Annual report on European SMEs 2020/2021 : digitalisation of SMEs : background document.
Date: 2021-07-01
Institutions: Executive Agency for Small and Medium-sized Enterprises
Subjects: EU Member State, activity report, aid to undertakings, business policy, competitiveness, coronavirus disease, corporate finance, digitisation, economic growth, economic recovery, investment, small and medium-sized enterprises, start-up

ANNUAL REPORT ON 

EUROPEAN SMEs  

2020/2021 

Background document 

July 2021 

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Annual Report on European SMEs 
2020/2021 
Digitalisation of SMEs 
Background document 

SME Performance Review 2020/2021 

Contract number: EASME/COSME/2020/SC/001 

July 2021 

EUROPEAN COMMISSION 
European Innovation Council and SMEs Executive Agency (EISMEA) 
Unit I-02. SMP / COSME Pillar 
E-mail: EISMEA-COSME-FWC-SME-PERFO@ec.europa.eu 
European Commission 
B-1049 Brussels 

DG Internal Market, Industry, Entrepreneurship and SMEs (DG GROW) 
Directorate A Strategy & economic analysis 
Unit A.2 – SMEs 
E-mail: GROW-SPR@ec.europa.eu 
European Commission 
B-1049 Brussels 

This background document was prepared in 2021 for the European Commission, DG Internal Market, Industry, 
Entrepreneurship and SMEs, Directorate A Strategy & economic analysis; Unit A.2 – SMEs by the consortium 
composed of: 
PwC EU Services 
CARSA 
DIW Econ 
LE Europe 

Authors: Patrice Muller, Shaan Devnani, Rohit Ladher, James Cannings, Emily Murphy and Nick Robin from LE 
Europe, Sandra Ramos Illán and Francisco Aranda from CARSA, Dr. Stefan Gorgels and Maximilian Priem from 
DIW Econ and Siemon Smid, Dr. Nuray Unlu Bohn, Dr. Virginie Lefebvre and Iakov Frizis from PwC. 

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Annual Report on European SMEs 
2020/2021 

Digitalisation of SMEs  
Background document 

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LEGAL NOTICE 

This document has been prepared for the European Commission however it reflects the views only of the authors, and 
the European Commission is not liable for any consequence stemming from the reuse of this publication. More 
information on the European Union is available on the Internet (http://www.europa.eu). 

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ISBN 978-92-9460-710-2 

doi:10.2826/120209 

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TABLE OF CONTENTS 

INTRODUCTION .................................................................................................................................................... 1 

1 

KEY FINDINGS OF THE RECENT LITERATURE ON DIGITAL TRANSFORMATION OF SMES ......................... 2 

1.1 

1.2 

1.3 

1.4 
1.5 

1.2.1 
1.2.2 
1.2.3 

THE PROCESS OF DIGITAL TRANSFORMATION .......................................................................................................... 2 
From Digitisation to Digitalisation and Digital Transformation .............................................................. 2 
1.1.1 
1.1.2  Main technologies adopted for Digital Transformation .......................................................................... 3 
1.1.3  Main purposes of digital transformation ................................................................................................ 4 
KEY DETERMINANTS OF DIGITAL TRANSFORMATION ................................................................................................. 5 
Internal factors ........................................................................................................................................ 6 
External factors ....................................................................................................................................... 6 
Impact of COVID-19 pandemic on SME Digitalisation and Sustainable Development ............................ 7 
TRENDS IN INTEGRATION OF DIGITAL TECHNOLOGIES BY EUROPEAN BUSINESSES ............................................................ 8 
1.3.1  Differences between integration of digital technologies ........................................................................ 9 
1.3.2  Differences between industries ............................................................................................................... 9 
1.3.3  Differences based on company size and age ......................................................................................... 11 
1.3.4  Differences between EU Member States ............................................................................................... 11 
1.3.5  Differences between EU-27 and non-EU Countries ............................................................................... 13 
THE BENEFITS AND IMPACTS BROUGHT ABOUT BY THE DIGITAL TRANSFORMATION OF SMES .......................................... 14 
THE DIGITAL TRANSFORMATION OF SMES AND THE IMPACT ON SUSTAINABILITY.......................................................... 15 
1.5.1 
Sustainable Development Goals (SDGs) for meeting the 2030 Global Agenda ..................................... 15 
1.5.2  Relevance of the Fourth Industrial Revolution (4IR), Industry 4.0 and the Circular Economy to SDGs .. 16 
Impact of 4IR, Industry 4.0 and the Circular Economy on SDGs ............................................................ 17 
1.5.3 
1.5.4 
Linkages between SMEs, DX and SDGs .................................................................................................. 21 
1.5.5  Drivers and challenges for SMEs to take sustainable actions using digital technologies ...................... 23 
POLICY RECOMMENDATIONS ............................................................................................................................. 25 
KEY TAKEAWAYS ............................................................................................................................................. 30 

2 

BUSINESS CASE STUDIES ........................................................................................................................... 33 

247TAILORSTEEL (NETHERLANDS) ..................................................................................................................... 33 
2.1.1 
Introduction ........................................................................................................................................... 33 
2.1.2  Digital Transformation activities ........................................................................................................... 33 
2.1.3 
Support for Digital Transformation ....................................................................................................... 34 
2.1.4  Key Success Factors ............................................................................................................................... 34 
2.1.5  Key Challenges and Issues ..................................................................................................................... 34 
2.1.6  Policy recommendations ....................................................................................................................... 34 
BÄCHER BERGMANN (GERMANY) ...................................................................................................................... 34 
2.2.1 
Introduction ........................................................................................................................................... 34 
2.2.2  Digital Transformation activities ........................................................................................................... 35 
2.2.3 
Support for Digital Transformation ....................................................................................................... 35 
2.2.4  Key Success Factors ............................................................................................................................... 35 
2.2.5  Key Challenges and Issues ..................................................................................................................... 35 
2.2.6  Policy recommendations ....................................................................................................................... 35 
DE TROG (BELGIUM) ....................................................................................................................................... 36 
Introduction ........................................................................................................................................... 36 
2.3.1 
2.3.2  Digital Transformation activities ........................................................................................................... 36 
2.3.3 
Support for Digital Transformation ....................................................................................................... 36 
2.3.4  Key Success Factors ............................................................................................................................... 36 
2.3.5  Key Challenges and Issues ..................................................................................................................... 37 

1.6 
1.7 

2.1 

2.2 

2.3 

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vi 

2.4 

2.5 

2.6 

2.7 

2.8 

2.9 

2.3.6  Policy recommendations ........................................................................................................................ 37 
FYZOKLINIKA (CZECHIA) ................................................................................................................................... 37 
2.4.1 
Introduction ........................................................................................................................................... 37 
2.4.2  Digital Transformation activities ............................................................................................................ 37 
Support for Digital Transformation ........................................................................................................ 38 
2.4.3 
2.4.4  Key Success Factors ................................................................................................................................ 38 
2.4.5  Key Challenges and Issues ...................................................................................................................... 38 
2.4.6  Policy Recommendations ....................................................................................................................... 38 
KATTY FASHION (ROMANIA) .............................................................................................................................. 38 
2.5.1 
Introduction ........................................................................................................................................... 38 
2.5.2  Digital Transformation activities ............................................................................................................ 39 
Support for Digital Transformation ........................................................................................................ 39 
2.5.3 
2.5.4  Key Success Factors ................................................................................................................................ 40 
2.5.5  Key Challenges and Issues ...................................................................................................................... 40 
2.5.6  Policy recommendations ........................................................................................................................ 41 
NORTEÑA DE APLICACIONES Y OBRAS (SPAIN) ...................................................................................................... 41 
2.6.1 
Introduction ........................................................................................................................................... 41 
2.6.2  Digital Transformation Activities ........................................................................................................... 41 
Support for Digital Transformation ........................................................................................................ 42 
2.6.3 
2.6.4  Key Success Factors ................................................................................................................................ 42 
2.6.5  Key Challenges and Issues ...................................................................................................................... 42 
2.6.6  Policy Recommendations ....................................................................................................................... 43 
ROYAL SNC (ITALY) .......................................................................................................................................... 43 
2.7.1 
Introduction ........................................................................................................................................... 43 
2.7.2  Digital Transformation activities ............................................................................................................ 43 
Support for Digital Transformation ........................................................................................................ 44 
2.7.3 
2.7.4  Key Success Factors ................................................................................................................................ 44 
2.7.5  Key Challenges and Issues ...................................................................................................................... 45 
2.7.6  Policy recommendations ........................................................................................................................ 45 
SKILL SOFTWARE GMBH (GERMANY) .................................................................................................................. 45 
2.8.1 
Introduction ........................................................................................................................................... 45 
2.8.2  Digital Transformation activities ............................................................................................................ 45 
Support for Digital Transformation ........................................................................................................ 46 
2.8.3 
2.8.4  Key Success Factors ................................................................................................................................ 46 
2.8.5  Key Challenges and Issues ...................................................................................................................... 47 
2.8.6  Policy recommendations ........................................................................................................................ 47 
VAN DEN BORNE AARDAPPELEN (NETHERLANDS) .................................................................................................. 47 
2.9.1 
Introduction ........................................................................................................................................... 47 
2.9.2  Digital Transformation activities ............................................................................................................ 47 
Support for Digital Transformation ........................................................................................................ 48 
2.9.3 
2.9.4  Key Success Factors ................................................................................................................................ 48 
2.9.5  Key Challenges and Issues ...................................................................................................................... 48 
2.9.6  Policy recommendations ........................................................................................................................ 48 
Z-APPLICATION (BELGIUM) ............................................................................................................................... 49 
2.10.1 
Introduction ........................................................................................................................................... 49 
2.10.2  Digital Transformation activities ............................................................................................................ 49 
2.10.3  Support for Digital Transformation ........................................................................................................ 49 
2.10.4  Key Success Factors ................................................................................................................................ 50 
2.10.5  Key Challenges and Issues ...................................................................................................................... 50 
2.10.6  Policy recommendation ......................................................................................................................... 50 

2.10 

3 

POLICY CASE STUDIES ................................................................................................................................ 51 

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3.1 

FRANCE NUM - FRANCE ................................................................................................................................... 51 
3.1.1 
Introduction ........................................................................................................................................... 51 
3.1.2  Digital Transformation Support Services ............................................................................................... 51 
3.1.3  Observed/Expected Impacts on SME Digital Transformation ............................................................... 52 
3.1.4  Key Success Factors ............................................................................................................................... 52 
3.1.5  Key Challenges and Issues ..................................................................................................................... 53 
3.1.6  Recommendations for roll-out as a best practice.................................................................................. 53 
FUND FOR ARTIFICIAL INTELLIGENCE, BLOCKCHAIN AND IOT (FONDO PER ARTIFICIAL INTELLIGENCE, BLOCKCHAIN E IOT) - 
53 

3.2 
ITALY 

3.3 

3.4 

3.2.1 
Introduction ........................................................................................................................................... 53 
3.2.2  Digital Transformation Support Services ............................................................................................... 54 
3.2.3  Observed/Expected Impacts on SME Digital Transformation ............................................................... 54 
3.2.4  Key Success Factors ............................................................................................................................... 55 
3.2.5  Key Challenges and Issues ..................................................................................................................... 55 
3.2.6  Recommendations for roll-out as a best practice.................................................................................. 55 
FIT 4 DIGITAL PACKAGES - LUXEMBOURG ............................................................................................................ 55 
3.3.1 
Introduction ........................................................................................................................................... 55 
3.3.2  Digital Transformation Support Services ............................................................................................... 56 
3.3.3  Observed/Expected Impacts on SME Digital Transformation ............................................................... 56 
3.3.4  Key Success Factors ............................................................................................................................... 57 
3.3.5  Key Challenges and Issues ..................................................................................................................... 57 
3.3.6  Recommendations for roll-out as a best practice.................................................................................. 57 
INICIATIVA NACIONAL COMPETÊNCIAS DIGITAIS E.2030 (PORTUGAL INCODE.2030) - PORTUGAL ................................ 58 
3.4.1 
Introduction ........................................................................................................................................... 58 
3.4.2  Digital Transformation Support Services ............................................................................................... 58 
3.4.3  Observed/Expected Impacts on SME Digital Transformation ............................................................... 59 
3.4.4  Key Success Factors ............................................................................................................................... 59 
3.4.5  Key Challenges and Issues ..................................................................................................................... 59 
3.4.6  Recommendations for roll-out as a best practice.................................................................................. 60 

4 

THE DRIVERS OF THE PERFORMANCE OF EU-27 SMES FROM 2008 TO 2016 ............................................ 61 

4.1 
4.2 
4.3 
4.4 

INTRODUCTION .............................................................................................................................................. 61 
EMPIRICAL APPROACH ..................................................................................................................................... 61 
EMPIRICAL RESULTS ........................................................................................................................................ 64 
MODEL PREDICTIONS....................................................................................................................................... 66 

5 

ANALYSIS OF FLASH EUROBAROMETER 486 SURVEY ON ‘SMES, START-UPS, SCALE-UPS AND 

ENTREPRENEURSHIP’ ......................................................................................................................................... 70 

5.1 
5.2 
5.3 

5.4 

INTRODUCTION .............................................................................................................................................. 70 
INDICATOR SELECTION ..................................................................................................................................... 70 
ECONOMETRIC ANALYSIS .................................................................................................................................. 85 
5.3.1  Approach ............................................................................................................................................... 85 
RESULTS ....................................................................................................................................................... 85 
5.4.1  Pairwise correlation ............................................................................................................................... 85 
5.4.2  Probit model estimation ........................................................................................................................ 97 

6 

THE DIGITALISATION OF SMES AND ITS LINK TO GHG EMISSIONS ........................................................ 109 

6.1 
6.2 
6.3 
6.4 

RESEARCH OVERVIEW .................................................................................................................................... 109 
DATABASE ................................................................................................................................................... 110 
DESCRIPTIVE EVIDENCE .................................................................................................................................. 110 
REGRESSION ANALYSIS ................................................................................................................................... 115 

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viii 

6.5 

CONCLUSION ................................................................................................................................................ 117 

INDEX OF FIGURES AND TABLES ...................................................................................................................... 118 

REFERENCES ..................................................................................................................................................... 120 

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ACRONYMS 

5G 
AI  
AML 
AR  
BEEHC 
CE 
CRM 
CSR 
D4SMEs 
DESI 
DLTs 
DX  
EC 
EPC 
ERM 
ERP 
ESG 
GDP 
GeSI 
GNI 
GRI 
HPC 
IaaS 
ICT 
IIoT 
IoT 
IR/4IR 
ISO 
KETs 
LEs 
ML  
NACE 
NFBS 
NFR 
OECD  
PaaS 
PPS 
RFID 
SaaS 
SAFE 
SBA 
SDGs 
UN 
VR  
VSEs 
WEF  

Fifth Generation of Broadband 
Artificial Intelligence 
Anti Money Laundering 
Augmented Reality 
Business Economy (except activities of holding companies) 
Circular Economy 
Client Relationship Management 
Corporate Social Responsibility 
Digital for SMEs Global Initiative 
Digital Economy and Society Index 
Distributed Ledger Technologies  
Digital Transformation 
European Commission 
European Policy Centre 
Enterprise Risk Management  
Enterprise Resource Planning 
Environmental, Social, and Governance  
Gross Domestic Product 
Global Enabling Sustainability Initiative 
Gross National Income 
Global Reporting Initiative 
High Performance Computing 
Infrastructure as a Service 
Information and Communications Technology 
Industrial Internet of Things 
Internet of Things 
Industrial Revolution / Fourth Industrial Revolution  
International Standardisation Organisation 
Key Enabling Technologies 
Large Enterprises 
Machine Learning 
Statistical classification of economic activities in the European Community 
Non-financial business sector 
Non-financial Reporting  
Organisation for Economic Cooperation and Development 
Platform as a Service 
Purchasing Power Standards 
Radio-frequency identification 
Software as a Service 
EC/ECB Survey on Access to Finance 
Small Business Act for Europe 
Sustainable Development Goals 
United Nations 
Virtual Reality 
Very Small Enterprises 
World Economic Forum 

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x 

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Introduction 

This background document provides detailed information for some of the topics addressed in 
the Annual Report on European SMEs 2020/21.  

• 

• 

• 

• 

• 

Chapter 1 provides an extensive review of the recent literature on the digitalisation of 
SMEs; 

Chapter 2 presents 8 detailed case studies of SMEs having digitalised their activities; 

Chapter 4 showcases 4 public programs aimed at supporting the digitalisation of SMEs; 

Chapter  5  presents  the  results  of  a  statistical  analysis  of  the  determinants  of  the 
performance of EU SMEs from 2008 to 2016; and, 

Chapter 6 provides a statistical analysis of the drivers of SME digitalisation using firm-
level data from the 2020 Flash Eurobarometer 486 Survey on ‘SMEs, Start-Ups, Scale-
ups and Entrepreneurship’. 

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2 

1  Key findings of the recent literature on Digital Transformation of SMEs  

1.1  The process of digital transformation 

Digital Transformation (to be denoted as DX across this literature  review), is the profound and accelerating 
transformation  of  business  activities,  processes,  competencies  and  models  to  fully  leverage  the  changes  and 
opportunities  of  digital  technologies  and  their  impact  across  society  in  a  strategic  and  prioritised  way,  with 
present and future shifts (i.e. socio-economic, environmental, technological etc.) in mind. DX, in the integrated 
and  connected  sense  of  the  term,  requires,  among  other  factors,  the  transformation  of  business  models; 
activities/functions;  processes;  ecosystems;  asset  management;  organisational  culture;  ecosystem  and 
partnership models; and customer, worker and partner approaches (i-SCOOP, 2021). 

Even  though  DX  brings  proven  benefits  to  firms,  the  public  sector  and  individuals,  it  comes  with  certain 
challenges, barriers and potential negative impacts that require proper understanding in order to establish the 
most appropriate ecosystem within a supportive policy and regulatory framework. This literature review aims to 
present key insights gathered from recent academic and semi-academic papers on DX, starting with its definition 
and continuing with explanations of the key digital technologies involved; their main purposes; the main actors; 
key  company  determinants,  both  internal  and  external;  the  impact  of  COVID-19  on  DX;  DX  performance  at 
national, sectoral and firm levels; the benefits and impacts created by DX; the impact of SME DX on sustainability; 
and proposed solutions for boosting DX.  

At the beginning of a new, yet challenging decade that started with a severe economic crisis caused by the COVID-
19 pandemic, in addition to pressing climate, environmental and societal challenges, it has become even more 
important  to  accelerate  the  twin  green  and  digital  transformations.  As  prioritised  under  the  new  growth 
strategy of the EU Green Deal,1 followed by the ‘EU Recovery Plan – NextGenerationEU’2 and the EC 2021 Work 
Programme3 this twin transformation will ensure compliance with Paris Agreement4 objectives on climate, as 
well as with the UN Sustainable Development Goals (SDGs),5 to realise the ‘2030 Global Agenda for Sustainable 
Development’, as defined by the UN.6 

1.1.1 

From Digitisation to Digitalisation and Digital Transformation 

Digitisation, digitalisation and DX are three commonly and interchangeably used terms, even though they have 
different meanings (i-SCOOP, 2021), thus it is key to understand the differences.  

Digitisation refers to the transformation of physical information into electronic sequences, from an analogue to 
a digital format, characterised by lower storage, processing and transmission costs  (Loebbecke & Picot, 2015; 
Yoo et al., 2010; Li et al., 2016; Sebastian et al., 2017; Vendrell-Herrero et al., 2017; Dougherty & Dunne, 2012). 
However, the concept of digitalisation is defined by Clerck7 as the use of digital technologies and data (digitised 
and natively digital) in order  to create revenue and improve business. In other words,  to transform business 
processes  (not  just  to  digitise  them)  and  to  create  an  environment  for  digital  business,  whereby  digital 
information  is  at  its  core,  using  digitalised  data  (resulting  from  the  concept  of  digitisation)  and  cutting-edge 
technologies  to  improve  existing  business  processes.  In  addition  to  redefining  existing  business  processes  by 
using digital technologies, digitalisation involves supplying new (digital) goods and services (Verhoef et al., 2021). 
Brennen and Kreiss (2014) define digitisation as the material process of converting individual analogue streams 
of information into digital bits, while digitalisation can be regarded as the restructuring of many domains of social 
life around digital communication and media infrastructures. 

DX  is  seen  as  the  next  step  after  digitalisation.  A  precise  definition  was  introduced  by  Hess  et  al.  (2016), 
highlighting that DX is concerned with “changes that digital technologies can bring about in a company’s business 
model, products, processes and organisational structure”. DX is about using digital capabilities (such as big data, 

1 See: https://ec.europa.eu/info/strategy/priorities-2019-2024/european-green-deal_en  
2 See: https://ec.europa.eu/info/strategy/recovery-plan-europe_en  
3 See : https://ec.europa.eu/commission/presscorner/detail/en/ip_20_1940  
4 See : https://ec.europa.eu/clima/policies/international/negotiations/paris_en 
5 See : https://sdgs.un.org/goals  
6 See : https://sdgs.un.org/2030agenda  
7 www.i-scoop.eu/digitization-digitalization-digital-transformation-disruption 

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IoT and cloud computing) to revolutionise the customer experience, to outdo the competition and to create an 
innovative  business  model  adapted  to  this  digital  era  (Westerman,  2011).  DX  involves  a  holistic  change, 
emphasising cultural, organisational and relational changes to better cater to both business and customer needs 
(Morandini, Thum-Thysen, & Vandeplas, European Commission, 2020; Observatory of Public Sector Innovation, 
2020; Bartholomae, 2018).  

Various  DX  frameworks  have  been  defined,  in  which  the  common  denominator  is  that  DX  is  not  just  about 
technology but about transformative changes that affect the way value is created and captured inside a given 
company.  For  instance,  according  to  the  DX  framework  proposed  by  the  MIT  Center  for  Digital  Business, 
companies must first transform the customer experience by building data analytics capabilities to more deeply 
understand their customers’ needs and preferences. Secondly, they must transform internal processes as well 
as their business model in order to move forward in their transformation journey (Westerman, 2011). According 
to the framework published by the Global Centre for Digital Business Transformation (Wade et al., 2017), during 
a  DX,  companies  must  actively  consider  their  strategy  on  go-to-market,  engagement,  operations,  and 
organisational  structure  and  ethos.  Rogers  (2016)  identified  five  domains  of  strategy  that  DX  is  changing: 
customer, competition, data, innovation and value proposition. These axes are also discussed by Uhl & Gollenia 
(2014),  who  found  that  customer-centricity,  innovation  capability,  operational  excellence  using  data 
capabilities  and  a  competitive  mindset  are  all  key  to  success  in  transforming  a  firm  digitally  to  remain 
competitive in the future. 

The European Commission (EC) defines DX as “a fusion of advanced technologies that integrates physical and 
digital  systems  and  when  combined  with  innovative business  models  and processes,  leads  to  the  creation  of 
smart products, services and significant improvement of productivity” (DigitaliseSME, 2020). The Organisation 
for  Economic  Cooperation  and  Development  (OECD)  defines  DX  as  “the  process  of  adoption  of  digital 
technologies  and  methods  by  an  organisation,  typically  those  that  have  either  not  been  including  the  digital 
factor  as  part  of  their  core  activities  or  have  not  kept  up  with  the  pace  of  change  in  digital  technologies” 
(Observatory of Public Sector Innovation, 2020).  

1.1.2  Main technologies adopted for Digital Transformation 

The EC’s working paper entitled ‘Shaping the digital transformation in Europe’ identifies basic information and 
communication technologies (ICTs) as the cornerstone to advanced digitalisation (DG CNECT, 2020). Based on 
the vision of the EC, there are two clusters of technologies that will build on a robust ICT environment to usher 
effective  DX across societies: enabling technologies and infrastructure, and high impact applied technologies. 
Enabling technologies and infrastructure include machine learning (ML) and artificial intelligence (AI), big data 
analytics, quantum and high performance computing (HPC), Internet of Things (IoT, including edge computing), 
next generation internet  and 5G/6G infrastructure, cloud  computing, digital platforms  and distributed ledger 
technology (DLT). High impact applied technologies include the use of advanced industrial robotics, industrial 
IoT  (IIOT),  virtual  and  augmented  reality  (VR/AR),  digitally  enabled  biotechnologies,  3D  printing  and  additive 
manufacturing. Also considered as high-impact applied technologies are multimodal deployments of different 
sets of advanced technologies for the development of smart cities, connected and autonomous vehicles (CAVs), 
digital energy innovation and sustainability, and advanced materials. The combination of these technologies with 
appropriate business processes and a digitally conducive institutional environment has the potential to support 
firms’ competitiveness and improve the quality of societal and environmental goods (Ferreira, Moreira, Pereira, 
& Durão, 2020).  

The  most  recently  launched  EU  Initiative  in  support  of  EU  industrial  policy,  ‘Advanced  Technologies  for 
Industry’,8  merges  the  previous  Key  Enabling  Technologies  (KETs)  Observatory9  and  Digital  Transformation 
Monitor10 initiatives, and focuses on the advanced technologies that will help industries to successfully manage 
a shift towards a low-carbon and knowledge-based economy. The following 16 advanced technologies have 
been  identified  under  this  initiative:  advanced  manufacturing  technology;  advanced  materials;  artificial 
intelligence;  augmented  and  virtual  reality;  big  data;  blockchain;  cloud  computing;  connectivity;  industrial 

8 https://ati.ec.europa.eu/  
9 https://ec.europa.eu/growth/tools-databases/kets-tools/kets-observatory 
10 https://ec.europa.eu/growth/tools-databases/dem/monitor/ 

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4 

biotechnology; internet  of  things; micro- and  nanoelectronics; mobility; nanotechnology; photonics; robotics; 
and security.  

Likewise, the ‘OECD Digital for SMEs Global Initiative (D4SME)’11 aims to support SME digitalisation by focusing 
on  accelerating  digitally-driven  transformations  in  supply  chains  and  business  models,  mainly  around  the 
following technology domains: big data and AI; blockchain & DLTs; cloud computing; digital platforms; fintech; 
and IoT / Industrial IoT.  

In addition to the previously mentioned technologies, the following are regarded as EU Internal Market policy 
agenda  priorities:  3D-printing  especially  in  advanced  manufacturing,  5G  &  6G,  next  generation  cloud,  edge 
computing, cybersecurity, and quantum technologies. Connectivity is a crucial building block of DX, enabling 
data  to  flow,  people  to  collaborate,  and  more  objects  to  connect  to  the  internet,  thereby  transforming 
manufacturing, mobility and logistic chains. As a result, gigabit connectivity, powered with secure fibre and 5G 
infrastructures,  has  been  indicated  as  an  investment  priority  in  ‘Shaping  Europe’s  Digital  Future’  and  also 
included as a pillar of EU strategic priorities for 2019-24 in ‘A Europe Fit for the Digital Age.12 Another EU priority 
is to increase public trust in digital technologies by ensuring maxiumum protection against cyber attacks through 
a combination of cybersecurity and ethical use of AI. As part of this goal, a European Common Data Space is 
therefore being planned, which will provide an ultra secure environment for big data collection and sharing. The 
EC  is  aiming  to  lead  a  global  standardisation  process  for  new  generation  technologies  such  as  algorithms, 
blockchain, quantum technologies and supercomputing, as has been successfully achieved for 5G and IoT, in 
order to allow the deployment of interoperable technologies and secure data sharing.13  

1.1.3  Main purposes of digital transformation 

The  ‘SME  Digitalisation’  study  commissioned  by  Vodafone  (Deloitte,  2020)  identifies  four  main  categories  of 
digital  technologies  used  by  SMEs  in  their  DX  process:  connectivity;  process  digitalisation  and  automation; 
cloud;  online  presence,  collaboration  and  communication.  Some  of  these  categories  provide  quick  wins  for 
SMEs,  requiring  only  minimal  investment  in  hardware,  integration  and  implementation.  The  categories  of 
connectivity  and  online  presence,  requiring  fixed  and/or  mobile  broadband  as  the  key  enabler,  allow 
deployment  of  various  online  tools  to  facilitate  business  processes  such  as:  online  communication  and 
collaboration; the online selling of goods and services; internet-based solutions to reduce customer interactions 
and  thereby  free  up  human  resources;  contactless  payments;  and  QR  codes  for  direct  ordering.  Process 
digitalisation and automation, on the other hand, uses connected devices, and can range from the relatively 
simple,  such  as  the  digitalisation  of  manual  business  processes  (e.g.  e-signing  documents)  to  the  more 
sophisticated, incorporating connected sensors and hardware ecosystems (IoT) to reduce contact processes (e.g. 
contactless check-in/check-out) and optimise industrial processes (e.g. Industry 4.0 manufacturing). The latter 
can be enhanced even further through automation, using connected devices with machine-to-machine (M2M) 
communication  capabilities  that  enable  an  increase  in  labour  productivity.  Meanwhile,  cloud  based  services 
reduce the need for ICT infrastructure and allow remote access to data and services from any place, at any time, 
through any internet-enabled device. This benefits SMEs in a variety of ways, such as: increasing efficiency and 
flexibility by converting expensive infrastructure investments into variable service costs; enabling data-driven 
innovation that brings productivity gains through the use of powerful cloud based computing power to leverage 
big  data;  making  use  of  services  provided  by  third  party  cloud  providers  to  ensure  smooth  and  reliable 
operations,  as  well  as  better  compliance  with  data  protection  regulations  (i.e.  GDPR),  by  implementing 
protection of the company’s own data as well as that of their customers.  

The  ‘Digital  With  Purpose:  Delivering  a  SMARTer2030’  report  (GeSI  &  Deloitte,  2019),  commissioned  by  the 
Global Enabling Sustainability Initiative (GeSI),14 provides another view of the main purposes of digital technology 
deployment under DX. The study first defines seven key technologies (i.e. digital access, fast internet, cloud, IoT, 
Cognitive, Digital Reality, Blockchain) as broadly representative of the way digital capability will evolve in the 
medium term and their critical influence on the world. In order to assess the impact of these technologies on the 

11 https://www.oecd.org/going-digital/sme/  
12 https://ec.europa.eu/info/strategy/priorities-2019-2024_en 
13 https://ec.europa.eu/info/sites/info/files/communication-shaping-europes-digital-future-feb2020_en_3.pdf  
14 https://gesi.org 

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Sustainable  Development  Goals  (SDGs)  (see  Chapter  3.5  of  this  report),  the  study  defines  ‘impact  functions’ 
grouped under 4 main categories as:  

•  Connect & Communicate: Connecting people to each other and to critical information; 
•  Monitor  &  Track:  The  real-time,  extensive  observation  of  the  world  and  its  natural  and  man-made 

systems; 

•  Analyse, Optimise & Predict: The development of insights from data, and the use of those insights to 

drive process efficiency and infer the future; 

•  Augment & Autonomate: Provision of an ‘active bridge’ between digital and physical, from simulation 

through augmentation to the creation of autonomous systems. 

The type of digital technology being adopted varies across sectors, in line with business needs. Based on the 
results of the 2019 EIB Investment Survey, the Internet of Things (IoT) is the main group of technologies currently 
being  implemented  by  manufacturing,  construction,  services  and  infrastructure  firms  (EIB,  2020).  DX  in 
manufacturing is associated with Industry 4.0, the use of real time data and information in networking all the 
individual  elements  of  the  manufacturing  process,  with  the  aim  of  decreasing  the  complexity  and  cost  of 
operation, whilst increasing the efficiency and effectiveness of production.  Industry 4.0 leverages Information 
Communication Technology (ICT) applications and is intended to integrate 3D printing, big data technologies, 
cyber-physical systems (CPS), distributed and decentralised control, embedded systems, IoT and robotics into 
the  production  process  (Almada-Lobo,  2014;  EFRA,  2013;  Kotynkova,  2016;  Schmidt,  et  al.,  2015).  Key 
technologies for the construction sector include 3D printing, drone technology, IoT, and virtual reality. Service 
companies mostly use big data, IoT, platform technology, and virtual reality. Infrastructure firms use 3D printing, 
big data, IoT, and platform technology. 

The process of DX requires a lot of research and experimentation with new technologies and business models 
(OECD, 2019). Not all firms are willing to take the risk of investing in DX. Among the ones that do, less than 30% 
succeed, and their success rates vary, depending on the industry. For digital savvy industries, such as high-tech, 
media  and  telecoms,  the  success  rate  is  around  26%;  while  for  traditional  industries  such  as  oil  and  gas, 
automotive, infrastructure and pharmaceuticals it varies between 4% and 11% (McKinsey & Company, 2018).  

1.2  Key determinants of digital transformation  

The ‘OECD Digital for SMEs Global Initiative (D4SME)’15 defines market conditions, regulatory environment and 
infrastructure as enabling framework conditions; skills, finance and innovation as firm level drivers; and digital 
technologies as all the digitally driven transformations in supply chains and business models. D4SME aims to 
promote  knowledge  sharing  and  dialogue  between  governments,  regulators,  business  sectors  and  other 
institutions concerning these different dimensions in order to accelerate SME DX. 

The use of data is central to this transformation process. The unison of data and digital technologies results in 
an  increasing  degree  of  overlap  between  the  physical,  digital  and  biological  realms  of  business  activities  (DG 
CNECT, 2020). For instance, 3D printing decreases the monetary and time costs of the production process, data 
analysis  improves  product  efficiency,  digital  databases  enable  real-time  monitoring,  thereby  facilitating 
management of the services being provided, and digital technology improves the production process, by allowing 
instant  and  seamless  exchange  of  information  between  the  production  and  procurement  departments  of 
manufacturing companies (DigitaliseSME, 2020).  

The  decision  to  invest  in  DX,  and  the  resulting  outcomes,  depend  on  company  capabilities  and  incentives. 
Complementarities exist between the two (Sorbe, Gal, Nicoletti & Timiliotis, 2019; OECD, 2018). The remainder 
of this section presents the factors affecting  DX in three parts. The first part discusses factors internal to the 
company; the second part presents the external factors; and the third part touches upon the impact of the COVID 
pandemic on SME DX. 

15 https://www.oecd.org/going-digital/sme/resources/D4SME-Brochure.pdf 

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6 

1.2.1 

Internal factors 

Managerial ability is an important factor in determining the likelihood of a companies’ decision to invest in DX 
and  the  returns  that  the  investments  will  generate.  Strong  managerial  skills  correlate  with  higher  digital 
adoption; poor managerial skills stifle productivity gains. High performance work practices also facilitate DX, by 
increasing  the  company’s  adaptability  to  technological  change  (Sorbe,  Gal,  Nicoletti,  &  Timiliotis,  2019;  Gal, 
Nicoletti, Renault, Sorbe, & Timiliotis, 2019; OECD, 2018).  

Access to talent is central to DX. The EC recently reported that more than 70% of firms indicated ‘access to talent’ 
as a key obstacle to new investments (European Commission, 2020). The capacity to advance the competencies 
of  existing  employees  and  swiftly  recruit  new  talent,  according  to  the  business  needs  of  the  company,  are 
essential to an effective digital transition. SMEs often have higher skill deficiencies compared to large companies. 
SMEs also tend to invest less in training for upskilling. According to the 2019 European Commission SME report, 
the key digital skills deficits are as follows: 41% of SMEs lack software development skills; 35% lack complex data 
analysis and mathematics skills; 33% cannot access digital strategy skills; 31% find it difficult to acquire digital 
project management skills; close to 30% lack either website development or data/database management skills; 
26% lack basic data input and processing skills. This shortage of digital skills is also highlighted in various academic 
papers (Abel-Koch, et al., 2019; Vogelsang et al., 2019). 

Capability and resource fit, coupled with the ability to connect a digital strategy with a concrete business model 
are all key to DX, together with a firm’s capacity to efficiently allocate its resources (APEC, 2020). The ability to 
easily adjust production volume, contingent on the degree of a venture’s success, reduces transition costs (OECD, 
2013;  OECD,  2019).  However,  SMEs  often  fail  to  fully  realise  the  consequences  of  digitalisation  for  their 
organisational structures, operations and strategies, resulting in difficulties in identifying the most suitable tools 
for their business needs (Thrassou , Uzunboylu, Vrontis, & Christof, 2020).  

Pre-existing business dependencies can influence DX. Dependency on a technology or security protocol for data 
storage and exchange, or on the compatibility of an existing infrastructure, can impose constraints on the use of 
digital technologies.  Technology dependencies may also stem from outside the company through interaction 
with  counterparts  (i.e.  partner  businesses,  governments,  customers).  Counterpart  needs  and  challenges  can 
influence transition; there is an interdependence between the DX of companies and the level of digital literacy 
of their counterparts (Iliescu, 2020; Peillon & Dubruc, 2019). 

Variation in behavioural characteristics at individual or group level can impact the likelihood of DX (Vogelsang, 
Liere-Netheler, Packmohr, & Hoppe, 2019). At individual level, fear of losing control, fear of data loss, fear of 
transparency/acceptance,  and  fear  of  job  loss  impairs  the  adoption  of  digital  technologies.  At  group  level, 
adherence to traditional roles/principles, risk aversion and fear of failure are likely to discourage the transition. 
All these contribute to a company’s innovation culture (APEC, 2020). 

1.2.2  External factors 

Good access to communication networks and services is a prerequisite  for DX (OECD, 2019). Availability and 
affordability of (high-speed) network connection, devices, software and applications increases the likelihood and 
benefits of DX for companies, governments and individuals. However, companies and individuals located in rural 
areas of OECD countries often have poor access to broadband, leading to low rates of DX (European Commission, 
2018; Probst et al., 2018; Shenglin et al., 2020). Additionally, there is a growing digital divide between SMEs and 
large firms in terms of the connectivity required to be able to access digital infrastructures and platforms. Smaller 
firms remain less connected (OECD D4SME).16 

Access to finance is vital to DX (OECD, 2019). It is often the case that firms, especially SMEs, do not have the 
financial reserves needed for DX. Access to appropriate external forms of finance correlates with higher digital 
adoption rates (OECD, 2019). Although the existence of venture capital, coupled with tax regimes that do not 
excessively  favour debt  over equity financing, can improve access to finance for SMEs, the principal cause  of 
variation in access to finance, based on company size, is information asymmetries. Credit institutions often lack 
access to information that will allow them to determine the expected profitability of projects or the quality of 

16 https://www.oecd.org/cfe/smes/latestdocuments/D4SME%20First%20Roundtable%20Proceedings.pdf  

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firm management. This increases the perceived risk of default, which leads to underprovision of finance (Altman, 
1968; Bester, 1987; Ennew & Binks, 1996; Stiglitz & Weiss, 1981). The emergence of information asymmetries is 
particularly common between credit institutions and SMEs pursuing DX. This is because SMEs pursuing DX tend 
to request sizeable investments in intangible assets or to seek financing to diversify into new business/markets.  

High take up of digital technologies is associated with the existence of a dynamic market setting (Calvino et al, 
2018).  Dynamic  markets  are  a  hotspot  for  digitally  innovating  firms.  The  markets  are  characterised  by  high 
allocative efficiency due to entry and exit dynamics. Highly innovative firms enter and capture market share by 
being more successful in commercialising new technologies; inefficient firms are pushed out of the market. This 
incentivises incumbent firms to remain as close as possible to the technological frontier (Hendersen, 1993). 

Access  to  data  is  another  key  determinant  in  the  ability  of  companies  to  leverage  AI/ML.  SMEs  have 
disadvantaged positioning due to their limited access to  the large  volumes of data  needed to train machine-
learning  algorithms.  They  are  also  more  vulnerable  in  terms  of  protecting  their  data  and  have  difficulty  in 
effectively managing data risks in compliance with regulations (OECD D4SME).17  

Indeed, the combination of previously mentioned internal and external factors result in specific challenges to 
SMEs on their DX journey related to: the availability of digital tools and technologies required for digitalisation, 
including  good  connectivity  and  awareness  of  the  availability  of  these  digital  tools;  the  capacity  of  SMEs  to 
engage with DX in terms of financial and time resources; and the capability of SMEs to gauge, plan, implement 
and optimise their DX (Deloitte, 2020). 

1.2.3 

Impact of COVID-19 pandemic on SME Digitalisation and Sustainable Development 

The COVID-19 pandemic has caused the greatest global economic slowdown since World War II. EU GDP is 
expected to contract by 7.35% in 2020, followed by a return to growth of 4.1 % in 2021, and a slower rise of 3.01 
% in 2022 (ECFIN, 2020; DG CONNECT, 2020). In many countries, the pandemic has brought business activity to 
a standstill, resulting in job and revenue losses. This has had a profound effect on global value chains, caused by 
transportation  system  disruption,  supply  chain disruption due  to  labour  input  constraints, and a sharp  fall  in 
demand (Fu, 2020). At an aggregate level, COVID-19 caused a rise in unemployment, including permanent lay-
offs and temporary furloughs, and a drop in business investment. The pandemic has also had an adverse effect 
on  job  matching,  firm-specific  capital  and  human  capital  (Portes,  2020).  The  ‘SME  Understanding  Survey’ 
conducted by Context Consulting (Deloitte, 2020) reports that the cautious optimism expressed by many SMEs 
prior  to  the  COVID-19  crisis,  regarding  a  positive  economic  outlook  for  growth  and  investment,  shifted 
dramatically during the pandemic as SMEs struggled for survival. The economic impacts on SMEs, caused by the 
COVID-19 pandemic, fall into three interrelated categories: a drop in customer demand and revenue, impacting 
cash flow and sustainability; supply chain disruptions; and challenges balancing employee capacity and welfare. 
The research highlights the shift in SME priorities from growing and resourcing their business, to finding new 
customers, managing business costs, streamlining business and staff,  and finding new revenue streams if the 
core business is no longer able to operate.  

The COVID-19 pandemic has affected both upstream and downstream activities of SMEs (Juergensen et al., 
2020). The SME presence is high (75% of total employment) in  those sectors most affected by the pandemic: 
transport,  manufacturing,  construction,  wholesale  and  retail,  trade,  air  transport,  accommodation  and  food 
services, real estate, professional services, and other personal services. The short term impacts of the crisis were 
more  or  less  similar  for  the  three  following  groups  of  manufacturing  SMEs,  but  their  medium  to  long  term 
recovery is likely to be different. Standalone SMEs, which trade under their own brand, producing final goods 
for the consumer or industrial markets, are facing significant logistical challenges and demand side repercussions 
due  to  the  reduction  in  consumers’  purchasing  power.  Specialist  supplier  SMEs,  which  provide  intermediate 
goods to larger firms, have also been hit severely both on the demand and supply sides. However, demand is 
likely  to  recover  for  this  type  of  SME  once  restrictions  are  lifted  and  production  restarts,  as  a  result  of  their 
exclusive agreements with key customers. Knowledge-based SMEs which supply specialised knowledge-based 
assets to other firms or consumer markets, face less critical supply side challenges, since their performance is 
largely  contingent  on  their  capacity  to  perform  day  to  day  tasks  remotely.  As  a  response  to  the  COVID-19 
repercussions, SMEs have increased debt leverage and, when possible, SMEs have attempted to digitalise their 

17 https://www.oecd.org/cfe/smes/latestdocuments/D4SME%20First%20Roundtable%20Proceedings.pdf 

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8 

activities. Information on debt finance since the onset of the COVID-19 crisis remains incomplete. Early estimates 
show: in Spain, the share of SMEs using bank finance jumped from 6% to 37% between October 2019 and March 
2020; in France, firms accounting for an already high debt to GDP ratio (70%), took on EUR 100 billion in debt, 
with ¾ of that total taken on by SMEs; in Italy, the debt-to-equity ratio is expected to rise by between 90% and 
110% (OECD, 2020). Social distancing rules motivated an accelerated adoption of digital services. A large portion 
of the workforce began to work remotely on a daily basis, consumers turned to e-commerce and online delivery 
services, students experimented with online learning and governments invested in digital services (DG CNECT, 
2020).  

DX can support business resilience. However, the investment required for transition is often prohibitive during 
periods of high uncertainty, such as a pandemic-induced economic crisis (Fitriasari, 2020). Digital technologies 
can introduce savings, allow the production process to become less contact intensive and support teleworking. 
Numerous  private  and  public  associations  throughout  the  EU  launched  digital  skills  initiatives  during  the 
pandemic that targeted SMEs in their localities. For instance, the ‘Digital Team Austria’ initiative offers three 
months of pro-bono digital services aimed at SMEs transitioning to mobile working. The Italian ‘Digital Solidarity’ 
initiative offers video conferencing, access to mobile data and cloud computing, free of charge, among other 
services. Latvia is developing FinTech initiatives to support DX through better access to finance for SMEs. Finland 
offers  EUR  500K  for  counselling  and  support  services  for  entrepreneurs.  Greece  gives  training  vouchers  to 
professionals  from  scientific  sectors.  Ireland  offers  mentoring  and  training  courses  to  help  SMEs  with  selling 
online.  

As  assessed  and  reported  in  the  Sustainable  Development  Report  2020  (Sachs  et  al.,  2020),  the  requisite 
measures taken in response to the immediate threat of COVID-19, including the shutdown of many economic 
activities for weeks, have led to a global economic crisis with massive job losses and major impacts especially 
with respect to vulnerable groups. COVID-19 is expected to have severe negative impacts on most SDGs. The 
only bright spot in this picture is the reduction in environmental impacts which has resulted from the decline in 
economic activity. The pandemic is anticipated to have profound short term implications for progress towards 
the SDGs, based on the emerging data and findings from around the world, up to June 2020, as follows: 

•  Highly negative impact for SDGs 1, 2, 3, 8, 10 that are linked to poverty, hunger, wellbeing, economic 

growth and inequalities;  

•  Mixed or moderately negative impact for SDGs 4, 5, 6, 7, 9, 11, 16, 17 that are linked to education, gender 
equality, clean water, affordable clean energy, industry, innovation and infrastructure, sustainable cities 
and communities, justice, and partnerships; 
Still unclear impact for SDGs 12, 13, 14, 15 related to responsible consumption and production, climate 
action, life below water, and life on land.  

• 

1.3  Trends in integration of digital technologies by European businesses  

Monitoring variation in the implementation of DX is key to addressing the adverse impacts of DX on economic 
convergence  as  well  as  on  income  and  wealth  inequality  (Qureshi,  2020).  The  European  Parliament  recently 
recognised the digital divide as an issue of concern (Negreiro & Madiega, 2019). The report argues that even 
though the digital divide has decreased in recent years, there are still economic dynamics that present cause for 
concern.  The  authors  highlight  the  existence  of  a  sizeable  gap  in  digital  skills  and  investment  (internet 
connectivity and AI), and also the limited number of digital champions and the deficit in advanced computing 
systems. 

