The impact of the policy mix
on service innovation
The formative and growth phases of the sectoral innovation
system for Internet video services in the Netherlands
proefschrift
Ter verkrijging van de graad van doctor aan de Technische Universiteit Delft, op gezag van de Rector Magnificus prof. ir. K.Ch.A.M. Luyben,
voorzitter van het College voor Promoties
in het openbaar te verdedigen op dinsdag 5 november 2013 om 15.00 uur
Martijn Alexander POEL
geboren te Rotterdam
Dit proefschrift is goedgekeurd door de promotor Prof. dr. Y. Tan Copromotor Dr. W.A.G.A. Bouwman
Samenstelling promotiecommissie:
Rector Magnificus Voorzitter
Prof. dr. Y. Tan Technische Universiteit Delft, promotor
Dr. W.A.G.A. Bouwman Technische Universiteit Delft, copromotor
Prof. dr. C.P. van Beers Technische Universiteit Delft
Prof. dr. E.F. ten Heuvelhof Technische Universiteit Delft
Prof. dr. N.A.N.M. van Eijk Universiteit van Amsterdam
Prof. dr. R.W. Hawkins University of Calgary
Dr. P. den Hertog Dialogic en Universiteit van Amsterdam
ISBN 978-90-79787-54-8
Printing Gildeprint Drukkerijen, Enschede
Published by Next Generation Infrastructures Foundation | www.nginfra.nl
This research was funded by Delft University of Technology, TNO and the Freeband research programme.
Graphic design Jantien Methorst, Rotterdam | www.jantienmethorst.nl
Copyright © 2013 by M.A. Poel. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior permission in writing from the copyright owner.
Colleagues at TNO increased my fascination for academic research and policy consulting. There are too many colleagues to be thanked so forgive me for only mentioning the people that hired me: Laurens Hoedemaker, Jos Leijten, Paul Rutten and Pascal Verhoest.
René Wagenaar, who sadly passed away in 2007, offered me the opportunity to write a PhD at the Delft University of Technology. His support was crucial for starting with a PhD.
Many thanks to my copromotor and supervisor Harry Bouwman for theoretical and methodological suggestions that were essential for the study. You helped me during climbs, flat stages, time trials and sprints.
I would like to thank my promotor Yao-Hua Tan and all members of the committee. Richard, thanks for travelling from Calgary to Delft.
I would like to thank all interviewees for taking the time and providing me with precious input.
A special word of thanks to Mark de Reuver. Mark and I worked together on the social network analysis (Chapter 6). Any mistakes are mine.
Maxim Oei assisted in analysing the interview transcripts. Laurie Blackman provided suggestions for improving my English. Cheers.
The lunch team, Jolien, Jo-Ann, Eveline and other colleagues at TU Delft: thanks. Jantien, many thanks for the graphic design of this thesis.
Big up to my friends in Rotterdam and to my dear friend Rob. A warm thanks to Rianne, Judith and Anneke.
Andra and Marjolein, paranymphs: merci.
Mum, dad, thanks for giving me support and freedom. Cees, Joke, thanks. Marjolein, Dennis, Benthe and Phileine: what’s for dinner?
1 Introduction
1.1. The increased importance of service innovation - 8 1.2. A synthesis perspective on service innovation - 9 1.3. Innovation policies and non-innovation policies - 13 1.4. Advances and challenges in impact assessment - 15 1.5. Market failures and system failures - 21
1.6 Conceptual framework and research question - 23 1.7 Research approach - 24
1.8 Contribution to academic literature - 30 1.9 Outline of the thesis - 33
2 Sectoral systems of innovation
2.1 Introduction - 36
2.2 Theoretical bases and main concepts - 36 2.3 Functions and phases of innovation systems - 49 2.4 Market failures and system failures - 54
2.5 Innovation policies and non-innovation policies - 63 2.6 Contribution to academic literature - 77
3 Specification: service innovation
3.1 Introduction - 80
3.2 Taxonomies of service sectors and innovation patterns - 82 3.3 Market failures and system failures - 87
3.4 Innovation policies and non-innovation policies - 96 3.5 Contribution to academic literature - 106
4 Domain description: Internet video services
4.1 Introduction - 108
4.2 Description, demarcation and statistics - 109 4.3 Value network - 118
4.4 Six dimensions of innovation - 126
4.5 Internet video services in the Netherlands - 129
5 Case study methodology
5.1 Introduction - 140
5.2 Selection of impact assessment methodology: case study - 141 5.3 Case study approach - 144
5.4 Case study selection: Internet video services in the Netherlands - 150 5.5 Selection of interviewees - 151
5.6 Interview protocol and interview questions - 154 5.7 Data analysis - 158
5.8 Limitations - 162
6 The innovation system: demarcation and tipping point
6.1 Introduction - 164 6.2 Demarcation - 164
6.3 Tipping point between the formative and growth phases - 177
7 Market failures and system failures
7.1 Introduction - 182 7.2 Market failures - 184 7.3 System failures - 192
7.4 Conclusion and validation - 217
8 Innovation policies and non-innovation policies
8.1 Introduction - 222 8.2 Market failures - 228 8.3 System failures - 239
8.4 Interactions in the policy mix - 249 8.5 Conclusion and validation - 260
9 Conclusion, contribution and future research
9.1 The impact of the policy mix on service innovation - 270 9.2 Contribution to academic literature - 285
9.3 Policy implications - 286
9.4 Limitations and suggestions for further research - 289
References - 292
Annex A Interviewees - 318
Annex b Study introduction and questions sent to interviewees - 322 Annex c Coding issues and solutions - 324
Annex D Coding of statements about the impact of policies - 326
Samenvatting - 330 CV - 338
1.1 | The increased importance of service innovation
This chapter introduces the mechanisms via which individual policies, and the mix of policies, have an impact on service innovation. First, we explain the increased importance of service sectors and service innovation.
Since the 1970s the importance of service sectors has increased, compared to manufacturing sectors (Eurostat, 2003, 2009; OECD, 2005a). In Europe, the contribution of service sectors to total gross added value is 70% (Eurostat, 2009). Around 75% of jobs are in service sectors (EC, 2010a, 2011a). There are five main explanations for the increased importance of service sectors in Europe, the US and other OECD countries (Bell, 1973; Cohen and Zysman, 1987; Miles and Boden, 2000; Preissl, 2000; OECD, 2005a; Eurostat 2009). The first explanation for this is innovation and growth in sectors that benefit from liberalisation and advances in ICT. Examples are transport, financial services, telecommunications and media. Gross added value increased, while employment remained constant or increased slowly. As a result, productivity increased. Second, service sectors benefit from outsourcing by manufacturing and service firms. Examples are outsourcing of accounting services, IT/ software services and R&D. Firms that specialise in these services are well positioned to develop online services and improve in situ services. Gross added value and employment increased. A third explanation for the increased importance of service sectors is increased employment in public service sectors such as health and education. This is due mostly to increased demand by citizens and politicians. Fourth, consumers are willing to spend more on services because of rising incomes, saturation with goods (such as cars) and the availability of innovative services. In short: a virtuous circle of demand and supply. Fifth, service sectors became more important because growth is limited in manufacturing sectors, construction and agriculture. There are large differences amongst manufacturing sectors. For instance, gross added value has grown in the electrical and optical equipment sector (e.g. microprocessors, communication and medical equipment).
