Exploring the impact of open innovation on national systems of innovation †A theoretical analysis Yuandi Wang a, Wim Vanhaverbeke a b c, Nadine Roijakkers a, â a Hasselt University, Faculty of business Economics, KIZOK/Innovation Management, Campus Diepenbeek, Agoralaan-Building D, 3590 Diepenbeek, Belgium b Vlerick-Leuven-Gent Management School, Reep 1, Gent, Belgium c ESADE Business school, Avenida Pedralbes 60-62,08034 Barcelona, Spain a r t i c l e i n f o a b s t r a c t Article history Received 24 october 2010 Received in revised form 12 august 2011 Accepted 13 august 2011 Available online 6 september 2011 This paper investigates the impact of open innovation on national systems of innovation. The open innovation concept has become widely established among scholars and practitioners However, an overview of its impact on national innovation systems is still lacking. Given that the innovating firm is at the core of national innovation systems, a better understanding of shifting innovation strategies at the firm level is of fundamental importance to the actions of policy-makers within the national innovation systems framework. Based on themain analytical approaches of national innovation systems and the current state of open innovation research we argue that open innovation practices have at least three critical effects on national systems of innovation:(a) they reinforce its importance;(b) they improve its effectiveness; and c) they diversify its networks  2011 Elsevier Inc. All rights reserved Keywords Open innovation National systems of innovation Innovation intermediaries Technology markets Corporate venture capital Innovation policy 1. Introduction Nowadays, no single company, not even the manufacturing giants, can monopolise the knowledge landscape as it used to be the case 1, 2. The knowledge landscape is fairly heterogeneous, with a wide variety of players (ï rms of various sizes, universities research institutes), each focusing on different pieces of the puzzle. As a result, the means of organising innovation have moved beyond the boundaries of the ï rm towards the concept of †open innovationâ€, a term coined by Chesbrough 3. Open innovation OI) can be deï ned as †the use of purposive inï ows and outï ows of knowledge to accelerate internal innovation and to expand the markets for external use of innovation, respectively†4, p. 1. According to Chesbrough 3, 4 OI requires that a ï rm creates value and captures value using technologies developed by others or enabling others to use its technology. When a company creates value using technology from the outside it can scout for several sources of technology or ideas. First, companies can license-in technologies. Second, they can establish different types of alliances to co-develop innovations with partners. Third, they can use brokers (e g. Innocentive) to reach a large number of researchers or technicians worldwide. Fourth, companies can spin-in or acquire high-tech companies to gain access to particular types of technology. Finally, they can source external knowledge from communities of practitioners, customers or specialists usually referred to as â€oeopen sourceâ€. These are all enablers of open innovation that coexist and jointly stimulate innovative performance The ability of ï rms to apply OI practices rests on a large number of external factors. In particular, open innovation practices are positively affected by: A continuous supply of outside knowledge; highly-educated personnel; ï nancial resources; effective legal systems; institutions protecting IP rights. Most of these factors are closely related to a country's national system of innovation NSI) 5, 6. A general deï nition of NSI is the set of institutions, actors, and relationships that individually and jointly inï uence a nation's cumulative innovative performance. Chesbrough 3 models the relationship between NSI and OI when he illustrates how Technological Forecasting & Social Change 79 (2012) 419†428 â Corresponding author. Tel.:++32 11268624 E-mail address: Nadine. Roijakkers@uhasselt. be (N. Roijakkers 0040-1625/$ †see front matter  2011 Elsevier Inc. All rights reserved doi: 10.1016/j. techfore. 2011.08.009 Contents lists available at Sciverse Sciencedirect Technological Forecasting & Social Change structural changes in the US'NSI have created a widely distributed knowledge landscape sinceworldwar II as a result of: Greater availability of highly-educated workers; venture capital; state-of-the-art knowledge. Furthermore, the author concludes that the resulting changes in NSI stimulated and accelerated the emergence of OI in the US Yet apart from Chesbrough's 2003 pioneering work, there are few studies exploring the important relationship between OI and NSI (a notable exception is De Jong, Kalvet, and Vanhaverbeke 7). This is because the burgeoning literature on OI has largely focused on ï rm-centred analyses, which generally disregard the relations between ï rms and their external context. Most recent publications in this ï eld focus on enhancing the OI framework and establishing its general validity 8, 9. Similarly, the large body of literature on NSI takes the structures, functions, and effectiveness of NSI as the main focus. This approach largely ignores new developments taking place at the ï rm level, where OI practices are used increasingly to organise companies'innovations 10 Therefore, few recent papers on NSI have analysed the paramount relationship between NSI and OI †despite the fact that it is precisely this relationship that may be crucial for policy-making Close scrutiny of the current literature on NSI reveals that there are three different approaches to examining NSI, which have been developed separately. We will brieï y describe these structural, functional, and effectiveness approaches to gain a balanced view of NSI. First, the structural approach is the most traditional and widely-used tool employed in NSI research. It focuses heavily on identifying structural elements that inï uence innovative performance within a system 11. Second, the functional approach is a new development for NSI research that concentrates on how various functions are served by the system. That is, what are the most important activities and processes taking place within an NSI? These functions constitute an intermediate level construct between the structural elements of an NSI and its performance 12. Third, there are many publications on NSI that focus on the effectiveness of innovation systems 13 where research covers performance assessment, system failures, and the efï cient evaluation of input-output This balanced view on NSI