e-Commerce and firm performance : an assessment using multiple survey sources and linked data.
Date: 2004-11-28
Institutions: Eurostat
Subjects: business policy, economic consequence, electronic commerce, performance measurement, statistics

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e-Commerce and
firm performance
An assessment using multiple
survey sources and linked data

4

THEME 4
Industry,
trade and
services

E U R O P E A N
C O M M I S S I O N

 
 
 
 
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Luxembourg: Office for Official Publications of the European Communities, 2004

ISSN 1725-0684
ISBN 92-894-7246-4

© European Communities, 2004
© Office for National Statistics (UK), 2004

e-Commerce and Firm Performance 

An assessment using multiple survey sources and linked data 

Report to the European Commission (DG Eurostat and DG Enterprise) 

Authors:  Tony Clayton, Chiara Criscuolo, Peter Goodridge 

Contents 

1.   Introduction and summary 

2.   Objectives of research 

3.   Data sources used 

4.   Summary of statistical results 

5.   e-Commerce adoption evidence 

6.   Productivity in manufacturing 

7.   Price effects  

8.   Service sector; retailing and stocks 

9.   R&D and innovation 

10.  Conclusions and next steps 

With grateful acknowledgements to Kathryn Waldron, Misha Tsvelik and members of the ONS 
/ Ceriba Business Data Linking team for support on analytical work, and to Debra Prestwood, 
Andrew Walton, Ray Lambert, Andrew Allen and Sue Smith for access to, and advice on data. 

Office for National Statistics   

July 2003 

1 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
INDEX 

Research Framework 

1.   Introduction and summary 
1.1  
1.2   Conclusions on e-Commerce Impacts 
1.3   Conclusions on Methodology and Surveys 

2.   Objectives of research 
2.1 
2.2 

Initial approach 
How the approach developed during research 
Table 2.1 

Observation numbers in each data set and the overlaps 

3.   Data sources used for economic analysis 
3.1 

E-commerce question format 

Annual Business Inquiry 
Figure 3.1 
Community Innovation Survey 
Figure 3.2 
Producer Price Inquiry 
e-Commerce survey 
Figure 3.3 
Capital Investment Surveys 

3.2 

3.3 
3.4 

3.5 

Data summary from surveys 

Linear innovation model 

4.   Summary of statistical results 
e-Commerce adoption 
4.1 
Productivity in manufacturing 
4.2 
Price effects 
4.3 
Service sector; retailing and stocks 
4.4 
R&D and innovation 
4.5 

5.   e-Commerce adoption evidence 
5.1 
5.2 

Year of adoption of network technologies by sizeband 
e-commerce sales as % of turnover, by sizeband 
Business % e-commerce sales via internet, and all e- networks, 2000  
Business % e-commerce sales via internet, and all e- networks, 2001 

Technology combinations and e-commerce activity 

Background 
Technology infrastructure in firms 
Figure 5.1       Distribution of 'connected' technologies in firms 
Figure 5. 2 
Rate and distribution of adoption 
Figure 5.3 
Figure 5.4 
Figure 5.5 
Figure 5.6 
Sector balance of e-purchasers and e-sellers 
Table 5.1         e-Purchasers vs. e-Sellers 
Perceived business benefits of e-commerce 
Figure 5.7 
Figure 5.8 
Leads and lags 
Figure 5.9 
Entry and exit 
Table 5.2 

e-Activity vs. time with a connected technology 

Specific benefits vs. first year selling over e-networks 
'Marketing' benefits vs. first year selling over e-networks 

2000/2001 responses linked; % sales over all networks 

5.3 

5.4 

5.5 

5.6 

5.7 

2 

 
 
 
 
 
 
 
 
6.   Productivity in manufacturing 
6.1 
6.2 
6.3 
6.4 

Background to selection of approach 
Regression analysis 
Interpreting the regression results 
Interpreting the data 
Figure 6.1 
Table 6.1 
Table 6.2 
Table 6.3 
Table 6.4 
Table 6.5 
Table 6.6 
Figure 6.2 

Value added / employee vs. e-commerce activity 
Regression results, 2000 
Regression results, 2000 - 2001 
Regression results, 2000 - 2001, smaller firms 
Regression results, 2000 - 2001, larger firms 
Descriptive statistics 2000 - 2001 
Distribution of observations by sector 2000 - 2001 
Value added / employee by SIC sector, 2000 

7.   Price effects  
7.1 
7.2 
7.3 
7.4 
7.5   Conclusions  

Outline 
Firm discussions 
Evidence from other sources 
UK survey data 

Table 7.1 
Table 7.2 
Figure 7.1 
Figure 7.2 
Figure 7.3 
Figure 7.4 
Figure 7.5 
Figure 7.6 

Regression results for effect of e-selling on producer prices 
Sectors covered by analysis 
SICs 1513 and 1581 
SICs 2852 and 2442 
SICs 2414 and 2420 
SICs 3210 and 2212 
SICs 24511 and 3430 
SICs 2430 

8.   Service sector; retailing and stocks 
8.1 
8.2 

Labour Productivity in Services 
Retail productivity 
Table 8.1  
SIC 52.1 Retail sale in non-specialised stores 
Table 8.2  
SIC 52.2 Retail sale of food, beverages and tobacco; specialised stores 
Table 8.3  
SIC 52.3 Retail sale of pharmaceutical and medical goods, etc 
Table 8.4  
SIC 52.4 Other retail sale of new goods in specialised stores 
Productivity across SIC 52 (2000) 
Table 8.9  
Table 8.10   Productivity across SIC 52 (2001) 
Table 8.11  
Retail Stocks 
Table 8.19  
Table 8.20  
Table 8.21 
Table 8.22 
Table 8.27 
Table 8.28 
Table 8.29 

SIC 52.1 Retail sale in non-specialised stores 
SIC 52.2 Retail sale of food, beverages and tobacco;specialised stores 
SIC 52.3 Retail sale of pharmaceutical and medical goods, 
SIC 52.4 Other retail sale of new goods in specialised stores 
Stock ratios - SIC 52 Retail (2000) 
Stock ratios - SIC 52 Retail (2001) 
Stock ratios - SIC 52 Retail (2000) by size and e-activity 

Size (employment) and e-commerce 

8.3 

8.4 Conclusions 

Figure 8.1:   Value added / employee, 2000, service industries 
Appendix A  Regression Analysis 

3 

 
 
 
 
 
 
 
 
 
 
 
 
Annexe to section 8; Size and sector - productivity; 

SIC 52.1 Retail sale in non-specialised stores 
SIC 52.2 Retail sale of food, beverages and tobacco in specialised stores 
SIC 52.3 Retail sale of pharmaceutical and medical goods, etc. 
SIC 52.4 Other retail sale of new goods in specialised stores 

SIC 52.1 Retail sale in non-specialised stores 
SIC 52.2 Retail sale of food, beverages and tobacco in specialised stores 
SIC 52.3 Retail sale of pharmaceutical and medical goods, 
SIC 52.4 Other retail sale of new goods in specialised stores 

SIC 52.1 Retail sale in non-specialised stores 
SIC 52.2 Retail sale of food, beverages and tobacco in specialised stores 
SIC 52.4 Other retail sale of new goods in specialised stores 

SIC 52.1 Retail sale in non-specialised stores 
SIC 52.2 Retail sale of food, beverages and tobacco in specialised stores 
SIC 52.3 Retail sale of pharmaceutical and medical goods, 
SIC 52.4 Other retail sale of new goods in specialised stores 

Table 8.5 
Table 8.6 
Table 8.7 
Table 8.8 
Sector and e-commerce effects 
Table 8.12 
Table 8.13 
Table 8.14 
Table 8.15 
Sector, size and e-commerce effects 
Table 8.16 
Table 8.17 
Table 8.18 
Stock trends by sector / size 
Table 8.23 
Table 8.24 
Table 8.25 
Table 8.26 
Stocks, e-commerce effects by sector 
Table 8.30 
Table 8.31 
Table 8.32 
Table 8.33 
Stocks, e-commerce, sector and size 
Table 8.34 
Table 8.35 

SIC 52.1 Retail sale in non-specialised stores 
SIC 52.2 Retail sale of food, beverages and tobacco in specialised stores 
SIC 52.3 Retail sale of pharmaceutical and medical goods 
SIC 52.4 Other retail sale of new goods in specialised stores 

SIC 52.1 Retail sale in non-specialised stores 
SIC 52.4 Other retail sale of new goods in specialised stores 

9.   R&D and innovation 
9.1 
Background 
9.2  Models for R&D and Innovation 
Studies based on UK CIS 
9.3 
Tabulations based on CIS3 
9.4 
R&D => Innovation 
9.5 
Figure 9.1 
Figure 9.2 
Innovation => output growth 
Figure 9.3 
Figure 9.4 
e-Business effects and the linear model 
Electronic networks and collaboration 
Figure 9.5 

9.7 
9.8 

9.6 

Innovation vs relative 'intellectual capital' R&D expenditure 
Innovation vs relative innovation related expenditure 

Relative growth vs relative innovation - manufacturing 
Relative growth vs relative innovation - services 

Collaboration section of CIS questionnaire 

10. Conclusions and next steps

4 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1 

Introduction and Summary 

1.1 Research Framework 

This report draws together work undertaken in ONS up to June 2002/3 to investigate the ways 
in which the use of electronic networks by firms affects their behaviour and performance. In 
undertaking these studies we have built upon progress made elsewhere, both in measurement 
techniques for use of ICT and e-commerce, and in analysis of the data from resulting surveys. 
We owe a great deal to: 
-  progress made by Eurostat and OECD in defining common approaches to measurement and 

enterprise surveys covering ICT use 

-  definitions of e-commerce developed by OECD, and applied in Eurostat's work 
- 

the international exchanges coordinated by OECD during 2002 on ICT impact work, and 
brought together in their forthcoming ministerial report. 

The purpose of the research is to investigate how far data collected in Eurostat based surveys 
on the information society (the enterprise e-commerce survey) can be linked with those on 
structural business statistics (the annual business inquiry for the UK) and on innovation (the 
community innovation survey) to develop methodologies and deliver conclusions on the 
economic impact of e-commerce. In doing this, we have analysed  
• 

the scope for linking between the various surveys, and the possible value of other sources 
for this type of work 

•  practical possibilities for obtaining statistically valid results from linked data 
•  conclusions from structural business statistics on productivity, using qualitative questions, 

• 

and the implications these appear to have for pricing 
feasibility of analysing effects of electronic business processes on logistics (as measured by 
stocks) using e-commerce questions linked to structural business statistics in the retail 
sector 

•  possible models for electronic business processes to impact on innovation outputs. 

1.2 Conclusions on e-Commerce Impacts 

The main conclusions from the programme of work to date are that: 
•  e-commerce survey results from the first two rounds of UK surveys show a high level of 

experimentation, entry and exit in electronic markets, and the sampling issues make overlap 
with the structural business statistics too small to generate statistically robust conclusions 
on firm performance (early indications from the third round suggest more stability)  
the e-commerce surveys do, however give valuable conclusions on the adoption of ICT and 
e-business processes, which inform research using other sources 

• 

• 

•  productivity effects of e-commerce from the much larger manufacturing samples available 
from the ABI give robust results for both 2000 and 2001, but show effects which are more 
complex than the simple 'production function' modelling which has been used in much of 
the macro and micro work to date 
in addition to 'efficiency effects' from e-commerce use which appears to be significant in 
statistical analysis of gross output in the manufacturing sector, there are much more 
pronounced price effects which are visible in analysis of value added which discriminate 
between the gains and losses to buyers and sellers 
in the majority of manufacturing sectors we have examined in detail (by linking firm level 
e-commerce use to price evolution as detected in price surveys) it appears that increased 
competition due to electronic trading exerts a restraining influence on prices, changing the 

• 

5 

 
 
 
 
 
 
• 

distribution of value added between buyers and sellers, and this affects the ability to detect 
productivity gains from ICT at firm level 
the impact of electronic transactions differs across manufacturing sectors, and can be very 
different in services; in retailing for example, the gains and losses associated with buying / 
selling appear the reverse of manufacturing, and apparent effects on stocks are unexpected 

•  while the analysis of innovation has failed to show significant benefits associated with e-

• 

commerce, this is in part due to methodological issues associated with measurement of the 
innovation process, and the limitations of initial data in both innovation and e-commerce 
the model suggested in our original proposal for innovation leading to enterprise growth 
and productivity gains is partly supported by this, and other, research; however the possible 
effects of electronic business processes in it are more likely to be connected with enhancing 
collaboration between firms than directly with access to markets 

Perhaps the most important conclusion of work to date is that the most visible effect of 
electronic networks on business behaviour and performance in our data is not due to internal 
efficiency gains or shifts in production functions. Instead it shows up in changes to the way 
markets work. The evidence in this research, which for the first time splits the impact of buying 
and selling in electronic markets, shows how e-commerce has influenced market efficiency. 

Taken together with case evidence from firms which have used electronic networks for 
procurement, it is consistent with assertions that reduction in search costs in electronic markets, 
the broadening of potential supply and the increased transparency of price and non-price factors 
have reduced 'friction' in markets. In manufacturing sectors where there is typically scope for 
buyers to make comparisons this is more likely than not to exert downward pressure on prices 

In the longer term, more efficient markets may speed up selection processes by which efficient 
producers replace the less efficient. But in the early stages of business adoption of buying and 
selling through electronic networks, the strongest statistical findings appear to be related to the 
efficiency of markets themselves. Therefore, to assemble evidence for productivity effects from 
ICT and e-business it is necessary to look at other output measures in addition to value added. 

1.3 Conclusions on Methodology and Surveys 

A general, conclusion from the work on data linking contained in this project is that overlaps 
between surveys restrict what can be achieved. Adequate coverage in overlapping samples is 
only achieved for larger firms, sampled at or close to the 100% level in at least one of the 
surveys linked. This problem may diminish as a longer panel of surveys is achieved, but even 
this is unlikely to give sufficient overlap data on smaller enterprises, typically sampled at low 
frequency, and therefore unlikely to appear in two separate surveys. The approach used by 
ISTAT - to add e-commerce questions to the structural business survey - seems the best 
approach to tackle SMEs. 

Another pointer from this work is the need to look beyond electronic buying and selling. It is 
clear from our limited conclusions on innovation and logistics that links between firms that do 
not involve buying and selling transactions affect behaviour. The current Eurostat survey, as 
applied in the UK in 2001/2 and 2002/3 captures e-business processes which are linked through 
e-commerce links; work on assessing their associations with productivity are under way. 
Results from the US Bureau of Census Computer Network Use survey suggests that electronic 
links through e-commerce  represent less than half of the links in use. Developing effective 
measures of wider e-business links is a challenge which EU surveys have yet to complete.

6 

 
 
 
 
 
 
 
2 

Objectives of Research 

Initial approach 

surveys used to produce structural business statistics (the Annual Business Inquiry or ABI) 
the Eurostat designed e-commerce inquiry 

2.1 
This project was designed as pilot research to investigate the feasibility of linking micro data 
from: 
- 
- 
-  other surveys including the Community Innovation Survey (CIS) 
Results from each of these surveys is reported separately by ONS or DTI, and the scope for 
micro-data linking with the financial and employment data in the ABI is discussed fully in 
ONS publications (Barnes and Martin 2002). The purpose of linking data at business level is to 
establish whether statistical evidence shows: 
- 
- 
- 
-  whether e-commerce users are more effective innovators than non-users 
Development of methodology in this area is seen in the UK as an important element in 
analysiing ICT impact in the economy, to assist policy management. To date UK based work 
on ICT productivity effects has been based on growth accounting studies at sector level (see 
London Economics study, 2003, and Oulton 2001).  By identifying how ICT and e-commerce 
adoption affects business behaviour and performance at the enterprise level, the use of survey 
data to assess policy impact for the Information Economy should be made more effective. 

links between e-commerce activity, ICT use , innovation, and business growth 
links between e-commerce use and labour productivity 
links between e-commerce use and working capital productivity 

The initial aim was to build as many links as possible between data-sets on output, innovation, 
e-commerce, ICT use, and to test integrated models using all of them simultaneously. This 
proved to be possible in some areas of statistical analysis, but not for the e-commerce survey, 
partly because of sample overlap issues and partly because of the high rate of entry and exit the 
survey itself shows from 2000 to 2001. 

How the approach developed during research 

2.2 
As the initial multiple linking approach - although practical - did not yield samples of 
businesses large enough for statistically significant results for parts of the work, an alternative 
was developed using linked analysis, based on single data-sets, or in some cases two linked 
data-sets. For example: 
- 

the e-commerce survey has been used to explore the relationships between ICT use and 
business take-up of e-commerce 
the ABI has been used, taking advantage of questions on e-commerce use included in 2000 
and 2001, for analysis of productivity.  

- 

However, linking has proved essential to achieve certain results. To analyse how far effects of 
e-buying revealed by analysis were due to price changes, we have taken data from ONS' 
producer price inquiry (PPI) series and separated it according to whether firms providing the 
original price data were actively selling through electronic networks or not. The identification 
of reporting units to do this was achieved through the UK business register, on the basis of 
responses to the ABI survey, rather than the smaller e-commerce survey for which the overlap 
sample with the PPI data-set would have been too small. 

7 

 
 
 
 
 
It is in the area of innovation that linking has proved most difficult, because of overlap 
problems with multiple surveys. While a substantial amount of work on innovation and 
productivity has been achieved by researchers at ONS using linkage between CIS and ABI, the 
additional three way link to the e-commerce survey has led to unworkably small samples. To 
get statistically workable data to relate e-commerce to innovation it has been necessary to use 
the internet usage questions from the innovation survey itself. 

Even for work on e-commerce and output data, the ABI source has proved more effective as a 
source of e-commerce use data because, as shown below, the ARD / e-commerce overlap 
provides only 707 linked observations, while the ABI data even after accounting for 
problematic or incomplete e-commerce items provides over 7000. 
The overlaps between ABI, CIS and e-Commerce surveys for 2000 are as follows: 
Table 2.1 

Observation numbers in each data set and their overlaps 

CIS3  ARD+ 

ARD 
(SEL) 

E-
COM 

E-
COMM 
7,318 
1,928 
DISTRIBUTION BY SECTOR 

8,172 

53,197 

ARD
+ 
 CIS3 
3,467 

ARD+CIS
3 + E-
COMM 
380 

75 

92 

297 

n/a 

n/a 

n/a 

n/a 

n/a 

127 

11,696 

2703 

3,440 

105 

4,456 

48 

150 

53 

947 

n/a 

n/a 

n/a 

707 

39 

78 

n/a 

n/a 

81 

1625 

41 

378 

n/a 

n/a 

n/a 

140 

16 

27 

14,400 

1764 

1,041 

569 

340 

102 

2,811 

2,564 

n/a 

177 

574 

790 

n/a 

773 

405 

10,163 

1112 

1,386 

1,562 

1,519 

3,457 

n/a 

n/a 

n/a 

n/a 

n/a 

n/a 

53 

150 

n/a 

332 

n/a 

n/a 

n/a 

n/a 

368 

n/a 

634 

n/a 

n/a 

n/a 

n/a 

50 

n/a 

45 

n/a 

n/a 

n/a 

  ALL SECTORS 

FORESTRY 

SIC 
CODE 
A  AGRICULTURE, HUNTING AND 
B  FISHING 
C  MINING AND QUARRYING 
D  MANUFACTURING 
E  ELECTRICITY, GAS AND WATER 
F  CONSTRUCTION 

SUPPLY 

G 

WHOLESALE AND RETAIL 
TRADE; REPAIR OF MOTOR 
VEHICLES, MOTORCYCLES AND 
PERSONAL AND HOUSEHOLD 
GOODS 

COMMUNICATION 

H  HOTELS AND RESTAURANTS 
I  TRANSPORT, STORAGE AND 
J  FINANCIAL INTERMEDIATION 
K  REAL ESTATE, RENTING AND 
BUSINESS ACTIVITIES 
M  EDUCATION 
N  HEALTH AND SOCIAL WORK 
OTHER COMMUNITY, SOCIAL 
AND PERSONAL SERVICE 
ACTIVITIES 

O 

Notes: Observations are reporting units. In terms of usable observations numbers are lower, for 
example: 
- 
-  ABI  questions  on  e-commerce  are  problematic  or  incomplete  for  all  except  34,645  of  the 

e-commerce has 7,318 observations, but 44 are problematic and 1 incomplete.  

53,197observations, but still provide by far the largest evidence base. 

