.7597 

EUR = XDC / 0.86979 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

447 

 
Metadata reporting template 

A3 

Annex 3 – Metadata reporting template 

-  Full view - 

INFOSOC_ETNSI_A_2024 

National Reference Metadata in SIMS structure for 

INFOSOC Enterprises 

Compiling agency: 

Eurostat metadata 
Reference metadata 
1. Contact 
2. Metadata update 
3. Statistical presentation 
4. Unit of measure 
5. Reference Period 
6. Institutional Mandate 
7. Confidentiality 
8. Release policy 
9. Frequency of dissemination 
10. Accessibility and clarity 
11. Quality management 
12. Relevance 
13. Accuracy 
14. Timeliness and punctuality 
15. Coherence and compara-
bility 
16. Cost and Burden 
17. Data revision 
18. Statistical processing 
19. Comment 
Related Metadata 
Annexes (including footnotes) 

For any question on data and metadata, please contact: Eurostat user support 

1.  Contact 

1.1. Contact organisation 

448 

European businesses statistics compilers’ manual for ICT usage and e-commerce in enterprises                                                                                          

 
 
 
 
 
 
 
 
 
 
 
A3 

Metadata reporting template 

1.2. Contact organisation unit 

1.3. Contact name 

1.4. Contact person function 

1.5. Contact mail address 

1.6. Contact email address 

1.7. Contact phone number 

1.8. Contact fax number 

2.  Metadata update 

2.1. Metadata last certified 

2.2. Metadata last posted 

2.3. Metadata last update 

3.  Statistical presentation 

3.1. Data description 

Data  on  the  Information  and  Communication  Technologies  (ICT)  usage  and  e-com-
merce  in  enterprises  are  survey  data.  They  are  collected  by  the  National  Statistical 
Institutes or Ministries and are in principle based on Eurostat's annual model ques-
tionnaires on ICT usage and e-commerce in enterprises. 
The legal basis for ICT enterprise statistics for survey year  2024 is Commission Imple-
menting Regulation (EU) 2023/1507 of 20 July 2023 laying down the technical specifi-
cations of data requirements for the topic 'ICT usage and e-commerce' for the reference 
year 2024.Large part of the data collected is used to measure the progress in the im-
plementation of one of the main political priorities of the European Commission for 2019 
to 2024 – A Europe fit for the digital age. 
Furthermore, ICT data facilitate the monitoring of the EU’s digital targets for 2030, set 
by the Digital Decade Policy Programme. Four of the key performance indicators (KPIs) 
of  the  current  programme  stem  from  the  statistics  for  which  the  implementing  and 

European business statistics compiler’s manualfor ICT usage and e-commerce in enterprises 

449 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Metadata reporting template 

A3 

delegated acts are enclosed for adoption: Artificial Intelligence, cloud, big data (data 
analytics)  and  the  digital  intensity  index  for  businesses  (DII)  -  a  composite  indicator 
reflecting the digital transformation of business. The aim of the European survey on 
ICT usage and e-commerce in enterprises is to collect and disseminate harmo-
nised and comparable information at European level. 
Name of data collection 

Please complete for the Metadata report. 

3.2. Classification system 

 NACE Rev.2 2008 - Statistical classification of economic activities  

3.3. Coverage - sector 

All economic activities in the scope of Annex of the Commission Regulation are in-
tended to be included in the general survey, covering enterprises with 10 or more em-
ployees and self-employed persons. These activities are:  

- Section C – “Manufacturing” 

- Section D, E – “Electricity, gas, steam and air conditioning supply”, “Water supply, 
sewerage, waste management and remediation activities” 

- Section F – “Construction” 

- Section G – “Wholesale and retail trade; repair of motor vehicles and motorcycles” 

- Section H – “Transportation and storage” 

- Section I – “Accommodation and food service activities” 

- Section J – “Information and communication” 

- Section L – “Real estate activities” 

- Section M – “Professional, scientific and technical activities” 

- Section N – "Administrative and support service activities" 

- Group 95.1 – “Repair of computers and communication equipment”. 

