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Editorial
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urning 40 is a special occasion, frightening for some and welcomed by others. The age of 40 is said to mark the end of youthfulness, but also the start of a period of maturity and wisdom. Turning 40 is also a special moment for an organization, and for that reason, we celebrate in this issue the 40th anniversary of the home institute of our journal, the School of Management and Governance at the University of Twente. This issue contains seven articles, of which four are contributions from members of this school, showing a wide variety of perspectives on creativity and innovation management. In addition, this issue contains an article that is co-authored by Henry Chesbrough, who is a key-note speaker at the anniversary festivities. To conclude this issue, a book by Chesbrough and a book by another key-note speaker, Charles Landry, are being reviewed. These articles make clear that 40 is the perfect age to combine youthful creativity with mature wisdom. The first article in this issue, written by Wim Vanhaverbeke, Vareska Van de Vrande and Henry Chesbrough, discusses the advantages of open innovation practices in corporate venturing. Part of these advantages can be explained by the real options approach. The authors argue that these benefits do not automatically materialize. Innovative firms have to learn new skills and routines to develop the full ‘real option’ potential of open innovation practices. Fuelled by the influential work of Richard Florida, the European economy sees a rise of cities that call themselves ‘creative cities’. Gert-Jan Hospers and Cees-Jan Pen take a look at the concept of ‘creative cities’ beyond the hype. They see ‘creative cities’ as competitive urban areas that combine concentration, diversity, instability as well as a positive image. Examples of creative cities and current ‘best practices’, in particular Øresund and Manchester, are elaborated in this article. Hospers and Pen show that local governments cannot plan knowledge, creativity and innovation from scratch, but they can increase the opportunity for urban creativity to emerge by providing the appropriate conditions. In the third article, Ariane von Raesfeld and Kaspar Roos present a longitudinal study of the development of a small firm in the printing © 2008 The Authors Journal compilation © 2008 Blackwell Publishing
industry. The paper shows how small firms can manage their network relations in order to balance efficiency in their current business and flexibility to develop new business. The paper concludes that different networking approaches drive business development. For successful business development, both strong and varied ties as well as the existence of intermediary functions of partners are necessary. Marianne van der Steen and Jürgen Enders criticize in their article the narrow view on the role of universities in knowledge-based economies. They propose extending the current policy framework of universities in national innovation systems (NIS) to a more dynamic one, based on evolutionary economic principles. The main reason is that this dynamic view fits better with the practice of innovation processes. The fifth contribution, by Rob Hoppe, explores the way policy systems are dealing with the often highly sensitive social, legal and ethical aspects of current medical technological innovations. The article includes examples and illustrations from the Dutch healthcare system. It concludes that the health policy system does not seem to be sufficiently robust and flexible to accommodate all necessary policy-making styles. In order to deal with unstructured problems, the author pleads for more participatory and deliberative design elements in health technology assessments. Christen Rose-Anderssen and his coauthors write about product innovation in the aerospace industry. The paper explores the effects of relationship development in the supply chain on innovation and competitive advantage. The findings of this study are useful to practitioners, for the successful implementation of supply chain change. It promotes risk-sharing partnerships as instruments for innovation. In the final article, Jan Buijs presents the results of two empirical studies on how experienced project leaders execute NPD projects. He found that inside real-life NPD projects the NPD activities rarely occur in the order described in the NPD literature. Activities may be skipped, run in parallel, or even have seemingly illogical timing. The reasons for these patterns are project specific, and relate to familiarity and complexity. On the basis of the
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second study, Jan Buijs proposes a minimal and a regular NPD process. Two book reviews conclude this issue. Dries Faems reviews Open Innovation: Researching a New Paradigm by Chesbrough, Vanhaverbeke and West. Cees-Jan Pen reviews The Art of City Making by Charles Landry. Looking ahead, we can say that 2009 will again be a special year for Creativity and Innovation Management. Firstly, because we have five special issues coming up: the CINet conference, TRIZ, the 2nd CIM Community meeting in Buffalo, the ECCI X, and, from a new track in the series of International Product Development Management Conferences, a special on Teaching Creativity and Innovation. Secondly, because two special people, both
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renowned scholars in the field of creativity and innovation, have agreed to strengthen our editorial board: Sören Salomo, professor of innovation management at Danish Technical University, and James Moultrie, lecturer in innovation and design management at the University of Cambridge. A final remark concerns next year’s Summer School on Technology Management. It will be organized in Volterra, Italy, hosted by our editorial board member Andrea Piccaluga from the Scuola Superiore Sant’Anna in Pisa (see http://www.eiasm.org). September 2008 Klaasjan Visscher Olaf Fisscher Petra de Weerd-Nederhof
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Understanding the Advantages of Open Innovation Practices in Corporate Venturing in Terms of Real Options Wim Vanhaverbeke, Vareska Van de Vrande and Henry Chesbrough Part of the advantages of using open innovation (compared to closed innovation) in corporate venturing can be explained by applying the real options approach. Open innovation in riskladen activities such as corporate venturing has the following advantages: (i) benefits from early involvement in new technologies or business opportunities; (ii) delayed financial commitment; (iii) early exits reducing the downward losses; and (iv) delayed exit in case it spins off a venture. We furthermore argue that these benefits do not automatically materialize. Innovative firms have to learn new skills and routines to develop the full ‘real option’ potential of open innovation practices.