provides us with a baseline for exploring the impact of OI on NSI. In this study, we explore how a country's NSI structures, functions, and effectiveness are inï uenced when ï rms change their innovation strategies towards an OI paradigm. The structure and functioning of a country's existing NSI also has an impact on theway companies deal with OI practices but this side of the coin has already been dealt with in another study 14. Here, we focus on how changing innovation practices at the ï rm level impact the NSI This study contributes to the literature and policy-making in the following ways. First, it encourages OI scholars to conduct their research within a broader economic growth perspective as this paper reveals that OI practices are connected closely to a country's speciï c innovative context. Second, it provides policy-makers with a better understanding of why an NSI needs to be adjusted when companies are adapting their approach to innovation. Given the new OI practices of companies, it is reasonable to assume that an NSI needs to evolve in the direction of a so-called national system of open innovation. Offering policy-makers an enhanced understanding of why current trends in corporate OI need to be taken into account when drawing up public policiesmay be very useful This paper is structured as follows. The ï rst section introduces themain NSI analyticalmodels. These approacheswill be used to get a balanced view of an NSI, which, in turn, provides us with a good starting point for examining how corporate OI practices inï uence an NSI. The second section explores the impact of ï rm-level OI practices on an NSI. The third and ï nal section sets out our conclusions and their implications for policy-makers 2. Three approaches to NSI Since the 1980s the NSI concept has been developed and built on by a host of scholars 11,15. A commonly used deï nition of NSI is: â€oethe network of institutions in the public and private sectors whose activities and interactions imitate, import, modify, and diffuse new technologies†11. Lundvall 15 draws a clear distinction between a narrow and a broad deï nition of NSI. Nelson et al 11 perceive NSI as a set of institutions whose interactions determine the innovative performance of a country. Although there is no single deï nition of NSI, most scholars share the core view that a country's NSI pursues some given goals, is spun by institutions organisations, and interrelationships and all these elements form the woof and the weft of nation-speciï c contexts. We ï nd that the current NSI literature is scattered and that at least three approaches have been developed separately over the last two decades We label them the structural, functional, and effectiveness approaches. To clarify several NSI dimensions we brieï y describe them below. Each of these dimensions will, in turn, be used as starting points for our analysis of the impact of OI practices on a country's NSI The structural approach identiï es and describes structural elements in different types of innovation systems. This approach which uses these structural elements to explain differences in innovative performances of various systems 11,15, has been the most common one since the NSI was articulated ï rst by Freeman in 1987. Structural analysis focuses on the national level of innovative activities and the structures spawned by companies, universities, research institutes, government agencies, public policies, institutions, and, in particular, the various relationships among them. Coherent corporate behaviour in the innovation ï eld is shaped by national culture, laws, norms, and conventions 16. The structural view of NSI primarily aims to identify the main determinants inï uencing the production, use, and dissemination of new technologies in a given country or region The functional approach to NSI focuses on the activities and processes that the main actors within an NSI carry out for the purpose of knowledge generation, dissemination, and application 12, 17†19. A function can be deï ned as the contribution of one or a set of activities to an NSI goal 19. Such functions might be: fostering technology markets; setting up linkages; stimulating education and training; setting up IPR protection; mobilising resources; providing knowledge 20. A combination of several NSI 420 Y. Wang et al.//Technological Forecasting & Social Change 79 (2012) 419†428 functions is referred generally to as a functional portfolio, which can be used to study NSI dynamics by mapping various portfolios over time 12 The effectiveness approach takes into account the complex nature of NSI where most actors and elements of NSI are †socially embedded†and the mechanisms used to coordinate them are non-market mechanisms: Institutional, networking, and policy mechanisms are employed to coordinate actors in an NSI 21. The effectiveness approach focuses on: assessing a system's performance; input-output efï ciency; diagnosing systemic failures hindering NSI development and operation; setting the benchmark and developing linked indicators 13 These approaches are developed separately and they emphasise different NSI characteristics. In order to discuss the impact of OI practices on NSI, a single approach cannot be relied upon as this might bias the results. Therefore, we prefer to have balanced a view on NSI based on the aforementioned approaches. This should give us a more detailed, complete picture of what an NSI is which, in turn, may facilitate our study of OI impact on NSI. As Lundvall 16 states, NSI are supposed purposively to achieve a certain goal in an attempt to capture the process of knowledge generation, dissemination, and use. Its ultimate goals are economic growth, job creation, and acquisition of international skills. Each innovation system performs differently in achieving its supposed aims. This principle is the underpinning for the effectiveness approach. Consequently, this approach provides a starting point for analysing the impact of OI on NSI: corporate OI and its effect on NSI effectiveness therefore lie at the core of our analysis Subsequently, we examine functions, processes or activities, following the functional approach. As a result, we can say to what extent functions in innovation systems are †achieved†through open innovation. However, the major weakness of the functional approach is a lack of clear and practical implications for policy-making. In contrast, the structural approach 12 pays sufï cient attention to identifying the most important factors in national systems of innovation. Thus it can help us determine OI's main policy implications. In the following section, we consider the impact of OI on NSI in detail, using the approaches just described 3. The impact of open innovation on NSI In contrast to the internally integrated model of innovation explained by Chandler et al. 22, the OI model presents a way of explaining how ï rms have come to organise their innovations so as to make full use of both internal and external innovations 9 Given that innovative ï rms lie at the heart of NSI and are vital to public policy design 16, we assume that this new development cannot be overlooked when studying NSI. 1 Consequently, investigating the impact of OI on NSI is likely to beneï t both streams of OI and NSI studies. In the ï rst two sections we describe the impact of open OI practices on the functions and effectiveness of NSI The last section addresses the impact of OI on the NSI structural perspective 3. 