Open innovation and globalisation: Theory, evidence and implications

Sverre J. Herstad

NIFU STEP Studies in innovation , research and education

Carter Bloch Danish Centre for Studies in Research and Research Policy

Bernd Ebersberger

Management Center Innsbruck

Els van de Velde Ghent University

April 2008

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About the report

This report is based on the work conducted through the project ”Open innovation and globalization – theory, evidence and implications”, financed through Vision Eranet. A main purpose of the project has been to theoretically and empirically examine the concept of “open innovation”, and how it can be used to understand changing corporate strategies and their impact on corporate innovation performance. Emphasis has been put on broadening the theoretical content of the concept, on demonstrating how existing innovation survey data can be used to describe various innovation practices across industries, on investigating their impact on corporate innovation performance, and on discussing the possible policy implications stemming out of the theoretical and empirical analysis. Participating institutions and countries have been Norway (NIFU STEP Studies in innovation , research and education), Denmark (CFA Danish Centre for Studies in Research and Research Policy), Austria (MCI Management Centre Innsbruck) and Belgium (Ghent University, Faculty of Economics and Business Administration). Each national team has been responsible for separate modules, and for running statistical analysis on country innovation survey datasets. The work has been coordinated by Sverre J. Herstad , NIFU STEP. The statistical models have been developed by Bernd Ebersberger of MCI, with assistance from Carter Bloch of CFA . Sverre J. Herstad has developed the theoretical framework presented in chapter 1 , with assistance from Carter Bloch . Els van De Velde of Ghent University has reviewed policy documents and policy measures, and coordinated interviews among policymakers in the different countries. She has also provided comments to all chapters. The work with preparing Belgian data and running the analysis was conducted by Andrè Spithoven . The group greatly appreciates the financial support received from The Research Council of Norway, The Danish Agency for Science, Technology and Innovation , Flemish Government - Department of Economy, Science & Innovation , Belgium; and Federal Ministry of Transport, Innovation and Technology, Austria. This report presents novel methods, insights and interpretations of the phenomena “global open innovation”. In short, it shows that open innovation - as defined here - matters. We therefore hope it can serve as a fruitful input to ongoing debates on innovation policy, as well as serve as input to and inspiration for academic debates on the boundaries of organizations and innovation systems. And we hope the analytical template we have developed will be applied on innovation survey data from other countries than those included here.

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Table of contents

Theoretical perspectives on open innovation ................................................................... 7 Introduction ................................................................................................................ 7 Levels of analysis ........................................................................................................ 7 Research strategy ........................................................................................................ 8 Drivers ........................................................................................................................ 8

Complexity and uncertainty ..................................................................................... 9 Globali zation and the internationali zation of value chains ...................................... 10 Institutional change ............................................................................................... 11

Towards a theory of open innovation ......................................................................... 12 The context of open innovation ............................................................................. 13 Open innovation and the firm ................................................................................ 15 Open innovation at the economy level.................................................................... 21 Open innovation and policy ................................................................................... 24

Synthesis ................................................................................................................... 25 Technological regimes ........................................................................................... 26

Empirical analysis of open innovation ............................................................................ 29

Introduction .............................................................................................................. 29 Data and methodology .............................................................................................. 30

Data ...................................................................................................................... 30 Constructing open innovation indicators ................................................................ 30 Open innovation practices vs. closed innovation .................................................... 32

Assessing the coverage of the indicators for open innovation practices...................... 35 Descriptive empirical analysis .................................................................................... 36

Description of the national data sets ...................................................................... 36 Innovation activities in the sample ......................................................................... 38

Exploring the open innovation indicators ................................................................... 42 Effect of open innovation on innovation performance ................................................ 48

Performance effects of open innovation practices .................................................. 48 Effect of globali zed innovation networks on performance ...................................... 56

Summary ................................................................................................................... 58 Conclusions and policy implications .............................................................................. 61

Main findings and policy implications ........................................................................ 61 Dimensions of open innovation ................................................................................. 62 Country differences ................................................................................................... 63 Regime differences .................................................................................................... 63 Regimes versus trajectories ....................................................................................... 64 Globalisation ............................................................................................................. 65 SMEs versus large enterprises .................................................................................... 65 Open innovation , performance and the theory of the firm .......................................... 66 Use of CIS data .......................................................................................................... 67 Implications for future firm level research .................................................................. 68 Implications for future economy level research .......................................................... 68 Implications for innovation policy .............................................................................. 69

References .................................................................................................................... 7 3 Appendix ...................................................................................................................... 8 3

Policy system interview guide .................................................................................... 83 CIS Questionnaire ...................................................................................................... 86

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Theoretical perspectives on open innovation

By Sverre J. Herstad and Carter Bloch

Introduction Claims have recently been made (OECD , forthcoming , Chesbrough 2003 , 2005) that industry is entering a new era of “open innovation”. An era of purposeful corporate strategies through which investments in intramural R&D are supplemented or even substituted (Herstad and Naas 2007 , Mariussen 2007 , Laz onick 2007) by extensive use of external knowledge sourcing and external paths to commerciali zation . New all -encompassing best practices are said to be emerging out of the modularity of technologies, increasing knowledge and information flows stemming from the explosive development and diffusion of digital information systems. This is claimed to create large opportunities for first mover advantages related to the establishment of radically new service or product architectures, and to a flattened distribution of productive competencies across actors and space (Friedman 2005 , Chesbrough 2003). A new landscape, rich of ideas and knowledge is “out there”; ready to be harnessed to those who master the trade of open innovation . We deviate from such simplistic views of a “new economy” or an “era of open innovation”, but recognize that we are witnessing forces in play which are transforming the industrial landscape. These can , we argue, be understood within the umbrella concept of “global open innovation”. We follow OECD (forthcoming) and reinterpret Chesbroughs open innovation concept by linking it to the interplay between subtle organizational processes and inter - organizational linkages and networks (ibid) – extending across space as a result of globalization (ibid , Unctad 2005 , Cooke 2007 , 2005 , Asheim 2005 , Bathelt et al 2004). We then argue that sustained competitive advantages are rooted in the ability of firms to tap into ideas and knowledge - wherever it may be located externally, and use these linkages to learn, accumulate speciali zed competencies, and be innovative . The strategies needed to address these challenges are not homogeneous across firms; there are diversities in technological, market and knowledge conditions that create diversity in firms’ innovation practices. This raises fundamental questions concerning new opportunities for public policy and the way these should be addressed . This study is a theoretical and empirical first - step investigation of the concept “global open innovation”. This report will first provide a theoretical framework for understanding open and global innovation practices; its various dimensions and drivers. Importantly, we also emphasize the diversity of open innovation practices, and argue that these can be best seen and understood through an analysis of technological regimes. Second , it will investigate the extent to which data already available from the European Community Innovation Surveys can be used to be able to grasp the different dimensions of global open innovation and their role in innovation performance. In doing so , we develop firm level indicators both of individual dimensions of open innovation and of firms’ overall open innovation practices. These are then used to examine the scope and characteristics of open innovation practices in Austria, Belgium , Denmark and Norway, and to analyze the relationship between these and innovation performance. Finally, it will discuss how policy – which is still predominantly national level policy – should understand , and react to, the phenomena of globalization and open innovation .

Levels of analysis Open innovation can be studied at the level of specific innovation projects. However, this mode of analysis faces three distinct problems. First , each development project will be characteri zed by a set of characteristics and conditions specific to it , making it difficult to generalize more broadly. Second , openness beyond specific project execution is not

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grasped by the analysis. For instance, the extent and impact of ongoing innovation search, the scanning of the environment for new ideas and information prior to the initiali zation of specific innovation project, will easily be neglected . Third , and most critical, the analysis loses sight of both prerequisites for openness and its implications at the level of the organization . It overlooks the often cumulative and collective nature of knowledge development and innovation (Lazonick 2005) and assumes that decisions concerning the governance of specific projects can be made independently of each other. For instance, outsourcing R&D may provide cost - efficient problem solving on a project -to - project basis; but comes with the organizational cost of lost knowledge accumulation . This may increase the dependence of organizations towards external R&D providers, and over time hollow these out. Outsourcing production , logistics or even marketing may similarly reduce operational costs, but may come with the cost of lost organizational “ears” towards markets and suppliers (Jacobides and Billinger 2006) and weakened integration between production and R&D (see e.g . Teece 1988). We therefore follow work within organizational studies and management studies, and study open innovation within the context of overall organizational boundaries and their permeability. We analyze firm level practices and strategies, and the impact of these on firm innovation performance. This level of analysis comes with its own set of challenges. First, we need to account for the fact that different means of interacting with the external environment – what we call dimensions of open innovation – may be more relevant to some firms than to others. We need indicators for open innovation which are not confined to specific dimensions. Second, we need to recognize that openness is more about the range of interfaces kept open than it is about the intensity of openness along specific dimensions or towards specific actor groups. Third , we need to acknowledge the distinction between business strategies maximiz ing individual firm private returns, and the social returns accruing to economies as a result of externalities and collective action dynamics.

Research strategy The project started out by conducting a review of literature dealing directly or indirectly with open innovation at the firm and economy levels respectively. This provided the basis for the theoretical analysis of open innovation which follows in the remaining part of chapter 1 . It draws on numerous research strands, ranging from economics through theories of innovation systems and into organisational and management studies. Community innovation survey data contain information about a wide array of inter -organisational linkages, and on innovation performance. This data was therefore used to produce descriptive statistics on open innovation . In addition , we identified certain weaknesses in the data. We then developed simple econometric models to test the relationship between overall organisational openness and its different dimensions, on the one hand , and innovation performance on the other. For the purpose of enabling the analysis to be run on other country datasets at later stages, emphasis was put on developing straightforward descriptive and analytical models based only on the core survey data. To provide a background for drawing policy implications we screened innovation policies and portfolios of innovation policy tools in the participating countries. We also presented our main findings to key representatives of different national policymaking institutions, and gathered information on the extent to which they feel existing policies and measures reflect these findings, and how. For this purpose we developed an interview guide . The results of these efforts are reflected in the concluding discussion .

Drivers Open innovation is not a new phenomenon . During the first decades of the 20th century, industrial enterprises in the United States co - operated and sourced R&D services from dedicated , external R&D labs (Teece 1988 , Hollingsworth 1991). Associative behaviour – from gentlemen agreements through cartels and co - ordination linked to trade associations - was common , and critical to the survival of what was then an industrial structure dominated by small firms (Hollingsworth 1991:291 - 292). The years following

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the Second World War saw this landscape change dramatically. In the 1950s and 1960 the “Fordist” regime of vertically integrated mass production grew and consolidated , and with it came a strong emphasis on internal R&D in so - called “first generation R&D organizations” (Roussel et al 1991). These celebrated the “…specialization and autonomy of the R&D professionals” (Lam 2000). Conditions critical to the growth of the regime were rapidly expanding domestic consumption markets linked to large public incentives for industry investments in R&D; low external mobility of labour, long - term governance of corporate enterprises by a managerial elite which operated independent of shareholder demand for returns, and US antitrust legislation – paradoxically – forcing vertical integration and closure. Hollingsworth (1991:40) point out how “…the absence of effective antirust law in Europe had the effect of perpetuating relative small family firms, whereas in America, antitrust law had the unintended consequence of accelerating the development of large-scale corporations”. This regime was severely challenged by the economic downturn of the 1970s. Throughout the 1980s, overall market saturation forced flexibility, responsiveness and product diversification; and created a new breed of “best practice” companies and industries, primarily outside the US. The Japanese success story is well - known , as are stories of so -called Marshallian industrial districts in Italy and Germany; i.e. networks of co - located small and medium sized enterprises (Asheim 1997). The vitality of the latter lead Piore and Sabel (1984) to go so far as to declare a “second industrial divide” characterized by the transition from large, vertically integrated companies (Fordism) to smaller, networked and thus more flexible modes of production and innovation (post - Fordism). During the 1990s, different innovation system approaches gained increasing influence as a basis for national industrial and innovation policy. This interest in the external organization of innovation was supplemented by studies and concept development at the level of organization and strategy; resulting in focus on “extended enterprises” and “collaborative advantages” (Dyer 1991), “virtual enterprises” (Chesbrough and Teece 1996) and “pragmatic collaboration” across organizational boundaries (Helper et al 2000). With this came the claimed transition from the “first generation R&D organization” through the intermediate market pull second generation model and into “third generation” modes of R&D organization through which internal R&D was to become integrated with other knowledge communities internal and external to the corporate enterprise. And innovation efforts were to be linked to and reflect long - term corporate planning and strategy – in contrast to the short - term focus on the second generation model (ibid). Emphasis was to be put on creating portfolios of innovation projects with internal complementarities between each other, drawing knowledge from different sources in - house , while simultaneously accelerating the degree of external knowledge sourcing . Concepts such as “learning organizations” came into fashion and were supplemented by a policy emphasis on clusters and “learning regions” as supporting infrastructures.

Complexity and uncertainty This increasing role of inter - organizational relations is driven by a variety of factors (Hagedoorn 1993 , Lichtenthaler and Ernst 2007), including the increasing complexity in technological content of products, processes and services – and the related divisions of labour. Cutting edge knowledge necessary for innovation tends to be dispersed across different actors and actor groups (Rothaermel et al 2006). According to Lam (2000), we are facing a situation where “knowledge is generated through the repeated combination and re-configuration of diverse disciplines and expertise in flexible forms of organization”. Industrial knowledge bases are only rarely disciplinary knowledge bases, feeding primarily on academic research . Rather, they are synthetic knowledge bases fed by inputs spanning from generic technologies such as biotechnology, nanotechnology or ICTs, to highly speciali zed knowledge that is accumulated only through experience or interaction with demanding customers or speciali zed suppliers. The more complex knowledge bases, products or processes become, the higher the dependence on various external sources of information , ideas and knowledge. These external sources may in turn be representatives of completely different technologies or “sectors” as traditionally understood; causing sectoral systems of innovation to blend with each other.

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Innovation in highly dynamic industries therefore often requires that the firm reaches beyond its own organizational boundaries, and beyond the boundaries of its immediate set of value chain transaction partners (Nooteboom 2001). What pieces of not - yet - linked knowledge that can feed future innovation processes is often uncertain and will only be revealed through search and experimentation . For instance , broad external search has led the development within fish - fodder to draw on laboratory techniques and equipment developed in pharmaceuticals combined with production technologies developed for potato chips manufacturers. Experimentation with production technologies developed for the purpose of knitting wool underwear has enabled recycling and refinement for the purpose of producing high - strength carbon fibre composites – which then has been transformed into naval warships Uncertainty also stems from dependencies towards external sources of knowledge which don’t themselves stand still, from rapid , unpredictable market changes and from the possible emergence of new platform technologies such as nanotechnology or biotechnology. Technological frontiers move in unpredictable directions; suppliers or competitors come up with ideas or solutions which may transform complete value chains; and future customer preferences may be very difficult to predict or dictate. Firms do not know all possible choices and outcomes, but choose from a number of satisfactory (Simon 1955) “known” options defined within the constraints of search strategy and information costs (Elster 1977 , Leiponen and Drejer 2007). But existing or known possible linkages easily end up optimiz ing the status quo (Nooteboom 2001). Radical innovations and the establishment of new trajectories may require inputs of unknown knowledge or ideas from sources not yet identified . Complexity and uncertainty combined suggests that there may be weakness in strong ties, and highlight the need to search broadly for ideas and information .

Globalization and the internationalization of value chains Policies building on the recognition of innovation as rooted in linkages have long remained focused on the role of local (clusters or Marshallian industrial districts), regional (regional innovation systems) or national (national systems of innovation) interaction patterns, and on traded - value chain - interdependencies. By the start of the 21st century it was becoming apparent that economic globalization was challenging this line of reasoning . Companies increasingly go abroad – and are forced to do so - to interact with the most demanding or competent customers, the cheapest or most competent suppliers, to seek ideas and knowledge within world leading research environments and seek new markets for their technologies (Lichtenthaler and Ernst 2007). Complexity combines with globalization and forces firms to internationalize – at earlier and earlier stages of their life - cycle (Smith 2000). The intensity of innovation - based competition is increasing , in part triggered by the rise of India and China as major international players. Symptomatic of all this is the internationaliz ation of corporate enterprises and innovation . Whereas we still see that market access or proximity to key users remains the single most important driver of such internationaliz ation in general, the proportion of corporate R&D performed outside domestic countries is increasing rapidly (UNCTAD 2005 , Granstrand 1999). The most important overall motive for this shifting of R&D activities remains customization of technologies to suit local market conditions, but there is clear evidence that technology sourcing plays an increasingly important role (van Pottelsberghe de la Potterie and Lichtenberg 2001 , UNCTAD 2005:158). This all means that national innovation systems or clusters are forced to open up . Useful knowledge has not necessarily become more evenly spread out across space , as Chesbrough (2003) claim; rather linkages are created between speciali zed knowledge development nodes located in places which are increasingly more geographically dispersed . Knowledge flows across actors and space as embodied in machinery or components; and between industries or firms with very different degrees of R&D - intensity and knowledge base characteristics. Low - tech firm users are linked to high - tech knowledge providers, and vice versa; innovation in individual firms – by necessity - becomes linked to interfacing with lead users located elsewhere; and to interfacing with leading suppliers, research institutes or universities that are more and more likely to be located outside of the immediate surrounding environment. Some of these nodes serve as gravitation points to which knowledge and ideas flow, and within which it accumulates

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and creates knowledge - rich environments. The Norwegian western coast is arguably a main centre of gravity in an intensely globalized shipbuilding industry, while its petroleum activities compete with Houston and Aberdeen for the same status. The hot -spots of biotechnology are found in a limited number of places such as San Diego , Boston and Munich (Cooke 2004). These all share the common characteristics of not being self -contained regional phenomena. Rather, their dynamics are dependent on strong pipelines towards external – by necessity often foreign - environments containing knowledge or complementary capabilities beyond what effectively can be developed and held within the regions themselves (Gertler and Levitte 2005 , Jacobsen and Onsager 2005 , Bathelt et al 2004). This “opening up” of firms, territorial innovation systems1 and sectoral innovation systems reflect a fundamental transition “from an internal knowledge base of firms to (more and more) open and globally distributed knowledge networks” (Asheim et al 2007 , Rothaermel et al 2006 , see also Smith 2000). Regional or national innovation systems deconstruct as sets of user - producer interaction , and must be reconstructed as global gravitation points (Cooke 2007) or “flow nodes” (Amin and Thrift 2002).

Institutional change Open innovation practices can to a certain extent be understood within the context of institutional constraints on closed innovation , i.e. the ongoing accumulation of in - house competences by way of internal long - term research , development and innovation; and institutional enablers of open innovation . Constraints stem from changes in modes of corporate control (Lazonick and O’Sullivan 2000 , Lazonick 2007), and from labour market mobility. Incentives or opportunities also stem from capital and labour market changes; capital markets are offering the option of technological renewal by tapping into the large pool of small, technology - based enterprises nurtured by seed and venture capital activity; and the option of commerciali z ing own technologies by spinning out firms; whereas external labour market flexibility (Lam 2000) eases diffusion of competencies across industrial enterprises and enables “hire - and - fire” strategies to source competencies according to need . Starting in the 1970s, what was then a large - firm dominated industrial system in the US was challenged by the slow general economic growth and high inflation of the period , and the entry of new players, in particular Japan (Fligstein and Shin 2007 , Laz onick and O’Sullivan 2000). According to Jensen (1993) “…corporate internal control systems… failed to deal effectively with these changes, especially slow growth and the requirement for exit”. This paved the way for a new principle of resource allocation . The capital allocation function of the economy was taken out of the conglomerate hierarchies of the “closed innovation” paradigm , and embedded in external capital markets. Governance according to the principle of “retain and reinvest earnings” was during the 1980s replaced by “distribute and downsize” (Lazonick and O’Sullivan 2000 , O ’Sullivan 2000). This has all paved the way for a new understanding of “professional” corporate management. Predictability, transparency, public disclosure of information and massive buy - backs of own stock on the industry side; in itself constraining the information privatiz ation and reinvestments conducive to closed innovation (Bah and Dumontier 2001) became linked to portfolio diversification and trading based on market indicators and mathematical modelling at the owner / investor side. The organizational result is concentration on core competencies (Teece 1998) and a correspondingly greater reliance on outsourcing and external knowledge for non - core activities. The other side of the capital market story is the growth of private equity capital (Chesbrough 2003). These include pre - seed and seed investors, investing in the early high - uncertainty phases of a company lifecycle; venture capitalists investing in the border territory between uncertainty and risk , and last but not least expansion phase or buyout actors focusing e.g . on entry into underperforming public enterprises in need of a committed , large owner. Incumbents, facing the constraints imposed from the corporate

1 The concept of territorial innovation systems refer to any innovation system tied to a

specific geographical entity, e .g . a cluster, region or nation .

