Systems and modes of ICT innovation.
Date: 2016-07-21
Institutions: Joint Research Centre
Subjects: economic growth, information technology, information technology industry, innovation, productivity, regional development, research and development, research report

Systems and Modes of  
ICT Innovation 

Author: René Wintjes 

Editor: Federico Biagi 

2016  

EUR 28005 EN 

 
 
 
 
This publication is a Science for Policy report by the Joint Research Centre, the European Commission’s in-house 
science  service.  It  aims  to  provide  evidence-based  scientific  support  to  the  European  policy-making  process.  
The  scientific  output  expressed  does  not  imply  a  policy  position  of  the  European  Commission.  Neither  the 
European Commission nor any person acting on behalf of the Commission is responsible for the use which might 
be made of this publication. 

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JRC102047 

EUR 28005 EN 

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ISBN 978-92-79-59836-4 

ISSN 1831-9424 

doi:10.2791/58656 

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Reproduction is authorised provided the source is acknowledged. 

How to cite: René Wintjes (2016) Systems and Modes of ICT Innovation; Joint Research Centre.  JRC Science for 
Policy Report  EUR 28005 EN; doi:10.2791/58656 

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Abstract  
This report focuses on modes of ICT innovation at the meso level of systems and the micro level of firms. After a 
summary of the literature on national innovation systems, and  its sectoral and regional applications, we discuss 
how the concept of innovation systems can be applied to the case of ICT innovation. A framework for indicators is 
proposed  to  characterize  the  performance  of  ICT  innovation  systems.  Due  to  the  multi-purpose  and  pervasive 
nature of  ICT,  it  is relevant to extend the  innovation  systems concept to the societal  level. The resource-based 
view of the innovative firm fits very well with the innovation systems perspective. Patterns in the firm-level sets 
(combinations,  or  mix)  of  resources  and  capabilities  can  be  identified  with  factor-analysis  and  they  give  rise  to 
the main types or modes of innovative behaviour.  
Monitoring  ICT  innovation  systems  is  important  as  it  allows  us  to  learn  how  to  improve  the  policy  and 
performance of current and future systems.  It allows us to design and improve a mix of ICT innovation policy, 
which is tailored to specific ICT modes of innovation, and also to  a mix of prioritized challenges (e.g. economic, 
social,  environmental,  health,  or  other  impacts).  Since  ICT  innovations  emerge  and  diffuse  fast,  the  timing  of 
institutional  change  is  crucial.  Setting  new  standards,  and  updating  regulations  can  create  a  competitive  edge. 
ICT innovation policy has become (and should be addressed as) a horizontal policy field which maintains systemic 
linkages with other policy fields.  At both the  system and firm  level, there are  several  modes of  ICT  innovation. 
Policy  makers  should  not  reduce  diversity  by  favouring  only  one  mode.  We  extend  Lundvalls’  theory  on 
interactive learning between producers and users of knowledge by adding that policy for ICT innovation should be 
produced in interaction with its users. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table of contents  

List of figures ..................................................................................................... 2 
List of tables ...................................................................................................... 2 
List of tables in the Annex .................................................................................. 3 
List of figures in the Annex ................................................................................ 3 

Executive summary ............................................................................................ 4 

1  Introduction .............................................................................................. 7 

2.1 
2.2 

2.2.1 
2.2.2 
2.2.3 

2  The innovation systems approach: towards an application for ICT 
innovation ....................................................................................................... 9 
Theoretical antecedents of the innovation systems concept ....................... 10 
Applications of the innovation systems concept ........................................ 15 
National and regional innovation systems ......................................... 15 
Sectoral innovation systems ............................................................ 20 
Innovation system approaches extending to the societal level ............. 23 
2.3  Monitoring  national  and  regional  systems  of  ICT  innovation:  an  operational 
framework .......................................................................................... 26 
Knowledge for ICT innovations ........................................................ 32 
Supply of ICT innovations ............................................................... 33 
Skills for ICT innovations ................................................................ 34 
Demand for ICT innovations ............................................................ 36 
Finance for ICT innovations ............................................................. 37 
Institutions for ICT innovations ........................................................ 39 
Internationalisation for ICT innovations ............................................ 39 
Towards a typology of regional systems of ICT Innovation in Europe .... 40 

2.3.1 
2.3.2 
2.3.3 
2.3.4 
2.3.5 
2.3.6 
2.3.7 
2.3.8 

3  How firms innovate: modes and models of ICT innovation ...................... 43 
Conceptual insights from the literature on innovating firms in general ........ 43 
Taxonomies of firm innovation modes in the literature .............................. 44 
Identifying ICT modes of innovation ....................................................... 48 
Conclusion and discussion ..................................................................... 52 

3.1 
3.2 
3.3 
3.4 

4  Conclusions and policy implications ......................................................... 54 

Literature ......................................................................................................... 59 

Annex: Tables and Figures ............................................................................... 65 

1 

 
 
 
 
 
 
 
 
 
 
 
 
List of figures 

Figure 1:   The coupling model of innovation ................................................... 12 

Figure 2:   The national innovation system: dynamics, processes and policy ........ 18 

Figure 3:   The regional innovation system: a conceptual framework .................. 19 

Figure 4:   Top ICT regions based on ICT manufacturing density and                 

contextual quality of environment ................................................... 22 

Figure 5:   Digital Economy and Society Index, by main dimensions (2014)......... 23 

Figure 6:   A framework to monitor ICT innovation systems at the societal level .. 26 

Figure 7:   Regional ICT innovation systems .................................................... 29 

Figure 8:   Three dimensions in EU digital scoreboard 2014 of use of internet:               

content, communication, transaction............................................... 37 

Figure 9:   Share of web entrepreneur’s projects funded by crowdfunding in EU 

countries, 2014 ............................................................................ 38 

Figure 10:  Average factor scores for 6 types of regional innovation systems                             
in Europe..................................................................................... 41 

Figure 11:  Typology of regional systems and modes of innovation in Europe ....... 42 

Figure 12:  Schumpeter types of innovation ..................................................... 43 

List of Tables 

Table 1:   Innovation studies: top 10 contributions .......................................... 15 

Table 2:   Operational framework to monitor ICT innovation systems:             

components, processes and purposes ............................................. 31 

Table 3:   Top 10 regions in EC funding for R&D on cognitive systems and            

robotics in FP7 ............................................................................. 33 

Table 4:   Innovation performance of the ICT sector versus all sectors in the EU . 34 

Table 5:   Overview of studies identifying innovation modes ............................. 45 

Table 6:   The 4 modes of Srholec & Verspagen (2012), hierarchical factor analysis  
(2nd stage) on ingredients of innovation strategies ........................... 47 

Table 7:   Innovation performance and types of innovators by sector                              

(share of firms) ............................................................................ 50 

Table 8:   Summary of ‘performance’ of each innovation mode for the ICT sector 

compared to the average of that mode over all sectors ..................... 51 

Table 9:   Occurrence of the ‘mixed modes’ of innovation from Frenz and Lambert 

(2012), by sector ......................................................................... 52 

Table 10  Blockchain applications: disruption and policy challenges................... 56 

2 

 
 
 
 
 
 
 
 
List of tables in the Annex 

Table a:   Share of ICT firms collaborating with others, universities,                                     
research institutes, suppliers and clients ................................................................ 65 

Table b:   Competitive Innovative performance of the ICT sector at national level ...... 65 

Table c:   Share of ICT firms which report a lack of qualified personnel as                   

an obstacle for innovation, CIS3 ............................................................ 66 

Table d:   Public funding, external funding and internal funding, % of                       

innovative ICT firms in Europe .............................................................. 66 

List of figures in the Annex 

Figure a:   EU digital agenda scoreboard E-health sub-indicators: medical data 

exchange, e-prescription ...................................................................... 66 

Figure b:   Revealed Comparative Advantages in Computer Manufacturing (ISIC 30) ... 67 

3 

 
 
 
 
 
Executive summary 

This report was prepared in the context of the three-year research project on European 
Innovation Policies for the Digital Shift (EURIPIDIS) jointly launched in 2013 by JRC-IPTS 
and DG CONNECT  of the European Commission. The aim of the project was to improve 
understanding of innovation in the ICT sector and of ICT-enabled innovation in the rest 
of the economy.1  

While innovation can be studied at micro, meso and macro level, this report focuses on 
the micro level (modes of innovation: how firms generate innovations), and on the meso 
level  (systems  of  innovation:  relations  between  firms,  customers,  suppliers,  the  public 
R&D  infrastructure  etc.).  It  does  not  look  at  the  macro-economic  level  of  the  impact  of 
innovation  on  social  and  economic  change  (e.g.  growth,  competitiveness,  employment 
etc.).  

The  report  has  four  main  objectives.  First,  it  summarizes  the  literature  on  innovation 
systems, from its origins to its development towards  sectoral and regional applications. 
Second, it discusses how the concept of innovation systems can be useful in the context 
of  ICT  innovation,  providing  insights  on  how  it  would  be  possible  to  characterize  the 
performance of an ICT innovation system using data at the firm, meso and macro levels. 
Third, it reviews the literature on firms’ innovation modes, paying particular attention to 
ICT  firms. Fourth, it provides useful insights on how it would be possible to  extend our 
empirical knowledge of ICT firms' modes of innovation using firm level data analysis.  

The underlying working hypothesis of the report is that in order to understand innovation 
one has to adopt an approach which looks beyond R&D investments, beyond intra-mural 
firm activities, and beyond market transactions, since innovative dynamics take place in 
a broader (global, national, regional, sectoral) context in which firms interact with other 
firms and non-firm actors, and with market and non-market factors. 

Towards ICT innovation systems 

The  innovation  systems  concept  was  defined  some  25  years  ago,  at  a  time  when 
economists  were  discussing  to  what  extent  technological  change  should  be  seen  as  an 
external publicly-available driver of change which benefits all, or as an internal one (e.g. 
private benefit for R&D performers). The innovation systems solution to this debate can 
be found at the meso level, where various actors learn from their own experiences and 
from interaction with others in a specific common context. At this meso level, innovation 
and  its  context  become  interdependent,  embedded  in  interactions  between  supply  and 
demand –respectively producers and users of knowledge- generating spill-overs that are 
available  to  the  other  stakeholders  in  the  system.  The  concept  of  national  innovation 
systems  -and  the  related  concepts  of  regional  and  sectoral  innovation  systems- 
emphasizes the importance of interactions between a diversity of actors, and a diversity 
of innovation activities, resources and institutions.  

Within this general context, ICTs play a special role, due to their distributive nature and 
pervasiveness in society (e.g. the role of ICT in emerging and ‘smart policy domains’), to 
the speed of change (yesterday’s ICT is outdated tomorrow), to their ability  to enhance 
productivity  and  to  offer  advanced  solutions  for  societal  problems,  and  new  services  to 
consumers.  The  disruptive  implications  of  ICT  innovation  go  beyond  industries, 
economies,  and  traditional  value  chains  and  business  models.  Therefore,  especially  for 
ICT,  it  is  relevant  to  extend  the  innovation  systems  concept  to  the  societal  level.  In 
terms  of  indicators  to  ‘measure’  innovation  systems,  this  implies  that  it  is important  to 
integrate indicators traditionally used to monitor the Information Society.  

Based  on  the  literature  review,  we  propose  a  matrix  as  a  framework  of  indicators  that 
can be used to measure  ICT innovation systems.  Horizontally, it distinguishes the main 

1   For more information, see the project web site:  
    http://is.jrc.ec.europa.eu/pages/ISG/EURIPIDIS/EURIPIDIS.index.html 

4 

 
                                           
components  of  an  ICT  innovation  system  (knowledge  and  technologies;  actors  and 
networks; institutions; mechanisms of interaction) and its main purpose (in the sense of 
the  impact  or  performance  it  aims  for).  The  following  processes  (functions/resources) 
are  distinguished  vertically:  knowledge  for  ICT  innovation;  skills  for  ICT  innovations; 
supply of ICT innovations; demand for ICT innovations; finance for ICT innovations; and 
internationalisation  for  ICT  innovations.  Examples  of  ICT  indicators  that  can  be  used  to 
fill in this operational monitoring system are provided. 

We also stress that it is important to continue the conceptual and empirical development 
process  of  the  last  few  decades,  by  further  broadening  the  perspective  on  innovation. 
This also requires a broadening of the relevant indicators used to measure and monitor 
what is happening at the system and firm levels, capturing the involvement of additional 
stakeholders and additional interactions.  

Towards firm-level modes of ICT innovation 

The resource-based view of the innovative firm fits very well with the innovation systems 
perspective. This view states that value creation arises from uniquely combining a set of 
complementary  and  specialized  resources  and  capabilities  (which  are  heterogeneous 
within an industry, scarce, durable, not easily traded, and difficult to imitate). Patterns in 
these firm-level sets (or combinations, or innovation mix) can be identified and they give 
rise to the main types or modes of innovative behaviour.  

Multivariate  methods  of  data-reduction  (e.g.,  factor  and  cluster  analyses)  seem  most 
appropriate for identifying these modes  of innovation as a coherent (jointly reinforcing) 
mix  or  combination  of  innovation  strategy  ingredients,  or  routine  components.  Overall, 
firms in the ICT sectors are more innovative than firms in other sectors, but the mode of 
innovation  differs  between  the  sub-sectors  of  ICT  products  (e.g.  telecom  and  ICT 
services).  These  multivariate  methods  are  useful  for  identifying  innovation  patterns  at 
the  firm  level,  and  at  the  systems  level.  Integration  of  factor  scores  on  ICT  modes  of 
innovation at firm level with regional indicators at system level would allow us to define 
an  EU  typology  of  regional  systems  of  ICT  innovation,  which  can  then  be  seen  as 
regional  ‘modes’  of  ICT  innovation.  This  would  reduce  the  complexity  in  studying  the 
interaction  between  firms  and  their  geographical  context  concerning  ICT  innovation,  by 
simplifying the diversity in system contexts, and the diversity in firm strategies. 

Policy implications 

Studying and monitoring ICT innovation systems is important as it allows us to learn how 
to  improve  the  policy  and  performance  of  current  and  future  systems.    It  allows  us  to 
design and improve an ICT innovation policy mix, which is tailored to the specific needs 
of ICT modes of innovation, and also tailored to the prioritized mix of societal needs (e.g. 
economic, social, environmental, health, or other impacts).   

Since  ICT  innovations  become  outdated  very  fast  and  new  disruptive  ICT  innovations 
emerge and diffuse equally fast, the relevant institutions and framework conditions also 
have  to  be  updated  relatively  quickly.  Technology  standards  or  regulations  are  very 
important  drivers  or  barriers  for  the  growth  of  ICT  innovation  systems.  The  policy 
implication of this is that timing in institutional change is crucial. Setting new standards, 
and  updating  regulations  can  create  a  competitive  edge.  The  relevant  regulations 
concern not only the ICT industries themselves, but also the industries and policy fields 
of  application,  e.g.  transport  (e.g.  automated  driving),  health  (E-health),  and  finance 
(e.g.  crowd-funding  and  block-chain  applications).  Due  to  the  increased  importance  of 
ICT innovation in all sectors and policy domains, ICT innovation policy has become (and 
should be addressed as) a horizontal policy field which maintains systemic linkages with 
the other policy fields. 

Taking  a  wider  systemic  view  of  ICT  innovation  at  a  societal  level  also  implies  a 
broadened perspective on policy that extends: 

•  Beyond  R&D  policy.  A  systemic  view  on  ICT  innovation  policy  goes  beyond  supply-
side innovation policy which promotes R&D in ICT, and complements it with demand-

5 

 
side innovation policies which promote the demand and diffusion of ICT innovations. 
For  instance,  a  region  such  as  Tampere  has  successfully  transformed  its  innovation 
policy  from  R&D  funding  to  supporting  innovation  platforms  such  as  Demola,  where 
student teams solve problems, allowing business opportunities to materialize faster.  

•  Beyond  promoting  science-industry  interactions.  For  many  years  innovation  policy 
has focused on supporting linkages between scientific research in ICT and producers 
in ICT industries, but these interactions should be broadened, to include ICT users in 
the public sector and civil sector. 

•  Beyond  triple-helix  innovation  policy  initiatives.  In  many  sectors,  regions  and  cities, 
the  concept  of  the  triple-helix  has  been  applied  in  developing  and  implementing 
innovation  strategies  which 
industry,  universities  and  government. 
Furthermore,  additional  actors  are  increasingly  involved,  most  notably  citizens  and 
civil society actors (including not-for-profit non-governmental organizations). 

involve 

•  Beyond aiming for a purely economic impact of ICT innovation. Traditionally the focus 
has  been  on  economic  impact,  but  awareness  of  the  impacts  of  ICT  innovation  in 
other policy fields has increased. Today, ICT innovation is seen as serving any policy 
field  or  government  department  and  ICT  innovation  should  be  part  of  policies 
addressing societal challenges. 

•  Beyond  policies  which  aim  for  technological  innovation.  In  the  past,  ICT  innovation 
policy  was  still  largely  focused  on  supporting  technological  innovation,  as  was 
innovation  policy  in  general.  Increasingly,  however,  it  has  become  apparent  that 
most  impacts  are  to  be  gained  from  a  wide  range  of  non-technological  innovations. 
Unleashing  these  impacts  calls  for  policies  and  policy  instruments  which  promote 
non-technological modes of innovation (including policies promoting new enabling ICT 
business/innovation  models,  or  policies  promoting  design-thinking  among  SMEs, 
etc.). 

•  Beyond old sector policy silos. There is a need for systemic interaction between policy 
domains,  and  between  government  departments/Ministries  and  DGs,  concerning 
emerging  ICT  innovations.  Governments  must  promote  synergies  from  (policy) 
interaction across sectors in the economy, across parts  of  society, and across policy 
domains. 

•  Beyond  policy  interventions  through  the  market/price-mechanism  for  ICT.  Lowering 
the  price  of  ICT  is  not  enough  to  enhance  uptake  and  diffusion.  Policies  should  aim 
for  behavioural  change 
(additionality)  by  promoting  experimentation  and 
demonstration  (e.g.:  by  organizing  hackathons,  ‘bankathons’,  ‘living-labs’,  boot 
camps, demonstrators, digital platforms, etc.). 

•  Beyond  copying  best  practice  in  ICT  innovation  policies.  Since  there  is  no  perfect 
‘one-size-fits-all’ standard policy solution which all policy makers should adopt, policy 
makers have to interact with stakeholders, and organize for entrepreneurial discovery 
of appropriate fields of ‘smart specialisation’ in order to develop systemic innovation 
strategies and learn how to improve the innovation policy mix.   

Since R&D and innovation supply-side policies have been dominant for decades, policies 
to strengthen the European demand-side of digital innovation are needed.  

At  both  the  system  and  firm  level,  there  are  several  modes  of  ICT  innovation.  Policy 
makers  should  not  reduce  diversity  by  favouring  only  one  of  these  (e.g.  by  mainly 
subsidizing  R&D  or  product  innovation).  Instead,  policy  makers  should  tailor  their 
innovation policy mix to the variety of modes of innovation. As an extension to Lundvalls’ 
theory  on  interactive  learning  between  producers  and  users  of  knowledge,  we  add  that 
policy for ICT innovation should be produced in interaction with its users. 

6 

 
 
 
1 

Introduction 

This report was prepared in the context of the three-year research project on European 
Innovation Policies for the Digital Shift (EURIPIDIS) jointly launched in 2013 by JRC-IPTS 
and  DG  CONNECT  of  the  European  Commission  in  order  to  improve  understanding  of 
innovation in the ICT sector and of ICT-enabled innovation in the rest of the economy.2  

The  objective  of  this  report  is  to  verify  how  the  concept  of  innovation  systems  can  be 
fruitfully  applied  to  ICT  innovation.  The  literature  on  (national,  regional  and  sectoral) 
innovation  systems  claims  that  in  order  to  understand  innovation  one  has  to  adopt  an 
approach  which  looks  beyond  R&D  investments,  beyond  intra-mural  firm  activities,  and 
beyond  market-transactions.  Innovative  dynamics  takes  place  in  a  broader  (global, 
national, regional, sectoral) context in which firms interact with other firms and non-firm 
actors, and with market and non-market factors. 

Basically, innovation can be studied at micro, meso and macro levels. Fagerberg (2013) 
defines these three levels as follows: 

• 

• 

• 

the making of innovations (at the micro-level: in firms and organizations),  

innovation  systems  (relations  between  firms,  customers,  suppliers,  the  public  R&D 
infrastructure, etc.), and 

the  impact  of  innovation  on  social  and  economic  change  (the  macro-level 
consequences for growth, competitiveness, employment etc.). 

•  This  report  has  its  focus  on  the  system  and  firm  level  of  innovation,  and  not  at  the 

macro-economic level.  

A side-effect of the insight that a broader and more dynamic view on innovation has to 
be  applied  in  trying  to  explain  innovation  (at  the  micro-level  of  companies,  the  meso 
level  of  networks  and  systems,  and  the  macro  level  of  economies  and  societies)  is  the 
acknowledgement  of this complexity. The acknowledgement that in social science there 
is  no  simplistic  ‘one-size-fits-all’  set  of  scientific  laws  or  equations  which  can  explain  it 
all,  is  one  of  the  main  reasons  why  system  approaches  have  been  developed  and  why 
these  approaches  have  become  popular  in  studying  innovation,  and  in  developing 
strategic policies.  Because of the inherent complexity and diversity, innovation systems 
literature  consists  mostly  of  conceptual  contributions  and  case-studies.  This  report  will 
not  discuss  the  benefits  and  limitations  of  quantitative  versus  qualitative  research 
approaches,  but  since  both  approaches  are  complementary  this  report  takes  up  the 
challenge of how to improve measuring the broad range  of relevant aspects concerning 
innovation at firm-level and system level. 

Section 1 of this report addresses the meso-level of relations in innovation systems and 
their performance at the level of countries, regions, and sectors. It provides an overview 
of  the  various  innovation  systems  concepts:  at  national,  regional  and  sector  level. 
Specific  focus  will  be  on  the  question  how  to  measure  the  various  aspects  of  the 
concerned  innovation  systems.  Since  the  interactions  between  producers  and  users  of 
innovations are central in the innovation systems approaches, the study will include both 
innovation  in  the  ICT  producing  sector  (s)  and  ICT-enabled  innovation  in  ICT  using 
sectors. 

Section  2  addresses  the  behavioural,  micro-level  question:  how  do  ICT  firms  innovate? 
After an overview of taxonomies of innovation modes or models, the focus will again be 
placed on the empirical question of how to measure, capture or indicate these modes.  

There  is  a  large  diversity  within  European  ICT  sub-sectors,  as  well  as  in  the  various 
national and regional contexts, e.g. the characteristics of ICT services and software are 
different  from  those  of  ICT  manufacturing  industries;  and  the  framework  conditions  for 

2   For more information, see the project web site:  
    http://is.jrc.ec.europa.eu/pages/ISG/EURIPIDIS/EURIPIDIS.index.html 

7 

 
                                           
the various sub-sectors differ between countries and regions. Indicator-based monitoring 
and  analysis  provides  insights  in  the  various  ICT  innovation  systems  and  the  different 
modes  of  innovation  among  firms,  which  serves  to  come  to  better  policies  to  promote 
the  performance  and  impact  of  the  concerning  ICT  innovation  systems.  Policy 
implications are addressed in Section 4. 

8 

 
 
 
2 

The innovation systems approach: towards an 
application for ICT innovation 

This section provides an overview  of the literature  on innovation systems (Sections 2.1 
and 2.2). It also addresses the question  of how these innovation systems  concepts  can 
be applied to ICT innovations, and how national and regional systems of ICT innovation 
can be measured and monitored (Section 2.3).  

In  a  discussion  of  analytical  and  methodological  issues  in  the  literature  of  innovation 
systems, Carlson et al. (2002) start with referring to definitions of the word ‘system’ in 
dictionaries,  where  a  system  is  often  defined  in  terms  like:  “a  set  of  rules,  an 
arrangement  of  things,  or  a  group  of  related  things  that  work  as  an  organic  whole 
towards a common goal”. Basically, systems consist of components, relationships among 
these,  and  their  characteristics.  Carlson  et  al.  (2002)  compare  the  various  concepts  of 
innovation systems and conclude that they all involve the creation, diffusion, and use of 
knowledge.  