8 

 
 
 
 
 
 
 
3 

Data Sources  for Economic Analysis 

Annual Business Inquiry 

3.1 
Included in the 2000 and subsequent ABI surveys for UK firms has been a question on use of 
electronic networks (internet or other networks) for placing or receiving orders. The format for 
these questions, which effectively use the OECD 'broad' definition of e-commerce is: 
Figure 3.1 

E-COMMERCE

If you use the Internet, Electronic Data Interchange or any other 
electronic network to:-
Place orders for goods or services, please enter '1'  in the box 
provided.  If not, please enter '2' 

Receive orders for goods or services, please enter '1'  in the box 
provided.  If not, please enter '2' 

This data, for e-commerce use and all the normal Structural Business Statistics set of 
employment, gross and net output, and inputs (including purchased ICT services as a specified 
item) is available for a sample of over 7,000 reporting units in the manufacturing sector, and 
has delivered highly significant results. It has also been used for initial analysis of an even 
larger number of reporting units in the service sector, but looking simply at labour productivity 
because the services data-set does not yet have accompanying estimates of firm level capital 
stock as the manufacturing data does. 

The UK ABI is available for analysis as a longitudinally linked dataset, for manufacturing back 
beyond the 1990s. Preliminary data for 2001 became available at the later stages of our work 
programme, and some results have been tested with this data, but most of the work is based on 
2000 data and earlier. 

In addition, the ABI / ARD has associated with it estimates of fixed capital stock for all 
manufacturing reporting units, built up from reported capital purchases using a perpetual 
inventory model at firm level, plus reported operating inventory levels (materials, work in 
progress and finished stocks) for all units (both manufacturing and services) which we have 
used for an exploration of e-commerce and operating efficiency in retailing. 

Community Innovation Survey 

3.2 
This survey is available for research in the UK for the years 1996 and 2000. Linking ABI 
output and productivity data to innovation surveys has been achieved, both for rounds. This has 
been used to test a number of relationships between inputs to innovation, innovation outputs 
(product and process), and productivity effects of  innovation. Much of this work has been done 
in a parallel research programme in ONS, some of whose results are used in this report. ( see 
Innovations and Productivity Growth,  Criscuolo and Haskel 2002, www.ceriba.org.uk). 

Links for the first two relationships in the linear innovation chain: 
Figure 3.2 

INNOVATION 
INPUTS 

INNOVATION 
OUTPUTS 

ENTERPRISE 
GROWTH 

PRODUCTIVITY 
INCREASE 

9 

 
 
 
 
 
 
 
 
 
 
 
 
 
have also been tested using CIS 2000 data as part of this project (see section 9), and the validity 
of the basic model supported within limits. 

Producer Price Inquiry 

3.3 
The third major data-set used in linked work to date was not anticipated in our original project 
plans, but became of interest as a source to explain the key result relating e-commerce use to 
productivity in manufacturing. When it became clear that  value added per employee is 
positively related to use of electronic networks for buying, and negatively related to electronic 
selling, this seemed strong prima facie evidence that at least part of the effect was as a result of 
price effects. 

The data available to test this possibility is the ONS' producer price database, made up of 
monthly quotes for specified products from thousands of producers. Each one is linked back, in 
quality corrected format, to a base year of 1995 and the data is used to produce sector price 
indices (representing prices for industrial output) which are used to deflate the output measure 
of GDP. Because the database carries both the business register number of the contributing 
firm, and the detailed sector identifier for the product it refers to,  it is possible to re-cast the 
data into indices which show separately the results for firms that sell using e-commerce, and 
the results for those that do not. 

Unlike the other data-sets discussed above (ABI, CIS, e-commerce)  the PPI data-sets are not 
available for analysis by external researchers, even under strict confidentiality conditions. This 
is because of the exceptional sensitivity surrounding price quote data. 

e-Commerce survey 

3.4 
Linking the e-commerce survey to structural business statistics has been achieved, through the 
UK business register. It has proved possible, mainly for larger enterprises, to achieve matching 
of e-commerce responses to ABI performance data on the same respondents for : 
•  gross value added per employee 
•  extra-UK activity 
•  capital investment, and ICT purchases,  as well as other inputs at enterprise level.  

However, the number of enterprises for which linking between the e-commerce survey and ABI  
has proved possible using the 2000 survey is 1928, of which only 707 are in manufacturing. 
This sample has been used for trial regression analysis of total factor productivity, without 
statistically significant results. For this reason, subsequent analysis on TFP (reported in section 
6) has been based on qualitative questions from the ABI (described in 3.1 above) on use of 
electronic networks for placing and receiving orders. 

The overlaps between ABI, e-commerce and CIS data-sets are too small to permit statistically 
robust conclusions on the effect of e-commerce (as measured in the e-commerce survey) to be 
derived. However, the CIS data-set itself contains questions on e-commerce use, and these have 
been used to test possible: 
- 
- 

influence of electronic networks on innovation inputs 
effects of electronic networks on exploiting innovation outputs. 

10 

 
 
 
 
 
 
 
 
 
 
 
Figure 3.3 
Relevant data from the three output / activity survey sources can be summarised as follows: 

Turnover 
Purchases 
Employment 
Value Added 
IT expenditure 
Advertising expenditure 
ICT usage 
Date of ICT adoption 
Benefits of technology 
E-commerce Activity 
(yes/no) 
Amount of e-commerce  
(% of sales) 
Split sales and purchases  
Innovation 
Collaboration 
Industry 
Exports 
Skills 

ABI / ARD 
, 
, 
, 
, 
, 
, 
. 
. 
. 
, 
(broad defn.) 
. 
, 
. 
. 
, 
, 
. 

CIS 
, 
. 
, 
. 
. 
. 
. 
. 
, 
, 
(narrow defn.) 
. 
. 
, 
, 
, 
, 
, 

E-commerce 
, 
. 
. 
. 
. 
. 
, 
, 
, 
, 
(both defns.) 
, 
, 
. 
. 
, 
. 
. 

The UK e-commerce survey for 2001 also contained, for 2001, questions on the use of e-
business processes other than buying and selling. This type of question, as we found from the 
work reported in section 6, has value. Although we have demonstrated that linking of this data 
to ABI data is possible, the number of observations is as yet too small for statistically reliable 
results 

Capital Investment Surveys 

3.5 
One additional data-set (or rather pair of data-sets) was considered for use in this work, but was 
found to be impractical in the time available. In addition to partial investment data collected 
with the ABI, ONS conducts two more detailed  capital investment surveys, one quarterly the 
other annual. In principle it will be possible for these to be used  to build a firm level data-set 
on ICT capital, covering both hardware and software.  

The benefit of having an ICT capital stock measure available at firm level is that it would be 
possible to distinguish in econometric analysis between capital invested in ICT infrastructure 
(from investment surveys), ongoing purchases of ICT services (from ABI) and measures of ICT 
usage (from the e-commerce and other surveys) which specifically measure behaviour and 
organisation. This would enable us to use UK data to investigate conclusions from other studies 
which have found the impact of ICT investment to be conditional on organisational and 
behaviour factors. 

However, the surveys have only been in operation since 1998 and 2000, and there is today 
insufficient history to create a capital stock measure at firm level using the perpetual inventory 

11 

 
 
 
 
 
 
methodology  - even given the relatively short life for ICT assets. Analysis using firm level ICT 
capital stocks will be tackled in a subsequent project. 

12 

 
4 

Summary of statistical results  

The main statistical results from analysis contained in this report are as follows: 

4.1 

e-Commerce adoption 

•  Multiple technology platforms for ICT support the firms with higher levels of e-commerce 

activity 

•  Typical lag between firms adopting connected technologies and moving to e-commerce is 

of the order of two years 

•  Non internet channels for e-commerce still dominate overall e-commerce volumes, except 

for smaller firms, for whom the internet is a point of entry 

•  The proportion of 'marginal' e-sellers, for whom e-commerce is 1% or less of sales, has 

fallen since 2000, but is still not insignificant 

•  Early adopters of e-commerce tend to have a more focused view of specific business 

benefits; later adopters tend to emphasise market access 

•  Levels of entry, exit and experimentation in electronic markets, underlying the growth of 

adoption, are high 

4.2 

Productivity in manufacturing 

•  Linked data using e-commerce surveys is not yet available in sufficient sample sizes to 

permit successful statistical analysis of productivity effects 

•  Linking productivity performance to qualitative questions on the structural business survey 
has established statistically significant productivity relationships across manufacturing 
firms 

•  The most significant, and largest, productivity gain associated with e-commerce use is 

associated with electronic procurement 

•  Electronic selling is associated with lower productivity, as measured in terms of value 

added 

•  A major part of this effect appears to be due to price effects caused by e-commerce, but 

there is also evidence for an overall efficiency gain effect 

•  Regression analysis shows that these results are not affected by selection bias, and that they 

apply to small as well as large firms 

•  While these conclusions hold good for the majority of manufacturing sectors, there are 

some (where services or information products are important) in which the negative effects 
of e-selling are not present 

4.3 

Price effects  

• 

• 

It has proved possible to link e-commerce indicators for firms to their responses to monthly 
price inquiries, and through this to test the price effects which appear to be present in the 
manufacturing productivity data 
In the majority of a selected set of sectors tested, the producer price data supports the 
conclusion that e-commerce use for selling tends to depress prices, relative to firms in the 
same sector (or producing the same products) which do not use e-commerce for selling 
•  There are, however, a number of sectors where this effect is not present, and a few where it 

is reversed; some of these may be explained by industry characteristics 

13 

 
 
 
 
 
 
 
 
•  Experts in e-commerce systems have suggested to us that industry structure plays a role in 
determining how e-commerce affects the balance of advantage between buyers and sellers  

4.4 

Service sector; retailing and stocks 

•  There are sectors in services (e.g. retailing) where e-selling seems to generate labour 

productivity gains - measured in terms of both value added per employee and gross output 
per employee 

•  Our analysis of operational effects of e-commerce use has focused on retailing because this 

is a relatively homogeneous sector in terms of technology and business processes; 
elsewhere the data is too heterogeneous to permit analysis 

•  E-procurement produces limited benefits for both stock turn and labour productivity in 

certain areas of retailing; size and sub-sector influence where the advantage lies 

•  E-selling (in the absence of e-procurement) usually demands higher stock levels relative to 

sales turnover, as well as generating higher productivity 

•  Through integration (e-buying and selling) is associated with stock turn advantages, and 

with higher productivity, in retail sectors other than food shops. 

4.5 

R&D and innovation  

•  Parallel research conducted in ONS using the Community Innovation Survey 1996 and 
2000 has produced regression evidence that links innovation output (presence and % of 
innovative sales in firm output) to R&D spending and to collaboration in innovation inputs 
•  The parallel work has also produced regression evidence linking both product and process 

innovation to  productivity gain, measured in terms of TFP 

•  Our analysis, based on CIS 2000, has shown firm level links between  

- relative innovation spending within sector and innovation output (% products new to firm) 
- R&D and IC spending within sector and innovation output (% products new to firm) 
- relative innovation output, and relative firm growth 

•  E-commerce use does not appear to be directly related to either of these relationships 
•  However, e-commerce use, measured by linking ABI and CIS data on electronic network 

use, is positively related to 
- the probability that firms collaborate in innovative activities 
- the number of collaborative relationships they are likely to have  
It appears that the role of electronic networks in facilitating innovation is easier to detect in 
the field of collaborative communication than in direct commercialisation of innovation.

• 

14 

 
 
 
 
 
5  e-Commerce adoption evidence 

5.1 

Background 

ONS has so far published two e-commerce surveys,  the initial survey for 2000, based very 
closely on the Eurostat model, and the second for  2001, published in stages during August - 
November 2002 (Prestwood, 2002). The 2000 survey contained questions on date of adoption 
of technologies, and on benefits and barriers related to e-commerce adoption. Data from the 
2000 survey is therefore most useful in detailed analysis of adoption behaviour by business, 
and much of the work reported in this section is based on it. It shows that patterns of e-
commerce use vary widely across UK industry.  

Comparisons of 2000 to 2001 e-commerce data have been made as part of the adoption 
analysis. They show a high level of experimentation and change under way, with firms 
declaring that they enter and leave the e-commerce arena in large numbers. This will be 
important to recognise when we come to look at impact on performance. 

Statistical work in other OECD countries, and case studies conducted for the DTI and DG 
Enterprise, have demonstrated common patterns of e-commerce adoption, and shared factors in 
the ability of businesses to capitalise on ICT benefits. Input from these studies has helped focus 
our analytical approach on three strands: 

- 

tabulation of the 2000 / 2001 e-commerce surveys, to understand the  relationships between 
technology use and adoption of electronic transactions 

-  modelling enterprise productivity levels and changes, taking into account the use of 

computer networks and electronic transactions, an extension of work already carried out in 
the US Bureau of Census (Atrostic and Nguyen, 2002). 

- 

analysis of R&D, innovation and output growth, to test whether use of electronic networks 
can improve the effectiveness of investment in innovation 

This section focuses on the comparisons we have been able to make covering the infrastructure 
required to support e-commerce, the rate and distribution of adoption, sector differences, 
perceived business benefits, leads and lags, and the year on year changes which suggest high 
rates of entry and exit.  

5.2 

Technology infrastructure in firms 

The ONS e-commerce survey launched in 2000, based on Eurostat's model contains questions 
on dates by which firms had made investment in specific forms of communications technology. 
It gathers data on the adoption patterns and use of Information and Communication 
Technologies (ICT)  by UK enterprises. The 2000 survey showed that the majority of UK firms 
had invested in some form of 'connected' technology, and that many were using more than one 
technology.  

Figure 5.1 shows the pattern of technology in use for connected firms, and the relationships 
between older technologies, such as intranet (internal networks within firms) and electronic 
data interchange (EDI, which links firms over closed transaction networks) and the more recent 
web technology. 

Only web access shows up as a 'standalone' technology in use by a significant number of firms 
on its own, although half of firms accessing the web were also using intranet or EDI. 20% of 
survey respondents, 1411 in total  were using all three - web access, intranet and closed links 

15 

 
 
 
with business partners via EDI. A similar number, 1385 or 19% of respondents, had no 
connection at all. 

Figure 5.1       Distribution of 'connected' technologies in firms 

575 

EDI 

76 

1411 

45 

1522 

Intranet 

163 

Web  

2097 

Source; e-Commerce Survey 2000  

Figure 5.2 shows that use of multiple 'connected' technologies is positively related to 
penetration of use of e-commerce. There are a small number of reporting businesses without 
any of these technologies who engage in e-commerce via third party web sites, but for the rest, 
more than one channel for e-business often means a higher proportion able to undertake 
electronic sales. In addition, ownership of an intranet, connecting internal processes within a 
firm, increases the likelihood that it will be able to buy and sell electronically. 

Figure 5. 2 

Technology combinations and e-commerce 
activity

40
35
30
25
20
15
10
5
0

None

Web
access

EDI

Intranet EDI and

Web
access

Technology

Intranet
and Web
access

Intranet
and EDI

Intranet
EDI and
Web

Source; e-Commerce Survey 2000 

Buying only

Selling only

Buying and selling

16 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5.3 

Rate and distribution of adoption 

EDI predates the internet by 20 years, but its role a vehicle for e-commerce is still important. 
Almost all large firms in the 2000 e-commerce survey used some form of connected 
technology, (often EDI) and 40% of large firms were already doing so by 1997 (Fig 5.3). The 
internet boom of 1999/2000 drew in many more firms, but more of the 'late joiners' were small 
and medium sized enterprises, under 250 employees.  

Figure 5.3 

Y e a r o f a d o p tio n  o f n e tw o rk  te c h n o lo g ie s  b y  
s ize b a n d

3 0

2 5

2 0

1 5

1 0

5

0

S m a ll 
M e d iu m
L a rg e

P re
1 9 9 5

1 9 9 5

1 9 9 6

1 9 9 7

1 9 9 8

1 9 9 9

2 0 0 0

Source; e-Commerce Survey 2000 

Because many larger firms that were ICT enabled since the mid 1990s used electronic 
exchange of orders or of information over closed systems, EDI dominates measures of value of 
e-commerce in both 2000 and 2001. Figure 5.4 shows, for the year 2000, business done over 
the internet and via 'all electronic networks', of which EDI is the largest element. In small 
firms, the proportion of business sold over the internet  is half of all electronic sales, which 
implies that EDI and internet sales are roughly comparable. For large firms internet sales are 
only around 12% of total network sales, with EDI and other systems accounting for the rest. 
This  suggests the internet is a point of entry to electronic trading for small firms, giving them 
access to e-business activity. 

Figure 5.4 

e - C o m m e r c e  s a le s  a s  %  o f  t u r n o v e r , b y  s iz e b a n d

l

s
e
a
s
-
e
%

9

8

7

6

5

4

3

2

1

0

S m a ll 

M e d iu m

F ir m  s iz e

L a r g e

Source; e-Commerce Survey 2000 

s a le s  In te r n e t o n ly
s a le s  a ll n e tw o r k s

17 

 
 
Analysis of the pattern of electronic transactions as a percentage of enterprise turnover (Figure 
5.5) shows a similar effect. The majority of businesses for which e-commerce constitutes over 
10% of sales value, use non-internet networks.   

Figure 5.5 

B u s in e s s  %  e -c o m m e r c e  s a le s  v ia  In te r n e t a n d  o v e r  a ll 
e le c tr o n ic  n e tw o r k s  - 2 0 0 0

s
e
s
s
e
n
i
s
u
b
%

7

6

5

4

3

2

1

0

In te rn e t
A ll e le c tro n ic  n e tw o rk s

0.01

0.1

0.5 1 5 10 15 25 50 75

100

%  s a le s  v ia  e le c tr o n ic  n e tw o r k s  

      Source; e-Commerce Survey 2000 

It is clear from the analysis in Figure 5.5 that a high proportion of businesses undertaking e-
commerce sales in 2000 were very 'marginal e-traders'. For well over half the businesses 
undertaking electronic sales, this activity accounted for 1% or less of their turnover. UK survey 
data for 2001 shows that this pattern has changed significantly. By 2001 there is a significantly 
higher proportion of e-traders in the 'over 1% of sales' bands, and a definite reduction in the 
proportion of very marginal users. A large part of the increase among the more intensive e-
commerce businesses appears to be among users of non-internet technologies. These year to 
year comparisons need to be read with some caveats, because of changes in the survey format 
and coverage, and because the survey operation itself is still developing. 

Figure 5.6 

Business % e-commerce sales via Internet and over all 
electronic networks - 2001

s
e
s
s
e
n
i
s
u
b
%

7
6
5
4
3
2
1
0

0.01

0.1

0.5 1 5 10 15 25 50 75

100

% sales via electronic networks 

     Source; e-Commerce Survey 2001 

Internet 
All electronic networks

18 

 
 
 
 
 
5.4 

Sector balance of e-purchasers and e-sellers 

The sector pattern of e-commerce sales has been published in value terms in ONS releases 
(Prestwood 2002).  It shows significant differences across sectors for values of electronic sales 
as a percentage of total sales, by broad SIC category. An alternative approach is to look at 
selling and buying behaviour, focusing on the proportion of businesses for whom e-commerce 
represents more than 1% of sales, and the proportion for whom it represents more that 1% of 
purchases.  This gives a pointer to how e-commerce is used by sector. Table 5.1 shows that: 

- 

- 

the food, drink and tobacco sector has by far the greatest proportion of 'non-marginal' e-
traders, over 40%. Sales from this sector via EDI to supermarkets and wholesalers have 
been an established business practice for several years.  

the wholesale / retail sector has among the highest proportion of electronic purchasers 
(22%), along with financial services (23%), business services (20%) and electrical / optical 
machinery (21%) 

The pattern in Table 5.1 seems to suggest that e-commerce involves a significant proportion of 
firms in sectors where the number of suppliers and / or buyers is low. This model of e-
commerce is likely to be a closed system of EDI. Where customers are more fragmented and 
the market structure suited to the internet, penetration appears slower. 

Table 5.1         e-Purchasers vs. e-Sellers 

Selected industry sectors 

Higher user sectors: 
Food / drink / tobacco 
Paper etc 
Chemicals 
Rubber / plastic 
Equipment / machinery 
Electrical / optical machinery 
Transport equipment 
Utilities 
Wholesale / retail 
Hotels / catering 
Real estate / business services 
Transport 
Financial services 
Lower user sectors: 
Textiles 
Leather 
Non-metallic products 
Metal products (11%) 
Manufacturing nes.(7%) 

% firms with more than 
1% e-purchases 

% firms with more 
than 1% e-sales 

12 
16 
17 
19 
18 
21 
16 
21 
22 
13 
20 
18 
23 

7 
8 
6 
10 
8 

45 
12 
19 
14 
10 
12 
12 
10 
14 
11 
6 
12 
17 

11 
7 
8 
11 
6 

Source; e-Commerce Survey 2000 

Sectors shown in italics have fewer >1% e-sellers than >1% e-purchasers 

This 'buyer / seller' analysis confirms that for the majority of sectors, the proportion of 'non-
marginal e-purchasers’ is greater than that of 'non-marginal e-sellers'. 