Because service sectors became more important, so did service innovation. Service innovation is relevant for service sectors and manufacturing sectors. Manufacturers rely on intermediate services that are provided by service providers (financial services, transport, etc.), and thus, innovation in intermediate services stimulates innovation
in final goods. Vice versa, service providers rely on intermediate goods that are provided by manufacturers (e.g. electrical and optical equipment). Innovation in intermediate goods stimulates innovation in final services. Moreover, services and goods can be complementary. For example: online services and end-user devices. Again, service innovation can trigger innovation in goods, and vice versa. This also applies to bundles of services and goods. Examples are car leasing, after sales service, and subscriptions on a weekly basket of vegetables.
The rest of this chapter is organised as follows. Section 1.2 elaborates on interactions between services and goods. Section 1.3 explains how service innovation is influenced by a range of innovation policies and by non-innovation policies such as product and market regulation. Section 1.3 also describes the societal relevance of the study. Section 1.4 addresses advances and challenges in assessing the impact of policies on innovation. Section 1.5 discusses the concepts of market failures and system failures, i.e. deficiencies in the innovation system. Section 1.6 introduces the conceptual framework and research question. Section 1.7 presents the research approach. Section 1.8 describes the contribution to academic literature. Section 1.9 contains the outline of this thesis.
1.2. | A synthesis perspective on service innovation
Since the 1980s academics have explored the peculiarities and the increased importance of service sectors and service innovation. Among the pioneers were Barras (1986, 1990), Sundbo (1991, 1994), Miles (1985, 1993), Djellal and Gallouj (1999), Rubalcaba (1999) and Den Hertog (2000). They analysed the service innovation process (e.g. the sources of innovation), innovation in the service itself (e.g. new concepts and new functionalities), innovation in the process of producing and delivering services (e.g. the use of ICT), innovation in the organisation of service providers (e.g. internal and external collaboration) and the economic and policy implications of service innovation. A variety of business sectors have been studied. The collection of studies in Metcalfe and Miles (2000), Malerba (2004a) and RENESER (2006) covers financial services, R&D services and other Knowledge Intensive Business Services (KIBS), retail, telecommunications, software, TV production, airports and airlines. In addition, academics have explored service innovation in public sectors (Koch and Hauknes, 2005; Windrum and Koch, 2008; Baumol, 2010; Fuglsang, 2010). Furthermore, service innovation contributes to addressing societal challenges such as environmental sustainability and active ageing (Gadrey, 2010). Examples are online services to provide households with immediate feedback about energy consumption; and online health services. Scholars that analyse the implications of service innovation at the macro and meso level (countries, regions and sectors) are reluctant to provide a definition of services
(Miles and Boden, 2000; Tether et al., 2001; Miles, 2005, 2008; Den Hertog, 2010). Instead, they stress the heterogeneity of services, the nuanced differences between services and goods, and the interactions between services and goods. Only two peculiarities apply to nearly all services: intangibility and interactivity/customer intensity (Miles, 2008; Den Hertog, 2010). Among the exceptions to this are services that rely on tangibles (e.g. prosthetic surgery) and services in which the role of customers is passive (e.g. traditional broadcasting). Although this study mainly builds on service innovation literature with an emphasis on the macro and meso level, we acknowledge the contribution by service innovation scholars that emphasise the micro level of services development, production and marketing. These scholars faced the same conceptual difficulties in defining service innovation (e.g. Grönroos, 1992; Vargo and Lusch, 2004; Chesbrough, 2011). Again, intangibility and interactivity have emerged as basic characteristics of service innovation (Grönroos, 1992; Vargo and Lusch, 2004; Bouwman et al., 2008). A clear definition of services is proposed by Vargo and Lusch (2004, p.2). A service is: “the application of specialized competences (knowledge and skills) through deeds, processes and performances for the benefit of another entity or the entity itself”. The definition reflects a service-centric logic in which the user perspective is emphasised. Users are looking for a solution/function that fulfils their needs. This may come in the shape of a service, a good or a combination of services and goods (Gummeson, 1995; Vargo and Lusch, 2004). The same point is made by service innovation scholars that focus on the macro and meso level (Gallouj and Weinstein, 1997). A nuanced view on the characteristics of services provides a realistic starting point for discussing the characteristics of service innovation. The characteristics of service innovation, compared to innovation in goods, are identified in case studies and in quantitative studies that compare service and manufacturing sectors (Miles, 1993; Gallouj and Weinstein, 1997; Miles and Boden, 2000; Tether et al., 2001; Howells, 2004; Miles, 2005; Tether, 2005; Nijssen et al., 2006; Tether and Tajar, 2008; Droege et al., 2009; Rubalcaba et al., 2010). The first characteristic of service innovation is a consequence of the intangible nature of services: service innovation often involves non-technological innovation such as innovation in service concepts, aesthetics and user experiences (in addition to the mere functionalities of a service). The second characteristic of service innovation is a consequence of interactivity/ customer intensity. Users play an active and important role in the service innovation process, although there are differences between groups of users, as well as between services. Other characteristics of service innovation are: the important role of suppliers in the service innovation process (with a less prominent role for R&D departments and universities); the highly iterative process of developing, testing, launching and adapting services (which requires involvement of several departments); and interaction between innovation in the service itself, innovation in the production and delivery processes (internal and external) and in goods and physical infrastructures that are used for producing and delivering a service. Again, the picture is nuanced.
For instance, not all services are developed in close collaboration with users and suppliers. Furthermore, service firms can contribute to the innovation process of manufacturing firms, and vice versa. Service innovations may even take place within manufacturing firms, e.g. innovation in after sales services. In addition, services and manufacturing firms collaborate on new bundles of services and goods. Hybrid firms, such as Apple, integrate innovation in services and goods.
There are three approaches for exploring the characteristics of service innovation and the implications for research and policy (Boden and Miles, 2000; Coombs and Miles, 2000; Drejer, 2004; Rubalcaba, 2006; Den Hertog et al., 2008). An assimilation approach emphasises those characteristics of service innovation that resemble innovation in goods, such as technological innovation. However, the assimilation approach might overlook the peculiarities of service innovation (Boden and Miles, 2000). The demarcation approach emphasises the peculiarities of services. The demarcation approach explores the need for services-specific concepts, theories, indicators and policies. Boden and Miles (2000), Coombs and Miles (2000) and Gallouj (2002) explain how this approach has contributed to increased understanding of service innovation. At the same time, it has helped to raise interest among academics, statisticians and policy makers. However, the demarcation approach fails to shed light on the interactions and similarities between service and manufacturing sectors (Boden and Miles, 2000; Coombs and Miles, 2000; Gallouj, 2002). It also fails to reveal how services and manufacturing sectors become more similar. For instance, ICT can increase standardisation and mass-production of services (with less influence for users) and can also increase customisation of goods (Sundbo, 1994; Freeman and Soete, 1997; Miles and Boden, 2000). A synthesis approach is advocated by Gadrey, Gallouj and Weinstein (1995), Boden and Miles (2000), Coombs and Miles (2000), Gallouj (2002), Drejer (2004), Tether and Metcalfe (2004), Rubalcaba (2006) and Den Hertog et al. (2008). The synthesis approach is also referred to as integrative or systemic approach. The synthesis approach acknowledges the incremental differences between services and manufacturing sectors (a continuum rather than a dichotomy), the importance of interaction between service innovation and other types of innovation (i.e. service-centric innovation) and the importance of service-good bundles. The synthesis approach stimulates researchers to explore which characteristics of service-centric innovation (such as the importance of non-technological innovation) also apply to a number of goods-centric innovations. “So, rather than seeing services as demanding new methods of innovation analysis, we suggest that it is the service and the service-like activities across all sectors of the economy that demand new approaches. In other words, the focus on service sectors has served to throw light on neglected elements in the whole economy.” (Boden and Miles, 2000, pp.257-258). The synthesis approach allows for studies about real-life innovations, without a priori restrictions to one type of sector or one type of innovation (Tether and Metcalfe, 2004). This study takes a synthesis approach towards service innovation, as reflected in the research approach (Section 1.7).