For micro-enterprises see the sub-concepts below. 

3.3.1. Coverage-sector economic activity for micro-enterprises - All NACE 
Rev. 2 categories are covered 

3.3.2. Coverage sector economic activity for micro-enterprises - If not all 
activities were covered, which ones were covered? 

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3.4. Statistical concepts and definitions 

The model questionnaire on ICT usage and e-commerce in enterprises provides a 
large variety of variables covering among others the following topics: 

-          Access to and use of the Internet 

-          E-commerce and e-business 

-          ICT specialists and skills 

-          ICT security 

-          Artificial Intelligence. 

The annual model questionnaires and the European businesses statistics compliers’ 
manual for ICT usage and e-commerce in enterprises comprise definitions and expla-
nations regarding the topics of the survey. 

3.5. Statistical unit 

Please complete for the Metadata report. 

3.6. Statistical population 

Target Population 
As required by Annex of the Commission Implementing Regulation, enterprises with 
10 or more employees and self-employed persons are covered by the survey. 

For micro-enterprises see the sub-concepts below. 

3.6.1. Coverage of micro-enterprises 

3.6.2. Breakdown between size classes [0 to 1] and [2 to 9] 

3.6.3. If for micro-enterprises different size delimitation was used, please 
indicate it. 

Please complete for the Metadata report. 

3.7. Reference area 

Detailed information on the provision of data on NUTS 2 regional level is available in 
“Annex I. Completeness“. 

Please complete for the Metadata report. 

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3.8. Coverage - Time 

Years 2023 and 2024. 

3.9. Base period 

Not applicable 

4.  Unit of measure 

Percentages of enterprises, Percentages of turnover, Percentages of employees and 
self-employed persons, Million euro (for selected indicators in some countries). 

5.  Reference period 

Please complete for the Metadata report. 

6.  Institutional Mandate 

6.1. Institutional Mandate - legal acts and other agreements 

Complementary national legislation constituting the legal basis for the survey on the 
use of ICT in enterprises: 

Please complete for the Metadata report. 

6.2. Institutional Mandate - data sharing 

Please complete for the Metadata report. 

7.  Confidentiality 

7.1. Confidentiality - policy 

Regulation (EC) No 223/2009 on European statistics (recital 24 and Article 20(4)) of 11 
March 2009 (OJ L 87, p. 164), stipulates the need to establish common principles and 
guidelines ensuring the confidentiality of data used for the production of European sta-
tistics and the access to those confidential data with due account for technical devel-
opments and the requirements of users in a democratic society. 

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At national level:  

Please complete for the Quality report. 

7.2. Confidentiality - data treatment 

Data are transmitted via eDAMIS (encrypted) and delivered to a secure environment 
where they are treated. Flags are added for confidentiality in case results must not be 
disclosed. 

At national level:  

Please complete for the Quality report. 

8.  Release policy 

8.1. Release calendar 

Please complete for the Quality report. 

8.2. Release calendar access 

Please complete for the Quality report. 

8.3. Release policy - user access 

Please complete for the Quality report. 

9. Frequency of dissemination 

Annual 

10. Accessibility and clarity 

Top 

10.1. Dissemination format - News release 

Please complete for the Quality report. 

10.2. Dissemination format - Publications 

Please complete for the Quality report. 

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10.3. Dissemination format - online database 

See detailed section 10.3.1. 

10.3.1. Data tables - consultations 

Results for selected variables collected in the framework of this survey are available for 
all participating countries on Digital economy and society of Eurostat website. 
At national level: 

Please complete for the Quality report. 

10.4. Dissemination format - microdata access 

Please complete for the Quality report. 