Introduction
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s each business matures over time, firms have to look for new growth opportunities. Extending products’ lifetime may be a short-term solution, but in the longer run companies have to invest in new business opportunities or explore new technological areas. Firms investing in new technologies or new applications face uncertain futures. We give two examples to illustrate this. First, when a new technology is in search of potential applications, the innovating firm usually has in the first stage(s) no well-defined idea of potential target customers and how the technology can create value. How the firm can create and capture value only becomes clear after extensive market research, lead user interaction and investments in application technology. Second, a company perceives a potential market opportunity but has to develop a technology to create the business. In both cases, committing prematurely to a new venture may impose considerable risks and the innovating firm should delay irreversible investments until it has gained sufficient information that reduces uncertainty to a manageable level.
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Historically, market leaders have tackled these types of uncertainties related to the introduction of radical innovations through the establishment of large, centralized R&D labs. The success of these labs is based on the exploitation of economies of scale and scope in R&D (Chandler, 1977, 1990). However, Chesbrough (2003, 2006) and other scholars claim that the internally oriented, centralized approach to R&D is becoming obsolete in many industries. Useful knowledge is widely disseminated and ideas must be used, or else should be sold to other organizations. R&D is becoming more costly and returns on it are diminishing because of increasing competition in product markets and shorter product life cycles. These are a few of the factors that are responsible for the emergence of the open innovation paradigm. In this approach innovating firms are searching for interesting ideas far beyond their organizational boundaries. Moreover, they are leveraging their internal ideas outside their own business by using external channels to market. In this study, we argue that the alleged benefits of open innovation can be partly explained by the real option approach. We focus on external corporate venturing as a
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management practice to stimulate corporate growth (Keil, 2002; Block & MacMillan, 2003). We have two reasons for doing so. First, it has been one of the major organizational vehicles to apply open innovation in innovating firms (Chesbrough, 2000, 2003). Second, it is a riskladen activity that can be used as a first but reversible step in a sequence of investments with increasing financial commitment on the part of the investing company. In this way, corporate venturing is an interesting area to apply real options theory. Corporate venturing can thus be analysed both in terms of open innovation and real options and it is rather surprising that nobody so far has connected open innovation to real options reasoning. Chesbrough (2003, 2006) developed the concept of open innovation independent of the real options approach, although the benefits of open innovation are implicitly using real option arguments. In this study, we make explicit linkages between the open innovation and real options literature. In particular, we assess the advantages of corporate venturing – as a particular open innovation mechanism – from a real options perspective. This is a first, explorative investigation on open innovation and real options and offers scholars some initial ideas on how real options theory can strengthen the theoretical foundation of the open innovation literature. We also argue that the benefits of the extended flexibility, so characteristic of open innovation, do not materialize automatically. Firms have to learn new skills and routines. We focus among other issues on the changing requirements to develop absorptive capacity to learn effectively from other companies.