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control side of the equation (O’Sullivan 2000), are offered the option of “outside - in” (Chesbrough 2005) open innovation strategies in the form of increased emphasis on equity investments in promising start - ups (see Carpenter et al 2003 for a US industry case). And they are offered a distinct mechanism for external technology commerciali zation and external sharing of risk . This governance system and capital market evolution is in turn interlinked with developments in the labour market. The inherent non - exclusive and often tacit nature of knowledge and competencies entail that they primarily move with the mobility of people . The ability to develop , accumulate and appropriate rents from internal knowledge development is therefore contingent on the ability of enterprises to develop internal labour markets (Lam 2000) through which knowledge is accumulated , diffused and recombined, and excessive external turn - over of personnel is avoided (see O ’Sullivan 2000 for a discussion of IBM during the 1950s and 60s). Part and parcel of the turn towards open innovation is therefore increasing complexity of careers for knowledge workers (Lepak and Snell 1999); the decreasing ability / willingness of in particular US enterprises to retain and create internal career paths for its personnel (Farber 2007) and the increasing emphasis on temporary (project) organizations as vehicles for problem -solving and learning . The absence of long - term internal knowledge accumulation leads businesses to increasingly rely on inter - organizational knowledge sharing , the formation of person - based networks across organizational boundaries, and collaboration (ibid: 3). What used to be the internal labour markets of firms are extending their roots into the surrounding environment. Seed and venture investments are shown to concentrate in geographical proximity to investment management companies. Inter - organizational mobility of knowledge workers is similarly linked to limited geographical mobility of the same . Consequently, speciali zed knowledge accumulates in regional labour markets or other “containing social structures” (Maurset and Verspagen 2002 , Verspagen and Schoenmakers 2004 , Lam 2000), and may form the basis for region - specific patterns of industrial dynamics which are reinforced by the presence of venture capital. Globali zation does not result in a “flattened” world (Friedman 2005). It rather results in geography of economic activity and innovation which is increasingly “spiky” (Florida 2005) – with linkages between its peaks. This is why “…the first implication of open innovation is that location matters” (Simrad and West 2005:233).

Towards a theory of open innovation It is difficult to delineate the research of direct relevance for the question of open innovation from the pool of theories and empirical work on the nature and boundaries of organizations, the systemic nature of innovation (Fagerberg et al 2005) or the linkages between innovation and performance – at firm and economy levels. All organizations connect with their external environments to source capital and labour, search for ideas, collaborate on innovation , purchase inputs and sell goods or services (Jacobides and Billinger 2006). Through these linkages they also contribute ideas, information and knowledge to this environment – what is often referred to as knowledge spill - overs. The activities of business firms therefore serve the dual purpose of sourcing and learning from the environment; and contributing to constructing it . A main weakness in Henry Chesbrough’s (2003) formulation of the open innovation concept is that only the former is considered . Following this line of reasoning , we distinguish between two main strands of literature , i.e. 1) research within management studies and organizational theory focusing on the firm level and industry private returns, and 2) research within the innovation system tradition and economics occupied with the larger dynamics of growth and development, consequently accounting for possible system dynamics and system failures (see section 2 .2 below) stemming from how knowledge and capabilities are linked across organizational boundaries, and the possibility of market failures leading to underinvestment in R&D . From these we draw essential findings to build a theory of open innovation .

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The context of open innovation Work within organizational theory has convincingly argued that the optimum mode of organization depends on a number of contingency factors, such as complexity of the environment faced , the strategic position of the firm , and the nature of the technology on which it is operating . The choice of organizational structure is seen as a question of “fit” to the environment faced or the knowledge involved (Birkinshaw et al 2002 , Cassiman and Veuglers 2006). We therefore start the discussion of open innovation and the firm by pointing to certain key conditions which on the one hand are either external to firms or beyond their direct control, but on the other creates strong incentives and constraints on their open innovation practices. These we summarize as the knowledge, cumulativeness, opportunity and appropriability conditions faced (Malerba and Orsenigo 1993). The nature of knowledge The nature of knowledge is found to be a strong predictor of organizational structure and modes of learning (Asheim and Gertler 2005 , Birkinshaw et al 2002 , Fey and Birkinshaw 2002 , Malerba and Orsenigo 1993) and patterns of external interaction . The literature on properties of knowledge, the role of knowledge as a contingency influencing organizational structure and related debates on knowledge management is vast, and cannot be given full justice here . Most studies and approaches converge on certain key insights and findings. First, different degrees of complexity are reflected in the number and nature of external interfaces which are necessitated . Many industrial knowledge bases are complex composites, developed by drawing on and integrating knowledge from a wide range of research disciplines (Smith 2000), and combining this with experience -based knowledge developed internally or sourced externally. Other knowledge bases are less complex , and can to a larger degree directly reflect e.g . advances within disciplinary research (see Asheim and Gertler 2005). This leads, second , to the issue of its contextual nature. Different degrees of transparency, tacitness or system embeddedness influence the degree to which knowledge easily can be communicated; which in turn influence both the need for internal organizational integration and challenge external interaction . Some knowledge assets may for instance be very context or firm specific (Blair 1997), making it difficult for competitors to identify, copy and use them , and making it difficult to communicate knowledge outwards during collaborative ventures (Lam 2000). Others are more codifiable and transparent – and consequently easier to communicate outwards intentionally while also increasing the potential for uncontrolled imitation (Fey and Birkinshaw 2005:599). Some problems may be easy to specify and communicate to outsiders, and left to these to be solved , whereas others cannot be understood by outsiders without “deep” interaction . We can therefore assume that there is a relationship between knowledge conditions and external collaboration which is mediated by the availability of a common (professional) language for communicating problems and solutions, and the appropriability conditions which will be discussed below. Third , different degrees of modularity and standardization in knowledge , products or services which enter into innovation processes influence the degree to which these can be sourced externally, and how – e.g . in the form of patents or as embodied in machinery or components, versus requiring deep collaborative interaction . Cumulativeness conditions Cumulativeness conditions refer to the extent to which the innovative activities of today – by means of knowledge accumulation - serve as the building blocks of innovations tomorrow (Malerba and Orsenigo 1993:48). It creates a self - reinforcing interplay between past , present and future innovative capabilities (Lazonick 2005), and is caused by factors ranging from the use of formal IPR measures through complexity and well into experience - based , firm - specific capabilities and competencies refined and accumulated on an ongoing basis. Cumulativeness at the level of an industry and / or at the level of a region follows when knowledge easily diffuses out of individual firms and is absorbed by either network partners or competitors who are able to tap into these externalities (Lam 2000). If cumulativeness is low at the firm level but high at the regional level, high external mobility of professionals may form the basis for development assets for larger groups of co - located , technologically related firms (Frenken et al 2007 , Asheim 2005).

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Constraints on internal knowledge accumulation due to external mobility is compensated by the availability of “fresh” competencies in the labour market; in sum contributing to ongoing diffusion and recombination of knowledge between firms (i.e. what Dosi (1988) refer to as “untraded interdependencies”). Based on this it is easy to understand why researcher skills may develop in a cumulative manner, so that centres that start early retain and even increase their lead (Unctad 2005). Firm level cumulativeness contains productive knowledge within the boundaries of the organization , whereas cumulativeness at the regional level contains R&D in specific places. Again we see why the global world of technology and innovation is not flat, but spiky (Florida 2005). Similarly, firm level cumulativeness trigger R&D abroad through the mechanism of acquisitions to get access to in - house competencies (Herstad (ed) 2005; Wilson et al 2006), alternatively through co - operation or alliances with actors holding these competencies, whereas region level cumulativeness motivate R&D abroad to tap into knowledge pools (Grünfeld 2004 , Unctad 2005). Opportunity conditions Search , sourcing or collaboration costs, and the risk of revealing private knowledge , may be defended if there are ample opportunities to innovate. These opportunities, however, vary significantly with the nature of different economic activities. High opportunities are created in the interplay between specific external conditions at the input and output side. On the input side, it implies that the knowledge relevant for innovation is easily accessible or can be developed at cost which is low compared to its returns; and at the output side that the clients and markets in question are willing to support – through willingness to pay for - a high rate of product change. The combined effect of availability of knowledge and demand for innovations may create a very dynamic industrial landscape of intense competition and high rates of new entrants on to markets. This characterized e .g . the ICT and internet bubble years of 1998 - 2000 , a period highly flavoured by the large supply of ICT entrepreneurs and seeming unlimited new markets for software and internet - based services. Low opportunity conditions are found in e .g. production of world market commodities, but are not necessarily limited to such activities. Conditions where opportunities are constrained at the user side we find in e.g . certain business - to -business markets for capital goods. A specific example is the Norwegian petroleum cluster, where the discrepancy between technological opportunities created on the supplier side and conservatism on the user side triggered the development of a support scheme where risk related to new production technologies is distributed among private sector actors, and the Norwegian state. In this case, knowledge and technologies exist to be recombined into products and solutions more radical than users by themselves would be willing to accept. Conditions where opportunities are constrained by both the user and knowledge conditions we similarly find in e.g . aviation; where even incremental product development involve large investments in R&D , testing and certification . Moreover, opportunity conditions are also related to pervasiveness. High pervasiveness exists when knowledge or technologies developed may be applied to a variety of products and markets, such as e .g . developments within biotechnology, ICTs or nanotechnology. This has of course been a defining characteristic of the ICT revolution; and has contributed to both channelling a large amount of capital into ICT innovations, and to opening up traditional industries to interaction with ICT software or hardware developers. Low pervasiveness means that new knowledge or technologies have a limited market outside its initial domain (Malerba and Orsenigo 1993), or are perceived so , hence limiting investments in the development of these and the search for alternative uses. Appropriability conditions Appropriability conditions refer, in general, to the possibilities of protecting innovations from uncontrolled imitation , consequently protecting own returns from investments in intramural R&D (Malerba and Orsenigo 1993) and raising the level of such R&D sustainable by these returns. But more specifically it refers to the possibilities of protecting own intellectual property when revealing knowledge through engagement in collaboration; and the possibility of “commodifying” knowledge as the basis for alternative means of commerciali z ation . Consequently, we expect that there is a relationship between appropriability and own investments in developing new knowledge;

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and between appropriability and external interfacing . Measures such as patents or copyrights provide formal means of protecting intellectual properties, whereas complexity or tacitness of, and firm speciali zation in , knowledge bases may provide effective functional substitutes of formal IP (Cassiman and Veuglers 2006:76 - 77). Informal means are particularly effective when the knowledge required cannot be easily incorporated into and diffused through products or documents, or the value of that knowledge is contingent its use within a large organizational or inter - organizational setting (Blair 1997 , Malerba and Orsenigo 1993:48)). Cassiman and Veuglers (2006) refer to the latter as strategic protection . Weak appropriability implies widespread existence of knowledge externalities (Malerba and Orsenigo 1993:48). Consequently, given such conditions, each individual firm will have less incentive to conduct intramural R&D; and strong incentives to tap into externalities produced by other firms in related industries. This may depress the overall rate of private sector R&D below the levels needed to sustain long - term private returns from innovation , and may therefore necessitate public support for intramural R&D . If means of avoiding such externalities are available in the form of formal IP, it is reasonable to assume that this will increase the willingness of companies to develop own technologies in - house . On the other hand , this could mean that less knowledge filters out into the economy, which in turn may necessitate public incentives for collaboration or other forms of knowledge sharing . A tight IP regime does mean that it is easier for firms to acquire technologies in the marketplace; and similarly easier to sell or license own technology. IP creates a platform for “commodification” and transfer of technology (Graham and Mowery 2004), critical also to the functioning of markets for technologies. Hence , a reduction in uncontrolled spill - overs could therefore be offset by more controlled processes of externaliz ation . This means that the effect of the formal IP regime on patterns of open innovation is not straightforward . A high degree of strategic protection may often be linked to high cumulativeness conditions (Malerba and Orsenigo 1993 , Castellacci 2008) in itself drawing in the direction of in - house development of knowledge rather than acquisitions or collaboration in the market – although this primarily applies for certain core, synthesiz ing learning processes and does not exclude the option of collaboration and in particular sourcing of modular or analytic knowledge components. Appropriability also reduces the risks related to knowledge leakages in external interaction . Cassiman and Veuglers (2006: 77) therefore argue and find empirical support for a high degree of strategic protection (e.g . complexity) being associated with an increased propensity of industry to simultaneously apply internal and external learning strategies.

Open innovation and the firm Below we review some of the literature relevant to the question of openness at the firm level, and how it can be related to performance. We first briefly discuss the issue of overall organizational openness, and the capacity of organizations to absorb knowledge and information from the external environment. We then turn to what we have labelled the different dimensions of open innovation; different mechanisms for interacting with the external environment for the purpose of enabling innovation and increasing performance. Last , we briefly discuss how the issue of corporate internationalization relates to open innovation , and what organizational challenges follow in the wake of these processes. Organizational learning, boundaries and absorptive capacity The question of “openness” has traditionally been analyzed within the framework of transaction cost theory (Coase 1937 , Williamson 1984), and has emphasized the governance of transactions rather than the overall boundaries of organizations. An early exception is the contribution from Teece (1988) in which he theoretically discussed the “…reluctance on the part of innovating enterprises to rely on external research facilities to procure new products and processes via the market”. A main point is made out of the need for organizational integration between production , marketing , research and development. Vertical integration of innovation processes is argued to “facilitates interaction between users and providers of new technology”, while avoiding “the difficulties associated with writing , executing and enforcing R&D contracts” and protecting innovations developed long enough for appropriation through mass

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production to occur. Teece (ibid) further warn that outsourcing could have the indirect effect of impairing the innovation process by inhibiting knowledge exchanges between different sub - processes. He also warned that outsourcing necessitates information sharing , and consequently comes with the risk of reducing appropriablity. In this sense , he departs from Williamson (1984), who leaves the question of make or buy more open and suggests that it is contingent on the specificities of the transaction processes in question . Extending this early debate on organizational boundaries was the contribution of Cohen and Levinthal (1989 , 1990), in which they argue that analysis of organizational absorptive capacity - the ability to absorb knowledge from the external environment - is contingent on in - house knowledge capabilities (i.e. R&D). However, as discussed in the introduction this focus on specific transactions, specific projects and specific dimensions overlooks overall firm level dynamics and implications. The firm , in essence, is conceptualised as a nexus of transactions which continuously and individually are subjected to make - buy decisions, the underlying assumption being that most knowledge and capabilities are available in the market. There is in this view very little taking place within the firm except minimising transaction costs and co - ordinating market transactions. In many ways, Chesbrough (2003 , 2005) is a prime representative of this view of the firm . Recent work (see e .g . Jacobides and Billinger 2006 , Rothaermel et al 2006) has however attempted to systematically analyze boundary construction , and the relationship between the “permeability” of these boundaries on the one hand , and organizational performance on the other. The concept of organizational absorptive capacity has been extended beyond the focus on how internal R&D is a prerequisite for absorbing externally (R&D) based knowledge, to include how different internal functions and competences may serve as “ears” towards different parts of the external environment – and in some cases be a prerequisite for the ability to successfully outsource and interact . Emphasis has shifted towards how boundary construction influence search , collaboration and sourcing capacity – and how decisions concerning the governance of specific functions or projects influence the overall knowledge accumulation and performance of organizations. This line of reasoning is reflected in attempts at constructing a more general “theory of innovative enterprise” (Lazonick 2005). Building on the so - called resource - based view of the firm , Lazonick (2005) points out how innovative capability at the firm level rests on the development and accumulation of specialised , internal capabilities. The development of these, in turn , is argued to rest on strategic control and financial commitment, and on organizational learning . Organizational learning depends on organizational integration: a set of relations that creates incentives for people who participate in hierarchical and functional divisions of labour to apply their skills and efforts to the innovation process (see also Helper et al 2000:483). Organizational integration is necessary for firms to absorb knowledge from the external environment, as this requires broad interfaces with this environment, and for these to be able to recombine, redevelop and exploit this knowledge through internal communication and diffusion . For instance, it is reasonable to assume that the marketing department has its ears more open towards customer preferences than the R&D department; but what is absorbed has little value if it does not reach and is understood by R&D . Similarly, the purchasing department is in a much better position to search the supplier base for ideas and knowledge , but the value of this may very well be contingent on diffusion to and understanding by R&D , marketing and top management . The narrower the interfaces to the external environment, the less knowledge and ideas are absorbed (see Lam 2000 , Wenger 1998). And the fewer people internally who come in contact with external ideas or knowledge, the lower is the likelihood that they will trigger innovation . In this perspective, absorptive capacity is understood as the coupling of interfaces towards the external environment and complex internal social evaluation processes (Cyert and March (1963), von Krogh and Grand (2000), with the “not invented here” (Chesbrough 2005) syndrome as but one of its many possible outcomes. Innovation search Search is the systematic scanning of external environments, using mechanisms ranging from the personal networks of employees and partners through participation at e .g. conferences or trade - fairs (Maskell et al 2006) and into the establishment of subsidiaries

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as “listening posts” to tap into knowledge externalities (Grünfeld 2004 , Asheim and Gertler 2005). Complexity and uncertainty imply that external search and internal signal processing (Tidd et al 2001) are critical. Firms search among customers, clients and competitors to increase their understanding of the market and the direction of market change; and among universities, research institutes, suppliers and again competitors for possible solutions or new directions to explore. This all exposes the organization to diverse inputs, allowing them to imagine , experiment with and establish new combinations of technologies and knowledge – and venture down new technological paths. Search processes can therefore be seen as a dynamic capability that allows firms to sustain their competitive advantage over time (Eisenhardt and Martin 2000). Fey and Birkinshaw (2005:616) find that openness to new ideas is the single most important predictor of R&D performance. In an earlier analysis of corporate search strategies, Laursen and Salter (2004) find that knowledge sources such as own R&D , suppliers and customers are the most commonly used by UK manufacturing firms. The direct use of universities as sources of ideas and information remain limited to a small number of firms, found either in a limited number of sectors and among those who use other information sources most extensively, and among those who have strong internal R&D capacities. Laursen and Salter (2006) find that innovation performance increases with both the breadth and depth of external search; i.e. with the diversity of external information sources used , and their intensity of use. These relationships are however found take on inverse U - shapes, indicating the possibility of excessive dependence on external information sources. The nature of search and its relationship towards performance is further argued to depend on the richness of technological inputs and opportunities available in the environment, on both input and output side , and the ease of which these sources can be tapped . Laursen and Salter (ibid) therefore point out that the relationship is not a simple one - to - one and that this has to do with different degrees of complexity in industrial knowledge bases, search costs and the possibility of over -searching (see also Katila and Ahuha 2002). Innovation collaboration, sourcing and embodied knowledge flows Collaboration is the development of knowledge through relationships with specific partner organizations, and involves mutual exchanges of knowledge. Industrial firms may collaborate with universities or research institutes (Balietta and Callahan 1992 , Conway 1995), “extend their enterprises” (Dyer 1991) to include collaborative relationships with suppliers and customers (von Hippel 1988 , Helper et al 2000 , Lettl et al 2006 , Knell and Srholec 2008); form alliances or joint ventures with other industrial firms holding complementary knowledge and engage in consortia within which competitors interact (Chiesa and Man z ini 1998 , Hagedoorn 1993). In general, collaboration is the “deepest” dimension of inter - organizational interaction . There may be a number of reasons why firms choose to cooperate on their innovation activities and in many cases close interaction may be a necessity to facilitate the transfer of knowledge . Among these are gaining access to proprietary technology, access to skills, know - how and other tacit knowledge , cost and risk sharing , and speciali z ation . Competitiveness effects reduce incentives for (horiz ontal) cooperation , unless firms are able to differentiate final products based on joint research (De Bondt, 1996). If appropriability is low, firms may have an incentive to cooperate in order to internali ze spillover effects, though from a social standpoint this may have adverse effects via reduced competition on product markets (d’Aspremont and Jacquemin 1988 , Kamien et al. 1992). As collaboration involves dense interaction and exposure of own knowledge, it also requires trust (Storper 1997 , Lundvall 1992), mechanisms to regulate opportunism (Helper et al 2000) and the development of mutual understandings concerning what is to be achieved . Some of this can be understood as relation specific, irreversible investments. It will, depending on the degree of intensity and success in the interaction , result in processes of mutual learning and adaptation , but contains the risk of each partner gaining less through inflows of knowledge than what is communicated outwards. In addition , it will easily require the allocation of substantial resources in the form of