This section concludes with a brief discussion of the definition of service innovation. The characteristics of services (discussed above) imply that a definition of service innovation must encompass technological and non-technological innovation. In addition, a definition should cover innovation in the service itself, in production and delivery processes, and in complementary goods and infrastructures. This is reflected in the OECD definition that applies to innovation in goods and services: “An innovation is the implementation of a new or significantly improved product (good or services), or process, a new marketing method, or a new organisational method in business practices, workplace organisation or external relations.” (OECD, 2005b, Oslo Manual, p.46). To clarify the dimensions of innovation that are covered by this definition, the OECD (pp.150-154) provides examples. These examples illustrate the relevance of the OECD definition for service innovation. Examples are provided for new services and innovation in the service itself (e.g. video on demand via broadband Internet), process innovation (e.g. automation equipment), marketing innovation (e.g. introduction of direct selling), organisational innovation (e.g. introduction of monitoring systems), innovation in workplace organisation (e.g. the introduction formal and informal teams to improve knowledge sharing between departments) and innovation in external relations (e.g. research collaboration with universities). The dimensions of service innovation in Internet video services will be discussed in the description of the empirical domain (Chapter 4). To be consistent with a synthesis approach towards service innovation, this study uses the ‘neutral’ OECD definition for innovation in goods/services, instead of definitions that emphasise the peculiarities of service innovation (such as definitions by Normann, 2000; Van Ark et al., 2003; and Den Hertog, 2010). This is explained in the theoretical chapter on service innovation (Chapter 3).
A complication in terms of definitions is that service innovation is an iterative process (see above). In practice, there may not be a strict demarcation between conducting R&D, managing the implementation of new services and processes, and improvements and improvisations while providing services to clients. However, the OECD definition of innovation puts the emphasis on implementation, as the result of a mostly linear process (cf. Godin, 2006). The OECD also defines innovation activities. “Innovation activities are all scientific, technological, organisational, financial and commercial steps which actually, or are intended to, lead to the implementation of innovations. Some innovation activities are themselves innovative, others are not novel activities but are necessary for the implementation of innovations. Innovation activities also include R&D that is not directly related to the development of a specific innovation.” (OECD, 2005b, p.47). This definition reflects a broader view on innovation. Phrasing still reflects a linear view on innovation, but the OECD provides examples of iterations between the various innovation activities (OECD, 2005b). The OECD definition of innovation activities will be used in the empirical part of our study, as we explore relevant actors, interactions, problems and policies in the innovation system.
1.3 | Innovation policies and non-innovation policies
While scholars continue to increase understanding of the dynamics of service innovation, policy makers have started to overtly stimulate service innovation. The first half of the policy mix consists of innovation policy. The main objective is to stimulate innovation (Chapter 2). Examples of innovation policies are European R&D programmes such as the Seventh Framework Programme (FP7) and Horizon 2020, the EU Framework Programme for Research and Innovation (EC, 2011b). These programmes support technological and non-technological innovation in a range of services and manufacturing sectors (EC, 2006a, 2011b).
The second half of the policy mix consists of ‘all other policies’. This includes policy areas such as industrial policy, science policy, education policy, environmental policy and competition policy. These policies can be referred to as non-innovation policies (Den Hertog et al., 2008). The primary objective is not to stimulate innovation but to stimulate economic development, achieve scientific breakthroughs, increase capabilities and knowledge, reduce energy consumption, stimulate competition, etc. Increasingly, the European Commission acknowledges the impact of non-innovation policies on service innovation (EC, 2003, 2004, 2006). An example of a policy area that combines innovation policies and non-innovation policies is ICT policy or information society policy. Programmes to support R&D are combined with regulation of products and markets (EC, 1999, 2005a, 2010b). Information society policy is relevant for service innovation, because service innovation takes place in the IT, telecom and media sectors and because ICT is a driver of service innovation in other sectors (Miles, 1993; Freeman and Soete, 1997; Freeman and Louçã, 2001; Perez, 2002).
Service innovation scholars have started to explore which innovation policies and non-innovation policies influence service innovation (Miles, 2005; Rubalcaba, 2006; Dodgson et al., 2011). Studies address individual policies and the overall policy approach for stimulating service innovation. One way of describing policy approaches is to distinguish between the assimilation, demarcation and synthesis approaches, as introduced in Section 1.2 (Boden and Miles, 2000; Coombs and Miles, 2000; Drejer, 2004; Rubalcaba, 2006; Den Hertog et al., 2008). In the assimilation approach, existing policy instruments are stretched to better fit the needs of service sectors, although they are designed according to the needs of manufacturing sectors. In the demarcation approach, new policy instruments are launched specifically for services. A synthesis approach would consist of policy instruments that focus on economic and societal challenges, without favouring specific types of sectors or technologies (Coombs and Miles, 2000; Rubalcaba, 2006; Den Hertog et al., 2008). The distinction between the three policy approaches is not always clear-cut, as the three approaches overlap; can be combined; and have little relevance for analysing non-innovation
policies (cf. Den Hertog et al., 2008). Of crucial importance is the design and implementation of individual policy instruments. Service innovation literature does not yet allow for clear recommendations on individual policy instruments. Instead, scholars have undertaken a menu approach (Den Hertog and Rubalcaba, 2010). Not only does this reflect a lack of solid empirical evidence, it also reflects the hetero-geneity of service sectors and innovation systems in which service innovation takes place. Furthermore, a menu approach reflects that policy makers need to acknowledge the phase of an innovation system. “We are already experiencing that there is no fixed recipe available as every individual innovation system (and for that matter sectoral innovation system) will need a customised (temporary) mix of instruments and policies that suits it.” (Den Hertog and Rubalcaba, 2010, p.642).