1. Open innovation reinforces the importance of NSI OI practices depend heavily on the internal capability of ï rms to leverage outbound and inbound knowledge and on the availability of sufï cient external knowledge and other important resources. The supply of external knowledge is largely determined by a well-equipped functional NSI. This strong interdependence makes NSI more important than ever before Hereafter, we focus on the formation of technology markets, linkages, IPR protection, knowledge development (basic research and education and training 3. 2. Undeveloped technology markets need to be cultivated in NSI OI broadens the range of external technology sourcingmodes by embracing alliances and acquisitions, technologymarkets, and corporate venture capital (CVC) 3, 23. In technology markets, ï rms purchase, sell, and use technologies developed by other ï rms to complement their internal technology base 24. Technology markets therefore play a key role in OI practices. Chesbrough 4 claims that technology markets (especially innovation intermediary markets) are a critical enabler for OI. Rigby et al. 25 use the term open-market innovation to map the burgeoning use of technology markets by ï rms for innovation purposes However, in contrast to most intangible markets, technology markets generally result in high transaction costs because they are imperfect 1, 26. Therefore, a reduction in imperfections would lead to a greater use of technology markets. Teece 27 emphasises the importance of improving the IPR regime and the legal system. Gambardella et al. 28 argue that the use of technologymarkets could be boosted if policies hindering the emergence of supporting institutions could be removed. Even so, the current literature on NSI and public policy largely overlooks this increasingly important function 29,30. Hence there is an urgent need to foster technology markets in NSI 1 Open innovation is a new term coined by Chesbrough in 2003 but collaborative innovation agreements between companies such as technology alliances joint ventures, technology licensing etc. have been analysed since the 1970s and 1980s. We believe, however, that open innovation represents a quantum leap with respect to the previous literature on collaborative innovation strategies. First, open innovation emphasises that innovating companies have to make full use of both internal and external innovations. External sources of innovations are as important as internal ones and this balance was not present in previous literature. Second, open innovation offers a uniï ed framework in which a ï rm's innovation strategy, the choice between external technology sourcing modes, the creation of absorptive capacity, and business model thinking are linked tightly to each other. Finally, the buzz on open innovation has triggered many ï rms to redirect their innovation strategy in new ways. We argue that open innovation and its inï uence on NSI deserve attention from researchers and policy-makers for two reasons: First, open innovation cannot be considered as a simple extension of the previous literature on collaborative research. Second, the balance between internal and external sourcing of ideas indicates that the way innovating ï rms reach out to other organisations in the NSI is of much greater importance than hitherto 421y. Wang et al.//Technological Forecasting & Social Change 79 (2012) 419†428 3. 3. Linkages within NSI need to be strengthened to facilitate the increasingly wide use of innovative networks In the OI realm, using inter-organisational collaborative agreements and networks to harness external knowledge ï ows among organisations is a key dimension. In itself, OI can almost be seen as a speciï c approach covering the links that innovating ï rms forge with other organisations 31. With a paradigmatic shift from closed innovation to open innovation has come a shift in the locus of innovation from large companies to networks 32. Prior research has demonstrated the major advantages of innovation networks for coordinating both tacit and explicit knowledge ï ows, and further research shows a positive effect of ï rms'presence in such networks on their innovative performance 33†36. However, existing NSI literature does not sufï ciently challenge the †closed innovation paradigm†for most networked innovations, which still centre on internal R&d 4. Open innovation scholars argue that ï rms use networks to source external knowledge for internal use and to market unused internal technologies 37 Inter-organisational networks are an increasingly important channel for implementing OI. However, this cannot be expected to take place in an NSI in which closed innovation thinking prevails 38 Innovation networks are likely to be used more widely in the OI era than ever before and this in turn means a need for stronger NSI linkages. Actually, this argument is consistent with the NSI core concept whereby multifarious efforts foster different kinds of collaboration and networking activity 39,40. Speciï cally, a stronger linkage between public and private sectors is imperative in the era of OI 37. Thus, the increasing development of OI practices is strongly reliant on forging ties among the key innovation players 41 3. 