10.5. Dissemination format - other 

Not requested 

10.5.1. Metadata - consultations 

Not requested 

10.6. Documentation on methodology 

The European businesses statistics compilers’ manual for ICT usage and e-com-
merce in enterprises provides guidelines and clarifications for the implementation of 
the surveys. 

At national level: 

Please complete for the Quality report. 

10.6.1. Metadata completeness - rate 

Not requested 

10.7. Quality management - documentation 

Please complete for the Quality report. 

11. Quality management 

Top 

11.1. Quality assurance 

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Metadata reporting template 

The European businesses statistics compliers’ manual for ICT usage and e-commerce 
in enterprises provides guidelines and standards for the implementation of the surveys. 
It is updated every year according to the changed contents of the model questionnaires. 

At national level: 
Please complete for the Metadata report. 

11.2. Quality management - assessment 

At European level, the recommended use of the annual Eurostat model questionnaire 
aims  at  improving  comparability  of  the  results  among  the  countries  that  conduct  the 
survey  on ICT  usage and e-commerce  in  enterprises.  Moreover,  the European busi-
nesses statistics compilers’ manual for ICT usage and e-commerce in enterprises pro-
vides guidelines and clarifications for the implementation of the surveys. 

At national level: 

Please complete for the Metadata report. 

12. Relevance 

Top 

12.1. Relevance - User Needs 

Please complete for the Metadata report. 

12.2. Relevance - User Satisfaction 

Please complete for the Metadata report. 

12.3. Completeness 

Detailed information is available in “ Annex I. Completeness “ - related to ques-
tionnaire, coverage, additional questions, regional data. 

12.3.1. Data completeness - rate 

Not requested 

13. Accuracy 

13.1. Accuracy - overall 

Comments on reliability and representativeness of results and completeness of 
dataset 

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These  comments  reflect  overall  standard  errors  reported  for  the  indicators  and  break-
downs in section 13.2.1 (Sampling error - indicators) and the rest of the breakdowns for 
national and European aggregates, as well as other accuracy measurements. The esti-
mated standard error should not exceed 2pp for the overall proportions and should not 
exceed 5pp for the proportions related to the different subgroups of the population (for 
those NACE aggregates for the calculation and dissemination of national aggregates). If 
problems were found, these could have implications for future surveys (e.g. need to im-
prove sampling design, to increase sample sizes, to increase the response rates). 

More detailed information is available in “ Annex II.  Accuracy “ - related to Euro-
pean aggregates, comments on reliability and use of flag. 

13.2. Sampling error 

For calculation of the standard error see 13.2.1.1. 

13.2.1. Sampling error - indicators 

Standard error (for selected indicators and breakdowns) 

Precision measures related to variability due to sampling, unit non-response (the size of 
the subset of respondents is smaller than the size of the original sample) and other (im-
putation for item non-response, calibration etc.) are not (yet) required from the MS for all 
indicators.  Eurostat will make basic assumptions to compute these measures for all indi-
cators produced (e.g. stratified random sampling assuming as strata the crossing of the 
variables “Number of employees and self-employed persons” and “Economic Activity” as 
it was defined in the 3 tables of section 18.1). 

More detailed information is available in“ Annex III. Sample and standard error ta-
bles 2024 “ – worksheets starting with “Standard error". 

13.2.1.1. Sampling error indicator calculation 

a) Name and brief description of the applied estimation approach 

b) Basic formula 

c) Main reference in the literature 

d) How has the stratification been taken into account?  

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e) Which strata have been considered?  

13.3. Non-sampling error 

See detailed sections below. 

13.3.1. Coverage error 

See concept 18.1.1. A) Description of frame population. 

13.3.1.1. Over-coverage - rate 

Please complete for the Quality report. 

13.3.1.2. Common units - proportion 

Not requested 

13.3.2. Measurement error 

Please complete for the Quality report. 

13.3. Non-sampling error 

See detailed sections below. 

13.3.1. Coverage error 

See concept 18.1.1. A) Description of frame population. 