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personnel (see e .g . Lam 2000). Collaboration is therefore presumably a more selective dimension of open innovation than search , and may be prone to lock - in2. Sourcing refers to the acquisition of knowledge or solutions on a market basis (Granstrand et al 1992 , Fey and Birkinshaw 2005). The sourcing firm is primarily concerned with the output of the contract, not the learning processes occurring through the development work . Sourcing therefore provides solutions without knowledge accumulation , and de facto leaves the contracting firm less control over assets developed . The contract partner, in turn , be it a research institute or a supplier, is free to use the experiences gained if not also the IPRs developed to serve other client companies (see e .g . Hagedorn and Sutton 1997), which in turn may have the effect of increasing the innovativeness of a larger population of companies. Fey and Birkinshaw (2005:603) however adhere to Teece (1988) when warning against the possibility of negative net impacts on firm R&D productivity and competitiveness stemming from excessive sourcing , while arguing that the impact on performance from R&D collaboration is positive . Innovation sourcing extends beyond contract R&D . It includes purchases of firms for the purpose of own technological renewal (Lazonick 2007 , van de Vrande et al 2006 , Chesbrough 2003), purchases of patents and licensing in technology. Further, it extends into purchases of knowledge “embodied” in machinery and components. Smith (2000) for instance show that a large proportion of innovation expenditures in small and medium siz ed enterprises in Norway are related to purchases of knowledge embodied in machinery, whereas Knell (2008) uses input - output for 25 European countries to investigate the relative contribution from different sources of R&D , and their weight in total innovation expenditures. This reveals the extremely high importance of embodied technology flows for sectors such as ICTs, automotives and machinery (ibid). Knowledge sourcing in the market for corporate control Last we look at a specific form of external knowledge sourcing , which links up to an alternative mode of commerciali zation . Sourcing can occur in the form of smaller or larger equity stakes in new technology based companies; preferably using own stock rather than cash as the currency used for entry and thus creating a direct linkage between own stock market appreciation and ability to source technology or knowledge externally (Lazonick 2007). Commerciali zation can occur as the establishment of new companies as vehicles for this commerciali z ation . Chesbrough (2003) argue that the development of a large -scale venture capital market in the US has created a continuously evolving stock of small technology based companies available for purchase, the existence of which enable industrial enterprises to think differently concerning their own technological renewal and new market entries. Van de Vrande et al (2006) build on the distinctions between risk and uncertainty, and between commitment and reversibility (see O ’Sullivan 2000). They develop the argument that conditions of high technological uncertainty – i.e. situations in which there is little or no information available to estimate in which way the technological or market frontier is moving (O’Sullivan 2000) – smaller equity stakes in companies experimenting with alternative technologies creates channels for search and provide options for later entry (se Carlsson and Eliasson 2002). It eliminates the need for premature commitment to specific technologies or R&D projects. Problems may arise, however, at the point of acquisition . In particular, it is often critical for the acquiring firm that it gains control over not just a

2 The dependence on trust, intense interaction and mutual adaptation leads to the

assumption that the “social” or “embedded” nature of collaboration draws these interfaces in the direction of lock - in and potentially excessive, from the point of view of radical innovations, dependence on already established linkages (Nooteboom 2001). Focusing on a limited set of transaction partners create trust and mutual understanding , and thus reduces transaction costs. The marginal costs and uncertainties associated with ongoing use of established partnerships are therefore low; while the marginal cost and uncertainties related to establishing collaborative ventures with new partners may be perceived as very high – and the partner choice itself subject to uncertainty.

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specific patent but the competencies embedded in the acquired organization and its personnel (Lazonick 2007). Carpenter et al (2003) show how many US firms in the optical networking industry has failed to retain the competencies of acquired firms, causing these to de facto pay excessively for access to specific component technologies. These processes of large - scale technological sourcing through the equity market occur large - scale only in a limited number of sectors; and build on a highly specific relationship between the stock market, the corporate economy, new technology based enterprises and – in cases such as ICTs and pharmaceuticals – university research (see Mowery and Sampat 2005 , Asheim and Gertler 2005). And the transaction costs involved may be very high . Against this background it is not surprising that Floricel and Miller (2003:31) find that start - ups represent important sources for ideas and knowledge only for a small subset of firms in their empirical material. This is not surprising against the background of venture investments in such enterprises being overwhelmingly oriented towards a very limited number of sectors, i.e . ICTs, biotechnology and healthcare (Herstad 2008). Laz onick (2007) warn against drawing general conclusions from the US “frenzy” of spinning in and spinning out during the late 1990s and early 2000s, and points out that focus on technological renewal through acquisitions come with the cost of a strong focus on own stock market capitali zation – boosted by stock repurchases financed by earnings and debt . The question from the perspective of the firm (Lazonick (2007:1031) is the extent to which costly stock repurchases support the innovation process by maintaining the ability of firms to recombine from the pool of new technology - based enterprises, or if they instead deprive the firm of resources better used for internal or collaborative knowledge development. Alternative modes of commercialization Knowledge development often has unintentional outcomes (Cyert et al 1972). Knowledge or ideas which are in “surplus” given existing business models may have alternative uses and be far more pervasive than what is obvious to the organization of origin . New technologies or ideas deviating from existing core competencies or activities can therefore be dismissed even before they are exposed to market selection (Cassiman and Ueda 2004 , Chesbrough 2005), or remain under - used (Danneels 2007 , Hargadon and Sutton 1997 , Shane 2000) from the lack of complementary capabilities or recombination with external knowledge (Carlsson and Eliasson 2002). The emergence of a vibrant private equity market, serving as a pull - factor for both controlled spin - offs and uncontrolled spill - overs; the increasing prevalence of open search and sourcing strategies among business in general, and the tightening of IPR regimes combine to pave the way for external technology commerciali zation (Lichtenthaler 2005 , Gassmann and Enkel 2006). It can take the form of licensing , establishment of new enterprises for the purpose of commerciali zation and the sale of IPRs. Through licensing the originating firm remains in control of the technology in question , but can utili ze the already available complementary capabilities (Teece 2001) of other firms. Licensing therefore combines organizational resources at their margins. By establishing new enterprises as vehicles for commerciali zation the company may utili ze external sources of funding to reduce its own risk , while remaining in possession of an option for later full re - internaliz ation . External technology commerciali zation remains an under - investigated phenomena, although anecdotal case evidence (Chesbrough 2005) as well as larger sample case evidence (OECD forthcoming , Herstad and Naas 2007) indicate it is becoming a broader trend (Lichtenthaler and Ernst 2007). Reasons include but extend beyond the prospects of generating additional revenues. More strategic reasons to systematically use external technology commerciali z ation mechanisms include influencing the establishment of industry standards, gaining access to the technology portfolio of other companies by sharing from its own portfolio , increased speed of own R&D through external learning and risk sharing effects and last but not least increased freedom of operation through cross - licensing of intellectual property (Lichtenthaler 2005).

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Internationalization and global knowledge sourcing Collaboration and alliance formation across national boundaries is increasing in extent (Knell 2008). Similarly, large international flows of technology occur as embodied in products and components (Knell and Srholec 2008). However, internationaliz ation through FDI creates direct, organizational linkages across national boundaries. Physical presence in certain business contexts if often considered a prerequisite for the ability of enterprises to search and tap into (Grünfeld 2004 , Unctad 2005) information and ideas not communicated intentionally. Individual firms belonging to internationalized corporate groups are linked to corporate networks spanning numerous business contexts, and may use these for the purpose of own innovation search (Ebersberger et al 2008). Access to knowledge and ideas is argued to be an increasingly important driver of FDI - based internationali zation (Unctad 2005). However, internationalization may come with large organizational costs, and it is not given that ownership - based linkages across national boundaries translate into flows of information and knowledge. The primary concern of the post - war “Fordist” MNE was production capacity and market access in an unprecedented demand - led business cycle upturn; co - ordination and in - house knowledge diffusion was a secondary issue (Granstrand 1999). The latter re - emerged as a strategic concern in the wake of the business cycle downturn in the 1970s, and has now - with the emergence of the “knowledge economy” – again been brought to the forefront. In general, MNEs still retain R&D at home when the costs of relocating or communicating knowledge are high (UNCTAD 2005:157 , Forsgren 1997), i.e. when knowledge is complex (Birkinshaw et al 2002) or has a high tacit component (Malerba and Orsenigo 1993), and when communication must occur across geographical, cultural and linguistic distance (Herstad 2005). These “centripetal” forces may be reinforced by scale economies in R&D , and by the necessity of linking R&D to other corporate headquarter functions. On the other hand, centrifugal forces may arise from the need to link R&D to already internationali z ed corporate functions such as production , by the need to conduct R&D in close proximity to markets or clients, and by the need to tap into speciali z ed knowledge environments abroad (Cooke 2007 , Grünfeld 2004). Similarly, the development of well -functioning internal systems for knowledge diffusion may come at the cost of a certain “closure” towards the external environment. Thus, the internationaliz ation of knowledge development is subjected to contradictory forces, resulting in diverse “global footprints” and means of coordinating knowledge flows across space (Doz et al 2006). The challenge for the corporate enterprise is finding the right balance between broader search and stronger linkages to a wide variety of communities and the ability to utili ze ideas and knowledge sourced on a broader basis internally – a challenge which increases exponentially with presence in an increasing number of locations (Owen - Smith and Powel 2004). Consequently, there are trade - offs between breadth in international presence and ability to utili ze knowledge or ideas sourced (Forsgren 1997), and between external “embeddedness” in home and host contexts and internal coherence of the corporate network (Herstad and Jonsodittir (eds) 2006 , Godoe and Guldbrandsen 2007; Blanc and Sierra 1999).

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Open innovation at the economy level A distinct dividing line between management studies on the one hand , and different innovation system approaches on the other, is how the former predominantly focus on firm decisions concerning structure and strategy; and their impact on firm level performance – whereas the latter predominantly has been occupied with the study of systematic interaction between private sector actors; and between these and the research infrastructures. The emphasis here is on how these patterns influence the innovative capabilities and channel the direction of development in economies or sectors at large; i.e. they include the external effects of decisions concerning structure, strategy and investments made by private enterprises. A clear distinction is therefore made between private returns of R&D and innovation at the firm level, and the social returns of R&D and innovation at the level of territorial (national, regional, local) economies. Innovation systems and system failures As we have seen , complexity and uncertainty forces linkages and relationships between entities. Through such linkages, new knowledge is developed (i.e. Lundvall's (1988) notion of “interactive learning”) ; and existing knowledge initially contained inside organizations spread out into society and form the basis for new rounds of selection , recombination and experimentation . This occurs inside value chains, through imitation and reverse engineering of what competitors already have done; through labour markets (Lam 2000) and third - party knowledge diffusion organizations – stemming the full range from universities through research institutes and into private sector services (Aslesen and Jacobsen 2007 , Aslesen 2007 , Asheim 2005 , Hagedoorn and Sutton 1997). These processes have at the economy level been studied under the heading of innovation systems. Open innovation strategies at the firm level can therefore be understood as attempts at linking up with innovation systems at the economy level; in the process contributing negatively or positively to the overall content and workings of these. The role of inter - organizational linkages in processes of knowledge development and diffusion at the economy level has led many European countries to develop innovation policies and measures focused on nurturing linkages. System failures necessitating public intervention are assumed to stem from the inadequate ability or unwillingness of individual actors to identify collaboration partners or environments in which to search; link up with these and engage in positive sum collective action games, i.e . games of interactive learning . Knowledge, economic growth and market failures The concept of market failures is closely related to work within the economics of R&D and knowledge. Market failures exist when individual firm level decisions concerning e .g . investments in intramural R&D lead to outcomes at the economy level which are suboptimal – in our example under - investments in such intramural R&D . These failures occur because firms only calculate own cost and utility and do not consider external effects, or so - called spill - overs. Both the new growth theory and industrial organization literature has been occupied with the role of R&D and spillovers. In their innovation activities, firms are able to draw on the aggregate stock of knowledge available to them , though clearly this knowledge will vary in its economic and technological relevance for each firm (Griliches 1979). Knowledge spillovers are a key driver of economic growth in New Growth models, though different mechanisms are highlighted in the literature. For example, in Romer (1990)’s model of endogenous growth , spillovers are cumulative: in their innovation activities, firms are able to draw on external knowledge and it is these knowledge spillovers that generate increasing returns. In contrast, in Aghion and Howitt (1992)’s model of growth through creative destruction and Grossman and Helpman (1991)’s model of growth through quality improvements, spillovers are only intertemporal. The innovation process is a ‘patent race’ (Tirole 1988) that builds on existing knowledge stocks, where the best innovations both render previous innovations obsolete and outcompete inferior innovations. The literature identifies a number of cases where social and private returns to R&D , or desirable amounts of R&D conducted by private sector enterprises, differ. First is the

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classic public good problem where , due to spillovers, firms are unable to appropriate gains from own R&D and thus invest less than socially desirable (Arrow 1962). Though , other factors such as absorptive capacity and product differentiation may offset this market failure. For example, in Romer (1990) spillovers do not have a negative impact on R&D incentives through an erosion of firms’ competitive position . However, another ‘failure’ can be identified here; firms do not take into account the positive impact that their R&D has on other firms’ innovation , thus leading to suboptimal levels of R&D investment (Romer 1990). Romer’s model is of horizontal or cumulative innovation . In models of vertical innovation (e.g . Aghion and Howitt, 1992), firms may invest too much in R&D , as they do not take into account the losses incurred by other firms whose innovations are rendered obsolete (the ‘business stealing effect ’ , Tirole 1988). The impact of spillovers depends greatly on the ability of firms to appropriate the gains from their innovation activities. By improving other firms’ benefits, a high level of spillovers reduces the competitive edge obtained by own innovation activities, thus incentives to engage in innovation (Arrow 1962). However, if firms are able to effectively differentiate their innovations, they will be better able gain more from their innovations despite high spillovers (De Bondt 1996). Hence, a positive ‘market share effect’ may offset this negative externality of spillovers3. Jaffe (1986) analyses the role of spillovers for US companies, using patent data to measure the relevance of external R&D for individual firms4. He finds evidence of both factors: spillovers have a positive impact on the productivity of own R&D , but also have a negative competitiveness effect on firm productivity. Other factors may counter the disincentives of spillovers on R&D and innovation . For example, if own investments in R&D and innovation improve firms’ ability to access and utili ze external knowledge, then high spillovers increase incentives to improve ‘absorptive capacity’ (Cohen and Levinthal 1989). In order to examine the effects of spillovers, a difficult challenge is to identify the externally available knowledge stock that is relevant for a firm’s research . A number of approaches have been used , which can be placed in two categories, transaction based and technology based . The basic idea is then that the potential stock of knowledge spillover for an individual firm can be calculated as a weighted sum of other firms’ R&D5. Transaction based approaches6 use input - output tables (i.e. the industry origin or destination of inputs or sold products) to calculate weights which measure the importance of R&D from each industry. This has, however, the major drawback that input - output measures may have little to do with the firm’s research activities. Following Jaffe (1986), technological approaches use firms’ (or industry level) patent data to construct firms’ “technological position”. The “potential spillover pool” of knowledge is then a “weighted sum of other firms’ R&D , with weights proportional to the proximity of firms in technological space”. Examples of studies on the impact of private R&D spillovers using technology based measures are Jaffe (1986), Acs et al. (1994), Los and Verspagen (2000) and Cincera (2005). All three papers find strong impacts of private sector spillovers on productivity. An extension of this line of reasoning is found in the recent emphasis on the composition of industrial bases and their related externalities. According to this line of reasoning , innovation and growth will be particularly strong in contexts characterized by the co - location of economic activities (Frenken et al 2007) which are not technologically homogenous but related . This forms the basis for knowledge diffusion and what Cooke (2007) label inter - sectoral absorptive capacity. 3 Implicitly, this type of effect is also in play in Romer’s growth model: firms are able to

differentiate, so that demand for their own products is unaffected by the spillovers of their research to other firms (Romer, 1990).

4 More specifically, he uses the technological profile of firms’ patents to estimate how correlated firms’ R&D activities are. See also Griliches (1979).

5 See Kaiser (2002) for a review and assessment of the different methods. 6 E.g . Goto and Su zuki (1989) and Los and Verspagen (2000)

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The above discussion points out that there may be asymmetry between extent of spillovers and appropriability. Cassiman and Veugelers (2002) take explicit account of this in an analysis of the interaction between incoming spillovers, appropriability and R&D cooperation . They find that both incoming spillovers and appropriability have a positive impact on incentives to cooperate , while cooperation with public research increases spillovers and vertical supply chain cooperation has a negative effect on appropriability. Globalization and system reconstruction The cumulative logic of territorial innovation systems – regional or national – leads these to speciali ze along specific development paths. Consequently, they need external linkages to feed on external knowledge development, otherwise the likelihood of lock - in to these paths becomes very high (Asheim and Herstad 2005 , Asheim 2005 , Narula 2002). Such linkages - in the form of search , sourcing and collaboration – is of particular importance to small, open economies; i.e . economies where national consumer markets are limited by demographic si z e; where domestic business - to - business markets are limited by speciali z ed users downstream and a speciali zed supplier infrastructure upstream (all limiting the formation of domestic user - producer based innovation systems); and where the domestic research base either is highly speciali z ed or of mediocre quality (Narula 2002). The importance of cross - national product embodied knowledge flows are but one indicator of this (Knell 2008). On the side of the government, this may call for a radical rethinking of policy if new opportunities stemming from this are to be harnessed , and challenges faced . The notions of knowledge pipelines (Bathelt et al 2004 , Maskell et al 2006) or places serving as gravitation points in international knowledge networks (Jakobsen and Onsager 2005 , Amin and Thrift 2002) has traditionally been linked to the activities of multinational enterprises (Unctad 2005), but is now extending into the study of international collaboration more generally (Knell and Srholec 2008). The econometric research on such spill - overs remain mixed in its findings (Unctad 2005). However, this is likely to at least in part reflect the diversities of involved companies, sectors and economies (see e.g . Grünfeld 2004 , Kvinge 2007) and the difficulties involved when attempting to directly measure spillovers. This points towards a need to investigate the behavioural preconditions for the occurrence of spill - overs; national absorptive capacity can be influenced by innovation system design and policy. A recent analysis using innovation survey data from the Nordic countries (Ebersberger and Lööf 2005) found that companies belonging to international corporate groups used their parent networks more extensively to search for ideas and information than companies belonging to national corporate groups, and consequently concluded that both inward and outward FDI may serve a pipeline role (see Ebersberger et al forthcoming). A follow - up project found that this effect may be weaker for inward FDI than for outward FDI. This suggests that headquarters and the immediate organizational contexts around these serve as gravitation points, and may in addition reflect how the build - up of networks around activities (outward FDI) is a process with different implications than integration into pre - existing international networks (inward FDI). In this perspective reverse technology transfers become a question of the absorptive capacity of the home economy more than a question of the strategies and networks of domestic multinationals (Herstad and Jonsdottir (eds) 2006). Supporting this line of reasoning , Knell and Srholec (2008) found the propensity to collaborate nationally to correlate with the propensity to collaborate internationally, although the former is influenced negatively by foreign ownership . Based on this it can be argued that the innovation systems of small, open economies are dependent on the presence of international networks of domestic multinational enterprises. These enterprises, in turn , face a trade - off between low marginal costs related to ongoing use of existing domestic organizational competencies and networks, linkages which are often nurtured by public incentives, and the high marginal cost of establishing contact with new actors or environments abroad . This contains the danger of “inertia” in the location of R&D and other knowledge - intensive activities, at the firm level, and a systemic lock - in between existing speciali zation of industry and the existing innovation system speciali zation (Narula 2002) at the economy level. At the level of the firm , this is presumably particularly problematic for high - tech SMEs, lacking the

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organizational competences and financial resources necessary to establish those foreign linkages to customers or research communities on which their long - term success may very well depend . This translates into a problem at the levels of territorial innovation systems and economies because it may limit the emergence, selection and successful incorporation of novel activities. Recent analysis have gone beyond the focus on multinationals and foreign direct investments, and launched the notion of “globalization mark II” as a new model of (open) industry organization (Cooke 2007), regional system construction and policy (Cooke 2005 , Asheim 2005) building on international pipelines coupled with the development and inter - sectoral diffusion of regional knowledge capabilities. In this perspective, it is less relevant to focus on the extent to which inter - sectoral reverse technology transfers are occurring , than on when and why these may occur and how the conditions necessary for this can be influenced by public policy This line of reasoning , emphasiz ing the changed although enduring importance of territorial innovation policies is supported if we consider the limitations to knowledge flows and experimentation within corporate networks. These have in part to do with the nature of knowledge as “embedded” in routines and experiences, i.e. the traditional arguments concerning its tacit and context - dependent nature. But it has also to do with the nature of organizational networks as consisting of designed interaction and knowledge diffusion processes, subjected to a priori definitions of what are to be transferred and constraints on who are to interact (see Wenger 1998 , Herstad 2005). Global open innovation does not equal open borders, but open slots. A set of knowledge can be defined and packaged for transfer across large geographical distances. But such designed interaction cannot reproduce unintentional interaction , knowledge sharing , externalities and resulting processes of experimentally recombining knowledge. Even if no longer exclusive to places, locali zed spillovers remain richer (Maurset and Verspagen 2002 , Verspagen and Schoenmakers 2004) than those over large distances (see also Mei and Verspagen 2006). IPRs Last , we briefly reflect on the issue of IPRs and their impact at the economy level. Protection mechanisms are assumed to increase the willingness of enterprises to invest in R&D because it protects private returns. On the other hand , the social returns from this R&D may be depressed by excessive protection of intellectual property, as it decreases competition and increases market prices; and may result in slower diffusion of new technologies. A lot of research emphasis has therefore been put on discussing what is assumed to be a trade - off between investments in new knowledge and technologies; and the diffusion of new knowledge into society at large . Emphasis has also been put on the so - called industry - IPR fitness problem; i.e. the problem of developing IPR regimes accounting for different industry conditions (see e .g Granstrand 2003).