4. Efï cient ï ows of knowledge heavily depend on stronger IPR protection OI practices involve abundant inbound and outbound ï ows of knowledge. Inside-out and outside-in ï ows entail patent licencing, technology-based acquisition, joint-ventures, non-equity R&d investments, and increasingly, direct technology buying or selling. Traditionally, consortia agreements have allowed information ï ows and technology collaboration only within a consortium, whose membership is limited to just a few companies 42. Obviously, OI is restricted not to these tight forms of technological collaboration and embraces the global innovation community as its knowledge base 43 However, the efï ciency of knowledge ï ows is linked directly to a country's regime for knowledge appropriation. Earlier NSI studies have shown that a well-deï ned system of intellectual property protection can ease knowledge ï ows 44. In view of this demand for knowledge ï ows, NSI scholars have listed IPR protection as one of the key functions within a portfolio and havewidely discussed it 6, 45†47. Therefore, enabling OI practices means improving and strengthening the IPR protection system and related legal regime 48 within the NSI framework. This is one of the most important requisites for OI practices The †seed corn†of OI relies on major public support for basic research OI shows that companies are shortening their time horizons for R&d expenses and are shifting the focus of their internal efforts from basic research to more immediate application-oriented innovations 3. This implies that industry can no longer be expected to underwrite the bulk of basic research costs. The spate of innovations from these laboratories over the past forty years is likely to dry up in the near future, given this shift in orientation made by major labs. This trend is also reï ected in the fact that most of the world -famous corporate R&d laboratories of the twentieth century have been downsized, broken up, or redirected to new goals 49 Basic research is critical as †seed corn†for new waves of innovations and greatly enriches the knowledge landscape. Public support for basic research has long been a tradition in most countries 50. However, in recent years, some counties, such as the U s.,have slashed public funding 51. This seems inconsistent with the development of the OI principle. A basic insight of OI is that there is a newdivision of innovative efforts between industry, government, and academiawith less basic research being conducted inside large corporate labs 49. Only two decades ago, large industrial companies had vast corporate R&d centres, which had greater scientiï c and technological capabilities than most universities. The majority of these central labs were dismantled †especially during the 1990s †as big companies were pressured by shareholders to focus on short-term proï ts. Long-term research was seen increasingly as too expensive. In consequence, as companies focused more and more on applied sciences and the development and commercialisation of technologies, universities became the sole institutions targeting basic research. In this way the position of basic research became weaker in the innovation ecosystem of different countries. Therefore, governments and universities face the challenge of stimulating efforts in basic research by providing public funds formost of the †seed corn†research This can in turn spur the major innovations of the next two decades 8, 52. Instead of weakening public support for basic research this NSI role may need to be beefed up 3. 5. The supply of high-quality labour is linked strongly to education and training One of the crucial elements holding the open innovation system together is its human and social capital. As Chesbrough 3 states, a high-quality labour force is one of the major prerequisites of OI as it allows knowledge to spill over to other organisations and boosts ï rms'ability to absorb innovations 53. This also applies to the recruitment of graduates by ï rms, which is probably one of the main mechanisms for making money from fundamental research 54. Hence the stress often laid on the roles played by education and training in stimulating OI 55. Better education and training will strengthen many of the behavioural aspects of OI including networking and collaboration skills, corporate entrepreneurship, the ability to license technologies, and carrying out R&d. Developing and maintaining a skilled labour force requires governments to deliver and implement high-quality education at all levels. In addition, policy-makers need to address postgraduate training and †lifelong learning†for a society's human capital as 422 Y. Wang et al.//Technological Forecasting & Social Change 79 (2012) 419†428 well 56. In general, education and training are at the heart of a public policy for fostering OI. Therefore we cannot ignore the importance of NSI in pursuing the open innovation model This section exploredhowopen innovation inï uences the functional perspective ofnsi. We argued that rising use of theoi approach boosts NSI importance in at least ï ve areas: fostering technology markets; establishing stronger linkages within NSI; IPR protection funding basic research; ensuring the supply of high quality personnel. All of these are linked closely to the performance of NSI functions 3. 6. Open innovation improves the effectiveness of NSI The effectiveness of NSI is the extent to which the system attains its general goal or carries out its mission 13. In reviewing the literature on the effectiveness of NSI, we ï nd that scholars typically concentrate on market distortions 57,58 and system failures 59†61. Empirical studies are linked directly to static (input/output) indicators such as R&d expenditures, patents, and bibliometric data 62. Generally speaking, such studies have detected most of the factors and mechanisms inï uencing the effectiveness of systems. However, as some authors claim, there are still a number of factors and mechanisms that have been poorly explored. For instance, Oreland et al. 63 states that knowledge transfer mechanisms have a critical impact on the effectiveness of systems. Porter 64 draws attention to the importance of institutional factors such as the effectiveness of the R&d system and networks between ï rms In our study, we suggest mechanisms through the lens of OI that may enhance our understanding of the effectiveness of NSI. In particular, we focus on: the social wellsprings of innovations; strong specialisation in innovative labour; the efï ciency of resource allocation; accelerating knowledge ï ows at lower costs 3. 