To  illustrate  the  progress  and  trends  related  to  the  integration  of  various  digital  technologies  by  European 
businesses, the following section will provide insights taken largely from the 2020 Digital Economy and Society 
Index (DESI),18 which monitors Europe's overall digital performance and tracks the progress of EU countries with 
respect  to  their  digital  competitiveness.  DESI  is  a  composite  index  that  observes  variation  in  the  digital 
performance  of  countries  across  five  key  dimensions:  connectivity,  human  capital,  use  of  internet  services, 
integration of digital technology, and digital public services. Among these key pillars, particular attention will be 
paid to the Integration of Digital Technology (DESI, 2020) since it is highly linked to the DX of SMEs. Within this 
category, DESI makes assessments within the following areas: integration of digital technologies, by measuring 

18 For more information on DESI, see: https://ec.europa.eu/digital-single-market/en/digital-economy-and-society-index-desi  

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business digitalisation (i.e. electronic information sharing, social media, big data, cloud) and e-commerce (i.e. 
SMEs’ online sales within and outside of their country, and their turnover from e-commerce); usage of different 
digital technologies at enterprise level, measured by using the Digital Intensity Index (DII); analysis of ICT skills; 
adoption of digital technologies (e.g. cloud based services such as Enterprise Resource Planning (ERP) software, 
Customer  Relationship  Management  (CRM)  systems,  social  media,  etc.);  analysis  of  key  technologies  such  as 
cloud computing, big data, national and cross-border e-commerce; web sales (i.e. B2B, B2C, B2G) in addition to 
emerging technologies (e.g. blockchain, high performance computing (HPC), quantum technology, data and edge 
computing) as well as cybersecurity.  

The  data  which  was  collected  under  the  ‘Integration  of  Digital  Technology’  pillar  just  before  the  pandemic 
showed large variations, depending on company size, sector and location. DESI 2020 shows that progress was 
being  made  by  all  EU  Member  States  and  in  all  key  areas  measured.  Finland,  Sweden,  Denmark  and  the 
Netherlands were the top 4 overall digital performers, closely followed by Malta, Ireland and Estonia. The sub-
sections which follow present a perspective on these major differences. 

1.3.1  Differences between integration of digital technologies  

The ‘Integration of Digital Technology’ pillar within DESI 2020 provides various DX performance insights, including 
a comparison between SMEs and Large Enterprises (LEs), as well as progress over time. 

Based on the results of the 2019 ‘Eurostat Community Survey on ICT usage and e-commerce in enterprises’ the 
DESI 2020 Report lists the following digital technologies as being used by both SMEs and LEs, in order of most to 
least frequently: enterprise resource planning (ERP); customer relationship management (CRM); social media; 
e-commerce; cloud computing; big data; cross-border e-commerce. SMEs lag behind LEs in adoption of nearly 
all digital technologies despite being connected to the internet at the same rate. The largest adoption gaps are 
in digitalisation of the internal organisational process (e.g. via ERP), where the greatest efficiency gains are to be 
made (Deloitte, 2020). 

In terms of cloud computing, since 2018, across the EU market, there has been  an increase in total revenues 
generated by public cloud services  such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and 
Software as a  Service (SaaS). Among these, SaaS represents almost  two thirds of total public cloud revenues 
generated across the EU market, with the trend rising from 2018 to 2021. The top four SaaS applications by total 
revenue  were  customer  relationship  management  (CRM),  followed  by  enterprise  risk  management  (ERM), 
workflow and management applications and collaborative applications, all of which increased their deployment 
rate between 2018-2021. However, in terms of revenue growth over the same period, the fastest growing SaaS 
application was software security (DESI, 2020). 

In 2018, on average, 12% of SMEs used big data analytics for analysing large volumes of data. SME take up was 
highest  in  Malta  (25%),  followed  by  the  Netherlands,  Belgium  and  Ireland  with  a  usage  rate  of  at  least  20%. 
However,  SMEs  in  Cyprus,  Hungary,  Austria  and  Bulgaria  barely  utilised  big  data  at  all.  In  terms  of  how  data 
analysis was carried out, nearly 6% of enterprises analysed big data using geolocation technology on portable 
devices, while 4% analysed data from their smart devices or sensors (DESI, 2020). 

One in five EU enterprises had already adopted e-commerce and online selling before the COVID-19 outbreak. 
As a result, the percentage of companies selling online between 2013 and 2019 increased by 3.5%. Prior to the 
pandemic, almost 15% of EU enterprises were active in online marketplaces in Europe using their own website 
or apps for selling online, while approximately 7% of all EU enterprises sold through e-commerce marketplaces. 
In terms of cross-border e-commerce, of the 17% of SMEs who sold online, only 7% sold online to customers in 
other EU countries, while 16% (representing almost all the enterprises who sold online) sold only to customers 
in their own country. Most of the enterprises selling online to other EU countries (62%) had not experienced any 
difficulties  in  doing  so,  while  the  rest  reported  obstacles  related  to  economic  factors  (e.g.  the  high  costs  of 
delivering or returning products, a problem reported by 27% of enterprises) and/or linguistic and legal problems, 
experienced by 11% of enterprises (DESI, 2020).  

1.3.2  Differences between industries 

The speed and the extent of DX varies greatly across sectors. EU companies active in the infrastructure, services, 
and manufacturing sectors are more likely to undergo DX than those in the construction sector, as illustrated in 

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10 

the figure below. According to the United Nations Conference on Trade and Development (UNCTAD), the top 10 
industries affected most by digitalisation are: media and entertainment; retail; high tech; healthcare systems 
and  services;  travel,  transport  and  logistics;  telecommunications;  professional  services;  financial  services; 
automotive and assembly; and consumer packaged goods (UNCTAD, 2017). 

Figure 1 Implementation of DX in parts of business or organised around the entire business (in % 
of all firms) 

Source: EIBIS 2019 

Industries undergo DX at different speeds, which highlights the importance of understanding why and how firms 
in different sectors adopt new technologies. For instance, despite the use of digital technologies by firms active 
in agriculture, mining, and real estate, these industries are still likely to underperform compared to other sectors, 
such as manufacturing, when it comes to indicators that focus on investment in ICT or software (OECD, 2019). 
Table  1  presents  a  taxonomy  of  sectors  by  usage  intensity  of  digital  technologies.  Transport  equipment 
producers, ICT, finance, administrative and advertising services are among the industries that  most use digital 
technologies and processes.  

Table 1 Taxonomy of sectors by digital intensity, overall ranking for 2013-2015 

ISIC Rev. 4 industry denomination 

Agriculture, forestry, fishing 

Mining and quarrying 

Food products, beverages and tobacco 

Quartile 
intensity 
Low 

Low 

Low 

ISIC Rev. 4 industry denomination 

Wholesale and retail trade, repair 

Transportation and storage 

Accommodation and food services 
activities 

Quartile 
intensity 
Medium-high 

Low 

Low 

Textiles, wearing apparel, leather 

Medium-low 

Publishing, audio-visual and broadcasting  Medium-high 

Wood and paper products, and printing  Medium-high 

Telecommunications 

Coke and refined petroleum products 

Medium-low 

IT and other information services 

Chemicals and chemical products 

Medium-low 

Finance and insurance 

Pharmaceutical products 

Medium-low 

Real estate 

Rubber and plastic products 

Medium-low 

Legal and accounting activities, etc.  

Basic metals and fabricated metal 
products 

Medium-low 

Scientific research and development 

Computer, electronic, optical products  Medium-high 

Advertising and other business services 

Electrical equipment 

Medium-high 

Administrative and support services 

High 

High 

High 

Low 

High 

High 

High 

High 

Machinery and equipment n.e.c. 

Medium-high 

Public administration and defence 

Medium-high 

Transport equipment 

High 

Education 

Furniture: other manufacturing: repairs  Medium-high 

Human health activities 

Medium-low 

Medium-low 

Electricity, gas, steam and air cond. 

Water supply; sewage, waste 

Construction 

Low 

Low 

Low 

Residential care and social work activities  Medium-low 

Arts, entertainment and recreation 

Medium-high 

Other service activities 

High 

Source: Calvino et al. (2018) based on annual national accounts, STAN, ICIO, PIAAC, International Federation 
of Robotics, World Bank, Eurostat Digital Economy and Society Statistics, national labour force surveys, US 
CPS, INTAN-Invest and other national sources.  

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1.3.3  Differences based on company size and age 

Only 17% of SMEs in Europe have already integrated digital technologies into their business, compared to 54% 
of large companies, according to the 2018 Digital Innovation Hubs Working Group report (DG CONNECT, 2018; 
European Commission, 2020; OECD, 2020). SMEs are often unaware of or ignore the potential benefits of DX, or 
find it challenging to identify their needs and to develop a strategy to address them, and/or cannot access the 
skilled  labour  force  and  financial  resources  necessary  for  effective  use  of  digital  technologies  to  boost 
productivity growth and innovation in the products/services they provide (Kopke et al., 2016). Since 2019, a large 
share of SMEs has already begun or intends to begin adopting digital practices. The 2019 ‘Going Digital European 
SME Survey’ (Abel-Koch et al (2019), conducted with SMEs from 5 large European economies (France, Germany, 
Poland, Spain and the United Kingdom) found that the most common digital activities adopted by SMEs were 
electronic invoicing and software to facilitate collaborative work (60% of respondents). Less frequently used by 
SMEs were enabling technologies and infrastructure, such as AI (20%) and big data analytics (32%), along with 
high impact applied technologies, such as 3D printing and additive manufacturing, advanced industrial robotics, 
Industrial IoT, and virtual and augmented reality (VR & AR). (Abel-Koch et al., 2019; Sorbe et al., 2019).  

The 2020 OECD statistics for ‘ICT Access and Usage by Businesses’, show that small EU SMEs (10-49 employees) 
are lagging behind larger businesses (250+ employees) in terms of adoption of virtually all digital technologies. 
This ranges from basic connectivity to more complex and higher return technologies such as cloud computing, 
big data analysis, CRM, ERP, e-Commerce, fast broadband, social media, and websites. Indeed, SMEs lag behind 
their larger counterparts even in basic connectivity, with the gap much larger in certain countries, such as Greece 
and  Hungary.  This  gap  is  even  greater  for  high  speed  connectivity  across  the  EU-27,  with  only  46%  of  small 
businesses using 30+ Mbps broadband compared to 80% of larger businesses, and a similar-sized gap for 100+ 
Mbps  usage:  19%  versus  53%.  For  the  more  advanced  technologies  enabled  by  high-speed  connectivity,  the 
digital divide between small and large businesses is even bigger (Deloitte, 2020).  

There is also a digital divide within sectors, between smaller and larger firms, as well as in terms of firm age, 
as indicated by the EIB study (Rückert, Veugelers, & Weiss, 2020) comparing manufacturing and services sectors 
in the EU and USA. For example, 30-35% of small manufacturing firms in the EU and the US were categorised as 
‘persistently non-digital’, compared to only 10-15% for large manufacturing firms. In services, this gap was lower, 
with younger small firms relatively closer to large firms in terms of digital adoption. However, older, small firms 
significantly lagged behind, with a digital adoption rate approximately 15 percentage points lower than that of 
their younger counterparts and large businesses. 

DESI 2020 compares the different digital technology adoption rates of SMEs (10-249 employees) and LEs. The 
largest gap can be seen in digitalisation of internal organisational processes (i.e. ERP) followed by cloud, big data, 
CRM, e-Commerce and social media. Similar results can also be seen in the DESI Digital Intensity Index (DII), 
which is based on the 2019 Eurostat Community Survey of ICT usage in enterprises. The same survey indicates a 
major  difference  in  the  employment  of  internal  ICT  specialists,  related  to  company  size.  ICT  specialists  are 
employed in 75% of LEs, 42.5% of medium-size companies (50-249 employees), but only 15% of small companies 
(10-49 employees).19 

1.3.4  Differences between EU Member States 

National economies undergo DX at different speeds, and also exhibit major regional differences. This section will 
present key findings from DESI related to the overall performance of EU Member States in implementing all 
five key dimensions (connectivity, human capital, use of internet, integration of digital technology, and digital 
public  services).  Each  country  is  given  a  general  ranking  based  on  their  scores  for  37  indicators  across  five 
dimensions. However, each dimension is weighted differently to arrive at the overall score: 25% for connectivity, 
25% for human capital, 20% for integration of digital technology, and 15% for use of both internet and digital 
public services.  

19 https://eufordigital.eu/wp-content/uploads/2020/06/DESI2020Thematicchapters-FullEuropeanAnalysis.pdf  

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12 

Finland, Sweden, Denmark and the Netherlands  were the top 4 digital performers, closely followed by Malta, 
Ireland  and  Estonia,  while  Italy,  Romania,  Greece  and  Bulgaria  were  the  4  lowest  scoring  digital  performers 
(Figure 2).  

Figure 2 EU-27 Digital Economy and Society Index 2020 Scores 

Source: DESI 2020 

Based on the progress made in 2015-20 across each dimension, on average, EU-27 Member States have mainly 
improved their performance in connectivity (demand and supply side of fixed and mobile broadband), use of 
internet (the number of people using the internet and the type of activities performed online) and digital public 
services (demand and supply of digital public services, and the availability/quality of open data). Integration of 
digital technology and human capital have progressed at a relatively slower pace. Overall, however, the majority 
of EU Member States have not progressed significantly with DX since 2015.  

The key conclusions on the overall performance of Member States across these five dimensions, based on the 
DESI 2020 results, are as follows20:  

▪ 

Even though connectivity has improved over time, still more efforts are needed to foster investment in 
‘Very High Capacity Networks’, both fixed and mobile, to address fast growing needs. So far, only 17 
Member States have provided access to the 5G radio spectrum; 

▪  More progress in digital skills is needed. 42% of the EU population still lacks at least basic digital skills, 

while enterprises report that filling vacancies for ICT specialists is a key challenge; 

▪ 

▪  Use of internet services (such as the consumption of online content like videos, music, and games, as 
well as video calls, online shopping and banking that are used by individuals for personal entertainment 
and as part of their professional or business life) have increased sharply due to the pandemic, despite 
already being at high levels before the crisis; 
Even though enterprises are increasingly integrating digital technologies, SMEs still lag behind large 
businesses in the adoption of nearly all technologies, despite being connected to the internet at the 
same rate. The biggest adoption gaps are in digitalisation of internal organisational processes, 
advanced cloud services, big data analytics and e-commerce;  
There is a rising trend in the usage of digital public services in the areas of eGovernment and eHealth, 
which  allows  for  increased  efficiency  and  cost  savings  for  governments  and  businesses,  improved 
transparency, and greater participation of citizens in political life.  

▪ 

The European Investment Bank Digitalisation Index is another tool for monitoring the comparative progress of 
EU Member States in terms of the digital technology adoption of firms and the quality of currently available 
infrastructure and investment. Member States are ranked on the basis of five digitalisation pillars, with firm 
level information collected through the ‘European Bank Investment Survey’ (EIB, 2020). Based on the 2019/2020 
results, Denmark, Netherlands, Czechia and Finland were the European digital frontrunners (outperforming the 
US). Slovenia, Sweden, Portugal, Estonia, Belgium, Luxembourg, Croatia, Slovakia and Austria outperformed the 
EU  average.  Greece,  Italy,  Lithuania,  Poland,  Ireland  and  Latvia  were  the  weakest  performers.  This  survey 

20 See: https://ec.europa.eu/commission/presscorner/detail/en/IP_20_1025  

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approach allows recent developments to be identified, and does not aim to capture hard data, such as frequency 
of internet usage or online transactions.  

Figure 3 EIBIS Digitalisation Index 

Source: European Investment Bank Investment Survey (EIBIS) 2019 (EIB, 2020).  

1.3.5  Differences between EU-27 and non-EU Countries  

The International Digital Economy and Society Index (I-DESI)21 aims to replicate EU DESI by using 24 datasets to 
benchmark  the  digital  performance  of  45  countries:  the  EU-27  and  18  non-EU  countries  around  the  world 
(Australia, Brazil, Canada, Chile, China, Iceland, Israel, Japan, Mexico, New Zealand, Norway, Russia, Serbia, South 
Korea, Switzerland, Turkey, United Kingdom, and United States).  

According to the findings of I-DESI 2020 (Foley et al., 2021), EU-27 Member States performed better than the 18 
non-EU countries in the dimension of ‘connectivity’ (the deployment and take-up of fixed and mobile broadband) 
and ‘digital skills’. Non-EU countries, on the other hand, performed better in the dimensions of ‘citizen use of 
internet services’ and ‘digital public services’. In general, EU-27 Member States compared well with the 18 non-
EU countries, with the highest performing EU-27 countries having similar or higher levels of digital performance 
than the best performing non-EU countries. Finland was the leading Member State in the I-DESI index and part 
of the ‘EU Top 4’ together with Sweden, the Netherlands and Denmark. The EU top 4 ranked among the top ten 
global performers, in second place after the US, followed by Switzerland, Norway, Iceland, Australia, and Israel. 
The EU Top 4 also outscored Korea and Japan. However, average EU digital performance was lower than all the 
previously mentioned countries, although better than that of China, Russia, the EU Bottom 4, Serbia, Mexico, 
Brazil and Chile.  

In terms of the Integration of Digital Technology dimension, which measures the digitalisation of businesses (i.e. 
technology availability and technology absorption) as well as e-commerce (i.e. selling online and using secure 
internet servers), average EU-27 performance increased in 2017 for the first time since 2013. However, it slipped 
behind again in 2018, when ten out of the 18 non-EU countries were ranked above the EU-27 average on this 
dimension, led by Switzerland and Israel, followed by the top four EU-27 Member States: Finland, Sweden, the 
Netherlands  and  Denmark.  The  normalised  performance  scores  of  EU-27  Member  States  in  2018  differed  to 
varying degrees from those of the non-EU countries under consideration, across the following indicators: 

•  Availability of the latest technology: EU-27 (51.8) vs non-EU countries (58.8);  
•  Business technology absorption: EU-27 (41.8) vs non-EU countries (47.7); 
• 
• 

e-Commerce - SMEs selling online : EU-27 (29.1) vs non-EU countries (43.6) ; 
e-Commerce - secure internet services : EU-27 (36.0) vs non-EU countries (27.9). 

21 For more information on I-DESI, see: https://ec.europa.eu/digital-single-market/en/news/i-desi-2020-how-digital-europe-compared-
other-major-world-economies  

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14 

1.4  The benefits and impacts brought about by the digital transformation of SMEs 

The wider benefits of digitalisation for SMEs include: improved financial performance as a result of optimising 
revenue channels and reducing costs; productivity gains leading to greater efficiency; access to new customers 
through expanded geographical reach; and more access to information along with more productive processes to 
foster innovation (Deloitte, 2020). 

DX  is  reported  to  bring  benefits  in  three  broad  areas:  industrial  management,  marketing,  and  access  to 
knowledge resources. Many researchers suggest that ICT innovations can boost the efficiency and effectiveness 
that is fundamental for economic growth and is also a critical factor for sustainable competitiveness in the global 
marketplace. A wide range of research emphasises the rapid growth of SMEs when they integrate ICTs into their 
business processes and thereby benefit from increased opportunities in international markets such as global 
collaborations, improved awareness of business opportunities across global value chains, and the ability to form 
new relationships with business partners and customers. Likewise, the digitalisation of business models offers a 
number of advantages: cost reductions, a wider selection of products, richer and more transparent/traceable 
information through blockchain solutions, and a rise in performance efficiency and sufficiency  by improving 
market orientation, task management and advanced market knowledge. In particular, the integration of internet 
and web-based technologies provides a digital means of delivery of services and/or products to the consumer. 
It also provides a mechanism for communication that enhances interactions with customers, as well as  image 
and product development, leveraging sales and revenues and expansion into international markets much earlier 
and faster than without the use of digital tools. Digitalisation also facilitates knowledge acquisition and enhances 
business networking, which empowers SMEs to interact more widely and closely with others, such as customers, 
investors,  local  authorities,  business  partners,  suppliers  and  multinational  enterprises,  all  of  which  creates 
sustainable  competitive  advantages.  In  addition,  it  allows  new  working  conditions  and  training  options  for 
employees (Thrassou, et al., 2020). 

DX improves  labour  productivity  and therefore firm productivity (Abel-Koch et  al.,  2019).  The use of digital 
technologies for monitoring and optimising production processes can improve the workflow and help optimise 
production inputs in terms of volume, as well as in terms of characteristics that allow customised production. It 
can also improve the production of goods and services through standardisation and increases  in quality and 
speed of production (DigitaliseSME, 2020). Overall productivity gains are a function of digital technologies,  a 
firm’s  organisational  capital  and  management  skills,  and  the  realisation  of  complementary  investments  and 
innovations associated with business process improvements and task automation. As a result, the full realisation 
of productivity gains can be expected to be a lengthy process (Sorbe et al., 2019). 

DX  permits  the  supply  of  digital  goods  and  services,  as  well  as  the  development  of  customised  goods  and 
services. In addition to the development of virtual platforms and other types of digital goods, companies making 
use  of  digital  analytics  can  harness  data  relating  to  customer  preferences  to  design  goods  that  are  perfectly 
tailored according to individual preferences (DigitaliseSME, 2020).  

DX allows access to new markets. Digital marketing and online platforms can be used to identify and reach out 
to  potential  consumers.  Digital  technologies  also  give  access  to  global  value  chains  through  collaboration 
technologies,  e-commerce  platforms,  and  production  of  digitally  advanced  goods  and  services.  DX  brings 
revenue  growth  to  SMEs  through  internationalisation,  as  well  as  increased  competitiveness  within  their 
national  market  (OECD,  2019).  Meanwhile,  the  adoption  of  digital  practices  by  one  firm  can  impact  the 
(global)  value  chain  collaborators.  Collaboration  platforms,  social  networks, 
performance  of 
augmented/virtual reality, cloud computing and crowdfunding generate value through linking  physical capital 
and labour across a given value chain. This involves three modes of adoption of digital technologies: digitalisation 
and integration of vertical and horizontal value chains; digitalisation and customisation of products and services; 
deployment of digital business models and customer access (Savastano et al., 2018). 

its 

The  adoption  of  digital  practices  by  one  firm  impacts  the  performance  of  its  competitors.  Providing  that  a 
welcoming  business  environment  and  good  access  to  skilled  labour  are  both  in  place,  DX  increases  market 
competition. Within any given industry, firms which are more productive often benefit most from the adoption 
of digital technologies, as a result of becoming early adopters and therefore becoming better placed to launch 
complementary reorganisation of production processes (Sorbe et al., 2019).  

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In terms of the deployment of specific digital technologies by SMEs, the following benefits were identified in 
the first roundtable of the OECD D4SME22: 

•  Digital infrastructure and platforms: Access to affordable and reliable digital infrastructure is critical 
for helping SMEs to compete in just-in-time and data-driven production systems and to scale up internal 
capacity (e.g. by accessing platform services such as cloud computing). Access to digital platforms can 
help SMEs lower transaction costs (e.g. related to finding information, negotiating prices, monitoring 
transactions or trading) and to reach new markets; 

•  AI: The deployment of AI, in combination with other technologies, can enable SMEs to make important 
productivity gains by providing unique insights into their business and market. Thanks to IoT, which is 
based  on  the  hyper-connectivity  of  sensors,  devices,  and  systems  that  support  machine-to-machine 
communication, a high quality and volume of data can be gathered and exchanged through high speed 
5G , and then leveraged using AI and data analytics to empower new data-driven business models; 
•  Blockchain and Distributed Ledger Technologies (DLTs): These technologies can be beneficial to SMEs 
by  allowing  an  immediate  and  secure  transfer  of  value  and  ownership  within  a  decentralised  digital 
network, without an intermediary and in total transparency. This facilitates international trade, supply 
chain management, payments and transparency, impacting productivity and access to finance, reducing 
transaction  costs,  and  enhancing  ecosystem  collaboration,  while  helping  address  the  counterfeiting 
issue in global trade flows. Financial services and systems architecture are currently the primary focus 
of blockchain-based solutions, but these solutions also have the potential to transform a wide range of 
other industries such as healthcare, social services, advertising, education, food and logistics;  
Fintech:  The  financial  sector  is  at  the  forefront  of  digital  innovation  and  fintech  has  already  largely 
transformed the industry. SMEs and entrepreneurs have benefited from these developments by gaining 
access to a broader range of financing sources that better meet their needs in terms of ease and access. 
Fintech holds promise for different types of SMEs, including innovative SMEs and startups, underserved 
segments and established SMEs that have previously used traditional lending channels, allowing them 
to benefit from lower transaction costs, better credit risk assessment and customised and rapid services. 

• 

Effective adoption of digital technologies and practices is expected to unlock net benefits of approximately USD 
100 trillion globally between 2016 and 2025 (World Economic Forum, 2018). While DX leads to job losses, the 
net outcome is expected to be positive; new jobs are expected to be generated due to complementarities with 
skilled labour (Sorbe et al., 2019). 

1.5  The digital transformation of SMEs and the impact on sustainability 

1.5.1  Sustainable Development Goals (SDGs) for meeting the 2030 Global Agenda 

The sustainable development goals (SDGs) of the ‘2030 Agenda for Sustainable Development’, adopted by the 
UN in Sept 2015, provide a shared blueprint for peace and prosperity for people and the planet, establishing an 
urgent call to action involving all countries in a global partnership.23 The Paris Agreement24 signed in Dec 2015, 
aims to strengthen the global response to the threat of climate change, while achieving UN SDGs. One of the 
most important characteristics of the 2030 Agenda is its universality, relevant to all communities, from local to 
global. SDGs cover a broad range of topics related to sustainable development, defined under 17 goals with 169 
targets. In general, SDGs, as an integrated framework, fall into 3 main categories25 related to the economy, the 
biosphere/environment, and the social/societal (Belyaeva & Lopatkova, 2020):  

•  Biosphere  covers  topics  related  to  climate  change  (i.e.  SDG  13  -  Climate  Action),  aimed  at  reducing 
emissions, ensuring resilience to natural disasters, and enhancing the ability to act; natural resources 
covers SDG 6 - Clean Water and Sanitation, SDG 14 - Life Below Water, and SDG 15 - Life On Land, with 
the goal of conserving these domains; 
Society covers the topics of basic human needs, amenities and utilities, and a fair and just society (i.e. 
SDG 1 - No Poverty, SDG 2 - Zero Hunger, SDG 3 - Good Health and Well-being, SDG 4 - Quality Education, 

• 

22 https://www.oecd.org/cfe/smes/latestdocuments/D4SME%20First%20Roundtable%20Proceedings.pdf  
23 https://sdgs.un.org/goals  
24 https://ec.europa.eu/clima/policies/international/negotiations/paris_en  
25 https://gesi.org/research/download/36  

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16 

• 

SDG  5  -  Gender  Equality,  SDG  7  -  Affordable  and  Clean  Energy,  SDG  11  -  Sustainable  Cities  and 
Communities and SDG 16 - Peace, Justice and Strong Institutions); 
Economy  covers  the  following  topics  related  to  inclusive  growth  and  sustainable  industry:  SDG  8  -
Decent Work and Economic Growth, SDG 9 - Industry, Innovation and Infrastructure, SDG 10 - Reduced 
Inequalities, and SDG 12 - Responsible Consumption and Production. 

Since 2015, the EU has been at the forefront of international efforts to implement the 2030 Global Agenda and 
the SDGs. The aim is to transform the EU into a modern, resource-efficient and competitive economy, with no 
net emissions of greenhouse gases by 2050, as targeted by its new post-2020 growth strategy, the EU Green 
Deal.26 This approach has been further highlighted in the EU’s new economic recovery plan, NextGenerationEU, 
announced in May 2020, which focuses on establishing an economy that works for  people and the planet by 
taking action in the areas of economic stability, social fairness, environmental sustainability and productivity and 
competitiveness. These high level strategic objectives are reflected in various other EU policies, including, among 
others, the new EU Industrial Strategy27, the new EU SME Strategy,28 Shaping Europe’s Digital Future,29 and the 
new Circular Economy Action Plan.30 The goal is to support industry  in becoming greener, more circular and 
more digital, in order to remain competitive, resilient, and sustainable on the global stage. As stated  in the EC 
2021 Work Programme,31 EC actions will remain guided by the 2030 Agenda and its SDGs, both internally and 
externally, as well as by the Paris Agreement. 

Taking into account the 2030 Global Agenda, this chapter aims to provide brief insights from recent academic 
publications  about  the  relationship  between  DX  and  its  impact  on  the  sustainability  commitments  of  SMEs, 
discussed from economic, societal and environmental perspectives. 

1.5.2  Relevance of the Fourth Industrial Revolution (4IR), Industry 4.0 and the Circular Economy to 

SDGs 

4IR is the current era, in which multi-modal adoption of different advanced digital technologies and Key Enabling 
Technologies (KETs) are constantly evolving and merging with other new technologies (such as bioengineering, 
geoengineering, the Internet of Everything (IoE), neurotechnology, and new computing technologies) to connect 
the different digital, physical and biological spheres. The digital sphere includes 3D printing, AI, big data analytics, 
blockchain, cloud technology, digital twins, industrial IoT, quantum computing, robotics, and virtual/augmented 
reality (VR/AR). The physical sphere includes advanced/nano materials, nano devices, next-generation batteries, 
organ  microchips,  and  wearables.  The  biological  sphere  includes  bioinformatics,  next-generation  genomics, 
personalised  medicine,  stem  cells,  synthetic  biology,  and  systems  metabolic  engineering.32  The  4IR  has  been 
evolving at an exponential pace since the middle of the last century, with a unique velocity, scope, and systems 
impact, disrupting almost every industry globally by transforming entire systems of production, management, 
and governance, as defined by the World Economic Forum in its 4IR Initiative.33 

The following 4IR technologies have been indicated as most  relevant for environmental applications by the 
World Economic Forum’s ‘Fourth Industrial Revolution for the Earth’ initiative34: 3D Printing used in additive 
manufacturing; advanced materials (including nanomaterials); advanced sensor platforms (including satellites); 
artificial  intelligence;  biotechnologies;  blockchain  (and  distributed  ledger);  drones  and  autonomous  vehicles; 
energy capture, storage, and transmission; geoengineering; internet of things; neurotechnology; new computing 
technologies such as quantum computing and DNA-based solid state hard drives; robotics; virtual, augmented 
and mixed reality.  

Likewise,  in  the  discussion  paper  drafted  by  the  European  Policy  Center  (ECP)  (Hedberg  &  Šipka,  2020),  the 
following digital technologies and digitally enabled solutions are presented as having potential for bringing about 
various sustainability solutions: 3D printing or additive manufacturing; AI; applications or apps for computers, 

26 https://ec.europa.eu/info/strategy/priorities-2019-2024/european-green-deal_en  
27 https://ec.europa.eu/info/sites/info/files/communication-eu-industrial-strategy-march-2020_en.pdf 
28 https://ec.europa.eu/info/sites/info/files/communication-sme-strategy-march-2020_en.pdf  
29 https://ec.europa.eu/info/sites/info/files/communication-shaping-europes-digital-future-feb2020_en_3.pdf  
30 https://ec.europa.eu/environment/circular-economy/ 
31 https://eur-lex.europa.eu/resource.html?uri=cellar%3A91ce5c0f-12b6-11eb-9a54-01aa75ed71a1.0001.02/DOC_1&format=PDF  
32 https://www.pwc.com/ee/et/publications/pub/innovation-for-the-earth.pdf  
33 https://www.weforum.org/agenda/2016/01/the-fourth-industrial-revolution-what-it-means-and-how-to-respond/  
34 http://www3.weforum.org/docs/WEF_Harnessing_the_4IR_for_the_Earth.pdf  

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tablets  and  mobile  devices;  blockchain;  cloud  computing;  data  sharing  built  on  barcodes,  blockchain,  quick 
response  (QR)  codes,  radio  frequency  identification  devices  (RFIDs),  and  watermarks;  digital  twins;  earth 
observation,  satellite  imagery,  sensors,  cameras,  drones  and  robots;  IoT  and  connected  devices;  and  online 
platforms. 

Even  though  the  term  Industry  4.0  evolved  simultaneously  and  is  used  interchangeably  with  4IR,  it  actually 
originates  from  a  strategic  initiative  in  Germany  (Industrie  4.0)  within  the  ‘High-tech  Strategy  2020’,35 
announced in 2010, aimed at making the German manufacturing industry more competitive. As a sub-domain of 
4IR,  Industry  4.0  has  became  a  global  term,  along  with  other  terms  such  as  ‘Smart  Factory/Industry’,  ‘Smart 
Manufacturing’, ‘Intelligent Factory’, ‘Factory of the Future’, etc. All of these terms are used to describe more or 
less  the  same  vision  of  modernisation  of  the  manufacturing  industry  achieved  through  implementation  of 
Industry  4.0.  The  term  can  be  defined  as  the  current  trend  for  utilising  automation  and  data  exchange  in 
manufacturing technologies by bridging the physical and digital worlds via cyber-physical systems enabled by 
Industrial  IoT  (IIoT),  cloud  computing  and  cognitive  computing,  allowing  for  personalisation/customisation  of 
smart products (i-SCOOP, 2021).  

Another focus of recent academic research is the combined impact of 4IR digital technologies and the circular 
economy (CE) concept on SDGs. The combination of 4IR technologies and CE, with its associated tools such as 
life  cycle  costing,  life  cycle  impact  assessment,  materials  passports,  and  circularity  measurements,  has  been 
implemented in a number of sectors, in a variety of countries, in order to move away from a linear ‘take, make, 
and  dispose’  model  to  a  more  circular  model,  with  demonstrably  positive  results  for  the  environment  and 
economy.  Three  types  of  technologies  can  be  used  to  transition  into  a  CE  and  sustainable  future:  Digital 
technologies  such  as  big  data,  blockchain,  IoT,  and  RFID  help  enterprises  track  capital  and  manage  excess 
capability  and  usage.  Physical  technologies  such  as  3D  printing,  energy  recycling  and  processing,  modular 
construction,  nanotechnology,  and  robots,  help  businesses  reduce  manufacturing  and  product  costs  and 
minimise  the  environmental  impact.  Biological  technologies  such  as  bio-based  materials,  biocatalysts,  bio-
energy, hydroponics and aeroponics, help businesses move away from fossil fuels (Hoosainet al., 2020).  

1.5.3 

Impact of 4IR, Industry 4.0 and the Circular Economy on SDGs 

Technological advances and innovative techniques have potential as powerful tools for achieving the UN SDGs 
(Chui et al., 2018), with some SDGs benefiting more than others. However, the impacts on SDGs are not always 
positive. There have also been studies showing negative impacts. For instance, a recent study (Vinuesa et al., 
2020), reports that while AI might act as an enabler of technological improvement for 134 targets (79% of all 
SDGs), for 59 targets (35% of all SDGs) the impact might in fact be negative. In order to better understand if and 
how 4IR technologies can be used positively to address SDGs, various arguments  concerning the positive and 
negative impacts will be briefly presented here.  

The ‘Fourth Industrial Revolution for the Earth’ publication series produced by WEF in collaboration with PwC 
and the Stanford Woods Institute, groups emerging applications of 4IR technologies for the earth into 5 main 
categories that could address climate change: clean power; smart transport systems, sustainable production 
and consumption; sustainable land use; smart cities and homes  (Herweijer & Combes, 2017). For instance, in 
terms of clean power, smart grids, connected via the cloud and utilising the IoT, big data analytics and ML, can 
significantly increase the energy efficiency of the existing grid. Enhanced predictability of demand and supply of 
renewables can also improve energy storage and load management and assist in the integration and reliability 
of renewables. The energy performance and affordability of renewables and battery storage solutions can also 
be  increased  using  innovations  such  as  nanobattery  solutions,  designer  carbon,  graphene  applications  and 
perovskite  solar  cell  coatings  (all  of  which  are  advanced  materials),  neural  network  controlled  solar  boost 
converters (AI), and 3D solar panels (3D printing). A distributed peer-to-peer grid, which leverages distributed 
renewables, can be facilitated through a fusion of AI, big data, blockchain, cloud and IoT. 

Sustainable production and consumption involving  adoption of IIoT, which  combines smart  machines, smart 
materials, and smart products across an entire industrial value chain, results in advanced production, optimised 
for the resource efficiency of energy, raw materials and water, while enabling connection with customer devices 
to optimise lifespan performance. Wider 4IR technologies incorporated into the IIoT platform to optimise smart 

35 https://ec.europa.eu/digital-single-market/en/blog/implementation-industry-40-strategy-german-plattform-industrie-40  

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18 

product design include: energy efficient robotics, industrial big data analytics, intelligent machine applications, 
and sensor-driven computing and VR product simulators. IoT connected sensors and blockchain can revolutionise 
the  ability  to  track  and  monitor  products  from  origin  to  supply  chains,  ensuring  increased  transparency  and 
accountability with regard to sustainable manufacturing and consumption.  

In addition, the following 4IR technologies can be combined to provide five game-changing climate solutions 
linked to SDGs 7 and 13 (Affordable and Clean Energy, and Climate Action)  and thereby underpin a net zero 
emissions economy: electrification of the transport system, fusing AI, advanced materials, big data, cloud and 
IoT; next generation distributed grid, fusing AI, advanced materials, big data, blockchain, cloud and IoT; smart 
and transparent land use management, fusing AI, autonomous vehicles (drones), big data, cloud and IoT; smart 
and automated road transport grid, fusing autonomous vehicles, cloud, big data and IoT; technology enabled 
urban planning and design, fusing 3D printing, AI, advanced materials, autonomous vehicles, big data, cloud and 
IoT. 

Despite  all  the  potential  benefits  that  can  be  gained  by  deployment  of  4IR  technologies  to  address  climate 
change, particular attention should be given to the unintended consequences which might also ensue, since a 
number of 4IR technologies could have unintended negative consequences if not designed and scaled in a smart 
and  sustainable  way.  Among  the  potential  negative  impacts  are  job  displacements,  infringements  of  data 
privacy,  cyber  security  problems,  and  biotechnology  ‘bio  errors’.  In  order  for  4IR  technologies  to  be  used 
successfully for the planet and for society, innovators, industry and governments need to come together to shape 
flexible and robust national and international technology governance structures that will enable a ‘responsible’ 
4IR (Herweijer & Combes, 2017). 

The Global Enabling Sustainability Initiative (GeSI)36 also defines seven digital technologies (digital access, fast 
internet,  cloud,  IoT,  cognitive,  digital  reality  (VR&AR),  and blockchain)  which  have  a  critical  influence  on  the 
world. In its ‘Delivering A Smarter2030’ report (GeSI & Deloitte, 2019), GeSI uses a framework of four ‘impact 
functions’ to assess the link between these technologies and their impact on SDGs:  

Connect & Communicate: Connecting people to each other and to critical information; 

• 
•  Monitor & Track: Real time, extensive observation of the world and its natural and built systems; 
•  Analyse, Optimise and Predict: The development of insights from data, and the use of those insights to 

drive process efficiency and infer the future;  

•  Augment & Autonomate: Provision of an ‘active bridge’ between digital and physical, from simulation 

through augmentation to the creation of autonomous systems. 

The report uses this framework to establish links between each target and the seven digital technologies, and to 
categorise how digital technologies can impact the SDGs. It focuses on 103 of the total 169 SDG targets which 
are not primarily reliant on policy, financial support or non-digital interventions. Analysis across a range of SDGs 
estimates  an  average  acceleration  of  22%  in  SDG  progress  and  a  23%  mitigation  of  downward  trends  by 
deploying existing digital technologies. However, while the increased adoption of digital technologies is expected 
to help close the gap in some of the 2030 targets, the performance of 8 out of the 25 indicators analysed is still 
expected to deteriorate.  

Analysing  the 
impact  of  digital  technologies  across  the  three  sustainability  pillars  of  economy, 
environment/biosphere and society, the report estimates the positive and negative impacts of DX for each pillar, 
as summarised in Table 2.  

36 https://gesi.org/  

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Table 2 Estimated positive and negative impacts of DX against major impact functions 

Estimated positive impacts  
on SDGs 
The global deployment of Industry 
4.0 is estimated to have the potential 
to increase global manufacturing 
value added per capita from around 
$1,800 to over $2,700 in 2030, of 
which 22% of this increase can be 
attributed to Industry 4.0. 

Adoption of digital technology is 
estimated to reduce CO2e by 668 
metric tons, equivalent to 1.3% of 
global emissions in 2030, as a result 
of increased efficiency via process 
optimisations in agriculture, 
transportation, energy networks, 
manufacturing, and by managing 
water use through deployment of 
smart water infrastructures.  

Adoption of digital technologies is 
expected to have positive impacts 
across different areas, from 
sustainable and productive 
agriculture, skilled birth attendance, 
youth literacy, access to electricity, 
renewable energy consumption, air 
pollution, and reproductive rights. 

Estimated negative impacts 
on SDGs 
• Greater divide between 

developed and less 
developed economies  

• Increase in consumption as 

wealth increases 

• Increase in job displacements 
• Increase in system-wide risk 
from centralised control and 
cyber attacks. 

• Increase in emissions directly 
related to ICT technology 
deployment  

• Increase in extraction of 

scarce resources  

• Increase in e-waste, such as 

increased proliferation of IoT 
devices. 

• Increase in inequalities  
• Reduction in resilience of 
core society supporting 
systems unless cyber security 
is ensured  

• Spread of misinformation 
leading to lack of trust in 
society unless transparency 
and truth are ensured. 

Major impact functions 

Key 
Pillars 

y
m
o
n
o
c
E

/
t
n
e
m
n
o
r
i
v
n
E

e
r
e
h
p
s
o
B

i

y
t
e
i
c
o
S

• Monitoring supply chains 

accurately to create 
transparency in production 

• Optimising processes to increase 

productivity while reducing 
energy and material usage and 
emissions. 

• Monitoring and tracking the 

state of the natural world (SDGs 
6, 14, 15);  

• Analysing and optimising energy 

and material usage across 
sectors to minimise the impact 
of climate change (SDG 13);  
• Augment and autonomate to 

reduce emissions across 
agriculture, industry and 
manufacturing. 

• Connecting the unconnected and 
vulnerable to basic digital access, 
to enable financial inclusion, 
education and empowerment 

• Analysing & predicting to 
accelerate drug and crop 
developments by use of AI/ML 
and the computing power 
promised by the cloud.  
• Monitoring, tracking and 

analysing complex datasets to 
reduce poverty and hunger, 
disaster impact, education and 
health outcomes. 

• Autonomating machines to 

transform agriculture as well as 
city utilities, services, and 
security provisions. 

Source: Authors’ interpretation of GeSI ‘Delivering A Smarter2030’ report (GeSI & Deloitte, 2019) 

The  previously  mentioned  impacts  of  DX  on  sustainability  have  also  been  highlighted  by  a  number  of  other 
researchers.  For  instance,  the  emergence  of  the  internet  of  things  (IoT)  has  enabled  the  delivery  of  huge 
amounts of data, providing valuable information on managing natural resources more effectively and thereby 
avoiding natural disasters. IoT in Agriculture has provided valuable information generated by connected devices 
which  analyse  the  soil  to  determine  water  needs  for  smarter  irrigation.37  Furthermore,  big  data  can  play  a 
powerful role with regard to the environment, due to its ability to help companies understand and measure the 
impacts  of  their  operations  on  the  environment  (Nardone,  2015).  In  addition,  data  has  always  been  key  to 
assessing  ecological  risks.  Another  added  value  of  big  data  relates  to  its  contribution  to  helping  companies 
optimise the usage of energy and other resources by tracking data in real time through the deployment of new 
generation smart meters and sensors (Yifat, 2017). 

A recent study (Corfe, 2020), focusing on the role played by 4IR technologies in improving the environment, 
identifies a number of channels for tackling the environmental challenges associated with air pollution and global 
warming.  These  include:  better  monitoring  of  air  quality  by  using  affordable  low  power  wide  area  networks 
(LPWANs); provision of more personalised advice on air  pollution; use of emerging technologies (e.g. robotic 

37 http://digital-me-up.com/2017/05/05/the-impact-of-digital-innovation-on-the-planet/  

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20 

trees, parasitic drones, air-cleaning buses and air separation plants) to remove pollution and carbon from the air; 
cleaning  up  transportation  by  shifting  to  electric  and  autonomous  vehicles,  car  sharing,  smart  public 
transportation  and  dynamic  road  pricing;  using  big  data  and  blockchain-based  solutions  to  encourage 
environmentally friendly decision making by consumers and businesses by presenting the carbon emissions of 
their  purchases  at  point  of  sale;  decarbonising  industry  by  using  green  commercial  vehicle  fleets,  cloud 
computing, virtual and augmented reality and 3D printing. 

Many more application areas of digital technologies and their various impacts on each SDG domain have also 
been mapped by Hoosain et al (2020) in their research on the use of digital technologies to address SDGs through 
circularity. 

As reported in the UN SDG progress report, the  circular economy (CE)  holds particular promise for achieving 
multiple SDGs, particularly those connected with clean energy, economic growth, sustainable cities, responsible 
consumption and production, climate change, oceans, and life on land.38  

Using 4IR technologies to achieve the goal of ‘Responsible Consumption and Production’ by adopting circular 
innovation  could  increase  energy  efficiency  and  reduce  waste  by  putting  re-use,  remanufacturing,  and 
recyclability  at  its  core.  Furthermore,  accelerating  circularity  across  global supply  chains  could  generate  over 
US$1 trillion annually by 2025 across the global economy (Herweijer & Combes, 2017), with the move from a 
linear model towards a more CE model generating as much as US$4.5 trillion globally by 2030 (Jose et al., 2020). 

There has been a significant acceleration towards the CE by using 4IR digital technologies (Ramkumar, 2018). 
Although  the  CE  applications  are  associated  with  SDGs  (Mattero  et  al.,  2018;  Schroeder  et  al.,  2018),  the 
combined impacts of digital technologies and the CE on SDGs  (Corfe, 2020) have only recently been the focus of 
research  (Ellen  MacArthur  Foundation,  2019;  Ghoreishi  &  Happonen,  2020).  Hoosain  et  al  (2020)  have 
investigated the relationship between DX and the CE in terms of achieving the SDGs. The 4IR technologies are 
viewed  as  enablers  to  enhance  the  CE  across  industries  in  three  main  ways:  through  circular  infrastructure 
optimisation; through building objects, parts, and components based on the circularity concept; and through 
the function of circular business  structures. The authors emphasise the importance  of bringing together  the 
concepts of 4IR digital technologies and the CE, in collaboration with international organisations and multiple 
stakeholders  from  politics,  industry,  academia  and  civil  society,  in  order  to  have  a  positive  impact  on 
sustainability and achieving the SDGs.  