The  notion  of  national  innovation  systems  was  first  proposed  by  Freeman  (1987)  and 
Lundvall (1992). It should be seen as an attempt to broaden the innovation perspective 
and  deepen  the  understanding  of  its  dynamic  processes  explaining  ‘how  the  economic 
system generates the force which incessantly transforms it’ (Schumpeter 1937, p. 158). 
The  innovation  systems  concept  was  defined  at  a  time  when  economists  discussed  to 
what extent technological change should be  seen as an  external (publicly available and 
benefitting  all)  or  internal  (e.g.  private  benefit  for    R&D  performers)  driver  of  change. 
The  innovation  systems  solution  to  this  debate  (as  is  the  answer  in  evolutionary, 
behavioral and institutional theories, and in concepts such as networks, open innovation 
or  ecosystems) is at the meso-level where  various actors interact in a specific common 
context. At this meso-level, innovation and its context become interdependent.  

Many  authors  have  used  the  concept  of  national  innovation  systems  and  have 
contributed  to  its  development  as  a  theoretical  concept,  but  there  is  no  commonly 
agreed  definition.  Often  when  new  concepts  emerge,  institutes  such  as  the  OECD 
manage  to  come  to  a  certain  level  of  standardization,  but  the  OECD  (1997)  report  on 
national  innovation  systems  does  not  provide  such  a  standardized  definition.  Several 
years  later,  the  Oslo  manual  (3rd  edition;  OECD  2005),  produced  by  the  OECD  and 
Eurostat in cooperation,  on how to measure innovation, shows that the development of 
the NIS concept has changed the conception of innovation:  

“innovation  is  a  more  complex  and  systemic  phenomenon  than  was  previously 
thought.  Systems  approaches  to  innovation  shift  the  focus  of  policy  towards  an 
emphasis  on the interplays between institutions, looking at interactive processes 
both  in  the  creation  of  knowledge  and  in  its  diffusion  and  application.  The  term 
“National Innovation System” has been coined for this set of institutions and flows 
of knowledge” (OECD 2005, p.6). 

Innovation  is  a  social  phenomenon:  it  does  not  occur  in  a  vacuum,  nor  can  it  be 
regarded  as  an  external  given,  which  becomes  accessible  to  us  all  like  ‘manna  from 
heaven’.  Many  different  actors  and  inputs  from  many  different  sources  are  involved  in 
innovation processes.  Innovation emerges from processes which take place in a certain 
context,  and  the  innovations  are  used  in  a  context,  where  they  serve  a  purpose.  The 
context  may  differ  across  technologies  and  sectors,  but  also  between  countries  and 
regions.  Various  different,  but inter-related  sources,  actors  and  processes  are  involved. 
The relevant context, from which new combinations emerge, can be specified in terms of 
institutional, political, historical and social aspects.  

All  this  implies  that  innovation  systems  can  be  defined,  studied  and  monitored  in  a 
variety  of  ways.  The  various  innovation  systems  concepts  which  have  been  developed 
include:  national  innovation  systems  (Freeman  1987;  Lundvall  1992;  Nelson  1993), 
regional innovation systems (Cooke 2010; Cooke et al. 1997; Braczyk et al. 1998), and 

9 

 
sectoral innovation systems (Malerba 2002; 2004). These three concepts will be defined 
and  discussed  (Section  2.2),  but  first  we  define  innovation,  and  we  discuss  some  older 
theoretical  antecedents  on  which  these  three  applications  of  the  innovation  system 
concept  are  based  (Section  2.1).  In  doing  so,  most  of  the  top  10  contributions  on 
innovation (Table 1) will be discussed. 

2.1  Theoretical antecedents of the innovation systems concept 

While  an  invention  concerns  the  creation  of  the  first  idea  of  a  new  product  or  process, 
innovation refers to the use of this new and better idea or method, the attempt to try it 
out in practice and to bring it on the market  or deliver it as a public service. So where 
inventions  can  be  seen  as  technological  ‘breakthroughs’  in  science,  innovations  can  be 
seen  as  ‘breakthroughs’  in  markets  and  societies.  Schumpeter  described  this  with  his 
concept  of  creative  destruction.  This  concept  is  more  dynamic  then  the  neoclassical 
strand of economics that emerged by then3, and that according to Schumpeter was a too 
passive view on economic life. He wanted to explain:  

“a source of energy within the economic system which would of itself disrupt any 
equilibrium that might be attained”. (Schumpeter 1937/1989, p.166).  

Innovation  is  this  dynamic  source  in  capitalism.  In  order  to  turn  an  invention  into  an 
innovation,  an  innovative  entrepreneur  combines  several  different  types  of  knowledge, 
capabilities,  resources  and  skills.  The  person  or  organizational  unit  which  combines  all 
these factors in new ways was labelled ‘entrepreneur’4. So, for Schumpeter the concept 
of  innovation  was  indeed  closely  related  to  entrepreneurship.  Although  he  often  wrote 
about  it  as  a  person  (and  is  sometimes  criticized  for  that),  he  referred  to  the 
entrepreneurial  function  of  coming  to  new  combinations,  which  replace  old  ones.  This 
process of creative destruction, is not limited to one person or a new product, or market, 
but also involves whole sectors or economies. The ‘new combinations’ include four types 
of  innovations  identified  by  Schumpeter  (product  innovation,  process  innovation, 
organizational innovation and market innovation). As such, these types are still visible in 
the definition of innovation as provided in the Oslo manual (3rd edition, 2005):  

“An  innovation  is  the  implementation  of  a  new  or  significantly  improved  product 
(good or service),  or process, a new marketing method,  or  a new  organizational 
method in business practices, workplace organization or external relations”.  

Besides building on ideas  of Schumpeter, the innovation systems literature that we will 
discuss  also  builds  on  work  of  other  authors.  The  theoretical  antecedents  are  basically 
evolutionary  and  institutional  theories.  Without  having  the  intention  to  discuss  all  the 
aspects  which  make  part  of  this  historical  trajectory  towards  the  concept  of  innovation 
systems, we will discuss a few.  

As  said,  Schumpeter  makes  an 
inventions  and 
innovations,  and  emphasizes  the  process  of  combining  several  different  kinds  of 
knowledge  and  resources.  Also  in  terms  of  actors  the  innovation  process  is  more 
complex, since the person with the first idea of a new technology (the inventor) may be 
different  from  the  innovator,  or  what  Schumpeter  called  the  entrepreneur.  Later  this 
distinction between invention and innovation has often been neglected, however:  

important  distinction  between 

3   As represented by Walras. Schumpeter admired his ideas on how price-mechanisms work, but 
he dis-agreed with the passive, steady state view on economies. Later this neoclassical school 
has  developed.  Some  have  formalised  some  insights  from  Schumpeter  into  macro-economic 
equilibrium models and labelled themselves as New Schumpeterian, but it is essential to note 
that according to Schumpeter the essence of capitalism is its non-steady, dis-equilibrium state. 
In  a  stationary  state  there  would  be  no  role  for  innovation  and  entrepreneurship.  Formal 
evolutionary  models  are  mostly  based  on  Nelson  &  Winter.  For  a  discussion  on  this,  see 
Fagerberg (2014), and Hall & Rosenberg (2010). 

4   More on the concept of entrepreneurship  will  be  provided in  Section 3  which  has its focus on 

the firm-level. 

10 

 
                                           
“most  important  innovations  go  through  drastic  changes  in  their  lifetimes  –
changes  that  may,  and  often  do,  totally  transform  their  economic  significance. 
The  subsequent  improvements  in  an  invention  after  its  first  introduction  may  be 
vastly  more  important,  economically,  than  the  initial  availability  of  the  invention 
in its original form” (Kline and Rosenberg 1986, p.283).  

So,  not  only  the  supply-side  of  inventions  matter,  but  also  the  demand-side,  the 
improvements from their interaction and the improved benefit of usage.  

The  ‘coupling  model’  of  innovation  developed  by  Rothwell  &  Zegveld  (1985)  also 
emphasizes  the  fact  that  both  the  supply-  and  demand-side  of  interactions  matter  for 
innovation. The model shows that there are many sources for innovation, many linkages 
and  feed-back-loops  from  interactive  learning  (Figure  1).    Generating  new  ideas  and 
technologies by performing R&D, e.g. by scientists, is part of the process and a valuable 
source, but as the ‘coupling model’ shows it is not the only source of innovation and the 
flow of knowledge does not automatically lead to new prototypes, production and sales . 
Also  the  learning-by-doing  from  experiments  in,  for  instance,  manufacturing  practices, 
and the lessons and new ideas from interacting with clients and suppliers are important 
sources of innovation. This implies that in order to explain innovation performance, one 
also  has  to  study  the  relations  between  all  the  involved  actors  and  (re)sources. 
Experimenting  with  existing  inputs  which  are  new  to  the  concerning  economy  (at  firm, 
region, sector  or  country level), such as new  materials, and with new ways to organize 
supply-chains,  and  buying  innovative  machines  which  already  exist  on  the  market,  are 
also innovative activities which should be taken into account (and measured) in case one 
wants  to  understand  innovation  dynamics  and  performance.  Figure  1  indeed  applies  to 
several levels of analysis (the micro-level of the firm, the meso-level of a sector, or the 
macro-level of a region, country or group of countries like the EU).  When we add to this 
conceptual  framework  the  policy  dimensions  of  supply-side  innovation  policy  (which 
refers  to  public  support  for  R&D  aimed  at  increasing  the  supply  of  new  technological 
innovation) and policies promoting the demand for innovation (which refers for instance 
to public procurement of innovations, or subsidies to promote the up-take of state of the 
art  robots  in  production,  or  support  for  the  commercialization  of  environmental  friendly 
innovations),  the  coupling  model  also  shows  that  at  many  instances  it  is  beneficial  to 
organize  communicative  linkages  between  the  innovation  supply  and  demand  side 
(Figure 1). This touches on the importance of interactive learning between producers and 
users  of  knowledge.  The  coupling  model  can  therefore  be  seen  as  a  theoretical 
antecedent, of Lundvall’s (1992) National Innovation Systems (NIS) framework that will 
be discussed in Section 2.2.    

11 

 
 
 
Figure 1: The coupling model of innovation 

Based on Rothwell and Zegveld, 1985. 

The ‘coupling model’ is also helpful in showing the important role of ICT as a pervasive 
and  general  purpose  technology.  ICT  can  affect  both  the  way  in  which  the  different 
elements  interact  (as  represented  by  the  arrows)  but  can  also  impact  the  elements 
(boxes)  of  the  framework  in  Figure  1.    To  give  a  few  examples  in  relation  to  Figure  1, 
new ICT-applications such as Big Data, web-based platforms and the Internet of Things 
are  affecting  the  red-arrows  in  the  graph,  and  the  communicative  interactions  which 
these arrows represent. Such new ICT applications result in new combinations and new 
business models.   

For  instance,  the  trend  of  crowdsourced  manufacturing,  or  the  ‘maker-economy’  shows 
how  ICT  platforms  can  actively  involve  customers  in  a  demand-driven  process  of 
prototyping  and  manufacturing 5 .  New  ideas  for  innovations  may  also  originate  from 
introducing new ways to communicate with the market place, and new analytical tools to 
make  predictions  based  on  Big  Data.  Innovations  may  also  emerge  from  automating 
analysis of large amounts of data on the concerning innovation or production processes. 
New prototypes or manufacturing processes or organizational innovations may also come 
from buying and experimenting with an existing, state-of-the-art 3D-printer.  The link to 
such a printer can also  be digital on a web-based platform. The case-studies and trend 
reports  of  the  Business  Innovation  Observatory 6  provides  many  examples  and 
explanations of how applying ICT leads to a wide variety of new ICT innovations.  

Concerning the impact of ICT on the ‘boxes’, governments might prioritize and promote 
certain ICT-based solutions to societal problems, for example ICT solutions for an ageing 
society.  Following  this  example,  the  needs  of  society  call  for  generating  new  ideas  and 
technologies  in  this  field,  e.g.  in  the  form  of  a  priority  in  the  Horizon  2020  research 
programme  of  the  EU.  At  regional  level,  some  regions  which  are  leading  in  terms  of 
ageing,  have  Smart  Specialisation  strategies  and  programmes  to  support  innovation  in 
the e-health sector. The policy instruments also include demand-side policy tools. E.g. In 
the  form  of  public  procurement  policy  which  promotes  the  development  of  new 
prototypes,  and  the  role  of  the  public  sector  as  launching  customer  or  lead-user.  With 
cluster-policies  buyers  and  suppliers  in  e-health  are  invited  to  interact  and  co-operate. 

5   http://ec.europa.eu/enterprise/policies/innovation/policy/business-innovation-
observatory/files/case-studies/27-smf-crowdsourced-manufacturing.pdf 

6   http://ec.europa.eu/growth/industry/innovation/business-innovation-

observatory/index_en.htm 

12 

new needneeds of society and marketsidea generationnew technologymarketplacestate of the art in technology and productiondevelopmentprototypemanufacturingmarketing & salesSupply-side:R&D policyInnovation demand-side:Problem-based: support user-, demand-driven processSolution-based: support commercialisation 
  
                                           
With the policy tool of living labs, solutions are tested by users, outside a research lab, 
but  in  real  life  conditions,  such  as  a  hospital.  Innovation  agencies  can  provide  loans  to 
producers  of  e-health  for  digitalizing  their  production  process  and  for  instance  to 
hospitals for investing in the internet of things infrastructure (Avigdor & Wintjes 2015).  

Rejecting  the  ‘linear  model  of  innovation’  studies  of  innovating  firms  have  indeed 
revealed  that  the  multiple  sources  of  knowledge  creation,  learning  and innovation have 
become  broader  and  more  complex,  regardless  of  the  R&D  intensiveness  of  their 
industry.  Innovation surveys (CIS) have for instance shown that R&D is indeed not the 
sole  source  of  innovation  for  firms  (Arundel  et  al.  2008;  Mairesse  and  Mohnen  2010). 
Also  Fransman  (2014)  draws  the  same  conclusion  from  studying  global  ICT  companies. 
The  traditional  neo-classical  notion  that  innovation  is  limited  by  the  rate  of  R&D 
investment is useful at the macro-level, but it is not very helpful for a firm, industry or 
policy maker in deciding how, and what kind of innovation should be pursued. Since, as 
argued  and  shown  by  the  innovation  systems  literature,  there  exists  a  large  variety  in 
the sources, nature and uses of innovations.  

The work of Nelson and Winter (1982) on the ‘Evolutionary Theory of Economic Change’ 
is  another  example  of  insights  that  have  served  as  building  blocks  of  the  innovation 
systems  literature.  They  combine  Schumpeter’s  work  with  behavioural  theories  such  as 
the idea of ‘bounded rationality’7 (Simon 1947), emphasizing the importance of routines 
and procedural and organizational knowledge. Due to the uncertainty and complexity of 
innovation  processes,  the  rationality  of  the  decisions  of  firms  is  ‘bounded’.  Nelson  and 
Winter explain that firms base their decision-making on ‘routines’ and habits for dealing 
with  problems.  Changing  routines,  disrupting  habits,  innovating  business  models 
(Chesbrough 2010) is not easy, but, these firm routines can accumulate (adopting new 
lessons) and be developed into ‘dynamic capabilities’ to address new problems, e.g.: the 
capability  to  absorb  the  technologies  developed  by  others  (Cohen  &  Leventhal,  1990).  
Instead of the term steady state, Nelson and Winter refer to steady change, a persistent 
process  of  transformation  in  an  economy  which  is  explained  by  the  continuous 
generation,  diffusion,  accumulation  and  substitution  of  innovations  by  a  diversity  of 
economic agents. Neither the characteristics of the economic systems, nor those of the 
decision-makers  are  regarded  as  fixed.  Rather  the  focus  is  on  the  non-equilibrium 
processes that emerge from actions of diverse agents with bounded rationality, who may 
learn  (and  so  to  say  ‘change  their  rationality  or  the  boundaries  of  it’)  from  experience 
and interactions, and whose differences contribute to the change.  

Policymakers  have  a  role  in  creating  the  conditions  for  firms  which  promote  innovative 
behaviour and interactions, and which enhance capabilities for innovation. In the words 
of  Metcalfe  (2005,  p.443):  “the  evolutionary  policymaker  is  not  an  optimizing 
supplement  to  the  market,  correcting  for  imperfect  price  signals  in  such  a  way  as  to 
guide private agents to a better innovation mix”. Policymakers are not perfect either and 
are  also  boundedly  rational.  So  a  policymaker  does  not  know  what  the  best  innovation 
mix would be for an SME, a sector or a region. This also means that there is no one-size-
fits-all, ‘best practice’ policy. The policy argument moves away from  a narrow  focus  on 
market failure arguments, to innovation support interventions which aim for a change in 
behaviour  and  routines  (Nelson  &  Winter  1982).  The  uncertainties  and  risks  involved 
with  technological  change,  put  a  premium  on  learning  by  doing,  learning  by  using  and 
learning by interacting. This actually applies to all the stakeholders in a concerning eco-
system,  not  only  to  firms,  but  also  to  the  policymaker,  the  investors,  the  users,  the 
teachers,  the  employees,  the  regulator,  etc.    All  these  actors  learn.  Also  policy  makers 
adopt new knowledge on ICT policies and influence each other towards adoption, e.g. on 

7   With  ‘bounded  rationality’  Simon  (1947)  rejects  the  notion  of  an  omniscient  'economic  man' 
capable  of  making  decisions  that  bring  the  greatest  benefit  possible.  When  individuals  make 
decisions, their rationality is limited by the information they have, the cognitive limitations of 
their minds, and the time available to make the decision. Decision-makers in this view can only 
seek a satisfactory solution, lacking the ability and resources to arrive at the optimal one. 

13 

 
                                           
trends  such  as  Big  Data  or  the  Internet  of  Things.  Nauwelaers  &  Wintjes  (2003) 
distinguish  policy  instruments  along  different  logics  of  intervention:  those  which  lower 
the price of inputs aiming to fund the best innovation projects (e.g. R&D subsidies) and 
those which aim for behavioural additionality (changing the boundaries of rationality) by 
providing  firms  a  learning  to  innovate  experience  (see  also  Asheim  et  al.  2013),  which 
can  be  an  eye-opening  experience,  an  opportunity  to  try  new  things,  to  increase 
capabilities,  to  get  to  know  new  partners,  to  get  inspired,  to  discover  export 
opportunities, etc. 

Also  on  the  top  10  list of  publications  in innovation  studies  is  the  contribution  of  Pavitt 
(1984) on Sectoral patterns of technological change. This study showed that firms from 
different  sectors  used  different  sources  for  innovation.  This  difference  in  sources  (or 
knowledge-basis) defines the different modes of innovation. Pavitt showed that firms in, 
for instance, biotech and pharmaceutical industries have a more ‘science-based’ mode of 
innovation,  which  involves  relatively  high  R&D  investments.  In  more  traditional  sectors 
such  as  textiles,  the  innovation  mode  is  more  based  on  linkages  with  suppliers.  These 
firms  often  innovate  by  buying  new  machinery  or  using  innovative  materials,  such  as 
new fibers developed by major global suppliers. The taxonomy developed by Pavitt has 
certainly  served  as  a  theoretical  antecedent  for  development  of  the  concept  of  sectoral 
innovation systems. However, this taxonomy will be discussed more  in-depth in section 
3.2 when we discuss taxonomies of firm innovation modes.  

Next  to  the  territorially  defined  innovation  systems  (2.2.1)  and  the  innovation  systems 
which  are  defined  by  sector  borders  (2.2.2),  we  therefore  also  discuss  more  recent 
innovation systems concepts which address certain societal challenges (2.2.3). 

Concerning the theoretical antecedents we conclude that the innovation systems concept 
was  defined  some  25  years  ago,  at  a  time  when  economists  discussed  to  what  extent 
technological  change  should  be  seen  as  an  external  (publicly  available  and  benefitting 
all)  or  internal  (e.g.  private  benefit  for    R&D  performers)  driver  of  change.  The 
innovation systems solution to this debate (as is the answer in evolutionary, behavioural 
and  institutional  theories,  and  in  concepts  such  as  networks,  open  innovation  or 
ecosystems) is at the meso-level, where various actors learn from own experiences and 
from interaction with others in a specific common context. At this meso-level innovation 
and  its  context  become  interdependent,  embedded  in  interactions  between  supply  and 
demand, between the producers and users of knowledge. 

14 

 
 
 
Table 1: Innovation studies: top 10 contributions 

Source: Fagerberg 2013. 

2.2  Applications of the innovation systems concept 

2.2.1 National and regional innovation systems 

With  the  national  innovation  systems  concept  (Freeman  1987,  and  Lundvall  1992)  the 
national boundaries are taken as the system boundaries, since there are many relevant 
institutional differences between countries.  This perspective challenges a simplistic view 
on  ‘globalization,  global  markets  and  global  economy’,  as  if  contextual  differences  no 
longer exist, or no longer matter for ICT innovations. One of the reasons why institutions 
concerning innovation differ between countries, is the difference in governance between 
countries  concerning  knowledge,  technology  and  learning  processes,  e.g.  concerning 
education, universities and industrial policies. In this respect, a main topic studied by the 
national  innovation  systems  literature  is  the  issue  of  science-industry  linkages  within 
countries. These science-industry linkages (and the technology ‘transfers’ between these 
knowledge  producing  and  using  sectors)  are  very  sensitive  to  (institutional,  political, 
historical and cultural) differences between countries.  

National innovation systems studies have broadened the view on innovation policy. R&D 
is  not  the  only  source  for  innovations  and  the  main  role  for  policy  makers  is  not  to 
secure funding for individual innovation projects, but in creating the conditions for firms 
that  promote  innovative  behaviour  and  interactions,  and  conditions  which  enhance 
capabilities  for  innovation.  In  this  respect  the  national  innovation  systems  studies  have 
been quite successful, since 25 years later, even neoclassical economists are  convinced 
that R&D is not the only driver of innovation.  

15 

 
 
The  main  authors  who  have  developed8   the  national  innovation  systems  concept  are: 
Freeman (1987), Lundvall (1992), and Nelson (1993). 

Freeman defined a national innovation system as: 

“  ..  the  network  of  institutions  in  the  public  and  private  sectors  whose  activities 
and interactions initiate, import, modify and diffuse new technologies.” (Freeman, 
1987).  

In  his  study  of  the  success  of  the  Japanese  economy,  Freeman  came  to  his  idea  (of 
national innovation systems) that could explain differences between countries in terms of 
technological innovations.  

In  his  publication  titled:  ‘National  Systems  of  Innovation:  Towards  a  Theory  of 
Innovation and Interactive Learning’ Lundvall builds on the ideas of Freeman and extents 
its theoretical background. Lundvall (1992) defined a national innovation system as:  

“ .. the elements and relationships which interact in the production, diffusion and 
use of new, and economically useful, knowledge ...” (Lundvall, 1992).  

This  definition  shows  that  this  approach  to  innovation  is  broadened  from  technological 
knowledge to economically useful knowledge. Lundvall has also extended the factors that 
promote innovation to also include social and cultural factors.   

The  important  theoretical  contribution  by  Lundvall  concerns  the  notion  of  interactive 
learning  between  producers  and  users  of  innovations.  This  notion  explains  how  many 
non-market  aspects  play  a  role  in  innovation  processes.  Users  and  producers  of 
knowledge and innovations learn from  communicating with each other, from interactive 
communication and not from exchanging price signals in mere market relations of buying 
and selling. With interactive learning actors adapt to each other and influence each other 
towards adoption: they co-evolve. Instead of passive selling (exchanging and extracting 
value), learning with users is about co-creation of value (Avigdor et al. 2014). Learning 
with  users  in  value  chains  and  systems  implies  a  role  for  users  and  consumers  that 
contradicts  the  dictionary  definition  of  “consume”:  meaning  “destroy”,  “use  up”,  or 
“waste”, since users of innovative solutions continue the value creation process through 
use  (Vargo  et  al.  2008).  In  Lundvall’s  theory  of  interactive  learning  between  producers 
and  users  of  economically  useful  knowledge,  the  starting  point  is  K.  J.  Arrow’s  1962 
paper  “Learning  by  Doing”,  which  shows  that  closing  knowledge  gaps  and  helping 
laggards learn are central to growth and development of any economy.9  

Concerning social and cultural factors, later  studies (as  for instance discussed by Soete 
et  al.  2009)  have  indeed  shown  empirically  that    differences  between  countries  (and 
regions)  in  terms  of  for  instance  social  capital,  trust,  and  entrepreneurial  culture,  do 
indeed explain differences in innovation performance. 

The third definition of national innovation system we provide is from Nelson (1993):    

“... a set of institutions whose interactions determine the innovative performance 
... of national firms.”  (Nelson, 1993). 