7. Eliciting social resources for innovation In modern society, innovation is vital for countries in building up their competitive advantage and providing sufï cient social welfare to their citizens 11,65. Successful innovation requires sufï cient resources to be earmarked to ongoing discoveries knowledge generation and dissemination, and technical development 29. Earlier NSI studies have focused almost solely on mobilising public and private resources for technological innovations and have suggested two complementary investment models 66. The ï rst is the †private investment†model, which assumes that innovation can be funded by private sources and that private returns can be appropriated from such investments 67. The second is the †collective modelâ€, which fosters innovation through direct public investments in public knowledge goods 68. This model also progressively employs policy instruments to promote private investments in R&d 69. Examples of such policy instruments are competition policies, tax policies, and subsidies Therefore, prior to the OI era, innovation came about either through private R&d spending in ï rms or through government investments in the public sphere 70. A vast range of social resources, such as retired skilled workers, and the valuable knowledge of former staff, have been excluded generally from the task of advancing the frontiers of innovation. OI scholars state that in the OI era, companies should tap into this large external pool of know-how to gain new ideas while at the same time move unused ideas outside the company 3. This leads to a large, valuable stock of knowledge for others and additional revenues for the ï rm itself Researchers in OI claim that valuable knowledge can be generated through resources other than those provided by ï rms and governments 36. Valuable knowledge can be produced through a broad range of instruments, including blogs and internet-based communities 38. A typical example of using social resources to innovate is the popular phenomenon of open source software 71 and the current Threadless Community 72, revealing †a third model†that draws upon extensive social resources to create valuable public knowledge. This model is termed the †private-collective†model by von Hippel et al. 71 and is an unusual collaborative effort where skills in ï rms, sectors, and nations offer a vast pool of public knowledge. OI thereby generates a unique way of eliciting and coordinating the efforts of scattered individuals in an internet-oriented knowledge base. This provides great potential for a country to increase the level of knowledge creation in society and further improve NSI effectiveness 3. 8. Beneï ting from strong specialisation in innovative labour Economists have shown that the growth of amore complex division of labour is closely bound to the growth of total production and trade, which strengthens a country's competitiveness 73. When closed innovation prevailed, the economy of specialisation was largely conï ned within ï rms 74. A study by Arora et al. 75 shows that a decline of research productivity inevitably occurs when research-oriented ï rms integratemanufacturing andmarketing units. They further claim that small innovating ï rmsmay be inefï cient at building downstream assets, making the commercial exploitation of new technology riskier and less efï cient. Large companies are generally better at exploitation than exploration and may be adapted better to making incremental improvements to existing technologies than pioneering new discoveries 76. There is a natural labour division in knowledge generation and commercialisation between ï rms of different sizes. Consequently, there is a major opportunity to beneï t from this strong division of labour through an open innovation model OI theory purports that innovators do not necessarily implement all innovation stages, thus proï ting from the division of labour in this ï eld. In particular, innovating ï rms can choose to sell technologies instead of investing in the downstream assets required to commercialise technologies or they can choose to buy new technologies instead of creating them in expensive in -house R&d labs 49. Through this innovative labour specialisation, companies may be able to focus their strengths on some parts of innovation value networks. This, in turn, boosts both the competitive capacity of the value network and the effectiveness of a country's NSI 77 423y. Wang et al.//Technological Forecasting & Social Change 79 (2012) 419†428 3. 9. Improving the efï ciency of resource allocation Innovation is a risky undertaking that requires the allocation of ï nancial and intellectual resources under speciï c conditions. At the company level, the innovative resource allocation process has been discussed widely in the context of the Bower†Burgelman model 78,79, which is aligned with the closed innovation paradigm where resource allocation is conï ned within ï rms 80. This generates two types of efï ciency losses, one for the company and the other for society at large In general ï rms are aiming for sustainable success while allocating scarce resources to costly R&d activities. The likely result is extended patent portfolios. According to Chesbrough's 49 survey, around 25%of patents are used never in any of the patent holders'businesses and most innovations and IP sit on the shelf unused. This is a major waste of scarce corporate resources. From society's point of view, the loss is even greater. If companies never use some of the technologies they are creating or license-out these technologies to other companies for the purpose of commercialisation, then the products covered by the temporary monopoly are brought never to market. Therefore, society never gets to experience the use of these new inventions, and policy -makers also empower companies to prevent anyone else from using the technology until the patents expire As innovation orientation changes from internal R&d to the growing use of external innovation, the Bower†Burgelman model gradually becomes obsolete and sub-optimised. As Maula 81 suggests for the OI process, allocation of resources will shift from the focal corporation to the developer community and external partners, such as joint ventures and university research. In this way, resource allocation efï ciency will be gained at two levels. At the ï rm level, innovating ï rms using this new model can manage the innovation community successfully over different time horizons 70 by leveraging internal and external innovators In addition, active R&d resource sharing generally widens ï rms'R&d reach and greatly reduces unnecessary duplication. From society's point of view, OI provides greater scope for jointly conï guring the best ideas and business models. In other words, OI strives to come up with the best ideas and the most suitable business models for commercialisation 49,82. As a result, OI improves resource allocation at the macro level and yields beneï ts for all concerned 57 3. 