13.3.1.1. Over-coverage - rate 

Please complete for the Quality report. 

13.3.1.2. Common units - proportion 

Not requested 

13.3.2. Measurement error 

Please complete for the Quality report. 

13.3.3. Non response error 

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See detailed sections below. 

13.3.3.1. Unit non-response - rate 

See detailed sub-concepts below. 

13.3.3.1.1. Unit response 

The following table contains the number of units (i.e. enterprises), by type of response to 
the survey and by the percentage of these values in relation to the gross sample size.  

Type of response 

0-9 (or 2-9) employees and 
self-employed persons 

10 or more employees and 
self-employed persons 

Enterprises 

Number 

% 

Number 

% 

100% 

100% 

Gross sample size (as in 
section 3.1 C) 

1. Response (question-
naires returned by the en-
terprise) 

1.1 Used for tabula-
tion and grossing up 
(Net sample or Final 
Sample; as in sec-
tion 3.1 D) 

1.2 Not used for tab-
ulation 

1.2.1 Out of 
scope (deaths, 
misclassified origi-
nally in the target 
population, etc.) 

1.2.2 Other rea-
sons (e.g. unusa-
ble questionnaire) 

2. Non-response (e.g. 
non returned mail, re-
turned mail by post office) 

Comments on unit response, if unit response is below 60% 

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13.3.3.1.2. Methods used for minimizing unit non-response 

Please complete for the Quality report. 

13.3.3.1.3. Methods used for unit non-response treatment 

1. No treatment for unit non-response 

2. Treatment by re-weighting 

2.1 Re-weighting by the sampling de-
sign strata considering that non-re-
sponse is ignorable inside each stra-
tum (the naïve model) 

2.2 Re-weighting by identified re-
sponse homogeneity groups (created 
using sample-level information) 

2.3 Re-weighting through calibra-
tion/post-stratification (performed us-
ing population information) by the 
groups used for calibration/post-strati-
fication 

3. Treatment by imputation (done dis-
tinctly for each variable/item) 

4. Method(s) and the model(s) corresponding to the above or other method(s) 
used for the treatment of unit non-response. (e.g. Re-weighting using Horvitz-
Thompson estimator, ratio estimator or regression estimator, auxiliary variables) 

13.3.3.1.4. Assessment of unit non-response bias 

Please complete for the Quality report. 

13.3.3.2. Item non-response - rate 

Please complete for the Quality report. 

13.3.3.2.1. Methods used for item non-response treatment 

1. No treatment for item non-response 

2. Deductive imputation 
An exact value can be derived as a known function of 
other characteristics. 

3. Deterministic imputation (e.g. mean/median, 
mean/median by class, ratio-based, regression-

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based, single donor nearest-neighbour) 
Deterministic imputation leads to estimators with no ran-
dom component, that is, if the imputation were to be re-
conducted, the outcome would be the same. 

4. Random imputation (e.g. hot-deck, cold-deck) 
Random imputation leads to estimators with a random 
component, that is, if the imputation were re-conducted, 
it would have led to a different result. 

5. Re-weighting 

6. Multiple imputation 
In multiple imputation each missing value is replaced 
(instead of a single value) with a set of plausible values 
that represent the uncertainty of the right value to im-
pute. Multiple imputation methods offer the possibility of 
deriving variance estimators by taking imputation into 
account. The incorporation of imputation into the vari-
ance can be easily derived based on variability of esti-
mates among the multiply imputed data sets. 

7. Method(s) and the model(s) corresponding to the above or other method(s) 
used for the treatment of item non-response. 

13.3.3.2.2. Questions or items with item response rates below 90% and 
other comments 

Other comments relating to the item non-response 

Additional issues concerning "item non-response" calculation (e.g. method used in nation  

Questions and items with low response rates (cut-off value is 90%) and item non-respons  

13.3.4. Processing error 

Please complete for the Quality report. 