The  role  of  Industry  4.0  within  the  4IR  has  also  became  another  research  focus  area.  Further  exploration  is 
required regarding the impact of Industry 4.0 on sustainability as it grows exponentially, as well as its implications 
for sustainability in terms of economic, environmental, and social impacts. The digital connectedness and real 
time  information  development  and sharing  offered  by  Industry  4.0  may have  contradictory  impacts  on  SDGs 
(Lopes de Sousa Jabbour et al, 2018a; Lopes de Sousa Jabbour et al, 2018b). Digitalisation of manufacturing and 
business processes and the deployment of smart machines and devices may offer numerous advantages, such 
as  increased  manufacturing  productivity,  resource  efficiency,  and  waste  reduction  (Tortorella  &  Fettermann, 
2018). However, increased productivity due to industrial automation is also likely to be associated with higher 
resource and energy consumption as well as elevated pollution concerns (Beier et al., 2017; Liu & Bae, 2018). DX 
of the manufacturing industry using labour saving technologies (e.g. intelligent robots, autonomous vehicles, and 
cloud solutions) is also expected to severely disrupt labour markets by replacing the majority of low skilled jobs 
with  new,  highly  skilled  jobs,  such  as  automation  engineering,  control  system  design,  machine  learning,  and 
software engineering (Brougham & Haar, 2018; Frey & Osborne, 2017). 

A recent study (Ghobakhloo, 2020) has attempted to develop an interpretive model of Industry 4.0 sustainability 
functions  by  explaining  the  processes  of  industrial  digitalisation  and  the  underlying  technology  trends  (i.e. 
additive/advanced  manufacturing,  AR/VR,  automation  and  industrial  robotics,  big  data  analytics,  blockchain, 
cloud computing, cyber-physical production systems, cybersecurity, IIoT, Internet of People, Internet of Services, 
semantic  technologies,  simulation  and  modelling)  and  design  principles  (i.e.  decentralisation,  horizontal 
integration,  interoperability,  modularity,  product  and  service  individualisation,  real  time  capability,  smart 
products  and  factors,  vertical  integration,  virtualisation)  in  order  to  assess  their  impact  on  SDGs.  The  study 
provides an explanation of ways in which the processes of Industry 4.0, including underlying technology trends 

38 The Sustainable Development Goals Report 2018: 
https://unstats.un.org/sdgs/files/report/2018/TheSustainableDevelopmentGoalsReport2018-EN.pdf  

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and  design  principles,  can  positively  contribute  to  the  achievement  of  SDGs.  The  modelled  Industry  4.0 
sustainability functions are expected to impact SDGs as illustrated in the figure below. 

Figure 4 Impact of Industry 4.0 sustainability functions on a firms’ SDG performance 

Source: (Ghobakhloo, 2020) 

The position paper published by the European Policy Centre (EPC) (Hedberg & Šipka, 2020) aims to show how 
digitalisation  can  support  environmental  protection  and  climate  action,  and  how  DX  can  be  made  more 
sustainable. In particular it looks at greening ICT and how digitalisation can enhance sustainable consumption 
and  production  (i.e.  the  circular  economy),  improve  biodiversity,  and  make  agriculture  and  mobility  more 
sustainable. Digital solutions using innovative business models could be utilised for greening the economy, and 
improving the design of products. They could support people’s right to repair, help improve resource and energy 
efficiency,  reduce  emissions,  minimise  waste  and  encourage  dematerialisation  (i.e.  doing  more  with  less 
materials). Digital solutions could also enhance governance, including the implementation and enforcement of 
relevant rules needed to protect biodiversity, and could boost the circular economy and help to achieve climate 
neutrality. On the other hand, concerns about the negative impacts of 4IR technologies have also been raised. 
These mainly concern the environmental footprint of the ICT sector itself (e.g. high consumption of energy and 
materials for data centres, digital devices and digital infrastructures; the waste created by the sector, etc.), along 
with an increase in wasteful consumption and energy- and transport-related emissions. In addition, there are 
negative implications for privacy, security (i.e. cyber attacks), health (i.e. radiation, hazardous substances), and 
potential job prospects, together with unwanted societal impacts, such as a rise in inequality. As a result, calls 
have been made for responsible use of DX by fully addressing the downsides of going digital in order to turn DX 
into a catalyst for creating a sustainable economy instead.  

1.5.4 

Linkages between SMEs, DX and SDGs 

A sustainable economy includes sustainable businesses; therefore, understanding the drivers and measurement 
of a firm’s successful transition to sustainability is necessary (Delmas, Lyon, & Maxwell, 2013). 

Even though there is plenty of academic research which aims to explore the roadmap to sustainability (Eccles & 
Krzus, 2015; Gray & Stites, 2013; Joyce & Paquin, 2016; Nidumolu et al., 2009), there are still only limited studies 
available which provide empirical evidence explaining the link between the drivers of DX and sustainability and 
the impact of DX on the sustainability commitment of companies. 

In a recent academic research paper (El Hilali et al., 2020), the link with/impact of DX on a firm’s sustainability 
is  investigated  empirically,  by  studying  Moroccan  SMEs  from  different  industries.  The  authors  define  five 

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different hypotheses in the light of current literature and use a partial least squares structural equation model 
(PLS-SEM) to test (empirically) the effect of DX on sustainability at firm level. The hypotheses are based on four 
drivers linked to DX: customers, competition, data, and innovation, as explained below: 

• 

• 

Customers  are at the heart  of any DX  and have a  positive influence on companies’ commitments to 
sustainability in the digital era; 
Competition  in  the  digital  era  has  a  positive  influence  on  companies’  commitment  to  sustainability 
through publication of their carbon footprints. Additionally,  the way that companies compete in the 
digital  era  has  changed,  as  they  frequently  collaborate  in  order  to  learn  from  each  other,  while  still 
remaining competitors; 

• 

•  Data (i.e. data related to business processes, products or services, and customer data on both supply 
and  demand  side)  as  a  strategic  asset  in  the  digital  era,  enhances  both  the  relationship  between 
company and customer, as well as the company’s commitment to sustainability; 
Innovation  in  the  digital  era  is  a  key  driver  of  companies’  commitment  to  sustainability,  especially 
through business model innovation (Foss & Saebi, 2016; Zott & Amit, 2017), digital social innovation 
(Andrea,  2017)  as  well  as  innovations  that  lead  to  the  emergence  of  platform  business  models, 
particularly  sharing  economy  solutions  (Porter  &  Kramer,  2011),  which  benefit  the  environment  by 
reducing  the  amount  of  goods  produced  and  waste  to  be  recycled  following  the  circular  economy 
concept.  (Evans  et  al.,  2017)  Proposals  which  set  out  the  foundational  concepts  for  innovation  in 
sustainable  business  models,  such  as  sustainable  value,  should  incorporate  economic,  social  and 
environmental benefits as value forms; an innovative business model also requires the design of a new 
business purpose, value proposition and governance; in addition it requires the creation of a system of 
sustainable value flows that include all the stakeholders involved (Evans et al., 2017). 

The results of the study of Moroccan SMEs affirm the positive influence of customers, data and innovation on 
the quest of companies to reach sustainability, whereas competition did not play a significant role. The authors 
conclude that on the road to achieving sustainability in the digital era, consideration should be given to adopting 
a customer-centric approach and building a culture that embraces data. Secondly, DX is not exclusive to large 
companies. SMEs could also adapt their business models, change the way customers are perceived, exploit data 
and rediscover innovation, not only for DX, but also to observe positive sustainability impacts (El Hilali et al., 
2020). 

In brief, DX is about using digital capabilities (such as big data, cloud computing  and IoT) to revolutionise the 
customer experience, to outdo the competition and to create an innovative business model adapted to the digital 
era  (Westerman,  2011).  To  link  it  with  sustainability,  a  DX  should  help  companies  to  increase  their  financial 
numbers and social footprint and reduce their negative externalities on the environment.  

Since there is a constant symbiosis between the three elements of sustainability: people (social aspect), planet 
(environmental aspect) and money (economic aspect), these must be embedded into the business model and 
corporate report of the company. The latest literature assumes that the integration of sustainable goals into the 
business  model  can  result  in  competitive  advantage,  increased  company  value,  and  a  positive  image  and 
reputation for the business (Evans et al., 2017). 

A sustainable mission statement should be translated into specific sustainable goals that the company will strive 
to achieve. The environmental and social goals are considered to be an integral part of the economic logic of 
the business. Frameworks such as the SDGs aim to help SMEs to understand the scope of the global sustainability 
issues,  while  providing  a  practical  blueprint  to  guide  companies  in  improving  their  economic,  social  and 
environmental performance. According to a study by the International  Trade Centre (ITC) (2019), investing in 
SMEs in developing countries contributes to the SDGs through four main impact channels as follows: employee 
impacts (SDGs 1, 2, 3, 8); business practice impacts (SDGs 5, 8, 9, 10, 12, 13, 14, 15, 16); sectoral impacts (SDGs 
2, 3, 4, 6, 7, 9, 11); and national economy impacts (SDGs 1, 8, 9, 10, 17). Through these channels, investments in 
SMEs can contribute to 60% of the 169 SDG targets.  The  two  SDGs which  stand out as  benefiting most  from 
strengthened  SMEs  are  ‘Decent  Work  and  Economic  Growth’  (SDG  8)  and  ‘Industry,  Innovation  and 
Infrastructure’ (SDG 9). Further benefits arising from the key role played by SMEs in achieving various SDGs have 
been  reported  by  Karlstorm  (2018).  These  include:  promoting  inclusive  and  sustainable  economic  growth; 
promoting sustainable industrialisation and fostering innovation; improving health and well-being; and reducing 
income inequalities at local level by providing good quality jobs and working conditions. Malaquias et al. (2016) 
found a positive and significant relationship between the IT usage of SMEs in Brazil and the four categories of 

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corporate social responsibility (CSR): economic, legal, ethical and discretionary. Jucan & Baier (2012) present the 
implications of the use of ICT for CSR activities in tourism businesses in emerging markets and also examine the 
relationship  between  e-sustainability  and  competitiveness.  ICT-based  services  can  improve  the  efficiency  of 
processes and systems for multiple stakeholders (Lakatos et al., 2015; Santoro et al., 2018), and can support the 
strategic (improved customer relations), tactical (improved contract administration) and operational (improved 
data  management)  sustainable  benefits  that  SMEs  can  gain  by  using  ICT  tools.  ICT  can  help  to  make  CSR 
information  more  easily  available  to  stakeholders  and  create  new  possibilities  for  linking  information  on 
company impacts with other sources, providing easier access to information online that can be used to develop 
sustainable awareness (Jucan & Baier, 2012). 

The business models of SMEs were compared against a framework of social and economic SDGs in a survey of 
750 European SMEs in the food and beverage industry in Western European (Germany, Spain, UK) and Central 
and Eastern Europe (Croatia, Poland and Russia). The empirical results revealed that the sustainable approach 
in Western and Eastern European countries was structured around the social and economic SDGs (1, 3, 4, 8, 
11,  12,  16,  17)  excluding  environmental  SDGs.  SMEs  from  both  Western  and  Eastern  European  countries 
contributed  to  ‘Good  Health  and  Well-Being’  (SDG  3)  by  providing  healthy  and  safe  working  conditions  and 
encouraging work-life balance. SMEs from Western Europe were more active in ‘Responsible Consumption and 
Production’ (SDG 12), implementing lean production to save resources, producing high quality products with 
minimal impact on the environment and providing full information about the origin of the product components 
on  the  packaging.  They  also  tried  to  achieve  ‘Peace,  Justice  and  Strong  Institutions’  (SDG  16)  by  meeting 
international  standards  and  obtaining  certificates  on  management  standards,  as  well  as  by  attaching  great 
importance to recruiting and training local community members (SDG 1 – No Poverty and SDG 8 – Decent Work 
& Economic Growth). Eastern European SMEs formulated their ethical principles in cooperation with business 
partners; raised the awareness of their employees and customers  about social and environmental  issues and 
encouraged them to participate in sustainable activities (SDG 4 – Quality Education); and communicated their 
commitment to socio-environmental activities to their stakeholders to encourage greater engagement (SDG 11 
– Sustainable Cities & Communities). Additionally, the survey findings indicate a positive relationship between 
the social responsibility strategy and ICT used by SMEs in all six countries. Engaging with the SDGs can provide a 
roadmap to business improvement and offer a way to stand out in the global arena. As a result of these empirical 
findings, combined with a literature review, the researchers conclude that the concept of digitalisation can be 
an effective factor in the sustainable development of SMEs (Belyaeva & Lopatkova, 2020).  

1.5.5  Drivers and challenges for SMEs to take sustainable actions using digital technologies 

SMEs face the following drivers and challenges, which are both internal and  external to their business, when 
taking sustainable actions by deploying digital technologies:  

1.5.5.1  External drivers and challenges 

The ICT sector plays a vital role as an enabler for a more sustainable economy and society via innovative business 
models that support the realisation of SDGs, particularly through CE practices, as previously explained. At the 
same time, the  existing  negative side effects of the ICT sector pose  key challenges that need to be tackled, 
especially  at  a  time  of  accelerated  deployment  due  to  the  COVID-19  pandemic  (Hoosain  et  al.,  2020).  Data 
centres, supercomputers, digital devices, IoT and digital infrastructures often require high levels of energy and 
resource consumption (i.e. initial mining of both abundant and rare materials and the impact of further activity 
across  the  value  chain).  The  waste  created  by  the  sector  is  another  growing  problem,  in  addition  to  the 
implications for privacy (i.e. misuse of personal data due to data leaks and non-compliance with GDPR); security 
(i.e.  cyber  attacks);  personal  health  (i.e.  radiation,  hazardous  substances);  job  prospects;  and  growing 
inequalities (GeSI & Deloitte, 2019; Hedberg & Šipka, 2020). The ICT sector currently accounts for around 2% of 
global greenhouse gas (GHG) emissions, which is comparable to the aviation sector (Avgerinou et al., 2017), and 
is expected to increase to more than 14% by 2040 (Belkhir & Elmeligi, 2018).  

The survey of SMEs in the food and drink industry in a selection of Western and Eastern Europe countries reveals 
that a diverse mix of emerging externalities forms a special environment for the development of sustainable 
business models. Among the analysed externalities, the surveyed SMEs attributed the greatest importance to 
three factors: 1) the increased importance of intangible business assets, such as image or long-term relationships 
with customers to meet their constantly changing and increasing needs; 2) the increased level of competition 
based on sustainability; and 3) the development of ICT. There are many ways, including adoption of the tools of 

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24 

Industry 4.0, that allow companies to update and create more innovative strategies in order to open up new 
opportunities for business development, as well as to impact social and environmental performance (Belyaeva 
& Lopatkova, 2020). 

The  positive  impact  of  the  CE  on  the  transition  to sustainability  can  be  considered  as a  driver  of  sustainable 
actions  by  SMEs.  However,  since  this  transition  to  the  CE  is  quite  slow  in  low  and  middle-income  countries 
compared to its fast acceleration in developed countries such as EU Member States, the CE cannot be seen as a 
meaningful  driver  in  those  countries  which  have  yet  to  boost  their  performance  on  implementing  the  SDGs. 
Major differences exist in terms of the perception of whether the implementation of CE activities can make a 
meaningful contribution to economic growth, employment, and sustainable development (Wright et al., 2019). 
The  shortfalls  in  implementation/contribution  are  associated  with  a  lack  of  the  following:  government 
interventions  and  policies  in  the  form  of  cross-departmental  collaborations;  business  incentives;  impact 
assessments on the economic, social, and environmental impacts on local communities; education; data required 
for monitoring and reporting; digital talent to improve existing technologies (Hoosain et al., 2020). 

Another important factor that can be seen as both a driver and a challenge in terms of the formation of business 
sustainability  is  standardisation.  Sustainability  reporting  is  mainly  defined  by  organisations  such  as  the 
International Standardization Organisation (ISO)39 and the Global Reporting Initiative (GRI), 40 among others. The 
aim is to define and unify non-financial reporting (NFR) as an indicator of efforts taken by firms to support UN 
SDGs (Bose, 2020). The GRI standards help organisations to understand their outward impacts on the economy, 
environment, and society by linking GRI standards with the SDGs and by providing a suite of tools to integrate 
SDGs  into  reporting.  This  increases  the  accountability  of  firms  and  enhances  the  transparency  of  their 
contribution to sustainable development. Under European Directive 2014/95/EU, large companies in EU Member 
States are required to provide a series of environmental, social and governance (ESG) disclosures, whereas this 
is  a  purely  voluntary  requirement  for  SMEs.  What  makes  NFR  challenging  is  the  lack  of  convergence  in  its 
definition  between  regulators,  quasi-regulators  and  standard-setters,  as  well  as  the  heterogeneity  of  NFR 
practices by leading sustainable firms (Stolowy & Paugam, 2018). Likewise, no generally accepted definition of 
this  term  exists  among  academics  (Tarquinio  &  Posadas,  2020).  It  is  mainly  referred  to  as  non-financial 
information or by using underlying concepts such as ‘social/environmental/human capital’ or ‘CSR reporting’ 
instead (Erkens et al., 2015). Researchers find a positive association between ESG disclosure levels and firm value, 
suggesting that improved transparency, accountability and enhanced stakeholder trust play a role in increasing 
firm value (Li et al., 2018). 

1.5.5.2 

Internal Drivers and Challenges 

Moore  and  Manring  (2009)  define  various  internal  drivers  which  optimise  SME  sustainability,  such  as:  i)  to 
become a valuable investment target for large firms, since large firms aiming to venture into new sustainable 
markets and/or business segments find it less expensive to do so through investing in or acquiring SMEs; ii) by 
creating highly competitive networks of sustainable SMEs across the value chain that help to generate progress 
and  provide  financial  and  organisational  efficiency  towards  sustainable  development;  iii)  by  becoming  highly 
efficient suppliers to global supply chains by implementing sustainable practices. 

The SMEs surveyed in the study of SMEs in the food and drink industry in Western and Eastern Europe reported 
the  following  benefits  of  taking  sustainability  actions:  an  increase  in  stakeholder  loyalty  and  brand  strength; 
improvement in staff motivation; and enhancement of financial indicators and sales. However, there were some 
differences  in  the  perceived  benefits  reported  by  Western  and  Eastern  European  SMEs.  Western  European 
companies  highlighted  an  increase  in  the  loyalty  of  internal  stakeholders,  improved  relations  with  the  local 
community and more positive perceptions of investors (external stakeholders), whereas Eastern European SMEs 
highlighted improved financial indicators as a key benefit (Belyaeva & Lopatkova, 2020). 

There  seems  to  be  a  correlation  between  SMEs  using  sustainable  strategies  and  the  following  business 
conditions:  (1)  a  sustainable  company  mission  statement;  (2)  knowledge  of  CSR  and  SDG  terminology;  (3) 
country of origin of SME; (4) company usage of ICT and e-Commerce (5) level of Gross Domestic Product (GDP) 
in the country in which the SME is located (i.e. the higher the GDP, the higher the number of SMEs implementing 

39 https://www.iso.org/news/ref2469.html  
40 https://www.globalreporting.org/about-gri/news-center/2021-01-21-enabling-companies-to-report-on-the-sdgs/  

Page | 24 

 
 
 
 
sustainability measures). The research findings indicate a positive relationship between the social responsibility 
strategy and ICT used by SMEs in six Western and Eastern Europe countries. Consequently, since digitalisation 
is linked with  sustainable  development, SMEs should be supported in achieving DX in order to realise these 
double benefits (Belyaeva & Lopatkova, 2020).  

Non-financial reporting (NFR) is gaining global momentum, most notably in relation to environmental matters, 
as  investors  and  the  public  at  large  ask  for  more  relevant  and  reliable  information  to  support  investment 
decisions in the context of sustainable finance. Conducting business sustainably by integrating ESG objectives 
into  the  business  model  has  more  or  less  become  mainstream  for  innovative  companies  and  is  seen  as  an 
opportunity for brand enhancement, attracting both talent and customers to the firm.41 

In spite of the reported benefits, there are particular challenges for SMEs in implementing sustainable solutions 
which mainly leverage digital technologies. The main challenges are: lack of access to finance for implementing 
sustainable  changes;  lack  of  knowledge,  skills  and  capacity,  particularly  regarding  business  development; 
insufficient  skills  in  marketing  and  strategic  management;  and  lack  of  time  (Belyaeva  &  Lopatkova,  2020; 
Johnson  &  Schaltegger,  2016;  Lessidrenska,  2019;  Morioka  et  al.,  2018).  The  biggest  concern  for  SMEs  is  the 
return on investment, therefore it is crucial to recognise that interventions which reduce a business’s negative 
impact can create long term value as well as save costs (Karlstorm, 2018). 

1.6  Policy recommendations 

As stated in the EC communication, ‘Shaping Europe’s Digital Future’,42 the EU aims to turn the ongoing DX into 
a  catalyst  for  creating  a  sustainable  economy.  This  requires  alignment  of  the  digital  and  green  transitions 
through recovery strategies that benefit from and contribute to greening the European economy by investing in 
the skills, sectors, products, services and digital technologies that can address climate, biodiversity and wider 
environmental  challenges.  The  European  Green  Deal  and  its  spinoff  policy  initiatives,  along  with  the  EU 
Multiannual  Financial  Framework  for  2021-27,  coupled  with  the  Next  Generation  EU  -  COVID-19  recovery 
package, all aim to define these high level strategies and corresponding action plans to make this twin transition 
happen.  

Based on this literature review, it is clear that DX comes with proven benefits as well as challenges for SMEs, 
while creating a digital divide between SMEs and LEs, smaller and bigger SMEs, and traditional and innovative 
SMEs, among others. Academics, along with  organisations  which support SMEs and  sustainable  development 
(e.g. the European Digital SME Alliance,43 SME United,44 the European Policy Centre (ECP),45 OECD D4SME,46 WEF 
4IR4Earth,47 GeSI48 etc.) have made key policy recommendations through various position and research papers 
for  supranational  and  national  policymakers  aimed  at  unlocking  the  full  potential  of  sustainable  DX,  as 
highlighted below.  

In terms of the various internal and external challenges faced by SMEs in their DX journey (e.g. lack of capacity 
and capability, time, funding, awareness, as well as  digital security, data privacy, availability and accessibility, 
regulatory  barriers,  connectivity,  interoperability,  etc.)  as  elaborated  in  the  previous  sections  (Thrassou, 
Uzunboylu,  Vrontis,  &  Christof,  2020),  there  are  various  clusters  of  recommendations  related  to  digital 
technologies  (e.g.  artificial  intelligence,  big  data,  blockchain  ecosystems,  cloud  computing  solutions,  fintech, 
online platforms, etc.) and their impacts on the economy, society, climate, and environment, in addition to cross-
cutting domains, all of which require that policy interventions be designed by taking into account the size, age 
and sector of SMEs (OECD, 2021).  

Promoting long term policy dialogue and cooperation among various stakeholder groups in order to define and 
set international standards and regulations for harmonisation is viewed as a priority by D4SME. Likewise, to 
ensure that inclusive and sustainable global growth delivers the SDGs, it is crucial that 4IR digital technologies 

41 https://insights.nordea.com/en/sustainability/why-small-businesses-should-bother-about-esg/  
42 https://ec.europa.eu/info/sites/info/files/communication-shaping-europes-digital-future-feb2020_en_3.pdf  
43 https://www.digitalsme.eu/  
44 https://www.smeunited.eu/  
45 https://epc.eu/en/  
46 https://www.oecd.org/going-digital/sme/  
47 https://www.weforum.org/projects/fourth-industrial-revolution-and-environment-the-stanford-dialogues  
48 https://gesi.org/ 

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26 

and circular economy concepts be brought together by global collaboration with multiple stakeholders from 
international  organisations,  politics,  industry,  academia,  and  civil  society.49  Additionally,  the  need  for  an 
integrated approach to policy making in the digital age is underlined, since DX impacts many policy domains. 
The recently published OECD ‘Going Digital Integrated Policy Framework’ defines its seven interrelated policy 
dimensions  as:  1)  access  to  communication  infrastructures,  services  and  data;  2)  effective  use  of  digital 
technologies  and  data;  3)  digital  and  data-driven  innovation;  4)  good  jobs  for  all;  5)  social  prosperity  and 
inclusion;  6)  trust  in  the  digital  age;  7)  market  openness  in  digital  business  environments.  It  also  defines 
intersecting  policy  domains  (e.g.  data,  digital  government,  skills  and  SMEs)  which  cut  across  several  policy 
dimensions  (OECD, 2020). The complementarities which exist between various policies aimed at addressing 
market failures impeding DX underline the benefits of taking concerted action, which can increase the benefits 
of reforms by 20% in the average OECD country. Based on OECD best practices, effective use of monitoring and 
evaluation techniques, in combination with impact assessment techniques, are also indicated as key elements 
in  improving  the  design,  efficacy  and  agility  of  a  policy  mix  aiming  to  achieve  DX  (Sorbe,  Gal,  Nicoletti,  & 
Timiliotis, 2019). 

Governments should seek to develop deeper understanding of the needs and challenges experienced by SMEs 
in  digital  adoption.  They  should  also  aim  to  raise  digital  awareness  by  sharing  best  practices,  ideas,  and 
interesting SME success stories about the benefits of digital technologies for addressing their specific business 
needs,  in  parallel  with  providing  incentives  for  digital  deployment.  Furthermore,  in  order  to  foster  digital 
adoption, it is crucial to provide hands-on guidance and support services which developing clear roadmaps, in 
combination with financial support. Financial instruments, tax benefits and targeted regulatory reforms can all 
improve access to finance (OECD, 2017). Supply side interventions aimed at improving access to finance should 
include grants, loans and guarantee schemes. These types of interventions should involve investment and capital 
gains tax incentives for entities investing in young firms, along with co-investment funds and fund of funds (OECD, 
2015).  Demand  side  interventions  should  focus  on  shaping  a  strategic  vision  and  improving  awareness  of 
available  sources  of  finance.  Such  interventions  should  leverage  networks,  such  as  cluster  organisations, 
incubators, accelerators, SME support associations, business angels, as well as matchmaking services, to help 
companies  access  finance.  Regulatory  interventions  should  target  access  to  finance  through  effective  and 
predictable  insolvency  regimes,  improved  market  competition  polices  (aimed  at  freeing  financial  resources), 
easing the development of fintech solutions. Through the decentralised consensus recording of successful debt 
repayment  or  default,  Distributed  Ledger  Technologies  (DLTs)  can  allow  high  quality  and  low  risk  SMEs  to 
credibly present their risk class to credit institutions, thereby reducing information asymmetries (Wang, Lin, & 
Luo, 2019; Benedikt & Weinhardt, 2019). Both trade and supply finance could benefit from more widespread 
use of blockchain technology, while blockchain-based smart contracts which define and automatically execute 
the  terms  and  penalties  of  an  agreement  could  support  trust  and  reduce  the  administrative  burden  (Morris, 
2019).  The  development  of  knowledge  brokers  could  support  DX  by  fostering  the  emergence  of  a  digital 
ecosystem, reducing the skills gap through effective knowledge and resource sharing (Lin, Strahonja, & Plačko, 
2019). Digital Innovation Hubs (DIHs)50 often function as knowledge brokers. Their operation is associated with: 
a) incentivising cooperation between SMEs and large enterprises, establishment of collaboration networks and 
sharing of best practices in risk taking, training and use of financial resources; b) the emergence of novel forms 
of  digital  organisation  and  digital  institutional  infrastructure  and  building  blocks,  such  as  new  standards  and 
modules; c) better access to finance for SMEs;  d) the  formation and maintenance of  networks of excellence, 
living labs, and platforms for learning and exchange of best practices; e) improved connectivity between research 
institutions, universities, SMEs and the public sector to address the lack of digital talent. 

In parallel, particular attention should be paid to skills and education by promoting dialogue on how to upgrade 
workforce skills, and by redesigning education to reap the benefits of digitalisation (D4SME). The development 
of training, mentoring and workshop services, as well as business advisory services has demonstrated a positive 
effect on DX  through upgrading  the  technical and managerial skills of the workforce  (Sorbe, Gal, Nicoletti, & 
Timiliotis, 2019; OECD, 2017). For example, the EC-funded DigitiliseSME initiative51 piloted a scheme for SMEs 
undergoing  digital  transformation.  Each  participant  SME  was  temporarily  paired  with  a  digital  enabler,  who 

49 https://www.weforum.org/projects/fourth-industrial-revolution-and-environment-the-stanford-dialogues  
50 https://ec.europa.eu/digital-single-market/en/digital-innovation-hubs-dihs-europe 
51 More information relating to the initiative can be found here: https://digitalisesme.eu/about-digitalisesme/  

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assessed  their  specific  needs  in  order  to  propose  the  most  appropriate  digital  solutions.  The  outcomes  were 
highly effective and useful, based on the testimonials of the beneficiary SMEs.52 

The recommendations that are directly linked to technologies (e.g. digital infrastructure, including connectivity, 
data accessibility and data platforms, can be categorised as follows (D4SME, 1st and 2nd Roundtables):53 

•  Digital 

infrastructure  and  platforms:  Ensure  reliable,  accessible  and  affordable  connectivity, 
cybersecurity  and  fair  access  to  data;  acquire  knowledge  and  develop  and  attract  skills;  develop 
effective e-government initiatives to stimulate SMEs to digitalise and to reduce red tape and regulatory 
burdens for SMEs; regulate digital platforms at international level in order to ensure fairness, access to 
data and fair taxation; make e-commerce platforms more affordable and remove regulatory constraints; 
•  AI:  Better  communication,  education  and  training  to  raise  SME  awareness  of  the  value  of  AI  by 
highlighting the importance of infrastructure, data, software and talent in democratising AI; enable data 
partnerships  and  data  sharing  across  the  economy  while  addressing  key  concerns  about  privacy 
protection  and  accountability;  coordination  between  national  AI  policies  and  global  AI  principles  to 
guide  new  technology  developments,  including  those  related  to  ethics,  cybersecurity  and  privacy; 
support international efforts to set global standards; 

• 

•  Blockchain  and  DLTs:  Develop  clear  and  effective  regulation  in  the  short  to  medium  term  through 
targeted approaches such as establishing technology hubs; encourage SMEs to use blockchain in their 
processes  by  funding  feasibility  studies  and  incentivising  pilot  projects;  ensure  global  policy 
coordination  with  regard  to  setting  international  standards  to  avoid  regulatory  inconsistencies 
between countries; foster dialogue between all stakeholders concerned; encourage firms to invest in 
blockchain  solutions;  educate  and  bring  together  policymakers,  regulators  and  entrepreneurs  to 
increase awareness about the technology, exchange best practices and discuss ways of accelerating its 
development;  implement  policies  on  developing  DLTs,  focusing  on  governance,  interoperability, 
privacy, security, transparency, scalability and accessibility; 
Fintech: There is a need for more and better data to improve policy design, evaluation and adaptation. 
Although  recourse  to  fintech  services  is  still  highly  concentrated  in  a  specific  section  of  the  SME 
population,  namely  high-growth  firms  with  a  global  outlook,  it  is  important  for  policymakers  and 
regulators to consider how fintech developments can respond to the diverse needs  of the wider SME 
population.  A  regulatory  framework  should  be  established  that  allows  experimentation,  while 
safeguarding consumers and investors, digital security and privacy, and increasing the financial literacy 
and skills development of SMEs. The push towards digitisation in 2020, especially in fintech, as a direct 
result  of  the  COVID-19  pandemic,  is  expected  to  push  fintech  into  a  digital  breakthrough  in  2021.54 
Cloud computing is seen as a key strategic technology for the DX of the financial sector and public cloud 
solutions are becoming increasingly important due to their flexibility and scalability, as well as for their 
high  quality  security  and  resilience  standards.  The  recent  gathering  together  of  European  financial 
institutions into the ‘European Cloud User Coalition’,55 to strengthen the public cloud ecosystem for 
the entire financial industry, can be seen as an evidence of this approach. 

In  terms  of  digital  infrastructure  and  platforms  and  the  importance  of  affordable,  high  speed  internet 
connectivity for accelerating digital adoption among firms, especially in remote (rural) areas (Sorbe et al., 2019), 
one solution for improving broadband coverage in rural areas would be to incentivise private investment, for 
example by offering special tax breaks, reduced spectrum costs or low interest loans. Alternatively, if private 
investment  is  not  financially  viable,  another  option  would  be  direct  government  investment.  Best  practice, 
according  to  the  OECD,  should  be  to  couple  better  access  to  affordable  internet  connectivity  with  technical 
enablers, such as 5G networks, and should also involve the introduction of pro-competitive reforms, such as 
encouraging the emergence of new industry entrants and enabling infrastructure sharing (OECD, 2017). Another 
related  key  recommendation  is  to  ensure  that  the  digital  infrastructure  becomes  more  sustainable  by 
introducing  requirements  and  financial  incentives  for  developing  and  deploying  ICT  equipment  that  utilises 
circularity and is energy-efficient, leading to fewer emissions and reduced material consumption. Governance 
and economic instruments should be used to reduce the climate footprint of data centres by enhancing their 

52 https://digitalisesme.eu/resources-digitalisesme/ 
53 http://www.oecd.org/going-digital/sme/, 
https://www.oecd.org/cfe/smes/latestdocuments/D4SME%20First%20Roundtable%20Proceedings.pdf 
54 https://www.fintechsurge.com/news/10-fintech-predictions-from-our-experts-on-what-will-define-2021  
55 https://www.euroclear.com/newsandinsights/en/press/2021/2021-mr-02-european-cloud-user-coalition.html  

Page | 27 

 
 
 
28 

energy efficiency and encouraging the use of renewable energy sources. For example, when the European Data 
Space is established, the European Policy Centre suggests  optimising the management and analysis of data 
relevant  to  climate  action  and  the  protection  of  the  environment.  In  addition,  the  ECP  also  recommends 
reducing barriers to the free flow of information across value chains to enable the development of a sustainable 
circular economy by keeping all stakeholders informed about the use, repair, recycling and disposal of products. 
Better coordination and exchange of information in value chains can enhance transparency, while creating the 
basis for utilising smart circular concepts, such as improved product environmental footprints and digital product 
passports  (Hedberg  &  Šipka,  2020).  Moreover,  as  underlined  in  the  ‘European  Social  Partners  Framework 
Agreement on Digitalisation’,56 protecting both workers and business functions from the potential health and 
safety  risks  of  new  technologies  should  also  be  an  important  consideration  within  the  relevant  policy 
frameworks.57 Additionally, because a crucial element of the digitalisation process for SMEs is the capability to 
work remotely and thereby continue their business activities online,  SMEUnited suggests supporting SMEs to 
adopt  cloud-based  teleworking  by  providing  channeled  financial  incentives  such  as  the  subsidising  of  digital 
hardware and software investments.58 

‘Manifesto  for  Europe’s  Digital  Future’,59  the  European  Digital  SME  Alliance  formulates 
In  the 
recommendations under priorities such as: introduction of a digital tax and ending tax inequalities; setting up a 
modern  and  fair  market  competition  framework;  unleashing  the  full  potential  of  the  data  economy  through 
public-private partnerships involving data, creation of data sharing pilot projects, open access to public sector 
data and enforcement of GDPR; reciprocity of access to public procurement markets; cutting down bureaucracy 
costs; strengthening the role of DIHs in supporting SMEs and boosting digital infrastructure to the next level; 
improving  access  to  finance  for  risk  prone  projects;  creation  of  geographic  areas  with  conditions  that  are 
favourable  to  the  development  of  new  technologies  (i.e.  a  ‘sandbox  environment’)  to  ignite  Europe-led 
innovation; unlocking standards by involving SME representation into the process; paving the way for EU-led AI; 
closing the digital skills gap; building a sustainable and inclusive digital Europe.  

In  order  to  achieve  sustainable  DX,  ECP  suggests  developing  and  deploying  digital  solutions  to  support  and 
accelerate the greening of the economy and society. This could be achieved by investing in digital solutions that 
could  help  to  enhance  biodiversity,  climate  neutrality,  the  circular  economy,  sustainable  consumption  and 
production and contribute to zero pollution as well as to the green transition, especially in agriculture, mobility, 
energy,  and  other  industrial  processes.  Additionally,  new  requirements  and  financial  incentives  could  be 
introduced for developing and deploying circular and energy-efficient ICT equipment, increasing product eco-
design requirements, such as recycled content quotas, and implementing the right to repair for smart devices. 
Market-based tools could also be introduced, such as  public  procurement for greener ICT (Hedberg & Šipka, 
2020). 

The European Digital SME Alliance suggests addressing the remaining hurdles in a holistic manner by taking all 
three components of a sustainable DX into account: sustainable B2B digitalisation; green(er) technologies and 
a circular economy; and an innovation-enabling policy and regulatory framework that builds on an openness to 
achieving the green and digital (twin) transitions.60 

In the report, Digital with Purpose: Delivering a SMARTer2030 (GeSI & Deloitte, 2019), the authors envisage a 
smarter  and  more  sustainable  world,  achieved  via  responsible,  ICT-enabled  transformation.  They  suggest 
developing and deploying digital technologies with positive societal impacts in mind, through a globally shared 
vision based on 3 pillars: 

• 

• 

Commitments for all: by embracing transparency and collaboration and by recommitting to the 2030 
agenda and SDGs through harnessing the power of digital technologies to support these commitments; 
Leadership by the ICT sector: by mitigating negative impacts, operating responsibly and collaborating 
with the key or partner sectors involved in delivering the 2030 agenda, such as agriculture and 
fisheries, consumer and industrial products, energy, financial services, healthcare, transport, and 
public services; 

56 http://erc-online.eu/wp-content/uploads/2020/07/Final-22-06-20-with-signatures_Agreement-on-Digitalisation-2020.pdf 
57 https://www.smeunited.eu/admin/storage/smeunited/digital-for-smes-2nd-roundtable-agenda-3-4-february-2021.pdf   
58 https://www.smeunited.eu/news/smeunited-participates-in-oecd-roundtable-digital-for-smes 
59 https://www.digitalsme.eu/manifesto/ 
60 https://www.digitalsme.eu/digital/uploads/Position-paper-Sustainable-Digital-Transformation_FINAL-2.pdf 

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•  Roles for each of the key stakeholder groups, such as individual businesses and key business sectors, 

citizens, governments, institutional investors and NGOs. 

Digital technologies will play a key role in helping the EU to achieve climate neutrality (i.e. a reduction of 55% in 
GHG emissions by 2030 and climate neutrality by 2050), in addition to strengthening its global competitiveness. 
For this reason, the digital and green transitions  should be mutually reinforcing.61 Meeting the EU’s climate 
ambition will require industry to reduce CO2 emissions from industrial processes by increasing energy efficiency, 
as  a  result  of  transitioning  to  renewable  energy  and  sustainable  resources,  and  increasing  circularity,  among 
other solutions.62 As reported by the European Economic and Social Committee (EESC),63 stepping up Europe's 
2030 climate ambition can only be achieved through a holistic approach and genuine participation and ownership 
by all actors at all levels, using the ‘European Climate Pact’ stakeholder platform, which is based on inclusiveness 
and transparency principles, to ensure the transformation is just and fair. 

Recently launched policies and regulations set out by the EC can be viewed as evidence of real commitment from 
the EU to achieving sustainable digital transformation throughout Europe. Some of the many examples include: 
the Digital Markets Act,64 to ensure fair and open digital markets; the Digital Services Act,65 to ensure a safe and 
accountable  online  environment;  the  Data  Governance  Act,66  to  establish  European  Data  Spaces  for  sharing 
public data; the proposed Climate Law,67 to achieve climate neutrality by 2050, mainly by cutting emissions and 
investing in green technologies; the new Circular Economy Action Plan,68 for a cleaner and more competitive 
Europe; the new EU Cybersecurity Act;69 the FinTech Action Plan,70 the Pan-European Blockchain Regulatory 
Sandbox,71 the White Paper on AI72 and an updated version of the Coordinated Plan on Artificial Intelligence73 
to be announced in April 2021, which will ensure the responsible use of AI, including human oversight.  

In order to achieve sustainable DX, the EC is also putting intensive effort into digital skills development aimed 
at  reskilling/upskilling  the  current  labour  force,  decreasing  youth  unemployment,  elevating  the  digital  skills 
literacy  of  European  citizens,  and  bridging  the  digital  skills  gap  between  labour  market  needs  and 
education/training providers, by defining sector specific curricula, among other approaches. The European Pillar 
of Social Rights74 is one of the most important social strategies of the EU in terms of ensuring socially fair and 
just  transitions  to  climate  neutrality,  digitalisation  and  equal  opportunities/access  to  the  labour  market.  The 
expected launch of the Action Plan to implement the European Pillar of Social Rights will take place in the first 
quarter of 2021. The EC is also encouraging Member States to include digital skills development actions in their 
national  recovery  plans  when  applying  for  financial  support  via  the  Recovery  and  Resilience  Facility.75  In 
addition, the European Skills Agenda76 sets objectives across 12 action lines to be achieved by 2025. The goal is 
to  develop  more  and  better  skills  in  order  to  achieve  the  twin  transitions  by  strengthening  sustainable 
competitiveness as defined in  the  European Green Deal;  ensuring  social fairness as the  first  principle of the 
European Pillar of Social Rights; and building resilience in responding to crises. The Pact for Skills77 was recently 
launched by the EC as the very first action set out in the European Skills Agenda. It builds on other EU initiatives 
for  multi-stakeholder  cooperation  such  as  the  Blueprint  for  Sectoral  Cooperation  on  Skills;78  the  renewed 
European  Alliance  for  Apprenticeships;79  and  the  Digital  Skills  and  Jobs  Coalition.80  Additionally,  Centres  of 

61 Commission Communication Stepping up Europe's 2030 climate ambition. 
62 Circular Economy Action Plan (COM (2020) 98 final.  
63 https://www.eesc.europa.eu/en/about  
64 https://ec.europa.eu/info/strategy/priorities-2019-2024/europe-fit-digital-age/digital-markets-act-ensuring-fair-and-open-digital-
markets_en  
65 https://ec.europa.eu/info/strategy/priorities-2019-2024/europe-fit-digital-age/digital-services-act-ensuring-safe-and-accountable-
online-environment_en 
66 https://ec.europa.eu/digital-single-market/en/news/proposal-regulation-european-data-governance-data-governance-act    
67 https://ec.europa.eu/clima/policies/eu-climate-action/law_en  
68 https://ec.europa.eu/environment/circular-economy/pdf/new_circular_economy_action_plan.pdf  
69 https://ec.europa.eu/digital-single-market/en/eu-cybersecurity-act  
70 https://ec.europa.eu/info/publications/180308-action-plan-fintech_en  
71 https://ec.europa.eu/digital-single-market/en/legal-and-regulatory-framework-blockchain  
72 https://ec.europa.eu/digital-single-market/en/artificial-intelligence  
73 https://ec.europa.eu/digital-single-market/en/news/excellence-and-trust-ai-brochure  
74 https://www.socialplatform.org/what-we-do/european-pillar-of-social-rights/ 
75 https://ec.europa.eu/info/business-economy-euro/recovery-coronavirus/recovery-and-resilience-facility_en  
76 https://ec.europa.eu/social/main.jsp?catId=1223&langId=en 
77 https://ec.europa.eu/social/main.jsp?catId=1517&langId=en  
78 https://ec.europa.eu/social/main.jsp?catId=1415&langId=en    
79 https://ec.europa.eu/social/main.jsp?catId=1147&intPageId=5234&langId=en  
80 https://ec.europa.eu/digital-single-market/en/digital-skills-and-jobs-coalition  

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30 

Vocational Excellence (CoVEs)81 have recently been established to bring together a wide range of local partners, 
such as providers of vocational education and training, employers, research centres, development agencies, and 
employment services, among others, to develop "skills ecosystems" that contribute to regional, economic and 
social development, as well as to innovation and smart specialisation strategies. The recently launched Digital 
Education  Action  Plan  (2021-27)82  outlines  the  EC’s  vision  for  high  quality,  inclusive  and  accessible  digital 
education in Europe and is a call for action to achieve stronger cooperation at European level to make education 
and training systems fit for the digital age. There are also plans to develop an EU Competence Framework for 
Green Skills83 which will define EU strategy for enhancing green skills and competences for all. 

1.7  Key takeaways 

First, it is key to better understand by all stakeholders what DX means along with potential benefits, negative 
and positive impacts before setting up policy and regulatory framework to unluck its full potential. Even though 
interchangeably used, it is a journey started with  digitisation that enabled digitalisation and collectively lead 
towards  DX  over  time.  It  is  defined  as  a  fusion  of  advanced  technologies  that  integrates  physical  and  digital 
systems  and  when  combined  with  innovative  business  models  and  processes,  leads  to  the  creation  of  smart 
products, services and significant improvement of productivity by the EC. DX is not just about technology but 
about transformative changes that affect the way the value is created and captured inside a given company such 
as transforming the customer experience by building on data analytics, transforming internal processes as well 
as  the  business  model.  Customer  centricity,  innovation  capability,  operational  excellence  using  data 
capabilities and a competitive mind-set are indicated as key success factors in transforming a firm digitally to 
remain competitive in the future. 

4IR  is  the  current  era  in  which  multi-modal  adoption  of  different  advanced  digital  and  KETs  are  constantly 
emerging  with  the  new  ones  such  as  bioengineering,  new  computing  technologies,  geo-engineering, 
neurotechnology  and  Internet  of  Everything  (IoE)  connecting  digital,  physical  and  biological  spheres  to  one 
another. Even though the term Industry 4.0 is evolved simultaneously and used interchangeably with 4IR, it can 
be defined as the automation and data exchange in manufacturing technologies by bridging the physical and 
digital  world  through  cyber-physical  systems  enabled  by  IIoT,  cloud  computing  and  cognitive  computing  that 
allows personalization/customization of smart products. Over the past years, the use of  4IR and Industry 4.0 
technologies together with CE concept gained more importance for the transition from a linear towards more 
circular model, which has shown positive results on the environment and economy.  

Key challenges that are internal to SMEs during DX can be grouped under three main categories as the lack of: 
awareness and availability of digital technology and tools required for DX due to lack of good connectivity, digital 
tools and services; capacity to engage in DX in terms of time and funding; capability to combine digital strategy 
with a concrete business model, integration with the existing technology and business processes, migration from 
previous  systems  and  decommissioning  old  technologies.  Thus,  SMEs  are  in  need  of  technical  support  for: 
defining their requirements; selecting the right products, technologies or suppliers; planning and initiating their 
DX; understanding the regulations in addition to financial support for implementation and training to fill digital 
skills  gap.  Lack  of  international  standards,  regulatory  barriers,  lack  of  affordable  and  accessible  digital 
infrastructure, interoperability, cyber attacks, lack of availability and access to public data and digital platforms 
etc. are indicated among key challenges external to SMEs.  