Open innovation and policy From the perspective of policy, the above points towards three essential issues. First, from the firm level analysis it becomes evident that what is a rational strategy at this level, targeting the maximization of private returns from innovation efforts, will vary significantly between different economic activities. Second , when interpreting the firm level analysis against the background of innovation systems and economics we see that what is a rational or desired mode of investing in and organiz ing innovation is not necessary the strategy which optimizes the social – economy level - returns from these efforts. As policy is about such returns, this distinction is critical. System failures may result in inadequate linkages across organizational boundaries; in lock - in to specific collaboration partners or sources of ideas and information , or excessive overall “closure” of learning processes. In the case of the latter, both private and social returns may be increased through tools focusing on increased interaction and permeability. Diffusion , recombination and exploration through linkages are the primary concerns of such policies.

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On the other hand, market failures due to low appropriability or perceptions of broad availability of knowledge in the external environment may result from downsiz ing of intramural R&D below the level desired from a social returns perspective. In this case, these returns may be increased through tools supporting intramural R&D . As opposite to tools focusing on linkages, the primary objective in this case is support of knowledge development and not least accumulation, i.e. the build up of specialised industrial knowledge bases, under the assumption that this has positive externalities. Third , it is no longer evident that innovation policy, the inevitable targets of which are to support innovation and growth at the economy level, equals policies containing innovation activities within those same economies. As the distinction between private and social returns account for the existence of spillovers, this distinction accounts for the role of global knowledge pipelines and thus the possibility of connections to environments or actors abroad triggering reverse technology transfers to the home economy. Such reverse transfers, in turn , may be contingent on both domestic intramural R&D and on domestic linkages. In sum , this means that the main challenge on innovation policy becomes one of balancing support for intramural R&D with support for collaboration; and balancing between focusing on establishing international (to source knowledge) and national (to diffuse and recombine knowledge) linkages. These must of course be placed within industrial and institutional contexts which are already there, and differ across countries. Consequently, the policy implications of global open innovation will diverge across countries; as a result of what “blend” of tools which are already in place , what degree of national or international openness is already consolidated and what the industrial structure is. There is no single best practice .

Synthesis Firms are rarely able to discretely choose between make, buy, collaborate or ally. The contexts these face determine the availability of relevant information sources and collaboration partners, where these are located and in what different forms knowledge may be acquired . Large firms within telecommunications or pharmaceuticals may very well source knowledge in the form of new enterprises; a strategy constrained by the lack of such new firm formation in many other industries. For some , knowledge may be easily purchased as embodied in machinery and components; in some cases even delivered by suppliers located nearby; whereas for others such purchases may require prior in - depth knowledge sharing with actors located in other countries or continents. Knowledge can be sourced as contract R&D at universities or research institutes; but this depends on the relevance of research - based inputs for the specific activity in question or the extent to which such universities or research institutes already have accumulated speciali zed competencies in the area on question . Free sharing of knowledge within a broader “ecosystem” may be sound when the rate of change is high and relevant contributors to innovation are broadly distributed within such ecosystems; factors we find driving open source software development; and it may be sound in battles for standards and dominant designs (Floricel and Miller 2003). But it does not necessarily make sense when the rate of change is slower, if there are fewer potential contributors than potential imitators and product complexity combined with experience - based customer understanding is the primary means of protecting own returns. In the empirical analysis we will therefore , by necessity, emphasize how open innovation practices diverge across different sub - groups of firms. Decisions in either one direction at any point in time expand or constrain the decision making space at any other given point in time; and are constrained and channelled by the broader technology and market contexts faced by firms – or what we have labelled technological regimes. Effective organizational design has to take into account the underlying characteristics of knowledge bases (Birkinshaw et al 2002) – both composition along the tacit - codified , synthetic - analytic or simple - complex dimensions, and their geography. It must account for the nature of existing markets (Miller and Floricel 2004), opportunities within those markets (Malerba and Orsenigo 1993), and account for

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appropriability (Cassiman and Veuglers 2004). The distribution across actor groups of ideas, information and competencies relevant for meeting different market requirements diverge; and so does its geography. This we assume triggers diversity in search , collaboration and sourcing profiles, and their geographical patterns. The notion of technological regimes therefore provides the most robust bridge between the theoretical perspectives presented above, and our empirical analysis.

Technological regimes The same characteristics that define technological regimes are driving factors behind open innovation practices. Two options are available when empirically operationalising technological regimes and each come with their own problems and limitations. It is, first, possible to use inductive methods such as factor analysis as the basis for identifying groups of firms independent of their assigned sectoral classification . This method is applied by e.g . Miller and Floricel (2004) and forms the basis for the identification of different innovation strategies at the firm level; and by Mariussen (2008) in his analysis of the Norwegian innovation system . As this method defines regimes according to homogeneity in innovation strategies and linkages (i.e. inputs), it is the solution most true to the original Pavitt (1984) line of reasoning and the one most robust against critique of the regime concept stemming from the confusion between these and sectors defined by assigned NACE or SIC codes (Leiponen and Drejer 2007)7. It opens up for the possibility of distinct regimes existing across sectors, and handles both sectoral and intra - sectoral heterogeneity. However, when using multi - country datasets or doing cross - country analysis it comes with the problem of either confusing regime or country characteristics, or, when comparing across country datasets, of identifying different regimes in each of the countries covered . This approach is therefore not an option here. The other main option is to depart from assigned sectoral classifications, and , by way of theory and empirical testing , group different sectors according to key regime characteristics. Marsili and Verspagen (2002)’s taxonomy takes Pavitt ’s taxonomy as a starting point and utili zes empirical data (such as patents, R&D statistics, scientific inputs, innovation surveys) to develop a typology of five regimes. The main criteria used to identify the regimes are: the nature of the knowledge base (in particular, technological diversity); technological opportunity conditions; and technological entry barriers. Manufacturing industries are then assigned to a regime based on the empirical data. The following describes the technological regimes. Basic descriptions of the regimes follow to some extent that in Marsili and Verspagen (2002). We then build on this by examining how firms source external knowledge in each regime and key factors that influence open innovation practices. In addition , Marsili and Verspagen’s typology only covers the manufacturing sectors. We introduce an additional regime , knowledge intensive services, in order to cover the important innovation activity in these sectors. The science-based regime characterizes innovative activities with a knowledge base in the life sciences and physical sciences. This includes pharmaceuticals, computers and other information processing equipment, electronics, and telecommunication equipment. This regime is characteri zed by high opportunity conditions, leading to intense competition and high levels of entry for new firms. As Marsili and Verspagen point out, there are technological entry barriers in terms of knowledge. This also implies that internal absorptive capacity is vital for performance which , among other things, makes it important that external sourcing does not hurt internal capabilities. Knowledge here is mainly analytical in character. It is not necessarily complex in terms of diversity, but is in many cases very advanced , requiring a high level of technological expertise to work with it . A particular example here is biotechnology where , for example have Zucker et al 7 A distinct regime is defined by homogeneity in knowledge, opportunity, cumulativeness

and appropriablity conditions, whereas a sector is defined by main area of activity or output . Failures to recognize this distinction leads to unwarranted critique of the regime concept, i.e . based on the argument that there is to much diversity within statistically defined sectors for these to be considered a coherent regime (see Leiponen and Drejer 2007 for an example).

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(2002) argued that while much biotechnology knowledge is codifiable , only a few people (“star scientists”) have the capabilities to utili z e it. There is a large presence of high risk capital, and venture capital funds play an important role here both in facilitating the entrance of new competitors, and in making spin - ins and spin - offs feasible options for firms’ knowledge sourcing strategies. Marsili and Verspagen (2002) state that science based regimes are characterized by high cumulativeness conditions, though they do not explain why. Patterns of innovation may not be so clear cut in this respect . On the one hand , the highly advanced nature of technologies and entry barriers in scale allow a continuous learning process for incumbent firms. On the other hand , rapid technological advance implies frequent occurrence of disruptive innovations that render existing technologies obsolete. Incumbents and start - ups thus coexist and are dependent on each other in their innovation activities. Large incumbents provide a ‘market’ for start - up technologies, and provide financing and know - how for a wide range of functions. Incumbents on the other hand rely on start - ups as important sources of new knowledge, based either on capabilities they don’t possess or projects deemed either too risky or ineffective to develop in - house. The fundamental process regime is associated with chemistry - based technologies and is mainly oriented towards process innovation . Technological opportunity is relatively low, a high component of knowledge is analytical (and homogeneous) and there are high entry barriers related to scale. These imply that innovation is very incremental and cumulative in nature. In addition , the nature of knowledge means that fundamental process firms may have relative ease in registering their intellectual property, and the scientific nature of their knowledge makes interaction with academic research important. In addition , user - producer interaction may be important here. The complex system regime involves the combination of mechanical, electrical and transportation technologies. It includes motor vehicles and other transport equipment. This regime has medium to high levels of technological opportunity and very complex knowledge. The latter is a key feature for these firms. Their innovation depends on a diverse set of knowledge and competences, which both comprise a technological entry barrier and make external cooperation essential. Commodification of knowledge may be relevant here , both for selling technologies developed in - house and for external acquisitions. However, the complex nature of both knowledge and business activities makes close and ongoing cooperation the most important type of external interaction for complex system firms. The product engineering regime involves the use mechanical engineering technologies, within areas such as machinery, equipment and instruments. There is a fairly high level of technological opportunity and a broad spectrum of firms here in terms of technological capabilities. Entry barriers are low, and firms’ ability to appropriate their innovations may depend to a large extent on their synthetic knowledge; i.e. on organizational abilities and ability to combine technical knowledge and knowledge of user needs. The continuous process regime includes a variety of production activities such as metallurgical process industries – metals and building materials – and chemical process industries – textiles and paper, food and tobacco . While there are some common features to these firms, the diversity of this regime makes it more difficult to characteri ze it. Technological opportunity is generally characterized as low, thus increasing the importance of non - technological forms of innovation . Knowledge bases are fairly complex , often relying on a variety of technologies and fields. There are also generally large potentials for applying technologies from other areas, such as ICT and biotechnology. This places specific importance on interaction with suppliers and on acquiring technologies that firms do not have the know - how to develop themselves. Finally, we add an additional regime to the typology suggested by Marsili and Verspagen , the knowledge intensive service regime, which includes telecommunications, computer services and technical business services. Technological opportunity is high , particularly within ICT services, and entry barriers are low. While registration of IP is possible to some degree within software and telecommunications, appropriation in general is more difficult for knowledge intensive services. This may act as a barrier to cooperation . On the other hand , rapid technological advance means that firms must continuously search for new

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knowledge. In terms of innovation systems, the knowledge intensive services regime has a central role in the promotion of open innovation , given that these firms are in the business of providing external knowledge to other firms. Hence knowledge development and exchange are difficult to separate from these firms’ day - to - day operations.

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Empirical analysis of open innovation By Bernd Ebersberger and Carter Bloch

Introduction As the theoretical analysis above clearly shows, open innovation is much more than the purchase and sale of intellectual property. A broader, more grounded theoretical examination reveals that open innovation involves a wide range of activities, such as search , sourcing , licensing and various forms of cooperation , and that open innovation practices are impacted by a wide range of framework conditions. Furthermore, the analysis above shows how the notion of technological regimes provides a useful vehicle for capturing the diversity of open innovation practices across firms. However, as stated in the introduction , we lack empirical evidence on open innovation . The theoretical discussion generates a number of questions. For example , how widespread are the use of open innovation practices? Which types of open innovation practices are most oftenly used , and how do open innovation practices vary across countries, firm size and technological regimes. And , finally, what are the impacts of open innovation practices? Can we find evidence of a positive impact of open innovation practices on performance, and which types of practices appear to be most important? The analysis here seeks to explore these dimensions using empirical data. The analysis is conducted using Community Innovation Survey (CIS) data for Austria, Belgium , Denmark and Norway. The clear advantage with this data is its harmonization , which greatly facilitates international comparative analysis. And , while there are limitations to what can be examined using this data, we are able to capture a number of dimensions of open innovation in the analysis. The objectives here are to:

1 . Operationalise open innovation practices through the construction of open innovation indicators

2 . Analyse these indicators across countries and technological regimes 3 . Analyse the impact of open innovation practices on innovation performance

Section 2 .2 describes the data and methodology used in the analysis. Section 2 .3 contains the empirical descriptive analysis, while section 2 .4 analyses the impact on performance.

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Data and methodology In this section we introduce the data sets utili zed in the analysis below. We then elaborate on the creation of indicators for open innovation practices and introduce the methodology used in the analysis. In particular we briefly discuss the econometric models used to assess the performance effect of open innovation practices. Data The data basis for this analysis is the Community Innovation Survey (CIS) of Austria, Belgium , Denmark and Norway. The CIS, launched in 1991 jointly by Eurostat and the Innovation and SME Program , aims at improving the empirical basis of innovation theory and policy at the European level by surveying the innovation activities of businesses in the Member States' economies and other participating European countries such as Norway or Iceland . In each country the CIS surveys firms using a largely harmonized questionnaire to generate comparable data related to innovation activities within the economy. Hence , to a large degree, the data are comparable on the European scale. Although data generation emphasizes a common approach across countries, the data access for research purposes is governed by national regulations, which are far from being consistent across countries. Due to differences in data availability the analysis bases on the CIS 3 covering the years 1998 to 2000 for Austria. It bases on the CIS 4 covering the years 2002 - 2004 for Belgium , Denmark and Norway. Although the reference period is three years8 (1998 - 2000 and 2002 - 2004) the data is a cross - section . The Community Innovation Survey closely reflects the definitions of the Oslo Manual9 (OECD 1997) and thus provides a good coverage of the items that could potentially be used to build open innovation indicators. The survey contains information on the innovation activities of firms, innovation collaboration , search for innovation , protection of intellectual property rights and some context information about the firm such as industrial sector of main activity, size , exports, major markets, organisational information etc. The core questionnaire of the CIS 4 in English language is contained in the appendix . Constructing open innovation indicators We structure the discussion with reference to the three pillars of open innovation brought forth by Gassmann and Enkel (2006): the inside - out process, the outside - in process and the coupled process. We find that we can only cover the latter two by the survey items contained in the Community Innovation Survey. In particular we cover the outside - in process by an indicator capturing the firm’s way to bring outside innovations to the market, employ new processes developed outside the firm boundaries and to integrate outside services in their innovation activities. In addition we build an indicator representing the firm’s search strategy that companies rely on for their innovation activities. The coupled process is covered by an indicator based on the information about innovation collaboration . The only data in the survey relating to the inside - out process is information about IP protection . Laursen and Salter (2006) introduce the notions of breadth and depth for their analysis of the search strategies of firms. Breadth refers to the variety of partners or activities and depth captures the intensity of the activity. We extend the concept from search activities to collaboration , protection and external innovation . Overall we capture open innovation practices in seven dimensions. The breadth (depth) related dimensions give rise to an overall open innovation breadth (depth) indicator. Finally, open innovation breadth and open innovation depth are collapsed into an overall indicator approximating all open innovation practices of the firm .

8 Though quantitative data on expenditures and innovative sales only refer to the last year

of the reference period. See the core questionnaire for CIS4 in the appendix . 9 Future CIS, such as CIS2008 , will be based on the recent revision of the Oslo Manual,

OECD /Eurostat (2005).

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The general approach for generating the indicators representing open innovation practices is to recode the appropriate items into dummies, to build an additive composite indicator and finally to scale the indicator to range from 0 – 10 , where 10 means the highest degree of openness in this dimension . Figure 1 illustrates the hierarchical structure of the indicator system of open innovation practices. The sections below give more detail about the creation of the indicators. Additionally Cronbach’s alpha is reported for each of the national data sets to illustrate the reliability of the composite indicators10. External innovation External innovation describes how open companies are with respect to sourcing of external knowledge. This includes both reliance on external sources for the development of final products and processes and purchases of external knowledge for in - house development activities. External innovation breadth describes the heterogeneity of the sources feeding into the company’s product development and commerciali zation process such as the purchase of external R&D , machinery for innovation and other preparations for the innovation process and the co - development of product innovations or process innovations by outside actors (Cronbachs alphaAT = 0 .67 , alphaBE = 0 .68 , alphaDK = 0 .63 , alphaNO = 0 .53). External innovation depth describes how intensive the outside contribution is by focussing on a high level of involvement. The expenditure for external R&D , for machinery and for other preparations is assessed relative to the sectoral level. More than the median is regarded as high . Involvement of outside actors is high if product innovations or process innovations are exclusively developed by outside actors (Cronbachs alphaAT = 0 .73 , alphaBE = 0 .75 , alphaDK = 0 .70 , alphaNO = 0 .76). Search Open innovation strategies and employment related practices makes a company more porous for taking up external ideas and benefiting from partners. Search captures the proactive component of this process. It is actively seeking in or screening of a company’s environment for new ideas. Search breadth is constructed in accordance with Laursen and Salter (2006). It gives the variety of information channels which are utili zed in the company’s innovation activities (Cronbachs alphaAT = 0 .93 , alphaBE = 0 .79 , alphaDK = 0 .79 , alphaNO = 0 .61). Search depth also follows Laursen and Salter (2006) and summarises the intensity of the information channels as proxied by the firm regarding the information source as important (Cronbachs alphaAT = 0 .93 , alphaBE = 0 .72 , alphaDK = 0 .68 , alphaNO = 0 .71). Protection For companies which pursue an open innovation strategy, protection IP is a crucial practice in securing positive economic returns from the inside - out process. Protection can be seen as the closed dimension of open innovation as the strict protection of IP can be conceived as a closed innovation strategy. However, registration of IP may also be used as a tool to commodify proprietary knowledge, potentially facilitating greater interaction . For the companies IP protection strategy we can only build a breadth indicator as data on the importance of the measures are not available. The dataset only contains information about the usage of certain measures of IP protection such as patents, trade marks, copyrights etc.. The construction of the protection breadth indicator is reliable (Cronbachs alphaAT = 0.77 , alphaBE = 0 .75 , alphaDK = 0 .76 , alphaNO = 0 .78). Collaboration The coupled process is a combination of the inside - out and the outside - in process as company boundaries are porous in two ways. Collaboration is seen as a way to access complementary assets and to internalize knowledge spillovers.