As such, service innovation literature started to explore which policies are relevant in which context. A leading service innovation expert writes: “There is a dearth of research concerning how innovation policies affect services, let alone analysis of service-related policies.” (Miles, 2005, p.452). The call for policy research on service innovation is repeated in numerous studies, including Den Hertog and Rubalcaba (2010), RENESER (2006) and EPISIS (2011). Dodgson et al. (2011, p.1154) have provided a recent and clear formulation: “Despite being the major components of contemporary economies, there remains a paucity of policy research on the connections between research and services and the symbiotic relationships between innovation in services and other sectors. Market failure models that elevate R&D spending as a policy aim contribute little to service sectors that undertake little standard R&D but enact important organisational and workplace innovations.” Miles (2005) and Dodgson et al. (2011) refer to the mechanisms or connections via which policies influence service innovation. Increased understanding of these mechanisms is crucial because 1) stimulating service innovation is relatively new, 2) services are heterogeneous, and 3) the design of effective support schemes is challenging, given the characteristics of service innovation (the heterogeneity of services; iterations between innovation, production and market activities; and interaction between service innovation and other types of innovation). Only to some extent, recent studies respond to the call for policy research on service innovation. Studies about innovation policies emphasise the rationale for stimulating service innovation; inventories of policies that are potentially relevant; and participation of services firms in R&D programmes (OECD, 2005c; RENESER, 2006; Rubalcaba, 2006; Den Hertog et al., 2008; Van Cruysen and Hollanders, 2008; Den Hertog and Rubalcaba, 2010). To some extent, indications about effective innovation policies are provided by case studies and statistical analysis in OECD (2005a) and RENESER (2006). A similar picture can be sketched for the impact of non-innovation policies on service non-innovation. Given the iterations between non-innovation, production and market activities, non-innovation policies are highly relevant for
service innovation (Van Ark et al., 2003; Miles, 2005; Den Hertog and Rubalcaba, 2010). The impact of non-innovation policies on service innovation can be indirect or direct, unintended or intended, negative or positive. A long list of non-innovation policies is mentioned as having a potential influence on service innovation (Van Ark et al., 2003; OECD 2005a, 2005c; Expert Group on Innovation in Services, 2007).
However, there have been few rigorous studies about the impact of non-innovation policies on service innovation, and about the underlying mechanisms. Notable exceptions are OECD (2005a) and RENESER (2006).
This brings us to the societal relevance of this study: while service innovation has become more important, policy makers lack information about innovation policies that stimulate service innovation, and about non-innovation policies that hinder or stimulate service innovation. In other words, the evidence base provides policy makers with few clues for improving the policy mix for service innovation. The ambition of policy makers to support service innovation is well documented by the OECD (2005a, 2005c), European policy makers (EC, 2003, 2004, 2006b, 2007, 2009) and national policy makers and advisory councils (e.g. AWT in The Netherlands, 2005; Tekes in Finland, 2007; Forfás in Ireland, 2006; and NESTA in the UK, 2008). To be able to advice policy makers on the policy mix for service innovation, academics need to increase understanding of the mechanisms via which innovation policies and non-innovation policies influence service innovation. This is discussed in the next four sections.
1.4 | Advances and challenges in impact assessment
The challenge of assessing the impact of innovation policies (and non-innovation policies) is not confined to service innovation. It applies to all types of innovation (IBO, 2002; Ruegg and Feller, 2003; OECD, 2006; European Court of Auditors, 2007; Dutch Court of Auditors, 2011; CPB, 2011; Expertwerkgroep Effectmeting, 2012). Impact assessment methods such as case studies, natural experiments and surveys have been improved over the course of decades. For instance, the indicators in Europe’s Community Innovation Survey (CIS) have matured. Indicators cover topics such as R&D expenses, participation in R&D programmes, external collaboration, patents, publications, the implementation of new goods/services and revenues from new goods/services. CIS data is available for several countries, sectors and years. This allows for econometric analysis about the impact of innovation policies. In addition, European policy makers use studies that assess individual R&D programmes such as the Framework Programmes (FPs). Through CIS and studies about individual R&D programmes, European policy makers acquired information about the implementation and output of R&D programmes. Less in known about the impact of these programmes on innovation. The European Court of Auditors looks back on the Framework Programmes, over a period of fifteen years: “For the period covered by this report, as required in the FP legislation, the ‘research DGs’ had an evaluation system
in place and the Commission can point to a sizeable body of evaluation studies. However, the fact remains that little or nothing is known about the achievement of programme objectives and the results of the FPs. This is because evaluations have generally focussed on short-term issues of programme implementation. As a result, the Commission’s evaluations were of limited usefulness to policy-makers, stakeholders and even to the Commission itself.” (European Court of Auditors, 2007, p.23).
This section proceeds as follows. It introduces academic literature about impact assessment of innovation policies. Next, this section describes how the impact of non-innovation policies on innovation is addressed in regulatory impact
assessment studies. Subsequently, it describes how literature about systems of innovation has explored the impact of innovation policies and non-innovation policies on innovation.
A rich body of literature is dedicated specifically to evaluation and impact assessment of innovation policies. Overview articles include Buisseret et al. (1995), Arnold and Balázs (1998), Georghiou (1998), Georghiou and Roessner (2000), Salter and Martin (2001), Kuhlmann (2003), Arnold (2004) and the collection of articles in OECD (2006). Toolboxes include Technopolis et al. (2001), Fahrenkrog et al. (2002), PREST et al. (2002), Ruegg and Feller (2003) and Polt and Vonortas (2006). The above authors and organisations have done substantial work on:
> Tools to make explicit the intervention logic or rationale for public R&D programmes and other support schemes (the theory and assumptions behind an intervention), > Qualitative and quantitative methods (with different methods and mixed methods
being used to assess different types of policies, and to assess policies ex ante and ex post),
> Quantitative and qualitative indicators at the level of input, innovation activities, output and impact (to be able to establish plausible links between input and impact), > Effective ways of involving stakeholders, experts and clients (to gather data and to
increase understanding, while avoiding capture and groupthink),
> Addressing the interactions within a portfolio of R&D projects (portfolio effects complicate the attribution of impact to one project or one public R&D programme), and,
> Sampling methods that acknowledge the skewed impact distribution of R&D projects (few projects will have a substantial impact, while many projects will have little impact).
In addition, there have been persistent pleas for a long-term perspective, with additional measurements taking place several years after a policy intervention (Georghiou and Roessner, 1998; Rush et al., 2004, among others). It takes time for impact to emerge. For instance, output such as patents may lead to patent licensing, new products and increased revenues. This cannot be measured immediately after filing of patents. Likewise, impact may emerge in sectors and organisations outside
the main target group; a challenging project with little output may lead to a second project that is successful; etc. One of the complications is that, in the meantime, policy makers and politicians may have already adapted policies, based on assumptions about impact.