13.3.5. Model assumption error 

Not requested 

14. Timeliness and punctuality 

Top 

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14.1. Timeliness 

See detailed section below. 

14.1.1. Time lag - first result 

Not applicable 

14.1.2. Time lag - final result 

Data are to be delivered to Eurostat in the fourth quarter of the reference year (due date 
for the finalised dataset is 5th October). European results are released before the end of 
Data are to be delivered to Eurostat in the fourth quarter of the reference year (due date 
for the finalised dataset is 5th October). European results are released before the end of 
the survey year or in the beginning of the year following the survey year (T=reference 
year, T+0 for indicators referring to the current year, T+12 months for other indicators 
referring to the previous year e.g. e-commerce). 

At national level:  

Please complete for the Quality report. 

14.2. Punctuality 

See detailed section below. 

14.2.1. Punctuality - delivery and publication 

Please complete for the Quality report. 

Data were delivered to Eurostat on …………; ……. days before (after) the deadline. 

15. Coherence and comparability 

15.1. Comparability - geographical 

Top 

The model questionnaire is generally used by the countries that conduct the survey on 
ICT usage and e-commerce in enterprises. Due to (small) differences in translation, in 
the used survey vehicle, in non-response treatment or different routing through the ques-
tionnaire,  some  results  for  some  countries  may  be  of  reduced  comparability.  In  these 
cases, notes are added in the data. 

Detailed information on differences in the wording of the questions in the na-
tional questionnaires is available in “ Annex I. Completeness “ - worksheets re-
lated to questionnaire, coverage, additional questions. 
Comparability between regions: 

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Please complete for the Quality report. 

Detailed information on the provision of data on NUTS 2 regional level is availa-
ble in “Annex I. Completeness“ – worksheets related to regional data. 

15.1.1. Asymmetry for mirror flow statistics - coefficient 

Not applicable 

15.2. Comparability - over time 

See section below. 

15.2.1. Length of comparable time series 

The length of comparable time series depends on the module and the variable consid-
ered within each survey module. Additional information is available in annexes at-
tached to the European metadata. 

Please complete for the Quality report. 

15.3. Coherence - cross domain 

Not applicable 

15.3.1. Coherence - sub annual and annual statistics 

Not applicable 

15.3.2. Coherence - National Accounts 

Not applicable 

15.4. Coherence - internal 

Not applicable 

16. Cost and Burden 

Cost:  
Please complete for the Quality report. 

Burden:   

Please complete for the Quality report 

Top 

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17. Data revision 

17.1. Data revision - policy 

Please complete for the Quality report. 

17.2. Data revision - practice 

Please complete for the Quality report. 

17.2.1. Data revision - average size 

Not requested 

18. Statistical processing 

Top 

18.1. Source data 

A) Frame population description and distribution 
For more information see concept 18.1.1. 

B) Sampling design - Sampling method 
Description of the sampling method used (e.g. stratified random sample, quota sampling, 
cluster sampling; one-stage or two-stage sampling) and information which variables were 
used to stratify, the categories of those variables, in particular for the NACE Rev. 2 cat-
egories related to the "possible calculation of European aggregates", and the final num-
ber of strata:  

Please complete for the Metadata report. 

C) Gross sample distribution 

More detailed information is available in “ Annex III. Sample and standard error 
tables 2024 “ (Worksheet: GROSS SAMPLE) 

D) Net sample distribution 

More detailed information is available in “ Annex III. Sample and standard 
error tables 2024 “ (Worksheet: NET SAMPLE) 

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18.1.1. Population frame 

A) Description of frame population 

a) When was the sample for the ICT usage 
and e-commerce in enterprise survey drawn? 

b) Last update of the Business register that was 
used for drawing the sample of  
enterprises for the survey: 

c) Indication if the frame population is the same 
as, or is in some way coordinated with,  
the one used for the Structural Business Sta-
tistics (different snapshots): 

d) Description if different frames are used dur-
ing different stages of the statistical process  
(e.g. frame used for sampling vs. frame used 
for grossing up): 

e) Indication the shortcomings in terms of time-
liness (e.g. time lag between last update 
e of the sampling frame and the moment of the 
actual sampling), geographical coverage,  
coverage  of  different  subpopulations,  data 
available etc., and any measures taken to 
 correct it, for this survey. 