10 We thank Bart Clarysse for pointing us towards the need to check the reliability of the

indicators by means of computing Cronbach’s alpha.

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The breadth indicator representing the collaboration dimension within the open innovation practices captures the variety of different collaboration partner types such as customers, suppliers, competitors etc. (Cronbachs alphaAT = 0 .79 , alphaBE = 0 .85 , alphaDK = 0 .84 , alphaNO = 0 .91). Collaboration depth represents high intensity of collaboration with a certain partner type , where high intensity means collaboration with at least one domestic partner and one international partner of this type (Cronbachs alphaAT = 0 .78 , alphaBE = 0 .81 , alphaDK = 0 .82 , alphaNO = 0 .85). Open innovation The overall employment of open innovation practices is summarized in an open innovation breadth indicator and an open innovation depth indicator. The open innovation breadth integrates all breadth dimensions of the open innovation practices (Cronbachs alphaAT = 0 .93 , alphaBE = 0 .77 , alphaDK = 0 .77 , alphaNO = 0 .59). Analogously, the depth indicator summarizes all depth indicators (Cronbachs alphaAT = 0 .81 , alphaBE = 0 .66 , alphaDK = 0 .62 , alphaNO = 0 .63). The combination of open innovation breadth and the open innovation depth gives the indicator for the usage of open innovation practices in the firm (Cronbachs alphaAT = 0 .79 , alphaBE = 0 .86 , alphaDK = 0 .86 , alphaNO = 0 .75). Overall the reliability of the indicators for the open innovation practices as measured by Cronbach’s alpha is given in Table 1 . Bearing in mind that the items measuring the open innovation practices are dichotomous variables, a threshold value of 0 .60 represents a reasonably good reliability. The majority of indicators exceed this threshold value for the analyzed countries. Table 1 Cronbach’s alpha Reliability of the inidcator AT BE DK NO

External innovation breadth 0 .67 0 .68 0 .63 0 .53

External innovation dept 0 .73 0 .75 0 .70 0 .76

Search breadth 0 .93 0 .79 0 .79 0 .61

Search depth 0 .57 0 .72 0 .68 0 .71

Protection breadth 0 .77 0 .75 0 .76 0 .78

Collaboration breadth 0 .79 0 .85 0 .84 0 .91

Collaboration depth 0 .78 0 .81 0 .82 0 .85

Open innovation breadth 0 .93 0 .77 0 .77 0 .59

Open innovation depth 0 .81 0 .66 0 .62 0 .63

Open innovation , total 0 .79 0 .86 0 .86 0 .75

Note: Indicator for the reliability of the indicator for open innovation practices, Cronbachs alpha Source: AT based on CIS 3 (1998 - 2000); BE, DK, NO based on CIS 4 (2002 -2004).

Open innovation practices vs. closed innovation The indicators for open innovation practices do not fully capture open innovation strategies. They do rather give a proxy for companies’ activities which can be thought of as the implemented practices of an open innovation strategy. Hence , we talk about the indicators for open innovation practices rather than indicating open innovation strategies. This is particularly relevant for the indicator capturing the IP protection . As closed innovation strategies will make extensive use of protection and open innovation strategies require systematic use of protection to facilitate the inside - out process, the protection indicator could potentially point towards the closed innovation strategies or towards open innovation strategies. This indicator alone will not allow us to distinguish closed from open innovation . However, as the overall open innovation indicators, be it the breadth , the depth or the overall composite indicator integrate characteristics of open innovation practices, the combination of indicators will point towards open innovation rather than to closed innovation . Collaboration , search and external innovation allows us

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to interpret protection as a part of the open innovation strategy rather than as a part of the closed innovation strategy.

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Figure 1 Structure of the indicators for the open innovation practices

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Assessing the coverage of the indicators for open innovation practices The current exercise illustrates that indicators for open innovation practices can be developed from existing firm level innovation survey data. Based on the internationally homogenized data set we are able to generate indicators for open innovation practices which are comparable across countries. However, not all aspects of firm level innovation activities are covered equally well by innovation survey data. The current status of the Community Innovation Survey allows for the coverage of the outside - in practices of open innovation by using the questions about who developed the product and process innovations, about the distribution of external and internal R&D activities, about innovation search and the assessment of the relevance of the partners. The coupled process is covered by the collaboration questions. Yet , the collaboration dummy variables only allow us to describe certain aspects of the corporate collaboration network . For more detailed analysis the collaboration intensity or the assessment of the relevance of certain collaboration partner (types) would be desirable . The third dimension of open innovation practices – the inside out process – is not adequately included in the current innovation survey. To be able to build indicators capturing also the inside - out one would require information about the license income of the firm , the income through patent sales, companies’ willingness and history in spinning out projects / companies, companies’ corporate venturing strategies etc. For the descriptive analysis we only use means and (relative) frequencies. We do not perform tests on cross - national or cross - sectoral differences. We rather try to give a picture about the structure of open innovation practices in the analyzed countries and the sectors. The dependent variables for the performance regressions are a dummy variable and a ratio which is bound below by zero and bound above by one. The dummy variable will be analysed by a standard probit regression. To analyse the ratio variable we use the fractional logit model developed by Papke and Wooldridge (1996).

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Descriptive empirical analysis The descriptive analysis in the sections below illustrates the structure and the utili zation of open innovation practices across countries, regimes and siz e classes.

Description of the national data sets The data sets in the analysis contain only innovation active firms from manufacturing (including Mining and Quarrying , NACE 10 - 14) and knowledge intensive service sectors11. These sectors are aggregated into technological regimes based on Marsili (2001), Marsili and Verspagen (2002) and OECD (2001). These technological regimes are described in detail in Chapter 1 above. Firms are classified as innovating if they reported the successful commerciali zation of a product innovation or the implementation of a new production process. In addition firms are also classified as innovation active if they reported positive innovation expenditures regardless of the success of the innovation project. Focussing on innovation active firms is necessitated by the structure of the innovation survey as some crucial information about the activities of firms is only surveyed in innovation active firms. Our definition of innovation activity corresponds to the filter question in the questionnaire; hence we include the maximum number of firms where the required information is available. Table 2 Size of the national data sets Regime AT BE DK NO Total

Continuous processes 118 319 173 455 1 ,065

Complex systems 10 8 2 35 55

Fundamental processes 14 90 26 46 176

Knowledge intensive serv. 32 256 288 403 979

Product engineering 92 296 272 459 1 ,119

Science based 30 54 98 110 292

Total 296 1 ,023 859 1 ,508 3 ,686

Note: Regimes are defined in accordance with Marsili (2001), Marsili and Verspagen (2002) and OECD (2001). Source: AT based on CIS 3 (1998 - 2000); BE, DK, NO based on CIS 4 (2002 - 2004).

The analysis bases on 3 ,686 observations from Austria, Belgium , Denmark and Norway. Table 2 displays the distribution of the national data sets on the technological regimes. The difference in the overall size of the national data sets is caused by differences in the initial selection of the sample and the different national response rates, where e .g . Norway stands out due to the participation in the innovation survey being compulsory. Table 3 Fraction of SMEs in the national data sets. AT BE DK NO

LE 28% 18% 24% 10%

SMEs 72% 82% 76% 90%

Note: SME denotes smeall and medium siz ed enterprises with 250 employees or less, LE are large enterprises with more than 250 employees. Source: AT based on CIS 3 (1998 - 2000); BE, DK, NO based on CIS 4 (2002 - 2004).

Table 3 illustrates the distribution of small and medium sized enterprises and large companies. The demarcation between SMEs and large companies is based on the number of employees of the companies. Companies with 250 employees or less are counted as SMEs and large enterprises have more than 250 employees. The annual turnover is not accounted for in the definition of SMEs here. In all national data sets in the analysis we 11 Knowledge intensive services here include: Telecommunications (NACE 64), Computer

services (NACE 72) and Technical business services (NACE 74 .2 - 3).

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find a majority of companies being SMEs. 90% of the companies in the Norwegian data set are small and medium sized . In the Austrian data set it is 72%12. Table 4 Average size of the firm (number of employees) Regime AT BE DK NO

Continuous processes 437 .74 242 .75 328 .29 191 .79

Complex systems 999 .15 2074 .88 22 .00 124 .54

Fundamental processes 307 .43 350 .43 343 .31 180 .50

Knowledge intensive serv. 205 .03 214 .64 286 .66 90 .57

Product engineering 298 .10 189 .85 245 .95 99 .73

Science based 627 .00 455 .13 305 .56 98 .34

Note: Average number of employees of the firm . Regimes are defined in accordance with Marsili (2001), Marsili and Verspagen (2002) and OECD (2001).Source: AT based on CIS 3 (1998 - 2000); BE, DK, NO based on CIS 4 (2002 -2004).

Table 4 reports the average size of the companies broken down by the technological regimes. In the Austrian and the Belgian data sets, the average companies in the complex systems regime are by far the largest. In all countries companies in knowledge intensive services and product engineering tend to be the smallest in the national data sets. Table 5 International market focus. Regime AT BE DK NO

Continuous processes 50% 87% 81% 53%

Complex systems 90% 100% 100% 69%

Fundamental processes 87% 92% 92% 78%

Knowledge intensive serv. 38% 60% 59% 61%

Product engineering 74% 89% 88% 70%

Science based 87% 87% 82% 75%

Note: Fraction of companies which operate on international markets. Source: AT based on CIS 3 (1998 - 2000); BE, DK, NO based on CIS 4 (2002 - 2004). Regimes are defined in accordance with Marsili (2001), Marsili and Verspagen (2002) and OECD (2001).

The analysis below will also utili ze the information about the international focus of companies. Table 5 summarizes the fraction of companies which operate on international markets13. The Belgian and the Danish data set stand out for their high international focus. Across countries we observe the least variation in the fundamental processes and the science based regime. Table 6 displays the shares of companies that are part of a corporate group . In general, shares are fairly similar across countries. Some exceptions are for continuous processes, where shares are much higher in Austria and Belgium , and product engineering where shares are higher in Denmark .

12 As weights are not available for all national data sets we base the following analysis

on the unweighted observations to achieve comparability across countries. 13 Note the slight difference between CIS3 and CIS4 for this question . For Austria (using

CIS3), the data refers to the share of companies which report main markets being international.

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Table 6 Part of a corporate group. Regime AT BE DK NO

Continuous processes 86% 75% 50% 51%

Complex systems 54% 57% 68% 63%

Fundamental processes 67% 78% 85% 78%

Knowledge intensive serv. 63% 57% 60% 55%

Product engineering 58% 54% 74% 53%

Science based 77% 76% 78% 65%

SMEs 47% 52% 61% 54%

LE 93% 93% 93% 91%

Note: Fraction of companies being part of a corporate group . SME denotes smeall and medium siz ed enterprises with 250 employees or less, LE denote large enterprises with more than 250 employees. Regimes are defined in accordance with Marsili (2001), Marsili and Verspagen (2002) and OECD (2001). Source: AT based on CIS 3 (1998 - 2000); BE, DK, NO based on CIS 4 (2002 - 2004).

Innovation activities in the sample In this section we investigate descriptively the innovation activities of companies in the sample. In a first step we compare the national data sets and break the national averages down between SMEs and large enterprises. We emphasize the innovation activities which are of particular relevance to open innovation practices. In particular we illustrate the collaboration behaviour (Table 7 andTable 8), the search for innovation (Table 9 and Table 10), the utili z ation of protection mechanisms (Table 11 and table 12), and the utili z ation of external R&D (Table 13 and Table 14). Table 7 Collaboration Collaboration AT BE DK NO

Domestic horiz ontal 6% 7% 15% 9%

Domestic science 20% 24% 27% 20%

Domestic vertical 13% 23% 30% 20%

International horizontal 5% 10% 14% 9%

International science 12% 12% 15% 9%

International vertical 17% 32% 34% 24%

Note: Fraction of firms with collaboration of the specific type. Source: AT based on CIS 3 (1998 - 2000); BE, DK, NO based on CIS 4 (2002 -2004).

Table 7 displays the fraction of companies with vertical, horizontal and science partners. Vertical collaboration captures the collaboration for innovation with customers, clients and suppliers. Collaboration with competitors is horizontal collaboration . Science partners include universities and governmental or non - profit research labs. Although we observe a heterogeneous pattern of vertical and horizontal collaboration across countries – e.g . the propensity to collaborate vertically with domestic partners varies from 13% in the Austrian data set to 30% in the Danish data set – the collaboration with science partners is comparably consistent across countries both for domestic and international partners.

39

Table 8 Fraction of companies with collaboration by size. AT BE DK NO

Collaboration LE SME LE SME LE SME LE SME

Domestic horiz ontal 7% 5% 14% 5% 21% 14% 16% 8%

Domestic science 41% 11% 48% 19% 44% 21% 53% 17%

Domestic vertical 20% 11% 40% 20% 45% 25% 39% 18%

International horizontal 11% 3% 23% 7% 24% 11% 19% 8%

International science 28% 6% 27% 9% 29% 11% 31% 7%

International vertical 33% 11% 59% 26% 55% 27% 45% 22%

Note: Fraction of firms with collaboration of the specific type. SME denotes smeall and medium siz ed enterprises with 250 employees or less, LE denote large enterprises with more than 250 employees. Source: AT based on CIS 3 (1998 - 2000); BE, DK, NO based on CIS 4 (2002 - 2004).

Table 8 reports the collaboration for innovation for SMEs and large enterprises separately. Consistent across countries is the fact that SMEs exhibit a lower propensity to collaborate with any type of collaboration partner. We find least variation across countries in large companies’ collaboration with scientific partners. The propensities to collaborate vertically or horizontally vary by a factor of two or three, respectively, between the Austrian data and the Danish data. Table 9 Search for innovation. Search channel AT BE DK NO

Clients and custormers 31% 43% 40% 42%

Suppliers 18% 31% 24% 20%

Competitors 10% 20% 12% 10%

R&D labs 2% 4% 1% 5%

Universities 5% 8% 8% 4%

Note: Fraction of companies utiliz ing specific partners in their search for ideas for innovation . Source: AT based on CIS 3 (1998 - 2000); BE, DK, NO based on CIS 4 (2002 - 2004).

Table 9 and Table 10 summarize the innovation search of the companies in the national data sets. The question in the innovation survey focuses on the relevant information for the companies’ innovation process either in the early phase of the project as an initial idea and inspiration or in later stages facilitating the finali zation of the project. In particular the question reads: “…Please identify information sources that provided useful information for new innovation projects or contributed to the completion of existing innovation projects.” (Eurostat 2004 , question 4 .1). The question hence measures the kind of channels to the external environment which are opened up by companies for their innovation activities. Consistent with the formal integration of external partners through innovation collaboration , the informal search predominantly focuses on customers, clients and suppliers. Utili zation of scientific partners as information sources is the least likely. Table 10 Search for innovation by size.

40

AT BE DK NO

Search channel LE SME LE SME LE SME LE SME

Clients and custormers 43% 26% 48% 42% 45% 38% 45% 42%

Suppliers 20% 18% 30% 31% 27% 23% 18% 20%

Competitors 13% 9% 27% 19% 12% 12% 14% 9%

R&D labs 1% 3% 6% 4% 3% 1% 7% 5%

Universities 6% 5% 11% 7% 9% 7% 8% 4%

Note: Fraction of companies utiliz ing specific partners in their search for ideas for innovation . SME denotes small and medium siz ed enterprises with 250 employees or less, LE denote large enterprises with more than 250 employees. Source: AT based on CIS 3 (1998 - 2000); BE, DK, NO based on CIS 4 (2002 - 2004).

Table 10 illustrates the differences in search strategies between large enterprises and SMEs. Across all countries the large enterprises have a higher propensity to utili ze any type of information source than SMEs have . Across all countries sourcing information for the innovation process from suppliers reveals the smallest difference between large enterprises and SMEs. About one in five or one in four companies use information from suppliers for their innovation activities. Table 11 and 12 summarize the utili zation of legal methods of protection . We observe no striking difference between the fraction of companies using patents or trademarks. Companies seem less likely to use copyrights. At least they are less likely to recognize or to report it. Table 11 Utilization of protection mechanisms Protection by AT BE DK NO

Patents 38% 20% 36% 24%

Trade marks 35% 19% 32% 23%

Copyright 16% 5% 16% 13%

Note: Fraction of companies using the measure to protect intellectual property. Source: AT based on CIS 3 (1998 - 2000); BE, DK, NO based on CIS 4 (2002 -2004).

Table 12 Utilization of protection mechanisms by size.

AT BE DK NO

Protection mechanism LE SME LE SME LE SME LE SME

Patents 65% 26% 40% 15% 52% 32% 47% 21%

Trade marks 48% 30% 33% 15% 51% 27% 40% 21%

Copyright 27% 12% 9% 5% 29% 13% 21% 12%

Note: Fraction of companies using the measure to protect intellectual property. SME denotes small and medium sized enterprises with 250 employees or less, LE denote large enterprises with more than 250 employees. Source: AT based on CIS 3 (1998 - 2000); BE, DK, NO based on CIS 4 (2002 - 2004).

The breakdown of the use of protection mechanisms is displayed in Table 12 . SMEs exhibit a lower propensity to protect than large enterprises do . In contrast to the observations above , large enterprises reveal a higher likelihood to patent than to apply for a trade mark . This particular observation holds for the Austrian data. It however does not hold for the Danish data.

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Table 13 Utilization of external R&D External R&D AT BE DK NO

New proc. developed extern . 48% 31% 41% 39%

New prod . developed extern . 22% 18% 24% 25%

Share of external R&D 5% 8% 10% 12%

Note: Share of firms with new processes or new products developed outside the firm . External R&D ex penditure share of total R&D expenditure. Source: AT based on CIS 3 (1998 - 2000); BE, DK, NO based on CIS 4 (2002 -2004)

Table 14 Utilization of exernal R&D by size

AT BE DK NO

External R&D LE SME LE SME LE SME LE SME

New proc. developed extern . 44% 50% 33% 31% 36% 42% 42% 38%

New prod . developed extern . 21% 23% 16% 19% 22% 24% 28% 25%

Share of external R&D 6% 4% 11% 7% 10% 10% 15% 12%

Note: Share of firms with new processes or new products developed outside the firm . External R&D expenditure share of total R&D ex penditure . Source: AT based on CIS 3 (1998 - 2000); BE, DK, NO based on CIS 4 (2002 - 2004).

Table 13 and Table 14 give an impression on the commerciali zation of externally developed goods, and services. It also refers to externally developed processes implemented by the firm . In addition it also gives the share of external R&D expenditure in total R&D expenditure. Although having found strong differences between large companies and SMEs in tapping into external sources of knowledge and collaboration , we find no striking difference between SMEs and large companies in their utili zation of external innovation . In some cases the descriptive data suggests that SMEs have a higher likelihood of using external innovation than large companies have.

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Exploring the open innovation indicators The indicators for open innovation practices are built as described above . This section summarizes the indicators for the open innovation practices. In a first step we analyze the open innovation indicators by country. In the second step we investigate patterns on the regime and the national level. Table 15 Open innovation indicators by country Open innovation indicators AT BE DK NO

External innovation breadth 3 .8 3 .5 3 .7 2 .0

External innovation dept 3 .4 3 .2 3 .3 3 .5

Search breadth 6 .3 7 .4 6 .9 7 .0

Search depth 1 .2 1 .6 1 .3 1 .3

Protection breadth 4 .1 1 .2 2 .2 1 .7

Collaboration breadth 1 .4 2 .4 2 .9 2 .4

Collaboration depth 1 .0 1 .8 2 .4 1 .8

Open innovation breadth 3 .9 3 .6 3 .9 3 .3

Open innovation depth 1 .8 2 .2 2 .3 2 .2

Open innovation , total 2 .9 2 .9 3 .1 2 .7

Note: Average indicator for the open innovation practices. Source: AT based on CIS 3 (1998 - 2000); BE, DK, NO based on CIS 4 (2002 -2004).