Despite persistent challenges in assessing the impact of policies on innovation, experts have succeeded in presenting results. For a range of public R&D programmes, calculations have been made about the impact of 1 Euro invested by governments (for an overview, see Salter and Martin, 2001; EC, 2005b). Likewise, much has been learnt about addressing specific types of actors and innovation activities. A brief summary of these studies would, however, fail to reflect that the devil is in the details. Each R&D programme is different in terms of design and implementation, and in terms of context (stakeholders, maturity of technologies and sectors, complementary policies, etc.). There are best practices but generalisation is difficult, even for well-established policy interventions such as R&D programmes. Compared to our understanding of R&D programmes, less is known about the impact of other types of innovation policies, such as tax credit schemes (Hall and van Reenen, 2002; Czarnitzki et al., 2011; Lokshin and Mohnen, 2012), foresight exercises (Kuhlmann, 2002; Georghiou and Keenan, 2005), innovation vouchers (Cornet et al., 2006; Bakshi et al., 2011) and supporting actor networks (Buhrer, 2002; Russo and Rossi, 2009). Academics also explore how innovation is influenced by non-innovation policies such as market regulation and product regulation (recent contributions include Coates, 2011; Blind, 2012; and Paraskevopoulou, 2012). Often, these studies are referred to as regulatory impact assessment (OECD, 2004; Renda, 2006, 2011). To assess the direct or indirect impact of non-innovation policies on innovation, is a substantial research challenge. Similar to innovation policies, it often takes years before non-innovation policies have impact. However, regulatory impact assessment generally takes place ex ante, i.e. before non-innovation policies are launched (Renda, 2006, 2011). This has implications for the data that is available; the methods that are feasible; and the level of uncertainty in the results of an assessment. There are many examples of non-innovation policies that were changed substantially, as empirical evidence about their impact became available. One example is regulation that allows Internet Service Providers to use the infrastructures of British Telecom, KPN and other telecom incumbents (Baake and Preissl, 2006; Cave, 2006; Bourreau et al., 2010). Systems of innovation literature explores the impact of innovation policies and non-innovation policies. Early contributions include Freeman (1987), Lundvall (1992), Nelson (1993), Carlsson and Stankiewicz (1991), Edquist (1997), Breschi and Malerba (1997) and Cooke et al. (1997). The systems of innovation framework is a heuristic that emphasises the interactions between technologies, knowledge, actors, networks and institutions. These concepts require a brief introduction.
Technologies mostly refers to physical artefacts such as devices, professional equipment, components and advanced materials, and virtual artefacts such as software. Technologies may also refer to production systems and business support systems such as factory automation and IT systems (Carlsson and Stankiewicz, 1991; Carlsson, 1995). Knowledge encompasses codified knowledge and tacit knowledge (Cowan et al., 2000). Examples of codified knowledge are manuals, patents and standards. Tacit knowledge includes experience, gut feeling, learning on the job, and other cognitive, verbal and manual skills. Technological developments benefit from advances in knowledge, e.g. information about user behaviour. Likewise, knowledge accumulation benefits from advances in technology, e.g. online collaboration tools. Actors refers to any organisation or individual that is relevant for the innovation process (Edquist, 1997; Malerba, 2004b). Networks refers to social networks between organisations and/or individuals (Granovetter, 1983; Burt, 1992). The concept of institutions encompasses formal rules (laws, regulations, support programmes and other policies) and informal rules (such as conventions and routines) (North, 1990; Edquist and Johnson, 1997). Interactive learning is at the heart of innovation systems (Lundvall, 1992). For example, knowledge about new technologies is shared and accumulated within actor networks.
In a period of 30 years, the systems of innovation framework has become highly prominent in the field of innovation studies (Lundvall and Borrás, 2005; Fagerberg et al., 2012; Martin, 2012). The framework stimulates researchers to look beyond ‘the basics’ such as technology, the production of codified knowledge, and traditional actors such as universities and high-tech firms. For instance, consortia of small firms, consultants and users may pursue a combination of technological and non-technological innovation, and exchange codified knowledge and tacit knowledge. Edquist (1997, p.14) provides a broad definition of innovation systems: it includes “…all important economic, social, political, organisational, institutional and other factors that influence the development, diffusion and use of innovations.” The overall function of innovation systems is to pursue innovation processes (Edquist, 2005). This can be specified based on normative and pragmatic criteria. For example, one can emphasise the contribution of innovation systems to economic growth, or to the creation and diffusion of knowledge across society (Lundvall, 2010). There are four different perspectives on systems of innovation: national, regional, technological and sectoral. These perspectives are different in terms of their demarcation and emphasis on specific concepts. Early studies with a national perspective emphasise technologies and policies (Nelson, 1993), interactive learning and informal rules/institutions (Lundvall, 1992). The regional perspective emphasises geographic and cultural proximity, collaboration and knowledge diffusion (Cooke et al., 1997; Braczyk et al., 1998; Cooke, 2001). The technological perspective emphasises the generation, diffusion and utilisation of technology (Carlsson and Stankiewicz, 1991). The sectoral
perspective emphasises the interaction between innovation, production and market activities (Breschi and Malerba, 1997; Malerba and Orsenigo, 1997; Malerba 2002, 2004b, 2007). This study takes a sectoral perspective, as will be explained in Section 1.6. In systems of innovation literature, as in service innovation literature, there is a strong interest in innovation policies and non-innovation policies. Because the systems of innovation framework is a heuristic, rather than a theory, it does not allow for building causal models of policies and other variables that influence innovation (‘a policy computer’). It does, however, guide researchers in conducting case studies and drawing policy conclusions that are context-specific (cf. contingency) and policy conclusions that have a broader relevance. To illustrate the policy relevance of systems of innovation studies, we list a number of valuable conclusions by leading authors. Examples of context-specific conclusions are Nelson (1993) on the important role of the military as a financer and client for high-tech innovation in the US, and Edquist (2003) on the importance of standardisation and liberalisation for mobile telecommunications. Examples of conclusions with a broader relevance concern the importance of consistency of polices (consistency across time); stimulating innovation by means of fiscal and trade policies (non-innovation policies); the importance of excellent research at universities and public research organisations (at least in disciplines that are relevant for high-tech sectors); the importance of collaboration between different types of actors; and alignment between education systems and the needs of industry (Nelson, 1993). Additional examples concern the balance between private and public actors in taking responsibility for innovation systems (Cooke, 2001); education systems that not only provide knowledge, but also a mentality of trust, honesty and social responsibility, in order to stimulate collaboration, learning and innovation (Dalum et al., 1992); and the importance of policy intervention during early phases of innovation systems, when uncertainty is high and when private actors are reluctant to invest substantially (Edquist, 2003; Malerba, 2004b).
The founding fathers of the systems of innovation framework have admitted that there is little appreciative theorising about the impact of specific types of policies on specific types of innovations, in specific contexts (Cooke, 2001; Edquist, 2005; Malerba, 2004b, 2006; Nelson, 2008; Lundvall, 2010). Theoretical and conceptual progress has been more substantial in identifying the key activities, actors, institutions and interactions in innovation systems.