Uncertainty and Real Options The creation of new businesses inherently involves a high level of uncertainty, especially in the early stages of new business development. One way for firms to cope with the technological and market uncertainty associated with new business development is by making small investments in multiple options on technology. These small, initial investments can be regarded as a real option. A real option is ‘the right, but not the obligation, to take an action in the future’ (Amram & Kulatilaka, 1999, p. 5), and typically consists of two distinct actions: option creation and option exercise. Option creation is the initial investment, which creates an option for the future. At some point in time, this option can be exercised through a follow-on investment. In the management literature, real options reasoning is often referred to as a tool for uncertainty reduction –
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making a small, initial investment under high levels of uncertainty allows one to create an option while waiting until the uncertainty about the opportunity has decreased. When the uncertainty has decreased, the investing firm can decide whether to make a follow-on investment or whether to abort the project (Adner & Levinthal, 2004; McGrath & Nerkar, 2004). The real options approach has been discussed frequently in the literature as a tool to reduce the uncertainty of innovation projects, corporate venturing and new business development (e.g., Bowman & Hurry, 1993; Teisberg, 1994; Huchzermeier & Loch, 2001; Miller & Arikan, 2004). Owing to its explicit nature to cope with uncertainty, real options reasoning may also provide us with a better understanding of how innovating firms evaluate sequential investment decisions concerning the external sourcing of technologies. In particular, this is applicable at the fuzzy front end of the innovation funnel where R&D co-operation with upstream technology providers and corporate venturing plays a crucial role in reducing the uncertainty inherently present in early phases of technology ventures. In these early phases with unacceptable levels of technological and market uncertainty, firms are better off creating options through learning investments: grants to universities to further explore new inventions or emerging technologies, joining a research consortium or establishing research agreements with partners, or investing in seed capital ventures or corporate ventures (Roberts & Berry, 1985; Dushnitsky & Lenox, 2005) are different possibilities to explore technologies or business opportunities in the first phase. They represent the ‘option creation’ phase. By investing in collaborative research or taking a minority position in high-risk (external) ventures, investing firms learn about this opportunity and in this way decrease the huge uncertainty related to the initial investment. Once the learning investments result in an improved understanding of the technology and uncertainty has dropped to an acceptable level, innovating firms may invest in more substantial ways using other external governance modes such as equity alliances, join ventures, spin-ins or outright acquisitions (Van de Vrande et al., 2006). In sum, real options are investments that can be characterized as sequential, irreversible investments made under conditions of uncertainty. The options create value by generating future decision rights and, in this way, providing strategic flexibility. This flexibility is more valuable the higher the level of uncertainty. Real options reasoning can also be applied to © 2008 The Authors Journal compilation © 2008 Blackwell Publishing
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the context of new business development and corporate venturing. Small initial investments made at an early stage of technology development allow a company to learn about the technology. In this way, it can defer additional investments and reduce the strategic risk of making irreversible commitments to a particular application of that technology. Corporate ventures could thus be considered as compound options where firms at each stage have the option to commit additional resources or to pull the plug. Consequently, the real options approach offers a framework to explain the sequential investment rounds in new technologies within a company. In the next section, we analyse in detail how the real option approach can explain the benefits of external corporate venturing as one of the most important open innovation practices in large companies in order to accelerate their internal innovation or to expand the markets for external use of their innovations (Chesbrough et al., 2006).