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Figure 2 illustrates this. There is some variation of the use of open innovation practices across countries. External innovation breadth seems to be particularly low in Norway whereas protection breadth seems particularly high in Austria. In accordance with the observation of the innovation activities above , collaboration both as the breadth and the depth is markedly low in Austria. Neither the Danish nor the Belgian data stand out indicating a particularly striking difference in the utili zation of open innovation practices. Although we observe some variation across countries the overall picture suggests a rather homogeneous utili zation of open innovation practices across industries. Regardless of the sectoral structure, open innovation practices seem to be distributed rather evenly across countries.

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Figure 2 OI indicators of by country

0

3

6

0

3

6

0

3

6

0

3

6

0

3

6

0

3

6

0

3

6

0

3

6

0

3

6

0

3

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AT BE DK NO

ExInno:bExInno:d

Search:bSearch:d

Prot:bColl:b

Coll:dO

I:bO

I:dO

I:a

Aver

age

scor

e

Note: Average indicator for the open innovation practices ExInno: external innovation Search: search , P: protection , Coll: collaboration , OI: open innovation . a: overall, b: breadth , c: depth . Source: AT based on CIS 3 (1998 - 2000); BE, DK, NO based on CIS 4 (2002 -2004).

45

For the analysis of common patterns among regimes and countries we rely on visual analysis of Figure 3 and Figure 4 which are built using summary statistics from the national analysis. 14 Figure 3 Open innovation indicators by country and regime

−2−1 0 1 2

−2−1 0 1 2

−2−1 0 1 2

−2−1 0 1 2

−2−1 0 1 2

−2−1 0 1 2

I i S s P C c O o a I i S s P C c O o a I i S s P C c O o a I i S s P C c O o a

AT BE DK NO

CPCS

FPKI

PESB

Devia

tion

from

the

natio

nal a

vera

ge

Note: Deviation of the average indicator score on the regime level from the national average. Negative deviation is orange, positive is dark gray. I: external innovation breadth , i: ex ternal innovation depth , S: search breadth , s: search depth , P: protection breadth , C: collaboration breath , c: collaboration depth , O: open innovation breadth , o: open innovation depth , a: overall open innovation . Regimes are defined in accordance with Marsili (2001), Marsili and Verspagen (2002) and OECD (2001). Source: AT based on CIS 3 (1998 - 2000); BE, DK, NO based on CIS 4 (2002 - 2004).

In this step we investigate whether technological regimes feature a special pattern in utili z ing open innovation practices. Figure 3 eliminates national peculiarities by showing only the deviation of the average score in the technological regime from the overall national average. To illustrate this: in Belgium the average score of the collaboration breadth is 2 .4 (see above). The average firm in the sectors of continuous processes (CP) exhibit a score of 1 .9 . Figure 3 displays the difference of - 0 .5 .

14 A comparative analysis based on statistical tests is impossible due to the regulation

that national data sets may not be pooled .

46

By and large we find in all countries a below average utili z ation of all dimensions of open innovation practices in the continuous processes regime (CP). The complex systems regimes (CS) show a below average score of search breadth and depth . External innovation in both dimensions of breadth and depth is above average. The overall OI score and the OI breadth are slightly positive . In the fundamental processes regime a consistent pattern shows up with the protection , the collaboration and the open innovation breadth being slightly above average . Figure 4 Open innovation indicators by country and regime

−2−1 0 1 2

−2−1 0 1 2

−2−1 0 1 2

−2−1 0 1 2

−2−1 0 1 2

−2−1 0 1 2

I i S s P C c O o a I i S s P C c O o a I i S s P C c O o a I i S s P C c O o a

AT BE DK NO

CPCS

FPKI

PESB

Devia

tion

from

the

cros

s na

tiona

l reg

ime

aver

age

Note: Deviation of the average indicator score on the regime level from the cross national regime average. Negative deviation is orange, positive is dark gray. I: ex ternal innovation breadth , i: external innovation depth , S: search breadth , s: search depth , P: protection breadth , C: collaboration breath , c: collaboration depth , O: open innovation breadth , o: open innovation depth , a: overall open innovation . Source: AT based on CIS 3 (1998 - 2000); BE, DK, NO based on CIS 4 (2002 - 2004). Regimes are defined in accordance with Marsili (2001), Marsili and Verspagen (2002) and OECD (2001).

The knowledge intensive services (KI) show a below average use of all open innovation practices. Firms in the product engineering regime (PE) are about average . On the contrary, firms in the science based regime (SB) exhibit above average usage of all open innovation practices in all participating countries. To investigate national patterns of open innovation practices we compute the average use of the practices on the regime level. The deviation of this and the cross national average use of the practice in the regime are displayed in Figure 4 . To illustrate this: Over all four countries the average company in a CP sector yields a sore of 2 .0 in collaboration

47

breadth . The average Austrian company in a sector belonging to the continuous process regime (CP) achieves a score of 1 .0 for its collaboration breadth . The difference of - 1 .0 is displayed in Figure 4 . To detect national patterns we compare along the national columns in Figure 4 . For Austria we do not observe a consistent pattern concerning external innovation . However, across all technological regimes search is below average in both the breadth and the depth dimension . The same also holds for collaboration . We also observe that Austrian companies exhibit a stronger protection breadth than the average company in the same technological regime. Across the board we find a slightly above average overall open innovation breadth . The overall depth and the aggregated overall open innovation indicator reveal below average utili z ation of open innovation practices in Austria. The national pattern in the Belgian data is not that strong . In the breadth of the external innovation practices the average Belgian firm in each of the technological regimes show an above average utili z ation . The breadth of the protection is below the four country average. Denmark , however, exhibits a strong national pattern with above average use of the breadth of external innovation and collaboration and above average use of the depth of collaboration . All indicators summariz ing the overall usage of open innovation practices are above average in all technological regimes. In the Norwegian data set – across all technological regimes – innovating companies reveal a below average use of open innovation practices related to the breadth of external innovation and protection . The overall usage of open innovation practices in Norway is below average both in the breadth dimension and as the overall indicator. Table 16 summarizes this national pattern . As a preliminary conclusion on of this section we can state that sectoral and national patterns in the use of open innovation practices can be detected . As this study does not attempt to explain the differences between sectors, regimes and countries, further research in this direction would be welcome . Yet, what this part of the exercise is able to illustrate is, that once policies are enacted to foster open innovation these difference have to be taken into account . As it is the case so often in innovation policy research one to one copying of existing and successful measures from one country to another may not yield the same favorable results due to sectoral or national difference. Table 16 Summary of national pattern Practices AT BE DK NO

Above average Prot:b ExInno:b ExInno:b Coll:b* Coll:d*

Below average Search:b Search:d Coll:b Coll:d

Prot:b ExInno:b Prot:b

Over all AT BE DK NO

Above average OI:b OI:b OI:d OI:a

Below average OI:d OI:a

OI:b OI:a

Note: * excluding CS,

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Effect of open innovation on innovation performance A central issue in the empirical analysis of open innovation is the impact of open innovation practices. What is the effect of open innovation practices on innovation performance? What types of open innovation practices are most important, or is it rather the overall open innovation strategy that matters most? This section examines these questions using innovation data. The open innovation indicators developed above will be used to assess the impact of open innovation practices on innovation performance . Two measures of innovation performance are used in the analysis. The first is a measure of innovative novelty; whether firms have introduced a product innovation that is new to their market. The second measure is the share of sales due to novel product innovations, thus measuring the scope or impact of new to market product innovations. The first can thus be considered as a measure of the ability of firms to create and implement novel innovations, while the second is a measure of the impact or success of the firm’s novel innovative activity. An important dimension of open innovation is collaboration . This section will also examine the impact of different types of innovation collaboration on innovation performance. In particular, the analysis distinguishes between collaboration with domestic and international partners, as this is both important in understanding the role of globalization and for policy design given that most policy measures focus on domestic collaboration and do not explicitly encourage international collaboration . Before we turn to the analysis of the performance effects of open innovation practices we investigate whether open innovation practices are “just” another measure of the corporate commitment to innovation activities. Table 17 reports the correlation coefficients of the indicators for open innovation practices and the R&D intensity of the firms. We observe that – although the correlation coefficients are positive and significantly different from zero for most of the open innovation practices – the correlation is moderately low, only rarely exceeding 0 .2 . We can thus argue that the indicators of open innovation practices capture additional and different information about innovation processes in firms than covered by R&D intensity. Table 17 Correlation of open innovation practices and R&D intensity

AT BE DK NO

External innovation breadth 0 .028 0 .072 0 .011 0 .057

Search breadth - 0 .004 0 .135 0 .166 0 .058

Protection breadth 0 .134 0 .226 0 .101 0 .111

Collaboration breadth 0 .091 0 .243 0 .237 0 .117

External innovation depth - 0 .029 0 .025 0 .057 0 .061

Search depth 0 .150 0 .149 0 .131 0 .067

Collaboration depth 0 .085 0 .217 0 .197 0 .101

Note: Correlation coefficient for open innovation practices and R&D intensity. Coefficients larger than 0 .13 (AT), 0 .07 (BE), 0 .10 (DK), 0 .05 (NO) are significant at the 5% level. Source: AT based on CIS 3 (1998 - 2000); BE, DK, NO based on CIS 4 (2002 - 2004).

Performance effects of open innovation practices This section assesses the impact of open innovation practices on innovation performance using the indicators developed above. We begin by examining the overall indicator of open innovation practices, and thereafter consider the individual dimensions of open innovation . Table 18 and Table 19 show the results of probit regressions for new to market product innovations. These regressions thus examine the effect of open innovation practices and other control variables on the propensity to successfully introduce a new, novel product on the firm’s market . As can be seen from Table 18 , in model I the overall open innovation practices have a positive impact on the propensity to introduce novel innovations. Coefficients for Belgium , Denmark and Norway are all highly significant. In

49

model II we break down the utili z ation of the overall open innovation practices into the breadth and the depth component . For Austria, coefficients for breadth and depth are of similar size with opposite signs, suggesting that effects of open innovation breadth and depth to a certain extent cancel each other out in the regression model I. The overall pattern for all countries is that open innovation breadth is driving the positive impact on novel innovativeness. The depth is insignificant for all countries. Examining the control variables, R&D intensity has a positive impact on the companies’ ability to innovate in Belgium , Austria and Norway, where we find significance only in case of the latter two countries. It is negative, yet insignificant, for Denmark . R&D intensity thus would appear to play a lesser role in companies’ ability to innovate in Denmark and Belgium . Finally, international orientation has a strong positive impact on novel innovation in Belgium , Denmark and Norway, while it is insignificant for Austria. However, it should be noted here that variables on international orientation for CIS3 (Austria) and CIS4 (Belgium , Denmark and Norway) are not fully comparable15.

15 More specifically, CIS3 data Austria indicates firms where international markets are

cited as most important, whereas CIS4 data simply indicates whether firms are active on international markets.

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Table 18 Performance regression, dep.var.: product innovation new to the market AT BE DK NO

Model I II I II I II I II

Open innovation

Open innovation , total 0 .033 - 0 .188*** - 0 .214*** - 0 .189*** -

Open innovation breadth - 0 .181* - 0 .214*** - 0 .22*** - 0 .123***

Open innovation depth - - 0 .153 - - 0 .041 - - 0 .024 - 0 .051

Controls

Si z e (log of number of empl) 0 .233*** 0 .183** 0 .06 0 .041 - 0 .033 - 0 .047** - 0 .011 - 0 .012

Part of a corporate group 0 .178 0 .062 0 .078 0 .047 0 .018 - 0 .004 - 0 .183** - 0 .192**

R&D intensity 1 .098 0 .962* 0 .255 0 .162 - 0 .194 - 0 .224 0 .663*** 0 .675***

International orientation - 0 .018 0 .031 0 .350*** 0 .288** 0 .420*** 0 .382*** 0 .218*** 0 .205***

Constant - 1 .191*** - 1 .604*** - 1 .526*** - 1 .169*** - 0 .488 - 0 .543 - 0 .834*** - 0 .879***

Observations 244 244 967 967 852 852 1406 1406

LR chi2 34 .92*** 38 .28*** 119 .86*** 128 .71*** 94 .83*** 102 .99*** 183 .11*** 185 .19***

Pseudo -R2 0 .11 0 .12 0 .09 0 .10 0 .08 0 .09 0 .10 0 .10

Note: Coefficients of a Fractional Logit - regression (Papke and Wooldridge 1996). Dependent variable: sales share of market novelties. *** (**,*) indicate significance at the 1% (5%, 10%) level. Standard errors of the estimates are available from the authors upon request. An additional 10 sector dummies are included in the regressions as controls, which are not reported here. Source: AT based on CIS 3 (1998 - 2000); BE, DK, NO based on CIS 4 (2002 - 2004).

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Table 19 Performance regression, dep.var.: productinnovation new to the market

AT BE DK NO

Model III IV III IV III IV III IV

Open innovation

Open innovation breadth - 0 .222** - 0 .217*** - 0 .229*** 0 0 .124***

Open innovation depth - 0 .009 - 0 .022 - 0 .026 - 0 .021 -

External innovation breadth 0 .035 - - 0 .003 - 0 .048* - 0 .071*** -

Search breadth - 0 .074 - 0 .028 - 0 .046** - 0 .021*** -

Protection breadth 0 .153*** - 0 .122*** - 0 .079*** - 0 .065*** -

Collaboration breadth - 0 .031 - 0 .051** - 0 .028 - 0 .034* -

External innovation depth - - 0 .062 - - 0 .058*** - 0 .008 - 0 .026

Search depth - 0 .024 - 0 .073** - - 0 .014 - 0 .014

Collaboration depth - - 0 .118* - - 0 .014 - - 0 .022 - 0 .01

Controls

Si z e (log of number of empl) 0 .186** 0 .180** 0 .033 0 .039 - 0 .045 - 0 .043 - 0 .017 - 0 .008

Part of a corporate group 0 .282 0 .122 0 .039 0 .046 0 .007 0 .003 - 0 .203** - 0 .193**

R&D intensity 0 .810 0 .927 0 .014 0 .056 - 0 .211 - 0 .206 0 .669*** 0 .678***

International orientation - 0 .018 - 0 .005 0 .279** 0 .275** 0 .365*** - 3 .79*** 0 .206*** 0 .206***

Constant - 1 .231*** - 1 .440*** - 0 .944*** - 1 .181*** - 0 .48 0 .558 - 1 .047*** - 0 .915***

Observations 244 244 967 967 852 852 1406 1406

LR chi2 52 .69*** 41 .03*** 142 .74*** 142 .21*** 105 .69*** 104 .67*** 195 .52*** 185 .73***

0 .16 0 .13 0 .11 0 .11 0 .09 0 .09 0 .10 0 .10

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In Table 19 we document model III and model IV of the regression analysis, where either the breadth or the depth of the open innovation practices are separated into individual dimensions. When disentangling the breadth of the open innovation practices a robust pattern across countries points to the highly significant correlation of the protection breadth of companies and their ability to innovate (model III). All four breadth dimensions (external innovation , search , protection and collaboration) are positive and significant for Norway, with similar – although in terms of significance weaker - results for Denmark . Results are mixed for the open innovation depth indicators, with most coefficients insignificant. An exception here is Belgium and Austria, where in Belgium external innovation depth has a negative effect and search depth a positive impact and in Austria collaboration depth has a negative effect on the ability to generate novel innovations.

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Table 21 show the results for fractional logit regressions on the share of sales due to market novelties. While the regressions above can be interpreted as examinations of the impact on ability or capacity to develop novel innovations, the regressions here measure the impact on innovation output or novel innovative performance. And , as can be seen , there are a number of important differences between the results here and those above . First, overall open innovation is positive and strongly significant in all four countries. For Austria, whereas overall open innovation had no significant impact on the propensity for novel innovation , the impact on innovative sales is highly significant as in all other three countries. For Austria, Denmark and Norway, it is open innovation breadth that is driving the positive impact on novel innovative performance. In contrast, for Belgium open innovation depth is positive and strongly significant while breadth is insignificant. I.e., while open innovation breadth is an important determinant of novel innovation in Belgium , open innovation depth impacts innovative performance. In particular, search depth is positive and strongly significant. Protection breadth , an important determinant of novel innovations in all countries, also has a strong impact on novel innovative sales in Austria, Norway and Belgium , though not in Denmark . External innovation breadth has a (weakly) positive impact for Norway, but a negative impact for Belgium . While in Denmark none of the individual dimensions are significant. Finally, we can also note that R&D intensity is positive and strongly significant for all countries in all model specifications. This can also be contrasted with the results of the probit regressions: while we find mixed results for the impact of R&D intensity on the propensity to innovate, there is a clear strong impact on the siz e of innovative sales (ie . innovation output).

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Table 20 Performance regression, dep.var.: sales share of market novelties

AT BE DK NO

Model I II I II I II I II

Open innovation

Open innovation , total 0 .289*** - 0 .153*** - 0 .100** - 0 .093*** -

Open innovation breadth - 0 .271** - 0 .035 - 0 .101* - 0 .107***

Open innovation depth - 0 .016 - 0 .121 - - 0 .006 - - 0 .043

Controls

Si z e (log of number of empl) - 0 .237* - 0 .279** - 0 .022 - 0 .015 - 0 .101* - 0 .107* - 0 .198*** - 0 .204***

Part of a corporate group 0 .096 - 0 .048 - 0 .075 - 0 .063 0 .222 0 .217 - 0 .287** - 0 .298**

R&D intensity 2 .578** 2 .527** 1 .388*** 1 .414*** 1 .709*** 1 .703*** 2 .045*** 2 .066***

International orientation 0 .110 0 .068 0 . 390** 0 .416** 0 .578*** 0 .562*** 0 .339** 0 .308**

Constant - 3 .190*** - 3 .270*** - 3 .131*** - 3 .149*** - 1 .959** - 1 .984** - 2 .653*** - 2 .822***

Observations 244 244 971 971 852 852 1430 1430

LR chi2 74 .58*** 72 .69*** 69 .43*** 63 .35*** 238 .10*** 238 .64*** 440 .71*** 436 .61***

Note: Coefficients of a Fractional Logit - regression (Papke and Wooldridge 1996). Dependent variable: sales share of market novelties. *** (**,*) indicate significance at the 1% (5%, 10%) level. Standard errors of the estimates are available from the authors upon request. An additional 10 sector dummies are included in the regressions as controls, which are not reported here. Source: AT based on CIS 3 (1998 - 2000); BE, DK, NO based on CIS 4 (2002 - 2004).