19 

 
 
 
 
 
 
 
5.5 

Perceived business benefits of e-commerce 

Responses to survey questions on the benefits associated with e-commerce use show that 
business objectives for electronic trading have changed over time. Firms which began using 
computer networks or the internet prior to 1997 are more likely to have reported their reasons 
for doing so in terms of very specific business benefits (Fig. 5.7). They report benefits 
including cost reduction for the firm, service quality improvement for customers, increased 
speed of operation and simplification of business processes. These are benefits which usually 
require electronic processes to be 'engineered in' to firms, affecting workflows, or the way 
firms interact with customers. 

Figure 5.7 

" S p e c ific "  b e n e fits  v s  firs t y e a r  s e llin g  o v e r  
e le c tr o n ic  n e tw o r k s

9 0
8 0
7 0
6 0
5 0
4 0
3 0
2 0
1 0
0

C o s t s a v in g s
S e rv ic e  q u a lity
S p e e d
S im p lific a tio n

P re
1 9 9 5

1 9 9 5

1 9 9 6

1 9 9 7

1 9 9 8

1 9 9 9

2 0 0 0

Source; e-Commerce Survey 2000 

Figure 5.8 shows, by way of contrast, that benefits reported by later adopters seem more related 
to marketing goals. The majority of these report their main objectives as geographic expansion 
or reaching customers. Often these objectives are less specific, and require less investment in 
business processes than cost savings, service quality, speed or process simplification. Further 
analysis may show whether businesses with primarily marketing priorities are among the 
'marginal' businesses in Figure 5.5 above. 

Figure 5.8 

 "M a rk e tin g " b e n e fits  v s . firs t y e a r s e llin g  o v e r 
e le c tro n ic  n e tw o rk s

9 0
8 0
7 0
6 0
5 0
4 0
3 0
2 0
1 0
0

1 9 9 5 1 9 9 6 1 9 9 7 1 9 9 8 1 9 9 9 2 0 0 0

P re
1 9 9 5

Source; e-Commerce Survey 2000 

R e a ch in g  cu sto m e rs
G e o  e xp a n sio n

20 

 
5.6 

Leads and lags 

Most important for identifying the impact of ICT and e-commerce use for business 
performance is an understanding of lags involved in the adoption process. From survey 
responses it is possible to identify how long firms are likely to take to move from initial 
investment in 'connected' ICT to trading on-line, either as a buyer or as a seller.  For example 
those investing in the technology pre 1995 had a 46% probability by 2000 of selling via e-
commerce and possibly buying as well. Those investing in 2000 only had a 21% probability of 
trading - and were nearly twice as likely to have only a marketing web site. The sharpest 
increase in probability of trading takes place in the first two years of adoption - which suggests 
a learning period for many firms to move from technology to business operations. 

Figure 5.9 

 e-Activity vs  tim e w ith  a n etw o rk tech n o lo g y

s
e
s
s
e
n
s
u
b
%

i

50
45
40
35
30
25
20
15
10
5
0

W eb site only

B uying

S elling or buying
and selling

pre
1995

1995

1996

1997

1998

1999

2000

C o m p u te r n e tw o rk  te c h n o lo g y in tro d u c e d

Source; e-Commerce Survey 2000 

This result may be related to the responses on benefits to e-trading reported in  Figure 5.7 
above. Time taken to embed electronic processes to enable effective trading and secure benefits 
is probably measured in years rather than months. We will need to recognise this in looking for 
'impact' benefits of technology. 

5.7 

Entry and exit  

While the analysis shown in Figures 5.5 and 5.6 above allow us to compare broad patterns of 
adoption and e-commerce usage for 2000 and 2001, using the whole sample for each year, we 
are also able for the first time to follow the progress of individual respondents. From this we 
can derive an initial assessment of how stable established e-commerce users might be, and the 
rate of new take-up. 

2716 reporting units appear in both the 2000 and 2001 survey. 1302 of these (48%) made no e-
sales in either year, as broadly defined, either over he internet or using other electronic 
networks. Of 858 firms which gave a non-zero estimate of electronic sales in 2000, 189 (22%) 
gave zero for 2001, and 166 (19%) reported that their share of e-commerce had fallen. For over 
40% of 2000 users e-commerce activity had declined. 

21 

 
 
 
 
 
 
 
By 2001 there were, of the total  linked set, 1225 units which gave a non-zero response for 
broadly defined e-commerce activity, up 42% on the year before. Of these 556 were reporting 
positive values for the first time, which means that of reported e-commerce users in 2001  45% 
had reported use for less than a year. 

Table 5.2 

2000/2001 responses linked; % sales over all networks 

% sales via 
networks in 
2000 
0 

> 0 and<5% 

5% to <50% 

50% to 100% 

Total 

Eliminated 
by 2001 

Reduced by 
2001 

No change 
by 2001 

Increased by 
2001 

Total 

0 

123 

58 

8 

189 

0 

40 

74 

52 

1302 

51 

31 

26 

166 

1410 

556 

219 

113 

63 

951 

1858 

433 

276 

149 

2716 

Source: e-commerce surveys 2000/2001 

These figures may not be representative of the whole population over which the surveys were 
conducted, and it is possible that some definitional differences affect comparisons from one 
year to the next. However,  the data does show very high levels of entry and exit from e-
markets, and the relatively low proportion of firms which appear to be consistent users of 
electronic networks for selling in 2000/2001. 

Over this period there was a high rate of business failure among dot.com businesses, many of 
which did not exist to be captured in a 2001 survey. Therefore this longitudinally linked 
analysis probably understates the amount of turbulence - especially the numbers of businesses 
leaving electronic markets. Similar results have been obtained from separate longitudinal 
linking of responses for e-commerce via internet, EDI and for intranet use. In each area 
measured, and across the size range of firms, the overall growth in activity hides significant 
entry and exit. 

The data shows a dynamic pattern, with experimentation and exit still widespread. This will 
affect the ease with which we can identify costs and benefits of e-commerce use.

22 

 
 
 
 
 
6 

Productivity in manufacturing 

6.1 

Background to selection of approach 

Micro-data work to investigate the effects of ICT investment and use is now being undertaken 
in a range of countries, drawing on data-sets from official and private sources. Most of these 
investigate the effects on firm productivity which can be attributed to ICT inputs, either 
through quantified measures of ICT investment, or through availability or use of ICT systems. 
Evidence from a range of sources has been brought together by OECD through a collaborative 
group which has contributed to the report 'ICT, seizing the benefits', which has informed the 
study reported here. 

The main approaches to this work include: 

- 

- 

- 

- 

inclusion of ICT capital stock at firm level as a separately identified capital input to labour 
productivity or TFP measurement (Brynjolfsson & Hitt, Hempell) 

including ICT capital alongside other measures of ICT use, such as internet use or number 
of employees using ICT (Maliranta & Rouvinen) 

including ICT capital stock together with measures on innovation and / or organisation 
change (van Leeuwen & van der Weil, Brynjolfsson & Hitt) 

including measures of computer network use as a determinant of labour productivity or TFP 
in a standard production function equation (Atrostic and Nguyen). 

In our linked data-sets for the UK, we do not yet have enough basic survey data to construct 
individual firm level ICT capital stock using perpetual inventory methods. This gap in data 
should be possible to remedy within the next year as the base of detailed capital expenditure 
surveys reaches a sufficiently long back series. But for our immediate work it is not available. 
The methodology adopted for initial analysis of ICT productivity effects is therefore based on 
the Atrostic and Nguyen approach, based on US Bureau of Census data, developed where 
possible to take account of additional dimensions in UK sources. 
Atrostic and Nguyen   show, using the US 1999 manufacturing census combined with a large 
scale computer network use supplementary survey,  that there is a significant positive effect on 
productivity associated with the use of computer networks, after allowing for management and 
other effects.  This conclusion is derived through a standard production function regression 
which takes into account: 

- 

- 

- 

- 

- 

labour and capital inputs 

size of reporting unit, and whether it is part of a multi-plant firm 

skills measured as occupation type 

industry dummies 

the use of computer networks by the unit for a range of business processes 

The US Computer Network Use Survey (CNUS) was completed by more than 38,000 firms. It 
consists mainly of qualitative (tick box) measures of the extent to which computer networks 
were used for buying and selling, logistics, operations, and other steps in the business' value 
chain. Of the firms reporting use of computer networks, only half were using them to buy or 
sell, so network use for 'e-business' as opposed to  'e-commerce' purposes is an important part 
of the overall picture. 

23 

 
 
From this data-set, Atrostic and Nguyen test whether the use of computer networks in 1999 had 
the effect of shifting the production function in such a way as to increase productivity. They 
conclude that it adds around 5% to output per employee, and that this result is robust to 
different model specifications. The results are also tested for selection bias. This successful use 
of qualitative ICT data on a large sample appeared to be the best model given the UK data 
available. 

The closest UK source to this US data is provided by the qualitative questions added to the UK 
structural business survey (ABI) from 2000 onwards, shown earlier in section 3. These ask firm 
to firms to indicate - yes or no - whether they use electronic networks to place orders for goods 
and services, or to receive orders. This is the OECD 'broad definition' of e-commerce use.  

There is interest in looking at the effect of e-commerce as a means of procurement or of supply 
chain management separate from its other applications, supported by case evidence. Adoption 
of electronic procurement systems by firms has been claimed to improve efficiency in several 
ways, by cutting internal administration costs and speeding up purchasing processes, by 
improving price transparency, by reducing search costs and therefore the efficient operation of 
choice in markets. A well documented example of case evidence was provided by Siemens to 
the DG Enterprise e-business w@tch workshop in November 2002, emphasising that 
procurement savings to the company came from both internal and external sources. 

The ABI data, separating buying and selling via electronic networks makes it possible to 
analyse the distinct impacts for a very large sample. 

6.2 

Regression analysis 

The ABI data provides the nearest equivalent, in type of question, and in ability to link to a 
large sample of performance data, to the US approach. It does not cover the 'e-business' 
application of computer networks as the US data does, but it does permit us to separate the e-
commerce applications between buying and selling. 

A TFP analysis using UK ABI data is restricted to the manufacturing sector, because firm level 
capital stock data is not yet available for services.  For manufacturing the ABI for 2000 
provides a total possible research data-set of over 11,000 reporting units (see Table 2.1 in 
section 2), reduced to around 7,000 after removing observations where the e-commerce 
response was invalid or missing. 

The framework for regression analysis is Cob-Douglas production function of the form:  

Q AK L Mα β

=

γ

where K, L and M are capital, labour and materials inputs (all available from the ABI) and  A is 
a technology change term which shifts the production function as a function of the use of 
computer / electronic networks for buying or selling, of the form: 

=

A

exp(

+
δ δ
CNETuse
1

0

)

and where CNETuse has the value 1 if e reporting unit uses  a network for buying or selling, 
and zero if it does not.  The equation on which regression is based is therefore: 

ln





Q
L





=

+
δ δ
CNETuse
1

0

+

α

ln





K
L





+

γ

ln





M
L





−

α β γ
(

+ + −

1) ln

L

The CNETuse term in the analysis is split into a number of dimensions for different 
specifications of the model, to show separately the effects for: 

24 

 
 
- 

- 

- 

- 

firms using computer networks for selling 

firms using computer networks for buying 

firms using networks for either buying or selling 

firms using networks for both buying and selling 

The reasons for doing this are to investigate during the analysis how possible 'market effects' 
due to use of computer networks can be separated from internal efficiency effects, and also to 
test a hypothesis that firms using networks to both buy and sell might be better integrated and 
so more productive. 

In addition to these factors, the analysis of 2000 data also takes account of: 

- 

- 

reporting unit size, as represented by number of employees 

industry sector and location 

-  ownership structure, (multinational status which parallel work has shown has major 

influence) 

- 

age of plant  

Results of this analysis for 2000 have been developed using gross (sales) and net (value added) 
measures of total factor productivity, and for value added per employee. All show statistically 
significant effects. Consistent positive correlation exists in all three specifications, between use 
of computer networks for buying and: 

-  value added per employee 

- 

- 

total factor productivity based on gross output, and 

total factor productivity based on value added 

Results in this format (shown in full in Table 6.1) are the most reliable, because they include 
multinational ownership as a productivity determining factor, which is highly significant in its 
own right. It is also in this specification that the regression has been tested for selection bias 
(using a two stage probit estimate, and employing ICT expenditure by the firm in prior years as 
an instrumental variable). The significance of the results is comparable to those achieved by 
Atrostic and Nguyen  

As we do not yet have multinational markers for 2001, the 2001 ABI data cannot be analysed in 
precisely the same way. However it has been possible to test the regression on 2000 and 2001 
data excluding these terms, and show that the productivity impacts for CNETuse terms is 
similar and significant in both years . Results for both years together are shown in Table 6.2. 

6.3 

Interpreting the regression results 

The regression results show, like the Atrostic and Nguyen analysis, an overall positive effect on 
firm productivity - on all the three measures listed above - associated with use of computer 
networks for trading. However, it is clear from comparing the gross output result with the value 
added results that pricing  effects play large part in this result. In both Table 6.1 and 6.2, the 
gross output results show a 2.5% gain in output associated with e-procurement. However, the 
value added results show gains associated with e-procurement between 7.7% and 8.9%, and a 
loss of value added associated with e-selling of between 5 and 6%. The most likely explanation 

25 

 
 
 
for the gain to buyers and the loss  to sellers appears to be due to pricing effects, with internal 
efficiency gains accounting for the difference. 

The relationships between the coefficients for the gross output and value added equations 
would be consistent with the interpretation that these results include the effects of lower prices 
in 'electronic markets'. Three results support this: 

-  value added / gross output ratios across the manufacturing sectors are typically below 50%, 
so price reductions would produce, pro-rata, much greater coefficients for value added than 
for gross output.  

- 

the effects of 'only e-buying' show up as weak and insignificant in the gross output 
equations, as might be expected if the main effect was on input prices. 

-  by contrast the effects of 'only e-buying' show up as strongest and most significant in the 

value added equations, which  would be affected by differential pricing. 

In effect, what is being measured in these equations is partly a 'measurement error due to price' 
in the specification of the production function, because the normal assumption that prices are 
similarly behaved within industries is not being met. Section 7 will explore direct evidence on 
this question, from linking UK pricing data to e-commerce use by producing firms. 

 The hypothesis that firms which both buy and sell are likely to be more 'integrated' in terms of 
their network use, and therefore more efficient, is not supported by the results. Units which 
both buy and sell, appear with a significant additional productivity effect in only one of the six 
forms of the model in tables 6.1 and 6.2 - the one for labour productivity omitting the 
multinational markers. It is at least possible that multinationals would be well represented 
among the firm types which would buy and sell electronically, if only to manage their internal 
transactions within the firm. 

Another hypothesis advanced on the basis of case evidence, and the results reported so far, is 
that the 'price effect' which appears to benefit firms through e-procurement is partly due to 
large firms using electronic markets to strengthen their position at the expense of smaller ones. 
For example, if a large multinational firm is able to construct a procurement system which 
enables it to put all its purchasing requirement out to international tender, and buy in a global 
market, while its smaller suppliers tend to be local, and to be price takers unable to access 
wider markets, then smaller firms could be disadvantaged. Alternatively, smaller buyers could 
find it difficult to buy electronically in international markets, and therefore unable to secure 
gains available to larger firms. 

To test this possibility, the productivity analysis for 2000 and 2001 has been split, in Tables 6.3 
and 6.4, between: 

- 

- 

reporting units which are smaller than the median size for their two digit sector, and so are 
likely to smaller firms with low market share (Table 6.3) 

reporting units which are larger than or equal to the median firm size in their two digit 
sector, which is likely to include market leaders (Table 6.4) 

The results indicate that the productivity effects associated with e-buying and with e-selling are 
almost equally strong in large and small firms. Both show value added productivity loss 

26 

 
 
 
 
 
associated with e-selling, and the coefficients are up to twice as large for large firms as for 
small. Both groups of firms show value added productivity gains associated with e-
procurement, with coefficients for the large firms only marginally bigger than for the small. 

Comparing our results with Atrostic and Nguyen suggests that they are at least consistent. 
However, it seems possible that both approaches may understate productivity impacts of ICT at 
firm level: 

- our analysis because it takes no account of e-business processes which are unrelated to buying 
or selling but are focused on other types of internal and external data management, which 
Atrostic and Nguyen found to be an important part of overall network use; in our data firms 
using networks in this way are categorised as 'non users'. 

- Atrostic and Nguyen because their data does not distinguish transaction related e-commerce 
between buying and selling, and the analysis may therefore be unable to separate out the partly 
offsetting effects on both ides of transactions. 

In order to overcome these analytical difficulties, the 'ideal' data-set for analysing computer 
network use effects would be long enough to allow time series analysis (which will take time, 
given that most EU countries only started collecting data in 2000), and would include: 

- distinction between network use for buying and selling 

- records of network use for other purposes (like the latest model Eurostat survey) 

- access to price indices for individual firms, to enable us to show productivity in 'real' terms at 
firm level, and investigate output changes over time without having to assume that price indices 
apply uniformly across sectors. 

UK e-commerce surveys from 2001 onwards contain data on e-business processes, but only 
linked to transactions. The Atrostic and Nguyen work suggests that we should consider 
widening the definition of e-business processes, and use an approach closer to the Bureau of 
Census CNUS. An initial analysis to test sensitivity of firm level price data to e-commerce use 
is considered in section 7. 

6.4 

Interpreting the data 

The effects shown in the regression results above are also evident in descriptive statistics. The 
data for 2000 and 2001 in Table 6.5, and in Figure 6.1 below shows that, for value added 
productivity measures, the best performing firms are those which use e-commerce for 
purchasing only.  

27 

 
 
 
 
 
 
 
 
 
 
 
 
Figure 6.1 

36

34

32

30

28

26

Value added / employee (£000)

2000

2001

No e-commerce

Either

e-sell

e-buy

Source; ABI surveys 2000/2001 

A detailed breakdown of 2000 data by main manufacturing sector is shown in Table 6.6, and in 
Fig 6.2. three sectors are omitted from this analysis to avoid data disclosure issues, but the 
overall patter is strikingly supportive of the conclusions reached in the overall analysis. In the 
majority of sectors, firms with the highest value added per employee are those which use e-
commerce for procurement. The raw data shows that - more often than not - firms which use e-
commerce for selling are ahead in terms of value added of those firms who do not use 
electronic networks, but this is likely to be due to other factors, which were controlled for in the 
regression analysis (scale, organisation, capital stock). 

Two sectors stand out in this industry analysis, where there are a large number of observations, 
and where e-selling firms come out ahead of those using e-commerce for procurement. These 
are: 

- 

sector 22, publishing and media, where the sector has shown significant growth, and where 
the sales content of many firms includes information and services in addition to physical 
products and commodities 

- 

sector 30, office equipment and computers, which shares these characteristics. 

Both these sectors are the ones which have been most revolutionised by the 'information 
economy', sector 30 as a supplier and sector 22 as a consumer of ICT. It is at least possible that 
firms in these sectors have moved further to change business processes and methods of working 
to match the new technology, and may therefore be able to achieve gains from e-selling.  In 
both, some firms have used electronic networks to 'leapfrog' the value chain to their eventual 
customers, cutting out intermediaries and appropriating part of their value added. 