An emerging theme in systems of innovation literature is the policy mix, i.e. looking at the interaction between policy instruments, the relative impact of individual policy instruments and the impact of the entire policy mix (Technopolis et al., 2001; Arnold, 2004; Mohnen and Roller, 2005; Schibany and Jörg, 2005; OECD, 2005d, 2007a; Freitas and Von Tunzelmann, 2008; Poel and Kool, 2009; UNU-MERIT et al., 2009;
De Heide, 2011; Flanagan et al., 2011). Flanagan et al. (2011) explain how the policy mix concept is discussed in public management literature about policy levels (e.g. interactions between regional, national and European policy), policy areas (e.g. horizontal coordination between ministries) and policy instruments (e.g.
combining carrots, sticks and sermon, as described by Bemelmans-Videc and Rist, 2003). Flanagan et al. (2011) describe the conceptual difficulties in defining and applying the policy mix concept. Policy mixes constantly change, through a series of negotiations with stakeholders about individual policy instruments, rather than about the policy mix. As such, the policy mix is an empirical observation (ex post) instead of a coherent plan (ex ante). One should be realistic about the possibilities to design and implement a coherent policy mix. Policy coordination cannot remove all tensions in the policy mix because individual policies differ in terms of public values to be served and objectives to be met. Innovation need not always be the number one priority. Still, the coherence and effectiveness of the policy mix can be improved substantially. “A key role for innovation policy studies should be to highlight the trade-offs and tensions inherent in any policy mix and to promote open debates about them.” (Flanagan et al., 2011, p.711). For instance, non-innovation policies, such as consumer protection, product regulation and competition policy, can hinder the innovation activities of actors, while innovation policies urge the same firms to develop and launch new services. An example of positive interaction is a combination of policy instruments to address all actors in the innovation system. Note that the concept of interactions in the policy mix refers to complementarities and tensions between policies rather than interaction effects in formal models and statistical analysis (cf. Flanagan et al., 2011).
The policy mix perspective raises the bar for impact assessment. Georghiou (1998) was among the first to mention the interaction between innovation policies and non-innovation policies, and the implications for impact assessment: “Having reviewed the practice of evaluation across the domain of innovation policy, one feature which emerges quite strongly is the general lack of cross-cutting evaluations, comparing the relative effectiveness of policies. […] However, if the emphasis in innovation policy is now upon its systemic characteristics, there would appear to be a need for the findings of evaluations to be similarly marshalled in order to understand the interaction between policies and the net effects of intervention. More broadly, innovation is affected by a wider range of policies and regulatory frameworks, which also need to be brought into the scope of such evaluation.” (pp.48-49). A similar point is made by Arnold (2004) and Flanagan et al. (2011), albeit with an emphasis on innovation policies. Each policy instrument is assessed ‘in splendid isolation’ because it has been in existence for two or four years, under the responsibility of ministry A or agency B, with objectives X or Y. Among the exceptions in the Netherlands is an integrated reflection on all innovation policies, every five or six years. A panel of policy makers and experts prepared a comprehensive study in 2001/2002 (IBO, 2002). However, the next study was undertaken by policy makers only, with a limited scope
and without information about impact. The national Court of Auditors criticised this decision (Rekenkamer, 2011).
The challenge of assessing the impact of both innovation policies and non-innovation policies is magnified in the context of service innovation. This is due to the characteristics of service innovation (see Section 1.2) and limited experience by policy makers and academics in designing policies to stimulate service innovation, and in assessing the impact of policies on service innovation. Given the increased societal importance of service innovation, there is much at stake when exploring the impact of the policy mix on service innovation. A crucial step is to understand how innovation policies and non-innovation policies influence service innovation. What are the mechanisms?
1.5 | Market failures and system failures
To understand the mechanisms via which policies influence service innovation, this study will identify deficiencies in the innovation system, and assess how they are influenced by innovation policies and non-innovation policies. In addition, this study will analyse how deficiencies hinder service innovation. The main reasoning can be summarised as follows: if innovation policies and non-innovation policies decrease the number of deficiencies and the magnitude of individual deficiencies, then the functioning of the innovation system is improved and service innovation is stimulated.
Market failures and system failures are the main concepts for analysing deficiencies in innovation systems. The concept of market failures is rooted in neo-classical economics, and is most applicable to transactions. Markets may fail to generate efficient allocation of resources and/or socially desired outcomes, because of four reasons (Bator, 1958; Coase, 1960; Katz and Rosen, 1998; Spulber, 2002):
> Market power,
> Information asymmetry, > Externalities/spillovers, and > Public goods.
The concept of market failures has been specified for technology and knowledge production markets (i.e. innovation markets) and has become the main rationale for innovation policy (Nelson, 1959; Arrow, 1962; Klette et al., 2000; Martin and Scott, 2000; Smith, 2000; Dosi et al., 2006a; Gustafsson and Autio, 2011). Firms with market power may lack incentives for (radical) innovation or may block access to important sources of innovation. Information asymmetry between investors and innovative firms can result in high uncertainty for investors and a lack of funding of firms. Externalities/spillovers refers to the reluctance of firms to invest in knowledge that
can easily spill over to other firms, and to invest in new services/goods that can easily be imitated (under IPR regimes that do not strike a balance between rewarding innovators and allowing for diffusion of knowledge). In the context of innovation, there are few public goods (Dosi et al., 2006a). The concept of public goods implies that innovation activities require a substantial scale, that activities are difficult to cut into parts (indivisibility) and that benefits spill over to a large number of actors, without any possibilities for restricting use by other actors, and without effective appropriability mechanisms (e.g. IPR, first mover advantages, learning curves and reputation effects). There are public good characteristics in large research facilities (e.g. clean rooms for developing microprocessors) and in large infrastructures such as Global Positioning Systems. In addition, there are public good characteristics in education and training (e.g. investing in students and employees). Cooke (2006) provides an example at a higher level of abstraction: innovation systems require a substantial scale, many actors benefit (directly or indirectly) and individual actors have few possibilities to recoup their investments in the innovation system. These public good characteristics provide governments with a rationale to improve functioning of innovation systems (Cooke, 2006).
System failures is a relatively new concept that emerged in the systems of innovation literature (Carlsson and Jacobsson, 1997; Smith, 2000; Klein-Woolthuis et al., 2005; Gustafsson and Autio, 2011). The concept is considered effective for analysing service innovation (Rubalcaba, 2006; Den Hertog and Rubalcaba, 2010). This study uses a typology of system failures that is adapted from Klein-Woolthuis et al. (2005): > Failures in infrastructural provision and investment, e.g. physical infrastructures
and science-technology infrastructures such as universities,
> Lock-in or path dependency failures, e.g. the development and adoption of new technologies and practices may be delayed or blocked because it requires changes in technological, economic and/or social systems (cf. technological regimes), > Institutional failures, e.g. laws, regulations, norms and routines that hinder
innovation,
> Interaction failures, e.g. weak networks between complementary technologies, knowledge, sectors and actors (weak network failures) and/or groupthink and overlooking new opportunities and combinations (strong network failures), and > Capabilities failures, e.g. a lack of competences and learning potential.
By means of introduction, it should be mentioned that the concept of system failures covers a number of specific deficiencies in the functioning of innovation systems, in addition to deficiencies that are covered by the concept of market failures. The concept of system failures is relevant for transactions, collaboration, knowledge exchange and other types of interaction. Chapter 2 addresses differences, similarities and interactions between market failures and system failures. This is preceded by a discussion of individual market failures and system failures. The discussion of
institutional failures will distinguish between institutional failures that are caused by policies (cf. hard institutional failures) and institutional failures that are caused by norms, routines and other informal rules in a sector or country (cf. soft institutional failures) (Carlsson and Jacobsson, 1997; Smith, 2000; Klein-Woolthuis et al., 2005).
1.6 | Conceptual framework and research question
This section presents the conceptual framework that is developed in this study (Figure 1.1). Subsequently, the research question is introduced.