Major benefits brought by DX to SMEs are: an increase of financial performance by optimising revenue channels 
and reducing costs; productivity gains leading to greater efficiency through greater use of digital; access to new 
customers  through  expanded  geographical  reach;  and  more  access  to  information  and  more  productive 
processes that fosters innovation. 

Connectivity,  online  presence,  process  digitalisation and  automation,  cloud-based  services,  collaboration  and 
communication seem to be the main purposes for SMEs to adopt digital technologies. Under the connectivity 
and  online  presence  where  fixed  and/or  mobile  broadband  are  the  key  enabler,  on-line  communication  and 

81 https://ec.europa.eu/social/main.jsp?catId=1501  
82 https://ec.europa.eu/education/education-in-the-eu/digital-education-action-plan_en  
83 https://www.eesc.europa.eu/en/our-work/opinions-information-reports/opinions/towards-eu-strategy-enhancing-green-skills-and-
competences-all-own-initiative-opinion  

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collaboration, e-commerce, internet-based solutions to reduce customer interactions, contactless payments, QR 
codes for direct ordering etc. are seen as common use cases. Process automation and digitalisation had a large 
spectrum of applications from e-signature to more sophisticated connected sensors and hardware ecosystems 
for  reduction  of  contact  processes  to  automation  using  connected  devices  with  machine-to-machine 
communication under Industry 4.0. Cloud-based services makes it possible to access data and services (e.g. CRM, 
ERM, ERP, collaborative apps, workflow and management apps etc.) remotely from any place at any time through 
any  internet-enabled  device.  When  looked  from  impact  function  perspective,  digital  technologies  mainly 
adopted  for  the  following  purposes:  Connect  and  Communicate;  Monitor  and  Track;  Analyse,  Optimise  and 
Predict; Augment and Automate.  

The global pandemic caused by Covid-19 seem to add both complexity and opportunities to the SMEs. In general, 
SMEs are in a struggle for survival since pandemic caused drops in customer demand and fall in revenues, supply 
chain disruptions and challenges balancing employee capacity and welfare among others. SME priorities shifted 
from growing and resourcing business to finding new customers, managing business costs, streamlining business 
and  staff,  finding  new  revenue  streams.  Additionally,  Covid-19  is  expected  to  have  profound  implications  on 
progress towards the SDGs, most of them to be impacted negatively while the impact on the ones related to life 
on land, below water, climate, and sustainable production and consumption are unclear at the moment.  

With regards to the impact of DX on SDG performance, 4IR technologies when used responsibly in combination 
with  CE  concept  are  stated  to  bring  sustainable  solutions  under  Clean  Power,  Smart  Transport  Systems, 
Sustainable  Production  and  Consumption,  Sustainable  land-use,  Smart  Cities  and  Homes  as  well  as  offering 
game-changing climate solutions  around energy and to underpin a zero-net-emissions economy. However, a 
particular  attention  should  be  given  against  potential  negative  impacts  that  can  vary  from  the  impacts  of 
automation on jobs, to data privacy, and cyber attacks. Regarding the effect of DX at firm level sustainability, 
adopting customer-centric approach and building a culture that embrace data and innovation seem to have a 
positive influence on companies’ quest to reach sustainability. There is also a positive relationship between the 
social responsibility strategy and ICT used by SMEs and digitalisation.  

There are external challenges such as negative side-effects of the ICT sector itself (e.g. data centers, computing, 
digital  devices,  IoT  and  digital  infrastructures)  requiring  high  levels  of  energy  and  resource  consumption  in 
addition to the waste created by the sector and other concerns around privacy, security; personal health; job 
prospects and growing inequalities that hampers the DX towards sustainability. However, if circularity measures 
are  taken into account, the negative  externalities of the sector can be turned into an  opportunity and when 
combined  with  government  interventions  and  policies  in  the  form  of  cross-departmental  collaborations, 
incentives  towards  businesses,  assessments  on  the  economic,  social,  and  environmental  impacts  on  local 
communities, education, data to monitor and report, talent to improve existing technologies, DX contributes to 
sustainability.  The  evolving  field  of  non-financial  and  ESG  reporting  which  is  voluntary  for  the  SMEs  pose 
challenges  to  the  firms  to  gather  right  data  for  reporting.  Particular  challenges  for  SMEs  to  implement 
sustainable  solutions  by  leveraging  on  digital  technologies  are  lack  of  access  to  finance  for  implementing 
sustainable  solution,  lack  of  knowledge,  skills  and  capacity  particularly  regarding  business  development, 
insufficient marketing and strategic management skills, and lack of time. 

Main  drivers  for  SMEs  to  optimise  sustainability  solutions  are  indicated  as  becoming  valuable  investment 
targets for larger firms, creating highly competitive networks of sustainable SMEs, and becoming highly efficient 
suppliers in global supply chains by implementing sustainable practices. Regarding benefits brought by taking 
sustainability actions are reported as an increase in stakeholder loyalty and brand strength, improvement  in 
staff  motivation  and  enhancement  of  financial  indicators  and  sales.  Conducting  business  sustainably  by 
integrating ESG objectives into the business model has seen as an opportunity for brand enhancement, attracting 
talent and customers by innovative SMEs. 

As  agreed  by  various  stakeholders,  meeting  the  ‘Climate  Neutrality’  as  well  as  the  ‘Global  2030  Agenda’ 
operationalised through  UN SDGs is possible if ongoing DX can be turned into  sustainable DX and used with 
purpose to act as a catalyst to ensure the alignment between the digital and green (twin) transitions as targeted 
under  the  EU  Green  Deal  and  its  spin-off  policy  initiatives.  It  requires  a  holistic  approach  and  continuous 
dialogue  and  collaboration  between  various  stakeholder  groups  at  the  global  level  especially  by  defining 
international  standards  and  regulations,  exchanging  best  practices,  investing  on  skills,  sectors,  digital 
infrastructure,  products,  services  and  digital  technologies  that  can  address  climate,  biodiversity  and  wider 
environmental challenges. 

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32 

Considering  the  key  enabling  role  yet  big  gap  on  accessible  and  affordable  connectivity  issues  observed, 
investing on digital infrastructure should be prioritised by the governments for boosting DX while reducing digital 
divide. It includes ensuring access to big data by establishing specific data spaces (e.g. EU Health Data Space) as 
well as ensuring data accessibility across the whole value chain by all relevant stakeholders through regulated 
digital platforms for boosting circularity. The digital infrastructure with strengthened public cloud is believed to 
accelerate the adoption of blockchain and DLTs, Fintech and other cloud based-solutions for SMEs particularly 
under SaaS applications (e.g. CRM, ERM, collaborative apps, workflow and management apps), PaaS and IaaS 
including cloud-based tele-work solutions. Ensuring cybersecurity and data privacy are key to ensure trust and 
transparency  while  increasing  security  on  deployment  of  digital  technologies  against  cyber  attacks.  When 
designing and investing, the deployment of green(er) technologies should be taken into consideration in order 
to minimise the carbon footprint posed by the digital infrastructure also by switching to the use of renewables 
and increasing energy efficiency particularly at the data centres as well as implementing CE concepts to reduce 
e-waste  through  recycle,  re-use  and  repair.  Social  dimension  is  another  key  point  to be  considered  in  policy 
making  in  order  to  eliminate  negative  impact  of  digital  on  worker’s  health  and  wellbeing,  impact  on  job 
transitions, and on growing digital divide.  

As cross cutting policy areas: raising awareness among SMEs on benefits to be brought by specific technologies 
and sustainable DX; continuously assessing their particular needs and challenges to provide tailored technical 
advisory  support  services  in  combination  with  financial  support;  supporting  skills  development  through 
mentoring,  training,  education  and  knowledge  brokers  such  as  Digital  Innovation  Hubs;  setting  up  fair 
competition and market dynamics; removing regulatory and administrative burdens; developing standards in 
collaboration  with  SMEs;  boosting  innovation;  rolling  out  e-Government  initiatives  to  boost  SME  DX; 
incorporation of green public procurement; evidence based policy making by taking size, sector, age, location 
as well as differences between traditional vs innovative, manufacturing vs services SMEs into account; boosting 
collaboration between ICT and other key sectors such as energy, agriculture, healthcare, industrial processes by 
putting circularity and bioeconomy at the centre are among many other recommendations.  

The European Green Deal and its spinoff policy initiatives, as well as the EU Multiannual Financial Framework 
for 2021-27, coupled with the Next Generation EU - COVID-19 recovery package, all aim to define high level 
strategies and to finance corresponding action plans to achieve the twin transitions required for a sustainable 
and inclusive future, not only in the EU, but globally, by taking a global leadership role and establishing global 
partnerships to realise the Global 2030 Agenda. The most recent policy and regulatory efforts undertaken by the 
European  Commission  in  terms  of  achieving  these  goals  fall  into  two  distinct  domains:  technology  and 
sustainability and skills development. The Digital Markets Act, the Digital Services Act, the Data Governance 
Act, the proposed Climate Law, the new Circular Economy Action Plan, the new Cybersecurity Act, the FinTech 
Action  Plan,  the  White  Paper  on  AI/updated  Coordinated  Plan  on  AI,  and  the  Pan-European  Blockchain 
Regulatory  Sandbox  all  fit  within  the  remit  of  technology  and  sustainability  and  are  expected  to  become 
operational in 2021-22. In terms of skills development, current actions include the Action Plan to implement 
the European Pillar of Social Rights, the new European Skills Agenda and the recently launched Pact for Skills, 
which builds on other EU initiatives involving multi-stakeholder cooperation, such as the Blueprint for Sectoral 
Cooperation  on  Skills,  the  renewed  European  Alliance  for  Apprenticeships  and  the  Digital  Skills  and  Jobs 
Coalition. These multi-stakeholder partnerships boost skills across the fourteen industrial ecosystems defined in 
the new EU Industrial Strategy. Along with the launch of the Digital Education Action Plan (2021-27) to make 
education and training systems fit for the digital age, the setting up of Centres of Vocational Excellence (CoVEs) 
to develop skills ecosystems, and the plans for developing an EU Competence Framework for Green Skills, these 
initiatives can all be viewed as evidence of a real commitment from the EC to turning high level strategies into 
action for achieving sustainable digital transformation for Europe. 

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2  Business case studies 

2.1  247TailorSteel (Netherlands)84 

2.1.1 

Introduction 

247TailorSteel B.V. is a sheet metal company founded by Carel van Sorgen in 2007 in Varsseveld, Netherlands. 
The company specialises in providing industrial manufacturers with on-demand production and supply of tailored 
laser-cut  metal sheets, tubes and bent  products.  247TailorSteel  leverages  its use of  advanced manufacturing 
technologies to supply added value to its clients in terms of convenience and customisation. 

2.1.2  Digital Transformation activities 

After growing and eventually selling his family’s sheet metalworking company in 1998, Carel van Sorgen spent 
several  years  working  in  different  industries.  However,  curious  about  how  new  digital  technologies  could  be 
leveraged to modernise the steel industry, in 2003 he decided to start a new project: developing smart software 
for metal processing factories. Eventually, he decided to implement  his vision for a  networked smart  factory 
himself. 

Today, 247TailorSteel is at the forefront of the digital transformation of the steel sector. The company’s business 
model is based on the on-demand production of tailored products through a digital and automated production 
process from design to delivery. To do so, the company relies on a software portal they developed in-house. The 
‘Sophisticated Intelligent Analyser’ (SOPHIA®) works as an online platform and order processing system, as well 
as an online assistant for customers. SOPHIA® allows customers to design and upload their own 3D models and 
receive a quote for production price and delivery time within one minute, as well as offering the convenience of 
being able to place an order 24 hours a day. SOPHIA® is connected to the company’s sophisticated machinery 
fleet,  logistics  and  delivery  system  for  production,  restocking,  transportation  and  delivery.  Robots  and 
automated guided vehicles have been implemented on the factory floor and software is used to plan efficient 
delivery routes. Thanks to the SOPHIA® portal, 247TailorSteel can deliver products within 48 hours of ordering, 
with a delivery reliability of 99.7 %. As an added bonus, the company has managed to significantly reduce its use 
of energy and raw materials. 

Another significant metalwork industry innovation has been 247TailorSteel’s launch of the Smart Bending Factory 
(SBF) Field Lab. This has enabled 247TailorSteel and eight key partner organisations to collaborate in sharing their 
know-how and resources, and to achieve scalable and flexible production capacities through the joint purchase 
and operation of machinery.  

The company has experienced significant growth, from 100 employees in 2014 to more than 500 today. Their 
goal is to grow further by expanding internationally. In 2020, they opened a new production facility in Hilden, 
Germany, and have further plans to expand into a second German location. In order to fund these ambitions, 
247TailorSteel  now  collaborates  with  the  private  equity  fund,  Parcom,  which  acquired  a  60%  stake  in  the 
company in 2019. 

84 Sources: https://www.247tailorsteel.com, Contributions from 247TailorSteel, A smart factory disrupting the production of sheet metal 
parts, WATIFY, 2017. Available at: 
https://ec.europa.eu/growth/tools-databases/dem/watify/boosting/communication-materials-support/smart-factory-disrupting-
production-sheet-metal-parts, Unterfrauner, E., Case study: Smart Bending Factory – The customizing products plug-in company, MAKE-IT, 
2017. Available at: 
http://make-it.io/2017/10/23/case-study-smart-bending-factory-the-customizing-products-plug-in-company/, Smart Industry Netherlands, 
Smart Bending Factory. Available at: https://smartindustry.nl/aan-de-slag/fieldlabs/smart-bending-factory, OP East Netherlands. Smart 
Bending Factory: The Plug-in Company Proj 00378. Available at: https://www.247tailorsteel.com/docs/default-
source/documenten/170906-p17-020087-bpri---smart-bendig-a3-poster-drukwerk-ebook.pdf?sfvrsn=1d32f1b3_0, Parcom, Partner 
companies: 24/7 Tailor Steel. Available at: https://parcom.com/en/partner-companies/tailor-steel/?cn-reloaded=1, Borghuis, M., State-To-
The-Art 247TailorSteel offers optimal delivery reliability. Available at: https://www.alurvs.nl/aluminium/nieuws/state-to-the-art-
247tailorsteel-biedt-optimale-leverbetrouwbaarheid_11622/, Metals Consulting International, Dutch sheet processor expands in Germany. 
Available at: https://metals-consulting.com/tag/247-tailor-steel/         

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34 

2.1.3  Support for Digital Transformation 

The  development  of  the  SBF  Field  Lab  was  undertaken  through  the  Smart  Industry  programme,  the  national 
Industry 4.0. initiative in the Netherlands, and was partially funded by the OP East Netherlands programme. The 
aim  of  this  joint  subsidy  programme  was  to  strengthen  the  economy  in  the  provinces  of  Overijssel  and 
Gelderland, with the goal of boosting the production of new products made by local SMEs. 

2.1.4  Key Success Factors 

Thanks  to  the  SBF  Field  Lab,  247TailorSteel  is  also  an  ambassador  for  the  Smart  Industry  initiative  in  The 
Netherlands.  

The SBF Field Lab has been fundamental to the company’s capacity to be a digital leader in the sector, creating a 
collaborative structure that allows them to share the high cost of machine ownership  to achieve flexible and 
scalable production capabilities at very competitive prices.  

Furthermore, 247TailorSteel attributes their successful scaleup and expansion ambitions to “knowing when to 
pass the torch” as the company puts it, by selling a 60% stake in 2019 to the private equity fund Parcom. 

2.1.5  Key Challenges and Issues 

The first challenge was the length of time it took for Carel van Sorgen and his small team of engineers to develop 
the concept and the software for a connected factory. In total, it took five years. The next challenge was that 
Varsseveld, the suburban town in which 247TailorSteel is headquartered, has a shrinking population and a lack 
of engineers. 

However, the SBF Field Lab has acted as an innovation pole for the region and the educational institutions which 
are  part  of  the  collaboration  –  Graafschap  College  in  Doetinchem  and  Anton  Tijdink  Technology  College  in 
Terborg – have established a Smart Industry education programme. Moreover, 247TailorSteel has contributed 
to the provision of training and the creation of interesting job opportunities that can help attract and retain 
young qualified people in the region. 

2.1.6  Policy recommendations 

The OP East Netherlands funding programme, from which the SBF Field Lab has benefited, achieved its policy 
objectives, since the initiative has contributed to the development of the local economy. The 247TailorSteel case 
study shows how innovative SMEs can become a digital transformation driving force and how adequate public 
support, in the form of project funding and public-private partnerships, can multiply the positive impacts of such 
schemes. Another policy-relevant lesson that may be drawn from this case is that innovative SMEs can also play 
a central role in promoting innovation support infrastructures. 

2.2  Bächer Bergmann (Germany)85 

2.2.1 

Introduction 

Bächer  Bergmann  GmbH  (BB)  is  a  small  German  carpentry  business  founded  in  2010  by  master  carpenters 
Sebastian  Bächer  and  Georg  Bergmann.  BB  combines  craftsmanship  and  innovation  to  provide  precisely 
manufactured  high-quality  products  with  complex  geometries  to  a  broad  array  of  clients  from  different 
industries, including major players such as Samsung, Porsche and DHL. 

85 Sources: https://digital.productions/, Contributions from Bächer Bergmann, Technological Transformation Success Story “Bächer 
Bergmann”, WATIFY 2017. Available at: https://ec.europa.eu/growth/tools-databases/dem/watify/inspiring/watify-video-channel/watify-
technological-transformation-success-story-b%C3%A4cher-bergmann.  

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2.2.2  Digital Transformation activities 

Recognising  the  digital  transformation  potential  in  carpentry,  the  company  adopted  digital  technologies  at  a 
relatively early stage so they could successfully meet new customer demands.  

BB offers high added value carpentry services, producing high-quality, high-precision, complex and customised 
objects,  such  as  sculptures,  furniture  and  prototypes,  combining  traditional  methods  and  tools  with  new 
technologies. These new technologies include Computer Numerical Control (CNC) milling machines, Computer-
Aided Design (CAD) software, 3D printing, laser technology and robotics. 

These new technologies have transformed and accelerated BB’s production processes and helped to overcome 
the limitations of traditional carpentry. As a result, BB has expanded its portfolio of products and services, moving 
from traditional craftsmanship to digitally  enhanced production. For  example,  for a  medical school client,  BB 
developed and fabricated larger than life-sized sculptures of the human body that were equipped with sensors 
in order to communicate with tablets and mobile phones. Other benefits of the modern technologies used by BB 
include increased operational flexibility and precision quality as well as reduced waste and energy consumption. 
BB’s digital craftsmanship model has been essential to its business success and to the high quality of the products 
and services it provides to its customers.  

BB’s digital capabilities also enabled them to leverage their flexible manufacturing capabilities during the COVID-
19 pandemic. They produced 80,000 face masks using 3D printing machines, which were sold on Veedelsretter, 
a local government funded e-commerce website set up to help small businesses in Cologne survive economically 
during the pandemic.  

2.2.3  Support for Digital Transformation 

BB  has  benefited  from  various  forms  of  public  support.  Firstly,  a  partnership  with  the  University  of  Cologne 
allowed them to experiment with and test the university’s CNC milling machines before investing in their own. 
Additionally,  German  government  funding  for  the  introduction  of  energy  efficient  technologies  helped  to 
partially fund BB’s digital transformation.  

2.2.4  Key Success Factors 

BB attributes their digital transformation success to combining digital technologies and know-how with creativity 
and craftsmanship. In order to optimise precision and quality, BB now uses CAD to design their products and CNC 
milling machines to manufacture them. However, traditional handcrafting is still used to put the finishing touches 
to each product.  

2.2.5  Key Challenges and Issues 

The key challenge for BB was identifying the specific digital technologies and tools to adopt. The opportunity to 
test  and  experiment  with  new  technologies  before  investing  in  them  was  instrumental  in  overcoming  this 
challenge. 

2.2.6  Policy recommendations 

Lack of access to expensive equipment and tools such as CNC milling machines for testing and experimentation 
could  be  a  deterrent  for  SMEs  wishing  to  digitalise.  The  case  of  BB  therefore  illustrates  the  importance  of 
providing SMEs with connections to knowledge and to research & technology institutions, and also to the right 
infrastructures,  including  technology  testing  facilities.  It  also  shows  how  synergies  and  cooperation  between 
SMEs/entrepreneurs and technology institutions can be an essential factor in progressing from initial concept to 
reality. 

Policymakers  should  help  facilitate  the  replication  of  success  stories  like  BB  by  actively  encouraging  and 
supporting knowledge and technology transfer and  by putting in place public-private support mechanisms for 
digital transformation. 

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36 

2.3  De Trog (Belgium)86  

2.3.1 

Introduction 

Founded in 1970 in Bruges, De Trog is a bio-label bakery that produces high-quality, organic bread. The company 
combines traditional breadmaking with advanced manufacturing and digital technologies to improve the quality 
and  efficiency  of  their  production  processes.  The  company  has  two  different  brands:  Bio  De  Trog  for  B2C 
customers and Pur Pain for B2B clients. 

2.3.2  Digital Transformation activities 

The bakery initiated its digital transformation journey in 2013, and now relies significantly on automation and a 
range  of  technologies  to  digitalise  its  processes,  such  as  custom  robotic  applications,  big  data,  apps  and 
augmented reality.  

De  Trog  has  also  gamified  the  training  of  its  employees.  Using  an  app  called  ‘Bakery  Battle’,  employees  can 
challenge  each  other  to  play  ‘knowledge  battles’,  enabling  them  to  maintain  and  update  their  professional 
knowledge in the process. The game format offers different levels, from easy to advanced, as well as different 
categories, such as ‘My first day’, warehousing, packaging, baking, etc. With its innovative training app, De Trog 
has  ensured  that  its  staff  is  skilled,  well-informed  and  kept  up-to-date  on  the  company’s  quality  and  safety 
protocols. 

De Trog’s transformation has been widely recognised in the industry and has resulted in several awards, including 
the prestigious ‘Factory of the Future’ award on two occasions. In 2018, BNP Paribas Fortis acquired a minority 
stake in De Trog’s parent group, Food Associates Group NV. 

The  efficiencies  enabled  by  process  automation,  in  combination  with  substantial  investments  in  green 
infrastructure, have resulted in De Trog becoming the first bakery to be granted the CO2-Neutral label in Belgium.  

De Trog has also recently announced a collaboration with Lokkal, an app-only fresh food supermarket, to deliver 
De Trog’s fresh bread directly to their customers’ homes. 

2.3.3  Support for Digital Transformation 

Although De Trog has relied on private finance for  their digital transformation journey, the company has also 
benefited from a number of collaborations with public and public-private knowledge and support institutions 
and training centres, from which they have received advice and guidance on how best to approach the different 
stages of their transformation process. 

In particular, De Trog has collaborated with the Flanders Agency for Innovation and Entrepreneurship and the 
Innovation Centre for the West-Vlaanderen region. The company has also benefited from knowledge exchange 
with  public  higher  education  institutions,  including  the  University  of  Ghent  and  KU  Leuven’s  Embedded  and 
Artificially Intelligent Vision Engineering (EAVISE) Research Group. 

2.3.4  Key Success Factors 

De Trog has relied heavily on knowledge exchange and collaboration with competence centres, leveraging these 
knowledge  ecosystems  to  explore  how  best  to  approach  their  objectives  and  obtain  the  necessary  support. 
Indeed,  De  Trog  has  collaborated  with  more  than  20  organisations  along  its  digital  transformation  journey. 

86 Sources: https://detrog.be/en/, Contributions from Food Associates Group NV, Combining craftsmanship & technology, WATIFY, 2017. 
Available at: 
https://ec.europa.eu/growth/tools-databases/dem/watify/inspiring/watify-success-stories/combining-craftsmanship-technology, WATIFY 
Technological Transformation Success Story "De Trog" - Combining craftsmanship & technology, 2017. Available at: 
https://ec.europa.eu/growth/tools-databases/dem/watify/inspiring/watify-video-channel/watify-technological-transformation-success-
story-de-trog-combining, De Trog once again Factory of the Future, Gumption, 2019. Available at: https://www.gumption.eu/article/de-
trog-once-again-factory-of-the-future, Our bank acquires a stake in Food Associates Group, BNP Paribas Fortis, 2018. Available at: 
https://companies.bnpparibasfortis.be/en/news?n=our-bank-acquires-a-stake-in-food-associates-group.  

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Furthermore,  the  company  has  not  just  focused  on  technology  in  its  pursuit  of  success.  The  company  also 
emphasises the importance of staff training, making significant efforts to ensure that its staff acquire the most 
appropriate  professional  skills  and  that  they  share  the  company’s  vision.  The  ‘Bakery  Battle’  app  has  been 
instrumental in supporting this tailored in-house training. 

2.3.5  Key Challenges and Issues 

De Trog’s goal was to scale up production without jeopardising the company’s values and commitment to both 
tradition and sustainability. Identifying the best path to follow was therefore a crucial challenge. Establishing 
collaborations  with  key  partners  and  gaining  access  to  knowledge  were  essential  elements  in  successfully 
overcoming this hurdle. By seeking external support and outsourcing digital solutions, De Trog has been able to 
maintain focus on their primary function: the production of high-quality bread.  

The upskilling of De Trog’s staff to use these new technologies was another important challenge, and one that is 
particularly prevalent in traditional industries, where employees may not be familiar with using digital tools in 
the workplace.  

2.3.6  Policy recommendations 

The  case  of  De  Trog  illustrates  how  local  support  agencies  for  SME  digitalisation,  innovation  and  business 
transformation can help SMEs to achieve successful digital transitions. Facilitating access to these services as well 
as to digital  skills training are concrete ways in which  policymakers can support  the digital transformation of 
SMEs. While advisory and training services could be provided and funded by either national or regional public 
institutions, there are also other ways in which access to such services could be facilitated. Examples include the 
provision  of  innovation  vouchers  to  help  pay  for  consulting  services,  public-private  partnerships  with 
competence centres and digital transformation support from Digital Innovation Hubs.  

2.4  FYZOklinika (Czechia)87 

2.4.1 

Introduction 

FYZOklinika is an SME located in Prague, Czechia. It was established as a small family business in 2011 by Zdenek 
and Iva Bilek and currently has 23 employees. It is a private health clinic offering physiotherapy and rehabilitation 
for the prevention and treatment of painful conditions of the musculoskeletal system, and in recent years  has 
started to offer modern services to accelerate wound healing. 

2.4.2  Digital Transformation activities 

FYZOklinika  aims  to  provide  customised  treatments  specific  to  each  patient.  Historically,  this  type  of  health 
practice used a paper-based reservation system and archived paper-based medical documents. Over time, some 
of these health practices have modernised their record keeping using Excel and similar software tools. This was 
the situation when FYZOklinika started its business 10 years ago. 

The company needed to find out more about exploiting digital solutions to further improve the efficiency of its 
management  and  its  relationship  with  its  clients.  As  a  result,  the  company  began  to  implement  various  IT 
solutions aimed at moving from a paper-based system to a digital database and so differentiating itself from its 
competitors. 

Based on the digital transformation support described in the following section, the company deployed a technical 
solution which not only uses a modern database platform, but also a digital communication function based on 
Asterisk,  an open  source  framework  for  building  communications  applications,  allowing  digital  connection  to 
clients/patients for the provision of customer care. Administrative staff are now able to communicate effectively 
and efficiently with clients before and after treatments. 

87 Sources: https://www.fyzioklinika.cz/en/, https://www.asterisk.org/, https://digitalisesme.eu/czech-health-company-receives-fruitful-
support-for-its-digital-strategy-from-lithuanian-digital-enabler/, Interview with Mr. Zdenek Bilek on 13.01.2021. 

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FYZOklinika implemented all the suggested solutions, and plan to advance their digital transformation further, 
by  continuing  with  the  integration  of  state-of-the-art  technologies  such  as  Artificial  Intelligence  (AI)  and  the 
Internet of Things (IoT) into their systems and client services. They are currently researching how they might 
carry out and fund this digital modernisation. 

FYZOklinika has already seen significant benefits. The staff responsible for customer care have reported increased 
effectiveness of communication and the ability to handle more calls per person on a daily basis. Furthermore, 
once an AI solution has been deployed, it should be possible to handle calls involving frequently asked questions 
by utilising machine learning. 

2.4.3  Support for Digital Transformation  

The company applied to the DigitaliseSME initiative, which matches the needs of applicants with an appropriate 
expert who can provide coaching in the specific digital solutions required. Sergey Matusevich, a digital expert 
from Lithuania, was assigned as their Digital Enabler to devise solutions for accelerating FYZOklinika’s processes. 
After collaborating for 4 months, FYZOklinika was highly satisfied with the support provided by the Digital Enabler 
and the digital solutions he proposed. 

2.4.4 

 Key Success Factors 

The  key  success  factor  for  FYZOklinika’s  digital  transformation  was  finding  a  digital  advisor  with  expertise  in 
nonproprietary technologies, such as Asterisk and other open source technologies, since technology platforms 
designed for large companies are usually not affordable for smaller companies. Another critical success factor 
was the flexibility of gradual deployment of the new technologies. 

2.4.5  Key Challenges and Issues  

Currently, although it is not difficult to find smart digital technical solutions, it is difficult to find the right solution 
that can be implemented by a small company  at an affordable cost. Once  a suitable technological solution is 
found, the next challenge is to find professional support for its proper implementation. 

2.4.6  Policy Recommendations 

Based on its own experience,  FYZOklinika would suggest to other SMEs seeking similar  digital transformation 
solutions, that they should avoid large proprietary technology platforms and instead research more affordable, 
cost-effective nonproprietary options.  

From a policy perspective, FYZOklinika would like to see continued roll-out of SME support services such as those 
offered by DigitaliseSME, whereby each SME can be assigned a specific Digital Enabler for a certain time period 
in which the company’s needs can be assessed, and company and advisor can then collaborate on determining 
the most suitable digital transformation options.  

2.5  Katty Fashion (Romania)88 

2.5.1 

Introduction 

Katty Fashion is an innovative SME founded in 2003 in Iași, in the Nord-Est development region of Romania. It is 
a manufacturing company for the textile/fashion industry, with 40 employees, experienced in producing a wide 
range of women’s outerwear, and specialising in short production runs and customised clothing. Katty Fashion 
is a member of the ‘Romanian Textile Concept Cluster Bucharest’ as well as the ‘European Textile Platform for 
the Future of Textile and Clothing’, from which it receives support in terms of news, ideas, funding opportunities, 
ecosystem and community, but not funding support. The company has been a successful exporter since entering 

88 Source: https://katty-fashion.com/, https://c-voucher.com/programme/, https://digitalisesme.eu/about-digitalisesme/, 
with Ms Caterina Ailiesei and Ms Claudia Irimiea on 14.01.2021. 

Interview 

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the  textile  market,  collaborating  with  European  partners  as  well  as  with  suppliers,  producers,  regional 
development agencies, educational and research institutions from the local and regional markets.  

Katty Fashion offers a wide spectrum of services, from original concept to the creation of new products, using 
computer aided (CA) pattern design and grading, sampling and prototyping, material sourcing, short production 
runs, technical support and quality control, and combining woven and jersey fabrics with very different  fibre 
compositions and structures. The company has implemented the Quality Management System ISO 9001:2008 to 
maintain high quality standards for its products and services. In 2010, the company developed its own project, 
‘Concept of Eco-Chic Ethical Wear’, to create highly fashionable collections using 100% organic materials. Their 
motivation was to comply with corporate social responsibility to protect the local and global environment, while 
producing stylish and environmentally friendly clothing.  

The company offers production and garment development services to brands in contemporary and bridge market 
segments (mid to high quality and price point). They are currently providing these bespoke product development 
and manufacturing services to over 50 high end EU fashion brands with diverse and complex requirements. 

2.5.2  Digital Transformation activities 

Katty  Fashion’s  decision  to  undertake  digital  transformation  was  based  on  the  desire  to  improve  overall 
efficiency, increase service diversification, gain a competitive advantage and consolidate customer loyalty.  

Their main digital transformation journey started at the beginning of 2019 when they were awarded financial 
support through ‘C-VoUCHER’, an EU scheme funded through the H2020 Research and Innovation Programme. 
It  is  the  first  pan-European  initiative  to  adopt  the  concept  of  the  circular  economy  to  redesign  value  chains, 
combining design and technology to help SMEs transition towards a circular economy.  

During  the  course  of  the  C-VoUCHER  programme,  Katty  Fashion  also  applied  for  technical  support  from  the 
‘DigitaliseSME’ initiative, which assigns a Digital Enabler to applicant SMEs by matching their particular needs 
with the specific expertise of the advisor. The Digital Enabler assigned to Katty Fashion first undertook a needs 
assessment by evaluating the firm’s activities, skills level and needs, in collaboration with the CEO. Together, 
they explored various opportunities and potential business directions for the company, broken down into short, 
medium,  and  long-term  strategies,  including  the  development  of  their  own  proprietary  analytics  platform 
powered by artificial intelligence (AI), which they named ‘KARE’, to showcase that they K(c)ARE about the impacts 
of today’s actions on the next generations. Through workshops on various topics, the Digital Enabler proposed 
concepts  for  both  rebranding  and  addressing  various  issues  arising  from  the  C-VoUCHER  scheme.  During  a 
month-long collaboration with their Digital Enabler, Katty Fashion accomplished the following: they introduced 
more efficient practices for pattern development and fit management and developed a pattern library protocol; 
identified and transferred the library to the most suitable cloud storage solution; identified appropriate Product 
Lifecyle Management (PLM) software for the development of layouts, graphics and front-end forms; worked on 
their  website,  logo,  imagery  and  web  content  for  rebranding.  Following  this  collaboration  with  their  Digital 
Enabler, the company implemented Computer-Aided Design (CAD) and Computer-Aided Manufacturing (CAM) 
software upgrades and 3D licenses. Training of the team for implementation of the 2D CAD/CAM upgrades was 
included in the purchase contract through C-VoUCHER funding, while the suggested implementation and use of 
3D licenses still requires additional help, study and follow-up.  

The proposed solutions were assessed in a feasibility study conducted by an outside consulting company using 
funding from the European Bank for Reconstruction and Development (EBRD) Small Business initiative. 

2.5.3  Support for Digital Transformation  

As a result of the funding support received via the C-VoUCHER scheme, combined with technical support from 
the Digital Enabler assigned by DigitaliseSME, Katty Fashion was able to purchase new 3D and CAD/CAM licences. 
Thanks  to  this,  the  company  is  now  able  to  provide  the  following  new  services:  virtual  prototyping,  digital 
validation of designs and expected consumption of materials for production, online catalogues and on-demand 
manufacturing.  

The addition of new features such as photo enabled pattern digitisation, pattern and fit development protocols 
and style history have improved the efficiency and speed of pattern development, while adopting 3D technology 

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40 

has the potential to reduce the number of samples required by half, which is expected to reduce the cost, waste 
and  lead  times  of  style  development,  while  helping  Katty  Fashion  to  diversify  its  portfolio  of  services. 
Furthermore, the new website now includes better and more appropriate visual and written content, improving 
the  company’s  attractiveness  to  top  brands  and  thereby  compensating  for  the  higher  production  and 
development costs involved. 

By adopting these new technologies, Katty Fashion has been able to start making the switch from a traditional 
business model towards a circular digitalised business model in line with its 10-year sustainable development 
strategy, developed in 2017-18. The company’s aim is to lower their environmental footprint and to offer better 
working  conditions  and  improved  business  solutions  in  order  to  make  a  vital  paradigm  shift  in  the  fashion 
industry.  

Currently,  the  company  is  aiming  to  develop  and  license  their  proprietary  KARE  digital  platform  to  other 
companies and to create a KARE micro-factory/demo-lab, utilising the latest technologies and machinery.  

Since the full implementation of all these new technologies is still ongoing and has been hampered as a result of 
the pandemic, it has not yet been possible to observe the full benefits, although the company has noticed an 
increase in labour productivity.  

In order to progress further, Katty Fashion has applied for additional funding support from the Romanian national 
POR 1.2 programme and the Romanian regional RIS3 programme. Both applications are still pending. 

2.5.4  Key Success Factors 

The technical support and guidance received from the Digital Enabler was cited by Katty Fashion as a key success 
factor,  along  with  continuous  progress  monitoring  and  the  studying,  testing  and  incorporation  of  proposed 
technology solutions into their workflow. Another factor mentioned was the firm’s experience of more than a 
decade  in  the  industry,  and  its  in-house  technical  skills  in  terms  of  assessing,  applying  and  comparing  new 
technologies.  

In October 2020, the company received an EIT Manufacturing Boost Up award for developing new solutions for 
sustainability, resiliency and creating social impact in manufacturing. 

2.5.5  Key Challenges and Issues 

The  biggest  challenge  for  Katty  Fashion  was  finding  the  most  appropriate  solution  for  optimisation  of  its 
administrative processes, and also the amount of time needed for its development. After several weeks of market 
research  to  identify  the  best  PLM  solution,  it  was  decided  to  develop  from  scratch  an  entirely  cloud-based 
platform,  accessible  from  any  device,  to  integrate  all  administration  processes,  and  to  include  a  number  of 
modules pertaining to the circular manufacturing model. The idea was to allow the company to address all of its 
process issues, while at the same time, adding unique value to their proprietary KARE platform in terms of its 
potential for being licenced to other companies, after identifying the needs and adaptations required. It was also 
challenging  to  work  out  how  to  integrate  the  PLM  with  CAD/CAM,  front-end  data  input  and  an  Enterprise 
Resource Planning (ERP) solution. A related challenge was how to avoid potential operational delays arising from 
the switchover to the company’s new digitalised administration processes. To address this issue, the company 
decided to develop temporary online forms, which could also be used as the basis for the development of their 
digital platform.  

At the present time, internal factors such as efficient management of production and challenges linked to the 
digitalisation  of  business  processes  are  more  pressing  for  Katty  Fashion  than  external  factors.  One  external 
challenge is a major increase in the number of low-cost manufacturers, particularly in the NE region, which has 
led  to  increased  competition  and  therefore  reduced  profit  margins.  As  a  result,  Katty  Fashion  is  planning  to 
diversify its services for higher added value by offering digital solutions and packages/toolkits for dissemination, 
replication  and  even  franchising  of  the  Katty  Fashion  business  model  which  can  be  deployed  by  other  SMEs 
operating in the textile sector. Their proprietary KARE platform will help the company to offer predominantly 
360 Degree software to other garment and textile companies as part of their main business model. The company 
intends to add new modules to the platform over time, such as a PLM module for real-time progress tracking in 

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the product manufacturing unit; a circular economy module for developing collaborative business models with 
other companies in the region and a collaborative 3D prototyping module.  

The vision and efforts of the company are totally in line with the regional RIS3 strategies of the of NE Region, 
which are: the implementation of (1) high-tech processes and applications (industry 4.0) and (2) digital fashion. 

2.5.6  Policy recommendations 

Since the company benefited a great deal from the two support initiatives, C-VoUCHER and DigitaliseSME, they 
would highly recommend that similar initiatives be replicated EU-wide to provide hands-on technical support to 
SMEs in assessing their specific digitalisation needs, defining relevant solutions and supporting implementation 
financially. They would also suggest adding sectoral experts to future schemes, in addition to digital experts. This 
would avoid wasting time explaining sector specificities to digital experts in instances where the digital experts 
do not have this sectoral expertise.  

2.6  Norteña de Aplicaciones y Obras (Spain)89 

2.6.1 

Introduction  

Norteña de Aplicaciones y Obras, located in Aranda de Duero, provides specialised services in roof waterproofing. 
They operate in Castile and León and neighbouring regions with a small core team of seven people, although 
they rely on collaborations with multiple freelancers to support their technical activity. 

2.6.2  Digital Transformation Activities 

Jorge  Bermejo,  Norteña’s  CEO,  started  the  company  in  2006,  just  before  the  financial  crisis  hit  the  Spanish 
construction sector. In order to survive, Norteña redirected its business model by focusing on the needs of local 
companies. It soon became apparent that digitalisation was going to be an essential tool in terms of offering the 
high-quality services the market demanded. In 2015, Jorge Bermejo decided to start with digitalising workers’ 
daily worksheets, which to that point had involved a cumbersome and time-consuming process: worksheets were 
completed by hand on the construction site every day, and then had to be scanned or introduced into an MS 
Excel spreadsheet again at the office. However, thanks to the implementation of Enterprise Resource Planning 
(ERP)  and  Customer  Relationship  Management  (CRM)  digital  tools,  workers  were  able  to  start  filling  their 
worksheets directly into their tablets and smartphones. 

Over  the  course  of  several  years,  Norteña  has  digitalised  most  of  its  processes,  which  has  allowed  them  to 
significantly enhance management and decision-making, as well as to offer high added value services to their 
clients, based on transparency and traceability. 

Internally, they have implemented an integrated system with several modules to closely monitor the company’s 
activity  at  all  levels.  Apart  from  fully  managing  clients  and  stock  using  the  CRM  and  ERP  systems,  different 
modules also allow for faster worksite incident management, real-time cost control of different projects, real-
time cost control per department and employee, digital management of invoices and payments and much more. 
All this information is displayed on a dashboard, from which the team can monitor the company’s finances in 
real time and control whether the budgets for all the projects and for the company overall are being implemented 
as planned, and then leverage this knowledge for better-informed business decisions. 

Norteña has applied its digital capacities to come up with innovative ways to cater to clients’ needs. For example, 
when they realised that clients would often misplace important documents for projects that had been finished a 
long time ago, they pioneered the inclusion of a QR code in their roofs. All their clients can now scan the code 
and  immediately  gain  access  to  all  documents  related  to  that  roofing  contract  whenever  needed,  from 

89 Sources: https://www.nortena.es/, La empresa arandina Norteña, premiada por su 'software' de gestión empresarial, El Norte de Castilla, 
2017. Available at: 
https://www.elnortedecastilla.es/economia/empresas/empresa-arandina-nortena-20171027140324-nt.html?ref=https:%2F%2F, Norteña 
gana el premio CEPYME, El diario de Burgos, 2019. Available at: 
https://www.diariodeburgos.es/noticia/z8853a5d1-0a4f-a83a-c9a3f9abdf80cec7/nortena-gana-el-premio-cepyme, Interview with Jorge 
Bermejo, CEO of Norteña, 18th November 2020. 

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42 

guarantees to permits, annual revision reports or maintenance manuals. Clients also receive a daily automatic 
email informing them of daily progress on ongoing projects, along with pictures of the site. Other client services 
relying on the company’s digital capabilities include automatic notifications sent to clients when their guarantees 
are about to expire, giving them the option to contract maintenance work, and the ability to check the resolution 
of incidents online.  

As  a  consequence  of  their  digital  strategy,  the  company  has  cut  the  time  required  by  workers  to  complete 
paperwork by 80 % and reduced the cost of budget control by 77 %. From 2011 to 2019, company turnover grew 
by an impressive 450 %, a performance that its CEO attributes to Norteña’s digital capabilities and innovative 
mindset. 

Jorge Bermejo also highlights the positive impact this transformation has had in terms of cost savings on paper 
and construction materials. 

2.6.3 

 Support for Digital Transformation 

In  2017,  Norteña  received  public  financial  support  for  the  development  of  its  ERP  system  from  the  regional 
government  of  Castile  and  León.  However,  Jorge  Bermejo  describes  his  experience  with  regional  digital 
transformation support programmes as quite unsatisfactory.  

According to Norteña’s CEO, in practice, accessing public support for digital transformation can often prove  to 
be too complicated and time-consuming for the scarce resources of SMEs.  

After some unfruitful attempts to obtain more public funding, he has decided to rely only on the company’s own 
funds in the next stages of the company’s digital transformation plan. 

2.6.4  Key Success Factors  

Norteña’s case has been widely recognised as a success and has won seven awards for innovation and excellence 
in management since 2017, including the Award for the Best SME Digital Transformation in 2019, granted by the 
Spanish Association of SMEs (CEPYME). 

Among its key success factors, Norteña emphasises digitalisation and continuous improvement as a core part of 
its  business  and  brand.  Despite  operating  in  a  traditional  sector,  the  company  strives  to  cultivate  a  startup 
mentality  and  has  been  very  active  in  promoting  its  story:  they  often  apply  for  digital  transformation  and 
management  awards,  participate  in  local  conferences,  forums  and  associations  to  share  their  digital 
transformation journey, and have been featured in the local and national press. 

A  second  factor  highlighted by  Norteña  is the  involvement  and  commitment  of  the  staff  to  the  digitalisation 
process. Norteña’s team is not only aligned with the company’s digital strategy, but is also closely involved in 
defining it. Jorge Bermejo points out that they all share a passion for realising the potential of digitalisation for 
Norteña, and that having everyone fully on board has been crucial for success. 

2.6.5  Key Challenges and Issues  

The first step – going paperless at the construction site – was a big challenge at the time. It was unclear how CRM 
and  ERP  technologies  could  be  integrated  into  the  process  and  the  costs  of  these  digital  tools  were  high. 
Overcoming  these  initial  obstacles  ultimately  required  significant  dedication  and  major  financial  efforts  for  a 
small company like Norteña. As digital tools helped to increase efficiency, the reinvestment of savings was used 
for further digitalisation steps.  

As the company’s transformation has been incremental, module integration has been an important challenge. 
Norteña are currently working out how to refine the communications between modules to take on their next 
objective: the automation of part of their financial control system.  

As with many other businesses, Norteña has faced challenges during the COVID-19 crisis. However, they highlight 
how being a digital company has helped them in dealing with the effects of the pandemic. Thanks to data-based 
decision making and digitalised processes, they expect to finish 2020 with the same turnover as last year. 

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Furthermore,  since  2012,  Norteña’s  office  team  has  teleworked,  benefiting  from  the  company’s  digital 
capabilities.  Consequently,  Norteña  was  fully  prepared  for  the  measures  required  to  control  the  COVID-19 
pandemic. 

2.6.6  Policy Recommendations 

Streamlining  application  processes  should  be  a  priority  in  all  programmes  targeting  SMEs.  Bureaucratic 
procedures  and  complicated  eligibility  conditions  can  discourage  application  and  put  a  strain  on  the  limited 
resources  of  SMEs.  Policymakers  at  both  regional  and  national  level,  who  are  aiming  to  promote  the  digital 
transformation  of  SMEs,  should  make  efforts  to  avoid  unnecessary  complexity  and  limit  the  administrative 
burden on applicants, thus encouraging them to apply.  