Real Options and Open Innovation So far, open innovation has not been linked explicitly to real options reasoning in its application to external corporate venturing. In our opinion, real options may provide us with a better understanding of how innovating firms evaluate sequential investment decisions in corporate venturing. Open innovation can be defined as ‘the use of purposive inflows and outflows of knowledge to accelerate internal innovation, and expand the markets for external use of innovation, respectively’ (Chesbrough et al., 2006, p. 1). Depending on its business model, a firm decides whether or not external and internal knowledge is valuable to be further developed and commercialized into a new business. When the venture project is expected not to be profitable enough or when it does not fit a firm’s business model, the firm will not simply abort the project (as in the closed innovation framework), but it will try to license or to sell it to other firms who can use the innovation productively because they have different business models. Comparing closed innovation versus open innovation practices in terms of real options reasoning, there might be several advantages working in an open innovation style in external corporate venturing. (We follow the typology of real options provided by Janney and Dess, 2004.) First, innovating firms benefit from early involvement in new technologies or business opportunities. Open innovation allows innovating companies to sense developments in a © 2008 The Authors Journal compilation © 2008 Blackwell Publishing
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wide range of externally developed inventions by buying minority stakes in (high-tech) startups, participating in venture capital funds, or by providing educational investments in promising projects at universities or research labs. This is an option-creation process in order to get more information and learn about projects or technologies with uncertain payoffs. The advantage of this strategy is that companies learn early on about new technologies: at that stage investments are small and reversible when investing companies exit. Moreover, tapping into externally developed technologies also enhances the upward potential of the real option because the company can scan a broad range of interesting ideas and projects. In real option terms, open innovation allows companies to scan a much wider range of the available technologies or new market developments, instead of just writing options on internal projects alone. The ability to access a broader range of technologies and market opportunities has financial value because there may be more varied opportunities, and some of these may be uncorrelated with internally perceived opportunities. The result is more alpha, in terms of higher return, and lower beta, in terms of robust diversification, enabling the open innovation firm to build a portfolio of projects that will be more resistant to problems in any one part of the business. Nokia, for instance, is continuously identifying opportunities in its own ventures organization; it systematically scans emerging trends and changes from the perspectives of technology, business and users. The knowledge gained from these multiple perspectives helps identifying potential indicators of change or disruption. By early identification of these indicators, Nokia can take steps to address change or disruption sooner. By identifying the disruptors, and understanding their business models, Nokia can develop its own response to otherwise unforeseen changes. Second, innovating firms also benefit from delayed entry or delayed financial commitment. The staged process in which new technologies are developed and commercialized into new business opportunities can be analysed as a compound option. In closed innovation, firms can only start with an internally developed idea/invention and pull it through the funnel. Open innovation practices offer firms more flexibility about when to start the internal portion of the innovation process: a company can start exploring the commercial possibilities of a technology outside initially, via relationships with universities, SMEs and other innovation sources. The ability to delay the investment in internal innovation activity
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enables the firm to consider a broader portfolio of entry options at the beginning, and also supports more ways to develop growth opportunities from a technology. This flexibility also creates the possibility to differentiate innovation strategies: some firms have developed the ability to scan widely for technologies and ideas early on, other firms prefer to invest in technologies at a later stage when the level of uncertainty has decreased to a level where the future market potential of the new venture becomes more predictable. Third, open innovation offers firms the advantage of an early exit, and the ability to realize some value from projects that do not go forward internally. Open innovation is characterized by the possibility that innovating firms can always license or sell technologies or spinoff ventures that are not promising enough and/or that do not fit with their business model or core competencies. Thus, a project that is determined to be unpromising as a business (but might be valuable as a complement to another part of the business) could be spun off to a supplier, a complementor, or other third party. Strategic initiatives can thus be pursued through multiple firms, with multiple sources of investment, rather than exclusively through the firm’s own capital. This implies one of two favourable outcomes: either the firm gets more ‘at bats’ with the same amount of capital, or the firm is able to pursue the same degree of innovative exploration with a lesser budget. There are two caveats to note here. First, firms may have to trade part of their intellectual property rights in order to enlist the investment and support of other firms. Second, the financial benefits of this are more interesting in the early stages of the innovation funnel, because application-specific investments in the later commercialization phase may be sunk costs, and harder to recover or redeploy (depending on the contestability of the market). Fourth, open innovation allows firms to benefit from delaying an exit. The creation of corporate ventures that reside outside the organization allows firms to monitor its developments while delaying the exit decision. While the venture grows further and matures, the corporation can decide whether to spin in the venture or whether to sell it to external capital providers such as venture capitalists. This decision depends, of course, on the strategic fit and the commercial success of the venture. If the firm chooses to syndicate its investment in the venture and invite other investors in, the firm also benefits from ‘other people’s money’ supporting the development of the venture. This is capital efficient for the firm, though it does relinquish a substantial
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degree of strategic control to the outside investors. In this way, the open innovation paradigm allows firms to maintain flexibility while keeping their different venture options open. These four arguments show that the alleged benefits of open innovation – i.e., improved access to other organizations’ technological capabilities or higher R&D productivity through the combination of internal and external channels to market – can only be fully explained using a real options perspective that focuses on the process of how firms cope with high levels of uncertainty through subsequent investments in new ventures. First, open innovation enables firms to get easy access to others’ technology because of their small and reversible educational investments in universities, research labs and high-tech startups. These small investments represent an option creation process which effectively copes with the substantial technological and market uncertainty in radical innovation projects. At the same time, these initial investments allow innovating companies to delay further financial commitment. Second, open innovation boosts the performance of companies because of the external channels to market such as licensing deals and spin-offs. These external channels to market represent alternatives for innovating firms to capture value from their innovation ventures. Open innovation allows innovators to choose in the option exercising phase to capture value from ventures in different ways, even when the firm is not active on the product market. Licensing and spin-offs are also effective means to delay an exit. Although the firm decides not to develop the venture as part of its own businesses, it can still benefit from extended control and delayed exit until it finally decides to break all ties with the venture. This delayed ‘exit’ is strategically interesting as long as spin-offs or licensees represent a potential competitive threat or when technological and market uncertainty prevent the making of a final decision because of the (lack of) strategic value of a particular technology or application for the innovating firm.