The  book  “National  Innovation  Systems”  (Nelson,  1993)  for  the  first  time  made  a 
systematic comparison between countries, discussing organizational innovation practices 
at system level. Together with the ‘Evolutionary Theory of Economic Change’ this book is 
in  the  top  10  (Table  1)  of  the  most  cited  publications  in  innovation  studies  (Fagerberg 
2013).  The  theoretical  perspective  builds  on  the  evolutionary  theory  from  Nelson  & 
Winter (1982). Firms in a given country have a certain commonality in their micro-level 

8   See for instance Soete et al (2009). 
9   In  “Creating  a  Learning  Society”  Stiglitz  &  Greenwald  (2015)  also  start  from  Arrows  paper 
when  they  explain  (as  Lundvall  did)  why  the  production  of  knowledge  and  innovation  differs 
from  that  of  other  goods  and  why  market  economies  alone  typically  do  not  produce  and 
transmit knowledge efficiently. Policy makers should promote learning and interactive learning. 

16 

 
                                           
‘routines’  which  can  be  explained  by  the  common  institutional  environment  of  the 
national  innovation  system  in  which  they  learn  and  innovate.    The  same  holds  for  the 
other type of stakeholders, including policy makers. As Malerba (2004, p. 14) puts it:  

‘in  an  evolutionary  framework  there  is  not  a  sharp  distinction  between  the 
learning environment and the unit of learning’.  

National systems  of technical innovation are based on a strong belief that technological 
capabilities of a nation's firms are a key source of national competitiveness, and can be 
built  by  national  action.  The  book  contains  studies  of  seventeen  countries,  including  a 
number of newly industrialized states10.  

One of the main contributions of Nelson (1993) was showing convincingly the importance 
of the State, the institutions and public policies in enhancing technological capabilities of 
firms in a country. The successes of companies from Japan, and Asian Tigers like South 
Korea  and  Taiwan,  could  be  traced  back  to  national  technology  strategies  and  national 
systems of technological innovations.  

The  concept  of  NIS  has  therefore  become  quite  popular  among  policy  makers,  as  it 
provides a framework for strategic action at the societal level. This strategic governance 
aspect has later also become apparent in strategies addressing grand challenges, and for 
instance in ‘Smart Specialization’.  

Although in the early studies on innovation systems the comparison between countries is 
based  on  descriptions  of  rather  static  profiles,  more  in-depth  and  qualitative  studies 
have focused on the dynamics and (path-dependent) change of systems over time, also 
including a policy perspective. See for instance the NIS framework synthesis provided by 
Fagerberg 2013 (as illustrated in Figure 2).   

In this framework knowledge, skills, demand, finance and institutions are highlighted as 
the  main  factors  (resources,  activities)  which  each  interact  with  technological  change. 
These  dynamic  processes  generate  innovation.  The  role  of  public  policy  is  complex, 
which is illustrated by the list of policy domains. Indeed, all the policy domains somehow 
touch  on  the  mentioned  processes.  We  can  interpret  the  domains  as  ministries  of  the 
concerning country.  In  order to prevent that  each ministry builds an innovation system 
for its own ‘purpose’ as a ‘policy silo’, strategic management is needed to organize for a 
systemic approach. The idea that the institutions of various policy domains influence the 
innovation performance of innovation systems has been part of the NIS concept from the 
beginning,  but  over  the  years  the  number  of  policy  domains  which  are  considered 
relevant has increased, moving  beyond the ministries of industry and economy and the 
ministries  of  science  and  education.  For  instance,  also  ministries  concerning  health, 
transport, energy, ICT, finance, and regional development, are involved. 

10   Empirically, the authors of the book edited by Nelson (1993) saw a ‘techno-nationalism’ in the 
rise  of  Japan  as  a  major  economic  and  technological  power  and  in  the  enhanced  technical 
sophistication of Newly Industrialized Countries such as Taiwan and South Korea. 

17 

 
 
 
                                           
Figure 2: The national innovation system: dynamics, processes and policy 

Source: Fagerberg 2013. 

After  the  emergence  of  the  national  innovation  systems  concept,  other  applications  of 
the  innovation  systems  concept  have  followed.  Focusing  on  the  geographical  aspect 
several  have  applied  the  NIS  concept  to  study  regional  innovation  systems.    Cooke 
(1997) applied the NIS theoretical background and suggested that: “this approach may 
be  complemented  in  important  ways  by  a  sub-national  focus”.  There  are  some 
differences with the NIS literature due to the reduced scale and policy competencies, but 
it  is  difficult  to  interpret  these  differences  as  conceptual.  Regional  Innovation  Systems 
(RIS) studies have developed into quite a large strand of literature and the RIS approach 
has been widely adopted by policy makers to develop regional innovation strategies. 

In Figure 3, one example of a schematic overview of the concept of regional innovation 
systems (Trippl 2006) is provided. The figure clearly illustrates that the RIS is not seen 
as  a  closed  system,  but  that  it  interacts  with  other  systems  at  national  and  regional 
level.  The  science-industry  linkages  have  been  the  central  focus  for  decades,  especially 
at  the  regional  level.  Trippl  discussed  this  as  interactions  between  sub-systems,  and 
added  a  policy  sub-system  (Figure  3).  The  importance  of  collaboration  between 
government,  firms  and  education  institutes  (defined  as  three  regional  sub-systems  in 
Figure  3 11)  has  also  been  emphasized  with  the  concept  of  ‘triple  helix’  (Etzkowitz  & 
Leydesdorff (2000).  

11   Namely:  the  regional  policy  sub-system,  the  knowledge  application  and  exploitation  sub-

system, and the knowledge generation and diffusion sub-system. 

18 

 
 
 
 
 
 
                                           
Figure 3: The regional innovation system: a conceptual framework 

Source: Trippl (2006). 

The  importance  of  the  interaction  between  the  three  RIS  subsystems  of  Figure  3, 
indicates  the  need  for  co-designed  regional  innovation  strategies,  which  is  also 
emphasized in the more recent concept of Smart Specialization developed by Foray et al. 
(2009,  2015)  and  the  RIS3  innovation  strategies  at  regional level.  The  notion  of  Smart 
Specialization  describes  the  capacity  of  an  economic  system  (a  region  for  example)  to 
generate new specialities through the discovery of new domains of  opportunity and the 
local concentration and agglomeration of resources and competences in these domains. 
Choosing the fields of regional specialization should not be done by scientific discovery, 
or  top-down  by  policy-makers,  but  in  a  joint  ‘entrepreneurial  discovery’  (Foray  et  al. 
2009,  2011)  process  based  on  interaction  with  companies  and  other  stakeholders.  As 
Foray  (2015)  explains  later,  they  (Foray  et  al.  2009,  2011)  referred  to  the  concept  of 
‘entrepreneurial discovery’ as a practical process in development economics, inspired by 
the  ‘self-discovery’  process  as  described  by  Hausmann  &  Rodrik  (2003).  Also  for  the 
design  of  policy  instruments  and  appropriate  framework  conditions,  interaction  among 
stakeholders is essential. Cooke (2007) and Asheim et al. (2013) refer in this respect to 
co-designing  of  regional  innovation  strategies  and  systems  in  terms  of  ‘constructing 
regional advantage’12. Nauwelaers & Wintjes (2002) explain in this respect the need for 
‘interactive policies’. 

Although some authors claim that either the national or the regional (sub-national level) 
is  more  important  for  innovation  than  the  other,  most  authors  appreciate  analysis  at 
multiple  geographical  scales:  e.g.  the  (Nuts1,  Nuts2  or  Nuts3)  regional  level  and  the 
national  level.  One  of  the  most  common  arguments  applied  when  selecting  the 
appropriate  level,  is  the  relevance  of  boundaries  of  jurisdictions,  but  the  way  in  which 
administrative  areas  are  organized  differs  to  a  large  extent  within  Europe  and  within 

12   See also Ron Boschma (2014) for a comparison between the concepts of ‘Smart specialization’ 

and ‘Constructing Regional Advantage’. 

19 

 
 
                                           
countries. In some countries the most relevant regulations, infrastructure, strategies and 
policy instruments are governed at the national level, while in other countries there can 
be quite remarkable differences between regions.  For example, in the case of Estonia or 
Luxembourg there is no reason why the sub-national level should be addressed, but for 
Spain there is.  

2.2.2 Sectoral innovation systems 

An  important  insight  of  the  sectoral  systems  approach,  developed  by  Malerba  (2002, 
2004),  is  that  innovation  systems  operate  at  multiple  levels  and  within  and  across 
(national/regional)  economies  and  technologies.  Indeed,  not  only  the  geographical 
differences  matter,  but  also  the  differences  between  sectors  and  their  sector-specific 
innovation  processes,  institutions  and  resources.  Malerba  (2002)  actually  labelled  the 
concept:  ‘Sectoral  systems  of  innovation  and  production’,  but  most  others  refer  to  it 
without adding the word ‘production’. According to Malerba:  

 “a sectoral system is a set of products and the set of agents carrying out market 
and  non-market  inter-actions  for  the  creation,  production  and  sale  of  those 
products.  A  sectoral  system  has  a  specific  knowledge  base,  technologies,  inputs 
and  demand.  Agents  are  individuals  and  organizations  at  various  levels  of 
aggregation.  They  interact  through  processes  of  communication,  exchange,  co-
operation,  competition  and  command,  and  these  interactions  are  shaped  by 
institutions. A sectoral system undergoes change and transformation through the 
co-evolution of its various elements.” (Malerba 2002, p. 247). 

In the above mentioned definition of sectoral innovation systems only products are used 
and  not  services.  Indeed,  one  of  the  critical  remarks  on  the  early  innovation  systems 
studies  was  their  focus  on  technological  innovation  in  manufacturing  sectors.  In  the 
definition  we  see  again  (as  in  the  definitions  on  national  innovation  systems)  the 
attention for both market and non-market factors. A difficulty concerns the boundaries of 
sectors  and  the  various  levels  of  aggregations,  because  the  boundaries  change,  new 
sectors  and  sub-sectors  emerge.  This  issue  of  sector-definition  is  especially  relevant 
when  studying  a  general  purpose  technology  such  as  ICT,  which  is  used  across  all  the 
sectors of the economy. The actors involved in sectoral systems also include those which 
are  classified  in  other  NACE  categories.  A  recent  study  of  Adams,  Montana  &  Malerba 
(2013) for instance  shows how important industrial users are in the sectoral innovation 
system for semiconductors. They show for instance that a large share of the inventions 
in  semiconductors  is  done  not  by  semiconductor  firms  but  in  user  industries  such  as 
instruments, industrial machinery, automotive and defence.   

Malerba  (2002)  specified  the  various  mechanisms  of  interaction  as  processes  of 
communication,  exchange,  co-operation,  competition  and  command.  These  interactions 
are  shaped  by  institutions  which  often  differ  by  sector.  Next  to  this  institutional 
theoretical  aspect,  also  the  evolutionary  aspect  is  addressed  in  the  above  definition  of 
Malerba  by  stating  that  the  transformation  is  the  result  of  co-evolution  of  its  various 
elements. This co-evolution creates development paths for the various sectors.  

The  concept  of  Sectoral  Innovation  Systems  (SIS)  is  less  often  used,  compared  to  the 
geographical  innovation  systems  concepts  of  NIS  and  RIS.  One  of  the  reasons  is  the 
popularity of the cluster concept, which is a ‘rival concept’ to the SIS concept. Although 
there  is  no  commonly  agreed  upon  definition  of  clusters,  they  often  combine  territorial 
and  sectoral  aspects.  For  example,  OECD  studies,  such  as  the  one  titled:  ‘Boosting 
Innovation:  The  Cluster  Approach’  describes  clusters  as  sector-specific  systems  of 
innovation,  which  operate  under  national  systems  of  innovation.  Also  the  OECD  study: 
‘Innovative  Clusters:  Drivers  of  National  Innovation  Systems’,  belonged  to  the  second 
phase  of  the  OECD  NIS  project  which  was  devoted  “to  deepening  the  analysis”  of 
national innovation systems.  

The term ‘ecosystem’ is another ‘rival concept’ which is often used (especially in the US), 
but  it  basically  refers  to  the  same  sectoral  innovation  systems  concept.  In  Europe, 

20 

 
another,  even  more  recent,  rival  concept  concerns  Smart  Specialisation.  As  a  pre-
condition  for  regions  for  the  new  period  of  Structural  Funds  (of  which  a  large  share  is 
available  for  innovation),  Smart  Specialisation  (RIS3)  strategies  revolve  around  a 
regionally as well as sectorally defined system of innovation approach.  

Although most studies on clusters and other types of sectoral innovation systems studies 
are  of  a  qualitative,  in-depth  and  case-study  nature 13,  there  are  some  studies  which 
have tried to test some innovation systems hypothesis in a more quantitative (indicator 
based  way)  for  certain  sectors,  such  as  the  ICT  sector.  For  instance,  Wintjes  & 
Dunnewijk  (2008)  report  on  ICT  clusters  at  regional  level.  ICT  clusters  settle  in  fecund 
regions, where fecundity is defined as a combination of high density of ICT employment 
and  high  quality  of  the  surroundings  or  contextual  quality.  Ranking  the  most  fecund 
European  regions  (EU25)  which  have  above  average  contextual  quality  and  above 
average ICT employment density (at the Nuts 2 level), the study reveals that Stockholm 
(se01), two Finnish  regions Pohjois-Suomi  (fi1a) and Etelä-Suomi (fi18), Île de France 
(fr10), Oberbayern (de21), Hampshire and Isle of Wight (ukj1), Dresden (ded2), Vienna 
(at13), Surrey, East and West Sussex (ukj2) hold the top-10 positions of this list. Figure 
4  exhibits  all  the  regions  with  above  average  contextual  quality  and  density  of  ICT 
manufacturing  activities.  Overall  this  analysis  supports  the  view  that  a  high  sector 
density  co-evolves  with  contextual  quality,  as  suggested  by  innovation  systems 
literature.  

The  IPTS  project 14  on  Poles  of  Excellence  in  ICT  provides  a  more  recent  and  more 
detailed  (e.g.:  at  NUTS3)  confirmation.  In  Nepelski  et  al.  (2014)  the  key  findings  are 
reported.  A  composite  indicator  shows  that  the  concentration  for  ICT  innovation  seems 
to have increased with Munich, London and Paris standing out. Other results in line with 
innovation  systems  literature  are  that  excellence  builds  on  high  performance  across  all 
activities, and that ‘diversity dominates’ (although ICT sub-sectors are not considered).  

13   e.g. Larosse et al. (2001) on ICT clusters. 
14   http://is.jrc.ec.europa.eu/pages/ISG/EIPE.html 

21 

 
 
 
                                           
Figure  4:  Top  ICT  regions  based  on  ICT  manufacturing  density  and  contextual 
quality of environment 

Source: Wintjes & Dunnewijk (2008). 

Also  within  the  sectoral  innovation  systems  literature,  most  of  the  attention  has  for  a 
long time, been given to research and technological development as input for generating 
business  innovation.  Soete  et  al.  (2009)  state  that  one  of  the  shortcomings  of  these 
early  systems  of  innovation  concepts  is  the  limited  attention  for  the  increase  of 
innovations  which  do  not  need  ‘particular  leaps  in  science  and  technology’:  the 
combination,  use  and  diffusion  of  known  practices  has  become  more  important  for 
innovation and its economic impacts.  

By  adding  the  word  ‘production’  to  the  definition  of  the  concept  of  ‘sectoral  systems  of 
innovation  and  production’  Malerba  (2002)  already  stressed  that  indeed  the  sectoral 
system should involve more than R&D. Especially concerning ICT innovations, more and 
more emphasis has been put on the innovation demand side, which, for  ICT goods and 
products, can be found in almost every other sector. In many ways a shift has occurred 
in the emphasis of many national ICT policies from targeting the R&D as the innovation 
supply side to supporting the innovation demand side. Even within R&D policies (e.g. the 
European  Horizon  2020  programme  or  the  EIT  institutes),  the  interaction  with  users  in 
Europe  has  been  given  more  attention.  Overall  the  awareness  has  increased  that  mere 
excellence  in  ICT  research  and  export  of  ICT  patents,  does  not  bring  much  economic 
impact, without production and usage of ICT in Europe.  

22 

 
 
 
 
Figure 5: Digital Economy and Society Index, by main dimensions (2014) 

Source: European Commission, Digital Agenda Scoreboard. 

Many initiatives in the EU involve stimulation of the market for  ICT by encouraging ICT 
take-up in every sector of the economy. ICT can serve as a means of achieving goals in 
many  policy  domains.    Also  for  the  future  ICT  developments  such  as  the  Internet  of 
Things,  the  applications  and  implications  are  linked  with  other  emerging  sectors  and 
smart policy domains. Also at the regional level the Smart Specialisation strategies often 
involve  a  focus  on  ICT  applications  in  certain  other,  existing  sectors.  While  the  old  ICT 
for  growth  narrative  had  its  focus  on  R&D  at  the  innovation  supply-side,  the  new  EU 
narrative  on  the  role  of  ICT  and  growth  is  more  on:  un-leashing  the  potential  and  by 
increasing focus on enabling the demand side (van Welsum et al. 2013). This implies a 
shift  to  a  more  systemic  ICT  narrative  where  the  interaction  between  producing  and 
using ICT knowledge is in focus. Also the OECD follows this new narrative, and suggests 
that  concerning  ‘measuring  the  digital  economy’,  this  shift  should  be  apparent  in  the 
monitoring  indicators  used.  Therefore  they  turn  to  indicator-sets  which  formerly  were 
referred  to  as  indicators  for  monitoring  the  Information  Society  (OECD  2015).  In  this 
respect  the  indicators  of  the  Digital  Economy  and  Society  Index  (Figure  5)  serve  to 
integrate the many dimensions (geographical, sectoral, institutional) of ‘e-readiness’ into 
a systemic approach to measure  ICT  innovation systems.  In Section 2.3 we  come back 
to this point. 

2.2.3 Innovation system approaches extending to the societal level  

The  earlier  mentioned  innovation  systems  literature  originally  focused  merely  on  firms 
and  the  general  economic  impact.  However,  innovation  and  innovation  policy  may  also 
have  other  aims,  addressing  challenges  in  other  domains  of  society.  In  recent 
applications  of  the  concept,  innovation  systems  are  no  longer  seen  as  merely 
instrumental for economic benefits, but also for addressing societal challenges (see also 
Turkeli & Wintjes, 2014). 

The  ‘socio-technical  system  of  innovation’  (Geels,  2004)  is  an  influential  example  of  an 
innovation systems approach designed to study ‘eco-innovations’ and the transformation 
to  a  society  which  is  not  depending  on  fossil  fuel.  Geels  (2004)  and  Coenen  (2013) 
describe  the  shift  from  sectoral  systems  of  innovation  to  what  they  call  socio-technical 
systems  of  innovation  (Figure  6),  which  adds  another  sub-system,  namely  a  user-sub-
system.  In  this  framework  a  functional/user  side  serves  as  the  selection  environment 
(Figure 6). This concept is however quite technocratic, since users and citizens only have 
a rather passive role. 

23 

 
 
Figure 6: Conceptual framework of socio-technical system of innovation 

Source: Geels (2004) 

Recently  there  has  been  an  increase  in  the  attention  for  the  role  of  customers  and 
citizens in innovation, and civil society is seen as valuable partner in innovation systems 
(Figure  7),  as  for  instance  also  discussed  by  Gabison  and  Pesole  (2014)  and  Türkeli  & 
Wintjes (2014). The role of citizens is not limited to expressing what they need or seek. 
Especially in the case of social innovation, citizens may also contribute to the design and 
implementation of solutions. The involvement of the  ‘crowd’ in innovation is enabled by 
ICT.  The ‘crowd’ has become a source of innovation in many ways.  E.g., in the case  of 
crowd-sourced  manufacturing  users  and  customers  provide  input  in  the  co-design  of 
innovative products. In new ICT based business models of the sharing economy such as 
Uber and AirB&B, citizens are integrated in the business model. Citizens are also actively 
involved  in  many  Smart  City  programmes  and  systems  aimed  at  addressing  societal 
challenges  at  city  level.  Citizens  have  also  been  involved  in  ‘entrepreneurial  discovery’ 
meetings,  which  regions  have  organized  to  development  strategies  for  Smart 
Specialization at regional level. 

Figure 7: The four types of stakeholders in societal innovation systems: beyond 
the triple helix 

Government 

Science  
Edu-
cation 

Citizen
s/ civil 
society 

Firms 

Source: Wintjes et al. (2014). 

24 

 
 
 
 
As  mentioned  before,  Carlson  et  al.  (2002)  pointed  at  an  aspect  which  is  not  often 
explicitly  addressed  in  innovation  systems  literature,  namely:  the  ‘common  goal’  of  the 
system, or the purpose, the ‘mission’ or ‘finality’ of innovation. The original concepts of 
national, regional and sectoral innovation systems, which have been developed some 25 
years  ago,  had  a  focus  on  firms  as  the  main  actors  and  on  the  economic  benefit  of 
innovations.  However,  the  issue  of  the  objective  of  innovation  deserves  our  attention, 
since it defines which components and system boundaries are most relevant, and which 
type  of  interaction  among  components  would  be  relevant  to  analyse.  The  original 
innovation systems concepts merely had a focus on the economic impact (or purpose) of 
innovation    (Foray,  Mowery  &  Nelson  2012),  while  more  recent  applications  of  the 
innovation  systems  concept  have  been  developed  which  also  have  broadened  the 
perspective  of  innovation  in  terms  of  impacting  on  societal  challenges,  e.g.:  climate 
change or healthy aging.  

Examples of ‘thematic’ systems of innovation addressing a specific societal challenge are 
those concerned with ‘security and safety’ (Weber, 2014), or ‘health’ (e.g. in relation to 
challenges of an aging society). As general-purpose-technologies, ICT innovations are of 
special  relevance  among  thematic  systems  of  innovations.  ICT  innovations  or  ICT-
enabled  innovations  can  be  instrumental  in  solving  problems  in  other  (than  economic) 
policy  domains  in  an  Information  Society  (e.g.:  health,  security,  education,  defence, 
social innovation, climate change, transport, public sector innovation).  

The conceptual model in Figure 6 is designed to monitor  ICT innovation systems at the 
societal  level.  It  is  based  on  the  monitoring  framework  developed  for  the  E-Flanders 
initiative  (Wintjes,  Dunnewijk  &  Hollanders  2002),  which  aimed  at  monitoring  the 
information society at the level of Flanders. The four type of actors (same as in Figure 7) 
on  the  ICT  innovation  supply-side  (namely:  firms,  governments,  knowledge  institutes 
and  citizens)  appear  also  on  the  innovation  demand-side,  since  each  of  these  so-called 
quadruple-helix  actors  can  be  both  a  producer  and  a  user.  Barriers  separate  these 
users 15  from  non-users,  and  users  outside  the  concerning  region  or  country.  ICT 
Infrastructures refer to the channels for interaction. The interaction mechanisms include 
not  only  trade  as  economic  transactions,  but  also  financing/investing,  learning,  co-
creation,  and  even  (institutional  changes  from)  regulation  and  standardisation  (Figure 
8). 

15   Typical  indicators  to  report  on  the  use  of  ICT  by  various  kinds  of  actors  refer  to  a  certain 

percentage of the sample which uses a specific ICT application. 

25 

 
 
 
                                           
Figure 6: A framework to monitor ICT innovation systems at the societal level 

Source: adapted after Wintjes, Dunnewijk & Hollanders (2002). 

ICTs  are  notably  a  distributive  source  for  increased  productivity,  of  advanced  solutions 
for  societal  problems,  and  of  new  services  to  consumers.  A  key  characteristic  of  ICT 
innovations  is  their  pervasiveness  in  the  whole  society.  The  disruptive  implications  go 
beyond sectors, economies, value chains and business models. As a result it has become 
difficult  to  distinguish  ICT  sectors  from  other  sectors.  Among  newly  emerging  sectors 
and  ‘smart  policy  domains’,  ICT  is  always  part  of  it  somehow.  Also  public  sector 
innovation and social innovation are to a large extent based on ICT innovation. 

We conclude that for ICT it is relevant to extend the innovation systems concept to the 
societal level.  In this respect we can refer to the concept of Information Society, which 
has  been  applied  to  national  and  regional  levels.  In  the  recent  OECD  (2015)  report 
‘Measuring  the  Digital  Economy’  this,  what  they  call  ‘new  perspective’,  is  presented.  It 
involves selecting indicators traditionally used to monitor the Information Society.  

Based  on  a  combination  of  the  three  types  of  innovation  systems  described  in  2.2,  the 
next section addresses the issue of how to monitor national and regional systems of ICT 
innovations. 