10. Accelerating knowledge ï ows at low transaction costs The NSI approach stresses the power of knowledge transfer and dissemination 83. Public policy thus often seeks to boost knowledge ï ows in order to synthesise and strengthen the general purpose of NSI. During the era of closed innovation, most innovating ï rms were reluctant to transfer knowledge, particularly outside the bounds of the company. For instance, patents (the most tradable knowledge assets) were treated commonly like The Crown jewels rather than media for knowledge exchange. The arrow information paradox, the NIH syndrome, untapped technology markets, and holdup problems used to prevent low-cost knowledge transfer 84†86 Conversely, in an OI era, knowledge transfer is driven primarily by ï rms'desire to advance current business models, nurture new businesses, or sell unused technologies to create additional revenues 49. These intrinsic reasons are fuelled by the growing number of innovation intermediaries, which, in turn speed up the circulation of global knowledge. Innovation intermediaries create a link between innovating ï rms and the global innovation community and help ï rms enhance their OI capabilities by exploiting the most valuable and context-speciï c tacit knowledge 43. At the same time, wider use of different types of networks boosts knowledge transfer, given that intermediaries'raison d'à tre is to convey ï rms'inbound and outbound knowledge 87,88 Increasingly efï cient technology markets mean that companies no longer need to acquire and sell other companies to access the underlying ideas and technologies. Consequently, much potentially valuable trade in innovation and its associated IP becomes less expensive 44,49, 89. Companies can extract value from technology without having to spend a lot of time and scarce resources on developing manufacturing, distributing, and marketing capabilities. As scholars are quick to claim, OI alleviates the AIP, NIH, and holdup problems 49 and so fosters knowledge transfer at low transaction costs, which, in turn, enhances NSI effectiveness 3. 11. Open innovation diversiï es the networks used in NSI Structural elements in NSI include institutions, other players and their myriad relationships 11. The most important players are ï rms, universities, venture capital organisations, and public agencies chargedwith innovation policy 29. Institutions embrace legislation, customs, and conventions. Culture and relationships refer to the various kinds of market and non-market relationships 15. In the OI age, we have to consider the emergence of new elements in NSI, such as innovation intermediaries, technology markets in physical and electronic forms, specialist technology suppliers and buyers, and so on 1, 8, 43,49. At the same time, the widespread use of external innovation tools such as collaborative communities dramatically affects players'relationship networks In this paper, we focus on networks. Furthermore, we show that OI diversiï es the networks used in NSI 3. 12. Networks used in NSI It is well known that one of NSI's most distinctive features is the numerous interactions among its components. Bearing inmind the importance of networks in NSI, pioneering researchers in this ï eld consider NSI to be the network driving knowledge generation, diffusion, and use 15. However, as several authors have shown, NSI networks only focus on formal knowledge exploration 90,91. Typical examples of these networks are producer†user relationships and the triple helix of university†industry†government 424 Y. Wang et al.//Technological Forecasting & Social Change 79 (2012) 419†428 Producer†user relationships marked the birth of the term †National System of Innovationâ€. In the 1980s, the strong producer†user relationships in the competitive Japanese manufacturing sector and Japan's resulting economic boom attracted the interest of scholars and policy-makers alike. In 1988, Lundvall claimed that research should focus on NSI instead of single producer†user networks. Focusing on the growing phenomenon of †centres of excellence†where industrial development seems to be closely linked to the best universities 92, Etzkowitz et al. 39 coined the term †triple-helix relations†to describe relations between university, industry, and government. Here, they stressed the role universities play in technical innovation and knowledge-based economies Such networks are popular in NSI research. However, many scholars have noticed that even when they include other types of networks, NSI research remains focused on the knowledge exploration phase and on formal players such as ï rms, universities, and government research entities 93. In general, current research ignores the new forms of innovation networks, such as the growing online social networks and other collaborative communities for knowledge exploration 36,94. Moreover, little work has been done on downstream networks exploiting existing knowledge 91 3. 13. Online social networks: Enlarging the knowledge exploration landscape The OI model stresses the importance of using a broad range of sources for innovation and commercialisation activities by ï rms. Its success therefore mainly depends on the continued supply of external sources 3. This supply of external knowledge can be sourced from traditional partners such as universities, users, and suppliers, as well as a range of other institutions and individuals. The knowledge they bring (often in the form of †tacit knowledgeâ€) is a key asset in NSI. Therefore, the ability to locate identify, and acquire valuable knowledge and generate various channels of knowledge transfer is of vital importance in a knowledge society. Online social networks are used frequently by ï rms as one of these channels but NSI scholars have not heeded them enough to date 95 An illustrative case of online social networks is the trend towards open source software 71. An oft-given example is the Linux Foundation. Open source involves collaboration between ï rms, suppliers, customers, and policy-makers of related products to pool software R&d and generate shared technology. Instead of the traditional extrinsic motivations, external innovators have strong intrinsic motivations such as reputation, fame, intellectual challenge, fun, and interest 72. More recently, various online communities and hosted service webs facilitating co-operation and knowledge-sharing among innovating ï rms, customers, and other interested parties, have been recorded in the literature 94,96. Social online networks, such as open source software communities, offer valuable beneï ts for society by encouraging community members to share their knowledge Oneway for policy-makers to elicit social knowledge is by building a wide range of internet-based platforms to bridge business sectors and create a broad range of knowledge, expertise, and skills