28 

 
 
 
 
 
 
MNE included 

LP 

TFP 

1 

2 

3 

4 

5 

6 

7 

8 

Table 

6.1 

9 

2000 Dependent variable: value added 

Dependent variable: gross output 

Dependent variable: value added 

buy_or_sell 

0.006 
 (0.014) 

 0.002 
 (0.009) 

 0.007 
 (0.016) 

 -0.054 
 (0.017)*** 
 0.077 
 (0.017)*** 

 -0.014 
 (0.010) 
 0.025 
 (0.010)** 

 -0.061 
 (0.020)*** 
 0.089 
 (0.019)*** 

 -0.052 
 (0.021)** 
 0.081 
 (0.026)*** 
 0.022 
 (0.018) 
0.142 
(0.023)*** 
0.274 
(0.036)*** 
0.191 
(0.031)*** 
0.088 
(0.007)*** 
0.006 
(0.007) 
-0.000 
(0.000) 
-0.030 
(0.040) 

0.143 
 (0.023)*** 
0.278 
 (0.036)*** 
0.195 
 (0.031)*** 
0.087 
 (0.008)*** 
0.006 
 (0.007) 
-0.000 
 (0.000) 
-0.030 
 (0.040) 

0.142 
(0.023)*** 
0.274 
(0.036)*** 
0.191 
(0.031)*** 
0.088 
(0.007)*** 
0.006 
(0.007) 
-0.000 
(0.000) 
-0.030 
(0.040) 

 -0.019 
 (0.011)* 
 0.016 
 (0.017) 
 0.012 
 (0.012) 
0.026 
(0.012)** 
0.070 
(0.017)*** 
0.031 
(0.014)** 
0.015 
(0.004)*** 
0.009 
(0.005)* 
-0.000 
(0.000)* 
0.036 
(0.023) 
0.088 
(0.011)*** 
0.614 
(0.014)*** 
5544 
0.81 

0.026 
(0.012)** 
0.071 
(0.017)*** 
0.032 
(0.014)** 
0.015 
(0.004)*** 
0.009 
(0.005)** 
-0.000 
(0.000)* 
0.036 
(0.023) 
 0.088 
 (0.011)*** 
 0.614 
 (0.014)*** 
5544 
0.81 

0.026 
(0.012)** 
0.070 
(0.017)*** 
0.031 
(0.014)** 
0.015 
(0.004)*** 
0.009 
(0.005)* 
-0.000 
(0.000)* 
0.036 
(0.023) 
0.088 
(0.011)*** 
0.614 
(0.014)*** 
5544 
0.81 

 -0.061 
 (0.024)** 
 0.088 
 (0.028)*** 
 0.029 
 (0.020) 
0.079 

0.135 

0.079 

0.135 

0.030 
(0.031) 
0.045 

0.030 
(0.031) 
0.045 

0.079 
(0.024)***  (0.024)***  (0.024)*** 
0.140 
(0.032)***  (0.032)***  (0.032)*** 
0.034 
(0.031) 
0.045 
(0.008)***  (0.008)***  (0.008)*** 
-0.004 
(0.008) 
0.000 
(0.000) 
-0.005 
(0.047) 
0.303 
(0.015)***  (0.015)***  (0.015)*** 

-0.005 
(0.008) 
0.000 
(0.000) 
-0.005 
(0.047) 
0.303 

-0.005 
(0.008) 
0.000 
(0.000) 
-0.005 
(0.047) 
0.303 

5438 
0.33 

5438 
0.33 

5438 
0.33 

29 

Observations 
R-squared 

9316 
0.21 

9316 
0.21 

9316 
0.21 

e_sell 

e_buy 

sell_no_buy 

buy_no_sell 

buy_sell 

UK MNE 

US MNE 

OTHER MNE 

ln_emp 

Age 

age2 

age_cens 

ln_k_emp 

ln_mat_emp 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
all firms 

2000 & 2001 

buy_or_sell 

e_sell 

e_buy 

sell_no_buy 

buy_no_sell 

buy_sell 

ln_emp 

Age 

age2 

age_cens 

ln_k_emp 

ln_mat_emp 

Table 

1 

2 

3 

4 

5 

6 

7 

8 

LP 

TFP 

Dependent variable: value 
added 
0.024 
 (0.011)** 

 Dependent variable: gross 
output 
 -0.001 
 (0.007) 

 -0.037 
 (0.012)*** 
 0.074 
 (0.012)*** 

 -0.014 
 (0.008)* 
 0.024 
 (0.008)*** 

 Dependent variable: 
value added 
 0.007 
 (0.012) 

 -0.050 
 (0.014)*** 
 0.071 
 (0.014)*** 

6.2 

9 

 -0.027 
 (0.015)* 
 0.089 
 (0.018)*** 
 0.034 
 (0.013)*** 
0.113 
(0.005)*** 

0.012 
(0.005)** 
-0.000 
(0.000)** 
-0.001 
(0.029) 

0.113 
 (0.005)*** 

0.114 
(0.005)*** 

0.012 
 (0.005)** 
-0.000 
 (0.000)** 
-0.002 
 (0.029) 

0.012 
(0.005)** 
-0.000 
(0.000)** 
-0.001 
(0.029) 

 -0.024 
 (0.009)*** 
 0.007 
 (0.014) 
 0.013 
 (0.008) 
0.018 
(0.003)*** 

0.010 
(0.003)*** 
-0.000 
(0.000)*** 
0.033 
(0.018)* 
0.084 
(0.009)*** 

 -0.049 
 (0.017)*** 
 0.073 
 (0.021)*** 
 0.021 
 (0.015) 
0.055 

0.055 
(0.006)***  (0.006)*** 

0.002 
(0.006) 
0.000 
(0.000) 
-0.007 
(0.036) 
0.287 
(0.011)***  (0.011)*** 

0.002 
(0.006) 
0.000 
(0.000) 
-0.007 
(0.036) 
0.287 

0.055 
(0.006)**
* 
0.002 
(0.006) 
-0.000 
(0.000) 
-0.008 
(0.036) 
0.288 
(0.011)**
* 

0.018 
(0.003)*** 

0.018 
(0.003)*** 

0.010 
(0.003)*** 
-0.000 
(0.000)*** 
0.033 
(0.018)* 
0.084 
(0.009)*** 

0.010 
(0.003)*** 
-0.000 
(0.000)*** 
0.032 
(0.018)* 
 0.084 
 (0.009)*** 

 0.611 
 (0.010)*** 
11660 
0.83 

Observations 
R-squared 

17671 
0.20 

17671 
0.20 

17671 
0.20 

0.611 
(0.010)*** 
11660 
0.83 

0.611 
(0.010)*** 
11660 
0.83 

11437 
0.33 

11437 
0.33 

11437 
0.33 

30 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Small 

LP 

TFP 

1 

2 

3 

4 

5 

6 

7 

8 

Table 

6.3 

9 

2000 & 2001 

Dependent variable: value added 

Dependent variable: gross output 

Dependent variable: value added 

buy_or_sell 

0.037 
 (0.017)** 

 -0.008 
 (0.015) 

 0.016 
 (0.024) 

 -0.014 
 (0.020) 
 0.063 
 (0.021)*** 

 -0.028 
 (0.018) 
 0.023 
 (0.020) 

e_sell 

e_buy 

sell_no_buy 

buy_no_sell 

buy_sell 

ln_emp 

Age 

age2 

age_cens 

ln_k_emp 

ln_mat_emp 

 -0.001 
 (0.025) 
 0.085 
 (0.030)*** 
 0.044 
 (0.021)** 
0.153 
(0.012)*** 

0.013 
(0.008) 
-0.001 
(0.000)* 
0.060 
(0.051) 

0.152 
 (0.012)*** 

0.154 
(0.012)*** 

0.013 
 (0.008) 
-0.001 
 (0.000)* 
0.059 
 (0.051) 

0.013 
(0.008) 
-0.001 
(0.000)* 
0.061 
(0.051) 

0.026 
(0.010)*** 

0.026 
(0.010)*** 

0.025 
(0.007)*** 
-0.001 
(0.000)*** 
0.095 
(0.046)** 
0.091 
(0.017)*** 

0.025 
(0.007)*** 
-0.001 
(0.000)*** 
0.092 
(0.046)** 
 0.091 
 (0.017)*** 

 0.588 
 (0.018)*** 
4053 
0.80 

 -0.034 
 (0.028) 
 0.061 
 (0.030)** 

 -0.030 
 (0.036) 
 0.067 
 (0.042) 
 0.025 
 (0.029) 
0.078 

0.078 
(0.016)***  (0.016)*** 

0.009 
(0.011) 
-0.001 
(0.001) 
0.064 
(0.088) 
0.290 
(0.017)***  (0.017)*** 

0.009 
(0.011) 
-0.001 
(0.001) 
0.064 
(0.088) 
0.290 

0.077 
(0.016)**
* 
0.009 
(0.011) 
-0.000 
(0.001) 
0.059 
(0.088) 
0.290 
(0.017)**
* 

 -0.031 
 (0.021) 
 0.019 
 (0.031) 
 -0.003 
 (0.019) 
0.026 
(0.010)*** 

0.025 
(0.007)*** 
-0.001 
(0.000)*** 
0.095 
(0.046)** 
0.091 
(0.017)*** 

Observations 
R-squared 

8224 
0.18 

8224 
0.18 

8224 
0.18 

0.588 
(0.018)*** 
4053 
0.80 

0.588 
(0.018)*** 
4053 
0.80 

3936 
0.34 

3936 
0.34 

3936 
0.34 

31 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
big firms 

LP 

TFP 

1 

2 

3 

4 

5 

6 

7 

8 

Table 

6.4 

9 

2000 & 2001 

Dependent variable: value added 

Dependent variable: gross output 

Dependent variable: value added 

buy_or_sell 

0.017 
 (0.013) 

 0.004 
 (0.008) 

 0.005 
 (0.014) 

e_sell 

e_buy 

sell_no_buy 

buy_no_sell 

buy_sell 

ln_emp 

Age 

age2 

age_cens 

ln_k_emp 

ln_mat_emp 

 -0.048 
 (0.014)*** 

 0.080 
 (0.014)*** 

 -0.007 
 (0.008) 

 0.024 
 (0.007)*** 

 -0.043 
 (0.017)** 
 0.088 
 (0.023)*** 
 0.031 
 (0.015)** 
0.092 
(0.009)*** 

0.003 
(0.006) 
0.000 
(0.000) 
-0.079 
(0.034)** 

0.093 
 (0.009)*** 

0.092 
(0.009)*** 

0.003 
 (0.006) 
0.000 
 (0.000) 
-0.078 
 (0.034)** 

0.003 
(0.006) 
0.000 
(0.000) 
-0.079 
(0.034)** 

 -0.017 
 (0.009)* 
 0.006 
 (0.015) 
 0.019 
 (0.008)** 
0.017 
(0.004)***  (0.010)*** 

0.062 

0.017 
(0.004)*** 

0.017 
(0.004)*** 

-0.000 
(0.003) 
0.000 
(0.000) 
-0.018 
(0.017) 
0.086 
(0.009)*** 

-0.000 
(0.003) 
0.000 
(0.000) 
-0.018 
(0.017) 
0.086 
(0.009)***  (0.015)*** 

-0.002 
(0.007) 
0.000 
(0.000) 
-0.055 
(0.039) 
0.283 

-0.000 
(0.003) 
0.000 
(0.000) 
-0.019 
(0.017) 
 0.086 
 (0.009)*** 

 0.625 
 (0.011)*** 
7607 
0.86 

 -0.060 
 (0.016)**
* 
 0.079 
 (0.015)**
* 

0.061 
(0.010)**
* 
-0.002 
(0.007) 
0.000 
(0.000) 
-0.057 
(0.039) 
0.282 
(0.015)**
* 

 -0.058 
 (0.019)*** 
 0.082 
 (0.023)*** 
 0.019 
 (0.017) 
0.061 
(0.010)*** 

-0.002 
(0.007) 
0.000 
(0.000) 
-0.057 
(0.039) 
0.282 
(0.015)*** 

0.624 
(0.011)*** 
7607 
0.86 

0.624 
(0.011)*** 
7607 
0.86 

7501 
0.36 

7501 
0.36 

7501 
0.36 

32 

Observations 
R-squared 

9447 
0.25 

9447 
0.26 

9447 
0.26 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2000 
GO/EMP  Obs 

 Mean 
 Med 
 Sd 

VA/EMP  Obs 

emp 

 Mean 
 Med 
 Sd 
Obs 
 Mean 
 Med 
 Sd 

2001 
GO/EMP  Obs 

 Mean 
 Med 
 Sd 

VA/EMP  Obs 

emp 

 Mean 
 Med 
 Sd 
Obs 
 Mean 
 Med 
Sd 

none 
4825 
91.45 
61.33 
197.76 
4825 
29.63 
24.74 
33.13 
5262 
129 
40 
343 

either 
3696 
100.71 
68.82 
146.77 
3696 
32.50 
26.54 
32.15 
4001 
260 
84 
729 

Sell 
3055 
100.09 
67.22 
153.60 
3055 
31.72 
26.11 
30.06 
3321 
256 
84 
664 

buy 
2349 
107.94 
70.33 
171.62 
2349 
34.45 
27.63 
36.45 
2528 
278 
78 
845 

Sell no buy 
1347 
88.10 
65.82 
86.62 
1347 
29.09 
24.73 
22.40 
1473 
228 
95 
466 

Table 

6.5 

Buy no Sell 
641 
103.66 
77.97 
108.48 
641 
36.19 
28.79 
40.47 
680 
279 
82 
987 

Buy and Sell 
1708 
109.55 
68.84 
189.98 
1708 
33.80 
27.21 
34.81 
1848 
278 
76 
787 

 none 

either 

Sell 

buy 

Sell no buy 

Buy no Sell 

Buy and Sell 

4917 
98.69 
61.54 
325.96 
4917 
31.61 
24.80 
89.89 
5359 
113 
32 
343 

4587 
104.83 
71.93 
201.32 
4587 
33.16 
27.88 
34.37 
4913 
232 
72 
619 

3705 
103.15 
70.60 
215.72 
3705 
32.39 
27.41 
33.25 
3982 
237 
73 
642 

3089 
110.86 
73.66 
235.59 
3089 
34.51 
28.84 
37.97 
3308 
246 
69 
690 

1498 
92.39 
68.26 
97.20 
1498 
30.36 
25.95 
25.14 
1605 
202 
80 
436 

882 
111.89 
77.77 
123.60 
882 
36.36 
30.28 
38.55 
931 
208 
68 
504 

2207 
110.44 
72.40 
267.56 
2207 
33.77 
28.53 
37.73 
2377 
261 
69 
749 

33 

 
 
 
 
  
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table     6.6 

19 

20 

97 

21 

17 

18 

15 

22 

407 

217 

212  106  165  127  141  271 
28.43  21.79  9.07  23.09  26.03  37.45  32.86 45.57  29.90  33.55  34.17  25.20 27.66  58.32  26.25 41.31 28.50  26.91 28.82 22.32 

2000: average value added per employee by industry and type of e-commerce activity  (obs: reports number of obs on which mean is calculated. 
25 
sic92 
2digit 
obs 
No e-
commerce 
obs 
e-buy or 
sell 
obs 
e sell 

83  210 
34.09  23.73  19.96  30.52  24.66  37.54  33.89 46.47  30.35  27.61  35.15  25.68 30.33  65.28  30.38 45.68 31.81  31.67 30.19 25.79 

56  182 
32.64  22.82  19.93  29.38  25.47  35.27  34.16 44.17  30.11  27.03  33.79  25.50 30.06  65.92  28.47 45.14 31.40  31.49 28.67 26.59 

165  385  303  304 

115  342  196  204 

140  383  245  238 

190  117  171  157 

214  213  666  564 

123  120  345  343 

97  102  291  261 

89  115  142 

152 

153 

383 

167 

429 

138 

35 

58 

24 

29 

28 

32 

48 

29 

30 

26 

46 

25 

39 

74 

64 

27 

31 

34 

65 

39 

33 

36 

obs 
e buy 

201 

67  116 
74 
39.12  25.63  22.24  26.19  26.71  37.81  32.70 49.31  30.95  28.41  37.87  26.86 30.74  63.57  32.10 47.79 33.10  33.83 31.99 29.20 

84  256  173  140 

63  203  255 

96  139 

125 

102 

99 

13 

58 

39 

41 

34 

 
 
 
 
 
 
 
 
 
 
 
 
  Figure 6.2
                                     Value added /employee  2000 by SIC_2digit sector

80.00

70.00

60.00

50.00

40.00

30.00

20.00

10.00

0.00

15

17

18

19

20

21

22

24

25

26

27

28

29

30

31

32

33

34

35

36

none

buy or sell

e sell

e buy

ll

35 

 
 
 
 
7 

Price effects  

Outline 

7.1 
Productivity analysis based on the ABI for UK manufacturing sectors shows that in 
most, but not all, industries covered there are positive value added effects associated 
with purchasing via electronic networks, and partial offsetting negative effect 
associated with selling. The suggestion from EU sponsored case studies is that price 
differences brought about by e-procurement are part of the 'value added' gain to firms 
which invest in use of electronic networks. This section reports preliminary discussions 
with industry experts, looks briefly at prior evidence on price effects of electronic 
markets, and tests the scope for detecting price differences by using official price 
statistics data linked to e-commerce survey information. 

Firm discussions 

7.2 
Interviews with firms which provide the infrastructure and databases on which 
electronic transaction systems are based have confirmed that gains through the 
management of backward supply chains are among the most important ex-ante 
justifications for investment for firms. The sources of cost saving for them are both 
internal and external, reducing the search and administrative costs associated with 
buying, and reducing purchase prices through access to a broader and better specified 
set of suppliers. 

One system supplier specialist interviewed qualified the experience of price effects by 
commenting that in their experience the effect of electronic buying and selling depends 
on the relative numbers of buyers and sellers, as well on the nature of the transactions. 
In markets where there are a large number of sellers making sales propositions to fewer 
buyers, the most likely outcome is downward pressure on prices, but where a larger 
number of buyers faces a small number of sellers the effect of electronic networks is to 
exert upward pressure on prices. These considerations were said to affect the design of 
buying / selling networks. 

Evidence from other sources 

7.3 
Literature on the price effects of digital markets (reviewed in Smith, Bailey and 
Brynjolfsson) covers a complex set of possible effects, which depend on the types of 
transactions covered. 

•  For simple products which can be specified in relatively few dimensions, electronic 

markets may increase price transparency and commoditisation, raising the 
importance of price in buying decisions which may tend to push prices down. For 
more complex products, where differentiation is possible there may be added scope, 
through the one to one relationship between buyer and seller permitted by e-
commerce, for price discrimination based on specific buyer circumstances, 
increasing both price dispersion and level. 

•  Electronic transactions may affect market boundaries in opposite ways. Lower 

search costs for buyers may enable them to seek more suppliers, tipping the balance 
of supply and demand in their favour and edging prices down. On the other hand, 
the investment required by suppliers in some EDI type closed electronic purchasing 
systems may limit entry, reducing scope for competition; but once made such 
investment may act as a barrier to exit, so that competition in supply increases over 
time. 

36 

 
 
 
 
 
 
•  E-commerce can also change the nature of transactions. Adoption of systematised e-
procurement models by firms often changes relationships with suppliers from a 
negotiation process towards long term agreement into a series of auctions and bids 
for specific, shorter term, contracts which are likely to be more intensely 
competitive. Against this, use of electronic networks for purposes in addition to the 
purchase decision can facilitate the delivery of value added services as part of a 
more intimate partnership between suppliers and customers in the value chain. 
•  The most dramatic effects of e-commerce can arise when suppliers use it to sell 

direct to their end users, cutting out a stage in the distribution chain; the best know 
example if this is  Dell computers, selling direct to consumers and by-passing PC 
retailers 

•  As a further difference between electronic sales and traditional marketing 

approaches, the ability of suppliers to change prices quickly and cheaply using web-
based price lists should be substantially greater. For example, traditional catalogue 
selling organisations were once restricted to changing prices, with their catalogues, 
two or three times a year to avoid unacceptably high marketing costs; with web 
based selling they can change prices from day to day, and aim special offers at 
selected customers whose buying patterns are known to them. Greater price 
flexibility and speed of response to shocks may be expected 

Most of these effects have been identified, but not often quantified, in the OECD E-
Business Impact Programme (EBIP) which brings together e-commerce experiences 
from a number of countries (see www.oecd.org/sti/information-economy). The 
quantified evidence quoted by Smith Bailey and Brynjolfsson tends to come from 
consumer internet markets (which still account for a small minority of e-commerce in 
the UK) rather than from digital business to business markets. This evidence seems to 
suggest that consumer prices are as likely to have risen as to have fallen over the 
internet, and that price dispersion has, on the whole, been unaffected. 

UK survey data 

7.4 
Based on our results from section 6, using ABI data to identify large numbers of 
manufacturing businesses which do, and do not, use electronic networks for selling, we 
are able to test the behaviour of prices in UK firms as a function of their use of e-
commerce, mainly in business to business markets. So far, we have conducted only a 
limited test covering firms identified for the year 2000, but one which suggests that 
results are statistically significant and worth  further investigation as additional years' 
data become available. 

To undertake this analysis, the following indirect data linking exercise has been 
undertaken. From the ABI responses to the e-commerce questions outlined in section 3, 
the use / non use of electronic networks for selling has been identified for firms in 40 
SIC sectors and sub-sectors in which e-commerce is known to be used by a significant 
proportion of firms. Through identifiers from the UK business register (IDBR) the firms 
have been matched with those that respond to the monthly UK producer price inquiry 
(PPI). This inquiry asks a large number of firms for quotes for specific, detailed, 
products on a consistent basis. 

PPI  results are summarised in a database which capture monthly movements for the 
prices returned, corrected for any changes in quality specification which occur. Based 
on January 1995 = 100, the 'price relatives' for each specified product show its 

37 

 
 
 
 
 
movement since the base date. These are weighted together according to value of output 
from each respondent to develop the UK Producer Price Index data-set. 