 B) Frame population distribution 

More detailed information is available in “ Annex III. Sample and standard error 
tables 2024 “ (Worksheet: FRAME POPULATION) 

18.2. Frequency of data collection 

Annual 

18.3. Data collection 

See detailed sections below. 

18.3.1. Survey period 

Survey / Collection 

General survey 

Micro-enterprises 

Date of sending out ques-
tionnaires 

Date of reception of the 
last questionnaire treated 

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18.3.2. Survey vehicle – general survey 

General survey - Stand-alone survey 

18.3.3. Survey vehicle – micro-enterprises 

The collection of micro-enterprises was integrated with the general survey 

18.3.4. Survey type 

Please complete for the Metadata report. 

18.3.5. Survey participation 

Mandatory 

18.4. Data validation 

Please complete for the Quality report. 

18.5. Data compilation 

Grossing-up procedures 

Please complete for the Quality report. 

18.5.1. Imputation - rate 

Please complete for the Quality report 

18.6. Adjustment 

Not applicable 

18.6.1. Seasonal adjustment 

Not applicable 

19. Comment 

Top 

Problems encountered and lessons to be learnt:  

Please complete for the Quality report. 

19.1. Documents 

Questionnaire in national language 

Questionnaire in English (if available) 

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National reports on methodology (if available) 

          Analysis of key results, backed up by tables and graphs in English  

          (if available) 

Other Annexes 

Related metadata 

Annexes 

Annex I. Completeness 2024 

Annex II. Accuracy 2024 

Annex III. Sample and standard error tables 2024 

Top 

Top 

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GETTING IN TOUCH WITH THE EU 

In person 
All over the European Union there are hundreds of Europe Direct information centres. You can find the 
address of the centre nearest you at: https://europa.eu/european-union/contact_en 

On the phone or by email 
Europe Direct is a service that answers your questions about the European Union. You can contact this 
service: 
– by freephone: 00 800 6 7 8 9 10 11 (certain operators may charge for these calls), 
– at the following standard number: +32 22999696 or  
– by email via: https://europa.eu/european-union/contact_en 

FINDING INFORMATION ABOUT THE EU 

Online 
Information about the European Union in all the official languages of the EU is available on the Europa 
website at: https://europa.eu/european-union/index_en 

EU publications 
You can download or order free and priced EU publications at: https://op.europa.eu/en/publications. 
Multiple copies of free publications may be obtained by contacting Europe Direct or your local information 
centre (see https://europa.eu/european-union/contact_en). 

EU law and related documents 
For access to legal information from the EU, including all EU law since 1952 in all the official language 
versions, go to EUR-Lex at: http://eur-lex.europa.eu 

Open data from the EU 
The EU Open Data Portal (http://data.europa.eu/euodp/en) provides access to datasets from the EU. 
Data can be downloaded and reused for free, for both commercial and non-commercial purposes. 

 
 
 
 
 
 
 
 
 
 
 
 
 
European business statistics  
compiler’s manualf or ICT usage  
and e-commerce in enterprises

The purpose of this publication is to provide the compilers of European 
statistics on information and communication technology (ICT) usage and 
e-commerce in enterprises with clarifications on how to apply the EU legal 
provisions. With the help of explanations and legal references, the Manual 
is meant to serve as a practical reference document for National Statistical 
Authorities. 

For more information
https://ec.europa.eu/eurostat/

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PDF: ISBN 978-92-68-25680-0

2023 edition