Real Options and Implications for Organizational Learning In the previous section we have shown that real options reasoning is an important rationale to understand the potential benefits from open innovation. But real options are not only interesting to increase the financial value of a firm’s innovative activities. It also requires a process of learning and competence building. The value of learning about new technologies © 2008 The Authors Journal compilation © 2008 Blackwell Publishing
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prior to the full-scale business development is enormous. As open innovation practices potentially enhance the upward potential of new venturing projects and limit downward risks, they increase the learning space for innovating companies. In this way, firms have the possibility to speed up their learning and development of new competencies in order to migrate towards more attractive technologies and applications. However, these advantages do not come automatically. Organizations have to develop new skills, routines and strategies. For example, firms cannot and should not explore externally developed technologies randomly. As Nobel Laureate Herb Simon once observed, ‘where there is a wealth of information, there is a poverty of attention’. They have to learn over time which technologies offer interesting, new growth opportunities. They have to develop the ability to scan efficiently trends in research and new technologies. They must become skilled at recognizing useful ideas, and separating those from the vastly more numerous ideas that are distractions. Hence, firms that try to open up their innovation process have to learn new skills to recognize and absorb externally developed technologies and innovations. Moreover, in open innovation, companies tap into external sources of knowledge. This is certainly not an easy process. It takes most firms years of learning before they effectively learn from external partners (Day et al., 2000; Schoemaker, 2002). How to build a learning relationship with partners? (Kale et al., 2002; Hoffmann, 2005, 2007; Heimeriks et al., 2007; Heimeriks & Duysters, 2007; Kale & Singh, 2007). How to build responsible partnership and trust? (Nooteboom, 2004). Hence, to work effectively, firms that adapt to the open innovation approach have to develop new competences and routines to become highly effective and to exploit the most benefits that can be obtained by the real options underlying the open innovation approach. We will explore this further by focusing on the development of interorganizational absorptive capacity (Lane & Lubatkin, 1998). Real options reasoning has major implications for the absorptive capacity of innovating firms. Absorptive capacity consists of three dimensions: identification, assimilation and exploitation of external knowledge (Cohen & Levinthal, 1990; Zahra & George, 2002; Todorova & Durisin, 2007). Real options reasoning has important implications for each of these three dimensions. First, making small learning investments allows firms to tap into different technologies at the same time. These small, initial investments allow them to learn about the different © 2008 The Authors Journal compilation © 2008 Blackwell Publishing
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technological opportunities ahead. In this way, the investing firm builds up absorptive capacity in a number of technologies simultaneously. Real options reasoning also indicates that absorptive capacity is not simply a by-product of a firm’s own R&D investments. It is not a passive process in which innovating firms automatically can profit from knowledge spillovers of other firms as has been described in the literature in the wake of the seminal publication of Cohen and Levinthal (1990). Firms identify and assimilate new technologies through purposively investing in interesting external technology sources, and then purposively develop processes in order to learn from them. Specific job functions such as technology scouts are developed for that purpose. R&D expenditure levels alone cannot explain the large variability in absorptive capacity among innovating firms. It is the organization of the internal R&D unit, its connection with external partners and its interaction with the other parts of the company that determine the innovative capability of a firm. Moreover, the stage-gate process of real options reasoning leads to a gradual improvement of a firm’s absorptive capacity. Firms learn about new technologies and opportunities by making small learning steps. This accumulation of absorptive capacity over time is an important precondition for an effective and efficient selection of options. By gradually improving its absorptive capacity, firms gain knowledge about the future potential of the projects. When a follow-on investment decision has to be made, this knowledge is necessary to select the best options ahead. As a consequence, real options reasoning is a dynamic approach that helps firms to improve their ability to identify, assimilate and exploit the external knowledge (Teece et al., 1997). As such, real options reasoning has important implications for the way in which firms build up absorptive capacity. Real options allow firms to build and strengthen their absorptive capacity in a broad range of technologies, by making small steps at a time and bringing down uncertainty in that way. The larger the portfolio of options, the stronger the absorptive capacity skills a firm will be able to build. In an open innovation paradigm, this is particularly important. Firms are constantly being confronted with the decisions whether to (further) develop a particular technology in-house, or whether to source it externally. The lack of absorptive capacity that may exist in the early stages of technology development can be gradually enhanced through the use of real options, in addition to the internal R&D activities. When uncertainty related to a venture decreases over time and the value
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of the opportunity becomes apparent, the increased absorptive capacity of the firm will help to make the right decision.