For this analysis we are only able to identify firms that sell electronically in the year 
2000, not the specific products they sell over electronic networks, or the year in which 
they might have started selling over electronic networks. To test for possible 
differences, the first approach has been to separate the PPI sets for firms which do, and 
do not, sell electronically within each SIC, taking the firm identifier as the definition of 
industrial classification (which may not be the same classification as the products 
quoted). We have then weighted the price relatives equally within the two sets (e-sell 
and non e-sell), because we are concerned to identify possible differences  between 
trends in individual observations the two sets of data than to reproduce the Producer 
Price Indices. Only in about 30% of cases do the two data-sets (PPI and ABI) overlap, 
so the price data is only available for a partial set of the forms on which our 
productivity evidence is based   

The results are shown in figures 7.1 to 7.6, with specific sectors shown as examples. 
Each graph shows the evolution of average price relative data for e-sellers and for non 
e-sellers month by month over the period January 1997 to December 2000. Each 
individual price relative series is based on January 1995 = 100, and the individual series 
are equally weighted to produce the average. They appear to show different effects in 
different markets, and we have grouped them into five main patterns of price behaviour 
over time. 

i) sectors where prices diverge in the period, with e-sellers' prices falling relative to 
non e-sellers. 
This group (Figure 7.1)includes pharmaceuticals manufacture (177 firms observed), 
where e-commerce systems have been adopted by major pharmaceutical wholesalers 
over the period as part of the process of increasing competition in a regulate market. It 
also includes mechanical engineering (32 firms observed), which is a relatively 
heterogeneous sector subject to increasing international competition over the period. 

ii) sectors where e-sellers' non e-sellers' prices diverged between 1995 and 1997, and 
where e-sellers prices remained lower through to 2000. 
This group (Figure 7.2)includes food products in both meat processing (125 
observations) and bakery production (67 observations) which sell a large part of their 
output to supermarkets, all of whom have used electronic procurement systems based 
on closed (EDI) systems for some time.  

iii) sectors where e-sellers prices had fallen relative to non e-sellers prior to 1997, and 
appear to re-converge by 2000 
 This group (Figure 7.3) includes basic organic chemicals (191 observations) and 
pesticides and agrochemical products (41 observations). In both these sectors there are 
effectively global markets, and there has been substantial international consolidation of 
supply during the later 1990s. These sectors are also subject to input price shocks from 
the oil market, to which they adjust with varying degrees of speed. 

iv) sectors where prices for the two groups are indistinguishable 
This group (Figure 7.4)  includes electronic components (137 observations) and 
newspaper publishing (118 observations), both areas related to sectors where e-

38 

 
   
 
 
 
 
 
commerce is well established, and has influenced markets for  considerable time. In 
such markets it is possible that competition has ensured that prices have stayed aligned. 
It is worth noting that in our productivity analysis by sector in section 6, publishing and 
computer manufacture were the two sectors in which value added / employee 
productivity measures for e-sellers were higher than for e-buyers. This would be 
consistent with a situation in which selling prices for e-sellers and non e-sellers were 
similar, and productivity gains associated with electronic network use were retained by 
firms rather than  being passed on to customers through lower prices. 

v) sectors where e-sellers' prices are higher than non e-sellers' 
This group (Figure 7.5) includes manufacture of  parts for motor vehicles (148 
observations) and manufacture of soap and detergents (58 observations). These are both 
sectors where intermediate demand includes both major brand owners who purchase for 
inclusion into branded consumer products, and 'spot' demand for other applications. It 
may be that differences in service levels or specification account for the differences. 

We have also encountered one sector, paints and varnishes, shown in Figure 7.6 and 
containing 136 observations, where e-sellers' prices have increased relative to non e-
sellers' over the four year period covered. This is a sector where there are major 
differences in price levels between consumer and industrial products, and where there 
have been technology developments in service delivery as well as producer 
consolidation over the period. 

7.5   Conclusions 
UK price evidence from this limited set supports the view from the literature that a 
range of forces are at work to affect prices in electronic markets. Across all the 21 
groups examined,  sectors in which e-sellers' prices are lower (groups i to iii) 
outnumber those where there is no difference, or where e-sellers' prices are higher. 
Overall therefore, it seems that the electronic sale of goods is more likely to have a 
negative impact on prices but there is a great deal of variation.  

This conclusion is supported by regression analysis for 2400 reported price series across 
forty four digit sectors in manufacturing for which we have data, each series monthly 
over four years. The results are presented in Table 7.1, and the sectors listed in Table 
7.2. They suggest that the electronic receipt of orders has a negative impact on relative 
prices which is statistically significant at the ten per cent level, after taking sector and 
size effects into account. The sample contains all firm level observations of relative 
prices for the selected sectors between January 1997 and December 2000 where real 
responses are available. Monthly price trends, and sector effects have been controlled 
for using monthly and industry dummies, the latter using four digit SIC codes. Firm size 
is measured by employment, but the equation works almost equally well using sales or 
market share. 

The model based on this initial data provides a reasonable “fit” with an R-squared of 
0.3 allowing for the fact that no other determinants of prices have been included. An 
attempt has been made to look at the 'number of suppliers' factors suggested by our 
industry interviewees, taking sector concentration (share of top four producers) or 
Herfindahl index into the analysis. This has been attempted in both the regression 
analysis, and in graphical analysis. In neither case does a clear pattern emerge - 

39 

 
 
 
 
 
 
 
probably because other factors covered in the literature - concentration in customer 
industries, presence of value added services or effect on distribution channels - are 
absent from our data-sets. 

Our provisional conclusion is that price effect of e-commerce in the sectors we have 
examined for the period up to 2000 is, on balance negative, and that this is consistent 
with the value added effects we see in productivity data. It is clear that effects differ 
across sectors, and in some sectors the effects of competition appear to even out price 
effects within a few years. However, the overall effect is to make the majority of 
markets more price competitive, which is likely to have both micro and macro effects 
within the economy. At micro level e-commerce is likely to speed up the selection 
processes favouring more efficient firms, and at macro level the evidence suggests that 
it may increase competitive downward pressure on prices. 

Table 7.1 

Regression results for effect of e-selling on producer prices (limited sample, 1997-
2000) 

Dependent variable: Relative Price 

Variable 

e-sell 

Employment 

Intercept 

Observations 
Adjusted R-squared 

Coefficient (P value) 
-1.352813 
(0.099)* 
-0.0007413 
(0.136) 
106.2562 
(0)*** 

107201 
0.2979 

*** Significant at 1% level 
** Significant at 5% level 
* Significant at10% level 

40 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 7.2 
Sectors covered by analysis so far 
4 digit 
SIC 

Description of sectors for which we have firms with observations from ABI which 
establish e-commerce use / non-use, plus time series of producer price relative data. 

Average number 
of time series in 
firms from SIC 
category 

1513 Production of meat and poultry meat products 
1581 Manufacture of bread; manufacture of fresh pastry goods and cakes 
2121 Manufacture of corrugated paper and paperboard and of containers of paper and 

paperboard 

2211 Publishing of books 
2212 Publishing of newspapers 
2213 Publishing of journals and periodicals 
2214 Publishing of sound recordings 
2215 Other publishing 
2221 Printing of newspapers 
2222 Printing not elsewhere classified 
2223 Bookbinding and finishing 
2224 Composition and plate-making 
2225 Other activities related to printing 
2231 Reproduction of sound recording 
2232 Reproduction of video recording 
2233 Reproduction of computer media 
2411 Manufacture of industrial gases 
2412 Manufacture of dyes and pigments 
2413 Manufacture of other inorganic basic chemicals 
2414 Manufacture of other organic basic chemicals 
2415 Manufacture of fertilizers and nitrogen compounds 
2416 Manufacture of plastics in primary forms 
2417 Manufacture of synthetic rubber in primary forms 
2420 Manufacture of pesticides and other agro-chenmical products 
2430 Manufacture of paints, varnishes and similar coatings, printing ink and mastics 
2441 Manufacture of basic pharmaceuticals 
2442 Manufacture of pharmaceutical preparations 
2451 Manufacture of soap and detergents, cleaning and polishing preparations 
2452 Manufacture of perfumes and toilet preparations 
2461 Manufacture of explosives 
2462 Manufacture of glues and gelatine 
2463 Manufacture of essential oils 
2464 Manufacture of photographic chemical material 
2465 Manufacture of prepared recorded media 
2466 Manufacture of other chemical products not elsewhere classified 
2470 Manufacture of man-made fibres 
2523 Manufacture of builders' ware of plastic 
2852 General Mechanical Engineering 
3210 Manufacture of electronic valves and tubes and other electronic components 
3430 Manufacture of parts and accessories for motor vehicles and their engines 

125 
67 
121 

72 
120 
104 
<10 
20 
<10 
135 
<10 
12 
<10 
19 
16 
<10 
11 
37 
95 
200 
38 
129 
10 
47 
161 
30 
165 
67 
98 
16 
22 
38 
17 
13 
79 
29 
80 
32 
141 
147 

41 

 
 
Figure 7.1 
1513 Production of meat & poultry products 

e-sellers

non e-sellers

e
c
i
r
P
e
v
i
t
a
l
e
R

110

108

106

104

102

100

98

96

94

92

90

199701

199703

199705

199707

199709

199711

199801

199803

199805

199807

199809

199811

199901

199903

199905

199907

199909

199911

200001

200003

200005

200007

200009

200011

1581 Manufacture of bread; manufacture of fresh pastry goods and cakes 

Date

e-sellers

non e-sellers

e
c
i
r
P
e
v
i
t
a
l
e
R

110

108

106

104

102

100

98

199701

199703

199705

199707

199709

199711

199801

199803

199805

199807

199809

199811

199901

199903

199905

199907

199909

199911

200001

200003

200005

200007

42 
200011
200009

Date

 
 
 
 
 
Figure 7.2 

2852 General Mechanical Engineering 

e-sellers

non e-sellers

108

106

104

102

100

98

96

94

92

199701

199703

199705

199707

199709

199711

199801

199803

199805

199807

199809

199811

199901

199903

199905

199907

199909

199911

200001

200003

200005

200007

200009

200011

2442 Manufacture of pharmaceutical preparations 

Date

e-sellers

non e-sellers

114

112

110

108

106

104

102

100

98

96

94

199701

199703

199705

199707

199709

199711

199801

199803

199805

199807

199809

199811

199901

199903

199905

199907

199909

199911

200001

200003

200005

200007

43 
200011
200009

e
c
i
r
P
e
v
i
t
a
l
e
R

e
c
i
r
P
e
v
i
t
a
e
R

l

Date

 
 
 
 
 
Figure 7.3 
2414 Manufacture of other inorganic chemicals 

e-sellers

non e-sellers

100

95

90

85

80

75

70

65

60

199701

199703

199705

199707

199709

199711

199801

199803

199805

199807

199809

199811

199901

199903

199905

199907

199909

199911

200001

200003

200005

200007

200009

200011

2420 Manufacture of pesticides and other agro-chemical products 

Date

e-sellers

non e-sellers

115

110

105

100

95

90

85

80

e
c
i
r
P
e
v
i
t
a
l
e
R

e
c
i
r
P
e
v
i
t
a
e
R

l

199701

199703

199705

199707

199709

199711

199801

199803

199805

199807

199809

199811

199901

199903

199905

199907

199909

199911

200001

200003

200005

200007

200011
200009
44 

Date

 
 
 
 
 
Figure 7.4 
3210 Manufacture of electronic valves and tubes and other electronic components 

e-sellers

non e-sellers

e
c
i
r
P
e
v
i
t
a
l
e
R

100

95

90

85

80

75

70

65

60

199701

199703

199705

199707

199709

199711

199801

199803

199805

199807

199809

199811

199901

199903

199905

199907

199909

199911

200001

200003

200005

200007

200009

200011

2212 Publishing of newspapers 

Date

e - s e lle r s

n o n   e - s e lle r s

e
c
i
r
P
e
v
i
t
a
l
e
R

1 2 2

1 2 0

1 1 8

1 1 6

1 1 4

1 1 2

1 1 0

1 0 8

1 0 6

1 0 4

1 0 2

1 0 0

199701

199703

199705

199707

199709

199711

199801

199803

199805

199807

199809

199811

199901

199903

199905

199907

199909

199911

200001

200003

200005

200007

200009

200011

D a t e

45 

 
 
 
 
 
Figure 7.5 

24511 Manufacture of soap and detergents 

e-sellers

non e-sellers

e
c
i
r
P
e
v
i
t
a
l
e
R

106

104

102

100

98

96

94

92

90

199701

199703

199705

199707

199709

199711

199801

199803

199805

199807

199809

199811

199901

199903

199905

199907

199909

199911

200001

200003

200005

200007

200009

200011

3430 Manufacture of parts and accessories for motor vehicles and their engines 

Date

e-sellers

non e-sellers

e
c
i
r
P
e
v
i
t
a
l
e
R

104.5

104

103.5

103

102.5

102

101.5

101

100.5

100

99.5

199701

199703

199705

199707

199709

199711

199801

199803

199805

199807

199809

199811

199901

199903

199905

199907

199909

199911

200001

200003

200005

200007

Date

46 
200011
200009

 
 
 
 
 
 
Figure 7.6 

2430 Manufacture of paints, varnishes and similar coatings, printing ink and 
mastics  

e-sellers

non e-sellers

e
c
i
r
P
e
v
i
t
a
e
R

l

110

108

106

104

102

100

98

96

94

92

90

199701

199703

199705

199707

199709

199711

199801

199803

199805

199807

199809

199811

199901

199903

199905

199907

199909

199911

200001

200003

200005

200007

200009

200011

Date

47 

 
 
 
8 

Service sector; retailing and stocks 

Labour Productivity in Services 

8.1 
The  scope  for  extending  the  productivity  and  pricing  analysis,  contained  in  section  6  and  7 
respectively, to the service sector is restricted. The first barrier is the absence of capital stock 
estimates at firm level. These are required to employ a TFP approach, there is also a lack of 
comprehensive price quotes, which have only started to be collected by ONS relatively recently. 
Therefore our analysis of e-commerce in the service sector is limited to associations between e-
commerce activity, labour productivity and operational ratios.  

A breakdown of labour productivity, measured as value-added per employee, by two digit SIC 
code is shown in Figure 8.1 The methodology employed to generate the data is different from 
that used in section 6, partly because there is no PPI data to adjust value-added per employee). 
Three  service  sectors  have  been  omitted,  one  due  to  disclosure  issues  and  two  for  lack  of 
analytical relevance. 

It  presents  a  very  different  and  more  varied  picture  of  e-commerce  impact  from  that  for 
manufacturing. In eight of seventeen sectors e-sellers enjoy the highest levels of value-added per 
employee, suggesting either price advantages or real productivity gains. Price advantages may 
arise  since  e-commerce  allows  firms  to  increase  value-added  through  provision  of  a 
differentiated service, or to deliver straight to the consumer, removing an element in the supply 
chain, as discussed in section 6. One example of this would be digital receipt of IT services (SIC 
72) rather than trading in physical form.  

However in five sectors it is firms that buy electronically that are the most productive, including 
Wholesale (SIC 51), Water / Air Transport (SIC 61 & 62) and Rental (SIC 71). This may be 
due  to  price  competition  effects  since  these  are  relatively  price  sensitive  and  commoditised 
sectors.  

Of the remaining sectors five show higher labour productivity for firms that do not conduct 
purchases or sales via e-commerce. One of these sectors is “Post and Telecommunications,” an 
information  service  for  which  there  are  apparently  no  productivity  gains  associated  with  e-
commerce activity; a surprising and unexpected result. However using e-commerce to buy is 
generally associated with higher labour productivity than not using e-commerce at all across 
most service sectors. 

It is quite possible that many service firms trading electronically are engaged in different types 
of  activities  from  those  that  do  not,  due  to  the  inherent  heterogeneity  of  services.  To  start 
comparative  analysis  of  e-commerce  effects  in  services,  we  have  therefore  chosen  retailing 
which has relatively simplicity value chains, and comparable customer / supplier relationships 
and technology. Also, since retail has been a strong and effective user of e-commerce for some 
time, mainly in the form of EDI (Electronic Data Interchange), the effects of e-commerce may 
be more established in this sector. 

Retail productivity 

8.2 
The  following  analysis  uses  simple  descriptive  data,  produced  using  the  ARD  (Annual 
Respondents Database) which is the longitudinal series of ABI surveys, to examine productivity 
trends in the retail sector over time, between different sub-sectors and according to e-commerce 

48 

 
 
 
 
 
 
 
 
activity.  The  analysis  only  included  firms  that  have  at  least  ten  employees,  positive  values 
reported  for value-added, and value-added to sales ratios of less than one hundred.  

The majority of firms in the retail sector fall under the following headings:  

- 

- 
- 
- 

Retail  sale  in  specialised  stores  which  includes  confectioners’  tobacconists  and 
newsagents (SIC52.1) 
Retail sale of food, beverages and tobacco in specialised stores (SIC52.2) 
Retail sale of pharmaceutical and medical goods, cosmetic and toilet articles (SIC52.3) 
Other retail sale of new goods in specialised stores (SIC52.4) (which mainly comprises 
of firms selling comparison goods)  

Tables 8.1 to 8.4 show labour productivity values in the four main sub-sectors of retail between 
1997 and 2001. Figures provided in parentheses refer to the number of observations, “Emp” 
refers to employment, “VA/emp” to value-added per employee and “GO/emp” to gross output 
per employee. All other cells are averages generated by weighting all responses equally. It can 
be seen that there is a clear upward trend, with some volatility, in value added per employee in 
all of the main sub-sectors of retail, but particularly in SIC52.1 and SIC52.4.  

Comparing against price changes shows that there have been real gains in labour productivity in 
all four sectors over this period. Gross output per employee has also grown in all sectors except 
SIC52.2.  (note  that  there  are  differences  in  the  methodology  to  generate  value-added  in  the 
following analysis to that employed in Figure 8.1, and that gross output is total turnover). 

Table 8.1: SIC 52.1 Retail sale in non-specialised stores 

Year 

1997 

1998 

1999 

2000 

2001 

 52.1 (296) 

52.1 (318) 

52.1 (290) 

52.1 (293) 

52.1 (334) 

Emp 
VA/emp 
GO/emp 

3178.15 
15.394 
59.220 

2369.14 
15.869 
65.567 

2378.96 
16.451 
65.513 

3464.04 
18.932 
65.525 

3256.18 
19.832 
76.873 

Table 8.2: SIC 52.2 Retail sale of food, beverages and tobacco in specialised stores 

Year 

1997 

1998 

1999 

2000 

2001 

 52.2 (231) 

52.2 (227) 

52.2 (274) 

52.2 (249) 

52.2 (285) 

Emp 
VA/emp 
GO/emp 

258.52 
13.979 
53.610 

115.98 
15.185 
48.578 

207.36 
13.667 
46.199 

211.49 
14.653 
52.274 

203.11 
16.679 
51.139 

Table 8.3: SIC 52.3 Retail sale of pharmaceutical and medical goods, cosmetic and toilet 
articles 

Year 

1997 

1998 

1999 

2000 

2001 

 52.3 (175) 

52.3 (164) 

52.3 (139) 

52.3 (145) 

52.3 (154) 

Emp 
VA/emp 
GO/emp 

202.51 
18.655 
77.257 

267.32 
19.305 
75.899 

173.09 
20.338 
85.351 

305.49 
23.186 
98.110 

320.17 
22.408 
93.415 

49 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 8.4: SIC 52.4 Other retail sale of new goods in specialised stores 

Year 

1997 

1998 

1999 

2000 

2001 

 52.4 (1161)  52.4 (1009)  52.4 (1005)  52.3 (1033)  52.3 (1145) 

Emp 
VA/emp 
GO/emp 

443.01 
22.733 
67.314 

309.01 
24.585 
69.952 

386.32 
33.660 
91.317 

566.29 
26.021 
70.443 

555.74 
29.368 
80.550 

Tables 8.5 to 8.8 (see annex) split the data by firm size, with the break point at 
hundred employees, as well as by sector, and suggest that labour productivity has 
risen in both larger and smaller firms, but has been most marked among the smaller.  

The next strand of analysis explores the effects of e-commerce on the retail sector as a whole, 
and results are shown in Tables 8.8 and 8.10. Unlike manufacturing, the data suggests that e-
selling  is  positively  associated  with  labour  productivity  (as  in  Figure  8.1),  as  is  using  e-
commerce to both place and receive orders. It seems retailers that sell electronically may be able 
to retain the productivity gain, and not pass it on in the form of lower prices, which is what 
appears to occur in manufacturing as shown in section 7. This is because retail consists mainly 
of business-to-consumer (B2C) activity, as opposed to business-to-business (B2B) activity, and 
consumers have less incentive to employ low search cost activities. 