Additionally,  more  emphasis  should  be  placed  on  the  once-only  principle  when  it  comes  to  administrative 
documents  and  information.  It  is  still  not  uncommon  for  companies  to  be  required  to  provide  the  same 
information repeatedly when applying for support. 

2.7  Royal snc (Italy)90 

2.7.1 

Introduction 

Royal snc is a family-owned SME managing a number of hotels in the North East of Italy. It was established in 
1985  and  currently  has  50  employees.  It  is  a  service  company  focused  on  the  delivery  of  hotel  and  catering 
industry services. 

2.7.2  Digital Transformation activities 

Royal snc recognises the importance of digitalisation for ensuring future success, and decided to deploy relevant 
digital technologies in order to stay up-to-date. In particular, they felt that data collection and analytics would 
be key to increasing their market outreach and improving the internal management of their hotels. 

For this reason, the company sought support for the identification and deployment of the most suitable digital 
tools  to  help  them  with  market  expansion,  improvement  of  management  capacity  and  energy  storage  and 
efficiency, in order to create a sustainable business model.  

They  started  with  leveraging  online  data  insights  to  gain  better  understanding  of  consumer  profiles  and 
behaviours, their competitors and their competitors’ offerings, and options for marketing communications, using 
digital tools from the main digital platforms, such as Google's Keyword Planner, Facebook IQ, etc. The company 
also collects guest information in compliance with General Data Privacy Regulation (GDPR) from the moment 
their guests check-in until they finally check-out. 

The company aims to make business decisions based on the customer insights generated by web analytics. By 
studying guest data, they hope to spot the trends and expectations of various customer segments (based on age, 
gender, cultural origin, etc.) and adapt their marketing strategy accordingly. Once they have accomplished this, 
the  company  plans  to  improve  their  search  engine  marketing,  site  user  experience,  email,  and  social  media 
marketing. 

Royal snc is also in the process of implementing automation of their beach umbrellas. A digital device powered 
by photovoltaic panels will automatically open and close beach umbrellas at the hotel’s private beach area, in 
order  to  benefit  from  solar  energy.  This  automation  of  operations  will  also  allow  the  company  to  minimise 
maintenance activities and optimise staffing levels by using the Internet of Things (IoT) together with information 
technology (IT) and operational technology (OT). 

90 Sources: https://www.baiadelmar.com/, https://digitalisesme.eu/about-digitalisesme/, https://digitalisesme.eu/digitalisesme-matching-
series-italian-hotel-company-expands-its-market-outreach-thanks-to-maltese-digital-enabler/, Interview with Ms Krista Boschian on 
14.01.2021.  

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2.7.3  Support for Digital Transformation 

Royal  snc  applied  to  the  DigitaliseSME  initiative,  which  matches  the  needs  of  applicants  with  an  appropriate 
expert who can coach the company in the development of digital solutions. Trevor Buhagiar, a digital expert from 
Malta, was assigned to Royal snc as a Digital Enabler, thanks to his expertise in integrating digitalisation tools 
into customer management to improve market outreach. 

The suggestions made by their Digital Eabler included the deployment of various digital solutions for both internal 
and external use, in order to transform the traditional hotel experience to follow global hospitality and tourism 
market standards, in line with the trending needs and preferences of guests. The following digital solutions were 
suggested: 

•  A  software  application  to  handle  goods  requests,  ordering  and  consumption,  in  order  to  eliminate 
traditional  manual  form  filling  processes  and  identify  trends  in  usage  and  consumption  of  goods, 
incorporating an alert system to send notifications of major changes and/or shortages. 

•  A  housekeeping  quality  control  system  to  contribute  to  the  maintainance  of  the  company’s  brand 

quality promise. 

•  Digital energy consumption monitoring for energy conservation and cost reductions. 

•  A Building Management System (BMS) to provide enhanced control of the technical plant of the hotel 
(such  as  automated  start-stop  of  equipment,  provision  of  alerts  to  the  operator  and  maintenance 
personnel) to improve provision of essential services such as air-conditioning and hot water supply. 

•  A Guest  Room  Management  System (GRMS) to both enhance the guest  experience  and support  the 

energy management and conservation strategy of the hotel. 

•  A digital menu ordering system to enhance the guest experience while providing a record of ordering 

trends to improve menu planning and food procurement. 

•  Voice activated controls within the guest rooms to provide a better experience for guests while also 

branding the hotel as innovative and future looking. 

•  A guest services application to provide a platform for the hotel owner to interact and better engage 
with guests in order to improve the experience of each guest throughout their stay, and also to provide 
a communication channel for digital marketing. 

•  A chat interface on the company’s website to allow customers to interact with the hotel reservation 
personnel in real-time, in order to increase customer confidence and thereby enhance the purchasing 
decision process.  

Royal snc was highly satisfied with the support provided by their Digital Enabler. The company is now looking 
into  further  funding  opportunities,  including  the  EU  recovery  fund,  to  help  implement  the  digital  solutions 
suggested.  

2.7.4  Key Success Factors 

Royal  snc  identified  a  number  of  success  factors  in  their  digital  transformation  journey.  Personal  curiosity, 
motivation, vision and the entrepreneurial skills of management were all viewed as being key, along with taking 
a step-by-step approach to deployment. As a result of their experience with DigitaliseSME, Royal snc feel that 
their  motivation  has  greatly  increased,  thanks  to  the  fruitful  exchanges  with  their  Digital  Advisor,  and  the 
contacts they have made with the digital world on both a human and knowledge-based level.  

Another key success factor mentioned was staff training. In order to maximise the benefits of digitalisation, the 
company  needed  to  train  employees  in  specific  skills  to  understand  the  functionality  of  the  software  and  its 
applications. Having a thorough familiarity with concepts like relational databases has allowed staff to manage 
the data appropriately.  

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2.7.5  Key Challenges and Issues 

Krista Boschian, Royal snc’s Project Manager, believes there is not enough support and funding for the digital 
transformation of SMEs, even though digitalisation has been widely recognised as providing immense potential 
for increasing productivity, improving efficiency, and empowering improved decision-making. SMEs are already 
burdened  by  the  costs  of  internal  management  and  taxation,  so  they  are  unable  to  cover  all  the  necessary 
investment on their own. As a result, Ms Boschian underlined that the high infrastructure and organisational 
costs incurred in implementing digital solutions present a major challenge for SMEs.  

Another key challenge is general lack of interest  from SMEs in undertaking digital transformation.  This is the 
impression Ms Boschian came away with after having presented Royal snc’s experiences at the final conference 
of the ‘DigitaliseSME’ initiative in Brussels, when she learned that the scheme had attracted fewer applicants 
than expected, indicating either lack of interest and/or lack of awareness among SMEs. 

2.7.6  Policy recommendations 

National governments need to introduce appropriate digital policies accompanied by support instruments for 
SMEs. Only then will SMEs be able to fully utilise new technologies to improve business performance.  

Specific  short  and  long-term  policies  which  make  the  costs  of  digital  transformation  sustainable  need  to  be 
identified in order to encourage investment by SMEs. 

Ms  Boschian  emphasised  the  growing  gap  between  the  skills  possessed  by  employees  and  the  digital  skills 
required by the labour market. As a result, policy measures are required to bridge this gap, as well as to ensure 
that  education  and  corporate  training  opportunities  are  boosted  to  equip  more  people  with  the  digital  skills 
needed to meet market demand.  

Finally, Ms Boschian stressed the importance of ensuring cybersecurity, both at firm level, in terms of helping 
firms  acquire  the  technical  skills  necessary  for  implementation,  as  well  as  at  policymaking  level,  in  terms  of 
defining the the most effective cybersecurity strategies, policies, and programmes.  

2.8  Skill Software GmbH (Germany)91 

2.8.1 

Introduction 

Skill Software GmbH is an innovative micro SME which develops digital solutions. It was founded 30 years ago by 
Edgar Reh, a nuclear physicist, in Frankfurt am Main in Germany, and has 8 employees.  

Skill Software focuses on software development and distribution, specialising in mobile B2B solutions using a 
cloud server database. The company provides radical simplification of complex business processes for use  on 
smartphones  and  tablets,  enabling  the  transfer  of  important  data  to  and  from  mobile  working  places,  and 
managing the on-site collaboration of teams and of main and sub-contractors. It offers Industry 4.0 solutions for 
construction and building management, energy and technology, and customer and visitor flow. Another focus 
area  is  project-oriented  Customer  Relations  Management  (CRM),  particularly  for  the  construction  industry, 
involving digitally supported customer acquisition and customer management. 

2.8.2  Digital Transformation activities 

Skill Software has been digitalising customer processes since it was founded 30 years ago. For its first project, it 
connected 15 advisors at Henkel to the Henkel head office with small mobile computers, networked customer 
services and set up a solution database for product problems.  

91 Sources: https://skillsoftware.de/, https://digitalisesme.eu/about-digitalisesme/, https://digitalisesme.eu/cs/digitalisesme-matching-
series-german-software-company-expands-it-market-outreach-with-the-support-of-czech-digital-enabler-6/, 
https://www.bmbf.de/de/kmu-innovativ-ressourcen-und-energieeffizienz-612.html, Interview with Mr Edgar Reh on 05.01.2021. 

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This led the company increasingly into customer acquisition and support, later known as ‘Customer Relationship 
Management’ (CRM). The next step was to connect BlackBerry mobile phones to CRM software, which led to the 
development of its first cloud-based CRM solution. 

When Skill Software’s largest CRM customer, Häfele, asked whether the company could digitalise planning and 
management documentation processes for the construction sector, the company developed BauDoc for tablet 
PCs and now smartphones.  

Another customer, KMW, wanted to digitalise the technical service management of freezers, so Skill Software 
developed TechDoc, and for energy consumption, EnergyDoc, using the Internet of Things (IoT).  

In  2018-2020,  in  collaboration  with  KMW  and  va-Q-tec  (a  major  provider  of  temperature-controlled  thermal 
containers for global distribution of the Covid-19 vaccine), Skill Software developed a freezer that uses only 50% 
of the usual energy requirements.  

Skill Software has also created a software suite called PropertyDoc comprised of: Skill Project CRM to help their 
customers increase sales through customer relatonship management; BauDoc for all construction management 
control  and  documentation  tasks  involved  in  the  building  and  construction  industry;  EnergyDoc  for  energy 
savings using mobile energy management software with integrated sensors and alarm functions;  TechDoc for 
delivery of important technical information to mobile workplaces to allow the undertaking of technical services 
and preventative maintenance, all in support of CO2 reduction. 

2.8.3  Support for Digital Transformation 

Mr Reh has taken part in the ‘DigitaliseSME’ initiative as both a Digital Enabler and also as a beneficiary. He first 
participated as a  Digital Enabler, helping an SME in Breda,  the  Netherlands, to digitalise the vehicle  approval 
process  for  special  types  of  vehicles  such  as  trucks,  caravans,  and trailers.  For  another SME,  in  Karlovy  Vary, 
Czechia, Mr Reh helped develop an energy management system for their customers. The concept was due to be 
implemented in 2020 but was halted by Covid-19 and the ensuing lockdowns. 

By this time, Mr Reh realised that he needed professional advice from a Digital Enabler with different expertise 
to his own, specifically on market analysis and digital marketing, with a view to expanding his market reach across 
Europe.  As  a  result,  he  registered  his  company  with  DigitaliseSME  and  was  matched  with  the  Czech  Digital 
Enabler,  Veronika  Kobylková,  who  has  exactly  the  kind  of  knowledge  and  skills  he  needed.  Through  this 
collaboration, Skill Software received digital marketing and sales support to enable it to expand into the Eastern 
European  B2B  market,  especially  in  Czechia  and  Poland.  Following  this  support,  Skill  Support  has  introduced 
several aspects of market analysis and digital marketing into the German market but has not yet done so for the 
Czech and Polish markets.  

Learning how to introduce new digital marketing and sales processes in a scalable and globally applicable way 
has been a fascinating experience for Mr Reh. The company is now in the process of expanding its business into 
the Netherlands and the United Kingdom. 

In terms of funding support, Skill Software benefited from an innovation fund provided by the State of Hesse and 
also from EU Regional Funds, which covered half the development costs of EnergyDoc. For the development of 
the low energy freezer, the company benefited from the ‘KMU Innovativ’ funding initiative run by the German 
Federal Ministry of Education and Research.  

The company is also connected to some B2B networks and clusters in Germany, such as BVMW, and cooperates 
with two universities.  

2.8.4  Key Success Factors 

Mr Reh pinpoints being open minded towards digitalisation as a key success factor. To this end, the company 
have attended a number of startup workshops, participated in networks and associations and cooperated with 
various  specialists  in  different  areas  of  digitalisation  via  LinkedIn  and  Xing  marketing,  Facebook,  Instagram, 
YouTube, internet forums, search engine optimisation, etc. As a result, they have worked out a modular strategy 
that has been tested and proven step-by-step. Based on the lessons they have learned, the company’s main 

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success driver has been to focus on the quality of their products as the best way of increasing the number of 
their business leads.  

2.8.5  Key Challenges and Issues 

Skill  Software  is  developing  software  solutions  in  difficult  domains  such  as  construction,  the  least  innovative 
sector in Germany. The key challenge identified by Mr Reh is that when companies learn and develop on their 
own, based on trial-and-error, the process is expensive, since it takes a long time to develop new solutions. He 
recommends taking a step-by-step approach by monitoring success, and by networking with other parties who 
are also digitalising their processes. 

2.8.6  Policy recommendations 

From a policy perspective, Mr Reh highlighted the importance of financial support for the digitalisation of SMEs 
as a key recommendation. However, he also highlighted that financial support on its own is not enough, it needs 
to be accompanied by technical support. Therefore, he recommends the roll-out of support initiatives such as 
‘DigitiliseSME’,  which  should be  made  easily  accessible  to SMEs  aiming  to  digitalise.  Bringing  together  digital 
experts  and  SMEs  to  collaborate  on  digital  solutions  to  meet  their  needs  and  expectations  triggers  cross-
fertilisation of ideas which is highly beneficial for the SMEs concerned. 

Another key recommendation made by Mr Reh, is to raise awareness of the benefits of digitalisation by sharing 
digitalisation success stories across as many communication channels as possible.  

Mr  Reh  also  drew  attention  to  the  way  in  which  the  recent  COVID-19  lockdowns  have  acted  as  a  powerful 
incentive to most companies to accelerate digitalisation in order to continue doing business. This momentum 
should continue to be supported during the pandemic and beyond.  

2.9  Van Den Borne Aardappelen (Netherlands)92 

2.9.1 

Introduction 

The Van Den Borne family farm, founded in Reusel (North Brabant) in 1952, consists of roughly 550 hectares of 
land, devoted primarily to potato crops, but also maize, wheat and sugar beet. Thanks to the innovative spirit of 
brothers Jacob and Jan Van Den Borne, it has now evolved into one of the most digitally advanced farms in the 
Netherlands, gaining industry recognition for its innovative practices, including a nomination in 2019 from the 
magazine ‘Future Farming’ as one of the 10 most innovative arable farms worldwide. 

2.9.2  Digital Transformation activities 

In 2006, the Van Den Borne brothers initiated the farm’s digital transformation journey with the aim of increasing 
the efficiency and quality of their crop yields through precision agriculture. 

The farm is now an agricultural leader in the application of a wide range of precision farming technologies. GPS, 
drones and sensor technologies have been deployed to monitor soil health and accurately determine cultivation 
requirements. The management of field operations is further supported by crop monitoring tools and software 
that  provide  real-time  data  and  key  insights  to  help  ensure  maximum  efficiency  and  productivity.  The  data 
collected helps the Van Den Borne brothers to make better informed decisions on irrigation and fertiliser use. 

92 Sources: https://www.vandenborneaardappelen.com/, Contributions from Van Den Borne Aardappelen, Smart precision farming, 
WATIFY, 2017 
https://ec.europa.eu/growth/tools-databases/dem/watify/inspiring/watify-success-stories/smart-precision-farming, Jacob Van Den Borne. 
Flagship Farmers, McDonalds, 2017 
https://www.flagshipfarmers.com/en/profile/jacob-van-den-borne/#tab3, Koerhuis, R. Precision farming technology as a management 
tool. Future Farming, 2019. 
https://www.futurefarming.com/Smart-farmers/Articles/2019/9/Precision-farming-technology-as-a-management-tool-463666E/.  

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48 

This digital transformation has enabled the farm to increase crop yields, reduce the use of inputs such as water, 
fertilisers and fuel, and to increase revenue. 

2.9.3  Support for Digital Transformation 

The  farm  has  received  public  financial  support  from  local,  regional  and  national  SME  innovation  and  rural 
economic development programmes to help finance the farm’s digital transformation ambitions. These support 
programmes  have  included  the  Noord-Brabant  subsidy  scheme  for  SME  innovation  stimulation,  the  Regional 
Economic Action Programme (REAP) of the province of Noord-Brabant and the Rural Development Programme 
POP3 Brabant 2014-2020. Over the years, the farm has also participated in a long list of publicly supported R&D 
and innovation projects. Additionally, the farm has received financial support for investment in precision farming 
equipment  from  the  ‘Programma  Precisie  Landbouw’  (Precision  Farming  Programme),  co-funded  by  the 
Netherlands government and the agricultural industry. 

2.9.4  Key Success Factors 

The Van Den Borne farm is an important example of how a rural SME from a traditional sector, supported over 
time  with  various  public  funding  programmes,  can  lead  the  way  in  smart  farming.  Because  of  these  funding 
schemes, the Van Den Borne brothers have been able to take risks in terms of the investments and experiments 
required to achieve their digital transformation goals.  

The success of their approach is based on running initial test trials and use cases, followed by scaling up the most 
successful trials company-wide. This has led to other farmers making the necessary investments in their own 
farms,  after  having  observed  the  results  of  the  scaling  up  and  company-wide  introduction  of  these  novel 
technologies.  

Aside from the practical deployment and adoption of technical equipment, the  Van Den Borne farm has also 
benefited from collaboration with knowledge institutions and other companies. This collaboration has been a 
mutual learning curve; for the institutions it has provided an opportunity to get closer to real-world challenges, 
while  for  the  Van  Den  Borne  brothers,  it  has  been  key  to  keeping  up-to-date  with  new  technologies  and 
identifying their future potential for the farm. 

Being able to leverage collaboration and public funding opportunities has been key to defining and funding the 
Van den Borne brothers’ vision for the farm.  

Furthermore,  the  efforts  of  the  Van  Den  Borne  brothers  to  share  their  knowledge  and  experience  of  smart 
farming have played a major role in raising the farm’s recognition and profile in the agriculture industry. 

2.9.5  Key Challenges and Issues 

The journey towards precision farming has been long but rewarding for the Van Den Borne brothers. Exploring 
the possibilities offered by smart farming initially required a significant investment in time and money to research 
the technologies available and to test use cases.  

2.9.6  Policy recommendations 

To replicate success cases like this, it is essential that SMEs which are interested in what novel technologies can 
do for their business but are unsure where to start – as was initially the case for the Van Den Borne brothers – 
receive  both  financing  and  guidance/training,  as  well  as  the  means  to  test  technologies  and  ideas.  Training 
programmes, innovation vouchers and Digital Innovation Hubs also play an important role here. 

Digital transformation support programmes that are tailored to the specific needs of rural SMEs would not only 
help to transform traditional sectors but  would also boost local economies and address depopulation in rural 
areas. 

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2.10  Z-Application (Belgium)93 

2.10.1  Introduction 

Z-Application  is  a  Belgian  micro  SME  established  in  2013  by  Francis  Appels  after  the  firm  for  which  he  had 
previously worked for 25 years closed down. He established Z-Application with his own funding and runs it as a 
sole entrepreneur utilising his educational background and expertise in electronics and informatics. The company 
specialises in the production of barcode scanning applications for warehouses, as well as customised warehouse 
logistics solutions for existing customers. The company’s warehousing and e-commerce customers are located 
mainly in France but also across Europe and other parts of the world, including the USA, Mexico, and South Africa. 
It has also recently partnered with an Australian company to cover distribution in the Asia-Pacific region. 

2.10.2  Digital Transformation activities 

Z-Application offers web-based client/server solutions, and utilises JavaScript/HTML5/CSS3 for user interfaces. 
It aims to help companies by offering custom development of Dolibarr open source Enterprise Resource Planning 
(ERP)  software  solutions  as  well  as  providing  Dolibarr  implementation  support  such  as  installation  support, 
problem solving, and data migration from other systems such as Sage/Ciel for automated stock management 
purposes.  

Mr  Appels  has  developed  a  ready-to-use  modular  base  product  which  eliminates  the  need  to  develop 
independent business solutions for each customer, requiring only customisation to suit their specific business 
needs. The base product uses the open source Dolibarr ERP system since it is easy to manage and install on any 
platform and has all the standard features a business needs. The base product is being continuously developed 
to add new features. The Z-Application ‘Mobilid’ app is a mobile Dolibarr warehouse application with a built-in 
barcode scanning feature for order collection and inventory counting, which can be installed on smartphones, 
tablets and next generation industrial handhelds. 

Improving the automation of their online sales process using digital technologies was important for Z-Application. 
As  a  result,  following  the  digital  transformation  support  which  Mr  Appels  received  (as  detailed  in  the  next 
section), the company developed a new website with an integrated Search Engine Optimisation (SEO) tool. The 
website includes a blog and a page of trusted business partners to attract more customers. It also links to the 
Mobilid product website for ease of product ordering and to track product interest. Mobilid is also marketed via 
the Dolibarr ‘Dolistore’ e-marketplace platform. 

Z-Application has already started to see significant benefits in terms of being able to understand its customers 
better and to be able to profile them. It is expecting to increase its sales as a result. 

Currently,  the  company  has  implemented  most  of  the  suggested  digital  transformation  solutions  and  is  now 
looking for additional support for the online branding and marketing of its products. 

2.10.3  Support for Digital Transformation 

When  first  setting  up  the  company,  Mr  Appels  received  financial  aid  from  the  Flemish  SME  e-wallet  training 
support scheme, which helped him to upskill on mobile app development.  

Recently,  the  company  applied  to  the  DigitaliseSME  initiative,  which  matches  the  needs  of  applicants  to  a 
relevant expert who can coach the company in the development of digital solutions. Bogdan Dumitrasconiu, a 
digital expert from Romania, was assigned to Z-Application as a Digital Enabler to suggest solutions for improving 
the automation and digitalisation of the sales process. After collaborating for two weeks, Mr Appels was highly 
satisfied with the support provided by his Digital Enabler. 

93 Sources: http://www.z-application.com/, https://www.dolistore.com/en/modules/407-Connector-for-Mobilid-Mobile-app.html, 
https://www.mobilid.eu/, https://www.vlaio.be/nl/andere-doelgroepen/flanders-innovation-entrepreneurship/subsidies-
entrepreneurs/subsidies, https://digitalisesme.eu/digitalisesme-matching-series-belgian-company-providing-solutions-to-warehouses-
aims-to-increase-sales-with-the-help-of-a-romanian-digital-expert/, Interview with Mr. Francis Appels on 01.11.2021. 

Page | 49 

 
 
 
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2.10.4  Key Success Factors 

Mr Appels considers his key success factors to be a strong product combined with personalised support. As he 
explained, there are many similar and cheaper products on the market, but none of them offer a customised 
product with customer support. Customers have the option of purchasing basic features with lifetime support or 
can purchase additional features with varying support options. Mr Appels mainly offers the direct support option 
to end customers. He also partners with trusted ERP integrators/resellers which buy and re-sell his product, since 
Z-Application does not carry out ERP integration and training. This allows customers in need of ERP support to 
receive it from his trusted partners instead. 

Further success factors mentioned by Mr Appels were his personal passion for digital technologies and a mindset 
which does not see digital transformation as a challenge. He also considers his home-based teleworking strategy 
to be a success factor, viewing it as more efficient, productive and environmentally-friendly, since it has entirely 
eliminated office commuting. As a result he has avoided many of the challenges faced by other companies in 
adapting to the new pandemic working conditions since, as the sole proprietor of a self-funded company, he has 
always carried out his business this way.  

2.10.5  Key Challenges and Issues 

Responding to multilingual requests from his international customer base, and undertaking digital marketing are 
the main challenges faced by Z-Application. Mr Appels is currently on the lookout for a reliable communication 
and translation company which offers a seamless e-translation solution that can be embedded into the existing 
website.  This  way,  requests  could  be  automatically  and  correctly  translated,  while  also  supporting  online 
branding and marketing in various languages on a variety of e-commerce websites.  

2.10.6  Policy recommendation 

Mr Appels is happy with the current regulatory and policy framework in Belgium, as well as across the EU, in 
terms of being able to provide business solutions cross-border via e-commerce throughout the EU. 

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3  Policy case studies 

3.1  France Num - France 

3.1.1 

Introduction 

France  Num  is  a  government  policy  initiative  led by  the  ‘Direction  Générale  des  Entreprises’.  It  was  officially 
launched in October 2018, based on the National Digital Council’s recommendations for supporting the digital 
transformation  of  SMEs,  particularly  VSEs  with  fewer  than  10  employees.  The  programme  was  developed  in 
partnership  with  the  ‘Régions  de  France’  organisation,  the  French  regions  and  a  number  of  professional 
organisations, with the aim of bringing together under one umbrella all the public services which support SME 
digital transformation.  

There are two strands to the France Num ‘brand’: ‘Advisor France Num’ for the public and private partners of 
the programme and ‘Enterprise France Num’ for SMEs seeking support for digital transformation. The overall 
France Num brand brings together under one banner all the actions carried out by the government and France 
Num stakeholders to support the digital transformation of SMEs at national and regional levels (e.g. the France 
Num  web  platform,  the  community  of  advisers  and  partners,  specific  regional  programmes,  events,  training, 
etc.). 

The high-level mission of the initiative  is to mobilise stakeholders around a  strong brand  in order to  provide 
support to SMEs for their digital transformation. The goal is to provide a tailored solution for each company by 
raising their digital awareness via the France Num website, and by the provision of online tools such as a digital 
maturity assessment, as well as search engines for finding funding opportunities, special events, and advisors 
(based on specific digital technology needs/industrial sector/location).  

There is also an extranet space within the France Num portal, reserved for public and private advisers, partners 
and government agencies, so they can share and promote relevant information and arrange collaborations aimed 
at providing digital support services and events for SMEs.  

3.1.2  Digital Transformation Support Services 

In a study carried out in 2019 by the Confédération des Petites et Moyennes Entreprises (CPME) and the software 
company, SAGE,94 only 34% of VSE managers stated that digital transformation was already deployed or was in 
the process of being deployed in their company. According to the study, digital transformation services such as 
website creation, digitalisation of business processes, and stock and order management processes were among 
those most highly requested by SMEs to meet their specific needs.  

France  Num  offers  tailored  services  based  on  three  types  of  company  profile:  companies  new  to  digital, 
companies which have already implemented digitalisation of some kind, and companies at an advanced level of 
digitalisation.  A  variety  of  services  are  on  offer  to  enterprises:  information  about  the  benefits  of  digital 
technologies; website creation; online marketing via social networks; use of e-commerce platforms to improve 
and increase sales; digital solutions for the organisation of business processes/automation of tasks/finding new 
customers/improving customer relations; and cyber resilience advice, for example, to help protect the company 
against cyber attacks and data leaks. Most recently, within the scope of the Recovery Plan established by the 
European Union to help repair the economic and social damage caused by the coronavirus pandemic, two new 
measures have been added to the existing initiative to accelerate the digital transformation of all enterprises. 
The first measure is the introduction of an Artificial Intelligence (AI) Booster. The objective is to assess the needs 
of SMEs and mid-caps from all sectors, which have already acquired a basic level of digital maturity, in order to 
support  them  in  implementing  various  AI  solutions.  The  second  measure  aims  to  provide  support  to 
manufacturing SMEs and mid-caps planning to invest in advanced Industry 4.0 manufacturing technologies (e.g. 
robotics,  additive  manufacturing,  cyber-physical  systems,  sensors,  production  management  software,  etc.)  in 
order to modernise their manufacturing processes.95 

94 https://www.sage.com/fr-fr/blog/transformation-digitale-tpe-etude/  
95 https://www.economie.gouv.fr/plan-de-relance/maitrise-diffusion-numerique 

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52 

A multi-phase implementation schedule is ongoing. During the 4th quarter of 2020, SMEs were given access to 
free diagnostics, a callout for the selection and mobilisation of  training providers was introduced, and France 
Num  loans  were  launched.  An  ongoing  awareness  raising  campaign  about  the  benefits  of  digitalisation  will 
continue  with  the  online  training  course,  “Ma  TPE  a  rendez-vous  avec  le  numérique”  (‘My  VSE  has  an 
appointment with digital technology’). Finally, from the second quarter of 2021, the France Num website will be 
further upgraded to provide direct access to new features.96  

3.1.3  Observed/Expected Impacts on SME Digital Transformation 

France Num aims to support every SME that wants to initiate the digitalisation process, with the goal of enabling 
1 million SMEs to benefit from a digital diagnostic over the 3-year period of the initiative. When it launched in 
2018, it comprised 25 partners and 900 advisors. By  2019, more than 1,500 advisors had partnered with the 
initiative to support VSEs/SMEs in their digital transformation. 

Based on the testimonials of SMEs that have already used the France Num platform, SMEs are generally satisfied 
with the services and guidance they have received. As a result of being matched with an advisor with specific 
expertise  in  the  digital  technology  field  appropriate  for  their  business sector  and  needs  (e.g.  developing  real 
estate digital platforms), SMEs have gained confidence and trust in the scheme, and in the recommendations of 
their advisors for transforming their business appropriately.97  

The impacts of  digitalisation on SMEs  have  differed depending on the  sector involved and the digital  actions 
implemented. The pandemic lockdowns have also created different impacts. For example, utilising social network 
marketing has led to greater business visibility for some SMEs, while the adoption of online booking platforms 
has  opened  up  new  revenue  sources  for  some  SMEs  and  helped  them  to  develop  services  for  their  local 
customers. Overall, the main impacts of digitalisation have been to provide companies with an improved online 
presence, new means of connecting with customers,98 and the ability to work with simple and easily accessible 
digital tools (email, phone, payment methods), all of which have allowed SMEs to remain competitive. Data from 
the scheme show that the larger the SME, the more likely it is to adopt digital transformation (43% of companies 
with 10-19 employees and 53% of those with 20-49 employees). 99 

Although  originally  the  main  goal  of  France  Num  was  to  enable  all  French  SMEs  to  launch  their  digital 
transformation within 3 years, the Covid-19 crisis has hampered progress of this aim. However, the European 
Union’s Recovery Plan is expected to help mitigate this challenge, enabling France to mobilise EU funding of EUR 
400 million until 2022 to help French companies recover and become more resilient through the adoption of 
digital technologies.  

3.1.4  Key Success Factors 

Access to finance for digitalisation activities is a major challenge  for  most SMEs. As a result, the France Num 
platform  provides  a  search  engine  for  digitalisation  funding  opportunities  specifically  dedicated  to  SMEs  and 
VSEs. It covers national and regional public funding instruments, such as digital vouchers, loans and guarantees, 
equity capital and other funding sources such as crowd sourcing platforms.100 Developing digital skills while at 
work is another important factor in increasing competitiveness and accelerating the business potential of SMEs. 
France  Num  therefore  provides  an  online  resource  on  its  website  which  defines  the  12  digital  skills  of  the 
future,101  along  with  another  online  resource,  aimed  at  the  construction  sector,  which  outlines  the  four 
advantages  of  digitalisation  for  businesses:  to  increase  the  visibility  and  responsiveness  of  the  company  and 
thereby boost competitiveness; to improve the customer experience; to introduce more efficient, time-saving 

96 https://www.economie.gouv.fr/plan-de-relance/profils/entreprises/aides-francenum-transformation-numerique 
97https://www.francenum.gouv.fr/comprendre-le-numerique/plugimmo-une-nouvelle-plateforme-immobiliere-numerique-concue-
pour-les-2 
98 https://www.francenum.gouv.fr/comprendre-le-numerique/un-primeur-developpe-son-commerce-de-proximite-avec-deux-applis-
commande-en 
99 https://www.gouvernement.fr/les-actions-du-gouvernement/economie-et-finances/comment-propulser-mon-entreprise-dans-le-
numerique 
100 https://www.francenum.gouv.fr/financer-son-projet 
101 https://www.francenum.gouv.fr/comprendre-le-numerique/quelles-sont-les-competences-numeriques-de-demain 

Page | 52 

 
 
 
 
administrative management and communication with suppliers; and to increase the overall productivity of the 
business by moving towards new digital forms of work organisation.102  

Other key factors in the success of the France Num initiative include being responsive to the specific needs of 
SMEs, and taking an active role as the intermediary between SMEs, digital advisors and all other stakeholder 
groups involved in the initiative. Also key to success is continually improving the scheme as a result of feedback 
from  stakeholders  and  beneficiaries.  A  final  success  factor  is  the  identification  and  promotion  of  companies 
which have successfully achieved digital transformation, as examples of best practice to inspire other SMEs. 

3.1.5  Key Challenges and Issues 

It is essential to start by identifying the precise needs of SMEs in taking their first step into the digital world. 
France  Num  has  pinpointed  four  obstacles  to  digital  transformation:  lack  of  digital  awareness;  lack  of 
understanding of digital technologies; lack of in-house digital skills; and lack of financial resources. All of these 
obstacles are addressed by the policy actions developed by the government.103  

To  avoid  confusing  SMEs,  it  is  important  that  all  the  digital  transformation  support  on  offer  from 
national/regional/local support institutions is made available in one place, in this case, under the France Num 
banner.  For  this  reason,  the  France  Num  initiative  is  conducted  jointly  by  the  government  and  the  regions, 
through  committees  organised  around  the  Association  of French  Regions,  the  Minister  for  the  Economy  and 
Finance, and the Secretary of State to the Prime Minister in charge of the Digital Agenda.  Other stakeholders 
(public and private-public partnerships, professional bodies working closely with SMEs, chartered accountants 
and banking networks) have also been encouraged to get involved in order to ensure the success of this policy.104 

3.1.6  Recommendations for roll-out as a best practice 

It is essential to centralise resources for SMEs so they can save time finding the information they need, quickly 
implement  the  most  appropriate  digital  solutions,  and  thereby  achieve  a  fast  return  on  their  time/money 
investments. A centralised platform allows SMEs to obtain online recommendations, find advisors in their region, 
identify  relevant  events  and  training,  assess  their  digital  maturity  and  find  funding  opportunities.  Further 
recommendations include the establishment of government guaranteed loans, which can be a crucial factor in 
facilitating the transition to new technologies, and taking a collaborative approach which involves government, 
regions and local partners, to ensure the effectiveness of the actions taken. 

3.2  Fund for Artificial Intelligence, Blockchain and IoT (Fondo per Artificial Intelligence, 

Blockchain e IoT) - Italy 

3.2.1 

Introduction 

The National Fund for Artificial Intelligence, Blockchain and IoT was  announced in the 2019 Italian budget.105 
Managed  by  the  Italian  Ministry  of  Economic  Development  through  its  in-house  agency,  Infratel,106  it  was 
endowed with EUR 45 million for the period 2019-2021 (EUR 15 million per year). Although the fund is not yet 
operational, its pilot project was launched in 2019. Subsequently, the ‘Simplification Decree’,107 published in July 
2020  and  converted  into  law  in  September  2020108,  established  a  new  regulation  simplifying  administrative 
procedures for the allocation of public funding, which requires the Ministry of Economic Development and the 

102 https://www.francenum.gouv.fr/comprendre-le-numerique/quels-outils-numeriques-pour-les-entreprises-du-batiment 
103 https://www.economie.gouv.fr/files/files/PDF/DP_FranceNum.pdf p. 8 
104 https://www.economie.gouv.fr/files/files/PDF/DP_FranceNum.pdf p. 8-9 
105 Budget Law number 145/2018, published in the Official Gazette, number 302 of 31/12/2018: 
https://www.gazzettaufficiale.it/eli/gu/2018/12/31/302/so/62/sg/pdf 
106 Infrastrutture e Telecomunicazioni per l’Italia S.p.A.: https://www.infratelitalia.it/  
107 Law Decree number 76/2020, published in the Official Gazette, number 178 of 16/07/2020: 
https://www.gazzettaufficiale.it/eli/id/2020/07/16/20G00096/sg   
108 Law number 120/2020, published in the Official Gazette, number 228 of 14/09/2020:  
https://www.gazzettaufficiale.it/eli/id/2020/09/14/20G00139/sg 

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54 

Ministry of Economy and Finance to define clear criteria for assigning resources. To what extent this will impact 
the operation of the fund is not yet known.  

The main scope of the fund is to pursue the digital and innovation growth of the country by leveraging emerging 
technologies and vertical applications in deep technology, such as Artificial Intelligence (AI), blockchain and the 
Internet of Things (IoT). The targeted beneficiaries are SMEs which could benefit from the integration of new 
solutions developed in these technology domains. 

The  establishment  of  the  fund  is  in  line  with  an  overall  national  strategy  that  since  2018  has  pursued  the 
development of emerging technologies and the uptake of applications in vertical sectors. The first step taken by 
the Italian Parliament in 2019 was to create the legal definitions of ‘Distributed Ledger Technologies’ and ‘smart 
contracts’.  Subsequently,  two  groups  of  national  experts  were  established  to  support  the  Italian  Ministry  of 
Economic Development  in devising national strategies for the promotion  of AI and blockchain applications in 
Italy. One group of experts developed a national strategy proposal for AI. The other group developed a national 
strategy proposal for blockchain, which was put out to public consultation until July 2020. These proposals will 
act as guidelines for the development of a national roadmap in these two fields. 

3.2.2  Digital Transformation Support Services 

The National Fund for ArtificiaI Intelligence, Blockchain and IoT was launched with the purpose of promoting: 

a) 

research and innovation projects to be carried out in Italy by public and private entities, including 
foreign actors, in national strategic areas, for the development of AI, blockchain and IoT, to 
increase the country’s competitiveness in these fields; 

b)  competitive challenges and new opportunities for the achievement of specific technological 

objectives and applications; 
the transfer of the resulting digital transformations to Italian businesses and specifically to SMEs. 

c) 

3.2.3  Observed/Expected Impacts on SME Digital Transformation 

The National Fund for ArtificiaI Intelligence, Blockchain and IoT is not yet operational. Therefore, there are no 
data  available on its implementation and impact. However,  other  data, available at national level, show that 
investments in blockchain are growing in Italy, doubling between 2018 and 2019 to a total of EUR 30 million. The 
main applications of blockchain are in supply chain traceability, digitalisation of processes, authentication and 
certification, tokenisation, marketplaces, data sharing, data integrity and security, digital identity management 
and smart contracts. The aim of most of these examples is to improve customer experience, avoid counterfeiting 
and promote and protect ‘Made in Italy’ products.  

In 2019, the Ministry of Economic Development launched a pilot project in partnership with IBM and a number 
of Italian textile and high fashion companies. The goal was to support the development of a case study to exploit 
the blockchain potential for ‘Made in Italy’ product protection and, more specifically, to prevent counterfeiting 
by increasing traceability within the textile sector. In 2020, the pilot resulted in a feasibility plan for the scalability 
of the project.  

As a result of the COVID-19 pandemic, blockchain solutions were also developed in 2020 to monitor and trace 
the evolution of the epidemic and to authorise actions to reduce infection risks.  

Finally, in September  2020,109  a study  was published,  conducted by the OECD and funded by the  Ministry of 
Economic Development, which provides an overview of the development of the blockchain ecosystem in Italy. It 
reviews  ongoing  projects  and  applications  and  their  impact  on  Italian  businesses,  in  particular,  on  SMEs, 
highlighting that many of these ongoing projects and applications are specifically aimed at SMEs. Most of the 
blockchain applications are focused on ensuring traceability, durability and transparency. The range of possible 
target sectors is broad, including agri-food, manufacturing and financial services. 

109 https://www.oecd-ilibrary.org/economics/blockchain-per-le-pmi-e-gli-imprenditori-in-italia_bdbbb4ea-it  

Page | 54 

 
 
 
 
3.2.4  Key Success Factors 

Even  though  it  is  not  yet  operational,  one  of  the  key  success  factors  behind  the  National  Fund  for  ArtificiaI 
Intelligence, Blockchain and IoT is the Italian innovation ecosystem, made up of skilled research teams and young 
startups  offering  solutions  based  on  emerging  technologies.  Another  key  success  factor  is  the  rise  in  Italian 
Venture Capital funds110 dedicated to companies offering solutions in these technology domains. The Iconium 
VC fund, established in 2018, is one such example, investing specifically in blockchain startups. Additionally, in 
recent  years,  various  incentives  and  support  measures  have  boosted  digitalisation  and  backed  startups  and 
innovative SMEs which offer advanced solutions in deep tech fields. One such example is the Italian government’s 
‘Smart&Start’ incentive scheme,111 which targets startups active in the blockchain field. A final success factor 
relates to the entry of Italy into the European Blockchain Partnership in 2019. This has led to Italy’s participation 
at an international level in the blockchain domain and has resulted in the establishment of strategic partnerships 
to enable further dissemination of digital technologies to citizens and businesses. 

3.2.5  Key Challenges and Issues 

The main challenges that may hinder the uptake of deep tech by SMEs include lack of a digital culture and lack 
of  internal  skills  and  competencies.  However,  there  are  other  issues  that  could  also  inhibit  the  adoption  of 
emerging  technologies,  such  as  difficulties  in  identifying  tangible  benefits,  allocating  adequate  financial 
resources and accessing available funds or support initiatives.  

In the blockchain field, for example, according to a survey conducted in 2019 by the ‘Blockchain and Distributed 
Ledger Observatory’ of the School of Management, Polytechnic University of Milan,112 only 20 % of SMEs had 
sufficient knowledge of technology applications and only 3 % considered that these technologies would have an 
impact on their business in the next five years. In addition, only 1 % of SMEs had carried out projects focused on 
the development of technology applications.  

3.2.6  Recommendations for roll-out as a best practice 

Similar policy initiatives, starting from a strategic approach to operational implementation, have already taken 
place  in  other  EU  countries,  so  this  type  of  policy  approach  has  already  proved  successful  (for  example,  in 
Germany, with its national blockchain strategy). In the case of Italy, the broad consultation process undertaken 
by  the  Italian  government,  involving  a  group  of  30  experts  selected  from  different  public  and  private 
organisations,  has  helped  to identify  the  challenges,  needs  and  requirements  in  the key  technology  domains 
which are specific to Italy. These identified factors will guide the development of a national strategy that will be 
turned  into  specific  action  lines  which  the  Fund  for  AI,  Blockchain  and  IoT  will  target.  In  this  way,  financial 
resources addressing the real needs of the beneficiaries can be most effectively channeled to projects and make 
a tangible impact on the target beneficiaries. In addition, establishing a legal definition of some of the major 
aspects of these technologies, such as Distributed Ledger Technology (DLT) and smart contracts, will contribute 
to the consolidation of knowledge and set up a framework for additional policy interventions and instruments in 
the field. 

3.3  Fit 4 Digital Packages - Luxembourg 

3.3.1 

Introduction 

‘Fit 4 Digital Packages’ is a national policy initiative developed and offered jointly by the General Directorate for 
Small and Medium-Size Enterprises of the Ministry of Economy, the House of Entrepreneurship of the Chamber 
of  Commerce,  and  Luxinnovation,  the  national  agency  responsible  for  the  promotion  of  innovation  in 
Luxembourg.113 It was first launched in 2019 for retail and services and  in 2020 was extended to the HoReCa 
(hotel/restaurant/café) industry, the skilled crafts industry, architects, and consulting engineers, all of which play 

110 https://www.iconium.it/en/home-en/   
111 https://ec.europa.eu/growth/tools-databases/regional-innovation-monitor/support-measure/centro-i/smartstart-italy  
112 https://www.osservatori.net/it/ricerche/osservatori-attivi/blockchain-distributed-ledger 
113 https://www.houseofentrepreneurship.lu/actualites/detail/le-programme-fit-4-digital-packages-elargit-son-perimetre/ 

Page | 55 

 
 
 
56 

an important role in Luxembourg with regard to employment and SMEs.114 The initiative will continue supporting 
SMEs throughout 2021. 

Fit 4 Digital Packages is a digitalisation aid programme which has been designed specifically to support Very Small 
Enterprises (VSEs) with fewer than 50 employees in their digital transformation efforts. The economic fabric of 
Luxembourg is made up mainly of VSEs and  therefore their impact on the local economy is significant. While 
bigger companies have already largely adopted digital solutions, VSEs are still struggling, particularly in terms of 
internal competencies and finding expert digital know-how and financial resources. 

The  programme  provides  VSEs  with  a  preliminary  needs  assessment,  help  with  implementation  of  digital 
marketing tools, and financial support.  It is aimed at VSEs which are hesitant about deploying a digitalisation 
strategy, and which lack knowledge about how to evaluate the work and costs involved. VSEs which implement 
one of the recommended packages at an investment level of EUR 6,650 - EUR 10,000, excluding value added tax 
(VAT), receive financial support of EUR 5,000 from the Ministry of the Economy’s General Directorate for Small 
and Medium-Size Enterprises.115,116  

A parallel initiative, Fit 4 Digital Transition,117 aims to provide similar support services to all size classes of SMEs. 
SMEs  can  either  apply  for  financial  support  of  EUR  5,000,  as  with  VSEs,  or,  alternatively,  after  digital 
implementation they can apply for a subsidy of up to 50% of the costs incurred. 

3.3.2  Digital Transformation Support Services 

Several types of support services are provided to VSEs during their digital transformation journey. The Fit 4 Digital 
Packages offer is built around the results of the preliminary analysis carried out by the House of Entrepreneurship 
of the Chamber of Commerce, which identifies the specific needs of each VSE and the corresponding solutions, 
such  as  digital  marketing  (development  of  website/social  media),  customer  relationship  management  (CRM 
software) or organisational management (i.e. specialised business software appropriate to the specific industry 
needs  of  the  VSE).  Once  agreed,  the  implementation  of  the  digital  tool  is  carried  out  with  the support  of  an 
approved service provider.118 The service providers are chosen and accredited by Luxinnovation,  and selected 
from providers who demonstrate specific digital competencies.119 

Since  the  Covid-19  pandemic,  with  its  impact  on  many  business  activities,  there  has  been  an  increase  in  the 
number of VSEs enquiring about digitalisation, due to the necessity of finding new ways of doing business (for 
example, in the HoReCa industry, by switching from restaurant catering to take-away services).  