Conclusion Open innovation has to our knowledge not been linked explicitly to real options reasoning. Real options may provide us with a better understanding of how innovating firms that are engaged in corporate venturing evaluate sequential investment decisions in sourcing external technologies. In particular, this is applicable at the fuzzy front end of the innovation funnel where R&D co-operation with upstream technology providers and corporate venturing play a crucial role. Open innovation in risk-laden processes such as corporate venturing has several advantages. First, firms can benefit from early involvement in new technologies or business opportunities. Because of investments in universities, or high-tech startups, investing firms have an early look at new or emerging technologies or trends. Second, firms can profit from delayed financial commitment as they can invest step by step, avoiding investing large up-front costs. Next, they can benefit from early exits as corporate venturing is a fairly flexible investment instrument. This reduces the risk of financial losses. Finally, investing firms can also delay exit in the case of spins-offs. Although the firm decides not to continue the venture as part of its own businesses, it still can benefit from extended control and delayed exit until it finally decides to break all existing ties with the venture. In a similar way, the creation of real options in the context of insourcing external technologies also increases the learning and absorptive capacity of investing firms. Linking absorptive capacity to real options reasoning has according to us a major potential to refine our understanding of the former. Absorptive capacity of firms in relation to radically new ideas in an early research phase is quite different from absorptive capacity related to proven technology that can readily be translated into new products or markets (Leifer et al., 2000). Hence, we have to differentiate the concept of absorptive capacity along the ‘innovation funnel’. It is different for technological exploration compared to exploitation and it has quite different strategic and organizational consequences. Combining both requires that companies become ambidextrous (Tushman & O’Reilly, 1996; O’Reilly & Tushman, 2004). Open innovation would add to this, by advising firms to become more open, as well as ambidextrous.
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This paper is a first, explorative study investigating the link between open innovation and real options theory. We are in particular interested in assessing how the advantages of external corporate venturing – as a specific open innovation practice – can be understood only by applying real option concepts. However, the current study is only an eye-opener; we invite other scholars to further apply real options theory to strengthen the theoretical foundation of the open innovation literature. We narrowed the scope to external corporate venturing to keep the analysis short and tractable; future research should analyse whether real options theory can be applied to other open innovation practices. Can it be applied to the use of intermediaries, crowd sourcing, open IP approaches, etc.? Next, describing the advantages of open innovation we assumed implicitly that managing open innovation was not an issue. In reality, however, open innovation poses considerable managerial challenges. Consequently, future research should investigate how open innovation can be effectively managed in order to reap the theoretical real option benefits. Finally, we should focus not only on the advantages but also on the disadvantages of open innovation compared to closed innovation. In which situations can open innovation damage a firm’s fortune and how can these disadvantages be analysed in terms of real options?