in cyberspace. The power of this type of network lies in mobilising dispersed local knowledge that can be applied to both problem-solving and problem-seeking. The result is a more open-ended, fertile innovation process. Online social networks may solve problems for current business development or pool ideas 97 for future business development. Incentives can be either extrinsic, intrinsic, or both 72. In this way, highly-skilled retirees, graduate students, professional workers in various disciplines, ordinary citizens, and research-based and proï t-seeking organisations are linked up in online social networks. Individuals are assigned carefully to certain communities, for example, those serving a given sector. These communities are then taskedwith innovating in a given knowledge ï eld. Community members share the right to use and collaboratively develop certain technologies. Usually they work for free to pool solutions, experience, ideas and other key knowledge for the business development of a ï rm 98,99 3. 14. Knowledge exploitation networks: Focus on commercialisation Research has shown repeatedly that innovation is a multi-stage process. It consists of several critical stages, of which research and development and commercialisation are the most commonly cited ones in the literature 100. However, the fact is that most innovation policy efforts focus on R&d progress rather than commercialisation. Open innovation stresses that a ï rm's business model acts as a ï lter, leaving just a few technology projects in the running for commercialisation. The upshot is that technological developments do not automatically lead to the successful launch of products and services 101. More importantly, considerable efforts may have to be put into commercialising the unused technology through spin-offs or through licencing to other companies in other markets. As such, commercialising technology is a complex process, involving different parties such as users, suppliers rivals, and other partners in the value network 3, 36,102. As March 33 noted, knowledge exploitation networks are entirely different from those covering universities and research-based knowledge-seeking organisations While the insights gained by knowledge exploitation networks have received less attention in the NSI literature, they are crucially important as they are directly responsible for the market success and proï tability of new technologies 16. OI may provide a newway to bridge this gap. For instance, Vanhaverbeke et al. 103 consider one of these exploitation networks by using the concept of value constellation. They suggest that value constellations differ from R&d networks insofar as they commercialise innovations with partners that have the wherewithal to bring the product or service to market 104. In addition, exploitation networks commonly embrace complex network governance †something that is in sharp contrast with the dyadic relations between single partners that are typically found in knowledge exploration networks 105. Scholars have suggested therefore that ï rmsmust set up and lead an entire value network to support their speciï c innovations 80. Although research on this issue is still at an early stage, it is foreseeable that exploitation networks will grow in popularity over the next few years due to the growing reliance on OI. The emergence of exploitation networks will affect NSI as companies with promising, game-changing innovations 425y. Wang et al.//Technological Forecasting & Social Change 79 (2012) 419†428 set up these new kinds of value) networks. An NSI can thus shift away from the link between research and educational institutions to focus instead on the commercialisation networks needed to launch new products. The government can play an important role in these networks. The growing importance of sustainability goals in the energy, car manufacturing, and chemical industries illustrates the changing role of governments in NSI today 4. Conclusions It may be unwise to limit the study of open innovation to the ï rm level. Companies are embedded in networks, industries, and national economies. In this study, we examine how open innovation practices in ï rms have an impact on the regional or national systems of innovation (NSI. An NSI is likely to be affected when companies change their innovation practices and the way in which they collaborate with external innovation partners. Surprisingly, the impact of OI practices on NSI has received so far scant attention from both scholars and policy-makers †a notable exception being De Jong et al. 7. Making the connection between OI practices at the ï rm level and their impact on the meso-or national scale is hampered by the different theoretical approaches found in NIS literature, namely the structural, functional, and effectiveness approaches. The various perspectives put forward by these approaches have yielded a sufï ciently detailed picture of NSI to study the impact of open innovation. As such, we have developed various arguments to demonstrate that companies'OI practices inï uence an NSI in different ways First, OI reinforces the importance of NSI more than ever. There is currently pressure to boost the effectiveness of technology markets given that innovating ï rms are increasingly interactingwith other NSI partners. Similarly, the role of innovation networks becomes more important as a result of ï rms'OI practices. The locus of innovation no longer lies in single ï rms but in the innovation network 106. Next, increasing inter-organisational knowledge ï ows raise the importance of having a reliable IP protection system. Companies work more and more on technology applications rather than on fundamental research. This is why the latter may need to be funded in new ways. Finally, OI hinges on the supply and mobility of highly-skilled knowledge workers Hence, education and training need to be linked closely to innovation policies Second, OI provides several mechanisms to improve the effectiveness of NSI. This effectiveness can be enhanced by elicitingmore resources for innovation. Besides private and public investments in R&d we also need to include a vast range of social resources that could be used in OI. Examples of such resources include: retired skilled workers; internet-based communities; innovation intermediaries. Next, NSI effectiveness can be boosted through greater labour specialisation in the innovation ï eld. Finally, NSI will be more effective when the unused technologies of large companies are commercialised through spin-offs or when they are made accessible to other ï rms through licencing Third, OI leads to the