The conceptual framework is elaborated on in Chapter 2 (sectoral systems of innovation) and Chapter 3 (service innovation). The conceptual framework is not intended to visualise sectoral innovation systems, but to position the components and mechanisms that are crucial for our study. The concept of service-centric innovation reflects the synthesis approach on service innovation. To increase readability, this study uses service-centric innovation and service innovation as synonyms.
Most central to this study is the assessment of the impact of innovation policies and non-innovation policies on market failures and system failures. As mentioned above, we address four types of market failures and five types of system failures, i.e. nine mechanisms via which policies can influence service innovation. Policies can increase or decrease market/system failures. The analysis of market failures and system failures is a crucial part of the analysis. Some types of failures emerge; others do not.
Innovation policies Non-innovation policies Market failures Service- centric innovation System failures
Some failures are persistent; others are temporary. Some failures interact; others do not. The analysis of market failures and system failures includes a description of functions of the innovation system, and the types of actors, that are affected (Bergek et al., 2008). Distinctions should be made between six functions of innovation systems: knowledge development and diffusion, influence on the direction of search, entrepreneurial experimentation, market formation, legitimation, and resource mobilisation (adapted from Bergek et al., 2008). This study will also assess how market failures and system failures (and the functions and actors affected) influence service innovation (Section 1.7).
The main objective of the study is to assess the impact of innovation policies and non-innovation policies on service innovation. To be able to provide an assessment, we identify and describe mechanisms - market failures and system failures - via which policies influence service innovation.
The research question is: what is the impact of the national policy mix of innovation policies and non-innovation policies on service innovation in the sectoral innovation system for Internet video services in the Netherlands, during its formative and growth phases?
The policy mix concept indicates an assessment of the impact of individual policies, interactions in the policy mix, and the impact of the policy mix. Furthermore, the research question makes explicit that we focus on national policies. Compared to local and international policies, national policies are highly relevant for service innovation (Den Hertog and Rubalcaba, 2010), for sectoral innovation systems (Malerba, 2004b) and for the empirical domain of Internet video services (Poel et al., 2007a, 2007b). A focus on national policies increases the feasibility of our study. To explore innovation policies and non-innovation policies already implies a broad scope. Still, this study touches upon local, regional, EU and international policies (Chapter 5). The research question makes explicit the case study: Internet video services in the Netherlands (see below).
1.7 | Research approach
First, this section explains that this study is about one specific type of service sector: information networks sectors. This is in order to acknowledge the variety of service sectors and service innovations. Next, this section describes that an in-depth case study allows for a synthesis approach on service innovation, and for revealing the mechanisms via which policies influence service innovation. Subsequently, it describes how this study will use the sectoral systems of innovation framework. Next, this section explains the research approach for the central part of the analysis: the identification and analysis of deficiencies in the innovation system, in terms of market failures and system failures; an assessment of the impact of innovation policies and
non-innovation policies on market failures and system failures; and on service non-innovation. This section concludes by presenting the subquestions of this study.
Information networks sectors
The analysis in this study is most relevant for one type of sector: information networks sectors (Miozzo and Soete, 2001). Examples are financial sectors, insurance and electronic communications (telecom). The analysis is less relevant for services that can be positioned in supplier-dominated sectors (e.g. retail and restaurants), scale-intensive physical networks sectors (e.g. transport and wholesale) and sectors that are science-based and/or highly specialised (e.g. R&D) (cf. Miozzo and Soete, 2001). Note the increased economic and social importance of services that rely on information networks, as ever more services are provided via the Internet.
Internet video services can be positioned in information networks sectors because Internet video services require information networks. One relevant sector - electronic communications - is an information networks sector. Parts of other relevant sectors - media and IT/software - can also be positioned as information networks sector. One of the strengths of the taxonomy of Miozzo and Soete (2001) is the description of characteristics of service innovation in information networks sectors: a combination of service innovation and process innovation; different sources of innovation (e.g. users, suppliers, engineering departments, R&D departments, consultants and universities); and interaction between service innovation and innovation in the underlying telecommunication networks and innovation in goods (e.g. end-user devices). In short: innovation patterns with a variety of actors and interactions. We elaborate on this in Chapter 3.
In-depth case study
Internet video services in the Netherlands (1997-2011) is analysed by means of an in-depth case study. The rationale for applying case study methodology is discussed in Chapter 5. Case studies are highly effective in studies that seek to reveal and understand the mechanisms between a range of concepts, especially in empirical domains that are relatively new (Eisenhardt, 1989; Yin, 1994; Flyvbjerg, 2006). More specifically, this point is made in the context of policy evaluation (Patton, 1987) and in the context of evaluating innovation policies (Stern, 2002; LLA et al., 2006). As mentioned above, there are persistent challenges in understanding the mechanisms between innovation policies and innovation. These challenges are amplified when studying service innovation, and when studying the mix of innovation policies and non-innovation policies.
Data has been gathered by means of interviews with service providers and experts. Interviewees were invited to first describe service innovations. These discussions allowed us to describe service innovation in terms of six dimensions that are defined
by OECD (2005b): new services, new processes, new marketing methods and new organisational methods in business practices, workplace organisation or external relations. In discussing service innovation, other qualitative and quantitative aspects of service innovation (such as the number of actors) were touched upon.
Subsequently, interviewees were asked about deficiencies/problems in the innovation process, and how they were influenced by policies. Interviewees discussed policies because they were perceived relevant for the innovation process, not because they were mentioned by the researcher. Interview results were validated by three types of documents: 1) studies about deficiencies in the innovation process for Internet video services, 2) official evaluation studies of individual policies, and 3) studies about the policy mix and/or the innovation system in the Netherlands.
Internet video services in the Netherlands, 1997-2011
The empirical domain of this study - Internet video services - was chosen because it can be positioned in information networks sectors (that are of increased importance); because it is rich in terms of relevant types of innovations, actors and policies; because innovation in media services is studied less often than service innovation in other information networks sectors (e.g. the financial sector); and because the economic and social relevance of Internet video services is increasing (compared to traditional broadcasting). We will introduce the empirical domain of Internet video services, before addressing the selection of the case study.
With increased adoption of Internet access services, and increased bandwidth, video has been added to text-based and audio-based websites. Dedicated Internet video services have also been launched. Our definition of Internet video services includes on-demand and streaming services that are available to a broad set of users, irrespective of their device or Internet access subscription. Examples are YouTube, Hulu, Netflix and the online services of public and commercial broadcasters. Our definition excludes services that are delivered only via proprietary devices and controlled service environments such as settop-boxes of cable networks. This study is most interested in the open environment of the Internet, with low entry barriers and few limitations in reaching consumers in several countries. However, we expect the open environment of Internet video services to interact or converge with controlled service environments. For instance, YouTube is installed on several cable settop-boxes, and has become available as an app on Google’s Android platform and Apple’s iOS platform. Internet video services contain several characteristics that are relevant for studying service innovation and the impact of the policy mix on service innovation. First, Internet video services have emerged as a new phenomenon, with opportunities for actors from a range of sectors. Each actor could bring their own resources and capabilities related to Internet technology, media technology, telecommunications, devices, media production, media programming, advertising, copyright, etc. This is
linked to a second characteristic: Internet video is service-centric but interacts with innovation in goods and infrastructures. Third, actors involved in Internet video services not only have to take into account (horizontal) non-innovation policies related to competition, consumer protection, taxation, etc. They also have to consider (vertical) policies for media pluralism and access to media by citizens/consumers. Moreover, the existence of public broadcasting organisations increases the range of relevant actors. Fourth, Internet video services increase internationalisation of media sectors. In this process, individual actors and countries become more relevant, at the expense of other actors and countries. Internationalisation is highly relevant for policy makers, as it affects the location of innovation activities, business activities and jobs. This is one of the reasons why policy makers stimulate innovation in the converged IT, telecom and media sector. Examples from the Netherlands illustrate this, such as the action plan electronic highways (1994), the digital agenda (1999) and the decision to prioritise media and other creative industries (2004).