2.3  Monitoring  national  and  regional  systems  of  ICT  innovation: 

an operational framework  

In  the  scientific  contributions  on  national  and  regional  innovation  systems,  often 
abbreviated  respectively  as  NIS  and  RIS  studies,  the  sector  component  has  always 
remained  an  important  aspect.  Several  case-studies  on  a  specific  national  or  regional 
system  of  innovation  have  focused  on  ICT  innovation  (its  emergence,  up-take  and 
impact on the wider economy and society) for the concerned territorial area. Studies on 
sectoral  innovation  systems  (SIS)  often  focused  on  certain  countries,  or  Europe,  or  for 
instance compared industries in different parts of the globe.  

Fransman (2014) for instance studied global ecosystems of ICT firms (structured in three 
ICT  sub-sectors)  and  sometimes  compares  US  versus  EU  and  Asian  companies.  So  the 
difference  between  the  sectorally  bounded  and  the  geographically  bounded  concepts  of 
innovation systems is often not very sharp. Meijers et al. (2008) is another example of a 
study that combines both the sectoral and geographical aspect of innovation. With their 
focus  on  internationalisation  of  European  ICT  companies,  they  touch  on  differences 
between  territorial  systems  of  ICT  innovation  in  which  the  international  ICT  companies 
operate.  

26 

 
 
In  his  book  on  ‘Sectoral  Systems  of  Innovation’  Malerba  (2004)  also  does  not  exclude 
the  territorial  dimension.  This  important  contribution  on  the  concept  of  sectoral 
innovation  systems  actually  consists  of  six  sectoral  systems  of  innovation in  Europe,  of 
which  two  chapters  address  ICT  sub-sectors,  namely:  ‘The  fixed  Internet  and  mobile 
telecommunications  sectoral  system  of  innovation:  equipment  production,  access 
provision  and  content  provision’  (Edquist  2004),  and  ‘The  European  software  sectoral 
system of innovation’ (Steinmueller 2004). These two case studies apply the concept of 
sectoral systems of innovation in a qualitative way, describing the processes which make 
the systems change. They also show that defining especially the ICT sector boundaries, 
or ICT sub-sectors is rather arbitrary16.  

Edquist (2004) makes clear that there “is a certain degree of arbitrariness when it comes 
to  the  specification  of  sectoral  boundaries.  [...]Some  minimum  degree  of  coherence  is 
nevertheless required to make it useful to talk about a sectoral system”. Since it is most 
useful to analyse the dynamics of sectoral innovation systems over time, it is interesting 
to  analyse  converging,  diverging,  emerging  and  barely  surviving  ICT  fields.  Edquist 
(2004)  discussed  several  converging  fields  in  ICT,  such  as  between  fixed  internet  and 
mobile telecoms. He first describes the functions (e.g: develop equipment, R&D, provide 
internet  access,  education,  create  standards)  and  organisations  in  the  system  who 
perform  these  functions.  Institutions  are  defined  as  “sets  of  common  habits,  routines, 
established practices, rules or laws that regulate the relations and interactions between 
individuals,  groups  and  organizations”  (Edquist  2004,  p.161).  He  then  described  the 
historical development of (and ways to get access to) the fixed internet and the various 
generations  of  mobile  telecommunication  starting  with  the  NMT  450  or  Nordic  Mobile 
Telephone standard in 1970. Later the GSM standard followed. A first policy conclusion is 
that  institutions  (including  technology  standards)  are  crucial  for  policy  and  the 
performance  of  sectoral  systems  of  innovation.  Organisations  provoke  institutional 
changes, and when the new institutions come into effect they may greatly influence the 
same or other organizations. The main policy conclusion of Edquist (2004) is that:  

“It  is  of  crucial  importance  that  public  policy  intervention  occurs  early  in  the 
development  of  the  sectoral  system.  Public  technology  procurement  was  crucial 
for  the  very  early  development  of  the  Internet  in  the  United  States  and  the 
formulation  of  standards  was  crucial  for  the  very  early  development  of  mobile 
telecommunications in the Nordic countries”. 

In  the  chapter  ‘The  European  software  sectoral  system  of  innovation’  Steinmueller 
(2004) describes more than three decades of growth dynamics in the software industry. 
According  to  Steinmueller  the  nature  of  the  sectoral  innovation  systems  for  software 
creation  and  exchange  activities,  and  the  technologies  supporting  these  activities,  are 
shaped by three fundamental issues: the nature of software as an economic commodity; 
the  historical  patterns  of  the  division  of  labour  involved  in  software  creation;  and 
distinctions in the design and use of software. 

In a more quantitative approach the earlier mentioned study on Poles of ICT Excellence 
(Nepelski et al. 2014) measures the level of excellence at the level of Nuts  3 regions in 
Europe  for  ICT  ‘as  a  whole’.  A  selection  of  indicators  is  based  on  the  idea  of 
interdependency  between  R&D,  innovation  and  business  activities,  which  implies  that 
these  are  often  co-located.  The  EIPE  project  looked  at  three  characteristics  of  these 
activities:  agglomeration,  internationalisation  and  networking.  The  result  allowed  us  to 
rank  the  top  cities  in  Europe  concerning  ICT  excellence  (‘as  a  whole’):  Munich,  London 
and Paris.  

The  issue  of  defining  ICT  subsectors  and  measuring  the  producer  and  user  side  of  the 
involved  ICT  knowledge,  has  recently  become  more  relevant.  Due  to  the  fact  that  ICT  

16   In  the  above  mentioned  study  of  Edquist  (2004)  the  equipment  production,  access  provision 
and  content  provision  are  studied  as  one  innovation  system,  while  in  the  above  mentioned 
study of Fransman (2014) these are actually considered as separate sectoral systems. 

27 

 
                                           
has  become  integrated  in  many  sectors  and  daily  life  and  not  only  serves  economic 
purposes,  it  has  become  rare  to  develop  innovation  system  frameworks  which  do  not 
involve any kind of ICT technology, any kind of ICT producing and using industry, as well 
as  any  kind  of  impact  (or  system  objective).  The  multi-purpose  or  general  purpose 
nature  of  ICT has led to the need for more specific granulated sub-system approaches, 
ranging  from  specific  local  ICT-clusters  (a  concept  defined  by  OECD  (2001)  as  micro-
systems  of innovations) or newly emerging technologies or industries, e.g. the  Internet 
of  Things  (PwC  2015).  This  need  for  more  granulation,  diversification  within  ICT  ‘as  a 
whole’ is especially a strategic (public and private) policy need which is most urgent for 
new emerging themes or fields of ICT applications; applications for which no institutions 
or  markets  may  exist  yet,  or  which  may  be  disruptive  to  existing  institutions  and 
markets. Defining the boundaries and objectives of an ICT innovation system can, in this 
respect, be part  of a politically-motivated strategy  concerning a certain niche of ‘Smart 
Specialisation’.  

Defining  the  most  relevant  sub-sector  boundaries  within  the  ICT  sector  remains 
therefore  a  challenge  for  those  academics  or  policy  makers  who  want  to  apply  an 
innovation systems approach to ICT.  Relevant sub-sectors within the ICT manufacturing 
as  well  as  ICT  service  sector  are  for  instance  those  which  supply  ICT  innovations  to 
industrial  markets,  e.g.  robotics  and  automation.  The  German  programme  Industry  4.0 
is  in  this  respect  linking  a  vision  on  the  Internet  of  Things  to  a  new  industrial 
renaissance17.  On  this  specific  theme  Ronald  Berger  (2014)  has  developed  an  indicator 
based  index  for  EU  Member  States  of  their  ‘readiness’  for  Industry  4.0  which  goes 
beyond  indicators  for  global  excellence  on  the  supply  side,  concerning  robotics  and 
automation. 

Other  examples  for  emerging  ICT  sub-sectors  can  be  found  in  case  studies  and  trend 
reports  of  the  EU  Business  Innovation  Observatory 18 (e.g.:  Wintjes  2013).  Based  on 
company interviews, innovation systems narratives emerge and show how a diversity of 
actors  are  involved  in  producing,  using,  financing,  regulating,  investing,  teaching  and 
standardizing  certain  ICT  innovations  in  an  interactive  learning  setting  of  a  national  or 
regional  ICT  innovation  system.  The  cases  (e.g.  concerning  Big  Data,  Advanced 
Manufacturing,  Internet  of  Things,  or  Sharing  Economy)  show  the  importance  of  being 
located  and  embedded  in  a local innovative  environment  that  is  particularly  relevant  to 
the  concerned  ICT  innovation;  a  region  with  potential  partners,  (lead)  clients, 
universities,  a  pool  of  high  skilled  experts,  end  -users,  access  to  finance,  and  relevant 
public  support  mechanisms  for  the  concerning  ICT  innovation.  Specific  for  the  newly 
emerging  ICT  sub-sectors  (such  as:  Big  data,  Advanced  Manufacturing,  Internet  of 
Things,  or  Sharing  Economy)  are  the  lack  of  institutions  such  as  standards  and 
regulations  and  laws.  The  risks  and  uncertainties  are  high,  markets  may  not  exist  yet, 
and typical concerns are about data security.  

As  an  example  we  refer  to  the  European  Roadmap  on  ‘Smart  Systems  for  Automated 
Driving’  (EPoSS,  2015).  The  roadmap  shows  that  many,  different  kinds  of  stakeholders 
have to cooperate on a wide range of activity fields, such as: technology inside the car, 
infrastructure,  big  data,  system  design,  standardisation,  legal  frameworks  and 
awareness measures. For example: cars have to communicate with traffic lights, and this 
makes the issue of software and data security a matter of life or death. The autonomous 
car  is  therefore  not  only  about  introducing  a  new  car  on  the  market,  but  about 
transforming  a  whole  traffic  system  (Swedish  Transport  Agency  2014).  This 
transformation is necessarily a collaborative public-private undertaking. Furthering such 
emerging fields of ICT innovation can therefore not be left to individual companies or to 
price-competition  in  existing  markets.  Institutions  and  (co-evolving)  processes  of 

17   For an innovation  systems approach  to  advanced manufacturing  at regional level, see the EU 

Regional Innovation Monitor (Wintjes 2014). 

18   http://ec.europa.eu/growth/industry/innovation/business-innovation-

observatory/index_en.htm 

28 

 
                                           
institutional change are crucial in understanding the dynamics of innovation in the field 
of ICT; in explaining how ICT enabled systems innovate.  

Institutions  also  have  soft  aspects  like  entrepreneurial  spirit,  innovative  culture, 
mentality,  awareness  of,  and  readiness  for  the  concerning  ICT  trends..  Linkages  with  a 
conducive  and  receptive  environment  and  partnerships  with  relevant  players  in  specific 
ICT  environments  provide  access  to  and  transform  relevant  resources  (knowledge, 
suppliers,  skills,  science,  end-users,  investors,  international  networks).  Each  of  these 
learning or transformation processes play a role in the  emergence  of  a path dependent 
specialization  trajectory  of  the  concerning  system  of  ICT  innovation  (e.g.  towards  an 
emerging Smart System for Automated Driving).   

As  sketched  in  a  simplified  conceptual  framework  of  regional  ICT  innovation  systems 
(Figure  9)  the  main  components  are  the  ICT  technologies,  the  actors  which  supply  ICT 
innovations  and  those  who  need  them.  The  arrows  represent  the  mechanisms  of 
interaction,  the  involved  institutions  which  govern  these  interactions  are  not  visualized. 
The societal and economic impact refers to the performance aspects or objectives of the 
concerning innovation system, e.g. employment, innovative turnover or health benefits.  

Figure 7: Regional ICT innovation systems 

Source: adapted from Wintjes (2014; 2013). 

Based  on  the  above  mentioned    examples  of  ICT  innovations  from  the  Business 
Innovation  Observatory  and  on  earlier  discussed 
literature 
(especially Carlsson et al. 2002; and Malerba 2004), we can conclude that the following 
are the main components of an ICT innovation system:  

innovation  systems 

1.  knowledge and technologies; 

2.  actors and networks;  

3.  institutions (regulation, habits, culture, policy, procedures); 

4.  mechanisms of interaction. 

In  addition  the  following  processes  (functions/resources/activities)  as  provided  by 
Fagerberg  (2013;  recall  Figure  2)  in  the  synthesis  of  the  innovation  systems  literature, 
and here specified for ICT innovations, concern: 

•  Knowledge for ICT innovation; 

•  Skills for ICT innovations;  

•  Supply of ICT innovations; 

•  Demand for ICT innovations; 

29 

 
 
•  Finance for ICT innovations; 

• 

Internationalisation for ICT innovations. 

These  processes  are  not  mutually  exclusive,  e.g.  there  is  knowledge  (and  learning) 
involved in all the processes, but studies in the past have  shown that they point at the 
main  potential  barriers  or  drivers.  By  combining  the  main  processes  with  the  main 
components  and  impacts,  as  is  done  in  Table  2,  an  operational  matrix  emerges  which 
serves as a tool to capture all the aspects which have been pointed out in the innovation 
systems literature discussed in the earlier Sections 2.1 and 2.2.   

Table  2  has  been  filled  with  illustrative  examples  which  could  capture  the  concerned 
aspect. This is not an attempt to define a quantitative macro-economic model, but a first 
step  in  identifying  the  building-blocks  and  dimensions  for  which  individual  indicators 
could be developed. The matrix framework of Table 2 should be seen as a tool to make 
the innovation systems concept operational. It translates the theoretical concept and fills 
it with a broad set of individual indicators (which could be fine-tuned to a chosen theme, 
e.g.:  e-health  innovation  systems  at  the  level  of  EU  Member  States,  or  Industry  4.0  at 
regional level). Later, in a second step (Section 2.3.8) a method will be provided on how 
to  come  to  systemic  patterns  and  composite  indicators,  which  can  be  used  to  analyse 
and  compare  the  dynamics  of  ICT  (sub-)  systems  of  innovation,  and  identify  the  main 
types.  

For  each  of  the  processes,  there  are  a  variety  of  relevant  components  (technologies, 
actors,  institutions  and  inter-action  mechanisms).  The  components  are  specified  by 
column:  e.g.  the  second  column  shows  the  diversity  of  actors  along  the  various 
processes.  A 
last  column  in  Table  2  refers  to  the  purpose  (outcome/aimed 
performance/impact)  of  the  concerning  process/function  in  the  ICT  innovation  system. 
But again these aspects of impact and performance are indicative, and in reality they are 
the  result  of  complex  variety  of  interactions.  Taken  together  they  generate  the  societal 
and economic impact from the  concerning ICT  innovation for the  concerning country or 
region. 

30 

 
 
 
Table 2: Operational framework to monitor ICT innovation systems: components, 
processes and purposes 

Components of ICT innovations systems 

Processes 
(functions/ 
activities) for ICT 
innovations: 

Knowledge for 
ICT innovations 

1  

2  

3  

4  

Technologies 

Actors 

Institutions  

R&D, patents, 
publications 
on ICTs such 
as: robotics, 
nanotechnolo
gy, software, 
Internet of 
Things 

ICT firms, 

Universities, 
public 
research 
institutes, 
start-ups 

Patent-system, 
patent 
application 
procedures 

Standardisation 

Tech Transfer 
Offices 

STI policy 
strategy 

Mechanisms of 
interaction, 
infrastructure 

Tech-transfer, 
licensing, 
technology 
platforms, co-
author-ship; 
citation 

Innovation 
systems 
purposes 

(5) 

Impact/ 

performance  

Scientific 
performance 
emerging 
industries 

Supply of ICT 
innovations 

Technological 
specialization 
of R&D on ICT 

RTD 
performance 
and structure  

Start-up 
support; RTD 
infrastructure 

ICT Sector and 
cluster, 
OEM-supplier 
network,  
incubators 

e-learning 
platforms, 
traineeships 

innovation 
output, 
turnover 

Employment 

Education, 
Certification 

Skills for ICT 
innovations 

Demand for ICT 
innovations 

ICT research 
skills, ICT 
manufact-
uring skills; 
coding, basic 
user skills  

KETs (Key  
Enabling 
Technologies) 
Strategic 
Objectives 
(societal 
challenges)  

Users and 
providers of 
skills (firms, 
governments, 
citizens, 
investors, etc. 

Actors from 
various 
private 
sectors, public 
sector, 
consumers, 
smart 
factories 

Finance for ICT 
innovations 

Subsidies for 
R&D, 
subsidies for 
demand 

Banks, VC, 

Crowd-
funding 

Inter-
nationalisation for 
ICT innovations 

Global 
excellence, 
lead suppliers   

Foreign firms, 
multi-
nationals,  

Licences, 
standards, 
regulation; 

Privacy 
concerns, lobby 

Public 
procurement 
platforms; 
Living Labs; 
demonstrators; 
user-innovation 

Catching-up, 
productivity 

e-health, 
advanced 
manufact-
uring 

Laws on crowd-
funding 

Web-based 
crowd-funding 
platform 

investments 

FP7, HORIZON 

EU Digital 
Agenda and 
internal market 

export/import 
collaborations 
integration,  
e-commerce 
platforms 

Trade, 
internal 
market 

For  each  cell  in  the  matrix  (Table  2)  one  can  think  of  indicators  which  identify  the 
interactions  or  systemic  linkages  (concerning  the  mechanisms  of  interaction  in  column 
4). For example, with patent applications one can indicate the generation of technology, 

31 

 
 
 
 
 
 
and  with  for  instance  patent-citations,  cooperation  or  licenses  one  can  also  capture  the 
involved systemic innovative interactions19.  

In the next  section each of the identified processes/functions in  innovation systems, as 
described in Table 2, will be addressed by suggesting some possible individual indicators 
to  measure  and  monitor  the  concerned  aspect  of  ICT  innovation  systems.  In  Section 
2.3.8  a  methodology  is  provided  which  combines  these  individual  ingredients  of  the 
operational  framework  (Table  2)  into  a  systemic  view  on  how  different  types  of  ICT 
systems in Europe innovate. 

2.3.1 Knowledge for ICT innovations 

Knowledge  for  ICT  innovations  concerns:  all  kinds  of  knowledge  involved  in  the 
generation, design, production, marketing and distribution of ICT innovations. Along the 
columns of Table 2 this can relate to specific ICT technologies; the activities of the actors 
involved  in  generating,  sourcing  and  diffusing  knowledge;  institutions  concerning 
knowledge (e.g. ICT patents, or ICT policy strategies) which serve as a barrier or driver 
of  learning;  and  mechanisms  such  as  licensing,  technology  transfer,  citations,  co-
authorship  and  other  forms  of  cooperation  between  actors.  In  order  to  capture  these 
aspects of knowledge for ICT innovations we can think of indicators concerning patents, 
publications,  citations  and  R&D  expenditure.  Besides  data  on  patents  and  publications 
per  ICT  sub-sector,  one  can  also  think  of  indicators  which  capture  certain  systemic 
mechanisms of interaction, e.g.: co-authorship and citation (in ICT patent applications or 
ICT publications).  

Knowledge  for  ICT  innovations  can  also  be  detailed  per  actor,  e.g.  specifying  the 
business expenditure in R&D for the various ICT sub-sectors in Europe. Although R&D is 
not the only relevant knowledge for ICT innovations, data availability allows for instance 
to measure ICT R&D intensity, as is done in the Digital Agenda Scoreboard 2014.  

The  EU  research  programme  FP7  is  an  institutionalized  mechanism  to  generate 
knowledge,  also  for  ICT  innovations.  Data  on  participation  in  this  policy  framework  can 
be used to indicate the embeddedness in collaborative European R&D networks in which 
a  diversity  of  public  and  private  actors  are  involved  focussing  on  specific  strategic 
objectives of ICT innovation. In the period 2007-2013, the FP7 has funded under the ICT 
Theme  €6.125  billion  for  almost  2000  R&D  projects,  which  involved  more  than  5,000 
different  organizations.  About  half  of  the  funding  (46%)  from  FP7  ICT  went  to 
governmental organizations; private commercial organizations have received 34.5%.  

In  the  period  2007  –  2012,  the  top  50  Regions  in  Europe  (NUTS3  level  classification) 
attracted  63%  of  total  FP7  funding.  In  terms  of  overall  EC  funding  received  over  this 
period,  Munich  is  the  European  city  that  leads,  followed  by  Paris,  London  and  Madrid. 
The available data can be used to differentiate between countries and regions the extent 
to  which  their  ICT  R&D  activities  are  specialized  in  certain  ICT  research  fields  (FP7 
Strategic Objectives). Germany for instance is the top recipient of funds in all categories 
but three and the country attracts 32% of funding in ‘ICT for the Enterprise’ and 29% in 
‘ICT for Transport’. The United Kingdom is the leading country in the Strategic Objectives 
‘ICT for learning’ and ‘Digital libraries’, with 19% and 18% of total funding respectively, 
and together with Germany accounts for 15% in ‘ICT for Health’. Italy is relatively strong 
in ‘ICT for ageing’ (15% of total funding), ‘Cognitive Systems and Robotics’ (14%), FET 
and  Language  Technologies  (13%).  France  has  a  relatively  strong  presence  in  nano-
electronics  (17%),  Future  Networks  and  Internet  (16%)  and  International  Cooperation 
(15%),  whereas  Spanish  organisations  excel  in  ICT  for  inclusion  (16%)  and  ICT  for 
Energy efficiency (14%). The Netherlands are relatively strong in Organic and large area 
electronics  (14%),  while  Belgium  has  a  relatively  strong  presence  in  nano-electronics 
research  (10%).  Greek  companies  are  active  mostly  in  ICT  for  Governance  and  Policy 

19   Possible indicators could also come from network analysis, e.g. indicating the centrality in the 

concerned network of interactions. 

32 

 
                                           
Modelling (11%) and ICT for Health (8%). Austria stands out for its share (13%) in ICT 
for  Governance  and  Policy.  These  details  on  the  ICT  sub-sectors  are  very  helpful  in 
deviating  from  the  generic  global  ICT  innovation  systems  (or  poles  of  excellence)  and 
identify  specific  systems  of  ICT  innovation,  specific  in  terms  of  sub-global  and  sub-
sectoral perspective, and the dynamics in terms of convergence and divergence between 
sub-systems. 

Table 3: Top 10 regions in EC funding for R&D on cognitive systems and robotics in FP7  

FP7 Strategic Objective: 05 Cognitive Systems and Robotics 

EC Funding in  

M EURO (2007-2013)  City 

Region level NUTS3 

        46  

         21  

         21  

         20  

         20  

         20  

         16  

         13  

         12  

         12  

         11  

         10  

         10  

           9  

MUNCHEN 

LONDON 

EDINBURGH 

GENOVA 

PARIS 

ZUERICH 

PISA 

NAPOLI 

ROMA 

BREMEN 

DE212 

UKI11 

UKM25 

ITC33 

FR101 

CH040 

ITI17 

ITF33 

ITI43 

DE501 

KAISERSLAUTERN  DEB32 

STOCKHOLM 

KOELN 

ENSCHEDE 

SE110 

DEA23 

NL213 

Regional level data are available for each of the 24 Strategic Objectives. This allows for 
developing  regional  level  indicators  concerning  for  instance  Strategic  Objective  5: 
Cognitive Systems and Robotics (Table 3). Also for individual organizations FP7 research 
data is available. Various mechanisms of interaction can be captured with FP7 data, e.g. 
concerning cooperation in consortia. 

2.3.2 Supply of ICT innovations 

Besides general indicators for the size and structure of national and regional ICT sectors, 
it  is  important  to  include  indicators  that  measure  the  innovativeness  of  these  ICT 
innovations supplying companies.  

The 2005 Trend Chart report “European Sector Innovation Scoreboards” (Hollanders and 
Arundel, 2005) analyses the innovation performance of European countries at the sector 
level.  They  use  12  indicators  for  constructing  the  Innovation  Sector  Index  (ISI)  which 
showed  that  ICT  is  the  most  innovative  sector  in  Europe.  Similar  indicators  could  be 
constructed  based  on  more  recent  survey  data.  For  most  of  the  indicators  used  for  the 
Innovation  Sector  Index  Table  4  provides  more  detail,  which  allows  us  to  identify 
differences  in  innovation  characteristics  between  ICT  (sub-sectors)  and  other  sectors  in 
the  EU.  It  shows  that  indeed  on  average  the  ICT  sector  is  more  innovative  than  the 
average  industry,  but  the  innovation  performance  gap  differs  per  indicator  and  per  ICT 
sub-sector.  Regarding  the  share  of  employees  with  higher  education  and  the  share  of 
firms  that  use  training,  the  performance  is  especially  high  in  the  Computer  service 
industry  (NACE  72),  where  almost  51%  of  the  employees  have  a  higher  education 
degree,  compared  to  13%  in  all  sectors  of  the  EU  (Table  4).  The  share  of  sales  due  to 
new-to-market  and  new-to-firm  products  is  way  above  average,  especially  for  ICT 

33 

 
 
hardware. This is in line with the notion that the life-cycle of ICT products is indeed very 
short. 