3.3.3  Observed/Expected Impacts on SME Digital Transformation 

VSEs seem, in general, to be highly satisfied with the initiative based on the testimonials received.120 Receiving 
customised  support  through  an  independent  scheme,  rather  than  one  affiliated  to  proprietary  software 
companies,  has  had  a  positive  impact  on  VSE  satisfaction  levels,  because  it  has  allowed  VSEs  to  define  their 
specific  needs  and  evaluate  different  solutions  that  make  sense  for  their  business.  As  a  result,  many 
entrepreneurs and VSE employees have learned about the different types of digital technologies and have also 
increased their digital skills. The greatest impact of the Fit 4 Digital Packages has been in the sectors of commerce, 
crafts, HoReCa and architecture. In the 3 months between the launch of the initiative in October 2019 and the 
end of the same year, a total of 30 companies participated in the initiative, while more than 180 companies took 
part in 2020.121 

Although VSEs with fewer than 50 employees and a turnover of less than EUR 10,000,000122 are eligible for the 
programme, in practice, VSEs with 1-10 employees (i.e. micro SMEs) comprise the majority of applicants. Specific 

114 https://www.cc.lu/actualites/detail/le-programme-fit-4-digital-packages-elargit-son-perimetre/ 
115 https://paperjam.lu/article/fit4-digital-packages-elargit- 
116 https://www.luxinnovation.lu/fit-4-digital-packages/ 
117 https://www.luxinnovation.lu/fit-4-digital-transition/ 
118 https://www.cc.lu/actualites/detail/le-programme-fit-4-digital-packages-elargit-son-perimetre/ 
119 https://www.luxinnovation.lu/fr/fit-4-digital-packages-prestataires/#prestataires 
120 https://www.luxinnovation.lu/fit-4-digital-packages/ 
121 Information provided by the House of Entrepreneurship, 15/12/20 
122 Interview with Luxinnovation 28/11/2020 

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operational changes within these companies have occurred as a direct result of the initiative, and, following the 
initial support, some VSEs have also proceeded further with their digitalisation process. By targeting additional 
sectors,  key  to  the  national  economy,  the  objective  of  this  policy  initiative  is  to  enable  the  largest  possible 
number of VSEs to benefit from digitalisation. Thanks to the financial support on offer, VSEs have been able to 
consider undertaking digital transformations that would not have been possible without it. 

3.3.4  Key Success Factors 

The  Covid-19  crisis  has  reinforced  the  need  for  many  VSEs  to  redefine  both  their  way  of  working  and  their 
business  models.  Indeed,  businesses  which  were  inititally  reluctant  to  digitalise,  have  been  encouraged  to 
digitalise their activities and processes to ensure the continuity of their businesses, thanks to the awareness and 
skills development programme for entrepreneurs set up by the House of Entrepreneurship.  

Another key success factor has been simplifying the digitalisation steps required. On average, the duration of 
each  project  is  around  three  months.  The  process  of  software  installation  seems  to  be  efficient,  with  skilled 
technical support provided by recognised partners, and some VSEs expressing their desire to digitalise further 
after the programme has ended. 

A  further  success  factor  has  been  the  establishment  of  regular  and  effective  communication  between 
beneficiaries, external service providers and the three institutions behind the Fit 4 Digital Packages initiative.  

3.3.5  Key Challenges and Issues 

During the digitalisation process, one particular challenge regularly mentioned by SMEs is the difficulty of finding 
employees  with  the  requisite  digital  qualifications.  Consequently,  digital  training  needs  are  high.  VSEs  also 
experience difficulty in finding the most appropriate digital solutions by themselves, as well as in accessing the 
necessary funding. The assistance provided by Fit 4 Digital Packages takes care of these three needs, by providing 
customised support, a network of affiliated experts, and financial aid supplied by the Ministry of the Economy in 
the form of a digital voucher.123 

One  of  the  lessons  learned  from  the  Fit  4  Digital  Packages  initiative  is  the  importance  of  supporting  VSEs 
technically as well as financially. One of the challenges faced by the initiative itself is the high number of requests 
received from VSEs due to the economic crisis caused by the pandemic, which has made it challenging to process 
all the applications and support them in a timely manner.124  

3.3.6  Recommendations for roll-out as a best practice 

As VSEs represent a large proportion of businesses in the EU-27 Member States, it is essential to continue to 
promote awareness of the benefits of digitalisation by guiding and advising VSEs on the steps to be taken and, 
above  all,  by  reassuring  them  about  best  practices.125  Many  CEOs  of  smaller  companies  do  not  always  know 
where to start their digitalisation process. For this reason, the introduction of simplified procedures enables VSEs 
to quickly implement new technologies. Furthermore, a fast procedure allows quick payment of the grant, thus 
facilitating the cash flow management of VSEs. Allowing VSEs to test their digital maturity is also key to ensuring 
that the advice they receive is as appropriate for their needs as possible.  

In  order  to  ensure  the  effectiveness  of  schemes  such  as  Fit  4  Digital  Packages,  another  key  essential  is  that 
government  agencies  and  any  external  partners  involved  maintain  high  standards  of  communication  and 
collaboration with each other. Furthermore, well-chosen trusted external service providers can play an important 
role in improving the visibility of this type of initiative and enhancing its effectiveness.  

A further recommendation is that the administrative roles in the initiative should be clearly defined. In the case 
of  Fit  4  Digital  Packages,  for  example,  the  initial  needs  assessments  are  carried  out  by  the  House  of 
Entrepreneurship  of  the  Chamber  of  Commerce,  service  provision  is  handled  by  Luxinnovation  via  external 

123 https://meetanentrepreneur.lu/en/video/fit-4-digital-packages/ 
124 Interview with Luxinnovation 28/11/2020 
125 https://www.cc.lu/actualites/detail/lancement-du-programme-de-digitalisation-des-pme-porte-par-le-ministere-de-leconomie-la-
chambre-de/ 

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58 

service  providers,  and  financial  support  is  the  remit  of  the  General  Directorate  for  Small  and  Medium-Size 
Enterprises of the Ministry of Economy.  

3.4 

Iniciativa Nacional Competências Digitais e.2030 (Portugal INCoDe.2030) - Portugal 

3.4.1 

Introduction 

One of the main factors hindering the digital transformation of SMEs is the shortage of digital skills. Indeed, the 
New Industrial Strategy for Europe126 points out that 7 out of 10 companies cannot  find  employees with the 
relevant skills. To tackle this issue, 120 million Europeans will have to upskill or reskill in the next five years alone. 
In Portugal, several studies have highlighted the scarcity of IT expertise,127 with 8 out of 10 companies facing 
difficulties  in  finding  specialised  IT  talent.  According  to  IT  industry  stakeholders,  universities  do  not  produce 
enough technicians and engineers to meet the existing needs of Portuguese SMEs.128  

In  this  context,  the  Portuguese  National  Government  launched  the  National  Digital  Competences  Initiative, 
‘Portugal INCoDe.2030’, in 2017. It commenced formally in 2018 with the publication of the Resolution of the 
Council  of  Ministers  26/2018  of  8  March  2018.129  The  ultimate  goal  of  the  initiative  is  to  improve  Portugal’s 
position and competitiveness worldwide in terms of digital proficiency. More specifically, Portugal INCoDe.2030 
130aims to:  

• 
• 

• 

Ensure digital literacy and inclusion for the whole population 
Stimulate employability, professional training and specialisation in digital technologies and 
applications 
Ensure strong participation in international R&D and digital knowledge networks. 

Both  public  and  private  stakeholders  are  participating  in  the  initiative:  from  national  and  regional  public 
administrations  and  municipalities  to  trade  associations,  SMEs,  educational  and  vocational  institutions, 
foundations, etc. 

The promotion and coordination of the initiative involves three permanent bodies: 

1.  The National Forum for Digital Competences, responsible for gathering and coordinating a broad 
range of private and public stakeholders to ensure widespread mobilisation for the initiative. 
2.  The INCoDe.2030 Coordination Structure, which oversees the initiative as a whole and promotes 

and coordinates the activities in each action line. 

3.  The INCoDe.2030 Technical Secretariat, which monitors, records and reports on the 

implementation of all the planned activities. 

3.4.2  Digital Transformation Support Services 

The implementation of Portugal INCoDe.2030 is structured in five axes / action lines:  

Inclusion: measures to ensure that the whole population has equal access to digital technologies. 

1. 
2.  Education: measures to ensure the education of the younger population by stimulating and 

reinforcing digital literacy and digital competences at all levels of schooling and as part of lifelong 
learning. 

3.  Professional qualifications: measures to upskill the working population by providing the knowledge 

required to become part of a labour market that relies heavily on digital competences. 

126 https://ec.europa.eu/info/sites/info/files/communication-eu-industrial-strategy-march-2020_en.pdf 
127 https://www.robertwalters.pt/hiring/hiring-advice/81-por-cento-empresas-com-dificuldades-para-encontrar-talento-tecnologico.html 
128 https://www.jornaldenegocios.pt/negocios-iniciativas/detalhe/o-drama-dos-recursos-nas-tecnologias-escassez-e-falta-de-competencias 
129 https://dre.pt/web/guest/pesquisa/-/search/114832288/details/maximized  
130 https://www.incode2030.gov.pt/sites/default/files/incode2030_en.pdf 

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4.  Specialisation: measures to promote specialisation in digital technologies and applications to 

improve employability and create higher added value in the economy. 

5.  Scientific research: measures to ensure conditions are in place for the production of new 
knowledge and active participation in international R&D networks and programmes. 

3.4.3  Observed/Expected Impacts on SME Digital Transformation 

Even though it is currently too early to assess the impact of the initiative on SMEs, some outcomes/results can 
already  be  highlighted,  based  on  the  Progress  Report  presented  at  the  2nd  Permanent  Forum  for  Digital 
Competences131: 

Table 3 Examples of outputs/results 

Axes / Action lines 

Inclusion 

Education 

Professional 
qualification 

Specialisation 

Scientific research 

Outcomes / Results 
Creation  of  10  Creative  Communities  for  Digital  Inclusion  with  750  direct 
beneficiaries, including 22 certified female mentors  
Over 200 primary schools integrated ICT in their curricula 
The ‘Programming and Robotics in Basic Education’ project was implemented in 
nearly 200 schools, reaching over 10,000 students 
Over 12,500 participants in digital skills training courses 
649  participants  have  benefited  from  40  courses  focusing  on  programming, 
information security, data science, digital marketing and similar topics 
Creation  of  several  Higher  Professional  Technical  Courses  in  direct  cooperation 
with polytechnic schools and private companies 
Launch of the Portuguese strategy on Artificial Intelligence 

The following table includes the SME-related indicators selected to measure the impact of the initiative: 

Table 4 Current situation vs Expected results 

SME-related Indicator 

Current (2015) 

% of ICT specialists in employment 
% of SMEs with a high level of digital intensity 
Business enterprise R&D (BERD) expenditure as 
percentage of GDP 

2.3 
17.7 

0.6 

Expected/targeted 
2025 
5 
30 

2030 
8 
40 

2020 
3 
20 

1 

1.5 

2 

3.4.4  Key Success Factors 

Portuguese  industry  considers  the  INCoDe.2030  initiative  to  be  an  important  tool  for  promoting  the  digital 
transformation  of  the  private  sector.132  However,  the  involvement  of  all  levels  of  government  and  society  is 
necessary, particularly in connection with digital literacy, for the initiative to be successful.  

3.4.5  Key Challenges and Issues 

Improving the digital skills of the Portuguese population is an enormous challenge in all dimensions: political, 
economic, cultural and social. For this reason, the Portuguese government has established a set of goals covering 
different  factors, including social inclusion and digital literacy, the physical and cognitive access of the entire 
population to digital services, and the intensive use of ICT in lifelong education. Another challenge facing the 
initiative is  related to  the various stakeholders involved. Portugal INCoDe.2030 is structured as an integrated 

131https://www.incode2030.gov.pt/sites/default/files/incode.2030_relatorio_de_progresso_12122018.pdf  
132 https://ec.europa.eu/information_society/newsroom/image/document/2019-32/country_report_-_portugal_-_final_2019_0D313AC4-
9C8E-F45B-AA1BFF19CC713C17_61218.pdf 

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60 

programme  for  Portugal,  to  be  promoted  through  the  collaboration  of  all  stakeholders,  with  their  different 
experiences  and  knowledge,  from  a  variety  of  public  and  private  organisations.  Consequently,  the  different 
priorities of the various stakeholders could affect the implementation of the initiative. Therefore, the continuous 
and active support of all stakeholders is needed in order to achieve the 2030 targets, for which there is a strong 
commitment.133 

3.4.6  Recommendations for roll-out as a best practice 

The INCoDe.2030 initiative was developed to adapt to the specificities of the Portuguese context and relies on 
stakeholders from both the private and public sectors. This specificity, combined with existing differences within 
the  EU-27  in  terms  of  digital  skills,  regulations,  culture  and  economic  structures,  could  therefore  prohibit 
replication of the initiative in other EU Member States.  

133 https://www.incode2030.gov.pt/sites/default/files/2019-incode2030-hlrc-finalreport_6.pdf  

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4  The drivers of the performance of EU-27 SMEs from 2008 to 2016 

4.1 

Introduction 

Cumulative growth from 2009 to 2016134 in value added generated by EU SMEs in the NFBS varies greatly across 
Member  States  (Figure  5).  The  present  chapter  assesses  empirically  the  factors  which  could  explain  these 
differences across Member States of the growth of SME value added from 2010 (i.e. from the post-great financial 
crisis year) to 2016 (i.e. the last year before major structural breaks occur in the SBS database135). 

Figure 5 Cumulative growth from in SME value from 2009 to 2016 

Source: Eurostat, National Statistical Offices, DIW Econ 

4.2  Empirical approach 

The variable of interest is the annual growth rate of value added by SMEs in the NFBS reported in Eurostat’s 
Structural Business Statistics database136. The explanatory variables include potential determinants of growth in 
value added by SMEs. These can be classified as structural or cyclical factors. Structural factors include:  

•  A country’s competitiveness, defined by the World Economic Forum (WEF) as the “set of institutions, 
policies, and factors that determine the level of productivity of a country” (Schwab and World Economic 

134 Because of structural breaks in the SME data, the end year of the analysis is 2016. According to Council Regulation (EEC) No 696/93 
of 15 March 1993 an 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. However, in past years, NSOs could 
not implement this definition of an enterprise due to a lack of data. Each legal unit used to be recorded as a separate enterprise in the 
SBS database. However, over the past few years, NSOs of a number Member States have started to report to Eurostat enterprise data 
reflecting the 1993 enterprise definition. Legal units (which are part one organisational unit, according to the definition above) are now 
recorded as a single enterprise in the SBS database instead if several SMEs. As a result, in the year in which the data are reported for the 
first time according to the correct enterprise defintion, the total number of SMEs decreases and the total number of large enterprises 
increases in the SBS database, which also implies a decrease in SME value added and employment (in contrast to an increase for large 
companies). Such a structural break is observed in 2017 in the case of FR and IT, and in 2018 for AT, BE, DE, ES, LV, PL and SE. 
135 According to Council Regulation (EEC) No 696/93 of 15 March 1993 an 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. However, in past years, NSOs could not implement this definition of an enterprise due to a lack of data. Each legal unit used 
to be recorded as a separate enterprise in the SBS database. However, over the past few years, NSOs of a number Member States have 
started to report to Eurostat enterprise data reflecting the 1993 enterprise definition. Legal units (which are part one organisational unit, 
according to the definition above) are now recorded as a single enterprise in the SBS database instead of several SMEs. As a result, in 
the  year  in  which  the  data  are  reported  for  the  first  time  according  to  the  correct  enterprise  defintion,  the  total  number  of  SMEs 
decreases and the total number of large enterprises increases in the SBS database, which also implies a decrease in SME value added 
and employment (in contrast to an increase for large companies). Such a structural break is observed in 2017 in the case of FR and IT, 
and in 2018 for AT, BE, DE, ES, LV, PL and SE. 
136 Eurostat (n.d.).  

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127%107%80%72%55%54%53%46%45%38%34%33%30%28%27%27%27%22%20%19%19%17%9%4%-2%-10%-12%-51% 
 
 
 
 
62 

Forum, 2016), and measured by the WEF’s Global Competitiveness Index (GCI)137. The GCI ranges from 
1 to 7 and has a broad scope: it is based on over 100 indicators138 which fall under 12 categories (or 
“pillars”), ranging from institutions to innovation. It is expected that growth in value added by SMEs is 
higher in more competitive economies.139 

•  A country’s score for starting a business140 – one of the 11 topics that are the focus of the World Bank’s 
(WB) Doing business project. The score accounts for the number of procedures typically carried out to 
start and run a limited liability company, the time and cost associated with these procedures, as well as 
the paid-in minimum capital requirement (Doing business, n.d.b). It is expected that higher scores are 
associated with higher business birth rates, which in turn are likely to increase overall value added by 
SMEs. 

•  A country’s score for resolving insolvency141 – also from the WB’s Doing business project. This indicator 
captures  the  “recovery  rate”  or  the  proportion  of  a  secured  creditor’s  unpaid  claim  that  can  be 
recovered through insolvency proceedings (taking into account  the time, cost  and outcome of these 
proceedings),  as  well  as  the  strength  of  an  economy’s  legal  framework  for  judicial  liquidation  and 
reorganization proceedings (Doing business, n.d.c).142 

• 

• 

The  share  of  SMEs  in  high  and  very  high  R&D  intensity  industries,143  based  on  Eurostat’s  Structural 
Business Statistics database144. It is expected that SMEs in economies with relatively important R&D-
intensive industries – as measured by the share of SMEs in these industries – will benefit from greater 
knowledge spillovers and therefore grow faster. 

The annual change in the structural balance of general government excluding interest, as a percentage 
of potential GDP at current prices, sourced from the AMECO database145. The change in the structural 
balance of general government is a measure of fiscal policy. A negative value indicates fiscal expansion, 
which is expected to stimulate the economy and promote growth in SME value added.146  

In addition, a number of cyclical factors were also included in the model specification:147  

• 

The output gap sourced from the AMECO database and lagged by one year148. It is measured as the gap 
between actual GDP and estimated trend GDP, and expressed as a percentage of trend GDP at constant 
prices. The output gap quantifies the difference between aggregate supply and demand. For example, 
a negative output gap indicates that aggregate demand is lower than what the economy is capable of 
producing when resources are used optimally (the latter is often referred to as “potential” output and, 
in this instance, measured through trend GDP) (see for instance Ladiray et al., 2003). It is expected that 

137 World Economic Forum (2018).  
138 These indicators are based on existing datasets (e.g. statistical data from the IMF, World Bank etc.) as well as a primary data collection 
exercise: the WEF’s Executive Opinion Survey (Schwab and World Economic Forum, 2016).  
139 The 12 pillars are: institutions, infrastructure, macroeconomic environment, health and primary education, higher education and 
training, goods market efficiency, labour market efficiency, financial market development, technological readiness, market size, business 
sophistication, and innovation (Schwab and World Economic Forum, 2016). For a detailed overview of the methodology for constructing 
the GCI, see for instance Schwab and World Economic Forum (2016).  
140 Doing business (n.d.a). 
141 Doing business (n.d.a). 
142 In order to utilize a broader measure of regulations and their enforcement, the use of the overall ease of doing business score was 
considered. However, because the computation of indicators within several topics was subject to methodological changes, this analysis 
focuses on the two topics which did not undergo substantial methodological revisions.  
143 As classified in Muller et al. (2019).  
144 Eurostat (n.d.).  
145 AMECO (2020a).  
146 Additional variables were also considered. For instance, in order to account for the possible impact of specific problems faced by SMEs, 
the inclusion of variables denoting the incidence of various problems (e.g. access to finance, availablity of skilled staff or experienced 
managers etc.) faced by enterprises (based on the survey on the access to finance of enterprises) was also considered. However, the causal 
relationship between these variables and the dependent variable would be unclear. Indeed, certain problems may hinder growth, but they 
may also be a result of high growth (e.g. a lack of skilled staff may prevent firms from scaling up their activities, but may also be a result of 
increased workload from rapid expansion). Therefore, these variables were not included in the analysis.  
147 These also include a variable capturing monetary policy, which is a key tool for managing cyclical changes in output. It should also be 
noted that some of these variables – growth in exports and inflation – include both a structural and cyclical component, so these variables 
are not purely ‘cyclical’. 
148 AMECO (2020b).  

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• 

• 

• 

the output gap in the previous period is negatively correlated with contemporaneous growth in value 
added, as a result of an economy’s adjustment towards its long-run equilibrium.  

The annual growth in exports of goods and services (nominal), sourced from Eurostat149. This proxy for 
foreign demand growth is expected to be positively associated with growth in value added by SMEs.  

The annual change in the short-term interest rate (nominal), taken from the AMECO database150. This 
variable  is  a  proxy  for  monetary  policy:  a  fall  in  the  short-term  interest  rate  indicates  a  monetary 
expansion, and is likely to ease SMEs’ access to financing, thereby allowing them to grow their activities. 
Therefore,  the  change  in  the  short-term  interest  rate  is  expected  to  be  negatively  correlated  with 
growth in SME value added. 

Inflation, as measured by growth in the average annual Harmonised Index of Consumer Prices (HICP), 
taken  from  Eurostat151.  Inflation  can  be  distortionary  when  it  is  unexpected,  thereby  hindering 
economic  activity  and  therefore  value  added  growth.  However,  growth  in  value  added  incorporates 
changes  in  prices,  meaning  that  higher  growth  in  value  added  is  likely  to  be  associated  with  higher 
inflation.152  

The analysis dataset is an almost perfectly balanced panel of 26 EU Member States and the estimation period 
ranges from 2011 to 2016.153 Table 5 presents summary statistics of the variables described above. 

Table 5 Summary statistics – analysis variables 
Count 
154 
154 

Growth in SME value added (%) 
Share of SMEs in high- and very high-
R&D intensity industries (% of NFBS 
population) 
Starting a business score (WB Doing 
business) 
Resolving insolvency score (WB 
Doing business) 
Global Competitiveness Index 
∆ Structural balance of general 
government excluding interest (% of 
potential GDP at current prices) 
∆ Nominal short-term interest rate 
Lagged gap between actual and 
trend GDP (% of trend GDP) 
Growth in the annual average HICP 
index (%) 
Growth in exports of goods and 
services (%) 

154 

154 

154 
154 

154 
154 

154 

154 

Mean 
3.4 
25.8 

87.0 

67.4 

4.7 
0.5 

-0.3 
-2.0 

1.3 

5.4 

Standard deviation 
6.4 
5.7 

5.7 

14.9 

0.5 
1.3 

0.6 
2.5 

1.6 

5.7 

Note: The above statistics are based on the estimation sample. ∆ denotes the first-difference operator. 
Source: Eurostat, National Statistical Offices, DIW Econ, AMECO, WEF, World Bank 

The model is fit using the Ordinary Least Squares (OLS) estimator. In order to check the robustness of results, 
variants  of  the  model  include  country  and/or  year  fixed  effects.  The  former  are  intended  to  account  for 
unobservable time-invariant country-specific factors, while the latter are included to control for EU-wide shocks 

149 Eurostat (2020a).  
150 AMECO (2020b). 
151 Eurostat (2020b).  
152 Inflation is also a key control variable, accounting for the potentially confounding effect of price-level variation on the relationship 
between the dependent variable and certain explanatory variables such as nominal growth in exports. 
153 Malta and Croatia enter the estimation sample in 2012. In recent years, Ireland has seen an increase in “globalisation activities” of 
multinational corporations (e.g. transfers of intangible assets for tax purposes) (see for instance Central Statistics Office, n.d.; European 
Commission, 2016; OECD, 2016; Regan, 2016; Beesley, 2017). This has impacted GDP – most notably in 2015, when it rose by over 25%. 
Although these sometimes-large changes in GDP resulting from “globalisation activities” of multinational corporations do not reflect 
developments in actual economic activity, they create structural breaks in several variables used in the analysis. For this reason, data on 
Ireland is not included in the analysis.  

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64 

to growth in SME value added. As unobservable factors may be correlated  within countries and across time, 
standard errors are clustered at the country level.  

4.3  Empirical Results 

The regression results in Table 6 provide some evidence that structural factors are associated with SME value 
added growth. The coefficient on the GCI achieves statistical significance at the 5% level and displays a positive 
sign in two of the specifications. Furthermore, the coefficient on the score for resolving insolvency is positive and 
statistically  significant  at  the  10%  level  in  one  of  the  models.  These  results,  however,  are  not  robust  to  the 
addition of country fixed effects (or time fixed effects, in the case of the score for resolving insolvency), and 
should therefore be interpreted with caution. The relationship between fiscal policy and growth in SME value 
added  is  more  robust  to  the  inclusion  of  fixed  effects.  Indeed,  the  differenced  structural  balance  of  general 
government  displays  a  negative  and  statistically  significant154  relationship  with  the  dependent  variable  in  all 
specifications, suggesting that expansionary fiscal policies are associated with higher growth of SMEs.  

Among cyclical factors, growth in exports displays the most robust relationship with the dependent variable as 
its coefficient estimate is positive and statistically significant in all specifications.155 The results also provide some 
evidence that SME economic activity has a tendency to adjust and revert back to its long-run equilibrium after 
actual GDP has deviated from potential GDP.156 Indeed, the coefficient on the lagged output gap is negative and 
statistically significant in two of the specifications.157  
Table 6 Regression results 

Share of SMEs in high- and very high-R&D intensity 
industries (% of NFBS population) 

Starting a business score (WB Doing business) 

Resolving insolvency score (WB Doing business) 

Global Competitiveness Index 

∆ Structural balance of general government excluding 
interest (% of potential GDP at current prices) 

∆ Nominal short-term interest rate (%) 

Lagged gap between actual and trend GDP (% of trend GDP) 

Growth in the annual average HICP index (%) 

Growth in exports of goods and services (%) 

Constant 

Country fixed effects 
Year fixed effects 
Observations 

(1) 
-0.0728 

(2) 
-0.153 

(3) 
-0.102 

(0.563) 
0.00433 
(0.969) 
-0.0985 
(0.173) 
4.054** 
(0.0379) 
-1.584*** 

(0.000443) 
0.0640 
(0.920) 
-0.282 
(0.371) 
-0.355 
(0.341) 
0.432*** 
(5.06e-06) 
-9.111 
(0.344) 

NO 
NO 
154 

(0.680) 
0.146 
(0.409) 
0.211* 
(0.0515) 
10.51 
(0.148) 
-0.537* 

(0.0597) 
0.881 
(0.123) 
-0.489* 
(0.0715) 
-0.565 
(0.144) 
0.409*** 
(0.000663) 
-70.80** 
(0.0456) 

YES 
NO 
154 

(0.420) 
-0.0286 
(0.789) 
-0.0984 
(0.141) 
4.391** 
(0.0241) 
-1.381*** 

(0.000356) 
0.0631 
(0.951) 
-0.407 
(0.188) 
0.781 
(0.326) 
0.442*** 
(0.00170) 
-11.95 
(0.217) 

NO 
YES 
154 

(4) 
-0.448 

(0.341) 
0.0846 
(0.641) 
0.188 
(0.165) 
8.065 
(0.234) 
-0.439* 

(0.0873) 
0.278 
(0.740) 
-0.691** 
(0.0103) 
-0.453 
(0.439) 
0.282* 
(0.0886) 
-43.56 
(0.262) 

YES 
YES 
154 

Note: ∆ denotes the first-difference operator. P-values in parentheses *** p<0.01, ** p<0.05, * p<0.1 
Source: Eurostat, National Statistical Offices, DIW Econ, AMECO, WEF, World Bank 

154 At the 10% level, and 1% level when country fixed effects are not included. 
155 At the 1% level in most models, and 10% level when both country and year fixed effects are included. 
156 It should be noted, however, that the output gap is computed at the level of the economy as a whole, whereas the dependent variable 
is restricted to the population of SMEs in the NFBS. Therefore, the estimated relationship between both variables cannot perfectly capture 
the equilibrium adjustment process.  
157 At the 5% and 10% level.  

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Coefficients from model (2) are presented graphically in Figure 6. For greater comparability, these coefficients 
are also presented in standardized form in Figure 7, where each bar represents the change in SME value added 
growth associated with a one standard deviation increase in a given explanatory variable (expressed as standard 
deviations of SME value added growth).  

Figure 7 suggests that a one standard deviation increase in the score for resolving insolvency is associated with 
half  of  a  standard  deviation  increase  in  the  dependent  variable.  The  standardized  coefficient  on  the  annual 
growth in exports of goods and services is roughly similar in magnitude albeit lower. A one standard deviation 
increase in the output gap in the previous period is associated with 0.2 of a standard deviation decline in the 
dependent variable. Finally, a one standard deviation increase in the differenced structural balance of general 
government is associated with a 0.1 standard deviation decrease in SME value added growth.  

Figure 6 Regression results (based on fixed effects model) 

Note: ∆ denotes the first-difference operator. 
Source: Eurostat, National Statistical Offices, DIW Econ, AMECO, WEF, World Bank 

Page | 65 

 
 
66 

Figure 7 Regression results – standardized (based on fixed effects model) 

Note: ∆ denotes the first-difference operator. 
Source: Eurostat, National Statistical Offices, DIW Econ, AMECO, WEF, World Bank 

4.4  Model predictions 

Coefficient estimates can be used to produce ‘fitted’ or ‘predicted’ values of the dependent variable – the annual 
growth rate of SME value added. The accuracy of the models’ predictions can be assessed visually in Figure 8 and 
Figure 9, which plot actual growth in SME value added across time for each Member State, along with fitted 
values based on models (1) and (2). Both models’ predictions appear to follow actual growth rates reasonably 
well.  

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Figure 8 Actual and predicted annual growth rates in SME value added in % between 2011 and 
2016 (based on pooled OLS model) 

Note: predicted growth estimated through an OLS model.  
Source: Eurostat, National Statistical Offices, DIW Econ, AMECO, WEF, World Bank 

Page | 67 

 
 
 
68 

Figure 9 Actual and predicted annual growth rates in SME value added in % between 2011 and 
2016 (based on fixed effects model) 

Note: predicted growth estimated through a country fixed effects model (country-specific effect included in the fitted values) 
Source: Eurostat, National Statistical Offices, DIW Econ, AMECO, WEF, World Bank 

Figure 10 and Figure 11 focus on the percentage change in SME value added between 2010 and 2016.158 The 
accuracy of growth predictions based on the fitted values from the OLS model varies markedly across countries. 
In contrast, predicted growth based on the fitted values from the fixed effects model is fairly accurate for all 
countries.  This  indicates  that  the  introduction  of  country-specific  intercepts,  which  capture  time-invariant 
determinants of the dependent variable in each Member State, are important in predicting growth.159  

158 This is calculated using the following formula: [∏
in country 𝑖 and in year 𝑡.  
159 Results based on models (3) and (4) (not shown) are very similar to those based on models (1) and (2) respectively.  

] − 1, where 𝑔𝑡

2016
𝑡=2011

𝑖)
(1 + 𝑔𝑡

𝑖 is actual or predicted annual growth in SME value added 

Page | 68 

 
 
 
 
 
Figure 10 Actual and predicted growth between 2010 and 2016 (based on pooled OLS model) 

Note: predicted 2010-2016 growth estimated through an OLS model. Growth in Croatia and Malta relates to the period 2011-2016.  
Source: Eurostat, National Statistical Offices, DIW Econ, AMECO, WEF, World Bank 

Figure 11 Actual and predicted growth between 2010 and 2016 (based on fixed effects model) 

Note: predicted 2010-2016 growth estimated through a country fixed effects model (country-specific effect included in the fitted values). 
Growth in Croatia and Malta relates to the period 2011-2016.  
Source: Eurostat, National Statistical Offices, DIW Econ, AMECO, WEF, World Bank 

Page | 69 

 
 
 
 
 
 
70 

5  Analysis of Flash Eurobarometer 486 survey on ‘SMEs, start-ups, scale-ups 

and entrepreneurship’ 

5.1 

Introduction 

Digitalisation  is  one  of  the  main  challenges  that  European  small  and  medium-size  enterprises  (SMEs)  face 
today160. SMEs can benefit from digitalisation by entering the data economy which in turn can improve business 
intelligence  and  consumer  research,  engendering  further 
innovation,  more  effective  marketing  and 
improvements  in  customer  satisfaction161.  Data  generated  by  SMEs  also  provides  the  opportunity  for  stock 
optimisation based upon consumer behavioural models.  

To assess what support could be most effective for SMEs, this section will analyse a number of characteristics 
which may encourage or deter SMEs to adopt digital technology. The data used for this analysis is sourced from 
the  Flash  Eurobarometer  486  on  Entrepreneurship,  Start-ups  and  Scale-ups.  Overall,  the  Eurobarometer 
response sample comprised 16,365 responses. For the purpose of the analysis in this report, the following survey 
responses were excluded: a) 3,750 responses from survey participants located in countries outside of the EU-27; 
b) 633 responses from survey respondents locacted in the EU-27 with 250 or more employees; c) 116 responses 
from  survey  participants  located  in  the  EU-27  who  did  not  provide  information  on  the  number  of  their 
employees; d) 1,225 responses from survey respondents who indicated that they had closed their business; and, 
e) 239 responses from survey respondents who reported that the age of their business was ‘9999’. As result, the 
response sample used in the analysis of the digitalisation of SMEs comprised 10,402 responses.  

Section 5.2 sets out the research question and rationale for the selection of indicators and section 5.3 presents 
the results of the econometric analysis. 

5.2 

Indicator selection 

To accurately measure the drivers of technology adoption amongst SMEs we utilized responses from the Flash 
Eurobarometer on Entrepreneurship, Start-ups and Scale-ups survey. Participants of the survey were asked to 
indicate which of the following options best describes your enterprise's approach to digital technologies? The 
respondents could choose one of the following options: 

1)  Your enterprise has adopted or is planning to adopt basic digital technologies such as email or a website 

but not advanced digital technologies; 

2)  There  is  a  need  to  introduce  advanced  digital  technologies  but  your  enterprise  does  not  have  the 

knowledge or skills or financing to adopt them; 

3)  There is a need to introduce advanced digital technologies and your enterprise is currently considering 

which of them to adopt; 

4)  There is a need to introduce advanced digital technologies and your enterprise has already started to 

adopt them; 

5)  Your enterprise does not need to adopt any digital technologies; 
6)  Other; 
7)  None; 
8)  DK/NA. 

For the purposes of this study, those SMEs which choose answer 1 and answer 4 are of interest, this is because 
in both cases enterprises have either adopted digital technologies or are planning to adopt digital technologies 
rather than simply considering or recognising the need for adoption.  

The most common response for SMEs overall is that the enterprise  has adopted or is planning to adopt  basic 
digital  technologies  such  as  email  or  a  website  but  not  advanced  digital  technologies.  The  answer  to  this 

160 European Commission (2019). Cybersecurity, Internet of things and big data for small and medium-size enterprises [online]. Available at: 
https://op.europa.eu/en/publication-detail/-/publication/82aa7f66-67fd-11ea-b735-01aa75ed71a1/language-en (Accessed: 17th December 
2020) 
161 European Commission (2019). Supporting specialized skills development: Big Data, Internet of Things and Cybersecurity for SMEs 
[online]. Available at: https://op.europa.eu/en/publication-detail/-/publication/bb5c6c09-6285-11ea-b735-01aa75ed71a1/language-en 
(Accessed: 17th December 2020) 

Page | 70 

 
 
 
 
particular question varies upon the size of SMEs whereby the adoption of advanced digital technologies is most 
prevalent among medium-sized SMEs, small SMEs are split between basic and advanced digital technologies and 
micro SMEs are more focused on basic digital technologies. 

Table 7 Frequency distribution (in %) of Q 22: Please indicate which of the following options best 
describes your enterprise's approach to digital technologies? (Single answer) 

Answer 
no. 

Which of the following options best describes your 
enterprise 

All SMEs 

Micro 
SMEs 

Small 
SMEs 

Medium-
sized 
SMEs 

1 

2 

3 

4 

5 

6 
7 
8 

Your enterprise has adopted or is planning to adopt basic 
digital technologies such as email or a website but not 
advanced digital technologies 
There is a need to introduce advanced digital technologies 
but your enterprise does not have the knowledge or skills or 
financing to adopt them 
There is a need to introduce advanced digital technologies 
and your enterprise is currently considering which of them to 
adopt 
There is a need to introduce advanced digital technologies 
and your enterprise has already started to adopt them 
Your enterprise does not need to adopt any digital 
technologies 
Other  
None  
DK/NA 
Total 

33.13 

36.76 

29.35 

26.74 

7.89 

8.2 

8 

6.65 

10.40 

8.55 

12.5 

13.37 

25.23 

19.68 

29.35 

37.69 

16.92 

19.97 

14.78 

1.12 
4.16 
1.15 
100 

1.17 
4.49 
1.17 
100 

0.96 
4.11 
0.96 
100 

9.84 

1.18 
3.12 
1.41 
100 

Note: Overall, the Eurobarometer response sample comprised 16,365 responses. For the purpose of the analysis in this report, the 
following survey responses were excluded: a) 3,750 responses from survey participants located in countries outside of the EU-27; b) 633 
responses from survey respondents locacted in the EU-27 with 250 or more employees; c) 116 responses from survey participants located 
in the EU-27 who did not provide information on the number of their employees; d) 1,225 responses from survey respondents who 
indicated that they had closed their business; and, e) 239 responses from survey respondents who reported that the age of their business 
was ‘9999’. As result, the response sample used in the analysis of the digitalisation of SMEs comprised 10,402 responses 
Source: LE Europe analysis of Eurobarometer data 

To ensure the consistency of respondents, we noted which specific technologies were adopted by SMEs who 
answered 1 or 4 to the above question. As per the table below, a number of enterprises who said that they were 
planning  to  adopt  basic  digital  technologies  have  actually  already  adopted  advanced  digital  technologies. 
Similarly, some businesses who said they had adopted advanced digital technologies failed to identify any specific 
advanced technologies they have adopted.  

Table 8 Summary of technologies adopted by enterprises (Q 23, multiple answers allowed) 

Technology 

Artificial intelligence, e.g. machine learning or 
technologies identifying objects or persons, 
etc. 
Cloud computing, i.e. storing and processing 
files or data on remote servers hosted on the 
internet 
Robotics, i.e. robots used to automate 
processes for example in construction or 
design, etc. 
Smart devices, e.g. smart sensors, smart 
thermostats, etc. 
Big data analytics, e.g. data mining and 
predictive analysis 
High speed infrastructure 
Blockchain 

Total 
Frequency 

Percentage 

Answered 1 to 
Q 22 

Answered 4 to 
Q 22 

651 

6.26 

127 

332 

4,741 

45.58 

1,358 

1,750 

815 

7.84 

2,615 

1,285 

3,244 
299 

25.14 

12.35 

31.19 
2.87 

158 

688 

259 

909 
73 

375 

1,032 

610 

1,229 
141 

Page | 71 

 
 
72 

None of these 
DK 

3,513  
151  

33.77 
1.45 

1,361 
54 

329 
24 

Note: Overall, the Eurobarometer response sample comprised 16,365 responses. For the purpose of the analysis in this report, the 
following survey responses were excluded: a) 3,750 responses from survey participants located in countries outside of the EU-27; b) 633 
responses from survey respondents locacted in the EU-27 with 250 or more employees; c) 116 responses from survey participants located 
in the EU-27 who did not provide information on the number of their employees; d) 1,225 responses from survey respondents who 
indicated that they had closed their business; and, e) 239 responses from survey respondents who reported that the age of their business 
was ‘9999’. As result, the response sample used in the analysis of the digitalisation of SMEs comprised 10,402 responses 
Source: LE Europe analysis of Eurobarometer data 

Whilst  the  survey  identified  cloud  computing  as  an  advanced  digital  technology,  we  categorise  it  as  a  basic 
technology due to its widespread use and adoption. We have grouped basic and advanced technology adopters 
as follows: 

•  Basic digital technology adopters are those firms that: 

o  Noted they have adopted basic technologies in Q22 and have not adopted any of the advanced 

technologies in Q23; 

o  Noted they have adopted basic technologies in Q22 and have only adopted cloud computing 

and/or high speed infrastructure in Q23; 

o  Noted  they  have  adopted  advanced  technologies  in  Q22  and  have  not  adopted  any  of  the 

advanced technologies in Q23; 

o  Noted  they  have  adopted  advanced  technologies  in  Q22  and  have  only  adopted  cloud 

computing and/or high speed infrastructure in Q23; 

•  Advanced digital technology adopters are those firms that: 

o  Noted they have adopted basic technologies in Q22 and have adopted cloud computing, high 

speed infrastructure and one or several of the advanced technologies in Q23; 

o  Noted they have adopted basic technologies in Q22 and have not adopted cloud computing or 
high  speed infrastructure but  have adopted one or several of the advanced technologies in 
Q23; 

o  Noted they have adopted advanced technologies in Q22 and have adopted cloud computing, 

high speed infrastructure and one or several of the advanced technologies in Q23; 

o  Noted  they  have  adopted  advanced  technologies  in  Q22  and  have  not  adopted  cloud 
computing  or  high  speed  infrastructure  but  have  adopted  one  or  several  of  the  advanced 
technologies in Q23; 

Table 9 Model specification 

Variable 

Dependent 1 

Dependent 2 

Independent variables 
Age 

Size 

Scale up 

Question 

22  

1 

2b 

5 

Comment 
Firms which have adopted basic digital technologies as 
per the definition above 
Firms which have adopted advanced digital technologies 
as per the definition above 

Value 

1; 0 

1; 0 

The size will correspond the definitions used in the 
report. 
This dummy will equal 1 where respondents answered 
that their company was found prior to 2015 and which 
have grown turnover by at least 30% since 2016162. 

Continuous 
Micro; Small; 
Medium;  

0;1 

162 This is consistent with the Eurobarometer definition for ‘scale-ups’ - enterprises founded prior to 2015, that have achieved significant 
growth since 2016 (thus in the last three years) in employment and/or turnover. Growth in turnover must have been at least 30% to qualify 

Page | 72 

 
 
 
 
 
Gazelle 

City 

GVC 

8 

9 

External funds available 

10 

Exporter 

Independent 

Innovation 

Country 

Sector 

11 

13 

19 

- 

- 

Barriers to digitalisation 

Lack of financial resources 

Lack of skills, including 
managerial skills 

Lack of information 
technology infrastructure, 
such as high speed internet 
connection 

Regulatory obstacles 

21 

IT security issues 

Uncertainty about future 
digital standards 

Internal resistance to 
change 

0;1 

0;1 

0;1 

0;1 

0;1 

0;1 

0;1 

0;1 

0;1 

0;1 

This dummy will equal 1 where respondents answered 
that their company is less than 5 years old and which 
have grown turnover by at least 30% since 2016163. 
This dummy will equal 1 where respondents answered 
that the enterprise is in a large town or city and 0 
otherwise. 
This dummy will equal 1 where respondents identified as 
not being part of a global value chain (option 5) 
This dummy will equal 1 where respondents said that 
they could probably or definitely obtain external 
financing in case of need (option 1 and option 2) 
This dummy will equal 1 where respondents said that 
they export (option 2 – 8) 
This dummy will equal 1 where respondents answered 
that their enterprise is solely owned by one person; 
owned by more than one person or predominantly 
family owned (option 1, option 2 and option 7) and 0 
otherwise 
This dummy will equal 1 where respondents said that 
they engaged in innovative activity (option 1 – 7) 
Country will be used as a dummy variable (EU27 only) 
Sector will be included at the highest level only due to 
data limitations 

This dummy will equal 1 where respondents said that a 
lack of financial resources represented a barrier to 
digitalisation (option 1) 
This dummy will equal 1 where respondents said that a 
lack of skills, including managerial skills represented a 
barrier to digitalisation (option 2) 
This dummy will equal 1 where respondents said that a 
lack of information technology infrastructure, such as 
high speed internet connection represented a barrier to 
digitalisation (option 3) 
This dummy will equal 1 where respondents said that 
regulatory obstacles represented a barrier to 
digitalisation (option 4) 
This dummy will equal 1 where respondents said that IT 
security issues represented a barrier to digitalisation 
(option 5) 
This dummy will equal 1 where respondents said that 
uncertainty about future digital standards represented a 
barrier to digitalisation (option 6) 
This dummy will equal 1 where respondents said that 
internal resistance to change represented a barrier to 
digitalisation (option 7) 

Source: LE Europe analysis of Eurobarometer data 

The majority of SMEs in the sample are micro SMEs (Figure 12), within this group, a total of 16.9% of enterprises 
are either gazelles or scales-ups (Figure 13). Gazelles are overrepresented within micro SMEs representing 4.6% 
of micro  enterprises as opposed to 3.7% of  enterprises overall (Figure  13).  Similarly, small SMEs have a  high 
proportion of scale-ups (20.2%) compared to SMEs overall (15.7%), however, given that small enterprises are 
only 26.9% of the sample, there are a higher number of scale-ups which are micro SMEs in this particular dataset 
(Figure 14). 

on a turnover basis. To qualify as a scale-up on the basis of employment, there must have been a growth of a t least 30% in the case of 
firms with 10 or more employees, or, in the case of micro firms, an increase of at least 3 employees. 
163 According the Eurostat a gazelle is a high growth enterprise that is up to 5 years old where high growth is defined as growth in number 
of employees greater than 10 % per year over a three-year period and having at least 10 employees in the beginning of the growth. 

Page | 73 

 
 
 
74 

Figure 12 Total distribution by SME size 

Source: LE Europe analysis of Eurobarometer data 

Figure 13 Distribution by SME size and type 

Source: LE Europe analysis of Eurobarometer data 

Figure 14 Distribution of gazelles and scale-ups according to SME size 

Source: LE Europe analysis of Eurobarometer data 

City 

Respondents were asked whether their enterprise is located in a large town/city, a rural area, industrial area or 
a small town or village. As shown in the Figure 15 below, firms located in a city have a slightly higher proportion 
of small and medium-sized SMEs.  

Page | 74 

56.7%26.9%16.3%0%10%20%30%40%50%60%70%80%90%100%MicroSmallMedium-sized4.6%3.1%1.5%12.3%20.2%20.1%83.2%76.7%78.3%0%10%20%30%40%50%60%70%80%90%100%MicroSmallMedium-sizedGazelleScale upOther70.5%22.7%6.8%44.3%34.7%20.9%58.5%25.6%15.9%0%10%20%30%40%50%60%70%80%90%100%MicroSmallMedium-sizedGazelleScale upOther 
 
 
 
 
 
Figure 15 Distribution of SMEs in city and non-city locale 

Source: LE Europe analysis of Eurobarometer data 

Global Value Chain (GVC) 

A global value chain (GVC) is when different  stages of a production process take place in different  countries. 
These processes all contribute to the manufacturing of a good or service. Approximately 8% of SMEs identified 
as being part of a GVC. As shown in Figure 16 below, nearly 35% of those enterprises that are part of a GVC are 
micro SMEs.  However,Figure 17 shows that only 5% of micro SMEs said that they are part of a global value chain 
but this figure increases to 10.1% and 16.3% with small and medium-sized SMEs respectively.  