There is no evidence of a productivity gain for e-procurement in the 2000 data (below or in 
Figure  8.1),  but  there  is  in  2001,  although  it  is  not  as  large  as  that  for  firms  that  only  sell 
electronically or those that do both. Therefore it appears that e-commerce enables retailers to 
improve the productivity of their employees by substituting capital for labour and improving the 
efficiency of their downstream supply chain.  

Table 8.9: SIC 52 (2000) 

N=1889 
Emp 
VA/emp 
GO/emp 

Either 
Yes (541)  No (1348)  Yes (204)  No (1685)  Yes (137)   No (1752)  Yes (200)  No (1689) 

 Buy only 

 Sell only 

 Both 

2418.45 
27.466 
86.723 

340.33 
22.181 
69.172 

1982.00 
22.226 
72.318 

808.80 
23.872 
74.427 

967.16 
30.357 
92.761 

933.02 
23.174 
72.747 

3857.76 
30.830 
97.281 

589.46 
22.850 
71.466 

Table 8.10: SIC 52 (2001) 
* Provisional data 

N=2103 
Emp 
VA/emp 
GO/emp 

 Either 
Yes (691)  No (1412)  Yes (531)  No (1572)  Yes (160)  No (1943)  Yes (258)  No (1845) 

 Buy only 

 Sell only 

 Both 

2117.06 
29.709 
91.141 

282.79 
24.288 
72.618 

2407.85 
28.140 
85.123 

371.26 
25.370 
76.536 

1152.02 
34.917 
111.114 

863.54 
25.341 
76.036 

3154.74 
32.042 
92.189 

568.16 
25.234 
76.819 

When the 2000 data is split by scale as well as e-commerce activity, as in Table 8.11, 
it shows that using e-commerce solely for e-procurement is only associated with 
higher productivity in larger firms. The data also tentatively suggests that using e-
commerce for selling is more beneficial for smaller firms than larger ones, whilst the 
opposite is true of those firms using it for both buying and selling. Using e-commerce 

50 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
to either sell or trade in both directions also appears to generate substantially higher 
levels of gross output per employee. 

Table 8.11 

Size (employment) and e-commerce 

N=1889  Either 

<100 

Emp 
VA/emp 
GO/emp 

Emp 
VA/emp 
GO/emp 

Emp 
VA/emp 
GO/emp 

Emp 
VA/emp 
GO/emp 

 Yes (321)  No (1029) 
28.33 
21.619 
68.916 

31.79 
27.134 
87.764 
 Buy only  <100 
 Yes (123)  No (1227) 
29.03 
22.974 
73.530 

30.41 
22.496 
72.075 
 Sell only  <100 
 Yes (72)  No (1278) 
28.70 
22.453 
71.880 

37.17 
31.408 
100.328 

<100 

 Both 
 Yes (126)  No (1224) 
29.06 
22.283 
71.081 

30.08 
29.220 
95.899 

 Either 
 Yes (220)  No (319) 

>=100 

1346.76 
23.993 
70.000 

5900.79 
27.950 
85.205 
 Buy only  >=100 
 Yes (81)  No (458) 

2897.83 
26.278 
76.828 

4945.53 
21.816 
72.686 
 Sell only  >=100 
 Yes (65)  No (474) 

1997.31 
29.192 
84.378 

3371.24 
25.116 
75.085 

>=100 

 Both 
 Yes (74)  No (465) 
  10375.15 
33.573 
99.633 

2064.58 
24.340 
72.478 

Further splitting of the 2000 data (Tables 8.12 to 8.15) suggests: 
- 
- 

the effect of electronic selling on labour productivity is evident in all sectors,  
the negative association with e-procurement is also evident in all sectors 
except SIC52.4  
the positive association with doing both is also evident in all sectors except 
SIC52.1 and appears remarkably strong in SIC52.3  

- 

It appears that e-sellers have a definite productivity advantage whilst e-buyers tend to 
suffer from lower levels of labour productivity, but there are no obvious reasons why. 

The final set of data for 2000 has been split according to size, e-commerce activity 
and sector, and is presented in Tables 8.16 to 8.18 (except for categories which might 
be disclosive). Results presented show that there tends to be a productivity advantage 
for larger firms from e-commerce activity. Splitting the data also helps reveal further 
scale related sector-specific effects. For instance: 
- 
- 

in SIC52.1 larger e-procurers suffer from lower productivity  
in SIC52.2 larger firms benefit from using e-commerce to either place or 
receive orders  
in SIC52.4, although e-procurement is associated with lower productivity for 
larger firms, smaller firms show the opposite tendency 
in the case of firms that both buy and sell electronically it is clear that it is the 
larger firms gain most in SIC52.4. This is reasonable since the figures for 
average employment indicate that this sector may have most scope for 
economies of scale. It is also the sector most likely to contain firms with the 
resources and market scale to benefit from integration of processes within their 
supply chain. 

- 

- 

51 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
The data for 2001 is not presented since it is provisional but it suggests that e-
procurement may result in productivity gains, especially for smaller firms. In terms of 
e-sellers it is again the smaller firms that experience the greatest productivity gain, 
supporting the findings for 2000.  The labour productivity value for those firms that 
both bought and sold electronically in SIC52.3 shows some differences from the 2000 
pattern, although there still a clear productivity gain for e-sellers in this sector. 

Further splits taking scale into consideration show that:  
- 

in SIC52.1 there is only a productivity gain for the smaller e-buyers, whereas any gain 
for  those  that  do  both  only  occurs  in  larger  firms,  which  is  acceptable  for  reasons 
discussed previously  
in SIC52.2 it is again the smaller e-procurers that enjoy higher productivity, whilst there 
is no such association for the larger firms 
in SIC52.4 it is the smaller firms that benefit the most from e-commerce activity, as 
they did in the 2000 data for all categories except “both”  
the  larger  firms  in  this  sector  that  trade  electronically  also  experience  higher 
productivity, which was not the case in 2000 data 

- 

- 

- 

8.3 

Retail Stocks 

Logistics, and more broadly supply chain management, are among the primary and most widely 
used applications of ICT. Theoretically ICT and e-commerce should allow firms to integrate, 
and  even  remove,  areas  in  the  B2B  part  of  their  supply  chain.  This  concept  can  be  easily 
understood  in  the  context  of  the  retail  sector  where,  with  the  correct  technology,  product 
information can be “read” at the electronic point of sale (EpoS) and transmitted up the supply 
chain, thus automatically controlling stock levels. 

One example of such a system is ECR (Efficient Customer Response) which auto-manages 
stock and replenishment between production and eventual sale. ICT allows retailers to become 
exponents of “pull logistics” and reduces risks of excess inventories by integrating their business 
processes. Increased concentration in the retail sector over the last decade may also mean that 
such practices are more evident since the scale of its major players may help them to force 
suppliers to hold stock further up the supply chain. 

The following data is presented in exactly the same format to that on labour productivity in the 
retail sector above, but shows stock ratios for the period 1997-2001, to sales, value added and 
employment. Much of the discussion that follows focuses on stocks / sales. 

Table 8.19: SIC 52.1 Retail sale in non-specialised stores 

Year 
N firms 
Emp 
Stock/Sales 
Stock/VA 
Stocks/Emp 

1997 
(296) 

1998 
(318) 

1999 
(290) 

2000 
(293) 
3178.15  2369.14  2378.96  3464.04 
0.0839  0.0794 
0.3627  0.3381 
5.2144  4.9779 

0.0877  0.0770 
0.3473  0.4391 
5.0764  4.6827 

2001 
(334) 
3256.18 
0.0736 
0.2929 
5.0113 

There  is  some  evidence  in  tables  8.19  to  8.22  of  a  decline  in  stock/sales  ratios  since  1997, 
although the time series is short. The stock/sales ratios in all sectors has fallen by 2001, which 
means that in operational terms rates of retail stock turn have risen. 

52 

 
 
 
 
 
 
 
 
 
 
Since  stocks  are  a  key  part  of  retailing  capital,  this  decline  represents  an  efficiency  gain. 
However, the ratio of most concern to retailers is stock / value added since this captures both 
operational and economic effects. For example, if lower stocks means less choice offered by 
retailers,  consumers  may  only  be  prepared  to  pay  lower  prices,  which  would  affect  'real' 
productivity. Stock / value added ratios, although more volatile, have also shown a downward 
trend since 1997. 

Table 8.20: SIC 52.2 Retail sale of food, beverages and tobacco in specialised stores 

Year 
N firms  
Emp 
Stock/Sales 
Stock/VA 
Stocks/Emp 

1997 
(231) 

1998 
(227) 
258.52  115.98 
0.0330  0.0316 
0.1579  0.1375 
2.2418  1.8795 

1999 
(274) 

2001 
2000 
(285) 
(249) 
207.36  211.49  203.11 
0.0297  0.0338  0.0298 
0.1386  0.1422  0.1061 
1.8488  2.2112  1.9695 

Results for retail sector 52.2 are harder to gauge. It appears that there may have been a slight 
decline in stocks but less than in the other three main sectors of retail. Also, whilst the decline in 
stock/sales  is  not  so  evident,  it  is  again  supported  by  the  other  two  stock  ratios,  as  is  the 
temporary rise in 2000. 

Table 8.21: SIC 52.3 Retail sale of pharmaceutical and medical goods, cosmetic and toilet 
articles 

Year 
N firms 
Emp 
Stock/Sales 
Stock/VA 
Stocks/Emp 

1997 
(175) 

1998 
(164) 
202.51  267.32 
0.0928  0.0919 
0.4432  0.4479 
7.0340  6.8284 

1999 
(139) 

2001 
2000 
(154) 
(145) 
173.09  305.49  320.17 
0.0932  0.0858  0.0767 
0.5033  0.5971  0.3702 
7.6076  9.2103  6.8217 

Decline in stocks is much more evident in SIC52.3 not only in terms of stock/sales but also 
stock/value-added, although stocks per employee are volatile. This area of retailing has smaller 
firms, as indicated by average employment, so volatility in the employment ratio may reflect 
variation in the sample selected. A similar statistical effect may be present in the data for SIC 
52.4.  

Table 8.22: SIC 52.4 Other retail sale of new goods in specialised stores 

Year 
N firms 
Emp 
Stock/Sales 
Stock/VA 
Stocks/Emp 
. 

1998 
1997 
(1161) 
(1009) 
443.01  309.01 
0.1582  0.1524 
0.503 
0.7409 
9.972 
10.341 

2001 
2000 
1999 
(1005) 
(1145) 
(1033) 
386.32  566.29  555.74 
0.155  0.1476  0.1417 
0.5982  0.5431  0.5948 
11.319  9.8412  11.173 

The importance of size is borne out by analysis splitting the data according to firm size, as in 
Tables 8.23 to 8.26. This reveals that it is the smaller firms that have experienced falling stocks 
whilst the larger firms have maintained fairly constant ratios in all sectors except SIC52.2 where 
the opposite is true. This suggests that scale effects vary from sector to sector. It also appears 
that larger firms tend to hold more stock relative to sales in all sectors except SIC52.4. 

53 

 
 
 
 
 
 
 
 
 
 
 
One  hypothesis  to  explain  the  downward  trend  in  stocks  over  time  is  that  it  is  due  to  the 
increasing use of electronic networks. Therefore the information on whether or not firms use e-
commerce to place/receive orders, collected using the ABI (Annual Business Inquiry) in 2000 
and 2001, was used to test whether there is any association between e-commerce and lower 
inventory levels.  

The results, shown in Tables 8.27 and 8.28, are broadly consistent between years but with some 
variation. For instance, in 2000 firms that use e-commerce to only place orders appear to hold 
lower stocks, but this is not evident in the 2001 data in terms of the stock/sales ratio. The data 
shows little or no effect for those firms that use e-commerce for either placing or receiving 
orders, or indeed both, thus not confirming the anticipated effects, at least when considering the 
retail sector as a whole. However, firms that use e-commerce to only receive orders hold more 
stock, which at first is a little surprising but possible reasons are explored below. It appears that 
e-commerce    effects  attributable  to  buying  and  to  selling  may  cancel  each  other  out  in  the 
“either” and "both" categories below. 

Table 8.27: SIC 52 Retail (2000) 

N=1889 
Emp 
Stock/Sales 
Stock/VA 
Stocks/Emp 

 Buy only 

 Either 
Yes (541)  No (1348)  Yes (204)  No (1685)  Yes (137)   No (1752)  Yes (200)  No (1689) 
589.46 
0.1129 
0.4663 
8.3465 

3857.76 
0.1148 
0.3920 
10.8164 

1982.00 
0.0991 
0.3576 
6.8246 

967.16 
0.1463 
0.4599 
12.9827 

2418.45 
0.1169 
0.3962 
9.8597 

933.02 
0.1106 
0.4584 
8.2659 

808.80 
0.1148 
0.4707 
8.8239 

340.33 
0.1116 
0.4835 
8.1056 

 Sell only 

 Both 

Table 8.28: SIC 52 Retail (2001) 
* Provisional data 

 Buy only 

 Either 
 Yes (691)  No (1412)  Yes (531)  No (1572)  Yes (160)  No (1943)  Yes (258)  No (1845) 
568.16 
0.1064 
0.4846 

3154.74 
0.1198 
0.3751 

1152.02 
0.1380 
0.5455 

2407.85 
0.1092 
0.3917 

2117.06 
0.1159 
0.4273 

863.54 
0.1055 
0.4651 

371.26 
0.1076 
0.4980 

282.79 
0.1041 
0.4926 

 Sell only 

 Both 

Emp 
Stock/Sales 
Stock/VA 

Stocks/Emp 

11.5044 

8.4810 

8.6739 

9.7448 

20.8979 

8.5338 

10.2655 

9.3638 

It may be that e-selling firms hold greater stocks due to increased variety and unpredictable sales 
patterns, since e-commerce can be used to offer greater customer choice. Against this stands the 
argument that since the order is electronic, the retailer does not need to hold the product in the 
form of stock since the information can be processed and delivery organised directly from the 
supplier.  However,  this  will  only  be  the  case  if  firms  have  the  electronic  networks  to  place 
orders and will also depend on the relative levels of market influence of the retailer and their 
supplier(s). E-selling firms may order in bulk and hold more stock in anticipation of greater 
sales; the data supports this picture. 

Since the above analysis provides no evidence of statistical significance, regression analysis, 
using stock/sales as the dependent variable and including dummies to reflect the nature of e-
commerce  activity  undertaken  by  firms  and  their  sector  within  retail,  was  conducted  and 
supports this data. An employment variable to capture any scale effects was also included. The 
results for the linear specification show significant coefficients in the direction shown in the 
descriptive data for e-procurement and e-selling. The results are shown in Appendix A.  

54 

 
 
 
 
 
 
 
 
 
 
 
The  next  analysis  considers  whether  the  effects  described  above  applied  consistently  across 
large and small firms. Therefore the firms were also split according to scale and, as can be seen 
in Table 8.29, the negative association between e-procurement and stock is stronger for larger 
firms, whilst the positive association between electronic sales and stocks is stronger for smaller 
firms. The latter is intuitive since it is the larger firms who will have the supplier relationships to 
ensure stock remains further up the supply chain. 

Table 8.29 

2000 

Emp 

Stock/Sales 
Stock/VA 
Stocks/Emp 
2000 

Emp 
Stock/Sales 
Stock/VA 
Stocks/Emp 
2000 

Emp 
Stock/Sales 
Stock/VA 
Stocks/Emp 
2000 

Emp 
Stock/Sales 
Stock/VA 
Stocks/Emp 

Either 
 Yes (321)  No (1029) 

<100 

 Either 
 Yes (220)  No (319) 

>=100 

31.79 

0.122 
0.425 
10.68 

28.33 

0.113 
0.517 
8.181 

5900.79 

1346.76 

0.109 
0.354 
8.663 

0.107 
0.374 
7.863 

Buy only 
 Yes (123)  No (1227) 

<100 

 Buy only 
 Yes (81) 

>=100 
No (458) 

30.41 
0.104 
0.388 
7.393 

29.03 
0.116 
0.506 
8.914 

4945.53 
0.091 
0.311 
5.962 

2897.83 
0.11 
0.376 
8.583 

Sell only 
 Yes (72) 

<100 
No (1278) 

 Sell only 
 Yes (65) 

>=100 
No (474) 

37.17 
0.164 
0.527 
15.586 

28.7 
0.113 
0.494 
8.391 

1997.31 
0.126 
0.386 
10.099 

3371.24 
0.105 
0.364 
7.927 

Both 
 Yes (126)  No (1224) 

<100 

 Both 
 Yes (74) 

>=100 
No (465) 

30.08 
0.116 
0.402 
11.086 

29.06 
0.115 
0.505 
8.537 

10375.15 
0.113 
0.375 
10.357 

2064.58 
0.107 
0.365 
7.844 

For the retail sector as a whole it appears that using e-commerce to place orders is 
negatively associated with stocks for larger firms. However, e-selling appears to 
require higher stocks, particularly for smaller firms. The effects are not exactly the 
same for each of the four main sectors in retail, as can be seen below in Tables 8.30 to 
8.33. For instance, in SIC52.1, SIC52.3 and SIC52.4, stores that use e-commerce to 
both place and receive orders hold less stock, suggesting benefits from integration of 
business processes. Data for those that use e-commerce only to sell is only available 
in SIC52.1 and SIC52.4, due to unsuitable sample sizes. However in each case they 
hold more stock than their counterparts that do not sell electronically. Firms that use 
e-commerce solely for e-procurement hold higher stocks in all sectors except SIC52.1. 

It has only been possible to split the data on three dimensions at once, according to e-
commerce activity, SIC code and scale, in cases where the sample size allows. 

The results, presented in Tables 8.34 and 8.35, suggest: 
- 

in SIC52.1 the e-procurement gain applies only to larger firms and may be 
partially hidden in the more aggregated data since smaller firms tend to 
experience the opposite effect 
it is the larger e-sellers that hold higher stocks  

- 

55 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
- 

- 

- 

- 

it is the smaller firms that benefit most from conducting both purchases and 
sales via e-commerce 
there is an e-procurement effect for smaller firms in SIC52.3 of a similar 
magnitude to that for the sector as a whole  
in SIC52.4 it is the smaller firms that benefit most from e-procurement and 
doing “both” in the form of lower stocks 
the now expected positive association with e-selling only applies to smaller 
firms in SIC52.4. Again this is reasonable for reasons given previously but 
may be more applicable in this sector due to the nature of the products and the 
scale of some retailers in this sector.  

In search of consistency between the two data sets the same analysis was conducted 
using the ARD 2001. Although descriptive data for 2001 has not been included, since 
it is provisional, the results for firms that use e-commerce to either place or receive 
orders, and those that use it to only receive orders, are very consistent with 2000, 
although the e-procurement effect for larger firms is not evident. 

More specifically: 
- 

- 

- 

- 

firms that use e-commerce solely for sales hold higher stocks in all sectors 
except SIC52.4, thus broadly supporting the 2000 data 
however firms that use e-commerce solely for e-procurement only enjoy lower 
stocks in SIC 52.3, whereas this was also the case in SIC52.2 and SIC52.4 in 
2000  
firms that both buy and sell electronically are only associated with lower stock 
holdings in SIC52.3, compared to SIC52.1, SIC52.3 and SIC52.4 in 2000 
the descriptive data for SIC52.1 and SIC52.2 in 2001 provides the perverse 
result that both buying and selling electronically is associated with higher 
stock, although it may be that these firms have identified this and trade 
electronically in an attempt to remove this problem.  

However it should be noted that the 2001 data is also less reliable in the sense that it is 
provisional and hence, to some extent, incomplete.  

The sample size did not allow for a complete split of the data according to sector, size and e-
commerce activity. Those results that were satisfactory in terms of observations showed: 
- 

an e-procurement effect for larger firms but not smaller firms in SIC52.1 and SIC52.2, 
thus supporting the 2000 data in the case of SIC52.1 (such data for SIC52.2 in 2000 
could not be presented)  
neither large nor small firms in SIC52.4 appear to benefit from buying electronically, 
unlike in 2000 when smaller firms in this group held lower stocks  
the e-selling effect in SIC52.4 applying only to larger firms, thus contradicting the 2000 
data when it applied only to smaller firms.  

- 

- 

There is enough consistency between the two years to suggest these effects are real and vary 
according  to  scale  and  sector,  as  would  be  expected.  However  it  should  be  noted  that  the 
descriptive data should not be read as conclusive evidence of determination since there may be 
an element of reverse causality (although in the case of e-buying this would mean the effect is 
understated in the descriptive data). 