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Lane, P.J. and Lubatkin, M. (1998) Relative Absorptive Capacity and Interorganizational Learning. Strategic Management Journal, 19, 461–77. Leifer, R., McDermott, C.M., O’Connor, G.C., Peters, L.S., Rice, M.P. and Veryzer, R.W. (2000) Radical Innovation: How Mature Companies Can Outsmart Upstarts. Harvard Business School Press, Boston, MA. McGrath, R.G. and Nerkar, A. (2004) Real Options Reasoning and a New Look at the R&D Investment Strategies of Pharmaceutical Firms. Strategic Management Journal, 25, 1–21. Miller, K.D. and Arikan, A.T. (2004) Technology Search Investments: Evolutionary, Options Reasoning, and Option Pricing Approaches. Strategic Management Journal, 25, 473–85. Nooteboom, B. (2004) Inter-Firm Collaboration, Learning and Networks: An Integrated Approach. Routledge, London. O’Reilly, C.A. III and Tushman, M.L. (2004) The Ambidextrous Organization. Harvard Business Review, 74–81. Roberts, E.B. and Berry, C.A. (1985) Entering New Business: Selecting Strategies for Success. Sloan Management Review, Spring, 3–16. Schoemaker, P.J.H. (2002) Profiting from Uncertainty: Strategies for Succeeding No Matter What the Future Brings. Free Press, New York. Teece, D.J., Pisano, G. and Shuen, A. (1997) Dynamic Capabilities and Strategic Management. Strategic Management Journal, 18, 509–34. Teisberg, E. (1994) An Option Valuation Analysis of Investment Choices by a Regulated Firm. Management Science, 40, 535–48. Todorova, G. and Durisin, B. (2007) Absorptive Capacity: Valuing a Reconceptualization. Academy of Management Review, 32, 774–86. Tushman, M.L. and O’Reilly III, C.A. (1996) Ambidextrous Organizations: Managing Evolutionary and Revolutionary Change. California Management Review, 38, 8–30. Van de Vrande, V., Lemmens, C. and Vanhaverbeke, W. (2006) Choosing Governance Modes for External Technology Sourcing. R&D Management, 36, 347–63. Zahra, S.A. and George, G. (2002) Absorptive Capacity: A Review, Reconceptualization and Extension. Academy of Management Review, 27, 185–203.
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Wim Vanhaverbeke (Wim.Vanhaverbeke@ econ.kuleuven.be) is Professor of Strategy and Organization at the Department of Business Studies at Hasselt University. He edited Open Innovation: Researching a New Paradigm (Oxford University Press, 2006) with H. Chesbrough and J. West. Current research areas include: alliances and acquisition of external technological capabilities, alliance management, and external corporate venturing. He serves on the editorial board of the Journal of Engineering and Technology Management, Strategic Organization and the International Journal of Technology Marketing. Vareska van de Vrande currently works as a post-doctoral researcher at the Ecole Polytechnique Fédérale de Lausanne (EPFL). She received an MSc and PhD in Industrial Engineering and Management Science from the Eindhoven University of Technology. Her research on open innovation focuses on the use of different organizational forms for external technology sourcing, such as corporate venture capital investments, strategic alliances, joint ventures, and mergers and acquisitions. Other research interests include corporate entrepreneurship and corporate venturing. Henry Chesbrough is Adjunct Professor and Executive Director of the Center for Open Innovation at the Haas School of Business at University of Calofornia at Berkeley. He is the author of Open Innovation (Harvard Business School Press, 2003) which was awarded one of the best business books of the year on national public radio, and Open Business Models (Harvard Business School Press, 2006), which was awarded one of the ten best books on innovation in 2006 by BusinessWeek magazine. Professor Chesbrough received a PhD from University of California at Berkeley, an MBA from Stanford, and a BA from Yale University, summa cum laude.
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A View on Creative Cities Beyond the Hype Gert-Jan Hospers and Cees-Jan Pen Fuelled by the influential work of urban guru Richard Florida, the European knowledge economy is seeing a rise of cities calling themselves ‘creative cities’. In this paper we have a look at the concept of creative cities and offer a view on them beyond the hype. We understand ‘creative cities’ as competitive urban areas that combine both concentration, diversity, instability as well as a positive image. Examples of creative cities in history and recent best practice of two such urban areas in Europe (Øresund and Manchester) show that local governments cannot plan knowledge, creativity and innovation from scratch. We conclude, however, that local governments can increase the chance that urban creativity emerges by providing the appropriate framework conditions.