Table 4: Innovation performance of the ICT sector versus all sectors in the EU 

ALL  

SECTORS 

ICT  

Computer 
Mnf.  

(NACE 30) 

Communication 
Mnf. (NACE 32) 

Services & 
Software 
(NACE 72) 

-% higher educated employment  13 

-% firms innovating in-house 

35 

-%  of  firms  co-operating  with 
others 

-%  sales  from  new-to-market 
products 

-% of firms that patent 

6 

6 

8 

-% of firms that use trademarks 

12 

18 

59 

13 

26 

19 

30 

25 

57 

16 

25 

20 

16 

51 

58 

15 

13 

9 

23 

Source: Community Innovation Survey 3, Arundel and Hollanders (2005). 

Collaboration is an important mechanism for interactive learning in innovation  systems. 
The ICT sector has the highest share of firms that innovate in collaboration with others. 
However,  some  types  of  collaborators  are  more  common  than  others.  Although  the 
number  of  ICT  firms  collaborating  with  others  has  increased  over  time,  and  it is  higher 
than  in  other  sectors,  the  large  majority  of  innovative  ICT  firms  still  do  not  collaborate 
with  national  research  institutes.  On  average  only  18%  of  all  innovative  ICT  firms  in 
Europe  collaborate  with  universities  and  11%  with  research  institutes,  28%  collaborate 
with clients and suppliers,  while 42% of the innovative  ICT firms collaborate with other 
(firms). The national differences in collaboration of innovative ICT firms with third parties 
are remarkably large (Annex Table a). Innovative ICT firms that collaborate with others 
are most often found in Norway, the UK, Sweden, Hungary, Lithuania, and Cyprus. Less 
collaborative  however  are  innovative  ICT  firms  in  Germany  and  Spain.  Least 
collaboration  with  research  institutes  are  reported  by  innovative  ICT  companies  in 
Southern European countries like Italy, Cyprus and Portugal. 

There  are  many  ways  to  measure  innovation  performance  of  the  suppliers  of  ICT 
innovations. The CIS data which is based on individual firm data includes an indicator on 
innovation output in terms of the share of turnover which is new (new to the firm or new 
to the market). There are also more macro-level indicators. An example of this is given 
in Table b in  the Annex, where indicators constructed by  Marin et al. (2008) are given. 
They have based their index on 3 indicators for competitive ICT innovative advantage for 
European  countries:  respectively  on  ICT  patenting,  ICT  export  and  total  factor 
productivity for the ICT sector.  

2.3.3 Skills for ICT innovations 

Skills for ICT innovations are relevant in many ways (at home, at the lab, office, factory, 
etc.)  and  for  many  of  the  other  innovation  processes.  According  to  an  OECD  (2015) 
analysis, changes in the number of jobs in various ICT industries indicate changes in the 
demand  for  skills.  One  could  however  argue  that  it  is  also  necessary  to  include 
information on functions and have task descriptions. Lack of qualified personnel is one of 
the main barriers for innovation. The share of ICT firms which report a lack of qualified 
personnel  as  obstacle  for  innovation  is  therefore  a  relevant  indicator  to  monitor  this 

34 

 
 
 
 
 
‘systemic  mismatch’  in  the  concerning  national  systems  in  Europe  (see  Table  c  in  the 
Annex).  

Skills in performing ICT R&D can be measured by using data on ICT  R&D personnel for 
the ICT sub-sectors in Europe. According to the 2015 PREDICT report, the rise in EU ICT 
R&D  personnel  was  driven  by  the  ICT  service  sector,  which  saw  its  personnel  increase 
from 161.6 thousand in 2009 to 192.7 thousand in 2012. On the other hand, the number 
of  R&D  personnel  in  the  ICT  manufacturing  sector  remained  constant  at  around  82 
thousand in the same period. They also report that:  

“The two ICT sub-sectors with the highest BERD share of total BERD in 2012 are 
Computer programming (5.96% of total BERD), which belongs to the ICT service 
sector, and Manufacturing of communication equipment (3.56% of BERD), which 
belongs to ICT manufacturing”. (Mas et al. 2015) 

While in the past the focus in measuring knowledge and skills for innovation was on R&D 
(which was often seen as the single source for new knowledge and innovation) there is 
an  increased  awareness  that  also  other  kinds  of  knowledge  and  skill  in  ICT  companies 
are  important,  e.g.  employees  involved  in  marketing  (innovations)  and  organizational 
(innovations). In this respect there are good arguments to include all employees in ICT 
sectors, expanding the type of skills relevant for innovation to include ‘learning by doing, 
using  and  interacting’  skills.  Measuring  ICT  employment  in  ICT  using  sectors  (which 
include almost all other sectors) is a complex issue and it is therefore necessary to have 
additional info on the use of ICT by firms/organizations and on the link between such use 
and innovation.   

A  further  relevant  extension  is  to  capture  the  skills  of  citizens  in  using  ICT.  The  skills, 
experience  and  eagerness  to  use  new  ICT  applications  at  home  are  relevant  for 
innovative companies. It not only increases the demand for certain ICT innovations, but 
in terms of interactions with these skilled users, ICT companies learn and these lessons 
feed back into the innovation processes as user-innovations. Irrespective of the sector in 
which  people  work  it  is  therefore  relevant  to  capture  the  ICT  content  and  relevance  of 
the Human Capital in general in a country or region. As an example, we refer to the EU 
digital  scoreboard  where  the  basic  and  advanced  ICT  skills  of  the  population  are 
captured as a dimension of human capital. 

According  to  Digital  Europe  (a  forum  representing  the  digital  technology  industry  in 
Europe)20, digital skills in Europe are lagging behind in term of population “IT literacy” as 
well as specialized ICT  competence  of the  skilled work  force.  This situation is discussed 
in  their  reports  as  a  barrier  for  the  development  of  a  digital  society  and  Digital  Single 
Market as well as for the innovation capacity  of the European  economy.  Data  of Digital 
Europe indicates that the demand for ICT practitioners in Europe is growing around 4% a 
year, which would imply a shortage of 509,000 jobs in 2015 compared to 274,000 today. 
According to data of the Digital Agenda Scoreboard 2014 the deficit of  ICT professional 
skills is estimated to reach 900,000 by 2020.21 Digital Europe provides data allowing for 
comparison of Member States, which for instance shows that the bottlenecks are largest 
in the UK, Germany, and Italy22. Institutionalized strategies such as The ‘Grand Coalition 
for Digital Jobs’ which have been launched to tackle the lack of digital skills could provide 
relevant  indicators  to  capture  the  efforts  and  improvement  concerning  skills  for  ICT 
innovations. 23  Other  EU  initiatives  which  could  generate  relevant  indicators  in  this 

20   47% of the EU  population has insufficient  digital skills, 23% has none  at all  (Digital inclusion 

and skills in the EU, 2014).  

21   Digital Agenda Scoreboard 2014 – Digital Inclusion and Skills. 
22   DigitalEurope.org 
23   https://ec.europa.eu/digital-agenda/en/grand-coalition-digital-jobs;  Davos  Declaration  on  the 

Grand Coalition for Digital Jobs 

35 

 
                                           
respect  include  “Opening  up  education” 24,  the  e-Skills  Campaign25,  and  the  European 
Coding  Initiative26.  The  EC  also  encourages  the  use  of  Massive  Open  Online  Courses 
(MOOCs) focused on web skills by establishing creation of a network of universities and 
business  schools  in  Europe  interested  in  developing  MOOCs  for  web  talent.  Massively 
Open  Online  Courses  (MOOCs)  are  indeed  gaining  importance  and  the  online  education 
market  could  also  be  considered  to  be  an  emerging  ICT-sub-sector.  Such  initiatives 
might  generate  data  which  could  be  used  for  monitoring  the  issue  of  skills  for  ICT 
innovation. Relevant in a systems perspective are measures of mismatches between the 
supplied  skills  and  demanded  skills,  as  well  as  the  mechanisms  and  institutions  which 
serve  to  improve  the  interactions,  e.g.  through  cooperation  between  industry  and 
education  institutes  concerning  for  instance  e-learning  platforms,  MOOCs,  traineeships, 
training on ICT-entrepreneurship.  

2.3.4 Demand for ICT innovations 

The  report  ‘Importance  of  Intelligent  Demand’  (OECD  2014)  discusses  the  increased 
importance  of  the  innovation  demand-side.  The  reason  of  its  importance  is  in  the 
interactive learning effects between producers and users of innovations.  A study on the 
decision  of  multinationals  on  the  location  of  their  R&D  and  innovation  activity  showed 
that  the  cost  of  doing  R&D  are  not  the  most  important  reasons,  but  the  location  and 
interaction with market demand are (Leitner et al. 2011). For some ICT products the EU 
is still in a leading R&D position, e.g. concerning robots (EC 2014), but since Europe is 
not a leading user, the concerning innovation capacity may deteriorate because of lack of 
the interactive learning between producers and users  of the  concerning ICT innovation. 
Even German machinery manufacturers have started to shift R&D expenditures to China, 
which  is  their  main  market 27 .  Another  example  is  the  US  company  GIRAFF  which 
produces robots for the health care sector, and which moved to Sweden because of the 
high reputation of its innovative health care system (Avigdor & Wintjes 2015). 

Several indicators of the EU Digital Scoreboard could be relevant to monitor the demand 
for certain ICT innovations; for instance, the demand for internet  services, as indicated 
by the use of internet for content, communications and transactions (Figure 10). 

24   eSkills  campaign  is  an  action  plan  to  facilitate  schools  and  universities  to  deliver  high  quality 
education  through ICT and digital content, as well as  the digital skills which  90% of  jobs will 
require  by  2020.  The  initiative  focuses,  inter  alia,  on  ICT-based  innovation  in  learning  and 
teaching, underpinning the delivering of skills for the 21st century.   
25   Initiative based on the Communication 'e-Skills for the 21st Century' 
26   Initiative led by ICT-companies and European Schoolnet to bring coding skills to teachers, kids 

and adults.  

27   See: 

http://eeas.europa.eu/delegations/china/documents/eu_china/research_innovation/4_innovati
on/sti_china_study_full_report.pdf 

36 

 
 
 
                                           
Figure  8:  Three  dimensions  in  EU  digital  scoreboard  2014  of  use  of  internet: 
content, communication, transaction 

Since  the  public  sector  is  an  important  customer  of  ICT  innovations,  the  provision  of 
digital  public  services  in  a  country  has  a  positive  impact  on  the  demand  for  ICT 
innovations. Innovative public procurement is, in this respect, an important mechanism 
to  enhance  interactive  learning  and  innovation  in  the  concerning  national  or  regional 
system  of  ICT  innovations.  The  EU  digital  agenda  scoreboard  sub-dimensions  of  digital 
public  services  concerning  e-Government  and  e-Health  are  in  this  respect  interesting 
indicators to capture the concerning demand for ICT innovations. 

When  the  objective  is  to  design  a  monitoring  framework  for  a  system  of  eHealth 
innovations, data on the use of medical data exchange and e-prescription are essential. 
This  has  been  indicated  by  eHealth  companies  interviewed  for  case  studies  of  the  EU 
Business Innovation Observatory. For example, a Spanish e-health company stated that 
the German market is not that interesting for them, because the use of Electronic Health 
Records is relatively low (Avigdor and Wintjes 2015). Figure a in the Annex does indeed 
show  that  the  eHealth  indicators  for  Germany  are  relatively  low  compared  to  other  EU 
countries. 

An  interesting  indicator  to  capture  the  demand  for  ICT  innovations  concerning  Smart 
Industry  and  Smart  Factories  could  be  the  number  of  enterprises  sharing  electronic 
information on the supply chain (data included in the EU Digital Agenda). This indicator 
refers  to  sending/receiving  all  type  of  information  on  the  supply  chain  (e.g.  inventory 
levels,  production  plans,  forecasts,  progress  of  delivery)  via  computer  networks  or  via 
websites,  but  excluding  manually  typed  e-mail  messages.  This  innovative  digital 
communication  in  manufacturing  actually  concerns  an  innovative  ICT  mechanism  of 
interaction which also serves the supply of ICT innovations.  

Whereas  the  public  funded  R&D  programmes  can  be  mainly  used  to  capture  the 
institutionalized knowledge generating activities and the strengthening of the innovation 
supply-side,  data  on  the  EU  Structural  Funds  programme  can  be  used  to  capture  the 
institutionalized effort to promote the up-take of ICT innovations. For 2007-2013 the EU 
Structural Funds allocated to regional policy support for ICT projects showed an increase 
to  over  EUR  15  billion or  4.4%  of  the  total  EU  cohesion  policy  budget.  There  is  a  clear 
shift in the investment priorities from infrastructure to support for content development, 
and  promoting  the  up-take  both  in  the  public  sector  (eHealth,  eGovernment,  etc.)  and 
SMEs (eLearning, eBusiness, etc.). 

2.3.5 Finance for ICT innovations 

The  diversity  and  complexity  in  financing  of  innovations  has  increased.  Important 
sources  include  internal  funding,  banks,  Venture  Capital  (VC),  public  funding  (e.g. 
subsidies/tax benefits), public procurement, but increasingly also  crowd-funding. In the 
CIS  survey  there  are  some  questions  concerning  finance  and  funding  for  innovation. 

37 

 
 
Answers  from  ICT  firms  to  three  of  these  questions  are  used  in  Table  d  in  Annex.  The 
first question was if companies had received public funding for innovation. For Norway, 
Finland  and  the  Netherlands  more  than  45%  of  the  responding  ICT  firms  reported  that 
they  have  received  public  funding  for  innovation.  The  concerning  CIS  question  also 
allows  to  differentiate  public  funding  from  EU,  National  and  regional  sources.  However, 
CIS  data  in  some  EU  countries  cannot  be  used  at  regional  level.  The  other  two  CIS 
answers are derived from the question to firms about barriers to innovation. The share 
of ICT firms stating that lack of internal finance is a barrier for innovation is higher than 
for external finance of innovation.   

Concerning finance and funding in ICT, Van Welsum et al. (2013) report three barriers: 
difficult  access  to  VC  for  small  innovative  projects;  legacy  of  vested  interest  in  old 
technology  and  systems;  difficult  access  to  funding  for  EU  ICT  SMEs.  These  three 
obstacles are also evident in other sectors, but due to the fast technological changes in 
the  ICT  sector,  the  second  barrier  seems  higher  in  ICT  sectors  than  in  many  other 
sectors. 

The  rules  and  practices  concerning  public  procurement  of  (ICT)  innovations  differ  per 
country  and  sometimes  even  regions,  but  we  are  not  aware  of  standardized  data  in 
Europe on this funding mechanism.  

A  similar  across-country  variability  of  rules  and  practices  can  also  be  found  for 
crowdfunding.  Although  crowdfunding  mobilizes  a  relatively  small  total  amount  of 
funding,  it  seems  especially  helpful  for  small  innovative  ICT  projects.  According  to  a 
report  of  the  Start-up  Europe  Crowdfunding  Network  (2014),  Germany  is  the  country 
where  the  largest  share  of  web  entrepreneurs’  projects  (24%)  has  been  crowdfunded, 
mostly  through  equity  and  reward  crowdfunding  platforms  (Figure  11).  The  German 
equity  crowdfunding  market  has  been  developing  with  good  results  for  a  few  years 
already,  which  might  contribute  to  the  large  number  of  web  entrepreneurs  using 
crowdfunding there. Spain holds a considerable percentage in the chart, too. However, in 
Spain  equity  crowdfunding  is  actually  not  yet  allowed  and  online  investing  is  forbidden 
by law. However, crowdfunding platforms can operate as matchmakers, bringing private 
investors  and  web  entrepreneurs  together.  Both  markets  also  do  not  tout  a  strong 
venture  capital  or  business  angel  industry,  so  the  funding  gap  might  be  more 
pronounced  than  for  example  in  countries  that  offer  tax  benefits  to  investors  in  early 
stage companies.  

Figure  9:  Share  of  web  entrepreneur’s  projects  funded  by  crowdfunding  in  EU 
countries, 2014 

Source: Start-up Europe Crowdfunding Network (2014). 

38 

 
 
 
 
2.3.6 Institutions for ICT innovations 

Institutions  refer  to  a  broad  range  of  formal  and  informal  rules,  arrangements  and 
procedures.  Formal  institutions  include  the  organizations  setting  the  ‘rules’  for 
metrology,  standards,  testing  and  quality,  etc.  Other  examples  of  formal  institutions 
affecting ICT innovation are State laws that govern for instance: universities, technology 
institutes,  education,  start-ups,  R&D  tax-credits,  patenting  law,  financing,  trade-tariffs, 
etc. 

Van Welsem et al. (2013) identified a number of institutional ‘policy’ barriers of the ICT 
growth  potential,  which  have  also  been  identified  by  the  Business  Innovation 
Observatory  Trend  reports 28.  Some  of  these  barriers  are  not  sector-specific,  e.g.  the 
barrier  of  red  tape,  especially  for  SMEs  and  the  lack  of  flexibility  in  the  labour  market. 
One  of  the  most  persistent  and  ICT  specific  barriers  to  innovation  are  the  fragmented 
legal  and  regulatory  frameworks,  which  are  not  adapted  to  the  fast  and  disruptive 
developments of ICT innovations. However, it is not easy to construct indicators on these 
formal institutional aspects for ICT innovations.  

Burgelman  and  Barrios  (2007)  show  that  regulatory  strictness  impact  economic  growth 
negatively. They use measures of strictness of regulation like business regulation, credit 
market  regulation,  and  labour  market  regulation.  They  have  shown  that  the  more 
stringent the regulation is, the higher the negative impact on GDP growth and the more 
application of ICT is hampered. 

Concerning  soft  and  informal  institutions  Wintjes  and  Dunnewijk  (2008)  report  on  an 
index  measuring  the  quality  of  the  socio-cultural  environment  for  innovation,  based  on 
indicators  for  cultural  capital,  consumer  behaviour,  social  capital,  organisational 
capacities and entrepreneurship. This index is however not ICT specific.  

Concerning  social  values  the  European  social  values  survey  can  serve  to  construct 
indicators, e.g. in terms of ‘trust’, and approach to ‘risk-taking’ and ‘entrepreneurship’.  

2.3.7 Internationalisation for ICT innovations  

The national and regional boundaries of national and regional systems of ICT innovations 
are not closed. International linkages can benefit all the innovation processes within the 
concerning systems of ICT innovations.  

Obvious indicators to capture internationalisation are data on ICT export/import, and on 
Foreign Direct Investments in ICT (Meijers et al. 2008).  

The CIS also enables to develop indicators on internationalisation in terms of export and 
interactions with other parts within an international corporation.  

A way to measure the international competitiveness of industries is by calculating RCA’s 
(Revealed Comparative Advantages) based on the specialisation (in terms of export and 
import)  in  that  industry.  Revealed  Comparative  Advantage  per  ICT  sub-sector  differs 
very much among countries. Figure b in the Annex gives an outdated example of RCA in 
Computer  manufacturing  (ISIC  30)  in  26  countries  during  1993  –  2003,  such  RCA’s 
would have to be up-dated. 

An alternative indicator constructed at a national level could be export of ICT goods and 
services.  The  definition  of  ICT  goods  include:  Computers  and  peripheral  equipment, 
Communication equipment, Consumer  electronic equipment, Electronic components and 
Miscellaneous. 
include:  Communications  services,  Computer  and 
information services. The value of exports covers both intra- and extra- EU. 

ICT  services 

28   http://ec.europa.eu/growth/industry/innovation/business-innovation-observatory/trend-

reports/index_en.htm 

39 

 
                                           
2.3.8 Towards  a  typology  of  regional  systems  of  ICT  Innovation  in 

Europe 

We  conclude  from  Section  2.3  that  a  broad  range  of  individual  indicators  is  needed  to 
capture the potentially relevant aspects of  emerging ICT innovation systems in Europe. 
This is also true for monitoring the development of ICT innovation systems over time, for 
comparing  ICT  innovation  systems,  and  for  explaining  differences  in  performance. 
Irrespective of some thematic choices (to focus on only a part of the ICT sector or a part 
of  Europe,  or  a  specific  objective)  following  the  (theory-based)  operational  framework 
suggested  in  Table  2  assures  a  sufficiently  broad  coverage  of  system  components, 
processes  and  purposes.    The  framework  could  be  used  to  design  a  qualitative  case-
study  analysis,  but  in  case  one  wants  to  analyse  more  than  a  few  ICT  Innovation 
Systems in Europe, statistical tools can help in reducing the complexity. 

In  order  to  identify  the  main  types  of  ICT  innovation  systems  at  regional  level,  multi-
variate  methods  of  data-reduction  (principal  component  or  factor-analysis,  and  cluster-
analysis) are very appropriate to identify patterns in the innovation indicators which can 
be  used  to  make  a  typology  of  systems.  As  explained  in  the  OECD/JRC  Handbook  on 
constructing composite indicators (Nardo & Saisana 2005) Principle Component Analysis 
and Factor analysis are useful in constructing composite indicators. Since there are many 
potentially  relevant  indicators  concerning  knowledge,  innovation,  economy  and  society, 
there  is  a  need  for  data  reduction  techniques.  These  statistical  methods  identify  the 
statistical  relations  between  the  various  individual indicators  and  based  on  that  provide 
the  main  factors  or  components.  The  same  methodology  is  used  in  the  literature 
discussed  in  Section  4  answering  the  question  ‘how  firms  innovate?’,  by  identifying 
different  modes  of  innovation  at  firm  level.  This  methodology  can  also  be  used  at  the 
systems level, for identifying different modes of innovation at systems level. 

As  an  example  of  applying  the  method  of  factor  analysis  for  identifying  types  of 
national/regional innovation systems in Europe, we refer to Wintjes & Hollanders (2011). 
After  applying  a  two-step  factor-analysis29,  they  use  a  cluster-analysis  to  come  to  the 
main types (or modes/models) of regional innovation systems (see Figures 12 and 13). 
Figure  12  shows  the  regional  averages  for  the  8  composite  indicators  (factors  scores 
which resulted from the first step of the factor analysis) per type of innovation system. 
The  High-tech  type  of  regions  and  the  Metropolitan  KIS  regions  have  similar  levels  of 
GDP  per  capita,  but  they  innovate  in  very  different  ways.  For  policy  makers  such 
typologies  of  innovation  systems  are  very  relevant  in  terms  of  policy  learning,  e.g. 
attempts to transfer good practice policies from totally different innovation systems are 
more likely to fail. The types of innovation systems displayed in Figures 12 and 13 refer 
to  innovation  in  general,  but  should  be  specified  for  ICT  innovation,  following  the 
framework  suggested  in  this  paragraph  for  selecting  indicators  for  ICT  innovation 
(Table 2).  Such  an  analysis  could  also  focus  on  a  specific  ICT  subsector  (e.g.  ICT 
products, telecom/network providers, or ICT services) or a more fine-grained focus on a 
newly  emerging  industries,  e.g.  concerning  the  Internet  of  Things,  e-health,  smart 
factories, the sharing economy, automated driving, et cetera.  

29   In  a  first  step  for  some  theoretically-justified  groups  of  indicators,  a  factor  analysis  was 
performed.  From  the  results,  8  factors  were  selected  for  a  second  factor  analysis  which 
resulted  in  3  main  components.  For  these  components,  a  cluster-analysis  was  performed  to 
make groups of regions which have similar scores on these main components. 

40 

 
   
 
                                           
Figure  10:  Average  factor  scores  for  6  types  of  regional  innovation  systems  in 
Europe 

Source: Wintjes & Hollanders (2011); Note: 0 is the average of all European regions. 