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Table 21 Performance regression, dep.var.: sales share of market novelties AT BE DK NO

Model III IV III IV III IV III IV

Open innovation

Open innovation breadth - 0 .285*** - 0 .041 - 0 .116* - 0 .111***

Open innovation depth 0 .069 - 0 .244*** - - 0 .003 - - 0 .048 -

External inno . breadth 0 .076 - - 0 .069* - 0 .020 - 0 .089* -

Search breadth - 0 .063 - - 0 .007 - 0 .016 - 0 .015 -

Protection breadth 0 .156*** - 0 .108*** - 0 .032 - 0 .072*** -

Collaboration breadth 0 .038 - - 0 .043 - 0 .029 - 0 .002 -

External innovation depth - - 0 .045 - - 0 .019 - 0 .000 - 0 .026

Search depth - 0 .077 - 0 .124*** - 0 .035 - - 0 .022

Collaboration depth - - 0 .002 - 0 .041 - - 0 .023 - - 0 .048*

Controls

Si z e (log of number of empl) - 0 .280** - 0 .279** - 0 .022 - 0 .015 - 0 .101* - 0 .107* - 0 .198*** - 0 .204***

Part of a corporate group 0 .166 0 .018 - 0 .075 - 0 .063 0 .222 0 .217 - 0 .287** - 0 .298**

R&D intensity 2 .235** 2 .391** 1 .388*** 1 .414*** 1 .709*** 1 .703*** 2 .045*** 2 .066***

International orientation 0 .023 0 .320 0 . 390** 0 .416** 0 .578*** 0 .562*** 0 .339** 0 .308**

Constant - 3 .478*** - 3 .225*** - 3 .131*** - 3 .149*** - 1 .959** - 1 .984** - 2 .653*** - 2 .822***

Observations 244 240 971 971 852 852 1430 1430

LR chi2 71 .55*** 74 .14*** 62 .49*** 63 .07*** 238 .52*** 238 .10*** 432 .97*** 433 .48***

Effect of globalized innovation networks on performance This section places focus on the role of globali zed innovation networks for innovation performance . Specifically, we examine the impact of three different types of innovation collaboration: vertical (collaboration with suppliers and customers), horizontal (with competitors) and science based (with universities and government research institutions). And , importantly, we examine the role of international versus domestic collaboration . As above , we examine the impact on novel innovativeness, however the other measure of innovation performance is slightly different than above. Instead of using the share of novel innovative sales, we use here an indicator (dummy variable) of whether the firm’s innovation performance as measured by the sales share of market novelties is above the median for the firm’s sector. Both regressions in Table 22 and Table 23 are estimated using Probit models. Table 22 Effects of innovation networks, dep.var: market novelties AT BE DK NO

Collaboration

Domestic vertical coll. - 0 .270 - 0 .027 0 .072 0 .283***

Domestic horiz ontal coll. 0 .371 0 .107 0 .239* 0 .008

Domestic science coll. - 0 .395 0 .071 0 .062 0 .137

International vertical coll. 0 .666* 0 .415*** 0 .344*** 0 .183*

International horiz ontal coll. - 0 .649 0 .287* - 0 .087 0 .095

International science coll. 0 .394 0 .207 - 0 .031 - 0 .018

Controls

Si z e (log of number of empl) 0 .260*** 0 .059 - 0 .004 0 .002

Part of a corporate group 0 .145 0 .065 0 .062 - 0 .148*

R&D intensity 1 .026 0 .176 0 .004 0 .741***

International orientation 0 .055 0 .325*** 0 .452*** 0 .268***

Constant - 1 .206** - 1 .117** - 0 .468 - 0 .401**

Observations 244 967 852 1406

LR chi2 41 .25*** 120 .20*** 75 .12*** 129 .70***

Pseudo R2 0 .13 0 .09 0 .06 0 .07

Note: Coefficients of the Probit - regression . Dependent variable: firm has commerciali z ed a product which is new to the market in the respective period . *** (**,*) indicate significance at the 1% (5%, 10%) level. Standard errors of the estimates are available from the authors upon request. An additional 10 sector dummies are included in the regressions as controls, which are not reported here. Source: AT based on CIS 3 (1998 - 2000); BE, DK, NO based on CIS 4 (2002 - 2004).

Both tables give the same clear message across all four countries: of all the different types of collaboration , it is international vertical collaboration that positively impacts innovation performance . In all four countries, the coefficient for international vertical collaboration is positive and strongly significant. Hence , while our findings indicate the importance of broad knowledge sourcing , the result here emphasizes the importance that this knowledge sourcing also includes close interaction with international sources. It can also be noted from coefficient estimates of the control variables that, even after accounting for different types of international collaboration , international orientation still has a significant, positive impact on innovation performance.

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Table 23 Effects of innovation networks, dep.var: top innovation performance AT BE DK NO

Collaboration

Domestic vertical coll. - 0 .372 - 0 .024 0 .055 0 .272***

Domestic horiz ontal coll. 0 .235 0 .039 0 .167 0 .037

Domestic science coll. 0 .034 - 0 .050 0 .038 0 .101

International vertical coll. 0 .715** 0 .205* 0 .339*** 0 .181*

International horiz ontal coll. - 0 .849** 0 .325** - 0 .235 0 .093

International science coll. 0 .171 0 .331** 0 .055 0 .003

Controls

Si z e (log of number of empl) 0 .140* 0 .046 - 0 .034 - 0 .013

Part of a corporate group - 0 .010 0 .085 0 .132 - 0 .149*

R&D intensity 1 .077 0 .437* 0 .199 0 .823***

International orientation 0 .095 0 .197* 0 .443*** 0 .333***

Constant - 0 .786*

- 0 .753*** - 0 .382 - 0 .376***

Observations 244 978 852 1436

LR chi2 24 .81* 59 .60*** 44 .33*** 126 .68***

Pseudo R2 0 .08 0 .05 0 .04 0 .07

Note: Coefficients of the Probit - regression . Dependent variable:firm shows top innovation performance i.e. firm’s innovation performance as measured by the sales share of market novelties is bettern than the median in the sector. *** (**,*) indicate significance at the 1% (5%, 10%) level. Standard errors of the estimates are available from the authors upon request. An additional 10 sector dummies are included in the regressions as controls, which are not reported here. Source: AT based on CIS 3 (1998 - 2000); BE, DK, NO based on CIS 4 (2002 - 2004).

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Summary Both the descriptive and regression results reveal a number of insights. This section summarizes the main results of the empirical analysis and ties them into the theoretical discussion above . We defer a discussion of potential policy implications until the end of the report. Open innovation matters. Open innovation practices have a strong impact both on the capacity for novel innovation and on actual innovation performance . In general it is the breadth of these practices – i.e. the range of interfaces with the external environment - that generates the positive effects. A partial exception here is Belgium where , while breadth is positively correlated with innovativeness, it is open innovation depth that positively impacts innovation performance. A broad based, holistic approach to open innovation may give greater returns than focus deeply on single aspect. Taking the results together, what seem to be most important are the overall strategies as opposed to individual dimensions of open innovation . For example for many individual dimensions of open innovation such as search , external innovation and collaboration , no impact was found . And even when effects are found , these are generally much smaller than for overall innovation . This is particularly the case for impacts on innovation performance, where overall open innovation is very strongly significant, while individual dimensions are generally either insignificant or only weakly significant. This is also somewhat the case for impacts on novel innovativeness, where in particular open innovation breadth is strongly significant. An exception concerning individual dimensions is Norway, where all individual dimensions are positive and significant. A strong internal capacity is still important. Two results point in this direction . First, the results indicate that R&D intensity is an important determinant of innovation performance . The second result concerns external innovation . There is some mixed evidence that external innovation breadth has positive impact (particularly in Norway), but none for external innovation depth (with a negative impact on novel innovativeness in Belgium). This could be interpreted that intensity of outsourcing research and innovation activities is not an important determinant, and may potentially have negative effects if it reduces R&D capabilities. Collaboration is a crucial determinant of high innovation performance. In particular innovation collaboration along the value chain – i.e. vertical collaboration – is significantly positively correlated with superior innovation performance of firms in all analyzed countries. The descriptive analysis shows that a fairly high share of firms has international collaboration , though shares are much higher for large firms. The most common type of collaboration is vertical collaboration , and shares are fairly similar for domestic and international collaboration . Though , our regression results indicate that it is vertical collaboration across national boundaries, which has the greatest impact. Open innovation practices less used by SMEs, though have important impact on performance. Recalling the descriptive discussion above we observe that across the board SMEs show a lower likelihood of implementing open innovation practices. However, over three fourths of the firms in our empirical analysis are SMEs, suggesting that positive impacts on performance that are found here are also present for SMEs. Supplementary analysis16 also confirms this – open innovation matters for SMEs.

16 Regressions run for SMEs yielded qualitatively the same results are those shown

above. Results are not shown here , but can be obtained from the authors.

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The descriptive analysis above also allows us to examine the relation between open innovation and technological regimes in greater detail. The Science based regime is characterized by high technological opportunity and ‘commodifiable’ knowledge . Hence , we would expect these firms to be most ‘open’ , and this is also what we find in the empirical analysis. With few exceptions, science based firms score highly across all four countries and all types of open innovation practices: search , external innovation , IPRs and cooperation . Continuous process regime in contrast is below national averages for all types of open innovation practices. This may to reflect to some extent low technological opportunity in these regimes; while there clearly are potential for innovation and the application of new technologies, this potential may still be lower than in other regimes. The potential for technology applications may be reflected in the external innovation indicator, which is around or slightly above average in the four countries. There are relatively few firms within Complex systems, making us cautious about interpreting the empirical results for this regime . However, a very tentative picture shows low scores for search and higher scores for external innovation and cooperation . This picture would appear to fit well with the characteristics of complex systems, where reliance on external sources is a necessity and the establishment of longer term collaborations perhaps more important than search for new knowledge . Like science based firms, fundamental process firms are able to commodify knowledge, though technological opportunity is lower and innovation more process oriented . The empirical results show low external innovation , and generally high scores for search , IPRs and cooperation . These results suggest that, while fundamental process firms are fairly ‘open’ in accessing new knowledge, they tend to conduct final product and process development in - house. For the Product engineering regime , almost all open innovation indicators are around average . This is perhaps surprising given that these firms would be expected to have fairly high technological opportunity and appropriability. This can possibly indicate that there is less focus here on open innovation , or simply that opportunities are slightly less than for other more open regimes. Knowledge intensive firms can be argued to have high technological opportunity, though they may have low appropriability. Furthermore , much knowledge for these firms may be embedded in individual employees. The empirical results show very low scores on all types of open innovation indicators, suggesting that open innovation in knowledge intensive firms may be hampered both problems in obtaining (and distributing ) new knowledge , and in gaining from it.

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Conclusions and policy implications By Sverre J. Herstad

Main findings and policy implications We have analysed the overall “openness” of organisational boundaries and innovation strategies by constructing composite indicators for open innovation practices. These indicators measure the range and intensity of overall interfacing with the external environment. They contain no biases towards specific dimensions of higher relevance to some industries than others, except from a bias against dimensions not covered by innovation survey data. We have argued that the broader the range of actors and actor groups firms interface with , the higher is the probability that ideas and knowledge complementary to own activity and capabilities is identified . And the higher is the likelihood of something novel emerging . This we understand as a direct product of increasing complexity of products and services, and increasing uncertainty concerning the direction of market and technological change . We have further pointed out that small, open economies are characterised by strong supply and demand side limitations in national innovation systems. Based on this, we have assumed that international search , sourcing and collaboration are of outmost importance to firms in these economies. The indicators developed have allowed us to investigate relationships between innovation performances on the one hand and overall as well as dimensional and geographical patterns of openness on the other hand . We have found that it is primarily the overall openness of organisations which impact positively on innovation performance, in addition to intramural R&D and international collaboration within the value chain , i.e. with customers or suppliers. This is all consistent with our theoretical assumptions; and can be linked to the occurring transition towards globally distributed knowledge networks. This transition does not imply that internal knowledge development and accumulation is becoming less important from both firm and economy perspectives; rather on the contrary. From the firm perspective , the ability to tap into, absorb from and serve as gravitation points within such networks is contingent on a strong , internal capacity. From the economy perspective, this capacity is essential as it serves to “anchor” companies to economies of origin and as it produces knowledge spillovers into these economies. Related to this, we warn against the conceptualisation of organisations as simple “nexuses” of market contracts which is built into the original formulation of the concept by Henry Chesbrough (2003). Organisations are social systems which serve the purpose of developing and accumulating specialised capabilities, based on “raw” inputs sourced from capital markets, labour markets and through innovation system linkages. It is still the ongoing development and accumulation of such capabilities within organisations and their surrounding environment which sustain innovation and industrial development; and these are not readily available in factor markets. They must first be developed internally, and may only then filter out into the environment and be made available for use by other firms. We find few reasons to argue that major changes in overall innovation policy and policy measures are needed in light of global open innovation – in the countries we have investigated here. The policy systems and measures of these countries are already,

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overall, recogniz ing the systemic and therefore open nature of innovation . This, of course , is attributable to the impact from innovation studies and certain strands of economics on innovation policy and innovation measure development during the last 15 - 20 years. This does not mean that there is not room for improvement. In general, policies must continuously must balance a very fine line between incentives and support for the build -up and use of speciali z ed knowledge bases embedded in firms (i.e . incentives for intramural R&D); and incentives or support for openness and the formation of linkages. The latter must balance finely between creating those ties, networks and platforms within economies which are essential to the ability of the economy to endogenously renew itself; and those international linkages which are increasingly important for the purpose of overcoming inherent national and regional supply and demand side limitations. The extension of this argument is that the interaction between different policies and measures must be considered very carefully. Different tools and measures can on their own only influence certain aspects of the larger dynamics sought achieved , reviews must therefore be conducted at the level of overall innovation policy and the portfolio of measures. Policies must also recognize how different industries face different innovation drivers; and draw on different sources of external knowledge when seeking inputs to innovation . When doing so , they learn and accumulate knowledge in different ways. Many policy measures and initiatives build on assumptions that certain drivers and inputs are more prevalent in – or important to – industrial development, than others. Policymakers should therefore carefully consider the compatibility between these assumptions, and the actual requirements of the industrial base sought supported , and similarly the composition of portfolios of different tools assuming different drivers and inputs. The open innovation practices of small and medium sized enterprises raise some concerns. Small size often means weak or narrow internal capacities, which in itself forces broad external networking . However, weak organisational and administrative capacities easily translate into limitations on the ability to successfully form linkages and absorb knowledge and ideas from these. Our analysis clearly indicates a need for policy attention towards SME growth (build - up of specialised internal knowledge bases) linked to international networking . Below we discuss the main project findings and implications in more detail.

Dimensions of open innovation In the empirical analysis we have distinguished between innovation , search , sourcing , collaboration and protection . These different ways of interacting with the external environment come with different opportunities, challenges and costs. We have argued that coupled open innovation strategies, proxied here as collaboration , are the most intense form of interaction , as they involve two or more parties sharing existing knowledge for the purpose of developing new knowledge. The initial investments and commitment involved in collaborative relationships contain an inherent danger of creating lock - in to existing partners (see e .g . Narula 2002). Our data show that collaboration is a highly selective dimension of open innovation . Scores on collaboration breadth , the range of different partner types used range from a very low 1 .4 in Austria to a maximum of 2 .9 in Denmark , which is still low compared to the possible score of 10 . Scores for depth , our proxy for intensity, are even lower. Broad search is believed to be essential for the ability of enterprises to keep track of what goes on beyond their specific sets of value chain transaction partners. This

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appears to be recognised by enterprises, as the differences between search and collaborations scores are striking . Firms across all countries show much broader search than collaboration patterns, with scores ranging from 6 .3 in Austria to 7 .0 out of 10 in Norway. These we interpret as very healthy patterns. Broad and ongoing search is a prerequisite for the ability of enterprises to avoid lock - in to low opportunity development trajectories. Community innovation survey data does not allow for us to investigate the use and impact of external technology commercialisation . Based on evidence from other studies, we do however believe that large - scale processes of combined spinning in and spinning out companies and technologies, portrayed as an ideal in Henry Chesbroughs (2003 , 2005) work , are limited to sectors and places with certain distinct knowledge, opportunity and institutional preconditions. There is, however, certain evidence from other studies (OECD forthcoming , Granstrand 2003) indicating that sale of IPRs is becoming more widespread , and that licensing is of higher importance to a wider range of industries than what has been assumed . The fact that innovation survey data only covers in - sourcing through such mechanisms, and not commercialisation through the same , is one of its major weaknesses.

Country differences There is some variation of the use of open innovation practices across countries, although the overall picture suggests that open innovation practices are fairly evenly distributed . Sourcing breadth – the number of means used to source knowledge externally - seems to be particularly low in Norway whereas protection breadth seems particularly high in Austria. On the other hand , sourcing depth is strong in Norway. Norwegian firms in our data set source on average 12% of their R&D from external actors, compared to a range from 5 % in Austria through 8 % in Belgium and on to 10 % in Denmark . This is not surprising , given the tendency of Norwegian firms to acquire R&D from each other or external research institutes (Herstad et al 2005 , Mariussen 2008). Norwegian firms in addition show the highest propensity to target external R&D labs in their search strategy; while showing the lowest average propensity to target universities. This reveals the importance of the so - called institute sector in Norwegian industry. In contrast to this, a distinct weakness in Denmark with respect to search among R&D labs is counteracted by the strongest focus on search among universities, which we assume is reflecting the set - up of the Danish science system combined with characteristics of the industrial structure . Collaboration both as the breadth and the depth is markedly low in Austria. Clients, customers and suppliers are frequently used sources of ideas and information across all four countries; however, Austrian firms emerge as weaker than the other countries with respect to searching among customers. Belgian firms are particularly strong with respect to supplier search .

Regime differences More striking differences are found across regimes. These differences materialise as a result of different availability and distribution of knowledge , across actors and in space; and as a result of different rates of market change and intensities in market selection . In addition , factors such as the possibility of using IPRs to a) protect own knowledge when interacting , and / or b) the use of in particular patents and licensing schemes as means of “packaging” and making knowledge available for external use, are assumed to influence on patterns of open innovation . The consistency of the regime profiles across the studied countries clearly points towards the need for policy to recognise that a variety of knowledge inputs enter into

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and are critical to industrial innovation processes. This is perhaps most striking for those industries presumed to be fed primarily by academic research . The broad range of complementary capabilities and knowledge inputs sourced in by firms in the science -based regimes clearly suggests that there is much more to the large - scale build - up and competitiveness of such industries than merely academic research . Similarly, policy must recognise that markets and therefore innovation drivers differ significantly across economic activities; in turn translating into different sensitivities towards different policy tools. Innovation may in certain fields such as biotechnology or nanotechnology be highly science - driven , and therefore supported by public investments in relevant scientific research . In others it is driven by the demands of professional clients; and may be influenced either by the public sector acting as a demanding customer, or through market regulations increasing the demand for new products. The latter apply, for instance , with respect to alternative energy technologies. Specific client demands may trigger radical innovations; but they may equally be very oriented towards long -term expected operational reliability, customization and maintainability – depending in turn on the conditions faced by the client. These drivers we find to dominate in e.g . the highly user - driven sub - sea technology or shipbuilding industries in Norway (Herstad and Naas 2007). Drivers may equally include culturally contingent preferences concerning design , appearances and other symbols built into products and services. In this case , innovation may be stimulated and enabled by the creation of linkages between creative industries and manufacturing industries – and not necessarily through public research schemes. A certain caution is warranted when interpreting the regime profiles. For instance , the average performance of firm in the product engineering regime most likely indicate that these firms have to rely heavily on internal knowledge development and innovation , as the knowledge inputs required are highly specialised . They are less open , but this does not necessarily indicate that there is a problem of insufficient openness. Similarly, KIBS emerge with far below average scores on openness – as measured here. Since the knowledge of service firms predominantly is embedded in individuals, this tends to suggest that knowledge sourcing through labour markets rather than direct interaction between organisational actors is the main mode of openness. Knowledge is embedded in the heads of KIBS professionals, and these are mobile between KIBS firms. This is not captured by our indicators. It also suggests problems related to appropriablity which can be assumed to limit the willingness of KIBS organisations to share knowledge; and it indicates the narrower band of relevant interfaces available. KIBS are, inherently, oriented towards client interaction , and innovation survey data do not capture the intensity of this specific interaction .

Regimes versus trajectories Caution is also warranted because the concepts of technological regimes and technological trajectories must not be confused . Regimes are external conditions of opportunities and constraints, which may or may not be reflected in actual patterns of innovation behaviour – i.e . the trajectories or paths followed by firms and industries. Opportunities and knowledge inputs must be discovered , a process which is contingent on search outside those existing value chain relationships and networks whose opportunities are already known . And it is contingent on experimentation with new collaboration partners and sources of knowledge . Recognising this distinction is critical to innovation and research policy. For instance, activities labelled by us as belonging to continuous process regimes – e .g . food , beverages and tobacco – reveal fairly weak patterns of open innovation and are presumably locked to low opportunity development trajectories and networks. These trajectories may however be broken . New platform technologies such as biotechnology or nanotechnology can serve to rejuvenate these industries if they search , collaborate and source outside existing networks. This, in turn ,

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can feed back and provide additional support for nanotechnology or biotechnology research This line of reasoning is related to the concept of cross - sectoral knowledge diffusion and absorption (Cooke 2007), and the need to create “platforms” through which ideas can flow between different value chains and sectors.

Globalisation Firms in our data are heavily oriented towards international markets. The lowest degree of internationalisation we find among knowledge intensive service providers in Austria, where only 38% of companies focus on international markets. In addition , only 50% and 53% of the firms belonging to the continuous process regimes in Austria and Norway respectively are oriented towards international markets, compared to between 81% and 87% in Denmark and Belgium The latter countries have , not surprisingly, a stronger and more internationally oriented food , beverage and tobacco industry which also searches more broadly and intensively (Belgium), or collaborate and source more broadly and intensively (Denmark). These exceptions aside we have seen that the share of firms focusing on such markets, across regimes and countries, predominantly are in the ranges between 70 % and 90 %. The role of clients and customers extend beyond merely absorbing products. Clients, customers and suppliers are the most frequently used sources of information and ideas as inputs into innovation . However, the extent to which companies engage in more in -depth collaborative relationships with these actors, and the extent to which they do so across national boundaries, diverge. Only 13 % of Austrian firms engage in domestic vertical collaboration , and 17 % engage in international collaboration . At the other end of the continuum we find Denmark with strong patterns of both domestic and international collaboration; 30% of innovation Danish enterprises co - operate domestically within the value chain , and as many as 34 % co - operate internationally. A larger proportion of companies co - operate with the domestic science system than do with international science systems. This picture is indicating a need for policy tools supplementing incentives for domestic science system collaboration with incentives to increase the propensity of enterprises to interface with foreign systems.