Figure 16 Distribution of SMEs which are part of a global value chain 

Source: LE Europe analysis of Eurobarometer data 

Figure 17 Respondents who identified as being part of global value chain, by SME size 

Source: LE Europe analysis of Eurobarometer data 

Page | 75 

56.3%57.2%27.1%26.8%16.6%16.0%0%10%20%30%40%50%60%70%80%90%100%CityNon-cityMicroSmallMedium-sized 
 
 
 
 
76 

The share of respondents who identified as being part of a GVC is below 10% in the majority of sectors. The real 
estate  activities  sector  had  the  lowest  GVC  participation  3.2%  and  financial  and  insurance  activites  had  the 
highest participation rate at 15.4%. 

Figure 18 SME respondents who identified being part of a global value chain, by sector 

Source: LE Europe analysis of Eurobarometer data 

Less than 1% of SMEs in Croatia, Lithuania and Romania identified as being part of a global value chain. SME 
participation  in  GVCs  is  low  throughout  EU  Member  States  with  Sweden  being  the  only  country  where 
participation is above 20%. 

Figure 19 SME respondents who identified as being part of a global value chain, by county 

Source: LE Europe analysis of Eurobarometer data 

External funds available 

Almost 70% of enterprises noted that their enterprise would definitely be able or probably be able to obtain 
external financing in case of need. The proportion of firms able to obtain external financing differs upon the firm 
size and the Member State in which  the firm is located. According to respondents,  medium-sized  SMEs have 
much greater access to external funds than micro SMEs. Similarly, 85.3% of SMEs located in Finland said they 
had access to external funds compared to just 29.7% in Estonia. 

Page | 76 

 
 
 
 
Figure 20 Availability of external funds, by SME size 

Source: LE Europe analysis of Eurobarometer data 

Figure 21 Availability of external funds for SMEs, by Member State 

Source: LE Europe analysis of Eurobarometer data 

Exporter 

The share of exporters amongst SMEs overall equates to 34.6% of the population (Figure 22). The largest share 
of exporters is amongst medium-sized SMEs where just over half of enterprises noted that they exported goods 
or services in 2019. This falls to 39.6% amongst small SMEs and 27.4% amongst micro SMEs. Naturally, the level 
of exporting varies greatly by sector. Manufacturing has the largest share of exporting SMEs at 62.1% and human 
health and social work activities has the smallest share of exporting at just 5.4%. 

Figure 22 Firm exporting by SME size 

Source: LE Europe analysis of Eurobarometer data 

Page | 77 

64.2%75.6%78.0%69.5%0%10%20%30%40%50%60%70%80%90%100%MicroSmallMedium-sizedTotal27.4%39.6%51.6%34.6%0%10%20%30%40%50%60%70%80%90%100%MicroSmallMedium-sizedTotal 
 
 
 
78 

Figure 23 SMEs exporting across sectors 

Source: LE Europe analysis of Eurobarometer data 

SME independence 

Participants were asked about the ownership structure of their enterprise and whether it is solely owned by one 
person, owned by more than one person, part of a national or international enterprise group, co-owned by a 
public entity, co-owned by venture capital firm, co-owned by business angel, predominantly family owned, jointly 
owned by its members (e.g. cooperative, mutual society).  

The vast majority of firms in the dataset are independent firms i.e. solely owned by one person, owned by more 
than one person or predominantly family owned. In fact, only 10.9% of firms are not independent (Figure 24). 
Firm independence seems to decrease with size whereby 94.8% of micro SMEs are independent, 86.7% of small 
SMEs and 72.9% of medium-sized SMEs. 

Figure 24 SME independence by SME size 

Source: LE Europe analysis of Eurobarometer data 

Firm independence varies greatly depending on which sector the enterprise is in. While less than half of SMEs in 
education are independent, over 95% of those SMEs in construction are independently owned (Figure 25).  

Page | 78 

5.4%7.3%9.1%10.9%14.8%16.5%16.6%19.1%25.5%29.9%32.8%33.3%34.4%47.8%51.6%62.1%0%10%20%30%40%50%60%70%80%90%100%Q - Human health and socialwork activitiesP - EducationD - Electricity, gas, steam andair conditioning supplyI - Accommodation and foodservice activitiesL - Real estate activitiesE - Watersupply,sewerage,wastemanagement/remediation…F - ConstructionR - Arts, entertainment andrecreationN - Administrative and supportservice activitiesK - Financial and insuranceactivitiesG - Wholesale and retail trade,repair of motor vehicles andmotorcyclesB - Mining and quarryingM - Professional, scientific andtechnical activitiesJ - Information andcommunicationH - Transportation and storageC - Manufacturing94.8%86.7%72.9%89.1%0%10%20%30%40%50%60%70%80%90%100%MicroSmallMedium-sizedTotal 
 
 
 
Figure 25 Independent SMEs across sectors 

Source: LE Europe analysis of Eurobarometer data 

Innovation 

The  Eurobarometer  survey  asked  SMEs  whether  their  enterprise  introduced  new  innovations  in  the  last  12 
months.  Innovations  included  improved  production  process,  products  or  services,  a  new  business  model  or 
method of selling goods and services or social and environmental innovations such as products or services which 
are more energy efficient or which improve society.  

The proportion of firms which engaged in innovation differed by firm size and Member State. Medium size SMEs 
had  the  highest  proportion  of  innovators  at  70.7%  (Figure  26).  Small  SMEs  had  the  second  highest  level  of 
innovation at 65.5% and micro SMEs had the lowest proportion of innovators at 52.4%. As shown in  Figure 27, 
over 70% of SMEs located in Spain (71.2%), Netherlands (71.5%) and Denmark (73.8%) engaged in innovation. 
The lowest level of innovation was found in Lithuania where 37.3% of SMEs engaging in innovative activities. Just 
under half of EU Member States have a higher proportion of SME innovation than Great Britain, 23 Member 
states have higher innovation than Japan and 21 Member states have higher levels of innovation amongst SMEs 
than the United States. 

Figure 26 Enterprise innovation by SME size 

Source: LE Europe analysis of Eurobarometer data 

Page | 79 

42.0%58.2%59.6%67.8%68.8%75.2%76.7%83.3%88.3%88.7%91.3%92.0%93.0%93.5%94.3%95.9%0%10%20%30%40%50%60%70%80%90%100%P - EducationD - Electricity, gas, steam andair conditioningsupplyE - Watersupply,sewerage,wastemanagement/remediation…R - Arts, entertainment andrecreationQ - Human health and socialwork activitiesL - Real estate activitiesB - Mining and quarryingK - Financial and insuranceactivitiesJ - Information andcommunicationN - Administrative andsupport service activitiesC - ManufacturingM - Professional, scientificand technical activitiesH - Transportation andstorageG - Wholesale and retailtrade, repair of motorvehicles andI - Accommodation and foodservice activitiesF - Construction52.4%65.5%70.7%58.9%0%10%20%30%40%50%60%70%80%90%100%MicroSmallMedium-sizedTotal 
 
 
80 

Figure 27 SME innovation by country 

Source: LE Europe analysis of Eurobarometer data 

Country 

The  distribution  of  SMEs  in  the  data  is  relatively  consistent  across  Member  States  (Figure  28).  The  largest 
concentration of gazelles are in Lithuania (11%) and are overrepresented in comparison to the proportion of all 
SMEs (4.4%) and scale-ups (3.8%) in Lithuania. Hungary has the highest proportion of scale ups out of all member 
states which represent 6.4% of all scale-ups. 

Figure 28 Distribution by country 

Source: LE Europe analysis of Eurobarometer data 

Sector 

The largest proportion of SMEs in the dataset are from the wholesale and retail trade, repair of motor vehicle 
sector. However, gazelles and scale-ups are underrepresented in this sector relative to SMEs overall. Similarly, 
the manufacturing; electricity, gas, steam and air conditioning supply; accommodation and food service activities 
and  education  sectors  have  a  lower  concentration  of  gazelles  and  scale-ups  compared  to  the  number  of 
enterprises overall. 

Gazelles and scale-ups are both over-represented in construction; administrative and support service activities; 
information  and  communication;  transportation  and  storage;  and  water  supply,  sewerage,  waste 
management/remediation activities.  

Page | 80 

 
 
 
 
Gazelles are dominant in the professional, scientific and technical activities; administrative and support service 
activities; arts, entertainment and recreation and mining and quarrying sectors. Finally, human and social work 
activities and finance and insurance activities are dominated by scale-ups. 

Figure 29 Sector breakdown 

Source: LE Europe analysis of Eurobarometer data 

Page | 81 

0%5%10%15%20%25%30%B - Mining and quarryingD - Electricity, gas, steam and airconditioningsupplyE - Water supply,sewerage,wastemanagement/remediation activR - Arts, entertainment and recreationK - Financial and insurance activitiesL - Real estate activitiesP - EducationQ - Human health and social work activitiesJ - Information and communicationN - Administrative and support serviceactivitiesH - Transportation and storageI - Accommodation and food service activitiesF - ConstructionM - Professional, scientific and technicalactivitiesC - ManufacturingG - Wholesale and retail trade, repair ofmotor vehicles andGazelleScale-upOther 
 
 
82 

Barriers to digitalisation 

As shown in Table 10 below, SME characteristics and barriers to digitalisation vary by country. Estonia and Italy have the largest number of SMEs who have adopted or are planning 
to adopt basic digital technology and a relatively low proportion of SMEs who have started to adopt advanced digital technologies. While 1.1% of SMEs are considered gazelles in 
Italy, 6.4% of SMEs in Estonia are considered gazelles. Both countries have a low level of SME participation in GVCs and cite access to finance as the largest barrier to digitalisation. 

Sweden and the Netherlands have the highest levels of advanced technology adoption amongst EU Member States. Additionally, Both Member States have a high proportion of scale 
ups, in the Netherlands scale-ups represent 19.6% of SMEs whilst in Sweden the percentage is even higher at 20.6%. Lack of skills, including managerial skills and internal resistance 
to change are the most common barriers to digitalisation in Sweden and the Netherlands respectively. Interestingly, SMEs in Sweden have the highest level of GVC participation 
amongst all Member States.  

France had the highest proportion of SMEs citing access to finance; lack of skills including managerial skills; regulatory obstacles and IT security as barriers to digitalisation. Changes 
to digital standards and internal resistance to change is most prevalent in Spain. Finally, 38.3% of SMEs in Denmark cited technology infrastructure as a barrier to digitalisation, the 
highest proportion across Member States. 

Table 10 Characteristics by country 

Country 

Total obs. 
per country 

Basic digital 
tech 
adoption 

Advanced 
digital tech 
adoption 

Gazelles 

Scale up 

Not part of 
GVC 

Access to 
finance 

Lack of skills 

Tech 
infrastructur
e 

Regulatory 
obstacles  

IT Security 

Changes to 
digital 
standards 

Internal 
resistance 

Barriers to digitalisation 

AT 

BE 

BG 

CY 

CZ 

DE 

DK 

EE 

ES 

FI 

FR 

419 

402 

377 

186 

429 

392 

366 

407 

410 

367 

421 

37.9% 

32.1% 

34.5% 

35.5% 

26.8% 

33.9% 

24.6% 

55.0% 

27.1% 

33.2% 

38.5% 

27.4% 

27.9% 

19.6% 

18.3% 

18.9% 

25.0% 

26.5% 

19.9% 

24.6% 

36.2% 

19.0% 

4.5% 

1.2% 

4.2% 

0.5% 

5.4% 

4.1% 

3.0% 

6.4% 

2.4% 

7.9% 

3.8% 

15.0% 

14.2% 

10.9% 

17.2% 

15.6% 

15.8% 

16.1% 

11.8% 

14.9% 

20.7% 

9.7% 

90.0% 

90.0% 

98.7% 

91.9% 

96.7% 

82.4% 

88.8% 

93.9% 

92.4% 

80.1% 

87.2% 

10.7% 

19.2% 

27.1% 

25.8% 

24.0% 

20.2% 

25.4% 

12.8% 

29.0% 

24.3% 

31.6% 

16.2% 

24.9% 

13.5% 

12.9% 

24.5% 

26.3% 

30.3% 

6.1% 

31.5% 

23.2% 

38.0% 

18.9% 

13.9% 

9.0% 

9.1% 

17.0% 

38.3% 

12.0% 

3.7% 

22.9% 

7.9% 

28.0% 

15.5% 

25.9% 

8.8% 

3.2% 

14.7% 

30.9% 

22.1% 

1.7% 

31.7% 

11.2% 

34.0% 

18.1% 

22.1% 

17.5% 

8.1% 

20.3% 

32.7% 

13.1% 

2.0% 

25.1% 

11.4% 

34.9% 

24.1% 

21.1% 

22.0% 

11.3% 

21.4% 

34.9% 

24.3% 

3.2% 

36.6% 

19.3% 

31.8% 

16.9% 

26.6% 

9.8% 

9.7% 

19.1% 

20.9% 

23.5% 

2.0% 

32.2% 

22.1% 

31.6% 

Page | 82 

 
 
 
 
 
 
 
 
 
Country 

Total obs. 
per country 

Basic digital 
tech 
adoption 

Advanced 
digital tech 
adoption 

Gazelles 

Scale up 

Not part of 
GVC 

Access to 
finance 

Lack of skills 

Tech 
infrastructur
e 

Regulatory 
obstacles  

IT Security 

Changes to 
digital 
standards 

Internal 
resistance 

Barriers to digitalisation 

EL 

HR 

HU 

IE 

IT 

LT 

LU 

LV 

MT 

NL 

PL 

PT 

RO 

SE 

SI 

SK 

430 

454 

451 

388 

439 

456 

162 

404 

183 

382 

396 

393 

438 

383 

439 

428 

31.9% 

44.1% 

28.6% 

26.3% 

61.0% 

35.3% 

30.2% 

33.4% 

39.9% 

34.6% 

33.6% 

33.8% 

17.1% 

35.2% 

34.2% 

33.4% 

23.5% 

18.5% 

22.6% 

30.7% 

10.7% 

13.6% 

31.5% 

24.3% 

22.4% 

38.0% 

23.0% 

23.4% 

17.6% 

35.8% 

29.8% 

21.0% 

5.1% 

1.1% 

2.2% 

1.5% 

1.1% 

9.2% 

3.1% 

2.2% 

0.5% 

3.7% 

6.8% 

3.1% 

2.5% 

6.0% 

1.1% 

3.3% 

15.6% 

15.6% 

23.3% 

18.3% 

8.2% 

13.6% 

9.3% 

14.6% 

13.1% 

19.6% 

16.2% 

20.9% 

15.1% 

20.6% 

17.5% 

17.1% 

93.7% 

99.1% 

98.0% 

87.1% 

98.4% 

99.1% 

89.5% 

86.4% 

94.0% 

91.4% 

91.9% 

85.5% 

99.3% 

74.2% 

96.1% 

94.9% 

20.7% 

21.6% 

12.6% 

25.8% 

13.9% 

24.3% 

23.5% 

30.4% 

18.0% 

11.8% 

27.5% 

24.4% 

29.0% 

22.2% 

13.2% 

20.6% 

11.9% 

10.8% 

12.6% 

36.9% 

10.5% 

10.3% 

30.2% 

23.5% 

31.1% 

23.0% 

8.3% 

14.2% 

7.8% 

30.8% 

13.7% 

11.4% 

18.6% 

17.8% 

10.2% 

30.7% 

5.9% 

3.9% 

12.3% 

10.1% 

13.1% 

14.4% 

17.7% 

10.9% 

6.8% 

9.1% 

9.3% 

11.0% 

7.7% 

5.3% 

9.1% 

29.9% 

11.4% 

4.4% 

22.8% 

20.0% 

16.9% 

20.2% 

26.5% 

22.1% 

19.6% 

13.8% 

6.6% 

7.5% 

7.4% 

8.4% 

6.2% 

24.2% 

4.6% 

5.0% 

30.2% 

15.3% 

24.0% 

20.2% 

22.5% 

22.1% 

4.6% 

24.0% 

10.3% 

8.2% 

13.3% 

7.5% 

11.3% 

29.9% 

7.1% 

6.4% 

17.9% 

27.2% 

19.1% 

18.8% 

26.0% 

31.6% 

4.3% 

24.5% 

11.4% 

6.1% 

6.3% 

6.8% 

6.2% 

31.2% 

8.0% 

6.8% 

30.9% 

21.0% 

26.2% 

28.3% 

18.7% 

24.4% 

7.8% 

26.4% 

11.4% 

7.2% 

Source: LE Europe analysis of Eurobarometer data 

The issues faced by SMEs differ by enterprise size. Micro SMEs, the most common enterprise size within the sample164, have the largest proportion of enterprises which have adopted 
or are planning to adopt basic digital technology. Additionally, micro SMEs also have the lowest proportion of enterprises which have started to adopt advanced digital technologies.  

Advanced technology adoption increases with enterprise size, 73.8% of small SMEs and 83.3% of medium-sized SMEs have adopted advanced digital technologies. While access to 
finance is the greatest barrier to digitalisation for micro SMEs, a lack of skills including managerial skills is cited as a more pressing issue for small and medium-sized SMEs. Interestingly, 

164 Micro SMEs represent 56.7% of the sample population, small SMEs represent 26.9% of the sample population and medium-size SMEs represent 16.4% of the sample population. 

Page | 83 

 
 
 
 
 
 
 
 
 
 
84 

internal resistance to change is the most common barrier to digitalisation identified by medium-sized SMEs which may be due to the greater adoption of advanced digital technologies 
as opposed to basic digital technologies. 

Table 11 Characteristics by SME size 

SME size 

Micro 

Small 

Medium-
sized 

Total 
enterprise 

Basic digital 
tech 
adoption 

Advanced 
digital tech 
adoption 

Gazelle 

Scale up 

Not part of 
GVC 

Access to 
finance 

Lack of skills 

Tech 
infrastructure 

Regulatory 
Barriers 

IT security 

Changes to 
digital 
standards 

Internal 
resistance to 
change 

5903 

2801 

1698 

38.7% 

31.8% 

17.7% 

26.8% 

24.6% 

39.9% 

4.6% 

3.1% 

1.5% 

12.3% 

20.2% 

95.0% 

89.9% 

22.0% 

21.4% 

20.1% 

83.7% 

21.2% 

16.6% 

21.8% 

23.9% 

13.4% 

15.3% 

15.7% 

15.6% 

17.1% 

17.4% 

14.0% 

17.7% 

18.9% 

17.6% 

20.4% 

20.3% 

12.1% 

21.3% 

28.0% 

Source: LE Europe analysis of Eurobarometer data

Barriers to technology digitalisation 

Page | 84 

 
 
 
 
 
 
 
 
 
5.3  Econometric analysis 

This section outlines the results of the empirical analysis which identifies the probability of an SMEs adopting 
basic or advanced digital technologies based upon a number of characteristics.  

5.3.1  Approach 

Using a  cross-sectional econometric model such as a probit  model we can identify  the likelihood of adopting 
digital  technologies  based  on  a  number  of  key  characteristics  provided  by  the  Flash  Eurobarometer  on 
Entrepreneurship,  Start-ups  and  Scale-ups  survey.  The  empirical  properties  of  each  indicator  included  in  the 
econometric  analyses  are  assessed  against  the  conventional  statistical  significance  threshold  (i.e.  up  to  a 
statistical significance level of 10%).  

The  results  of  pairwise  correlation  analysis  are  presented  as  a  first  step  in  understanding  the  relationships 
between variables. The results of the econometric analysis are then presented. 

5.4  Results 

5.4.1  Pairwise correlation 

Pairwise correlations provide an initial sense of the indicators’ relationships with the dependent variable. Prior 
to conducting the pairwise correlation for all of the variables, we checked the correlation between gazelles and 
scale ups. Due to their overlap in characteristics of having high growth and being relatively young enterprises 
there is a concern that gazelles and scale ups are highly correlated. Surprisingly, the analysis shows that gazelles 
and scale-ups have a small negative association, significant beyond the 1% level. As the variables are not highly 
correlated we can include both of them in our econometric model. 

Table 12 Pairwise correlations of gazelle and scale-ups 

Indicator 
Gazelle 

Scale up 

Note: * p<0.1 
Source: LE Europe analysis of Eurobarometer data 

Gazelle 
1 
-0.0844 * 
(0) 

Scale up 

1 

In  the  interest  of  identifying  any  differences  in  the  adoption  of  technology  due  to  enterprise  size,  we  first 
conducted  the  analysis  on  all  SMEs  and  then  ran  separate  analysis  for  micro,  small  and  medium-sized  SMEs 
below.  

The table below provides the pairwise correlations between the dependent variables measuring the adoption of 
digital  technology  and  firm  level  characteristics.  Almost  all  of  the  independent  variables  show  different 
coefficient signs in the adoption of basic versus advanced digital technologies, this is intuitively correct as the 
characteristics  of  firms  adopting  basic  digital  technology  are  expected  to  be  different  than  firms  adopting 
advanced digital technology. Age is an exception showing a positive and statistically significant association with 
both basic and advanced digital technologies. In the case of micro SMEs, age has a positive association with basic 
digital  technologies  and  a  negative  association  with  advanced  digital  technologies.  In  contrast,  age  has  the 
opposite association with basic and advanced technology adoption for medium-size firms. 

SMEs which identify as scale-ups, have external funds available, export and innovate show positive associations 
with advanced digital technology adoption and negative associations with basic digital technology adoption. In 
contrast, SMEs that identify as not being part of a global value chain show a positive association with basic digital 
technology adoption and a negative association with advanced digital technology adoption. 

Gazelles show a positive association with advanced digital technology adoption and a negative association with 
basic  digital  technology  adoption  however,  this  relationship  reverses  in  the  case  of  medium-sized  SMEs. 
Independent SMEs exhibit a positive association with basic digital technology adoption and a negative association 

Page | 85 

 
 
86 

with advanced digital technology adoption, the opposite is true for small and medium-sized SMEs although the 
association is not significant.  

The majority of the barriers to digitalisation have a negative statistically significant association with basic digital 
technology adoption and a positive statistically significant association with advanced digital technology adoption. 
Lack of financial resources is the only barrier to digitalisation which has a negative association to the adoption of 
both  basic  and  advanced  digital  technologies.  Disaggregating  the  results  by  firms  size,  we  find  that  a  lack  of 
financial resources in the case of small SMEs show a negative statistically significant association with basic digital 
technology adoption and a positive statistically significant association with advanced digital technology adoption.  

The associations with the dependent variables differ across industries. Additionally, the only industries which 
show consistent associations regardless of form size are the Information and communication; and professional, 
scientific and technical industries. In general, manufacturing; electricity, gas, steam and air conditioning supply; 
and  water  supply,  sewerage,  waste  management/remediation  activities  show  a  positive  association  with 
advanced digital technology adoption and a negative association with basic digital technology adoption.  

Table 13 Pairwise correlations for all SMEs 

Variables 

Age 

Scale-up 

Gazelle 

City 

Not part of a global value chain 

External funds available 

Exporter 

Independent 

Innovation 

Lack of financial resources 

Lack of skills, including managerial skills 

Lack of information technology infrastructure, such as high 
speed internet connection 

Regulatory obstacles 

IT security issues 

Uncertainty about future digital standards 

Basic digital 
technology 
adoption 

Advanced digital 
technology 
adoption 

0.0168* 

0.0859 

-0.0531* 

0 

-0.0251* 

0.0104 

0.0026 

0.7876 

0.0654* 

0 

-0.0539* 

0 

-0.0706* 

0 

0.0303* 

0.002 

-0.0988* 

0 

-0.0572* 

0 

-0.0740* 

0 

-0.0285* 

0.0036 

-0.0751* 

0 

-0.0617* 

0 

-0.0617* 

0.0365* 

0.0002 

0.0755* 

0 

0.0023 

0.8156 

-0.0087 

0.3744 

-0.1162* 

0 

0.1145* 

0 

0.1235* 

0 

-0.0543* 

0 

0.1893* 

0 

-0.0175* 

0.0735 

0.0407* 

0 

0.0218* 

0.026 

0.0805* 

0 

0.0681* 

0 

0.0525* 

Page | 86 

 
 
Variables 

Internal resistance to change 

AT 

BE 

BG 

CY 

CZ 

DE 

DK 

EE 

ES 

FI 

FR 

EL 

HR 

HU 

IE 

IT 

LT 

LU 

LV 

MT 

Basic digital 
technology 
adoption 

0 

-0.0744* 

0 

0.0145 

0.1379 

-0.0105 

0.2862 

-0.0004 

0.971 

0.0026 

0.7915 

-0.0339* 

0.0006 

-0.0027 

0.7854 

-0.0401* 

0 

0.0868* 

0 

-0.0319* 

0.0011 

-0.0053 

0.5861 

0.0169* 

0.0851 

-0.0118 

0.2276 

0.0426* 

0 

-0.0267* 

0.0064 

-0.0343* 

0.0005 

0.1169* 

0 

0.0033 

0.7352 

-0.0114 

0.2436 

-0.0049 

0.6188 

0.015 

0.1268 

Advanced digital 
technology 
adoption 

0 

0.0826* 

0 

0.0176* 

0.0723 

0.0192* 

0.0502 

-0.0189* 

0.0536 

-0.0174* 

0.0752 

-0.0239* 

0.0149 

0.0057 

0.5643 

0.0122 

0.2136 

-0.0184* 

0.0606 

0.004 

0.6799 

0.0559* 

0 

-0.0231* 

0.0187 

-0.0014 

0.8831 

-0.0265* 

0.0069 

-0.0058 

0.5516 

0.0318* 

0.0012 

-0.0645* 

0 

-0.0512* 

0 

0.0227* 

0.0204 

0.0022 

0.8196 

-0.0043 

0.6584 

Page | 87 

 
88 

Variables 

NL 

PL 

PT 

RO 

SE 

SI 

SK  

B - Mining and quarrying 

C - Manufacturing 

D - Electricity, gas, steam and air conditioning supply 

E - Water supply, sewerage, waste 
management/remediation  

F - Construction 

G - Wholesale and retail trade, repair of motor vehicles and 
motorcycles 

H - Transportation and storage 

I - Accommodation and food service  

J - Information and communication 

K - Financial and insurance  

L - Real estate  

M - Professional, scientific and technical  

N - Administrative and support service  

P - Education 

Q - Human health and social work  

Basic digital 
technology 
adoption 

Advanced digital 
technology 
adoption 

-0.0001 

0.9949 

-0.0041 

0.6745 

-0.003 

0.7571 

-0.0769* 

0 

0.0028 

0.7763 

-0.0018 

0.8565 

-0.005 

0.6067 

0.0024 

0.809 

-0.0394* 

0.0001 

-0.0223* 

0.0227 

-0.0291* 

0.003 

0.0236* 

0.0163 

0.0122 

0.2129 

-0.0148 

0.1313 

0.0158 

0.1071 

-0.0297* 

0.0024 

-0.0048 

0.6269 

0.01 

0.308 

0.0239* 

0.0149 

0.0033 

0.7374 

0.0109 

0.2677 

0.0105 

0.0650* 

0 

-0.0038 

0.7016 

-0.0017 

0.859 

-0.0306* 

0.0018 

0.0550* 

0 

0.0299* 

0.0023 

-0.0134 

0.1715 

0.0079 

0.4233 

0.0449* 

0 

0.0309* 

0.0016 

0.0113 

0.2511 

-0.0395* 

0.0001 

-0.0419* 

0 

-0.016 

0.102 

-0.0257* 

0.0089 

0.0492* 

0 

0.0289* 

0.0032 

0.0096 

0.3254 

0.0067 

0.4913 

0.0012 

0.901 

0.0053 

0.588 

0.0075 

Page | 88 

 
 
Variables 

Note: * p<0.1 
Source: LE Europe analysis of Eurobarometer data 

Table 14 Pairwise correlations for micro SMEs 

Basic digital 
technology 
adoption 

0.2845 

Advanced digital 
technology 
adoption 

0.4434 

Variables 

Age 

Scale-up 

Gazelle 

City 

Not part of a global value chain 

External funds available 

Exporter 

Independent 

Innovation 

Lack of financial resources 

Lack of skills, including managerial skills 

Lack of information technology infrastructure, such as high 
speed internet connection 

Regulatory obstacles 

IT security issues 

Uncertainty about future digital standards 

Internal resistance to change 

AT 

BE 

Basic digital 
technology 
adoption 

0.0656* 

0 

-0.0270* 

0.0378 

-0.0476* 

0.0003 

-0.0011 

0.9325 

0.0467* 

0.0003 

-0.0277* 

0.0335 

-0.0372* 

0.0042 

0.0134 

0.3027 

-0.0573* 

0 

-0.0791* 

0 

-0.0679* 

0 

-0.014 

0.2828 

-0.0733* 

0 

-0.0475* 

0.0003 

-0.0492* 

0.0002 

-0.0440* 

0.0007 

0.0055 

0.6746 

-0.001 

Advanced 
digital 
technology 
adoption 

-0.0355* 

0.0064 

0.0471* 

0.0003 

0.0238* 

0.0675 

-0.02 

0.1245 

-0.0769* 

0 

0.1016* 

0 

0.0967* 

0 

-0.0277* 

0.0334 

0.1577* 

0 

-0.0042 

0.7445 

0.0204 

0.1179 

0.0148 

0.2565 

0.0814* 

0 

0.0634* 

0 

0.0511* 

0.0001 

0.0378* 

0.0036 

0.018 

0.1676 

0.0254* 

Page | 89 

 
90 

BG 

CY 

CZ 

DE 

DK 

EE 

ES 

FI 

FR 

EL 

HR 

HU 

IE 

IT 

LT 

LU 

LV 

MT 

NL 

PL 

PT 

RO 

SE 

0.938 

-0.0096 

0.4597 

0.0126 

0.334 

-0.0434* 

0.0008 

-0.0266* 

0.041 

-0.0620* 

0 

0.0953* 

0 

-0.0211 

0.1049 

0.0121 

0.3535 

0.0192 

0.1394 

-0.0451* 

0.0005 

0.0382* 

0.0033 

-0.0453* 

0.0005 

-0.0172 

0.1875 

0.1241* 

0 

-0.0181 

0.1645 

-0.0191 

0.1428 

0.0068 

0.602 

0.0187 

0.1506 

0.0238* 

0.0679 

-0.0006 

0.9648 

0.0047 

0.7153 

-0.0952* 

0 

0.0298* 

0.0508 

-0.017 

0.1927 

-0.0260* 

0.0457 

-0.0209 

0.1091 

0.0084 

0.5206 

-0.0042 

0.7467 

-0.0247* 

0.0581 

-0.0012 

0.9241 

0.0245* 

0.0599 

-0.016 

0.2192 

0.0221* 

0.089 

-0.0169 

0.1939 

0.0154 

0.2372 

0.0270* 

0.0378 

-0.0570* 

0 

-0.0574* 

0 

0.0178 

0.172 

-0.0042 

0.7464 

-0.0105 

0.4195 

0.0662* 

0 

-0.0066 

0.6137 

-0.0032 

0.8085 

-0.008 

0.5402 

0.0486* 

Page | 90 

 
 
SI 

SK  

B - Mining and quarrying 

C - Manufacturing 

D - Electricity, gas, steam and air conditioning supply 

E - Water supply, sewerage, waste 
management/remediation  

F - Construction 

G - Wholesale and retail trade, repair of motor vehicles 
and motorcycles 

H - Transportation and storage 

I - Accommodation and food service  

J - Information and communication 

K - Financial and insurance  

L - Real estate  

M - Professional, scientific and technical  

N - Administrative and support service  

P - Education 

Q - Human health and social work  

Note: * p<0.1 
Source: LE Europe analysis of Eurobarometer data 

Table 15 Pairwise correlations for small SMEs 

Variables 

Age 

Scale-up 

0.0221 

-0.0028 

0.8295 

0.0001 

0.9941 

-0.0043 

0.7389 

0.0038 

0.7721 

-0.0290* 

0.0261 

-0.0116 

0.3731 

0.016 

0.2182 

0.0067 

0.6066 

-0.0218* 

0.0941 

-0.0037 

0.7741 

-0.0295* 

0.0234 

0.001 

0.9397 

0 

0.9985 

0.0131 

0.3137 

-0.007 

0.5934 

0.0077 

0.5545 

0.01 

0.4429 

0.0002 

0.0388* 

0.0029 

-0.0189 

0.1467 

0.0046 

0.7223 

0.0002 

0.9899 

0.0331* 

0.0109 

-0.0069 

0.5953 

-0.0135 

0.2995 

-0.0392* 

0.0026 

-0.0244* 

0.0613 

-0.0082 

0.5284 

0.0570* 

0 

0.0214* 

0.0998 

0.0182 

0.161 

0.0306* 

0.0188 

-0.007 

0.5901 

-0.003 

0.8171 

0.0175 

0.1782 

Basic digital 
technology 
adoption 

0.0552* 

0.0035 

-0.0621* 

0.001 

Advanced 
digital 
technology 
adoption 

0.0051 

0.7865 

0.0817* 

0 

Page | 91 

 
92 

Variables 

Gazelle 

City 

Not part of a global value chain 

External funds available 

Exporter 

Independent 

Innovation 

Lack of financial resources 

Lack of skills, including managerial skills 

Lack of information technology infrastructure, such as high 
speed internet connection 

Regulatory obstacles 

IT security issues 

Uncertainty about future digital standards 

Internal resistance to change 

AT 

BE 

BG 

CY 

CZ 

DE 

DK 

Basic digital 
technology 
adoption 

Advanced 
digital 
technology 
adoption 

-0.012 

0.5272 

0.0095 

0.6156 

0.0367* 

0.0523 

-0.0473* 

0.0123 

-0.0415* 

0.0281 

-0.0073 

0.7012 

-0.1086* 

0 

-0.0407* 

0.0314 

-0.0456* 

0.0158 

-0.0460* 

0.0149 

-0.0616* 

0.0011 

-0.0581* 

0.0021 

-0.0630* 

0.0008 

-0.0457* 

0.0156 

0.0450* 

0.0173 

-0.0219 

0.2458 

0.0057 

0.7623 

-0.0087 

0.6472 

-0.021 

0.2663 

0.0117 

0.5346 

-0.0095 

0.6138 

0.0076 

0.6866 

0.0166 

0.3791 

0.0874* 

0 

0.0889* 

0 

0.0774* 

0 

0.0003 

0.9859 

0.1836* 

0 

0.0193 

0.3075 

0.0233 

0.217 

0.0108 

0.5681 

0.0721* 

0.0001 

0.0630* 

0.0009 

0.0248 

0.1887 

0.0618* 

0.0011 

0.0017 

0.9301 

0.0289 

0.1265 

0.0202 

0.2844 

0.0185 

0.3286 

0.0116 

0.5406 

0.0014 

0.9417 

0.0022 

0.9073 

Page | 92 

 
 
Variables 

Basic digital 
technology 
adoption 

Advanced 
digital 
technology 
adoption 

EE 

ES 

FI 

FR 

EL 

HR 

HU 

IE 

IT 

LT 

LU 

LV 

MT 

NL 

PL 

PT 

RO 

SE 

SI 

SK  

B - Mining and quarrying 

0.0701* 

0.0002 

-0.0350* 

0.064 

0.0001 

0.9963 

0.0168 

0.3731 

0.0271 

0.1522 

0.0390* 

0.039 

-0.016 

0.3965 

-0.0475* 

0.0119 

0.0836* 

0 

0.0036 

0.8503 

-0.0059 

0.7565 

-0.0271 

0.1519 

0.014 

0.4588 

-0.004 

0.8344 

-0.0062 

0.7415 

-0.0142 

0.454 

-0.0533* 

0.0048 

-0.0271 

0.1519 

-0.0007 

0.9715 

-0.0132 

0.4853 

0.0128 

0.4969 

0.0046 

0.8061 

0.015 

0.4276 

0.0753* 

0.0001 

0.0297 

0.1165 

0.0216 

0.254 

0.0341* 

0.0709 

0.0417* 

0.0271 

0.0485* 

0.0102 

0.0646* 

0.0006 

0.0295 

0.1187 

0.0328* 

0.0828 

0.0104 

0.5823 

0.0175 

0.3551 

0.0591* 

0.0018 

0.0235 

0.2129 

0.0144 

0.4459 

0.0586* 

0.0019 

0.0782* 

0 

0.0211 

0.2642 

0.0037 

0.8455 

0.0107 

0.57 

Page | 93 

 
94 

Variables 

C - Manufacturing 

D - Electricity, gas, steam and air conditioning supply 

E - Water supply, sewerage, waste 
management/remediation  

F - Construction 

G - Wholesale and retail trade, repair of motor vehicles 
and motorcycles 

H - Transportation and storage 

I - Accommodation and food service  

J - Information and communication 

K - Financial and insurance  

L - Real estate  

M - Professional, scientific and technical  

N - Administrative and support service  

P - Education 

Q - Human health and social work  

Note: * p<0.1 
Source: LE Europe analysis of Eurobarometer data 

Table 16 Pairwise correlations for medium-sized SMEs 

Variables 

Age 

Scale-up 

Gazelle 

City 

Basic digital 
technology 
adoption 

Advanced 
digital 
technology 
adoption 

-0.0352* 

0.0626 

-0.0187 

0.3236 

-0.0268 

0.1564 

0.0318* 

0.0923 

-0.028 

0.1382 

-0.0084 

0.658 

0.0515* 

0.0064 

-0.0399* 

0.0347 

0.0155 

0.4135 

0.0358* 

0.058 

0.0217 

0.252 

0.0049 

0.7962 

0.026 

0.1686 

-0.0128 

0.4978 

Basic digital 
technology 
adoption 

-0.0335 

0.1679 

-0.0614* 

0.0114 

0.018 

0.4587 

0.0125 

0.0127 

0.502 

0.0107 

0.57 

0.0083 

0.6606 

0.0657* 

0.0005 

0.0202 

0.2846 

0.0036 

0.8498 

0.0499* 

0.0083 

0.0614* 

0.0011 

0.0154 

0.4156 

0.0044 

0.814 

0.0061 

0.7481 

0.0143 

0.4485 

0.011 

0.5589 

0.0098 

0.6056 

Advanced 
digital 
technology 
adoption 

0.036 

0.138 

0.0529* 

0.0292 

-0.0134 

0.5816 

0.0231 

Page | 94 

 
 
Variables 

Not part of a global value chain 

External funds available 

Exporter 

Independent 

Innovation 

Lack of financial resources 

Lack of skills, including managerial skills 

Lack of information technology infrastructure, such as high 
speed internet connection 

Regulatory obstacles 

IT security issues 

Uncertainty about future digital standards 

Internal resistance to change 

AT 

BE 

BG 

CY 

CZ 

DE 

DK 

EE 

ES 

Basic digital 
technology 
adoption 

0.6081 

0.0816* 

0.0008 

-0.0863* 

0.0004 

-0.1133* 

0 

-0.0063 

0.7967 

-0.1494* 

0 

-0.0081 

0.7398 

-0.0978* 

0.0001 

-0.0322 

0.1846 

-0.0955* 

0.0001 

-0.0833* 

0.0006 

-0.0867* 

0.0003 

-0.1185* 

0 

0.0087 

0.719 

-0.0235 

0.3334 

0.0202 

0.4047 

-0.017 

0.4846 

-0.0197 

0.4161 

0.0709* 

0.0035 

-0.0028 

0.9086 

0.0660* 

0.0065 

-0.0677* 

Advanced 
digital 
technology 
adoption 

0.341 

-0.1158* 

0 

0.0845* 

0.0005 

0.0947* 

0.0001 

0.0146 

0.547 

0.1732* 

0 

-0.0457* 

0.0597 

0.0495* 

0.0416 

0.0315 

0.1939 

0.0806* 

0.0009 

0.0431* 

0.0761 

0.0744* 

0.0022 

0.0729* 

0.0027 

0.029 

0.2328 

-0.0139 

0.5679 

-0.0174 

0.4726 

0.0131 

0.5905 

-0.0540* 

0.0261 

-0.0121 

0.6195 

0.0474* 

0.0508 

-0.0141 

0.5623 

-0.0007 

Page | 95 

 
96 

Variables 

FI 

FR 

EL 

HR 

HU 

IE 

IT 

LT 

LU 

LV 

MT 

NL 

PL 

PT 

RO 

SE 

SI 

SK  

B - Mining and quarrying 

C – Manufacturing 

D - Electricity, gas, steam and air conditioning supply 

Basic digital 
technology 
adoption 

Advanced 
digital 
technology 
adoption 

0.0053 

-0.0830* 

0.0006 

-0.0104 

0.6699 

0.0460* 

0.0581 

0.0606* 

0.0125 

0.0249 

0.3047 

-0.0564* 

0.0202 

0.1160* 

0 

0.0787* 

0.0012 

0.0104 

0.6675 

-0.0267 

0.2714 

0.0154 

0.5252 

-0.0594* 

0.0143 

0.0135 

0.5774 

-0.0351 

0.1484 

-0.0522* 

0.0316 

-0.0568* 

0.0193 

-0.0063 

0.7967 

-0.0032 

0.8955 

0.0121 

0.6171 

-0.0678* 

0.0052 

0.001 

0.9775 

0.1202* 

0 

-0.0138 

0.5707 

-0.0415* 

0.0875 

-0.0358 

0.1409 

-0.0044 

0.856 

0.0005 

0.982 

-0.0587* 

0.0155 

-0.0496* 

0.0409 

0.0177 

0.4658 

0.022 

0.365 

-0.0338 

0.1645 

0.0574* 

0.0181 

-0.0017 

0.9453 

0.0396 

0.1029 

-0.0520* 

0.0322 

0.0507* 

0.0368 

0.029 

0.2319 

-0.0229 

0.3448 

-0.008 

0.7434 

0.0336 

0.1665 

0.0326 

Page | 96 

 
 
Variables 

E - Water supply, sewerage, waste 
management/remediation  

F – Construction 

G - Wholesale and retail trade, repair of motor vehicles 
and motorcycles 

H - Transportation and storage 

I - Accommodation and food service  

J - Information and communication 

K - Financial and insurance  

L - Real estate  

M - Professional, scientific and technical  

N - Administrative and support service  

P – Education 

Q - Human health and social work  

Note: * p<0.1 
Source: LE Europe analysis of Eurobarometer data 

5.4.2  Probit model estimation 

Basic digital 
technology 
adoption 

0.9671 

-0.0435* 

0.073 

0.0256 

0.292 

-0.0061 

0.8013 

-0.006 

0.8035 

0.0157 

0.5169 

-0.0299 

0.2175 

-0.0332 

0.1711 

-0.0057 

0.8134 

0.0085 

0.7263 

0.0456* 

0.0601 

0.0331 

0.1728 

0.0916* 

0.0002 

Advanced 
digital 
technology 
adoption 

0.179 

0.0163 

0.5028 

-0.0573* 

0.0182 

0.0058 

0.811 

-0.0135 

0.5782 

-0.0221 

0.3622 

0.0368 

0.1299 

0.0375 

0.1225 

0.0258 

0.2884 

0.0283 

0.2444 

-0.0073 

0.7635 

-0.0055 

0.8208 

-0.0692* 

0.0044 

SMEs which are categorised as scale-ups, have external funding available, export or innovate are more likely to 
adopt advanced digital technologies and less likely to adopt basic digital technologies, regardless of the firm size. 
These results are intuitively correct, high growth firms such as scale-ups may need to adopt advanced digital 
technologies in order to continue growing. Firms which have access to external funding may want to invest that 
funding in advanced digital technologies which can engender efficiency gains. Exporting firms are competing on 
a global stage, as such, the adoption of advanced digital technologies may be in an effort to stay competitive or 
to  become  more  competitive.  Similarly,  innovative  firms  may  adopt  advanced  technologies  to  create  new 
products or enhance existing products or work processes. 

Similar  to  scale-ups,  SMEs  which  are  categorised  as  gazelles  show  a  positive  relationship  with  advanced 
technology adoption and a negative relationship with basic digital technology adoption. This result fails to hold 
for medium-sized SMEs, however this is not statistically significant.  

SMEs that are not part of a GVC are less likely to adopt advanced digital technology and more likely to adopt 
basic digital technology..  

It is expected that independent firms may find it harder to obtain financing, making the investment in digital 
technology difficult. The aggregate results show that firms which are independent are more likely to adopt basic 

Page | 97 

 
98 

digital  technology  and  less  likely  to  adopt  advanced  digital  technology.  This  result  fails  to  hold  for small  and 
medium-sized  SMEs  whereby  independent  firms  are  more  likely  to  adopt  both  basic  and  advanced  digital 
technologies, although the results are not statistically significant.  

Firms who cite uncertainty about future digital standards as a barrier to digitalisation were more likely to adopt 
advanced digital technology. This result may be due to reverse causality whereby those firms that have adopted 
advanced  digital  technologies  may  be  concerned  about  future  digital  standards  but  basic  digital  technology 
adopters may not take digital standards into consideration. Contrary to the aggregate result, small SMEs show a 
negative  relationship  between  future  digital  standards  and  the  adoption  of  basic  and  advanced  digital 
technologies, the result is not statistically significant.  

SMEs which cite internal resistance to change as a barrier to digitalisation are less likely to adopt basic digital 
technologies but more likely to adopt advanced digital technologies. Again, this may be as a result of reverse 
causality,  whereby  internal  resistance  to  change  arises  when  companies  start  implementing  advanced  digital 
technologies but not when basic digital technologies are being adopted. The same reasoning could be used to 
explain  regulatory  obstacles  and  IT  security  issues,  only  firms  adopting  advanced  digital  technologies  will 
encounter  these  barriers.  Interestingly,  IT  security  issues  act  as  a  barrier  to  basic  and  advanced  technology 
adoption for medium-sized SMEs, although the relationship is not statistically significant.  

A lack of financial resources and a lack of skills including managerial skills reduce the likelihood of adopting basic 
and advanced digital technology. These findings hold for micro and small SMES but fail to hold for medium-sized 
SMEs. 