41 

Metropolitan KIS regions-1.5-1.0-0.50.00.51.01.52.0ProductivityPublic knowledgePrivate technologyActivitySkilled workersCreative workersHigh-tech manufacturingKnowledge-intensive servicesKnowledge absorbing regions-1.5-1.0-0.50.00.51.01.52.0ProductivityPublic knowledgePrivate technologyActivitySkilled workersCreative workersHigh-tech manufacturingKnowledge-intensive servicesPublic knowledge centres-1.5-1.0-0.50.00.51.01.52.0ProductivityPublic knowledgePrivate technologyActivitySkilled workersCreative workersHigh-tech manufacturingKnowledge-intensive servicesSkilled industrial East Europe-1.5-1.0-0.50.00.51.01.52.0ProductivityPublic knowledgePrivate technologyActivitySkilled workersCreative workersHigh-tech manufacturingKnowledge-intensive servicesHigh-tech regions-1.5-1.0-0.50.00.51.01.52.0ProductivityPublic knowledgePrivate technologyActivitySkilled workersCreative workersHigh-tech manufacturingKnowledge-intensive servicesSkilled technology regions-1.5-1.0-0.50.00.51.01.52.0ProductivityPublic knowledgePrivate technologyActivitySkilled workersCreative workersHigh-tech manufacturingKnowledge-intensive services 
 
 
 
 
 
 
 
 
Figure 11: Typology of regional systems and modes of innovation in Europe 

Source: Wintjes & Hollanders (2011) 

Pink: Metropolitan 
knowledge-intensive 
services regions 

Yellow: Knowledge 
absorbing regions 

Red: Public 
knowledge centres 

Purple: Skilled 
industrial Eastern EU 
regions 

Turquoise: High-tech 
regions 

Green: Skilled 
technology regions 

Blue: Traditional 
Southern EU regions 

42 

 
 
 
 
 
3 

How firms innovate: modes and models of ICT 
innovation  

How firms innovate, is traditionally a micro-level issue, which builds on the theory of the 
(innovative)  firm.  Again  we  will  start  with  a  short  overview  of  theory  and  concepts 
concerning  innovating  firms  in  general  (3.1)  and  will  then  discuss  examples  from  the 
literature in identifying modes of innovation mostly based on quantitative analysis of firm 
level  data-sets.  Each  firm  innovates  in  its  own  way,  but  there  are  some  existing 
taxonomies  of  innovation  modes  that  identify  certain  patterns  on  how  firms  innovate 
(3.2).  Subsequently,  we  will  discuss  some  possibilities  to  identify  specific  modes  of  ICT 
innovation (3.3). 

3.1  Conceptual insights from the literature on innovating firms in 

general  

Schumpeter  distinguished  five  different  types  of  innovations:  new  products,  new 
methods  of  production,  new  sources  of  supply,  the  exploitation  of  new  markets,  new 
ways to organize business and new inputs (material, components).  In economics, most 
of  the  focus  has  been  on  the  two  first  of  these.  The  terms  “product  innovation”  and 
“process innovation” have been used to characterize the occurrence of new or improved 
goods and services, and improvements in the ways to produce these good and services, 
respectively.  Together  with  organizational  innovation  and  market  innovation  these  four 
types  of  innovation  (see  Figure  12)  are  still  distinguished  in  many  innovation  surveys, 
such  as  the  Community  Innovation  Survey  (CIS) 30 .However,  the  distinction  between 
these  four  types  of  innovation,  does,  not  capture  very  well  the  fact  that  innovation 
involves  new  combinations  of  these  four,  which  are  often  very  hard  to  separate  from 
each  other.  This  is  especially  the  case  concerning  new  ICT  services  and  ICT  enabled 
business models (See Figure 12) 

Figure 12:   Schumpeter types of innovation 

The Resource-Based View of the ﬁrm (Penrose, 1959), built on Schumpeter’s perspective 
on  value  creation,  views  the  ﬁrm  as  a  bundle  of  resources  and  capabilities.  The 
Resource-Based  View  states  that  uniquely  combining  a  set  of  complementary  and 
specialized  resources  and  capabilities  (which  are  heterogeneous  within  an  industry, 
scarce,  durable,  not  easily  traded,  and  difficult  to  imitate),  may  lead  to  value  creation 
(Penrose,  1959).  Teece  and  Pisano  (1994)  applied  this  evolutionary  view  of  the  firm  to 
innovation  and  extended  it  into  the  concept  of  “dynamic  capabilities”,  defined  as  “the 
skills, procedures, organizational structures and decision rules that firms utilize to create 
and capture value” (Teece 2010, p. 680).  

30   Although, what Schumpeter referred to as ‘market innovation’, has been changed to a question 

in the CIS asking about innovative ‘marketing’. 

43 

 
 
 
                                           
Many  authors  have  showed  that  indeed  the  way  firms  innovate  involves  a  complex 
combination  of  resources,  activities  and  capabilities  concerning  innovation.  Some  of 
them  use  survey  data  to  identify  different  modes  (types,  models,  strategies)  of 
innovation, mostly based on CIS data which relates to a variety of answers from firms to 
questions  concerning  innovations.  A  selection  of  these  taxonomies  of  firm  innovation 
modes will be discussed in Section 3.2. 

As  can  be  concluded  from  the  development  of  the  concept  of  innovation  systems,  the 
micro-view  on  innovation  at  firm  level  as  a  unique  combination  of  complementary 
resources and capabilities fits quite well  with the innovation systems view at the meso- 
and macro level (Martin 2012).  Differences in the modes of innovation at firm level are 
therefore often used (as is done in Section 2) to indicate differences at the systems level 
(e.g.  between  national/regional  systems  of  innovation,  or  between  sectoral  systems  of 
innovation).  

However,  although  sector  and  country  are  indeed  useful  in  explaining  differences 
between  firms  in  how  they  innovate,  Srholec  and  Verspagen  (2012)  show  that  there  is 
still  a  high  level  of  heterogeneity  among  firms  within  such  systems.  An  evolutionary 
interpretation  of  the  resource-based  theory  of  the  firm  holds  according  to  Srholec  and 
Verspagen (2012, p.1224) that: 

“firms  may  adopt  widely  differing  strategies,  even  in  a  similar  selection 
environment  such  as  a  sector  or  country,  because  they  start  from  different 
resource  bases,  interpret  the  environment  differently,  and  use  different  models 
for reaching decisions on their strategy”.  

In  the  next  section,  we  will  discuss  some  studies  which  identify  in  an  indicator-based 
way (mixing theory and empirical observations) taxonomies of firm innovation modes. 

3.2  Taxonomies of firm innovation modes in the literature 

As  a  first  taxonomy  of  innovation  modes  we  can  refer  to  the  distinction  between  the 
early  and  later  work  of  Schumpeter,  often  referred  to  as  Schumpeter  Mode  1  and  2. 
Mode  1  refers  to  his  work  on  the  individual  entrepreneur  who  sees  and  tries  new 
opportunities  before  others  do.  His  later  work  acknowledges  the  importance  of  team 
work  in  departments  and  between  departments  in  large  firms,  including  a  research 
department.  

Pavitt  (1984)  showed  with  his  taxonomy  of  innovating  firms  that  the  sources  and 
purposes  of  innovation  are  diverse  and  that  one  can  identify  different  modes  of 
innovation. He also  related the types and modes of innovation to sectors,  showing that 
they  are  industry-specific  in  the  sense  that  some  modes  are  more  frequent  in  certain 
industries.  ‘Science-based’  innovating  firms  are  dominant  in  high-tech  industries  which 
mainly innovate by performing in-house R&D for product innovation, or external R&D at 
universities. ‘Supplier-dominated’ include mostly small firms in traditional industries like 
textiles,  where  the  process  innovations  of  new  machines  coming  from  suppliers  are 
typically  more  important  (or  the  more  dominant  innovation  mode/strategy/or  routine). 
Scale-intensive  refers  mainly  to  large  firms  producing  basic  materials  and  consumer 
products,  e.g.  automotive  industries.  Sources  of  innovation  may  be  both  internal  and 
external  to  the  firm.  The  ‘specialised  suppliers’  in  Pavitt’s  taxonomy  refer  to  smaller, 
more specialized firms producing technology which is sold to other firms, e.g. specialized 
machinery  and  instruments.  There  is  a  high  level  of  appropriability  of  the  knowledge 
concerned.  

44 

 
 
 
 
 
Table 5: Overview of studies identifying innovation modes 

Source: Frenz and Lambert (2012). 

Table 5 gives an overview provided by Frenz and Lambert (2012) of some studies which 
have followed-up the search for patterns in firm-level data, hence identifying main types 
or modes of innovation. 

Frenz and Lambert (2012) refer to these modes as ‘mixed modes’, as they indeed refer 
to  certain  combinations  of  innovation  resources,  activities  and  outputs  which  often  can 
be  found  in  (the  CIS  answers  for)  one  firm.  There  are  two  methods  to  come  to  such  a 
typology: either prescriptive or exploratory.  

Arundel  and  Hollanders  (2005)  and  Jensen  et  al.  (2007)  use  a  prescriptive  method,  as 
they a-priory ‘design’ their modes informed by theory. Firms which satisfy certain criteria 
are  assigned  to  the  corresponding  mode.  Hollanders  and  Arundel  (2005)  first  define 

45 

 
 
criteria to differentiate innovators from ‘non-innovators’31, and they subsequently report 
on four types of innovators based on CIS data:  

•  Strategic  innovators  are  active  on  international  and  national  markets  and  have 
introduced (at least) a product or process innovation that they developed (partly) in-
house. Their R&D is a continuous activity and they did introduce at least one product 
that  is  new  to  their  market  as  well.  These  firms  are  the  source  of  many  innovative 
products and processes that are also adopted by other firms. 

• 

Intermittent  innovators  develop  innovations  (at  least  partly)  in-house  and  have 
introduced new-to-market innovations. But, they are unlikely to develop innovations 
that diffuse to other firms.  

•  Modifiers  all  developed  an  innovation  (at  least  partly  in-house)  but  none  of  them 
perform  R&D.  If  they  are  active  on  national  or  international  markets,  they  have  not 
introduced  a  new  to  market  innovation  (otherwise  they  would  be  classified  as  an 
intermittent  innovator).  If  they  are  active  in  local  and  regional  markets,  they  may 
have  introduced  a  new  to  market  innovation  and  have  slightly  modified  it  for  this 
market. 

•  Adopters  depend  on  adopting  innovations  developed  by  other  firms,  hence  innovate 

through diffusion. 

Others use exploratory methods, they ‘let the data speak’ by identifying patterns with for 
instance  factor  analysis  (also  known  as  data-reduction  and  principle  component 
analysis). We focus in this report on this second methodology, because it combines the 
insights from theory and empirical observations. An example of this approach is Srholec 
and  Verspagen  (2008;  2012) 32  who  take  the  broadest  set  of  CIS  variables  into  the 
analysis  and  use  a  two-step  factor  analysis33.    In  a  first  round  they  perform  a  factor 
analysis on the variety of innovation activities, ranging from:  performing in-house R&D, 
to acquisition of inputs, and the activities of marketing and design. This results in three 
types  of  activity  sets  (mix  or  constellations)  which  can  be  differentiated.  Another  first 
round  of  factor  analysis  identifies  three  types  of  effect  of  innovation;  another  identifies 
the  three  main  sources  of  information  for  innovation:  science,  clients  and  suppliers. 
Another  first  step  factor  analysis  on  variables  regarding  knowledge  appropriation 
behaviour  identifies  a  formal  and  an  informal  method  of  IP  protection.  A  last  first  step 
identifies  a  factor  addressing  non-technological  innovation.  On  these  ingredients  of 
innovation strategy (the 13 factor-scores of the first stage, see first column of Table 6) a 
second stage factor analysis is performed. The result leads to four innovation strategies 
or  modes,  which  Srholec  and  Verspagen  have  given  the  following  labels:  ‘Research’, 
‘User’, ‘External’ and ‘Production’ (Table 6).  

31   They first constructed a set of criteria and then selected only those firms which met the criteria 

of the concerning mode, the remaining are considered to be non-innovators. 

32   Srholec and Verspagen (2008) in Table 10 refer to a working paper which has been published 

in 2012. 

33   However,  they  do  not  include  all  possible  variables  since  factor  analysis  is  sensitive  to  the 

inclusion of similar or overlapping variables. 

46 

 
 
 
 
 
                                           
Table 6: The 4 modes of Srholec & Verspagen (2012), Hierarchical factor analysis (2nd 
stage) on ingredients of innovation strategies 

(1) 

(2) 

(3) 

(4) 

Research 

User 

External 

Production 

R&D 

Marketing 

External inputs 

0.70 

0.07 

0.16 

0.07 

0.65 

−0.16 

0.09 

0.01 

−0.16 

−0.13 

0.65 

0.02 

Product effects 

−0.01 

0.69 

−0.03 

0.15 

Process effects 

−0.08 

0.06 

0.02 

Social responsibility 

0.08 

−0.07 

0.01 

Information from science 

0.62 

0.01 

0.31 

0.81 

0.83 

0.06 

Information from clients and 
industry 

−0.01 

0.61 

0.28 

−0.07 

Information  from  suppliers 
and events 

−0.07 

0.23 

0.69 

0.10 

Formal protection 

Informal protection 

0.36 

0.42 

Non-technological innovation 0.00 

0.37 

0.35 

0.53 

−0.27 

0.05 

−0.18 

0.01 

0.02 

0.12 

Innovation co-operation 

0.78 

−0.06 

0.06 

−0.09 

Source: Srholec & Verspagen (2012, p.1237). 

The ‘Research’ mode puts together strong R&D capabilities, extensive use of information 
from  science,  and  a  tendency  to  participate  in  joint  innovation  projects  with  other 
organizations. This mode is similar to the  ‘Science-based’ mode of Pavitt (1984), which 
has also been identified by Hollenstein (2003), and Leiponen and Drejer (2007).  

The  second  mode  is  the  ‘User-driven/oriented’  mode  which  is  dominated  by  product 
effects as a  reaction to information from clients for which marketing and organizational 
innovations are important. According to Srholec and Verspagen (2012) this dimension is 
similar  to  the  ‘Product  market-orientation’  of  Hollenstein  (2003)  and  ‘Market-driven’ 
innovation  by  Leiponen  and  Drejer  (2007).    The  ‘External’  mode  “exploits  opportunities 
for  innovation  from  diffusion  of  technology  embodied  in  new  capital  goods  and 
acquisition of existing technology from other  organizations by purchase of  rights to use 
patents,  licenses,  or  software”  (Srholec  &  Verspagen  2012,  p.1238).  Also  part  of  this 
dimension  is  the  importance  of  external  sources  of  information,  especially  from 
suppliers.  Methods  to  protect  IP  do  not  seem  to  matter  for  firms  following  this  route. 
According to Srholec & Verspagen (Ibidem):  

47 

 
 
 
“This  pattern  seems  to  reincarnate  the  “IT”  dimension  of  service  innovations 
detected  by  Hollenstein  (2003),  although  we  do  not  have  direct  variables  on 
diffusion of information technologies in our dataset; and the “supplier-dominated” 
factor identified by Leiponen and Drejer (2007)”. 

The  “Production”  mode  is  oriented  at  process  effects  of  innovation  (Table  11).  Again, 
firms that use this mode do not tend to use any methods of protection extensively.  

“This  dimension  appears  to  be  most  closely  related  to  the  principal  factors 
identified  as  the  “cost  reductions  based  on  process  innovation”  by  Hollenstein 
(2003)  and  “production-intensive”  by  Leiponen  and  Drejer  (2007),  while  the 
“process modernizing” mode detected by Frenz and Lambert (2009) overlaps with 
both  of  the  External  and  Production  factors.”  (Srholec  &  Verspagen  2012, 
p.1238). 

The  OECD  report  by  Frenz  and  Lambert  (2012)  on  mixed  modes  of  innovation  argues 
that  the  data  allow  the  characterization  of  5  innovation  modes,  which  are  named  as 
follows: Mode 1: ‘IP/technology innovating’; Mode 2: ‘Marketing based innovating’; Mode 
3:  ‘Process  modernising’;  Mode  4:  ‘Wider  innovating’;  and  Mode  5  ‘Networked 
innovating’.  Mode  4  refers  to  a  non-technological  mode  of  innovation  based  on 
innovations.  The  5th  mode  combines 
organizational  innovations  and  marketing 
cooperation in research and buying-in of R&D, e.g. through licensing. Although internal 
R&D also loads high in this 5th mode, it is way more open than the first mode.   

to 

try 

kind 

which 

which 

measure 

Studies 
innovations 
(Product/process/organisational/marketing)  generate  more  growth  in  terms  of  turnover 
or  jobs  give  mixed  results.  Lachenmaier  and  Rottmann  (2010)  conclude  that  process 
innovations  have  a  higher  positive  effect  on  employment  than  product  innovations. 
Product  innovations  on  the  other  hand  are  more  often  associated  with  growth  in 
turnover.  For  policy  makers,  however,  the  lesson  is  that  there  are  no  good  reasons  to 
promote  only  one  type  of  innovation,  or  one  mode  of  innovation.  Innovation  policies 
which apply to a broader understanding of innovation and which are not limited to R&D 
or  product  innovation,  are  more  likely  to  impact  on  growth  of  firms,  and  SMEs  in 
particular (Wintjes 2014).  

of 

The  fact  that  there  are  clear  similarities  in  the  modes  which  are  found  in  the  various 
studies (using various methods and indicator sets, and with different focus of analysis in 
terms of countries or sectors), support the conclusion of Srholec and Verspagen (2012) 
that to a high degree these modes (and the heterogeneity they represent) can be found 
in  all  sectors  and  all  countries.  There  is,  so  to  speak,  no  convergence  to  a  single  best 
practice  mode  of  innovation.  From  an  evolutionary,  (eco-)system  perspective,  it  is 
healthy  to  have  this  diversity,  which  allows  for  novel  combinations.  So,  policy  makers 
should  not  reduce  the  variety  of  modes,  but  rather  maintain  the  diversity,  and  for 
instance strengthen ‘weak modes’. This also implies that there is no single best practice 
policy  which  policy  makers  can  copy  as  a  ‘one-size-fits-all’  policy  from  other  regions  or 
sectors. Designing the appropriate innovation policy mix for a given innovation system, 
calls for an interactive process in order to come to a tailored policy mix. 

3.3  Identifying ICT modes of innovation  

Based  on  qualitative  analysis,  Fransman  (2014)  proposes  a  taxonomy  for  global  ICT 
firms,  making  a  distinction  between  three  types  or  sub-sectors  and  their  related 
innovation  ecosystems: 
ICT  equipment  providers;  network  operators;  and 
platform/content and application providers.  The entrepreneurial mode of the ‘app  firms’ 
that  operate  on  internet  platforms  has  actually  some  similarities  with  the  Schumpeter 
Mark  I  Mode  of  innovation,  which  is  characterized  by  low  entry  costs/barriers.  This 
contrasts  with  the  mode  of  the  telecom  sector  (network  operators)  which  has  some 
similarities  with  the  Schumpeter  Mark  II  situations  with  high  entry  barriers/costs.  The 
third model of Fransman (2014) refers to the IT hardware manufacturers. 

48 

 
In  the  more  quantitative  empirical  attempts  to  identify  modes  of  innovation  (e.g.  the 
studies listed in table 5), the information concerning innovation inputs, outputs, activities 
and  effects,  are  not  ‘ICT-specific’,  except  in  one  study:  Hollenstein  (2003).  Based  on  a 
Swiss  survey  with  a  large  set  of  innovation  indicators  which  includes  data  on  IT  inputs 
(hard+software)  and  outputs,  Hollenstein  (2003)  identified  with  cluster-analysis  5 
different  groups  of  ICT  service  firms  which  can  be  interpreted  as  specific  “innovation 
modes” within ICT services: 

1.  “Science-based high-tech firms with full network integration”; 

2.  “IT-oriented network-integrated developers”; 

3.  “Market-oriented incremental innovators with weak external links”; 

4.  “Cost-oriented  process  innovators  with  strong  external  links  along  the  value 

chain”; 

5.  “Low-profile innovators with hardly any external links”. 

The other studies do not focus on ICT sectors in particular, nor are the modes based on 
ICT  variables  like  those  that  Hollenstein  (2003)  had  in  his  Swiss  survey.  However, 
interestingly the modes he identified have similarities with some  of the modes found in 
the  other  studies,  as  we  have  above  referred  to  while  discussing  the  results  of  Srholec 
and Verspagen (2012). Hollenstein (2003) concluded that although R&D is less important 
for  ICT  service  firms  (actually  only  in  mode  1),  and  although  non-technological 
innovation is more important for innovation in ICT services than for ICT manufacturing, 
the  differences  between  ICT  services  and  manufacturing  are  not  as  large  as  it  is  often 
claimed to be. Hollenstein (2003) also found that the economic performance of the five 
groups of ICT service firms did not differ that much.  

One  way  to  identify  if  ICT  sectors  (and  sub-sectors)  innovate  in  a  different  way  is  by 
comparing the firm-level data of firms in ICT sector(s) with those in other sectors. This is 
mostly done by first identifying the modes of innovation for all firms in the dataset, and 
then analysing which of these modes can more frequently be observed in ICT sectors. 

An example is given by Wintjes & Dunnewijk (2008) which make use of the prescriptive 
method  of  Arundel  and  Hollanders  (2005)  and  Hollanders  (2007)  to  identify  their  types 
of  innovators  (Strategic,  Intermittent,  Modifiers  and  Adapters)  for  firms  in  several  ICT-
sectors (Table 7). 

Table  7  shows  that  across  all  firms  6%  are  strategic  innovators,  17%  are  intermittent 
innovators,  11%  are  technology  modifiers  and  4%  are  technology  adopters.  When 
comparing  this  with  the  total  for  the  ICT  sectors  we  notice  that  in  the  ICT  sectors  the 
share  of  strategic  (18%)  and  intermittent  innovators  (29%)  are  much  higher  than  for 
firms across all sectors (Hollanders 2008; Wintjes & Dunnewijk 2008). 

49 

 
 
 
 
 
 
 
 
Table 7: Innovation performance and types of innovators by sector (share of firms) 

Industry 

Average 
innovation 
performance  

Type of innovator 

Strategic  Intermittent  Modifiers  Adopters 

NACE 30 Office machine 
etc. 

NACE 32 Radio, TV & 
Comm Eq. 

65% 

20% 

37% 

3% 

5% 

61% 

21% 

18% 

8% 

5% 

DL Electrical and Optical 

63% 

NACE 72 Computer 
services etc 

63% 

19% 

18% 

24% 

7% 

29% 

11% 

3% 

5% 

ICT (total) 

61% 

18% 

29% 

10% 

5% 

D All Firms 

47% 

6% 

17% 

11% 

4% 

Source:  Wintjes  &  Dunnewijk  (2008),  based  on  the  typology  prescriptions  of  Hollanders  and 
Arundel (2005) and Hollanders (2007). 

Table 8 summarizes the ‘performance’ characteristics of each innovation mode in the ICT 
sector  compared to the average performance  of that mode  over all industries.  It shows 
that  the  share  of  innovative  firms  among  Strategic  innovators  and  Intermittent 
innovators are higher than for the same modes in other sectors. The turnover from new-
to-market  products  and  the  growth  of  total  turnover  for  Strategic  and  Intermittent 
innovators is higher in ICT than in other sectors. 

The  fact  that  the  average  ICT  firm  is  smaller  than  the  average  firm  in  other  sectors  is 
especially  true  when  we  compare  strategic  innovators  in  the  ICT  sector  with  strategic 
innovators  in  other  sectors.  Strategic  ICT  innovators  have  on  average  313  employees, 
whereas for all sectors Strategic Innovators are on average three times larger, with 948 
employees. The average size of Strategic innovators in NACE 30 is 108, and 250 in NACE 
72.  Strategic  innovators  in  NACE  32  with  an  average  of  699  are  also  smaller  than  in 
other  sectors,  but  almost  7  times  as  large  as  strategic  innovators  in  NACE  30  (these 
classifications refer to the previous: Rev. 1.1 NACE classification). 

The  share  of  high-educated  employees  is  for  each  of  the  innovation modes  for  firms  in 
the  ICT  sector  higher  than  for  same  modes  in  other  sectors.  The  share  of  higher 
educated employees is especially high in the NACE 72 (computer services). 

Among  the  innovating  ICT  firms,  turnover  growth  is  highest  in  NACE  32,  employment 
growth and export growth is highest in NACE 72. Among the Strategic Innovators in the 
ICT  sub-sectors,  those  from  NACE  72  outperform  those  in  NACE  30  and  32  in  terms  of 
growth in turnover, employment and export. 

Unfortunately,  Frenz  and  Lambert  (2012)  mostly  use  the  results  of  their  typology  of 
innovation  modes  to  analyse  differences  between  countries.  They  test  and  confirm  the 
National  Innovation  Systems  concept,  and  devote  less  attention  to  the  differences  by 
sector.  Also  the  relevance  (or  use,  occurrence)  of  the  5  modes  of  innovation  differs  by 
sector,  but  their  sectors  are  rather  broadly  defined.  For  our  purpose  we  selected  the 
three sectors which include ICT sub-sectors (Table 9).  