SMEs versus large enterprises Whereas the industries of Austria, Norway, Denmark and Belgium in general show well -developed patterns of open innovation , the picture for small and medium sized enterprises is more nuanced . There are few substantial differences between these SMEs and large enterprises (LE) with respect to search practices; except for Belgium where only 26% of SMEs search among customers and clients, compared to 43% within the Belgian LE group . However, when it comes to the more organisationally demanding process of collaboration we find striking differences. Collaboration is in general is far weaker for SMEs than large enterprises. For instance, whereas 53 % of Norwegian large enterprises and 48% of Belgian large enterprises collaborate with their domestic science systems, only 17% and 19 % respectively of SMEs do so . Given that the growth of new activities is considered critical to the renewal of national and regional innovation systems, and that this growth relies on external system support, the weak linkages between SMEs and the science system begs for policy attention . More disturbing , but consistent with other research on corporate internationalisation , is perhaps the weak patterns of international collaboration revealed by SMEs. These weaker patterns are to be expected on the background of weaker organisational resources and management capabilities, and weaker ability to carry the financial burdens and risks associated with internationalisation . On the other hand , the need for such enterprises to network internationally may be very strong . Only between 6 %

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(Austria) and 11 % (Denmark) of SMEs have collaborative relationships with science systems outside their domestic economies. SMEs are stronger in international vertical collaboration , but still only between 11 % of Austrian and 27 % of Danish innovation active SMEs engage in international value chain collaboration . The numbers for large enterprises are 33 % and 55 % respectively. This points towards the importance of support mechanisms aiding search and serving as door openers for international collaboration . Small enterprises may transcend own resource limitations by acquiring knowledge in external markets, i.e . in forms ranging from machinery through components and into contract R&D and patents. However, SMEs across all four economies contract out a lower proportion of their R&D than do large enterprises. This may on the downside indicate that they are unable to successfully source R&D; but it may also on the upside indicate that they are more oriented towards building up own internal knowledge bases. Norwegian SMEs emerge as particularly strong in this respect, outsourcing a total of 12 % of their R&D , while Austrian SMEs emerges as particularly weak with only 4 % outsourced . Denmark is again an outlier, with 10 % of total R&D outsourced by both small and large enterprises. On the other hand , this could indicate that Norwegian enterprises are particularly weak with respect to the build - up of own specialised capabilities. Austrian , Belgian and Danish SMEs to a larger extent commercialise products or processes developed externally. Combined with the weaker collaboration and sourcing patterns of SMEs, this raises the issue that there may be additional mechanisms involved in feeding knowledge and technologies into SMEs than what is captured by our data, i.e. other mechanisms than direct search , sourcing or collaboration . A likely mechanism is labour market mobility.

Open innovation, performance and the theory of the firm Our analysis clearly shows that open innovation matters. Open innovation practices have a strong impact both on the likelihood of introducing novel innovation and on actual innovation performance as proxied by the sales share of products which are new to the market. In general it is the breadth of these practices – i.e. the range of interfaces with the external environment - that generates the positive effects. This reveals the complex , synthetic nature of organisational knowledge bases, and their resulting need to draw on a wide variety of inputs from the external environment. A partial exception here is Belgium where , while breadth is positively correlated with innovativeness, it is open innovation depth that positively impacts innovation performance. Complexity combines with value chain disintegration to produce a landscape characterised by distributed knowledge networks. From the firm perspective, this means that a broad based , holistic approach to open innovation may give greater returns than focus on single aspects. In the empirical performance analysis, individual dimensions of open innovation such as search , external innovation and collaboration , did not impact innovation performance significantly. And even when effects are found , these are generally much smaller than for overall openness of innovation . This is particularly the case for impacts on innovation performance, where overall open innovation is very strongly significant, while individual dimensions are generally either insignificant or only weakly significant. This is also somewhat the case for impacts on novel innovativeness, where in particular open innovation breadth is strongly significant. These knowledge networks we have assumed to be increasingly global; linking specialised competencies and actor groups such as customers and suppliers across

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national boundaries. The empirical analysis has given the same clear message across all four countries: of all the different types of collaboration , it is international vertical collaboration that positively impacts innovation performance . The coefficients for international vertical collaboration are positive and significant. Further, the only positive and significant impact found from domestic collaboration is vertical collaboration in Norway, presumably revealing strong clustering effects in certain key Norwegian industries. It must be highlighted that this co - exists with the positive impact from international vertical collaboration . We find no positive impact from collaboration with the science system , except for international science system collaboration among Belgian firms. This lack of overall impact is interesting against the background of recent policy emphasis on building domestic science system - industry linkages. Yet, this finding does not call all this policy emphasis into question as the regressions display the effect on the mean of the companies. Positive effects of the international science system can be relevant for certain pockets and niches. It can also be emphasised that, even after controlling for different types of international collaboration , international market orientation still has a significant, positive impact on innovation performance . However, our findings also indicate that a strong internal capacity is still important, and that industry intramural R&D cannot be substituted by public research – as Chesbrough (2005) would claim . Two results point in this direction . First, the results indicate that R&D intensity is an important determinant of innovation performance . The second result concerns external innovation . There is some mixed evidence that external innovation breadth has positive impact (particularly in Norway), but none for external innovation depth (with a negative impact on novel innovativeness in Belgium). Outsourcing of R&D can therefore not be considered a functional substitute for intramural R&D and the build - up of internal knowledge capabilities. Combined we interpret these findings as consistent with the so - called competence -based view of the firm . Firms grow and remain competitive not from control over specific technologies or product, but from their ongoing development of knowledge and capabilities not readily available in external markets. Broad search – beyond existing transaction partners - provides information and ideas; channels organisational learning processes and reduces the risk of lock - in . Still, inputs gained through search must either serve as the basis for own R&D and knowledge accumulation or form the basis for entering into committed collaborative ventures. All this requires internal capacity; and adds to the stock of such capabilities. When search and collaboration must occur on a global scale and potentially target a wide array of actors outside existing value chain relationships, this increases the demand on internal corporate capacities. Organisational challenges increase dramatically; and what is tapped into will have little or no value of not absorbed or linked to specialised competencies internally. In contrast, it can be argued that Chesbrough (2003 , 2005) in his work on open innovation borders on portraying organisations as “nexuses of contracts”; of assuming that knowledge or “pieces of technology” are readily available out there for sourcing on a transaction - to - transaction basis. We warn against such conceptualisations of industrial enterprises, and in particular against the policy implications which could stem from it.

Use of CIS data The current exercise illustrates that indicators for open innovation practices can be developed from existing firm level innovation survey data. Based on the internationally harmonized data set we are able to generate indicators for open innovation practices which are comparable across countries. However, not all aspects of firm level innovation activities are equally well covered by innovation survey data. The current status of the

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Community Innovation Survey allows for the coverage of the outside - in practices of open innovation by using the questions about who developed the product and process innovations, about the distribution of external and internal R&D activities, about innovation search and the assessment of the relevance of the partners. To further disentangle the issue of when and how collaboration matters we would require data on the stated importance of different collaboration partners; similar to the data on innovation search . To further disentangle the issue of globalisation , we would similarly require data on the geography of innovation search . Combined , this translates into distinct limitations on the analysis of global open innovation practices. A critical dimension of open innovation , external technology commercialisation , is not covered at all in the current innovation survey. The survey only covers technology that is commercialised through in - house use in new products or processes, which is a major weakness and contains the danger of systematically underestimating innovation performance . To be able to build indicators capturing external paths to commercialisation one would require information about the license income of the firm , the income through patent sales, the companies willingness and history in spinning out projects / companies, the companies corporate venturing strategies, etc.

Implications for future firm level research The findings above raise critical issues concerning absorptive capacity. If open innovation was about research system search and collaboration only, understanding absorptive capacity as a product of intramural R&D capacity would make obvious sense . But we have argued that open innovation is about variety and breadth; about the ability of enterprises to utilise a wide range of interfaces to search for ideas, collaborate to develop knowledge or source knowledge which is readily available – simultaneously and from numerous sources. This requires a different understanding of absorptive capacity. For instance, it is reasonable to believe that the purchasing department is in a better position to search the supplier side than is the R&D department; and the marketing department in a better position to scan customers and clients. It is also reasonable to assume that these departments do so as part of their daily activities. But what is absorbed by these departments must on an ongoing basis be communicated to – and evaluated in interaction with – R&D . This point back towards the importance of certain critical social conditions (Lazonick 2005) of absorptive capacity and innovative enterprises. Similarly, arguments have been made that the ability to tap into external knowledge sources, and successfully outsource functions ranging from R&D through production to marketing – is contingent on having strong internal competencies related to what is outsourced . Work within organisational theory and management studies have therefore suggested that open , vertical architectures combining in - house capabilities with external sourcing and sales of external knowledge increases the ability of organisations to scan their environments, absorb ideas and knowledge , collaborate and outsource successfully. These issues are particularly pressing against the background of increasing value chain fragmentation offshoring and the internationalisation of R&D and innovation . Future research at the firm level should therefore carefully investigate the nature and determinants of absorptive capacity.

Implications for future economy level research The issue of how national economies in general should build innovation systems and adapt to economic globalisation is the object of intense contemporary academic debates. Among the critical issue of these debates is the tension between the need for industry and economies to establish international linkages, and the losses of externalities to the home economy which are the immediate outcome of knowledge

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development moved abroad . On the other hand , such internationalisation may actually trigger reverse technology transfers to the home economy, and consequently serve to feed these . However, the empirical work on the existence of such reverse transfers is inconclusive. Future research is needed to explicitly investigate their determinants, and how policy as well as the overall innovation system set - up can be designed for the purpose of harnessing such . In Chesbrough’s (2003 , 2005) work , open innovation is a corporate strategy targeting the maximisation of private returns from R&D by utilising knowledge readily available externally – and minimiz ing own production of uncontrolled spillovers. But, as Chesbrough’s (2005) own cases paradoxically show, the open innovation strategy of one company in a certain sector may presuppose the existence of spillovers from large investments in intramural R&D (i.e . “closed innovation”) by other companies – in the same sector or technological field . Further research is needed concerning the impact of open innovation practices on intramural R&D for sectors and the economy as a whole, and under what conditions, if any, an increased reliance on open innovation can lead to lower aggregate levels of R&D . As an extension of this, additional research is needed to uncover if and how intramural R&D in certain spheres of the economy, for instance among incumbents, impact on new firm formation and innovation in other spheres of the economy.

Implications for innovation policy Small, open economy innovation policies can no longer build on the assumption that knowledge development, diffusion , accumulation and exploitation can be collapsed into sets of dynamic domestic network relationships. Policy must recognize the need for international interfaces, while simultaneously making sure that knowledge accumulates domestically and filter out into the economy for re - use, recombination and experimentation . All this calls for dynamic innovation policies continuously balancing between incentives for build - up and use of internal knowledge resources (i.e. intramural R&D), and incentives for external search , collaboration and sourcing . The latter, in turn , must balance between domestic linkages, broadly defined , and international linkages. We have argued that open innovation practices are fairly evenly distributed across the investigated countries, and that these countries have already in place innovation support measures serving most if not any of these purposes. Although we have not conducted a complete policy system and measure analysis, our impression is that the overall balance between different tools is fairly good . Investigating in detail how each policy measure system function in real life is of course very far beyond the scope of this project; so is investigating the broader issue of the relationships between innovation policies narrowly defined – and broader science, labour market and education policies. These relationships are present; and they call for horizontal policy co - ordination . Numerous schemes targeting linkages are in place , in particular at the regional level. These are – and should - increasingly be focused towards the construction of what one could label “platforms” for knowledge diffusion outside value chain interaction and across sectors narrowly defined (Asheim 2005 , Cooke 2005). This recognises that value chain interaction increasingly will have to occur internationally, and that regional systems should serve as platforms for experimentation with knowledge across a set of related sectors. Trends towards regionalisation we interpreted as a recognition of the role of labour market mobility and social networks in supporting such knowledge diffusion – and as a recognition of the need for measures and interventions that are sensitive to specific industrial and social contexts (Asheim et al 2007)

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The interviews we have conducted among policymakers reveal that the importance of internationalisation is increasingly recognised , and is reflected in already existing tools nurturing international linkages (e.g . EU framework programme participation ,) and supporting international collaboration . Similarly, a shift towards increased recognition of the often user - driven nature of innovation processes is reflected in particular in Denmark; and is combining with and to a certain extent correcting for a perhaps excessively strong emphasis on technology - push commercialisation of university research . However, we still urge policymakers to carefully consider if incentives for national networking and sourcing built into policy tools is to strong , if there are sufficient support mechanisms in place for e.g international lead customer or research collaboration – and if existing tools relevant in this context actually serve the purpose . We have pointed to the weaker open innovation performance by SMEs and the weak ability of the science system to provide support for such enterprises. Policy tools that directly target the international value chain interaction of SMEs, such as the Norwegian Industrial Development Contracts scheme, are vital in this context, in particular if they combine with tools supporting their build - up of internal knowledge bases domestically. Such a tool is Norwegian tax - deduction scheme skatteFUNN . The same would apply for tools directly supporting networking and collaboration between SMEs and relevant science system actors.. Norwegian firms show strong patterns of innovation - driving vertical collaboration both domestically and abroad; while underperforming on overall open innovation practices. As this is shown to correlate with performance , we interpret this as pointing towards a need to stimulate broader – the use of a wider range of interfaces - interaction with the external environment, domestically and abroad . There appear to be strong ties both within the Norwegian economy and within the international, vertical networks of Norwegian firms. International science system collaboration , on the other hand , is distinctively low in Norway, and should receive policy attention . This could for instance take the form of “loosened up” requirements of national science collaboration baked into innovation support schemes. Similarly, Austrian firms are weaker than the four -country averages on search and collaboration , breadth and depth; and they under -perform on all dimensions of collaboration except domestic and international science collaboration . Both domestic and international search and collaboration schemes should therefore be strengthened . A seemingly paradoxical implication is that all countries should consider the extent to which they offer sufficient incentives for the build - up and use of internal knowledge resources. This includes in particular intramural R&D and knowledge intensive workers, but also other elements such as organisational practices and IT - based solutions that enhance the development and use of internal knowledge. This is particularly important for SMEs with a high growth potential and activities novel to national innovation systems, and consequently for the ability of these systems to develop new technological development paths through the growth of such firms. The obvious reason for this is that the international competitiveness of firms is rooted not in their control over specific patents or technologies, but in their control over speciali z ed knowledge resources which serve as the basis for generating steady streams of new technologies and products – by drawing from the external environment and synthesiz ing with knowledge contained internally. There are several more specific reasons why this is important. First, such R&D generates externalities into the surrounding economy, consequently serving as the basis for new firm formation and open innovation strategies of other firms. Second , because in - house capacity is a vital determinant for the ability of enterprises to absorb and utili z e inputs from the external environment. Third , and last, because large organizationally embedded knowledge bases serve as “gravitation points” in international knowledge networks, and as the “anchor points” ensuring that necessary

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linkages to globally distributed knowledge networks don’t result in relocation altogether. Open innovation at the firm level goes hand in hand with absorptive and synthesiz ing capacity. The ability of national and regional economies to feed on linkages to globally distributed knowledge networks may therefore presuppose – somewhat paradoxically – a strong focus on the internal build - up and embedding of knowledge resources in firms.

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Appendix Policy system interview guide The purpose of this interview is to find out if the current national innovation policy measures and instruments are suitable to respond to the findings and advice stemming out of openING. It is not the aim to provide a complete mapping of national innovation policies and instruments.. 1. A finding seem to be that it is the breadth (and not depth) of open innovation

practices positively correlates with innovation performance. a. Which policy measures do currently exist to encourage

companies / universities to engage in searching , sourcing or collaborating broadly?

b . How could policy makers expand the current policy measures to better respond to these tendencies or is there a need for new policy measure to respond to these tendencies?

c. What would be the objections policy makers would have in adjusting or formulating new policy measures to respond to these tendencies?

d. What assumptions concerning breadth or depth are built into the current policy system? Does it attempt to nurture strong ties between actors, or broad interaction?

2. We also found that international collaboration, rather than national

collaboration, correlates positively with innovation performance. a. Which policy measures do currently exist to encourage international

collaboration between companies? b . Which policy measures do currently exist to encourage international

collaboration between universities? c. Which policy measures do currently exist to encourage international

collaboration between companies and universities? d . How important is the “national” component in the policy measures

stimulating collaboration? e . What geographical levels have these been focused on? Local linkages

(clusters, regions), national linkages, international linkages. f. How could policy makers expand the current policy measures to better

respond to these tendencies or is there a need for new policy measure to respond to these tendencies?

g . What would be the objections policy makers would have in adjusting or formulating new policy measures to respond to these tendencies?

3. Another finding underscores the importance of intramural R&D.

a. How important is the encouragement of intramural R&D for policy makers compared to policy measures that stimulate collaboration?

b . How would you describe the relative emphasis on incentives for intramural R&D vs. incentives for collaboration and knowledge diffusion in your national innovation policies?

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c. If emphasis on collaboration and linkages, what linkages have / are you primarily focusing on building and nurturing? Intra - industry linkages, university - industry linkages, clusters or centres of expertise , etc. Within or outside value chains. Triple helix reasoning , cluster reasoning , etc.

d . Do you have , or have you considered , direct on indirect support for the build - up of knowledge bases and research capacity (i.e . intramural R&D) in private sector organizations? If not, why – if, how and targeting what enterprise groups (small, large) or sectors (all, high tech only, etc)

e . Have you ever considered / systematically analyzed where policy tools / funding schemes should focus on building up research capacity – in industry or in public research (universities, institutes)? The balance between the two?

f. Do you have, or have you considered , direct measures to support knowledge diffusion – between different sectors of industry, and between industry and public research institutes? On this point they’ll point to most of the traditional innovation systems informed tools.

g. Do you have , or have you considered , tools or mechanisms to support researcher mobility between private and public research?

h . How could policy makers expand the current policy measures to better respond to these tendencies or is there a need for new policy measure to respond to these tendencies?

i. What would be the objections policy makers would have in adjusting or formulating new policy measures to respond to these tendencies?

4. Users or markets exert a strong influence on innovation – by channeling

and/or directly contributing specialized knowledge. Has your national policy system considered either one of the following options:

a. Use of public procurements to actively stimulate innovation in own industries b . Use of public regulations or incentive schemes to establish markets more in

demand of innovation (obvious example is environmental technologies, alternative energy sources etc)

5. Economies, in particular if these are small, often do not contain a broad range of

lead users or markets, supplier and research environments, which are necessary to keep the process of evolution going without negative technological lock-ins/path dependencies. This means that also economies need to create networks to environments abroad – to sustain and supplement national knowledge development.

c. To what extent does the current policy system reflect this tendency? d . To what extent have additional tools / policies been considered? e . What are the problems policy makers would have in adjusting or formulating

new policy measures to respond to these tendencies? f. Have you established , or are you considering establishing , incentives or

support for industry international interfacing (beyond EU framework program participation?) (e .g . funding of industry co - operation with or sourcing of research from foreign universities or research institutes, suppliers, customers, etc.

g . Do you have, or have you considered , special support schemes targeting international networking for small and medium sized enterprises? Small enterprises have weaker international networks and organisational capabilities – but may, if their activities deviate from the existing national industrial speciali zation – be in strong need of international interfacing at very early stages of their life - cycles (i.e. so - called born globals).

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h . How could policy makers expand the current policy measures to better respond to these tendencies or is there a need for new policy measure to respond to these tendencies?

i. What would be the objections policy makers would have in adjusting or formulating new policy measures to respond to these tendencies?

6 . Against the background described above – how do you think the innovation

policy system would react to policy advice suggesting a supplementary focus on the interplay between:

o Incentives for broad , international collaboration o Incentives for domestic, intramural R&D

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