Illustrations at the EU level are the eEurope initiative (1999), the digital agenda for Europe (2010) and ICT being a priority in the Framework Programmes for research (from the nineties onwards).
An additional reason to study Internet video services is that media sectors are often studied from the perspective of technological innovation, standardisation and business models, while it is less common to apply innovation concepts such as systems of innovation, innovation networks and service innovation. Notable exceptions include Zook (2000), Gilsing and Nooteboom (2005), Cook and Pandit (2007), Storz (2008), Cooke and Porter (2011) and Karlsson and Picard (2011). In addition, innovation in media sectors is addressed in studies about creative industries (Florida, 2002; Miles and Green, 2008; Stoneman, 2010) and in studies on IT-related clusters such as Silicon Valley (Adams, 2011). As mentioned above, other information networks sectors have received more attention from innovation scholars. For example, innovation in the financial sector is studied in Barras (1990), Sundbo (1991), Oliveira and Von Hippel (2011) and in numerous studies about Knowledge Intensive Business Services (as observed by Gallouj and Savona, 2009).
The main reason for selecting the Netherlands as the focal point of the study, is that researchers, established firms, start-ups, public broadcasters and users in the Netherlands were among the pioneers in Internet video. Around 1997, a sectoral innovation system for Internet video services in the Netherlands emerged. Actors benefited from Internet access, interaction between different types of actors, timely policy interventions, and convergence between IT, telecom and media sectors. This will be elaborated on in Chapter 5. Internationally, the market is now dominated by YouTube (Google), Netflix, Akamai and other US-based firms. Only four or five firms from the Netherlands have acquired a (small) position in international markets. Other Dutch actors are successful in the Netherlands only. This process has evolved
over a period of 15 years. As such, this study analyses an innovation system with mixed results instead of a successful innovation system such as Silicon Valley.
Sectoral systems of innovation framework
For two reasons, this study applies the sectoral ‘version’ of the systems of innovation framework (Breschi and Malerba, 1997; Malerba and Orsenigo, 1997; Malerba, 2002, 2004b). First, the sectoral perspective emphasises the interaction between innovation, production and market activities for a set of services/goods for specific uses. This fits the characteristics of service innovation. As mentioned in Section 1.2, among the characteristics of service innovation are the involvement of users and iterations between innovation, production and market activities. The sectoral perspective takes into account other key variables such as technologies and knowledge, actors and networks, and institutions. Second, and related to this, the sectoral systems of innovation framework has been considered effective in several studies on service innovation (e.g. Metcalfe and Miles, 2000; Edquist, 2003; Steinmueller, 2004; Tether and Metcalfe, 2004; Gotsch, Hipp, Gallego and Rubalcaba, 2011).
The (sectoral) systems of innovation framework acknowledges the relevance of innovation policies and non-innovation policies. Malerba (2004b, p.502): “…a sectoral system approach emphasizes that innovation and technology policies affect and are linked with other types of policies, such as science policy, industrial policy, policies related to standards and intellectual property rights, and competition policy.” Furthermore, the systems of innovation framework allows us to address characteristics of service innovation such as non- technological innovation and the role of suppliers and users. It also allows for a dynamic perspective on the demarcation of sectors and innovation systems. The framework is agnostic in terms of methodologies. There is no fixed set of causal models, methodologies, tools and indicators. The systems of innovation framework does, however, provide the concepts and mechanisms for designing a case study approach. In applying the sectoral system of innovation framework, this study incorporates two conceptual advances in literature on technological innovation systems. It differentiates between two phases of innovation systems and six functions of innovation systems (see below). Although there are several implications of taking either technologies or sectors as the point of departure in innovation studies, there are many commonalities between both types of studies. As mentioned above, all versions of the systems of innovation framework address interactions between technologies, knowledge, actors, networks and institutions. Therefore, we can test whether conceptual advances in the technological version of the systems of innovation framework, can also improve the sectoral version of this framework. The sectoral systems of innovation framework emphasises the evolution of sectors. The focus is on reduced technological uncertainty and actor variety, as sectors grow (Breschi and Malerba, 1997; Malerba and Orsenigo, 1997; Malerba, 2002, 2004b). Concepts are taken from technological paradigms
(Dosi, 1982, 1988) and technological regimes (Nelson and Winter, 1982; Winter, 1984). Bergek et al. (2008) propose a clear distinction between two phases. They distinguish between the formative and growth phases of technological innovation systems, based on the concept of technological regimes and on empirical studies of technological innovation systems in the field of sustainable energy. Furthermore, a distinction is made between different functions of innovation systems or activities in innovation systems (Smits and Kuhlmann, 2004; Hekkert et al., 2007; Negro et al., 2007; Bergek et al., 2008; Suurs and Hekkert, 2009; Suurs et al., 2010). This study differentiates between six functions: knowledge development and diffusion, influence on the direction of search, entrepreneurial experimentation, market formation, legitimation, and resource mobilisation (adapted from Bergek et al., 2008). The relative importance of each function can differ between the formative and growth phases. Moreover, the emphasis within individual functions is different between phases (e.g. the type of resources, knowledge and actors). In addition, interactions between functions can be different between phases. For example, the formative phase often involves close interaction between the functions of resource mobilisation, knowledge development and diffusion, influence on the direction of search, and entrepreneurial experimentation. Gradually, interactions with market formation and legitimation become more important, and the scale of resource mobilisation increases. As such, the analysis of functions contributes to the delineation of phases. Based on Bergek et al. (2008), we developed (rough) indicators for recognising the tipping point between the two phases (Chapter 2). The distinction between two phases allows for exploring innovation policies and non-innovation policies that influence service innovation during the formative phase and/or the growth phase. When phases are different in terms of prominent functions of innovation systems, leading actors, technological maturity, market size, etc., there are likely to be differences in terms of relevant policies.
Another innovation in the application of the sectoral system of innovation framework, is that this study uses social network analysis to demarcate the innovation system. Network analysis can be used to explore knowledge exchange between actors, between sectors, between regions, and between countries (Powell and Grodall, 2005; Provan et al., 2007; Ozman, 2009). In this study, interviewees were asked about actors that were involved in the innovation process. This allows for the identification of firms from different services and manufacturing sectors. It also allows for identifying other types of actors, and actors from countries other than the Netherlands. A network analysis was undertaken for both the formative and growth phases, to acknowledge that innovation systems evolve over time.