50 

 
 
 
 
 
 
 
 
 
Table 8: Summary of ‘performance’ of each innovation mode for the ICT sector 
compared to the average of that mode over all sectors 

Strategic 
innovators 

Intermittent 
innovators 

+ 

++ 

Share of innovative firms  ++ 
Turnover of new-to-firm 
products 
Turnover of new-to-
market products 
Firm size 
Turnover growth 
Employment growth 
Labour productivity 
Share of employees with 
higher education 
International markets 
most significant 

-- 
++ 
+ 
- 

++ 

-- 

++ 

+ 

+ 

- 
-- 
+ 
- 

++ 

-- 

Innovation activities (top 
3 used by most firms) 

Innovation expenditures 
(top 3 highest spending 
shares) 

- own R&D 
- buying advanced 
machinery 
- training of 
personnel 

- own R&D 
- training of 
personnel 
- buying advanced 
machinery 

- own R&D (64%) 
- buying advanced 
machinery (29%) 
- buying external 
R&D (4%) 

- own R&D (59%) 
- buying advanced 
machinery (29%) 
- buying external 
R&D (6%) 

Use of formal IP 
(most used) 

Use of non-formal IP 
(used by most firms) 

0 
(trademarks) 
++ 
(lead-time 
advantage) 

Use of non-technological 
change (used by most 
firms) 

- 
(aesthetic 
changes) 

Source: Wintjes & Dunnewijk (2008). 

+ 
(trademarks) 
++ 
(lead-time 
advantage) 
+ 
(new 
organisational 
structures) 

Technology 
modifiers + 
Technology 
adopters 
- 

- 

+ 

-- 
+ 
+ 
-- 

++ 

- 

- buying advanced 
machinery 
- training of 
personnel 
- market 
introductions 
- buying advanced 
machinery (54%) 
- buying other 
external 
knowledge (29%) 
- own R&D (49%) 
0 
(trademarks) 
++ 
(lead-time 
advantage) 

0 
(new corporate 
strategies) 

Within the ‘Machinery,  electrical, communication, medical equipment’ sector firms more 
often  apply  the  ‘IP/Technology’  mode  of  innovating,  and  the  ‘Marketing’  mode.  The 
‘Process  modernising’,  and  ‘Wider  innovating’  modes  of  innovation  are  under-
represented in that sector.  

Firms  in  the  ‘Post  and  telecoms’  sector  are  highly  ‘Marketing  based’  and  also  the  non-
technological  mode  of  ‘Wider  innovating’  occurs  more  often  in  this  sector  than  in  the 
average for all firms. The ‘IP/Technology’ mode is less often used by firms in this sector. 
The occurrence of the modes in the sector which includes: ‘KIBS, computer, R&D, legal, 
accounting’, is closer to the average (0) of all firms, but  values for ‘IP/Technology’ and 
‘Process  modernising’  are  lower,  and  those  for  ‘Wider’  and  ‘Networked’  modes  of 
innovating are higher (Table 9). 

51 

 
 
Table 9: Occurrence of the ‘mixed modes’ of innovation from Frenz and Lambert (2012), 
by sector 

Industry 

IP/Techno
logy 
innovating 

Marketing 
based 
innovating 

Process 
modernising 

Wider 
innovating 

Networked 
innovating 

Machinery, 
electrical, 
communication, 
medical equipment 
Post and telecoms 
KIBS, computer, 
R&D, legal, 
accounting 

0.39 

0.22 

-0.07 

-0.13 

0.14 

-0.21 
-0.09 

0.17 
0.03 

-0.09 
-0.13 

0.18 
0.11 

-0.12 
0.12 

Source:  Frenz  and  Lambert  (2012,  p.  51)  Note:  cross  country  average  factor  scores  per  sector. 
When positive the mode is more often used by firms in the concerning sector, when negative the 
mode is less often used by firms in the concerning sector (less often than in other sectors).  

3.4  Conclusion and discussion 

We conclude from this paragraph on firm-level innovative behaviour that the theoretical 
concept  of  the  Resource  Based  View  (RBV)  on  innovation  at  firm-level  fits  with  the 
innovation systems view. The innovative firm uniquely combines a value creating set of 
complementary  and  specialized  resources  and  capabilities.  These  firm-level  sets  (or 
combinations,  or  innovation  mix)  may  be  unique,  but  with  firm-level  indicators  it  is 
possible to identify some patterns which refer to the main types or modes of innovative 
behaviour. 

Overall  the  method  to  identify  modes  of  innovation  by  multi-variate  methods  of  data-
reduction  (factor  and  cluster-analysis)  seems  most  appropriate  to  identify  modes  of 
innovation  as  a  coherent  (jointly  reinforcing) mix  or  combination  of innovation  strategy 
ingredients, or routine components. 

The availability of data on ICT specific aspects of innovation at firm level is rather limited 
in  Europe.  However,  the  studies  referred  to  in  this  paragraph  show  that  firms  in  ICT 
sectors are more innovative than firms in other sectors. The kind of innovation behaviour 
(or in other words the mode, or model of innovation) differs between the ICT sub-sectors 
(of ICT products, telecom and ICT services).  

Towards the future, it would be interesting to have an up-date on some of the referred 
indicator  based  work.  Moreover,  it  would  be  interesting  to  have  a  more  fine  grained 
coverage  of  the  ICT  subsectors,  and  to  compare  for  instance  the  preference  for  certain 
modes  in  the  ICT  manufacturing  industries  with  the  average  factor  score  for 
manufacturing industries. Also a comparison between the average  factor scores for  ICT 
services  with those for  other services, would be relevant for identifying ICT specificities 
in  the  innovative  behaviour  of  firms  in  service  industries.  In  order  to  identify  modes 
which  are  specific  for  ICT  innovation,  one  could  also  run  a  separate  factor  analysis  for 
firms  in  the  ICT  sector,  but  then  the  results  (the  factor-scores  and  modes)  will  be 
difficult to compare with those of other sectors. 

Since the Community Innovation Survey does not include specific questions on ICT, e.g. 
ICT  inputs,  it  is  difficult  to  specify  an  ICT-enabled  mode  of  innovation.  An  option  to 
differentiate  between  ICT  producing  and  ICT  using  sectors,  could  be  based  on  macro-
data (e.g. input-output data from national accounts) at national level, and subsequently 
compare  the  modes  (factor  scores)  for  the  ‘ICT  producing’  sectors  versus  the  ‘non-ICT 
producing’  sectors,  and  the  ‘ICT  using’  sectors  versus  the  ‘non’  or  ‘less  ICT  using’ 
sectors. 

52 

 
Besides modes of innovation for firms, taking a wider view on ICT innovations in society, 
implies that also modes of innovation of other type of actors are relevant. Innovation in 
the public sector for instance34. 

34   http://ec.europa.eu/enterprise/policies/innovation/files/epsis-2013_en.pdf 

53 

 
 
 
                                           
4 

Conclusions and policy implications 

Innovation systems: from concept to operational suggestions on how to apply it 
to ICT and extend it to the societal level  

Concerning  the  theoretical  antecedents  discussed  in  Section  2,  we  conclude  that  the 
innovation systems concept was defined some 25 years ago, at a time when economists 
were  discussing  to  what  extent  technological  change  should  be  seen  as  an  external 
(publicly-available  and  benefitting  all)  or  internal  (e.g.  private  benefit  for  R&D 
performers)  driver  of  change.  The  innovation  systems  solution  to  this  debate  is  at  the 
meso-level  where  various  actors  learn  from  their  own  experiences  and  from  interaction 
with others in a specific common context. At this meso-level, innovation and its context 
become  inter-dependent,  embedded  in  interactions  between  supply  and  demand, 
between the producers and users of knowledge. 

The  concept  of  national  innovation  systems  emphasizes  the  importance  of  interactions 
between  a  diversity  of  actors,  a  diversity  of  innovation  activities,  resources  and 
institutions.  The  innovation  systems  concept  has  also  been  applied  to  regions  and 
sectors.  

ICTs  are  a  distributive  source  of  increased  productivity,  advanced  solutions  for  societal 
problems, and new services to consumers. The disruptive implications of ICT innovation 
go  beyond  industries,  economies,  and  traditional  value  chains  and  business  models.  A 
key characteristic of ICT innovations is their pervasiveness in society as a whole.  ICT is 
always  part  of  it  in  some  way,  especially  in  newly  emerging  sectors  and  ‘smart  policy 
domains’. Also, public sector innovation and social innovation are to a large extent based 
on  ICT  innovation.  We  have  concluded  that,  especially  for  ICT,  it  is  relevant  to  extend 
the innovation systems concept to the societal level.   

In terms of indicators to ‘measure’ innovation systems, this implies that it is relevant to 
integrate indicators traditionally used to monitor the Information Society, e.g. indicators 
for  the  readiness  of  an  innovation  system  for  the  Internet  of  Things  (in  terms  of  the 
interaction  mechanisms,  knowledge  generation  and 
various  actors, 
transmission,  supply,  skills,  demand, 
ICT 
innovations).  Suggestions  have  been  given  on  how  to  apply  the  systems  of  innovation 
approach to ICT innovation. 

internationalization  of 

finance  and 

institutions, 

In short, we propose that the conceptual and empirical development process of the last 
few decades should be continued, by further broadening the perspective on innovation. A 
broadened perspective would increase the number of relevant indicators to measure and 
monitor  what  is  happening  at  systems  and  firm  levels,  and  it  would  also  increase  the 
complexity  through  the 
involvement  of  additional  stakeholders  and  additional 
interactions. We have suggested a framework to ensure broad coverage, when selecting 
relevant  indicators,  and  have  also  given  some  examples  of  indicators  from  this 
framework. 

Innovation  modes  at  firm-level:  from  concepts  to  operational  suggestions  on 
how to identify modes of ICT innovation 

In Section 3, we turned to innovation at the firm-level, and concluded that the resource-
based view of the innovative firm fits very well with the innovation systems perspective 
(Martin  2012).  The  resource-based  view  states  that  a  unique  combination  of 
complementary  and  specialized  resources  and  capabilities  (which  are  heterogeneous 
within an industry, scarce, durable, not easily traded, and difficult to imitate), may lead 
to value creation (Penrose, 1959). These firm-level sets (or combinations, or innovation 
mix) may be unique, but with firm-level indicators it is possible to identify some patterns 
which refer to the main types or modes of innovative behaviour.  

Multivariate  methods  of  data-reduction  (factor  and  cluster-analysis)  seem  the  most 
appropriate to identify models or modes of innovation as a coherent (jointly reinforcing) 
mix  or  combination  of  innovation  strategy  ingredients,  or  routine  components.  Overall, 

54 

 
firms  in  ICT  sectors  are  more  innovative  than  firms  in  other  sectors,  but  the  mode  of 
innovation  differs  between  the  ICT  sub-sectors  of  ICT  producers,  telecoms  and  ICT 
services.  

The  exploratory  method  of  factor  -  or  principal  component  -  analysis  (and  cluster-
analysis) is useful for identifying innovation patterns  not only at the firm-level, but also 
at the systems level. Advancements in identifying firm-level ICT modes of innovation, as 
discussed in Section 3, could subsequently be used to improve the monitoring of national 
and  regional  systems  of  ICT  innovation,  as  discussed  in  Section  2.  The  deviation  from 
the average factor-scores (referring to ICT modes of innovation) could serve to identify 
national or regional-specific modes of ICT Innovation. Integration of factor-scores on ICT 
modes of innovations at firm-level with regional indicators at systems level would allow 
us to arrive at an EU typology of regional systems of ICT innovation, which could be seen 
as regional ‘modes’ of ICT innovation35. This would reduce the complexity in studying the 
interaction  between  firms  and  their  geographical  context  concerning  ICT  innovation,  by 
simplifying the diversity in system contexts, as well as the diversity in firm strategies.  

Policy implications 

Studying and monitoring ICT innovation systems is important as it allows us to learn how 
to  improve  policy  and  the  performance  of  current  and  future  systems.    It  will  make  it 
possible  to  design  and  improve  an  ICT  innovation  policy-mix,  which  is  tailored  to  the 
specific  needs  of  ICT  modes  of  innovation,  and  also  tailored  to  the  prioritized  (mix  of) 
societal needs (e.g. economic, social, environmental, health, or other impacts).   

Before  we  address  the  policy  implications  of  this  (which  are  linked  to  the  ‘general 
purpose’  nature  of  ICT  and  its  pervasiveness  in  society),  we  first  address  the  policy 
implication  of  another  characteristic  of  ICT  innovation,  namely  the  rate  of  technological 
change in ICT, which tends to be very high.  

ICT  innovations  become  outdated  very  quickly  and  new  disruptive  ICT  innovations 
emerge  and  diffuse  equally  quickly.  This  means  that  the  relevant  institutions  and 
framework  conditions  need  to  be  up-dated  at  relatively  often.  We  have  seen  in  the 
previous  paragraphs  that  institutions  such  as  technology  standards  or  regulations  are 
very important drivers or barriers for the growth of ICT systems of innovation. The policy 
implication of this is that timing in institutional change is crucial. Setting new standards, 
and  up-dating  regulations  can  create  a  competitive  edge.  The  relevant  regulations 
concern not only the ICT industries themselves, but also the industries and policy fields 
of application, e.g. transport (e.g. automated driving), health (E-health records), finance 
(e.g.  crowd-funding  and  block-chain  applications).  Due  to  the  increased  importance  of 
ICT  innovation  in  all  sectors  and  policy  domains,  ICT  innovation  policy  should  be 
addressed as a horizontal policy field.  

We  mention  one  example  (Wintjes  et  al.  2016):  the  policy  response  to  the  disruptive 
impact  of  block-chain  applications  (Table  10).  Block-chain  applications  utilize  an 
innovative open source IT architecture that allows financial transactions to be carried out 
with anybody, while keeping an authoritative record of all the changes made. They were 
originally  developed  in  the  context  of  peer-to-peer  digital  payment  systems  such  as 
Bitcoin.  They  make  decentralised  and  verifiable  exchanges  possible,  overcoming  the 
need  for  verification  processes  provided  by  a  trusted  third  party  (e.g.  a  bank).  A  clear 
example  of  their  potentially  disruptive  impact  is  given  by  the  time  it  currently  takes  to 
move funds between different institutions and geographic. 

35   For  those  ICT  innovation  processes  which  take  place  at  international  or  global  level,  the 

national perspective would be more relevant. 

55 

 
 
 
                                           
Table 10 

Blockchain applications: disruption and policy challenges 

Disruption        Block-chain  technology  provides  a  safer,  faster  and  more  cost-

effective way to implement transactions 

Impact on 
societal 
challenges 

Market 
opportunity 

Optimise  cost,  transparency,  time  efficiency,  stability  of  the  financial 
system 
Enhance the operability of Internet of Things networks 
Facilitate  blockchain-based  transactions  by  a  large  portion  of  the 
population 

Reduction  of  banks’  infrastructure  costs  by  EUR  13.8-18.4  billion  per 
annum by 2022.36 
Fintech-related business contribution of more than EUR 28.2 billion to the 
UK economy in 2014.37 
Opportunities  in  industries  such  as  finance,  insurance,  engineering, 
manufacturing 
Can redefine role of cloud-based computing   

Policy 
challenges 

Stable and clear regulatory framework 
Provision of support for creation of standards 
Foster awareness 

Source: Wintjes et al. (2016, p.10). 

face 

important  barriers 

from  governments  and 

Block-chain  start-ups 
financial 
institutions,  for  instance  when  they  want  to  register  as  legal  entities  or  open  trading 
bank  accounts.  A  major  policy  challenge  is  to  promote  collaboration  between  the  ICT 
start-ups  and  the  established  banking  sector,  and  the  respective  government 
departments  (Ministries)  and  DG’s  at  EU  level.  In  this  respect,  the  2015  “green  paper” 
from  the  European  Commission  ‘Building  a  Capital  Markets  Union’  only  mentions  ICT 
innovation twice. While the paper acknowledges it as “an important driver of integration 
of  capital  markets”,  it  remains  vague  on  how  this  transformation  will  affect  the  sector. 
This rather reserved and conservative approach could be due to the obstacle identified in 
the  case  study  on  block-chain  applications  concerning  lobbying  pressures  from  the 
establishment 38 .  Most  promotion,  demonstration  and  awareness-raising  about  block-
chain applications has perhaps been done in the preparation of the Digital Single Market 
initiative.  Events  such  as  the  recently  held  workshop  on  block-chains  and  digital 
currencies39 organized by the European Commission in the context of the Digital Agenda 
for  the  EU,  have  contributed  to  an  increased  awareness  of  the  benefits  of  block-chain 
applications.  

Organizing formal platform meetings is not the only support provided for the creation of 
standards.  Informal  ‘playgrounds’  also  play  a  part.  For  instance,  in  2015  the  first 
‘bankathons’ (https://www.bankathon.net/) were organised in Frankfurt (Germany). At a 
bankathon, banks, fintech start-ups, developers and designers come together ‘to create 
products  customers  dream  of’.  In  a  few  days,  30  hours  in  total,  a  new  product  is 
developed from scratch and launched    

36   Santander InnoVentures, Oliver Wyman and Anthemis Group. (2015). The Fintech 2.0 Paper : 

Rebooting Financial Services. 
Available  at:  http://santanderinnoventures.com/wp-content/uploads/2015/06/The-Fintech-2-
0-Paper.pdf 

37   Sillicon 

Valley 

Available 
Investment 
http://www.finextra.com/finextradownloads/featuredocs/SVB-Fintech-Report-2015-digital-
version.pdf 

Fintech. 

(2015). 

trends 

Bank 

in 

38   http://ec.europa.eu/growth/industry/innovation/business-innovation-observatory/case-

studies/index_en.htm 

39   https://ec.europa.eu/digital-agenda/en/news/blockchain-and-digital-currencies-workshop 

at 

56 

 
                                           
Coming  back  to  the  ‘general  purpose’  nature  of  ICT  and  the  pervasiveness  of  ICT 
innovations in society, we can draw several conclusions. Taking a wider systemic view of 
ICT  innovation  at  a  societal  level  has  policy  implications,  as  it  implies  a  broadened 
perspective on policy: 

•  Beyond R&D policy: a systemic view of ICT innovation policy goes beyond supply-side 
innovation  policy  which  promotes  R&D  in  ICT,  and  complements  it  with  more 
demand-side  innovation  policies  which  promote  the  demand  and  diffusion  of  ICT 
innovations.  The  Tampere  region  has  successfully  transformed  its  innovation  policy 
from R&D funding to supporting innovation platforms such as Demola (see text-box), 
where  student  teams  solve  problems,  often  leading  to  quicker  take  up  of  business 
opportunities.  

•  Beyond promoting science-industry interactions: for many years innovation policy has 
focused  on  supporting  linkages  between  scientific  research  in  ICT  and  producers  in 
ICT industries, but these interactions should be broadened to include ICT users in the 
public sector and civil sector. 

•  Beyond  triple-helix  innovation  policy  initiatives:  in  many  sectors,  regions  and  cities 
the concept of triple-helix (industry, universities, and government) has been applied 
to developing and implementing innovation strategies. Increasingly, additional actors 
are  involved,  most  notably  citizens  and  civil  society  actors  (including  not-for-profit 
non-governmental organizations). 

•  Beyond focusing on the  purely economic impacts of  ICT innovation: traditionally the 
focus has been on economic impact, but today there is more awareness of the impact 
of ICT innovation on other policy fields. ICT innovation now serves any policy field or 
Ministry, and ICT innovation policy affects many societal challenges. 

•  Beyond  policies  which  aim  for  technological  innovation:  in  the  past,  ICT  innovation 
policy was largely focused on supporting technological innovation, as was innovation 
policy  in  general.  Nowadays,  however,  it  has  become  increasingly  apparent  that 
impacts  can  be  gained  from  all  kinds  of  non-technological  innovations.  Un-leashing 
these  impacts  calls  for  policies  and  policy  instruments  which  promote  non-
technological modes of innovation, for example new ICT business models, or design-
thinking among SMEs. 

•  Beyond old sector policy silos: there is also  a need for systemic interaction between 
policy  domains,  government  departments/Ministries  and  DG’s  as  regards  emerging 
ICT  innovations.  Governments  have  to  promote  synergies  from  (policy)  interaction 
across sectors in the economy, across parts of society, and across policy domains. 

•  Beyond  policy  interventions  through  the  market/price-mechanism  for  ICT:  lowering 
the price of ICT is not enough to enhance up-take and diffusion. Policies should aim 
for  behavioural  change 
(additionality)  by  promoting  experimentation  and 
demonstration  (e.g.:  by  organizing  hackathons,  ‘bankathons’,  ‘living-labs’,  boot 
camps, demonstrators, digital platforms, etc.). 

•  Beyond  copying  best  practice  in  ICT  innovation  policies:  there  is  no  single  perfect 
‘one-size-fits-all’ standard policy solution which all policy makers should adopt. Policy 
makers  should  therefore  interact  with  stakeholders,  and  encourage  entrepreneurial 
discovery  of  appropriate  fields  of  ‘smart  specialisation’.  Thus,  they  would  be  able  to 
develop  systemic  innovation  strategies  and  learn  how  to  improve  the  innovation 
policy mix.   

Traditionally,  the  policy  focus  in  NIS,  RIS  and  SIS  was  on  promoting  science-industry 
interactions,  and  the  main  aim  was  to  have  impact  on  economies  (at  firm,  sector, 
country  and  regional  level).  If  we  build  on  Information  Society  concepts,  however,  a 
wider  symbiotic  view  emerges  in  what  can  be  called  the  societal  system  of  ICT 
innovations.  

57 

 
the 

global 

Demola 

for 
talents 

for  multi-disciplinary, 

these  platforms 
and 

Since  R&D,  or  innovation  supply-
side  policies  have  been  dominant 
for  decades,  policies  to  strengthen 
the  European  demand-side  of 
innovation  are  needed. 
digital 
to  a  strengthened 
Linking  up 
demand-side  would  help 
firms 
cross 
‘valley-of-death’  by 
linking  global  excellence  and 
regional 
through 
relevance 
problem-based  and  solution-based 
policy  modes.  Centres  of 
ICT 
research  excellence  still  receive 
public  funding  mainly  for  R&D. 
funding 
They 
for 
less 
receive 
diffusion  and 
interaction  with 
potential SMEs or civil users within 
the  innovation  systems  in  which 
they  operate  in  Europe.  In  this 
respect,  we  have  argued  in  this 
report  in  favour  of  paying  more 
attention  to  the  ‘demand-side’  of 
ICT  innovations.  An  obvious  policy 
implication  is  the  call  for  more 
‘demand-side  policies’.  This 
for 
policies 
includes 
instance 
concerning:  the up-take of certain 
cross-sector 
ICT 
innovation 
innovation 
initiatives; 
platforms; regulation as a driver of 
demand 
innovations; 
ICT 
developing  lead-markets  for  ICT 
living-labs, 
innovations; 
ICT 
ICT 
standards 
developing 
innovations;  investments  in  the 
skills of potential users; facilitating 
demonstrations and proof-of-concept; public procurement programmes for ICT; support 
for internationalization.  

Although  Tampere  is  one  of  the  most  R&D-intensive 
(and ICT-intensive)  regions in Europe,  the  Council of 
the Tampere region, has shifted from traditional R&D-
driven cluster policies to innovation platforms such as 
Demola, 
collaborative 
innovation  and  demonstration.  Demola  platforms 
combine people, processes and facilities, and provide 
a  new  ‘needs-driven  or  problem-based’  method  of 
funnelling the flow of innovation in a sustainable way. 
include 
The  key  drivers 
communities, 
ecosystem 
orchestrators.  In  addition,  they  create  attractive 
environments  for  co-creation  (‘trying  out  new  stuff’) 
speed-up  going-to-market,  build  capacity,  facilitate 
systemic  projects,  and  react  to  demand  pull  instead 
of technology push. The platform concept is based on 
create  new 
the 
combinations  of  knowledge  (by  tapping  into  young 
talent);  move  faster  (from  an  idea  (kick-off)  to 
prototype  (pitch)  in  just  4  months);  and  demos  do 
not  require  a  lot  of  financial  support.  The  Demola 
platform  matches  companies  or  other  organisations 
which  have  problems 
solved,  with 
multidisciplinary  teams  of  students  and  university 
staff  who  will  come  up  with  solutions  which  they 
demonstrate  in  a  short  time.  The  platforms  have 
achieved  encouraging 
innovation 
projects  which  respond 
life  problems, 
challenges  and  needs;  2500+  innovation  community 
members  and  170  partner  companies;  100+  start-
ups; 500+ jobs; and €18 million funding for start-ups 
and  innovators.  Demola  is  applied  at  13  (mostly  EU) 
locations. 
see: 
further 
http://tampere.demola.net/ 

following  guiding  principles: 

Source: Wintjes et al. 2014, p.22 

innovations; 

results:  535 

information, 

to  real 

to  be 

for 

for 

For 

At  both  the  system  and  firm  levels,  there  are  several  modes  of  ICT  innovation.  Policy 
makers  should  not  reduce  diversity  by  favouring  one  mode  of  innovation  over  another 
(e.g.  by  only  subsidizing  R&D  or  product  innovation).  Instead,  policy  makers  should 
tailor their innovation policy mix to the variety of modes of innovation. As an extension 
to Lundvall’s theory on interactive learning between producers and users of knowledge, 
we add that policy for ICT innovation should be produced in interaction with its users. 