emergence of many new structural elements such as innovation intermediaries and technology markets To a great extent, OI will simultaneously diversify the networks used in NSI by creating two new types of networks: online ones fostering knowledge exploration, and exploitative ones targeting knowledge commercialisation As we mentioned earlier, these insights may have important policy-making implications. First, policy-makers need to realise that the rapid proliferation of ï rms'OI practices is changing the way companies innovate. Since IP ultimately rests on the activities and initiatives of companies, it is vital that policy follows this trend to foster amore open innovation environment. We have shown in this study how ï rms'OI practices inï uence the NSI and that policy-makers need to examine how their decisions can foster and speed up OI practices. First, the government can help by encouraging ï rms towork together inmulti-partner innovation networks Second, policy-makers are advised to broaden the scope of innovation to include not only joint research but also joint development and commercialisation activities. Third, policies may need to ï nd new ways for improving NSI effectiveness by considering the roles played by innovation intermediaries and newly-emerging technology services facilitating open innovation. Fourth, more attention might be paid to the IPR system: it needs to be reliable and affordable for (smaller) innovators and policy-makers may need to stimulate the dissemination of unused knowledge. This requires a new policy towards large companies that hoard technologywithout using it. Fifth, governments can:(i) promote the generation and dissemination of high-quality knowledge;(ii foster and support institutions channelling human and ï nancial resources towards promising technologies and business models iii) highlight the reworking of good ideas through suitable business models and highly-efï cient markets for technology and knowledge workers. Sixth, policy-makers may need to move away from funding innovation in single ï rms and towards networks of ï rms (given that this is the locus of innovation today Our study is broad in scope and examines how emerging corporate OI practices have an impact on the functioning of the NSI Chesbrough et al. 4, p. 287 stated that â€oeoi is practiced within the context of a given set of political and economic institutions including regulation, intellectual property law, capital markets, and industry structureâ€. We have focused on the impact of corporate OI practices on the NSI. Even so, we only provided a general picture and did not tackle a given research issue. We encourage future researchers to delve more deeply into the links between OI practices and the NSI. In particular, we advocate studies on the link between corporate OI practices and: the education system; technology and business networks; IPR system industry structure; regulatory system; funding rules for innovation. Empirical, comparative analyses of NSI in various nations could be one way of discovering which OI practices inï uence the NSI and how particular NSI features shape ï rms'OI practices References 1 U. Lichtenthaler, H. Ernst, Innovation intermediaries: why internet marketplaces for technology have not yet met the expectations, Creat. Innov. Manage. 17 1)( 2008) 14†25 2 R. Kirschbaum, Open innovation in practice, Res. Technol. Manage. 48 (4)( 2005) 24†28 426 Y. Wang et al.//Technological Forecasting & Social Change 79 (2012) 419†428 3 H. 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Q 41 (1)( 1996) 116†145 Yuandi Wang is a postdoctoral fellow at Technical University of Denmark. He received his Phd from Hasselt University in Belgium. He received his Masters degree from Dalian University of Technology (People's republic of china. From 2005 to 2007 he was a lecturer at China University of Mining and Technology. Since 2008 he has been working for his Phd thesis in the ï eld of open innovation and national systems of innovation Wim Vanhaverbeke is professor of strategy and innovation at the University of Hasselt (Belgium. He is also visiting professor at ESADE (Spain) and the Vlerick Leuven Gent Management School. He is published in international journals such as Journal of Management Studies, Organization Science, Organization Studies Journal of Management Studies, and Journal of Business Venturing. He serves on the editorial board of several international journals. He is co-editor with Henry Chesbrough and Joel West of †Open Innovation: Researching a New Paradigmâ€, a book about the research challenges associated with Open Innovation. He is extending his research on open innovation and open business models by performing joint research with various universities around the globe Nadine Roijakkers obtained her Phd degree from the United nations University-MERIT (Netherlands) in 2002. For two years she worked as a policy researcher for the European commission. From 2004 to 2007 she was an assistant professor of Open Innovation at Eindhoven University of Technology (Netherlands. For the past two and a half years she has been working as a strategy consultant at Atos. Since 2009 she has been working in Belgium alongside Wim Vanhaverbeke to further develop the theory and practice of Open Innovation. Outlets for her research include Long Range Planning, Harvard Business History Review, and Research Policy 428 Y. Wang et al.//Technological Forecasting & Social Change 79 (2012) 419†428 Exploring the impact of open innovation on national systems of innovation †A theoretical analysis 1. Introduction 2. Three approaches to NSI 3. The impact of open innovation on NSI 3. 1. Open innovation reinforces the importance of NSI 3. 2. Undeveloped technology markets need to be cultivated in NSI 3. 3. Linkages within NSI need to be strengthened to facilitate the increasingly wide use of innovative networks 3. 4. Efficient flows of knowledge heavily depend on stronger IPR protection 3. 5. The supply of high-quality labour is linked strongly to education and training 3. 6. Open innovation improves the effectiveness of NSI 3. 7. Eliciting social resources for innovation 3. 8. Benefiting from strong specialisation in innovative labour 3. 9. Improving the efficiency of resource allocation 3. 10. Accelerating knowledge flows at low transaction costs 3. 11. Open innovation diversifies the networks used in NSI 3. 12. Networks used in NSI 3. 13. Online social networks: Enlarging the knowledge exploration landscape 3. 14. Knowledge exploitation networks: Focus on commercialisation 4. Conclusions References

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