56 

 
 
 
 
 
 
 
 
8.4 Conclusions 

It appears that the using e-commerce only for procurement may have a negative 
impact on stocks, especially for larger firms. Using it to only receive orders appears to 
have the opposite effect. 

In terms of labour productivity the clearest association is with receiving orders 
electronically but firms that use it for both also tend to experience higher levels of 
labour productivity. However, the effects vary across sectors 

The varied pattern of productivity advantage in e-procurement in retail may reflect 
different systems and standards within the sector and also the different implications 
for prices in B2C markets compared to B2B markets.  

Other studies (PWC 2000) suggest that retailers, particularly smaller ones, may not be 
fully utilising the capabilities of e-commerce as they are concerned that it may 
provide larger manufacturers with the means to discriminate in terms of what they 
supply and at what price. The same source also provides evidence that only 28% of 
retailers perceived a positive impact of e-procurement, compared to 52% that held 
positive expectations. Therefore, despite the extent of use of e-commerce in the retail 
sector it may take time to realise benefits due difficulties in changing relationships, 
and inertia in the supply chain. 

57 

 
 
 
 
 
 
 
Figure 8.1   

Value Added/employee 2000: Services 

1 8 0

1 6 0

1 4 0

1 2 0

1 0 0

8 0

6 0

4 0

2 0

0

5 0

5 1

5 2

5 5

6 0

6 1

6 2

6 3

6 4

7 0

7 1

7 2

7 3

7 4

9 1

9 2

9 3

n o n e

b u y  o r sell

e sell

e b u y

59 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Appendix A 

Regression Analysis 

As can be seen below the results suggest that scale does have a negative effect but is not 
significant. However since larger firms are more likely to use e-commerce and be 
classified in SIC52.4, then it may be that the dummies are removing part of the scale 
effect. More encouraging are the significant coefficients for two of the e-commerce 
dummies in the direction suggested in the descriptive data since the dummy for only 
buying electronically is negative and significant at the 10% level, whilst that for e-selling 
has a positive coefficient significant at 1%. Using e-commerce for both does generate a 
negative coefficient but it is not significant. The results also show that sector is a very 
important determinant of stocks with a significant positive coefficient for SIC52.4 and a 
significant negative coefficient for SIC52.2, reflecting the different purchase frequencies 
of food and comparison goods. 

Dependent variable: 

(1) 
stock/sales 

(2) 
log (stock/sales) 

employment  

log (employment) 

-3.25E-07 
(0.28) 

ebuy only 

esell only  

Both 

dummy 52.1 

dummy 52.2  

dummy 52.3 

dummy 52.4  

intercept  

Adjusted R-squared 
Observations 

-0.01105 
(0.097)* 
0.031157 
(0)*** 
-0.00402 
(0.553) 
-0.01151 
(0.181) 
-0.05676 
(0)*** 
-0.00521 
(0.603) 
0.051632 
(0)*** 
0.091761 
(0)*** 
0.1757 
1907 

0.025593 
(0.028)** 
-0.043644 
(0.507) 
0.328346 
(0)*** 
0.048985 
(0.461) 
0.115725 
(0.177) 
-0.935274 
(0)*** 
0.386424 
(0)*** 
0.710197 
(0)*** 
-3.039248 
(0)*** 
0.3036 
1874 

*** Significant at 1% level 
** Significant at 5% level 
* Significant at 10% level 

Since that these relationships may not be linear the model has also been run under a logged 
specification. This improves the fit of the model with the adjusted R-squared rising to 0.3. 
However there is a positive and significant coefficient for employment, which is difficult 
to explain from a theoretical point of view, but is supported by the descriptive data in 2000 
for all sectors except SIC52.4. Although economies of scale were anticipated they may be 
may not be a reality in all sectors of retail. The implications of the rest of the coefficients 
are broadly similar to the linear model but stronger due to the greater significance of the 
other sector dummies. Both regressions broadly support the previous analysis and suggest 
that e-commerce activity is associated with clear patterns in stock levels. 

60 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ANNEX 8 

Size and sector - productivity 

Table 8.5: SIC 52.1 Retail sale in non-specialised stores 

52.1 
<100 
Emp 
VA/emp 
GO/emp 
>=100 
Emp 
VA/emp 
GO/emp 

1997 
(147) 
29.18 
12.881 
49.617 
(149) 
6284.85 
17.874 
68.695 

1998 
(189) 
28.44 
13.191 
59.345 
(129) 
5798.53 
19.794 
74.684 

1999 
(154) 
29.98 
13.362 
54.834 
(136) 
5038.83 
19.949 
77.605 

2000 
(159) 
31.31 
13.501 
56.254 
(134) 
7537.19 
25.376 
76.526 

2001 
(195) 
31.63 
19.023 
78.091 
(139) 
7779.83 
20.968 
75.165 

Table 8.6: SIC 52.2 Retail sale of food, beverages and tobacco in specialised stores 

52.2 
<100 
Emp 
VA/emp 
GO/emp 
>=100 
Emp 
VA/emp 
GO/emp 

1997 
(184) 
24.90 
14.072 
53.983 
(47) 
1173.11 
13.618 
52.147 

1998 
(232) 
29.63 
15.331 
48.026 
(45) 
561.18 
14.433 
51.428 

1999 
(216) 
26.46 
13.471 
46.476 
(58) 
881.09 
14.394 
45.170 

2000 
(197) 
25.75 
14.207 
52.485 
(52) 
915.15 
16.342 
51.474 

2001 
(232) 
26.13 
16.882 
51.756 
(53) 
977.81 
15.792 
48.437 

Table  8.7:  SIC  52.3  Retail  sale  of  pharmaceutical  and  medical  goods,  cosmetic  and  toilet 
articles 

52.3 
<100 
Emp 
VA/emp 
GO/emp 
>=100 
Emp 
VA/emp 
GO/emp 

1997 
(143) 
32.13 
18.910 
79.130 
(32) 
963.94 
17.515 
68.891 

1998 
(141) 
27.00 
19.600 
77.457 
(23) 
1740.57 
17.501 
66.350 

1999 
(114) 
25.28 
20.349 
88.126 
(25) 
847.12 
20.286 
72.698 

2000 
(123) 
28.25 
24.103 
101.254 
(22) 
1855.50 
18.060 
80.531 

2001 
(132) 
29.31 
22.779 
93.604 
(22) 
2065.32 
20.187 
92.283 

Table 8.8: SIC 52.4 Other retail sale of new goods in specialised stores 

52.4 
<100 
Emp 
VA/emp 
GO/emp 
>=100 
Emp 
VA/emp 
GO/emp 

1997 
(842) 
28.11 
22.702 
67.266 
(319) 
1538.14 
22.814 
67.441 

1998 
(782) 
27.76 
24.279 
68.352 
(227) 
1277.90 
25.636 
75.464 

1999 
(742) 
28.76 
36.162 
97.166 
(263) 
1395.13 
26.600 
74.814 

2000 
(746) 
29.18 
25.818 
69.317 
(287) 
1962.40 
26.548 
73.368 

2001 
(813) 
28.72 
30.292 
81.701 
(332) 
1846.32 
27.105 
77.731 

61 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Sector and e-commerce effects 

Table 8.12: SIC 52.1 Retail sale in non-specialised stores 

52.1 

N=293 

Emp 

VA/emp 

GO/emp 

Either 

  Buy only 

  Sell only 

  Both 

Yes (87) 

No (206) 

Yes (52) 

No (241) 

Yes (13) 

No (280) 

Yes (22) 

No (271) 

10406.47 

17.778 

65.186 

532.04 

19.420 

65.668 

6566.35 

2794.66 

2923.08 

3489.15 

23905.14 

1804.61 

16.675 

64.111 

19.419 

65.830 

23.155 

70.173 

18.736 

65.309 

17.207 

64.781 

19.072 

65.585 

Table 8.13: SIC 52.2 Retail sale of food, beverages and tobacco in specialised stores 

52.2 
N=249 
Emp 
VA/emp 
GO/emp 

Either 
Yes (35)  No (214)  Yes (15)  No (234)  Yes (11)  No (238) 

 Buy only 

 Both 

824.14 
18.374 
85.5 

111.29 
14.044 
46.84 

344.93 
12.321 
61.977 

202.93 
14.803 
51.652 

1817.64 
17.418 
54.106 

137.25 
14.525 
52.189 

Table 8.14: SIC 52.3 Retail sale of pharmaceutical and medical goods, cosmetic and 
toilet articles 

52.3 
N=145 
Emp 
VA/emp 
GO/emp 

Either 
Yes (39)  No (106)  Yes (24)  No (121)  Yes (10)  No (135) 

 Buy only 

 Both 

147.1 
28.742 
106.813 

363.76 
21.142 
94.908 

91.42 
20.82 
92.112 

347.95 
23.655 
99.3 

251.3 
47.722 
114.9 

309.5 
21.368 
96.866 

Table 8.15: SIC 52.4 Other retail sale of new goods in specialised stores 

52.4 
N=1033 
Emp 
VA/emp 
GO/emp 

Either 
Yes (308)  No (725)  Yes (104)  No (929)  Yes (84)  No (949)  Yes (120)  No (913) 

 Buy only 

 Sell only 

 Both 

1005.45 
28.442 
78.954 

379.72 
24.993 
66.827 

506.14 
25.881 
70.556 

573.02 
26.037 
70.430 

984.43 
28.182 
77.562 

529.28 
25.830 
69.812 

1452.89 
30.842 
87.206 

449.76 
25.387 
68.239 

62 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Sector, size and e-commerce effects 

Table 8.16: SIC 52.1 Retail sale in non-specialised stores 

52.1 
N=293 
Emp 
VA/emp 
GO/emp 

Either 
>=100 
Yes (36)  No (123)  Yes (51)  No (83) 

Either 

<100 

 Buy only  <100 
 Yes (22)  No (137)  Yes (30)  No (104) 

Buy only  >=100 

33.17 
13.147 
56.926 

30.77  17728.80 
21.047 
71.017 

13.605 
56.057 

1274.88 
28.037 
79.910 

33.41 
12.748 
56.987 

30.98  11357.17 
19.555 
69.336 

13.622 
56.136 

6435.28 
27.056 
78.599 

Both 
Yes (11)  No (148)  Yes (11)  No (123) 

>=100 

<100 

Both 

Emp 
VA/emp 
GO/emp 

27.09 
11.280 
55.646 

31.63  47783.18 
23.135 
73.916 

13.666 
56.299 

3937.96 
25.577 
76.759 

Table 8.17: SIC 52.2 Retail sale of food, beverages and tobacco in specialised stores 

52.2 
N=249 
Emp 
VA/emp 
GO/emp 

Either 
Yes (19)  No (178)  Yes (16)  No (36) 

Either 

>=100 

<100 

32.11 
16.156 
95.621 

25.07 
13.999 
47.881 

1764.69 
21.008 
73.482 

537.58 
14.268 
41.692 

Table 8.18: SIC 52.4 Other retail sale of new goods in specialised stores 

52.4 
N=1033 
Emp 
VA/emp 
GO/emp 

Either 
Yes (193)  No (553)  Yes (115)  No (172)  Yes (70)  No (676)  Yes (34)  No (253) 

Buy only  >=100 

Buy only  <100 

Either 

>=100 

<100 

30.95 
28.045 
78.786 

28.56 
25.041 
66.012 

2640.91 
29.107 
79.235 

1508.75 
24.837 
69.446 

30.99 
27.022 
71.992 

28.99 
25.694 
69.04 

1484.41 
23.532 
67.599 

2026.64 
26.954 
74.144 

 Sell only 
 Yes (42)  No (704)  Yes (42)  No (245)  Yes (81)  No (665)  Yes (39)  No (248) 

Sell only 

>=100 

>=100 

<100 

<100 

Both 

Both 

Emp 
VA/emp 
GO/emp 

34.83 
28.288 
78.551 

28.84 
25.671 
68.766 

1934.02 
28.076 
76.572 

1967.27 
26.286 
72.819 

28.9 
28.803 
84.78 

29.21 
25.455 
67.433 

4410.41 
35.079 
92.246 

1577.44 
25.207 
70.4 

63 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Stock trends by sector / size 

Table 8.23: SIC 52.1 Retail sale in non-specialised stores 

52.1 
<100  
Emp 
Stock/Sales 
Stock/VA 
Stocks/Emp 
>=100  
Emp 
Stock/Sales 
Stock/VA 
Stocks/Emp 

1997 
(147) 
29.18 
0.0854 
0.3325 
4.3556 
(149) 
6284.85 
0.0900 
0.3620 
5.7875 

1998 
(189) 
28.44 
0.0709 
0.5334 
3.9977 
(129) 
5798.53 
0.0860 
0.3010 
5.6864 

1999 
(154) 
29.98 
0.0826 
0.3974 
4.3069 
(136) 
5038.83 
0.0853 
0.3234 
6.2420 

2000 
(159) 
31.31 
0.0730 
0.3563 
4.1114 
(134) 
7537.19 
0.0870 
0.3165 
6.0060 

2001 
(195) 
31.63 
0.0635 
0.2811 
4.2016 
(139) 
7779.83 
0.0878 
0.3093 
6.1471 

Table 8.24: SIC 52.2 Retail sale of food, beverages and tobacco in specialised stores 

52.2 
<100  
Emp 
Stock/Sales 
Stock/VA 
Stocks/Emp 
>=100  
Emp 
Stock/Sales 
Stock/VA 
Stocks/Emp 

1997 
(184) 
24.90 
0.0303 
0.1398 
2.0516 
(47) 
1173.11 
0.0436 
0.2291 
2.9865 

1998 
(232) 
29.63 
0.0310 
0.1254 
1.7086 
(45) 
561.18 
0.0352 
0.1996 
2.7604 

1999 
(216) 
26.46 
0.0289 
0.1407 
1.7456 
(58) 
881.09 
0.0325 
0.1308 
2.2332 

2000 
(197) 
25.75 
0.0326 
0.1433 
2.0513 
(52) 
915.15 
0.0385 
0.1383 
2.8171 

2001 
(232) 
26.13 
0.0289 
0.1002 
1.8667 
(53) 
977.81 
0.0338 
0.1321 
2.4191 

Table 8.25: SIC 52.3 Retail sale of pharmaceutical and medical goods, cosmetic and 
toilet articles 

52.3 
<100  
Emp 
Stock/Sales 
Stock/VA 
Stocks/Emp 
>=100  
Emp 
Stock/Sales 
Stock/VA 
Stocks/Emp 

1997 
(143) 
32.13 
0.0913 
0.4161 
7.1600 
(32) 
963.94 
0.0995 
0.5643 
6.4706 

1998 
(141) 
27.00 
0.0929 
0.4583 
6.8957 
(23) 
1740.57 
0.0863 
0.3846 
6.4156 

1999 
(114) 
25.28 
0.0928 
0.5309 
7.9165 
(25) 
847.12 
0.0948 
0.3777 
6.1991 

2000 
(123) 
28.25 
0.0846 
0.6276 
9.5432 
(22) 
1855.50 
0.0924 
0.4265 
7.3486 

2001 
(132) 
29.31 
0.0735 
0.3457 
6.6208 
(22) 
2065.32 
0.0956 
0.5168 
8.0266 

Table 8.26: SIC 52.4 Other retail sale of new goods in specialised stores 

52.4 
<100  
Emp 
Stock/Sales 
Stock/VA 
Stocks/Emp 
>=100  
Emp 
Stock/Sales 
Stock/VA 
Stocks/Emp 

1997 
(842) 
28.11 
0.1659 
0.8330 
10.7197 
(319) 
1538.14 
0.1380 
0.4979 
9.3402 

1998 
(782) 
27.76 
0.1560 
0.5145 
10.0178 
(227) 
1277.90 
0.1402 
0.4634 
9.8143 

1999 
(742) 
28.76 
0.1632 
0.6494 
11.9397 
(263) 
1395.13 
0.1321 
0.4537 
9.5662 

2000 
(746) 
29.18 
0.1532 
0.5941 
9.8419 
(287) 
1962.40 
0.1330 
0.4105 
9.8392 

2001 
(813) 
28.72 
0.1471 
0.4658 
11.8281 
(332) 
1846.32 
0.1282 
0.9107 
9.5682 

64 

 
 
 
 
 
Stocks, e-commerce effects by sector  

Table 8.30: SIC 52.1 Retail sale in non-specialised stores 

52.1 
N=293 
Emp 
Stock/Sales 
Stock/VA 
Stocks/Emp 

Either 
Yes (87)  No (206)  Yes (52)  No (241)  Yes (13)  No (280)  Yes (22)  No (271) 

 Buy only 

 Sell only 

 Both 

10406.47 
0.0863 
0.3368 
5.4465 

532.04 
0.0765 
0.3386 
4.7800 

6566.35 
0.0830 
0.3534 
5.1628 

2794.66 
0.0786 
0.3348 
4.9380 

2923.08 
0.1428 
0.4596 
9.6139 

3489.15  23905.14 
0.0608 
0.2248 
3.6545 

0.0764 
0.3324 
4.7626 

1804.61 
0.0809 
0.3473 
5.0853 

Table 8.31: SIC 52.2 Retail sale of food, beverages and tobacco in specialised stores 

52.2 
N=249 
Emp 
Stock/Sales 
Stock/VA 
Stocks/Emp 

Either 
Yes (35)  No (214)  Yes (15)  No (234)  Yes (11)  No (238) 

 Buy only 

 Both 

824.14 
0.0572 
0.2417 
5.2943 

111.29 
0.03 
0.126 
1.707 

344.93 
0.0279 
0.1565 
1.9062 

202.93 
0.0342 
0.1413 
2.2308 

1817.64 
0.0468 
0.1806 
2.8717 

137.25 
0.0332 
0.1405 
2.1807 

Table 8.32: SIC 52.3 Retail sale of pharmaceutical and medical goods, cosmetic and 
toilet articles 

52.3 
N=145 
Emp 
Stock/Sales 
Stock/VA 
Stocks/Emp 

Either 
Yes (39)  No (106)  Yes (24)  No (121)  Yes (10)  No (135) 

 Buy only 

 Both 

147.1 
0.0773 
0.4465 
8.8644 

363.76 
0.0889 
0.6525 
9.3375 

91.42 
0.0738 
0.468 
6.7783 

347.95 
0.0882 
0.6227 
9.6926 

251.3 
0.0754 
0.3673 
9.1764 

309.5 
0.0866 
0.6141 
9.2128 

Table 8.33: SIC 52.4 Other retail sale of new goods in specialised stores 

52.4 
N=1033 
Emp 
Stock/Sales 
Stock/VA 
Stocks/Emp 

Either 
Yes (308)  No (725)  Yes (104)  No (929)  Yes (84)  No (949)  Yes (120)  No (913) 

 Buy only 

 Sell only 

 Both 

1005.45 
0.1387 
0.4211 
10.0159 

379.72 
0.1514 
0.5949 
9.7669 

506.14 
0.1273 
0.3821 
8.5150 

573.02 
0.1499 
0.5611 
9.9896 

984.43 
0.1585 
0.4738 
11.4198 

529.28 
0.1466 
0.5492 
9.7014 

1452.89 
0.1346 
0.4180 
10.3340 

449.76 
0.1493 
0.5595 
9.7764 

65 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Stocks, e-commerce, sector and size 

(for non-disclosive sectors only) 

Table 8.34: SIC 52.1 Retail sale in non-specialised stores 

52.1 
N=293 
Emp 
Stock/Sales 
Stock/VA 
Stocks/Emp 

Buy only  <100 
Yes (22)  No (137)  Yes (30)  No (104) 

Buy only  >=100 

33.41 
0.0962 
0.4785 
5.6990 

30.98  11357.17 
0.0733 
0.2617 
4.7697 

0.0693 
0.3367 
3.8565 

6435.28 
0.0909 
0.3323 
6.3626 

 Both 
 Yes (11)  No (148)  Yes (11)  No (123) 

>=100 

<100 

Both 

27.09 
0.0425 
0.2113 
2.0056 

31.63  47783.18 
0.0792 
0.2384 
5.3033 

0.0753 
0.3671 
4.2679 

3937.96 
0.0876 
0.3235 
6.0688 

Table 8.35: SIC 52.4 Other retail sale of new goods in specialised stores 

52.4 
N=1033 
Emp 
Stock/Sales 
Stock/VA 
Stocks/Emp 
52.4 
N=1033 
Emp 
Stock/Sales 
Stock/VA 
Stocks/Emp 

Buy only  <100 
Yes (70)  No (676)  Yes (34)  No (253) 

Buy only  >=100 

 Sell only  <100 
 Yes (42)  No (704)  Yes (42)  No (245) 

Sell only  >=100 

30.99 
0.1268 
0.3680 
8.9059 

28.99 
0.1560 
0.6175 
9.9388 

1484.41 
0.1284 
0.4110 
7.7102 

2026.64 
0.1336 
0.4105 
10.1253 

34.83 
0.1813 
0.5527 
12.6277 

28.84 
0.1516 
0.5966 
9.6757 

1934.02 
0.1357 
0.3949 
10.2119 

1967.27 
0.1325 
0.4132 
9.7753 

Both 
Yes (81)  No (665)  Yes (39)  No (248) 

>=100 

<100 

Both 

28.90 
0.1359 
0.4256 
10.0178 

29.21 
0.1553 
0.6146 
9.8205 

4410.41 
0.1319 
0.4023 
10.9908 

1577.44 
0.1332 
0.4119 
9.6581 

66 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9 

R&D and Innovation 

Background 

9.1 
The concept that ICT and electronic business processes add to the capabilities of firms, 
and of the whole economy, to undertake and benefit from innovation is built into the 
EU's  Lisbon strategy to strengthen Europe as a competitive, sustainable, knowledge 
based economy.  Electronic transactions and exchanges, between firms and their 
business partners, are seen as an essential part of developing the information economy 
within this strategy. 