58 

 
 
 
   
 
 
Literature 

Arundel, A. & H. Hollanders (2005). EXIS: An Exploratory Approach to Innovation 
Scoreboards. European Trendchart on Innovation. European Commission. 

Arundel, A. & H. Hollanders (2011). A taxonomy of innovation: How do public sector 

agencies innovate? Results of the 2010 European Innobarometer survey of public 
agencies. ProInno Europe, INNO METRICS. 

Arundel A, Lorenz E, Lundvall B-A, and Valeyre A, (2007). How Europe's economies 

learn: a comparison of work organization and innovation mode for the EU-15, 
Industrial and Corporate Change, 16:1175-1210. 

Avigdor, G., N. Gauders, H. Hollanders, R. Lucas, N. Mielech and R. Wintjes (2014). 

Smart factories, clean tech and customer experience; how to scale-up the 
success of learning with users? Third Trend report of the Business Innovation 
Observatory. 

Avigdor, G. & R. Wintjes (2015). Disruptive innovations and forward looking policies 

towards smart value chains. Trend Report, EU Business Innovation Observatory. 

Borrás, S., and Edquist, C. (2014). Institutions and Regulations in Innovation Systems: 

Effects, Problems and Innovation Policy Design (CIRCLE Working Paper 2014/29). 
Lund University, Sweden: Centre for Innovation, Research and Competence in the 
Learning Economy. 

Boschma, R. and K. Frenken (2011). “Technological relatedness and regional branching”, 

in H. Bathelt, M.P. Feldman and D.F. Kogler (eds.), Beyond Territory. Dynamic 
Geographies of Knowledge Creation, Diffusion and Innovation, London and New 
York: Routledge. 

Boschma, R. (2014). “Constructing Regional Advantage and Smart Specialisation: 

Comparison of Two European Policy Concepts.” Italian Journal of Regional Science 
13 (1), 51–68.  

Carlsson, B., Jacobsson, S., Holmén, M., & Rickne, A. (2002). Innovation systems: 

analytical and methodological issues. Research policy, 31(2), 233-245. 

Chesbrough, H. (2010). “Business Model Innovation: Opportunities and Barriers”. Long 
Range Planning, Volume 43, Issues 2–3, April–June 2010, Pages 354-363. 

Coenen, L. & F.J. Diaz López (2009). “Comparing systems approaches to innovation and 

technological change for sustainable and competitive economies: an explorative 
study into conceptual commonalities, differences and complementarities”. Paper 
no. 2009/12. CIRCLE, Lund University. 

Cohen, W.M. & D. A. Levinthal (1990). “Absorptive Capacity: A New Perspective on 

Learning and Innovation”. Administrative Science Quarterly, Vol. 35, No. 1, pp. 
128-152. 

Cooke (2010). Regional innovation systems: Development opportunities from the ‘green 

turn’. Technology Analysis and Strategic Management, 22, 831-844. 

Corrocher N, Cusmano L, Morrison A. (2009). ‘Modes of innovation in knowledge-

intensive business services evidence from Lombardy, Journal of Evolutionary 
Economics, 19:173-196. 

Doloreux, D. and S. Parto (2004). Regional Innovation Systems: A Critical Synthesis. 

UNU-Intech Discussion Paper Series, nr: 2004-17. 

Dunnewijk, T. and R. Wintjes (2006). “Governance forces shaping economic 

development in a regional information society; a framework and application to 
Flanders”. In: W. Dolfsma and L. Soete (Eds.). “Understanding the dynamics of a 
knowledge economy”, pp.157-181. Cheltenham Glos, Edward Elgar. 

59 

 
 
 
EPoSS (2015). The European Roadmap "Smart Systems for Automated Driving”, edited 

by the European Technology Platform on Smart Systems Integration. Available 
at: http://www.smart-systems-
integration.org/public/documents/publications/EPoSS%20Roadmap_Smart%20Sy
stems%20for%20Automated%20Driving_2015_V1.pdf 

European Commission (2009). “Lead Market Initiative for Europe. Mid-term progress 

report”. 

European Commission, Information Society and Media, Unit F4 (2009). New 

Infrastructure Paradigms and Experimental Facilities. Living Labs for user-driven 
open innovation. An overview of the Living Labs methodology, activities and 
achievements:, Accessible: 
http://ec.europa.eu/information_society/activities/livinglabs/docs/brochure_jan09
_en.pdf) 

European Commission (2013). “Measuring innovation output in Europe: towards a new 

indicator”. COM (2013) 624. 

European Commission (2014) “Advancing Manufacturing - Advancing Europe' - Report of 

the Task Force on Advanced Manufacturing for Clean Production”. SWD(2014) 
120; 19-3-2014. 

Etzkowitz, H. & L. Leydesdorff (2000). The dynamics of innovation: from National 
Systems and ‘‘Mode2’’ to a Triple Helix of university–industry–government 
relations. Research Policy 29; 109–123. 

Fagerberg J. (2013). Innovation –A new Guide. TIK Working Papers on Innovation 

Studies, No. 20131119. Centre for technology, innovation and culture, Oslo. 

Fagerberg (2014). Schumpeter and the revival of evolutionary economics: an appraisal 
of the literature. Journal of Evolutionary Economics, 13(2), p. 125-159. 

Foray, D., David, P. and Hall, B. (2011). Smart Specialisation: From academic idea to 

political instrument, the surprising career of a concept and the difficulties involved 
in its implementation, MTEI Working Paper 2011-001, EPFL, Lausanne. 

Foray, D., P.A. David and B.H. Hall (2009). Smart specialization. The concept, 

Knowledge Economists Policy Brief No. 9, June 2009, 5 pp. 

Foray, D., D. Mowery, R. Nelson (2012). Public R&D and societal challenges: What 

lessons from mission R&D programmes? Research Policy, Vol. 41, 10, pp. 1697-
1702. 

Foray (2015). Smart Specialisation: Opportunities and Challenges for Regional 

Innovation Policy. Routledge, Oxon. 

Fransman, M. (2014). “Models of Innovation in Global ICT Firms: The Emerging Global 

Innovation Ecosystems”. European Commission - JRC-IPTS, Seville. Available at: 
https://ec.europa.eu/jrc/sites/default/files/jrc90726.pdf?search 

Frenz, M. and R. Lambert (2012). “Mixed Modes of Innovation: An Empiric Approach to 

Capturing Firms' Innovation Behaviour”, OECD Science, Technology and Industry 
Working Papers, 2012/06, OECD Publishing. 
http://dx.doi.org/10.1787/5k8x6l0bp3bp-en 

Freeman, C. (1988). ‘Japan: A new national innovation system?’, in G. Dosi, C. Freeman, 

R. R. Nelson, G. Silverberg and L. Soete (eds.) Technology and economy theory, 
London: Pinter. 

Gabison, G. and A. Pesole (2014). Open Innovation, User Innovation and Social 

Innovation; An Overview of Models of Distributed Innovation. IPTS, Seville. 

Gault, F., & Hippel, E. A. V. (2009). The Prevalence of User Innovation and Free 

Innovation Transfers: Implications for Statistical Indicators and Innovation Policy, 
MIT Sloan Working Papers. MIT Sloan School of Management. Available at: 
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1337232 

60 

 
Geels, F. (2004). From sectoral systems of innovation to socio-technical systems: 

Insights about dynamics and change from sociology and institutional theory. 
Research Policy, 33, 897-920. 

Hall, B. & N. Rosenberg eds. (2010). Handbook of the Economics of Innovation, Volume 

1. Elsevier, Amsterdam.  

Hausmann, R. and D. Rodrik (2003). “Economic development as self-discovery”, Journal 

of Development Economics, 72(2): 603-633. 

von Hippel, E. (1976). The dominant role of users in the scientific instrument innovation 

process. Research Policy, 5(3), 212-239. 

Hollanders, H., (2007). Innovation modes. Evidence on the sector level. Europe Innova 

Sectoral Innovation Watch deliverable WP4. European Commission, Brussels. 

Hollanders, H. (2008). “Strategic innovators drive innovation performance at the sector 

level: a sectoral analysis of innovation modes”. 

Hollenstein, H. (2003). Innovation modes in the Swiss service sector: a cluster analysis 
based on firm-level data. Research Policy, Volume 32, Issue 5, May 2003, Pages 
845-863. 

Iizuka, Michiko (2013). Innovation systems framework: still useful in the new global 

context? UNU-Merit Workingpaper, #2013-005. 

Jensen MB, Johnson B, Lorenz B, Lundval BA. (2007). Forms of knowledge and modes of 

innovation. Research Policy 36:680-693.  

de Jong JPG and Marsili O (2006). ‘The fruit flies of innovations: A taxonomy of 

innovative small firms’, Research Policy 35:213-229. 

Kline, S.J. & N. Rosenberg (1986). “An overview of innovation.” In R. Landau & N. 

Rosenberg (eds.), The Positive Sum Strategy: Harnessing Technology for 
Economic Growth. Washington, D.C.: National Academy Press, pp. 275–305. 

Lachenmaier and Rottmann (2010). Effects of innovation on employment: A dynamic 

panel analysis. International Journal of Industrial Organization, 29, 210-220. 

Larosse, J. and P. Slaets, J. Wauters, S. Bruninx, P. Simkens, R. Wintjes (2001). “ICT 
Clusterorganisations in Flanders: Co-operation in Innovation in the New 
Networked Economy”. In: “Innovative Clusters; Drivers of National Innovation 
Systems”, OECD-Enterprise, Industry and services. Paris, OECD.  

Leitner, Karl-Heinz; Jeff Butler, Giovanni Cerulli, Theo Dunnewijk, Franziska Kampik, 

Andrea Kasztler, Huub Meijers, Bianca Poti, Meirion Thomas, Eva Trier, Stig 
Slipersæter, René Wintjes & Jan Youtie (2011). Analysis of the evolution of the 
costs of research – trends, drivers and impacts; Final Report to the European 
Commission, DG Research and Innovation. 

Lundvall, B. A., ed. (1992). National Systems of Innovation: Towards a Theory of 

Innovation and Interactive Learning. London: Pinter. 

Lundvall, B.-Å. (2010). National Systems of Innovation–Towards a Theory of Innovation 

and Interactive Learning, New York: Anthem Press. 

Malerba, F. and L. Orsenigo (1997). Technological Regimes and Sectoral Patterns of 

Innovative Activities. Industrial and Corporate Change 6: 83-117. 

Malerba, F. (2002). Sectoral systems of innovation and production. Research Policy, 31, 

247–264. 

Malerba, F. (2004). Sectoral Systems of Innovation: Concepts, Issues and Analyses of 

Six Major Sectors in Europe. Cambridge University Press. 

Malerba, F. (2004). Sectoral Systems of Innovation: basic concepts. In: Malerba, F. (ed.) 
Sectoral Systems of Innovation: Concepts, Issues and Analyses of Six Major 
Sectors in Europe. pp. 9-41. Cambridge University Press. 

Malerba, F. and R. Nelson (2011). Learning and catching up in different sectoral 

systems: evidence from six industries. Industrial and Corporate Change (2011) 
20 (6): 1645-1675. 

61 

 
Marin, A., Pari Patel and Caroline Paunov (2008). “Benchmarking National Sector Specific 
Environments in the ICT Industry”. Systematic Report, Sectoral Innovation 
Watch. 

Martin, Ben R. (2012). The evolution of science policy and innovation studies. Research 

Policy 41, pp. 1219–1239. 

Mas, M., J. Fernández de Guevara Radoselovics, G. De Prato (Ed.) and I.K. Rohman (Ed.) 
(2015). An Analysis of ICT R&D in the EU and Beyond; The 2015 PREDICT Report. 
IPTS, Seville. 

Meijers, H., B. Dachs, and P. Welfens (eds.) (2008). Internationalisation of European ICT 

Activities Dynamics of Information and Communications Technology. Springer-
Verlag: Heidelberg. 

Nardo, M. & Michela Saisana (2005). OECD/JRC Handbook on constructing composite 

indicators.  

Nauwelaers, C. & R. Wintjes (2002). “Innovating SMEs and Regions: The Need for Policy 

Intelligence and Interactive Policies”. Technology Analysis & Strategic 
Management, vol. 14, 2, pp.201-215. 

Nelson, R. and S. Winter (1982). An Evolutionary Theory of Economic Change (Harvard 

University Press. Cambridge, MA, 1982). 

Nelson, R.R. Editor (1993). National innovation systems. A comparative analysis; Oxford 

University Press, New York and London. 

Nepelski Daniel, Giuditta De Prato, Editors: Marc Bogdanowicz, Zoe Kay (2014). Key 

Findings and Implications of the European ICT Poles of Excellence Project. JRC 
Scientific and Policy Report EUR 26582. Available at: 
http://is.jrc.ec.europa.eu/pages/ISG/EIPE.html 

OECD (1997). National Innovation Systems, OECD Publications, Paris. 

OECD (1999). Boosting Innovation: The Cluster Approach. OECD Publications, Paris. 

Available at: 
http://www.oecd.org/sti/inno/boostinginnovationtheclusterapproach.htm 

OECD (2001). Innovative Clusters: Drivers of National Innovation Systems. 

OECD (2005). The Measurement of Scientific and Technological Activities; Oslo Manual, 

Guidelines for Collecting and Interpreting Innovation Data, 3rd Edition. OECD, 
Statistical Office of the European Communities, Luxembourg. 

OECD (2015). “Measuring the Digital Economy: A New Perspective”. OECD Publications, 

Paris. 

OECD (2014). “Intelligent Demand: Policy Rationale, Design and Potential Benefits”, 

OECD Science, Technology and Industry Policy Papers, No. 13, OECD Publishing. 

Pavitt K. (1984). ‘Sectoral Patterns of Technical Change - Towards a Taxonomy and a 

Theory’, Research Policy 13:343-373. 

PwC (2015). The Internet of Things. Business Innovation Monitor case-study. 

Roland Berger (2014). Think Act – Industry 4.0: The new industrial revolution – How 

Europe will succeed. 

Rothwell, R. & W. Zegveld (1985). “Reindustrialization and Technology”. Harlow, 

Longman. 

Schumpeter, J. A. (1937). “Preface to the Japanese Edition of “Theorie der 

Wirtschaftlichen Entwicklung,” reprinted in Schumpeter, J. A. (1989). Essays on 
Entrepreneurs, Innovations, Business Cycles and the Evolutions of Capitalism, 
edited by R. V. Clemence, New Brunswick NJ: Transaction Publishers, p. 165-168. 

Simon H. (1947). Administrative Behaviour: A Study of Decision-Making Processes in 

Administrative Organization. – 4th ed. in 1997, The Free Press. 

Steinmueller, Ed (2004). The European software sectoral system of innovation. In: 

Malerba, F (ed.) Sectoral systems of innovation: concepts, issues and analyses of 

62 

 
six major sectors in Europe. Cambridge University Press, Cambridge, pp. 193-
242. 

Startup Europe Crowdfunding Network (2014). Support services to foster the crowd 

funding environment in Europe focused on web entrepreneurs, final report. 
Brussels. 

Soete, L., Verspagen, B. and ter Weel, B. (2009). ‘System of innovation’, UNU-MERIT 

Workingpaper series no. 2009-062, Maastricht: UNU-MERIT. 

Srholec M., and B. Verspagen (2012). The Voyage of the Beagle into innovation: 

explorations on heterogeneity, selection, and sectors. Industrial and Corporate 
Change, Volume 21, Number 5, pp. 1221-1253. 

Stiglitz, Joseph E. & Bruce C. Greenwald (2015). Creating a learning society: a new 
approach to growth, development, and social progress. Columbia: Columbia 
University Press. 

Swedish Transport Agency (2014). Autonomous driving: Short version/Summary. Ref. 

no. TSG 2014-1316. 

Teece D.J. and G. Pisano (1994). The Dynamic Capabilities of Firms: an Introduction. In: 

industrial and Corporate Change, 3 (3), pp.537-556. 

Teece, D. (2010). ‘Technological innovation and the theory of the firm: The role of 
enterprise-level knowledge, complementarities and (dynamic capabilities)’, 
Chapter 16 in Hall and Rosenberg (2010), Handbook on the Economics of 
Innovation, Volume 1, (North-Holland, Elsevier). 

Tödtling, F. and Trippl, M. (2005). One size fits all? Towards a differentiated regional 
innovation policy approach, Research Policy, Vol. 34(8), pp. 1203-1219. 

Türkeli, S. and R. Wintjes (2014). Towards the societal system of innovation: The case 

of metropolitan areas in Europe. UNU-MERIT Working Papers Series, number 
2014-040. Available at: http://www.merit.unu.edu/publications/working-papers/ 

Vargo, S.L., P. Maglio & M. Akaka (2008). On value and value co-creation: A service 

systems and service logic perspective. European management Journal (2008) 26, 
145-152. 

Weber, K. M., H. Rohracher (2012). Legitimizing research, technology and innovation 

policies for transformative change. Combining insights from innovation systems 
and multi-level perspective in a comprehensive “failures” framework. Research 
Policy, 41, 1037-1047. 

Welsum, D. van, W. Overmeer, B. van Ark (2013). Unlocking the ICT growth potential in 
Europe: Enabling people and businesses Using Scenarios to Build a New Narrative 
for the Role of ICT in Growth in Europe. 

Wintjes, R., T. Dunnewijk and H. Hollanders (2002) ICT Monitor Vlaanderen: Eindrapport 

van een haalbaarheidsstudie. IWT study 39, Brussels. 

Wintjes, R. (2006). “ICT: Scoping Paper”, Research Report commissioned by the 

European Commission in Innovation Watch – Systematic, Maastricht.  

Wintjes, R. (2006). “Internationalisation of European ICT activities; Evidence from case 

studies”. Chapter VI in final report for IPTS, Seville.  

Wintjes, R. (2007). “Monitoring and analysis of policies and public financing instruments 

conducive to higher levels of R&D investments: the “policy mix” project: sector 
case study ICT, The Netherlands”.  Report for DG Research, European 
Commission, Brussels. 

Wintjes, R. (2008). “From Case studies; International Division of Labour within ICT 

Companies”. In H. Meijers, B. Dachs, and P. Welfens (eds.), Internationalisation 
of European ICT Activities. Springer-Verlag: Heidelberg, pp.186-200. 

Wintjes, R. and T. Dunnewijk (2008). “Sectoral Innovation Systems in Europe: the case 

of the ICT sector”. Sector report for the Innovation Watch – Systematic project 

63 

 
within the Europe INNOVA initiative. Research Report commissioned by the 
European Commission (DG Enterprise). 

Wintjes, René and Hugo Hollanders (2011). “Innovation pathways and policy challenges 

at the regional level: smart specialization”. UNU-MERIT Working paper series, 
number 2011-027. UNU-MERIT, Maastricht. 

Wintjes, René (2012). “Demand-side innovation policies at regional level”, Regional 

Innovation Monitor, Thematic Paper 3. Available at: http://www.rim-europa.eu/ 

Wintjes, René (2013). Unlocking the potential of business and societal innovation; how 
to scale-up successful new business and production models? Trend report of the 
Business Innovation Observatory. European Union, Brussels. Available at: 
http://ec.europa.eu/enterprise/policies/innovation/policy/business-innovation-
observatory/ 

Wintjes, R., G. Avigdor, N. Gauders, N. Mielech and F. Santuccio (2014). Design for 

innovation, smart living and innovative business models; how to scale-up 
success? Second Trend report of the Business Innovation Observatory. Available 
at: http://ec.europa.eu/growth/industry/innovation/business-innovation-
observatory/index_en.htm  

Wintjes, R., D. Douglas, J. Fairburn, H. Hollanders and G. Pugh (2014). “Beyond product 

innovation; improving innovation policy support for SMEs in traditional 
industries”. UNU-MERIT Working Papers Series, number 2014-32. 
http://www.merit.unu.edu/publications/working-papers/?year_id=2014 

Wintjes, R.J.M. (2014). Supporting advanced manufacturing activities at the regional 

level. RimPlus thematic paper. Available at: https://ec.europa.eu/growth/tools-
databases/regional-innovation-
monitor/sites/default/files/report/RIM%20Plus_Supporting%20ADMA_Thematic_P
aper%203.pdf 

Wintjes, R., G. Avigdor, G. Christopoulos (2016). “Optimal recycling, big data from 

space, and blockchain applications: disruption and policy response”. Sixth Trend 
Report of the Business Innovation Observatory. Available at: 
http://ec.europa.eu/growth/industry/innovation/business-innovation-
observatory/index_en.htm 

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Annex: Tables and Figures 
Table a: Share of ICT firms collaborating with others, universities, research 
institute, suppliers and clients  

cz  de  gr  es 

fr 

it 

cy 

lt 

hu  nl  pl  pt  sk 

fi 

se  uk  no 

Others 

45  25  45  25  43  27  50  48  51  41  49  37  48  49  48  40  36 

Universities  22  16  24  11  : 

12  14  24  20  16  8 

17  : 

40  20  11  16 

Research 
Institutes 

12  7 

12  7 

10  2 

0 

17  : 

12  11  6 

: 

27  : 

: 

14 

Suppliers 

37  8 

20  10  26  11  41  45  37  30  32  28  38  41  33  27  24 

Clients 

35  12  28  10  26  11  14  36  : 

29  20  25  41  48  39  28  30 

Note: =no data available 

Table b: Competitive Innovative performance of the ICT sector at national level 

Country 

Index 
Level of 
innovation 

Patenting 
Advantage 

(1a) 

(1) 

Market 
Advantage 
(export) 

(1b) 

0.75 
0.58 
0.48 
0.47 
0.46 
0.45 
0.41 
0.25 
0.23 
0.13 
0.11 
0.06 
0.05 
-0.13 
-0.19 
-0.54 
-1.18 

Finland 
Netherlands 
France 
UK 
Belgium 
Germany 
Austria 
Sweden  
Denmark 
Spain 
Portugal 
Italy 
Norway 
Hungary 
Poland 
Czech Rep 
Slovakia 
Ireland 
Greece 
USA 
Japan 
Average 
Source: Marin et al. (2008) 

0.50 
0.43 
0.17 

2.33 
1.93 
0.91 
0.66 
0.72 
1.75 
0.99 
1.55 
1.04 
0.27 
0.05 
0.49 
0.62 
0.12 
0.04 
0.05 
0.03 
0.45 
0.18 
0.97 
1.45 
0.76 

2.94 
4.50 
0.82 
1.11 
2.26 
1.11 
1.49 
1.66 
1.30 
0.50 
0.66 
0.39 
0.46 
1.69 
0.19 
0.60 
0.20 
7.68 
0.22 
0.56 
1.24 
1.83 

Total Factor 
Productivity 

(1c) 

3.66 
1.50 
3.97 
4.15 
3.29 
1.94 
2.61 
-0.46 
0.37 
0.84 
0.96 
-0.30 
-0.68 
-2.66 
-2.53 
-7.24 
-15.08 

4.19 
2.20 
0.02 

Index of Growth 
in innovation 
performance 

(2) 

0.79 
0.74 
0.20 
0.28 
0.32 
0.67 
0.49 
0.57 
0.62 
0.28 
0.38 
0.22 
0.31 
0.35 
0.20 
0.19 
0.14 

0.35 
0.18 
0.38 

65 

 
  
 
 
 
 
 
 
 
 
 
 
Table c: Share of ICT firms which report a lack of qualified personnel as 
obstacle for innovation, CIS3 

cz  de  gr  es  fr 

it 

cy 

lt  hu  nl  pl  pt 

fi  no 

lack  qualified 
personnel 

15  5 

13  10  13  11  27  26  11  12  5 

31  11  4 

Table d: Public funding, external funding and internal funding, % of innovative 
ICT firms in Europe 

cz  de  gr  es  fr 

it 

cy 

lt  hu  nl  pl  pt  ro  fi 

se  no 

public funding 

22  21  41  32  23  41  41  7 

23  49  9 

17  8 

48  0 

56 

lack of external 
finance  

lack of internal 
finance  

: 

14  35  31  : 

27  27  26  20  17  23  17  27  11  19  16 

10  18  54  35  34  25  45  42  : 

26  29  20  : 

18  27  19 

Source: Wintjes & Dunnewijk 2008, based on most recent CIS data available in 2008. 

Figure a: EU digital agenda scoreboard E-health sub-indicators: medical data 
exchange, e-prescription 

66 

 
  
 
 
  
 
 
 
 
 
 
 
 
 
Figure b: Revealed Comparative Advantages in Computer Manufacturing (ISIC 
30) 

67 

 
 
 
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doi:10.2791/58656 

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