The mining and quarrying; electricity, gas, steam and air conditioning supply; information and communication; 
financial and insurance activities and real estate activities industries are more likely to adopt advanced digital 
technologies  and  less  likely  to  adopt  basic  digital  technologies,  regardless  of  firm  size.  Similarly,  the 
manufacturing; water supply, sewerage, waste management/remediation activities; professional, scientific and 
technical activities and administrative and support service industries have a positive relationship with advanced 
digital technology adoption and a negative relationship with basic digital technology adoption. These results hold 
at the aggregate level and for small and medium-sized SMEs but fail to hold for micro SMEs. 

Amongst  medium-size  firms,  all  sectors  apart  from  human  health  and  social  work  activities  have  a  positive 
relationship with the adoption of advanced digital technologies and a negative relationship with the adoption of 
basic digital technologies. For small SMEs, two industries have a negative relationship with advanced technology 
adoption, accommodation  and  food  service;  and  construction.  Interestingly,  construction  also  has  a  negative 
relationship with basic digital technology adoption. In the case of micro SMEs, over half of the industries have a 
negative relationship with advanced digital technology adoption. 

Austria, Belgium, Bulgaria, Estonia, Finland, Luxembourg,  Latvia, the Netherlands, Sweden and Slovakia show 
positive  relationships  with  the  adoption  of  both  basic  and  advanced  digital  technology.  These  results  are 
generally  consistent  across  firm  sizes  with  a  few  exceptions.  Firms  located  in  Cyprus,  France,  Hungary,  Italy, 
Lithuania, Malta and Poland are less likely to adopt advanced technologies and more likely to adopt basic digital 
technologies. Czechia, Spain and Romania are the only Member States where firms are, in general, less likely to 
adopt both digital and advanced technologies. Germany, Denmark, Greece and Portugal fail to show a consistent 
pattern of technology adoption across different firm sizes. 

Table 17 Probit model results for all SMEs 

Variables 

Age 

Scale-up 

Gazelle 

City 

Basic digital technology 
adoption 
0.00107 
(0.111) 
-0.124*** 
(0.000887) 
-0.197*** 
(0.00661) 
0.00267 
(0.923) 

Advanced digital 
technology adoption 
0.000881 
(0.225) 
0.149*** 
(8.08e-05) 
0.0261 
(0.732) 
-0.0378 
(0.204) 

Page | 98 

 
 
Variables 

Not part of a global value chain 

External funds available 

Exporter 

Independent 

Innovation 

Lack of financial resources 

Lack of skills, including managerial skills 

Lack of information technology infrastructure, such as high 
speed internet connection 

Regulatory obstacles 

IT security issues 

Uncertainty about future digital standards 

Internal resistance to change 

AT 

BE 

BG 

CY 

CZ 

DE 

DK 

EE 

ES 

FI 

FR 

EL 

HR 

HU 

Basic digital technology 
adoption 
0.205*** 
(8.18e-05) 
-0.0498* 
(0.0909) 
-0.139*** 
(5.29e-06) 
0.0958** 
(0.0368) 
-0.140*** 
(4.52e-07) 
-0.0960*** 
(0.00413) 
-0.103*** 
(0.00510) 
0.0126 
(0.752) 
-0.138*** 
(0.000452) 
-0.0676* 
(0.0931) 
-0.0544 
(0.149) 
-0.125*** 
(0.00123) 
0.195** 
(0.0313) 
0.0418 
(0.652) 
0.0372 
(0.689) 
0.0525 
(0.648) 
-0.164* 
(0.0721) 
0.118 
(0.210) 
-0.162* 
(0.0959) 
0.545*** 
(1.37e-09) 
-0.0423 
(0.652) 
0.0856 
(0.362) 
0.275*** 
(0.00262) 
-0.0316 
(0.727) 
0.288*** 
(0.000991) 
-0.141 
(0.117) 

Advanced digital 
technology adoption 
-0.261*** 
(1.91e-07) 
0.230*** 
(0) 
0.242*** 
(0) 
-0.111** 
(0.0173) 
0.437*** 
(0) 
-0.0947*** 
(0.00905) 
-0.0504 
(0.194) 
-0.000644 
(0.988) 
0.189*** 
(2.54e-06) 
0.0705* 
(0.0899) 
0.0288 
(0.469) 
0.114*** 
(0.00353) 
0.106 
(0.287) 
0.132 
(0.191) 
0.0284 
(0.785) 
-0.0383 
(0.771) 
-0.160 
(0.113) 
0.0251 
(0.809) 
0.0154 
(0.880) 
0.0988 
(0.335) 
-0.0136 
(0.893) 
0.389*** 
(9.61e-05) 
-0.141 
(0.174) 
0.154 
(0.123) 
-0.0514 
(0.606) 
0.124 
(0.209) 

Page | 99 

 
100 

Variables 

IE 

IT 

LT 

LU 

LV 

MT 

NL 

PL 

PT 

RO 

SE 

SI 

SK (omitted) 

B - Mining and quarrying 

C - Manufacturing 

D - Electricity, gas, steam and air conditioning supply 

E - Water supply, sewerage, waste management/remediation  

F - Construction 

G - Wholesale and retail trade, repair of motor vehicles and 
motorcycles 

H - Transportation and storage 

I - Accommodation and food service  

J - Information and communication 

K - Financial and insurance  

L - Real estate  

M - Professional, scientific and technical  

N - Administrative and support service  

P - Education 

Basic digital technology 
adoption 
-0.134 
(0.163) 
0.677*** 
(0) 
0.0299 
(0.736) 
0.0219 
(0.859) 
0.0894 
(0.327) 
0.254** 
(0.0264) 
0.102 
(0.275) 
0.125 
(0.175) 
0.112 
(0.225) 
-0.523*** 
(6.40e-08) 
0.187** 
(0.0448) 
0.0446 
(0.618) 
- 
-0.133 
(0.615) 
-0.205* 
(0.0694) 
-0.602*** 
(0.00667) 
-0.547*** 
(0.00175) 
-0.107 
(0.355) 
-0.103 
(0.353) 
-0.189 
(0.118) 
-0.0998 
(0.409) 
-0.309** 
(0.0157) 
-0.131 
(0.353) 
-0.0621 
(0.647) 
-0.0127 
(0.913) 
-0.150 
(0.233) 
0.0740 

Advanced digital 
technology adoption 
0.244** 
(0.0157) 
-0.346*** 
(0.00155) 
-0.155 
(0.137) 
0.240* 
(0.0593) 
0.0495 
(0.621) 
-0.0252 
(0.840) 
0.375*** 
(0.000160) 
-0.0432 
(0.672) 
-0.0595 
(0.558) 
-0.0437 
(0.666) 
0.329*** 
(0.000942) 
0.282*** 
(0.00343) 
- 
0.304 
(0.297) 
0.103 
(0.405) 
0.678*** 
(0.00138) 
0.275 
(0.124) 
0.0188 
(0.883) 
-0.0330 
(0.787) 
-0.0208 
(0.876) 
0.0193 
(0.885) 
0.306** 
(0.0248) 
0.140 
(0.356) 
0.214 
(0.147) 
0.0878 
(0.489) 
0.137 
(0.322) 
0.202 

Page | 100 

 
 
Variables 

Q - Human health and social work  

Constant 

Observations 

Note: *** p<0.01, ** p<0.05, * p<0.1, ++ p<0.2, + p<0.25 

Source: LE Europe analysis of Eurobarometer data 

Table 18 Probit model results for micro SMEs only 

Basic digital technology 
adoption 
(0.585) 
0.0246 
(0.848) 
-0.373*** 
(0.00853) 
10,402 

Advanced digital 
technology adoption 
(0.170) 
0.138 
(0.327) 
-1.117*** 
(0) 
10,402 

Variables 

Age 

Scale-up 

Gazelle 

City 

Not part of a global value chain 

External funds available 

Exporter 

Independent 

Innovation 

Lack of financial resources 

Lack of skills, including managerial skills 

Lack of information technology infrastructure, such as high 
speed internet connection 

Regulatory obstacles 

IT security issues 

Uncertainty about future digital standards 

Internal resistance to change 

AT 

BE 

BG 

CY 

Basic digital technology 
adoption 
0.00353*** 
(0.000782) 
-0.0239 
(0.656) 
-0.240*** 
(0.00620) 
-0.0404 
(0.262) 
0.213** 
(0.0109) 
-0.0103 
(0.784) 
-0.0870** 
(0.0346) 
0.0453 
(0.575) 
-0.0601* 
(0.0943) 
-0.170*** 
(0.000113) 
-0.150*** 
(0.00336) 
0.0939* 
(0.0790) 
-0.162*** 
(0.00227) 
-0.0453 
(0.413) 
-0.0135 
(0.792) 
-0.0732 
(0.201) 
0.0286 
(0.815) 
0.0380 
(0.757) 
-0.0468 
(0.701) 
0.0614 
(0.690) 

Advanced digital 
technology adoption 
-0.00312** 
(0.0231) 
0.0615 
(0.296) 
0.0284 
(0.765) 
-0.0544 
(0.198) 
-0.236*** 
(0.00585) 
0.219*** 
(1.41e-06) 
0.249*** 
(9.53e-08) 
-0.0775 
(0.380) 
0.399*** 
(0) 
-0.0679 
(0.188) 
-0.0679 
(0.247) 
-0.0560 
(0.368) 
0.219*** 
(0.000156) 
0.0855 
(0.160) 
0.0752 
(0.197) 
0.0187 
(0.768) 
0.241 
(0.105) 
0.250* 
(0.0911) 
0.0981 
(0.518) 
-0.0845 
(0.682) 

Page | 101 

 
102 

Variables 

CZ 

DE 

DK 

EE 

ES 

FI 

FR 

EL 

HR 

HU 

IE 

IT 

LT 

LU 

LV 

MT 

NL 

PL 

PT 

RO 

SE 

SI 

SK (omitted) 

B - Mining and quarrying 

C - Manufacturing 

D - Electricity, gas, steam and air conditioning supply 

E - Water supply, sewerage, waste management/remediation  

Basic digital technology 
adoption 
-0.263** 
(0.0303) 
-0.139 
(0.284) 
-0.430*** 
(0.00129) 
0.550*** 
(3.22e-06) 
-0.0442 
(0.720) 
0.141 
(0.251) 
0.218* 
(0.0687) 
-0.319** 
(0.0113) 
0.176 
(0.128) 
-0.328*** 
(0.00664) 
-0.0891 
(0.493) 
0.623*** 
(7.59e-08) 
-0.0937 
(0.419) 
-0.161 
(0.349) 
0.0874 
(0.461) 
0.223 
(0.152) 
0.179 
(0.159) 
0.0720 
(0.575) 
0.0597 
(0.614) 
-0.653*** 
(2.63e-07) 
0.258** 
(0.0333) 
-0.0441 
(0.704) 
- 
-0.318 
(0.482) 
-0.0278 
(0.854) 
-0.665* 
(0.0523) 
-0.326 

Advanced digital 
technology adoption 
-0.0807 
(0.588) 
0.128 
(0.413) 
-0.0252 
(0.871) 
0.0757 
(0.609) 
0.0321 
(0.831) 
0.315** 
(0.0324) 
-0.0477 
(0.751) 
0.369** 
(0.0128) 
0.102 
(0.486) 
0.331** 
(0.0212) 
0.327** 
(0.0334) 
-0.200 
(0.197) 
-0.247 
(0.115) 
0.308 
(0.109) 
0.0705 
(0.631) 
-0.000649 
(0.997) 
0.536*** 
(0.000308) 
-0.0122 
(0.939) 
0.0233 
(0.873) 
0.126 
(0.383) 
0.437*** 
(0.00240) 
0.404*** 
(0.00348) 
- 
0.112 
(0.835) 
-0.0978 
(0.573) 
0.643** 
(0.0404) 
-0.108 

Page | 102 

 
 
Variables 

F - Construction 

G - Wholesale and retail trade, repair of motor vehicles and 
motorcycles 

H - Transportation and storage 

I - Accommodation and food service  

J - Information and communication 

K - Financial and insurance  

L - Real estate  

M - Professional, scientific and technical  

N - Administrative and support service  

P - Education 

Q - Human health and social work  

Constant 

Observations 

Note: *** p<0.01, ** p<0.05, * p<0.1, ++ p<0.2, + p<0.25 

Source: LE Europe analysis of Eurobarometer data 

Table 19 Probit model results for small SMEs only 

Variables 

Age 

Scale-up 

Gazelle 

City 

Not part of a global value chain 

External funds available 

Exporter 

Independent 

Innovation 

Lack of financial resources 

Basic digital technology 
adoption 
(0.215) 
-0.0280 
(0.855) 
-0.0162 
(0.912) 
-0.129 
(0.421) 
-0.137 
(0.391) 
-0.210 
(0.210) 
-0.0692 
(0.720) 
-0.0562 
(0.749) 
0.0398 
(0.793) 
-0.0685 
(0.682) 
0.0700 
(0.716) 
0.107 
(0.558) 
-0.402** 
(0.0444) 
5,894 

Advanced digital 
technology adoption 
(0.725) 
-0.0340 
(0.847) 
-0.151 
(0.368) 
-0.190 
(0.308) 
-0.0125 
(0.946) 
0.194 
(0.300) 
0.0651 
(0.764) 
0.196 
(0.325) 
0.0296 
(0.865) 
-0.108 
(0.581) 
-0.0307 
(0.891) 
0.138 
(0.505) 
-1.126*** 
(1.30e-06) 
5,903 

Basic digital technology 
adoption 
0.00297** 
(0.0176) 
-0.166** 
(0.0118) 
-0.0992 
(0.512) 
0.0347 
(0.522) 
0.0527 
(0.557) 
-0.0679 
(0.269) 
-0.0264 
(0.651) 
0.00146 
(0.986) 
-0.211*** 
(0.000129) 
-0.0495 
(0.453) 

Advanced digital 
technology adoption 
-0.000385 
(0.774) 
0.198*** 
(0.00268) 
0.0524 
(0.729) 
-0.0325 
(0.562) 
-0.152* 
(0.0842) 
0.213*** 
(0.00132) 
0.0743 
(0.214) 
0.0395 
(0.653) 
0.462*** 
(0) 
-0.0599 
(0.381) 

Page | 103 

 
104 

Variables 

Lack of skills, including managerial skills 

Lack of information technology infrastructure, such as high 
speed internet connection 

Regulatory obstacles 

IT security issues 

Uncertainty about future digital standards 

Internal resistance to change 

AT 

BE 

BG 

CY 

CZ 

DE 

DK 

EE 

ES 

FI 

FR 

EL 

HR 

HU 

IE 

IT 

LT 

LU 

LV 

MT 

Basic digital technology 
adoption 
-0.0271 
(0.686) 
-0.136* 
(0.0726) 
-0.0738 
(0.323) 
-0.103 
(0.170) 
-0.118 
(0.101) 
-0.0188 
(0.783) 
0.412** 
(0.0161) 
-0.0317 
(0.861) 
0.210 
(0.261) 
0.0360 
(0.861) 
-0.0103 
(0.953) 
0.271 
(0.122) 
-0.000359 
(0.998) 
0.539*** 
(0.00243) 
-0.0470 
(0.793) 
0.149 
(0.412) 
0.299* 
(0.0812) 
0.275* 
(0.0885) 
0.388** 
(0.0224) 
0.0187 
(0.912) 
-0.124 
(0.479) 
0.588*** 
(0.00106) 
0.0481 
(0.787) 
0.100 
(0.638) 
0.0458 
(0.807) 
0.292 
(0.175) 

Advanced digital 
technology adoption 
-0.0875 
(0.206) 
0.0311 
(0.691) 
0.149** 
(0.0445) 
0.115 
(0.127) 
-0.0503 
(0.494) 
0.0849 
(0.217) 
-0.0670 
(0.708) 
0.175 
(0.328) 
-0.158 
(0.419) 
-0.179 
(0.402) 
-0.143 
(0.420) 
-0.0507 
(0.779) 
0.00335 
(0.986) 
0.172 
(0.360) 
0.0227 
(0.897) 
0.446** 
(0.0139) 
-0.254 
(0.165) 
-0.0472 
(0.779) 
-0.230 
(0.210) 
-0.216 
(0.225) 
0.237 
(0.162) 
-0.490** 
(0.0191) 
-0.00616 
(0.974) 
0.209 
(0.317) 
-0.0276 
(0.882) 
0.0773 
(0.726) 

Page | 104 

 
 
Variables 

NL 

PL 

PT 

RO 

SE 

SI 

SK (omitted) 

B - Mining and quarrying 

C - Manufacturing 

D - Electricity, gas, steam and air conditioning supply 

E - Water supply, sewerage, waste management/remediation  

F - Construction 

G - Wholesale and retail trade, repair of motor vehicles and 
motorcycles 

H - Transportation and storage 

I - Accommodation and food service  

J - Information and communication 

K - Financial and insurance  

L - Real estate  

M - Professional, scientific and technical  

N - Administrative and support service  

P - Education 

Q - Human health and social work  

Constant 

Observations 

Note: *** p<0.01, ** p<0.05, * p<0.1, ++ p<0.2, + p<0.25 

Source: LE Europe analysis of Eurobarometer data 

Basic digital technology 
adoption 
0.134 
(0.427) 
0.206 
(0.225) 
0.115 
(0.555) 
-0.276 
(0.138) 
0.00697 
(0.971) 
0.123 
(0.492) 
- 
-0.00356 
(0.993) 
-0.231 
(0.275) 
-0.542 
(0.197) 
-0.479 
(0.134) 
-0.0414 
(0.848) 
-0.179 
(0.390) 
-0.156 
(0.502) 
0.128 
(0.569) 
-0.391 
(0.111) 
0.0393 
(0.884) 
0.159 
(0.554) 
-0.0115 
(0.958) 
-0.127 
(0.597) 
0.140 
(0.565) 
-0.0757 
(0.749) 
-0.320 
(0.221) 
2,801 

Advanced digital 
technology adoption 
0.203 
(0.223) 
-0.247 
(0.154) 
-0.226 
(0.267) 
-0.398** 
(0.0424) 
0.363* 
(0.0513) 
0.147 
(0.417) 
- 
0.388 
(0.361) 
0.0948 
(0.673) 
0.306 
(0.444) 
0.0521 
(0.872) 
-0.115 
(0.622) 
0.0808 
(0.715) 
0.0426 
(0.861) 
-0.183 
(0.453) 
0.366 
(0.144) 
0.144 
(0.611) 
0.0669 
(0.815) 
0.0545 
(0.816) 
0.183 
(0.467) 
0.0821 
(0.752) 
0.0294 
(0.906) 
-1.125*** 
(3.65e-05) 
2,801 

Page | 105 

 
106 

Table 20 Probit model results for medium-sized SMEs only 

Variables 

Age 

Scale-up 

Gazelle 

City 

Not part of a global value chain 

External funds available 

Exporter 

Independent 

Innovation 

Lack of financial resources 

Lack of skills, including managerial skills 

Lack of information technology infrastructure, such as high 
speed internet connection 

Regulatory obstacles 

IT security issues 

Uncertainty about future digital standards 

Internal resistance to change 

AT 

BE 

BG 

CY 

CZ 

DE 

DK 

EE 

ES 

Basic digital technology 
adoption 
-0.000980 
(0.522) 
-0.164* 
(0.0799) 
0.0539 
(0.843) 
0.0999 
(0.192) 
0.0701 
(0.525) 
-0.150* 
(0.0726) 
-0.253*** 
(0.00565) 
0.136 
(0.116) 
-0.258*** 
(0.00105) 
0.0209 
(0.817) 
-0.109 
(0.248) 
-0.0310 
(0.768) 
-0.291*** 
(0.00620) 
-0.0777 
(0.463) 
-0.122 
(0.219) 
-0.199** 
(0.0290) 
0.351 
(0.138) 
0.0197 
(0.939) 
0.305 
(0.205) 
-0.0879 
(0.811) 
-0.113 
(0.642) 
0.568** 
(0.0171) 
0.247 
(0.301) 
0.568** 
(0.0193) 
-0.202 
(0.476) 

Advanced digital 
technology adoption 
0.000262 
(0.847) 
0.0560 
(0.493) 
-0.127 
(0.622) 
0.0733 
(0.292) 
-0.170* 
(0.0689) 
0.152* 
(0.0609) 
0.153* 
(0.0618) 
0.0272 
(0.734) 
0.387*** 
(2.71e-07) 
-0.166** 
(0.0430) 
0.00855 
(0.918) 
0.0928 
(0.321) 
0.206** 
(0.0237) 
-0.0227 
(0.803) 
0.0642 
(0.459) 
0.0569 
(0.472) 
0.162 
(0.464) 
0.0701 
(0.763) 
0.0661 
(0.778) 
0.364 
(0.263) 
-0.258 
(0.265) 
-0.0443 
(0.847) 
0.260 
(0.243) 
0.0581 
(0.808) 
-0.0709 
(0.759) 

Page | 106 

 
 
Variables 

FI 

FR 

EL 

HR 

HU 

IE 

IT 

LT 

LU 

LV 

MT 

NL 

PL 

PT 

RO 

SE 

SI 

SK (omitted) 

B - Mining and quarrying 

C - Manufacturing 

D - Electricity, gas, steam and air conditioning supply 

E - Water supply, sewerage, waste management/remediation  

F - Construction 

G - Wholesale and retail trade, repair of motor vehicles and 
motorcycles 

H - Transportation and storage 

I - Accommodation and food service  

J - Information and communication 

Basic digital technology 
adoption 
-0.450 
(0.157) 
0.235 
(0.387) 
0.417* 
(0.0794) 
0.591** 
(0.0123) 
0.375 
(0.119) 
-0.303 
(0.254) 
0.960*** 
(0.000132) 
0.649*** 
(0.00571) 
0.334 
(0.333) 
0.127 
(0.616) 
0.401 
(0.167) 
-0.270 
(0.327) 
0.380 
(0.108) 
0.138 
(0.609) 
-0.277 
(0.277) 
-0.148 
(0.591) 
0.257 
(0.293) 
- 
-0.218 
(0.739) 
-0.442 
(0.118) 
-0.585 
(0.195) 
-0.895** 
(0.0192) 
-0.336 
(0.256) 
-0.268 
(0.350) 
-0.415 
(0.176) 
-0.205 
(0.513) 
-0.531 

Advanced digital 
technology adoption 
0.715*** 
(0.00350) 
-0.0199 
(0.937) 
-0.0873 
(0.711) 
-0.0856 
(0.710) 
0.0835 
(0.713) 
0.0504 
(0.828) 
-0.332 
(0.194) 
-0.201 
(0.393) 
0.287 
(0.371) 
0.177 
(0.448) 
-0.260 
(0.349) 
0.329 
(0.167) 
0.0775 
(0.727) 
0.241 
(0.308) 
-0.161 
(0.491) 
0.292 
(0.215) 
0.267 
(0.239) 
- 
0.122 
(0.839) 
0.267 
(0.371) 
0.948** 
(0.0287) 
0.514 
(0.153) 
0.141 
(0.649) 
0.212 
(0.480) 
0.200 
(0.529) 
0.228 
(0.494) 
0.573* 

Page | 107 

 
108 

Variables 

K - Financial and insurance  

L - Real estate  

M - Professional, scientific and technical  

N - Administrative and support service  

P - Education 

Q - Human health and social work  

Constant 

Observations 

Note: *** p<0.01, ** p<0.05, * p<0.1, ++ p<0.2, + p<0.25 

Source: LE Europe analysis of Eurobarometer data 

Basic digital technology 
adoption 
(0.126) 
-0.157 
(0.659) 
-0.274 
(0.453) 
-0.119 
(0.700) 
-0.280 
(0.373) 
-0.0135 
(0.966) 
0.102 
(0.734) 
-0.188 
(0.589) 
1,698 

Advanced digital 
technology adoption 
(0.0907) 
0.235 
(0.494) 
0.460 
(0.196) 
0.383 
(0.228) 
0.370 
(0.251) 
0.498 
(0.140) 
-0.00127 
(0.997) 
-1.061*** 
(0.00229) 
1,698 

An additional area of interest would be to run the analysis for digital and non-digital sectors, however the data 
is not granular enough to achieve this. 

Page | 108 

 
 
 
 
6  The digitalisation of SMEs and its link to GHG emissions  

According  to  a  recent  narrative,  digitalisation  may  not  only  increase  businesses´  competitiveness  and 
productivity  but  may  also  enhance  their  sustainability  by  reducing  greenhouse  gas  (GHG)  emissions.  The 
digitalisation of SMEs could thus contribute to achieving the overall goal of emission neutrality in 2050 set by the 
European Commission.165  

Digitalisation may increase productivity through improvements in supply chain management, which may lead to 
higher energy efficiency (substitution effect). The production of the same output can thus be achieved with lower 
GHG  emissions.  The  potential  for  improvements  in  productivity  and  resulting  reductions  in  GHG  emissions  is 
particularly high for SMEs for two reasons. First, SMEs account for a large share of economic activity in most 
countries. Second, SMEs are generally less productive than large firms: across the EU27, SME productivity was 
about EUR 40 000 in 2020, compared to EUR 66 300 for large companies.166 

However, higher productivity also enables firms to produce more cost-efficiently and to lower prices, thereby 
incentivizing higher consumption and production (income effect). An increase in the total level of production 
thus leads to a “rebound effect” in GHG emissions. Theoretically, it is not clear which of two opposing effects - 
the reduction in GHG emissions due to an increase in energy efficiency or the increase in GHG emissions due to 
higher output - prevails.  

This chapter provides an empirical analysis of how digitalisation paths across different industries impact GHG 
emissions  and  whether  the  impact  on  overall  GHG  emissions  depends  on  the  share  of  SMEs  in  an  industry. 
Descriptive statistics indicate that GHG intensity of companies depends more on the sector than on the country. 
A  regression  analysis  leads  to  the  conclusion  that  industries  with  higher  digitalisation  levels  may  show 
significantly lower levels of overall GHG emissions but only in sectors with a lower share of SME employment. In 
sectors  with  a  higher  share  of  SME  employment,  higher  digitalisation  levels  are  associated  with  higher  GHG 
emissions.  The  latter  must  be  vetted  against  the  finding  that  a  higher  share  of  SMEs  in  a  sector  in  general 
indicates lower GHG emissions. 

6.1  Research overview 

The counteracting effects outlined above are reflected in mixed findings in the emerging field of research linking 
digitalisation and sustainability. On the one hand, studies find or forecast that digitalisation has or will have a 
positive impact on sustainability through its impact on productivity, resulting in a decrease in GHG emissions. 
The GLOBAL e-SUSTAINABILITY INITIATIVE, for example, estimated in their SMARTer 2030 report that ICT enabled 
solutions offer the potential to reduce GHG emissions by 20 % until 2030 (GeSI, 2015). However, other studies 
come to the opposite conclusion. They find that ICT can also adversely affect sustainability depending on country 
characteristics and the level of ICT, leading to an increase in GHG emissions. Salahuddin et al. (2016), for example, 
found that a 1% increase in internet usage results in a rise of 0.16% in GHG emissions across OECD countries.  

Higon et al (2017) analysed the impact of ICT on CO2 emissions for a panel of 142 countries over the period from 
1995  to  2010.  They  found  that  rising  ICT  levels  lead  to  a  reduction  in  CO2  emissions  in  developed  countries, 
whereas the opposite holds for developing countries. The authors concluded that the relationship between ICT 
and CO2 is best described as an inverted U-shape. Similarly, Khan et al (2020) showed that ICT reduces emissions 
in  developed  countries  and  increases  emissions  in  developing  countries.  However,  for  the  full  sample  of  91 
countries, ICT reduces emissions. In contrast, Park et al. (2018) concluded that more usage of ICT led to higher 
CO2 emissions for a panel of 23 EU Member States.  

Empirical research focusing on environmental effects across industries, instead of countries as a whole, is scarce. 
Bernstein & Madlener (2008) analysed the impact of ICT capital on electricity intensity, not GHG emissions, in 
five  major  European  industries.  To  measure  ICT  capital,  they  used  data  from  the  EU-KLEMS-dataset  and 
combined it with data from EUROSTAT on electricity intensity. The key finding of the panel analysis with cross-
section fixed effects was that the effect of computer and hardware on electricity-intensity is sector-specific. In 
contrast, communication technologies were  found to have an energy-saving effect in  all industries. Similarly, 

165 https://ec.europa.eu/info/strategy/priorities-2019-2024/european-green-deal_en 
166 Source: SME PR 2020 

Page | 109 

 
 
 
110 

Collard et al. (2005) found that electricity intensity decreased with a rise in communication devices in French 
service sectors, whereas electricity intensity of production increases with computers and software.  

The explicit link between digitalisation in SMEs and GHG emissions has not been intensively studied yet. Isensee 
et  al.  (2020)  proposed,  after  conducting  an  extensive  literature  review,  that  SMEs  should  adopt  green 
digitalisation tools to improve their sustainability. However, the authors themselves regard the available data 
linking SMEs, digitalisation and GHG emissions as insufficient. In the following, we aim to fill this research gap by 
linking the SME PR 2020 database with macro data on digitalisation and industry-level GHG emissions over the 
period from 2008 to 2016. 

6.2  Database 

The  dataset  used  in  the  following  analysis  covers  16  countries  and  12  sectors  of  the  non-financial  business 
economy for the years 2008 to 2016. Following the approach of Berstein & Madlener (2008), the current state 
of digitalisation across industries is measured by data from the EU-KLEMS data set on the capital net stock167 of 
the capital types computer and hardware (IT), telecommunication equipment (CT) and software and databases 
(Soft_DB).168 In the following, these variables are abbreviated as ICT. The data  for  GHG  emissions have been 
taken from air emissions accounts, published by EUROSTAT. GHG emissions are reported as CO2-equivalent for 
64 industries according to the NACE classification.169 Air emissions accounts follow the residential principle, i.e. 
regardless of where the emissions take place geographically, emissions are assigned to the country where the 
operator  causing  the  emissions  resides.170  To  link  digitalisation  and  GHG  emissions  with  SMEs,  the  data  are 
merged  with  the  information  from  the  2021  SME  PR  database.  The  professional  activities  (M)  and  the 
administrative activities (N) sectors are aggregated as specified by EU-KLEMS. Not all countries of the SME PR 
database could be included in the analysis due to missing data within the EU-KLEMS or EUROSTAT database. In 
2016, the countries included in the analysis represented 88.2 % of value added and 78.4 % of employment in the 
EU27. 

6.3  Descriptive evidence 

The following figure describes the development of digitalisation, productivity, absolute GHG emissions and GHG 
emissions per employee aggregated across all 16 countries and all 12 sectors from 2008 to 2016. 

167 Capital net stock: “The stock of assets surviving from past periods and corrected for depreciation is the net (or wealth) capital stock. The 
net stock is valued as if the capital good (used or new) were acquired on the date to which a balance sheet relates.” https://www.oecd-
ilibrary.org/economics/national-accounts-at-a-glance-2009/net-capital-stock_9789264075108-23-en;jsessionid=J3Op_e-
QonnGsnHDRTizlj1u.ip-10-240-5-21  
168 https://euklems.eu/download/ 
169 https://ec.europa.eu/eurostat/web/climate-change/data/database 
170 https://ec.europa.eu/eurostat/statistics-
explained/index.php/Greenhouse_gas_emissions_by_industries_and_households#Greenhouse_gas_emissions 

Page | 110 

 
 
 
 
Figure 30 Development of central indicators from 2008 to 2016 

Note: The share of ICT is weighted by the respective net capital of all assets in (1).  
Source: EUROSTAT (3) + (4), EU-KLEMS (1), SME PR (2)+(4) 

Figure 30’s first panel depicts the share of ICT capital of the overall net capital stock. The share of ICT capital – 
also referred to as ICT intensity in the following analysis - stagnated between 2008 and 2014 at a level of about 
2.1 % and grew in 2015 and 2016 to about 2.3 %. The development of productivity, defined as value added per 
employee, is displayed in panel (2) of  Figure 30. Until 2012, productivity growth was strongly affected by the 
global financial crisis and the following European debt crises. However, from 2012 onwards, productivity grew 
from EUR 50 600 to EUR 54 100 per employee in 2016.  

Panel  (3)  displays  the  path  of  the  absolute  amount  of  GHG  emissions  in  million  tonnes  of  CO2  equivalent. 
Emissions fell from 2 400 million tonnes in 2008 to 2 000 million tonnes in 2014 and stagnated from then on. CO2 
emissions per employee, depicted in Figure 33, followed a similar trend. In 2008, the average employee emitted 
26.2 tonnes in CO2 equivalent. The value dropped to about 21 tonnes per employee in 2016.  

However, one should be cautious to draw any conclusions on the relationship between ICT capital, productivity 
and  GHG  emissions  based  on  these  aggregated  data.  There  is  substantial  variation  in  ICT  capital  and  GHG 
emissions across industry sectors and countries which is masked in the aggregate data. The following plots take 
a  closer  look  at  the  relationship  between  GHG  intensity  and  ICT  capital  both  within  and  across  sectors  and 
countries.  

Figure 31 shows the relationship between GHG emissions per employee and the share of ICT net capital on all 
assets171. Each dot represents an observation for a sector within a country for a certain year and the different 
colours mark the different sectors.  

171 GHG emissions are displayed in logs (logarithmic scale), a common practice for skewed variables. 

Page | 111 

00.511.522.5200820092010201120122013201420152016(1) Share of ICT on all assets in % 0102030405060200820092010201120122013201420152016(2) Productivity in EUR 1,00005001,0001,5002,0002,5003,000200820092010201120122013201420152016(3) GHG em. in mil. tonnes051015202530200820092010201120122013201420152016(4) GHG em. per employee in tonnes   
 
 
 
112 

Figure 31 Share of ICT capital in % vs. GHG emissions in tonnes per employee (in logs) by sector 

Note: The information and communication sector has very high shares in ICT net capital with maximum values larger than 60 %. However, 
for illustration purposes, the graph is cut at the 20 % threshold.  
Source: EU-KLEMS, EUROSTAT 

First, it is notable that the observations are clustered by industries. Sectors tend to have similar levels of ICT 
capital  and  GHG  emissions  per  employee  across  countries  and  periods.  Additionally,  there  seems  to  exist  an 
overall negative correlation between digitalisation and emissions per employee. However, this correlation seems 
to  be  driven  by  variation  of  GHG  emissions  across  sectors  with  different  levels  of  ICT  capital  rather  than  by 
variation  in  ICT  capital  within  sectors.  As  expected,  the  electricity  and  gas  sector  and  the  water  supply, 
manufacturing and transportation show the most GHG emissions per employee. The least GHG emissions per 
employee are emitted by service sectors. Within sectors the dots are horizontally  distributed, indicating that 
there is no correlation between ICT capital per employee and GHG emissions if one compares GHG emissions in 
the same sector across different countries or periods.  

Page | 112 

 
 
 
 
Figure 32 Share of ICT capital in % vs. GHG emissions in tonnes per employee (in logs) by country 

Note: The information and communication sector has very high shares in ICT net capital with maximum values larger than 60 %. However, 
to remain informative, the graph is cut at the 20 % threshold.  
Source: EU-KLEMS, EUROSTAT, SME PR 

Figure  32  depicts  the  same  data  as  Figure  31  but  differentiated  by  countries.  It  illustrates  the  challenges  in 
analysing  the  impact  of  ICT  capital  on  GHG  emissions  on  the  country  level.  Each  country’s  economy  is 
characterised by a different composition of sectors. Figure 31 has shown that ICT capital stock and GHG emissions 
differ  systematically  and significantly  across  sectors.  Therefore,  when  analysing the  relationship  between  ICT 
intensity  and  GHG  emissions  in  a  country  over  time,  shifts  in  the  sectoral  distribution  should  be  taken  into 
account. A country shifting from an industry-oriented to a more service-oriented economy will experience an 
increase in the share of ICT capital mainly because service sectors tend to have more ICT capital. If GHG emissions 
decrease during the shift, the reduction in emissions should be attributed to the shift in the sectoral distribution 
of the economy rather than the digitalisation paths of companies within a sector.  

Page | 113 

 
 
 
 
114 

Figure 33 Share of employees in SMEs in % vs. share of ICT in % by sector 

Note: The information and communication sector has very high shares in ICT net capital with maximum values larger than 60 %. However, 
to remain informative, the graph is cut at the 20 % threshold.  
Source: EU-KLEMS, SME PR 

Figure 33 plots the share of employees in SMEs and ICT intensity within a sector in a country in a certain year. 
Again, the data points are clustered by sectors. The graph does not reveal any clear relationship between the 
share of employees in SMEs and ICT intensity. While for sectors with a share of employees in SMEs below 75 
percent, a slightly positive correlated may be detected, the relationship seems to become negative for companies 
with  a  high  share  of  employees  in  SMEs  above  75%.  There  are  some  sectors  with  particularly  high  shares  of 
employees of SMEs and low shares of ICT capital, including the real estate activities sector, the accommodation 
and food services sector and the construction sector. There is also no clear relationship between the share of 
employees in SMEs and ICT intensity within sectors.  

Page | 114 

 
 
 
 
 
 
Figure 34 Share of employees in SMEs in % vs. GHG emissions in tonnes per employee (in logs) 
by sector 

Note: The information and communication sector has very high shares in ICT net capital with maximum values larger than 60 %. However, 
to remain informative, the graph is cut at the 20 % threshold.  
Source: EU-KLEMS, SME PR 

Finally, the share of employees in SMEs is plotted against GHG emissions per employee across sectors. Once 
again,  the  data  are  clustered  by  sector:  Sectors  tend  to  have  similar  shares  of  employees  in  SMEs  and  GHG 
emissions across countries and periods. Strikingly, sectors with high  shares of SMEs tend to have lower GHG 
emissions per employee. However, within sectors, no clear relationship between the two variables is observable. 

6.4  Regression analysis 

The  graphical  analysis  revealed  that  differences  across  sectors  account  for  a  large  portion  of  the  observed 
variation in ICT capital, GHG emissions and the share of SMEs across countries and time periods. To investigate 
the impact of digitalisation paths of companies on GHG emissions, this variance across sectors should thus be 
explicitly controlled for in the following regression analysis.  

The  regression  analysis  aims  to  estimate  the  impact  of  SMEs  and  ICT  on  absolute  GHG  emissions  and  GHG 
emissions  per  employee.  To  construct  an  experiment-like  setting,  the  whole  sample  was  divided  into 
observations with a relatively high share and those with a relatively low share of ICT capital. A dummy variable 
takes the value 1 if the observation – i.e. the value of ICT capital in a sector in a country in a particular year - is 
among the highest three deciles of ICT intensity within a sector and year. The dummy takes the value 0 when the 

Page | 115 

 
 
116 

observation is amongst  the lower  3 deciles.172 The dependent  variable, GHG  emissions, is included in logs to 
simplify the interpretation of the estimated effects in percentages.  

The explanatory variables of interest in the regression are the share of employees working in SMEs, ICT-intensity 
and an interaction of the share of employees in SMEs and ICT-intensity. The interaction term allows investigating 
in how far the effect of digitalisation on GHG emissions depends on the share of SME employment in the sector. 
Additionally, the regression model controlled for time (years), sectors and countries. The standard errors are 
clustered at the sector level. 

Figure 35 Coefficients of Regression of GHG Emissions on Digitalisation and Share of SMEs 

Note: N = 1,107, regression with industry-clustered standard errors, controlling for time, country and industry, 90%-significance level 
confidence interval. Own calculations.  
Source: EU-KLEMS, EUROSTAT, SME PR 

Figure 35 focuses on the regression results with GHG emissions as dependent variable173, showing the effect size 
and the corresponding p-value of the main variables of interest. Both the coefficient of SME employment and 
the digitalisation dummy are estimated to be negative, indicating a reducing effect on GHG emissions. However, 
to correctly interpret  the  effect of both variables, the interaction term  must  also be taken into account. The 
coefficient of the interaction is estimated to be positive, implying that the decreasing effect of digitalisation on 
GHG emissions is weaker the higher the share of SME employment in a sector.  

Figure 36 illustrates how the estimated effect of digitalisation varies with the share of SME employment. While 
the effect of a sharp increase in the share of ICT capital (i.e. digitalisation dummy equal to 1) is estimated to be 
negative for sectors with a low share of SME employment, it turns positive at a threshold SME employment share 
of around 66%. For example, at a SME employment share of 60%, a sharp increase in digitalisation is estimated 
to reduce emissions by around 8%; whereas at an SME employment share of 70%, digitalisation is estimated to 
increase emissions by around 4%.  

172 A transition from 0 to 1 may be interpreted as a treatment of digitalisation. Observations that fall in-between the status of “treated” 
and “untreated” are not covered by the regression approach. 
173 The regression with GHG emissions per employee as dependent variable showed no significant results. 

Page | 116 

 
 
 
 
 
Figure 36 The effect of a sharp increase in digitalisation on GHG emissions dependent on the 
share of SME employment 

Note: N = 1,107, pooled regression with industry clustered standard errors, controlled for time, country and industry. Own calculations.  
Source: EU-KLEMS, EUROSTAT, SME PR 

However, it should be emphasised that – despite the positive interaction effect – the overall effect of the share 
of SME employment on GHG emissions is estimated to be negative. For sectors with a high level of digitalisation, 
an  increase  in  SME  employment  of  1%-point  reduces  GHG  emissions  by  an  estimated  2.2%,  compared  to  a 
reduction of around 1% for sectors with high levels of digitalisation. 

6.5  Conclusion 

The above analysis provided several insights into the relationship of digitalisation, the share of SMEs and GHG 
emissions across sectors. First, all three variables – the ICT capital stock, the share of SME employment and GHG 
emissions – differ significantly and systematically across sectors. Therefore, any empirical analysis should control 
for the type of sector. Second, the regression analysis has shown that the effect of digitalisation depends on the 
share of SME employment. For sectors with low shares of SME employment, digitalisation is estimated to reduce 
emissions; this effect changes direction when the sectoral SME employment share exceeds 66%.  

However, one should be cautious to interpret the presented results causally. More research is needed to identify 
the causal relationship between digitalisation paths of SMEs and GHG emissions. In particular, the above analysis 
highlighted the challenges in analysing the relationship of digitalisation and GHG emissions at an aggregate level. 
Future  research  would  thus benefit  from  disaggregated,  company-level  data  on  digitalisation  paths  and  GHG 
emissions. 

Page | 117 

-100%-80%-60%-40%-20%0%20%40%60%05101520253035404550556065707580859095100Share of SMEs in employment 
 
 
INDEX OF FIGURES AND TABLES 
Index of Figures 

67 

10 
12 
13 
21 
61 
65 
66 

Figure 1 Implementation of DX in parts of business or organised around the entire business (in % of all firms) 
Figure 2 EU-27 Digital Economy and Society Index 2020 Scores 
Figure 3 EIBIS Digitalisation Index 
Figure 4 Impact of Industry 4.0 sustainability functions on a firms’ SDG performance 
Figure 5 Cumulative growth from in SME value from 2009 to 2016 
Figure 6 Regression results (based on fixed effects model) 
Figure 7 Regression results – standardized (based on fixed effects model) 
Figure 8 Actual and predicted annual growth rates in SME value added in % between 2011 and 2016 (based on pooled 
OLS model) 
Figure 9 Actual and predicted annual growth rates in SME value added in % between 2011 and 2016 (based on fixed 
68 
effects model) 
69 
Figure 10 Actual and predicted growth between 2010 and 2016 (based on pooled OLS model) 
69 
Figure 11 Actual and predicted growth between 2010 and 2016 (based on fixed effects model) 
74 
Figure 12 Total distribution by SME size 
74 
Figure 13 Distribution by SME size and type 
74 
Figure 14 Distribution of gazelles and scale-ups according to SME size 
75 
Figure 15 Distribution of SMEs in city and non-city locale 
75 
Figure 16 Distribution of SMEs which are part of a global value chain 
75 
Figure 17 Respondents who identified as being part of global value chain, by SME size 
76 
Figure 18 SME respondents who identified being part of a global value chain, by sector 
76 
Figure 19 SME respondents who identified as being part of a global value chain, by county 
77 
Figure 20 Availability of external funds, by SME size 
77 
Figure 21 Availability of external funds for SMEs, by Member State 
77 
Figure 22 Firm exporting by SME size 
78 
Figure 23 SMEs exporting across sectors 
78 
Figure 24 SME independence by SME size 
79 
Figure 25 Independent SMEs across sectors 
79 
Figure 26 Enterprise innovation by SME size 
80 
Figure 27 SME innovation by country 
80 
Figure 28 Distribution by country 
81 
Figure 29 Sector breakdown 
111 
Figure 30 Development of central indicators from 2008 to 2016 
112 
Figure 31 Share of ICT capital in % vs. GHG emissions in tonnes per employee (in logs) by sector 
113 
Figure 32 Share of ICT capital in % vs. GHG emissions in tonnes per employee (in logs) by country 
114 
Figure 33 Share of employees in SMEs in % vs. share of ICT in % by sector 
115 
Figure 34 Share of employees in SMEs in % vs. GHG emissions in tonnes per employee (in logs) by sector 
116 
Figure 35 Coefficients of Regression of GHG Emissions on Digitalisation and Share of SMEs 
Figure 36 The effect of a sharp increase in digitalisation on GHG emissions dependent on the share of SME employment 117 

Index of Tables 

Table 1 Taxonomy of sectors by digital intensity, overall ranking for 2013-2015 
Table 2 Estimated positive and negative impacts of DX against major impact functions 
Table 3 Examples of outputs/results 
Table 4 Current situation vs Expected results 
Table 5 Summary statistics – analysis variables 
Table 6 Regression results 
Table 7 Frequency distribution (in %) of Q 22: Please indicate which of the following options best describes your 
enterprise's approach to digital technologies? (Single answer) 
Table 8 Summary of technologies adopted by enterprises (Q 23, multiple answers allowed) 
Table 9 Model specification 
Table 10 Characteristics by country 
Table 11 Characteristics by SME size 

10 
19 
59 
59 
63 
64 

71 
71 
72 
82 
84 

Page | 118 

 
 
 
 
Table 12 Pairwise correlations of gazelle and scale-ups 
Table 13 Pairwise correlations for all SMEs 
Table 14 Pairwise correlations for micro SMEs 
Table 15 Pairwise correlations for small SMEs 
Table 16 Pairwise correlations for medium-sized SMEs 
Table 17 Probit model results for all SMEs 
Table 18 Probit model results for micro SMEs only 
Table 19 Probit model results for small SMEs only 
Table 20 Probit model results for medium-sized SMEs only 

85 
86 
89 
91 
94 
98 
101 
103 
106 

Page | 119 

 
 
 
 
 
 
 
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Annual Report on 
European SMEs 
2020/2021 
Digitalisation of SMEs 

BACKGROUND DOCUMENT 

ISBN 978-92-9460-710-2 
doi:10.2826/120209 

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