Micro evidence that R&D and innovation contribute to productivity growth, and to 
other measures of competitiveness, has been researched from a wide range of sources. 
(Baldwin - Canada, van Leeween - Netherlands, Loof - Sweden, Clayton and Turner - 
DG Enterprise,). Up to now, however, evidence that ICT or electronic networks might 
affect the innovation process been limited. As part of this work programme, we have 
made an assessment of data from the UK version of the Community Innovation Survey, 
and the effects we are able to observe related to e-commerce data collected within the 
UK CIS, and information society data linked from other sources including the ABI. 

9.2  Models for R&D and Innovation 
Micro data analysis of  innovation drivers and effects have been based on a range of 
measures of outputs. In most, the measures of innovation itself are based on Oslo 
manual definitions of: 
-  use of innovative processes, new to the firm, or to the industry 
sales of innovative products, new to the firm, or to the market. 
- 

Measures of innovation input are based on information, relationships, skills, R&D 
spending (internal or external), research personnel, patents and licenses, and spending 
on other inputs (marketing, plant, other services) associated with innovation.  Measures 
of innovation output have been based on competitive position / change in market share 
(Baldwin 2001, Clayton and Turner1996), or changes in productivity - measured as 
gross output or value added per employee (van Leeuwen). 

Most of this work has been based on 'linear models' of innovation, conceptually 
predicated round innovation projects, with research activities, spending and other inputs 
leading to new product concepts, which result in measurable new product sales in 
markets and then leading to growth in output and / or productivity. This type of linear 
project model was included in the proposal for this study: 

INNOVATION 
INPUTS 

INNOVATION 
OUTPUTS 

ENTERPRISE 
GROWTH 

PRODUCTIVITY 
INCREASE 

This type of model depends on separately defined inputs at each stage in the chain, and 
has no feedback mechanisms, which may be a weakness. However, most of the existing 
evidence, from CIS and its equivalents outside the EU and from private data, is 
structured in this way. Models based on feedback or on a cycle of innovation 
management, and data to support them, are still limited by data. (eg. A Virtuous Cycle - 
Innovation, Consumer Value and Communication, Kashani, Miller and Clayton, IMD)  

67 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Studies based on UK CIS 

9.3 
In parallel to work on this project, a research programme to test the productivity effects 
of innovation (as measured in the UK CIS for 1996 and for 2000) has been running in 
ONS over the last year, undertaken by Jonathan Haskel and funded by the Department 
of Trade and Industry. It was envisaged in our proposal that we would draw on this 
work, and it has so far focused on the 'linear model' approach, identifying inputs 
associated with product and process innovation, and the productivity outputs associated 
with product and process innovation. 

Methodological issues associated with interpreting precisely what is measured in the 
surveys have been complex. The treatment of innovators who succeed in producing 
marketable new products without any reported spending or innovation activity has 
given problems in assessing probabilities in a reliable way. In addition, it is possible 
that interpretation by firms of apparently similar questions on innovation may have 
changed between 1996/7 and 2000/1, changing the 'amount' of reported innovation and 
its relationship to inputs. UK CIS results are consistent with the possibility that what 
firms may have declared as innovation in 1996/7 may be treated as 'normal business' in 
2000/1. 

Even with these limitations, the academic work on UK data has demonstrated, in 
regressions using both rounds of the data within CIS: 
- statistically significant (but not always consistent) relationships between internal 
innovation expenditure at firm level, and success in marketing innovative products 
- statistically significant relationships between existence of collaborative relationships 
and firm success in producing innovative product output, and also with the probability 
that a firm will adopt innovative processes (although the significance of the relationship 
depends on specification of the regression model). 

To investigate productivity effects, this work has linked CIS2 data to UK structural 
business data for 1992-1996 (for manufacturing only) to test 'before and after' 
productivity for firms which do, and do not, innovate in product or process. A similar 
linking process and analysis (again for manufacturing only) has been applied to CIS3, 
looking at the period 1996-2000, although the first round of this work was undertaken 
with provisional firm output data (now updated in early 2003). 

The CIS2 results (Criscuolo and Haskel ,ONS productivity workshop 2002) show: 
- 

the significance of collaboration arrangements (along with plant purchase, training, 
patent information and other information sources) in process innovation adoption 
the strong relationship between process innovation and productivity growth. 

- 

CIS3 results from this programme have not yet been published, but early results show:  
links between internal innovation expenditure, collaboration arrangement (with 
- 
varying degrees of significance)  and adoption of innovative products and processes 
that product innovations in the CIS3 data-set are more likely to show significant 
links to productivity growth. 

- 

Tabulations based on CIS3 

9.4 
Evidence from the work programme above broadly supports the linear model round 
which our approach to innovation was specified, but suggests that there are both 
conceptual and measurement issues which may require its development. Rather than re-

68 

 
 
 
 
 
 
 
invent, or pre-empt, the innovation work programme, to begin an analysis of the impact 
of electronic business processes with available knowledge we have: 
-  developed a limited number of tabulations based on CIS3 to show how robust a 

linear model might be 
sought evidence using e-commerce data within CIS3 for influence of electronic  

- 
-  networks on relationships in the basic R&D => Innovation => Growth / 

- 

Productivity model 
looked for evidence relating electronic network use to other drivers of the process, 
using linked data. 

The tabulations are reported here only where statistically significant results arise. 

R&D => Innovation 

9.5 
The relationship between R&D expenditure and product innovation output has been 
tabulated at two levels: 
- expenditure on 'intellectual capital', on intramural R&D, external R&D and external 
knowledge acquisition (each of which is separately identified  in CIS3) 
- expenditure on total 'innovation related expenditure', which includes all of the above, 
plus machinery expenditure, design, training and marketing. 

In the summary tabulations below we have first separated out the 'no R&D' firms, then 
classified each of the remaining firms within their two digit sectors according to 
whether they spend up to the mean R&D / sales ratio for R&D performers in the sector, 
or spend above the mean level. The innovation output of each group is then measured as 
percentage of new or improved products within firm sales. 

For the majority of manufacturing and service sectors this shows that the firms which 
spend above sector average on intellectual property creation or acquisition also have 
significantly higher levels of new product (or service) content in their sales. This result 
remains significant at the 5% level taken across the whole of manufacturing and 
services, as shown in Figure 9.1. In both manufacturing and services, firms which spend 
anything have more sales from innovative products or services than firms which spend 
nothing,  and firms spending above their sector means have more than firms spending 
below sector means. However, even the 1350 firms which report spending zero on 
R&D have, a mean innovation output of 7% to 8% of sales. 

Figure 9.1 

n
o
i
t
a
v
o
n
n

i

t
c
u
d
o
r
p
)

%

(

Innovation vs relative 'Intellectual Capital' R&D 
Expenditure

30
25
20
15
10
5
0

Manufacturing
Services

no intellectual
cap.r&d

int. cap.r&d/sales
<= sector mean

int cap.r&d/sales
> sector mean

(%) R&D/sales

Source UK Community Innovation Survey 2000 

69 

 
 
 
 
 
 
 
 
 
 
A similar analysis taking into account all innovation related expenditure is slightly less 
clear; it is here that the issue of firms spending nothing shows the limits of a linear 
model. The data shows fewer than 30 manufacturing firms which spend nothing on any 
category of innovation related expenditure, but on average they have innovation output 
of 20% of sales - almost equal to the manufacturing firms that spend above their sector 
averages. It is clear that the survey shows innovation in some cases without measured 
expenditure. This may be due to timing, to response error, to lack of knowledge by 
respondents or to innovation expenditure being incurred outside the reporting unit (or 
even the country) where new products are sold  In any event, the range of innovation 
output within this group is so large that the mean result is not statistically significantly 
different from the 'low spending group', although the data still creates difficulties in 
regression work. 

The data in Figure 9.2 seems to show R&D => innovation relationships across the 
services sector to be rather better behaved than for manufacturing. In both sectors, the 
relationship holds when we compare low spending vs. high spending firms at 2 digit 
sector level. 

Figure 9.2 

Innovation vs relative Innovation Related 
Expenditure

t
c
u
d
o
r
p
)

%

(

l

s
e
a
s
/

n
o

i
t

a
v
o
n
n

i

25

20

15

10

5

0

no IRE

IRE/sales <=
sector mean

IRE/sales >
sector mean

(%) IRE/sales

Source UK Community Innovation Survey 2000 

Manufacturing
Services

Innovation => output growth 

9.6 
The tabulations below treat the innovation => growth data in a similar way, separating 
businesses surveyed in CIS3 into three groups: 
- 
- 

those reporting no new or improved products at all 
firms which innovate, but  whose proportion of new or improved output is less than 
the mean (excluding non-innovators) in their two digit SIC category 
innovators whose new or improved output equals or exceeds the mean for 
businesses in their SIC category (again excluding non-innovators). 

- 

For each group we have looked at the growth in sales reported within the CIS survey for 
the three years ending in the year for which innovation output  is measured. The 
analysis compares percentage growth performance of businesses relative to the mean 
growth of their 2 digit category - effectively relating change in the business’ share of its 
sector to relative innovation activity. The results are shown below, for selected broad 
sector groups. 

70 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
For most  production and service sectors, these results show that firms with no 
innovation grow slowest - usually slower than the sector mean. Firms with moderate 
innovation do better (although not in every case), and firms with the highest innovation 
levels increase their sales significantly faster than the sector mean, and thus implicitly 
increase market share. The effect is present in both manufacturing and service sectors. 

Figure 9.3 

R elative g ro w th  v s relative in n o vatio n

r
o
t
c
e
s

s
v
h
t
w
o
r
g
m

r
i
F

0.25

0.2

0.15

0.1

0.05

0

-0.05

Food / D rink

T extiles

M achinery

E lectrical / optical

N one

B elow  M ean

A bove M ean

%  Innovative sales vs secto r m ean

 Source UK Community Innovation Survey 2000 

Figure 9.4 

R e la tiv e  g ro w th  v s  re la tiv e  in n o v a tio n
S e rv ic e s  

r
o
t
c
e
s

s
v
h
t
w
o
r
g
m

r
i
F

0 .2 5

0 .2

0 .1 5

0 .1

0 .0 5

0

-0 .0 5

D is trib u tio n

T ra n s p o rt

B u sin e s s  S e rv ice s

F in a n c ia l S e rvic e s

N o n e

B e lo w  M e a n

A b o v e  M e a n

 %  in n o v a tiv e  s a le s  v s . s e c to r m e a n

Source UK Community Innovation Survey 2000 

These relationships provide a base from which may be  possible to assess anecdotal 
evidence that electronic interactions increase the benefits firms reap from innovation.  

9.7 

e-Business effects and the linear model 

We have used CIS3 data to test: 

-  whether  innovation output is influenced by the presence of electronic networks 
(measured by e-commerce use as captured in the CIS3 survey); the method used 
was to add 'electronic network use' as a term in the innovation output regression 
described in 9.3 above, and the result did not show a relationship at any significant 
level 

71 

 
 
 
 
 
 
 
 
-  whether ICT and electronic transactions help, through improved marketing 

communication, to speed up or increase the sales growth effect associated with 
innovation; the method used was to test the tabulations shown in Figures 9.3 and 9.4 
above for differences in the  growth rates of firms with similar relative innovation 
rates, but  with and without the use of electronic networks. In no sector examined 
was there a statistically significant effect on growth. 

The evidence from these analyses suggests that direct, statistically significant, links 
between e-business process use and innovation output measures in our linear model 
may be difficult to find: 

- at the innovation output stage because of the range of factors which impact 
performance, and the heterogeneity of innovation environments and types 

- at the commercialisation (output growth) stage because we do not yet have a 
conceptual or  data framework which  captures marketing communication, although the 
ABI data-set identifies spending by firms on purchased marketing services. 

9.8 

Electronic networks and collaboration 

There is, however, a simpler qualitative variable which describes the behaviour of 
innovating  firms which it has proved possible to correlate with electronic network use. 
In addition to the CIS regressions reported in 9.3 which confirm the significance of 
collaborative relationships, other international studies have shown collaboration as an 
important factor. 

The CIS3 survey asks for responses on co-operation arrangements, first by asking 
whether firms engage in them or not, then if yes asking for the types of co-operations to 
be indicated in the following categories: 

Figure 9.5  Collaboration section of questionnaire 

Type of partner 

Local  National  Europe  US  

Other 

Internal 

Market 

Other enterprises within your 
enterprise group 

Suppliers of equipment, materials, 
components or software 

Clients or customers 

Competitors 

Consultants 

Commercial laboratories / R&D 
enterprises 

Institutional  Universities or other higher 

education institutes 

Government research 
organisations 

Specialised 

Private research institutes 

72 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
One possible impact of electronic networks in firms is to facilitate such collaborative 
arrangements, to increase the probability the firms are able to operate them, and to 
enable them to 'network' involving more partners. Although the survey does not tell us 
how many co-operation partners a firm has, it does indicate whether or not co-operation 
takes place and the number of different types and locations - which must as a minimum 
indicate the number of co-operators. 

In order to increase the coverage of the sample on e-commerce  use, we have matched 
the CIS responses with the ABI responses. The reason for this is that CIS response on e-
commerce use is less comprehensive than that for ABI. The result is a data-set which 
contains nearly 7,000 observations with both e-commerce and collaboration responses. 

The incidence of co-operation is surprisingly small, around 10% probability for those 
firms without electronic networks compared to a little over 15% for forms which 
indicate on one survey or the other that they use electronic networks for transactions. 
Regression including this relationship shows it as significant after controlling for firm 
size and sector. In addition, for the 800 firms which report co-operation arrangements, 
the use of electronic networks is associated with a larger spread of collaborator types 
(average number  =  4.8) compared to those which do not (average number = 3.8). 

The evidence from this linked data therefore suggests that use of electronic networks 
for commercial purposes is positively related to: 

- 

- 

the probability that a firm will be involved in collaboration for R&D / innovation 

the variety of collaborators that it will have. 

However, as the mechanisms used for electronic research collaboration are in most 
cases different from those used for buying and selling, this is an indirect measure of 
behaviour. If we had data on use of networks for purposes other than buying and selling 
for a large sample (for example via the ABI, through direct questions on collaboration 
mechanisms in CIS, or from linked e-business questions on the e-commerce survey) it 
is likely that evidence for use of electronic networks to facilitate active research 
collaboration by firms would show up with greater clarity. 

73 

 
 
 
 
 
 
10  Conclusions and Next Steps 

The work outlined in this report has demonstrated that data linking between surveys, 
and longitudinal linking across survey years, can help to identify market effects and 
firm behaviour influenced by electronic networks. It has shown: 

turbulence, entry and exit in electronic markets 

-  patterns of technology adoption 
- 
-  productivity effects dependent on types of transaction 
-  price effects in markets, dependent on a range of structural factors still to be 

measured 

-  use of electronic networks for innovation co-operation between firms   

An important conclusion from the work to date is that straightforward production 
function approaches to productivity assessment, or linear models for innovation effects, 
have their limits. For example: 
- 

the price effects we have identified undermine normal measurement approaches to 
productivity assessment as a function of e-commerce use 
the innovation models we have investigated leave out of account a large number of 
intermediate influences. 

- 

Future ONS work, supported by the UK Department of Trade and Industry, will 
therefore focus on 'steps in the chain' from technology use to productivity, and from 
R&D and know-how to innovation. The approach we plan to take will be based on the 
Porter value chain model, examining the possible influence of ICT investment, and 
computer network use, on specific processes in firms. This approach has already been 
shown to work in this project insofar as we have demonstrated: 
- 
- 

the relationship between network use and innovative collaboration 
the major benefits from e-procurement 

A programme of work is being formulated during 2003/4 to extend the approach to 
other business process areas, in consultation with firms and with government 
policymakers. This will also draw on the outcome of studies by other EU member 
states, and on work from the US, Japan, Australia and elsewhere, shared under the 
OECD programme of work on ICT impact. 

Some of these new studies show that ICT use has a complex interaction with innovation 
in firms, and that the combination of new technologies, new processes and new outputs 
can be linked to productivity gains. Others show that different measures of ICT 
penetration in firms - based on people using technology - are useful explanatory 
variables. These measures are captured in the current Eurostat surveys on e-commerce 
and innovation (CIS3) as implemented in UK, and it is intended to test them in this 
programme of work.

74 

 
 
 
 
 
 
 
 
References 

Section 2 

Business Data Linking; an Introduction, Mathew Barnes and Ralf Martin, UK Office 
for National Statistics, Economic Trends no 581 April 2002 

ICT and GDP growth in the United Kingdom, a Sectoral Approach, London Economics 
2003 

ICT and productivity growth in the United Kingdom, Nick Oulton, 2001, Bank of 
England working paper no 140  

Section 3 

Innovations and productivity growth Chiara Criscuolo and Jonathan Haskel, 2002, 
www.ceriba.org.uk 

Section 5 

Prestwood D (2002) 2001 online trading by UK business. ONS First release, 8th 
October 2002 

Atrostic BK and Nguyen S (2002) Computer Networks and US Manufacturing Plant 
Productivity, New evidence from the CNUS Data. Centre for Economic Studies, 
January 2002 

Prestwood D (2002) 2001 e-commerce survey: online trading by UK business by sector. 
ONS First release, 31st October 2002 

Section 6 

Brynjolfsson and Hitt, 1995; Computers as a factor of production, the role of 
differences between firms, Economics of Innovation and New Technology 

OECD, 2003; Seizing the Benefits of ICT - an international comparison of the impacts 
of ICT on economic performance, DSTI/IND/ICCP(2003)2 

Hempell,T. 2002; Measuring the productivity impacts of ICT at firm level, Centre for 
European Research 

Mika Maliranta and Petri Rouvinen, 2003; Productivity effects of ICT in Finnish 
business, discussion paper 852, Research Institute of the Finnish Economy 

Van Leeuwen and van der Weil, 2003; ICT, innovation and productivity,  CAED 
conference paper, London 2003 

Atrostic BK and Nguyen S,  2002; Computer Networks and US Manufacturing Plant 
Productivity, New evidence from the CNUS Data. Centre for Economic Studies, 
January 2002 

Presentation by Michael Nenninger, Siemens, to DG Enterprise e-business w@tch 
workshop, Brussels, November 2002 

75 

 
 
 
 
 
 
Section 7 

Michael Smith, Joseph Bailey and Erik Brynjofsson, 2000, Understanding Digital 
Markets; Understanding the Digital Economy, ed. Brynjolfsson and Kahin, MIT Press 

E-business Impact Programme (EBIP) at www.oecd.org/sti/information-economy 

Section 8 

Pricewaterhouse Coopers, July 2000, e-commerce impact study, Retail Overview, at 
www.ecrnet.org/conference/index.html 

Section 9 

Criscuolo C and Haskel J (2002)  Innovation and Productivity Growth in the UK. paper 
for ONS / DTI productivity workshop, November 2002 

John Baldwin and David Sabourin, 2001; Impact of the Adoption of Advanced 
Information and Manufacturing Technologies on Firm Performance in the Canadian 
Manufacturing Sector,  OECD reference DSTI/EAS/IND/SWP/AH(2001)5 

Tony Clayton and Graham Turner, 1996, Building Business, Panorama of EU Industry, 
DG Enterprise 

George van Leeuwen, 2002; Linking innovation to productivity growth using two 
waves of CIS, STI working paper, OECD 

K.Kashani, J.Miller and T.Clayton,2000; A virtuous cycle - innovation, consumer value 
and communication, IMD Lausanne  

76