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f anything, the 21st century’s economy is a knowledge economy. In the highly developed area of Western Europe, knowledge has become a determining competitive factor, not only in the commercial world but also in regions and cities. Greater investments will have to be made in the knowledge economy if we wish to maintain present levels of European welfare (Cooke, 2002). Development of knowledge, in fact, underlies new products, services and processes (innovations) that end up constituting the engine of economic progress. To express it in the words of the well-known economist Schumpeter: knowledge-intensive activities set off a process of ‘creative destruction’ whereby the existing disappears and something new is born (Schumpeter, 1943). New knowledge can lead to a wide range of innovations, varying from breakthroughs in information technology, life sciences and nanotechnology (radical innovation) to small changes in everyday objects (incremental innovation). However, where knowledge and innovation are concerned, it does not necessarily have to be about new technologies; innovation is possible as well in the field of organization, marketing and logistics, as for example the McDonald’s fast food chain has demonstrated. Throughout the centuries knowledge and innovation have, of course, always played an © 2008 The Authors Journal compilation © 2008 Blackwell Publishing
important part in economic life – here we need only think of the steam engine that heralded the Industrial Revolution. But in contrast to earlier times, innovations follow one another much more quickly nowadays (Cooke, 2002; Rutten, 2003). It is, for instance, estimated that between 1966 and 1990 there were as many innovations as between 1900 and 1966. And in a country such as the United States, in 1999 more than half the economic growth came from activities that had scarcely, if at all, existed ten years previously. No company, region or city can hold itself aloof any longer from this ‘knowledge race’ and its economic consequences. In turn, policy makers struggle with the question how their locality can become a ‘winner’. This article analyses in particular what the knowledge economy means for cities in the European Union. Fuelled by the work of urban guru Richard Florida, cities are increasingly seen as the most suitable locations where knowledge, creativity and innovation flourish (Landry, 2000; Florida, 2002, 2005, 2008). Especially in Europe this has led to the popularity of the concept of ‘creative cities’. In fact, the creative city is developing into a hype (Hemel, 2002; Peck, 2005; Landry, 2006; Di Cicco, 2007). Mayors and councillors of many cities, big and small, look for recipes for how to implement the appealing message of the knowledge economy in their local context. In this paper, which is particularly written from such a
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policy perspective, we suggest that Europe’s knowledge economy indeed asks for creative cities and supporting policies – but that there is much more to explore than Florida’s hype. Rather, we see creative cities as phenomena of all ages and define them as competitive urban areas that are able to combine both concentration, diversity, instability as well as a positive image. After having discussed these elements we deal with the policy question, what city authorities can do to create and reinforce them. Then the focus is on the experience of local authorities in two European urban agglomerations – namely Copenhagen/ Malmö (Øresund) and Manchester – with targeted policy initiatives in the field of the knowledge economy. Finally we round off the article with brief conclusions and policy recommendations.
Cities in the European Knowledge Economy The rise of the knowledge economy in Europe is closely linked to a structural trend in the world order familiar to all of us as ‘globalization’. Globalization is a far-reaching form of internationalization that has slowly but surely led to a worldwide integration of spatially spread activities since the 1980s (Dicken, 2003). The movement towards European Union, the fall of the Berlin Wall, and with it the collapse of Communism, have led to an increasing belief in the advantages of free trade and the market mechanism. Indicators for the trend towards globalization are the gradual disappearance of borders, the rise in exports and imports, an increase in foreign investments and the lively mobility of labour and capital. On the one hand, the countries of Western Europe benefit from this development because companies have found new markets and investment opportunities abroad. On the other hand, globalization gives rise to new players competing against the West European economy. The rise of areas where labour costs are far lower, such as Eastern Europe, South-East Asia and Latin America, has not only sharpened international competition but has also radically changed its character (Krugman & Obstfeld, 2003). It is no longer sufficient for highly developed countries such as Germany, Denmark and France to compete on the basis of cost; instead they have to draw their competitive advantage from knowledgeintensive and high-quality innovations. It is not only countries, but large companies and employees – the ‘knowledge workers’ in Drucker’s words – that are having trouble keeping their feet (Drucker, 1999). The same
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applies to cities: they too have to ask themselves how they can compete in an intelligent manner in the globalized knowledge-based economy. The consequences of the worldwide knowledge economy for cities are not immediately obvious. Some authors are pessimistic and see the growing increase in integration as a threat to the continuing existence of the traditional city. They point to the major effect of what are known as ‘space-shrinking technologies’, which have made the knowledge society and the global community possible (Dicken, 2003). These are technologies that make the world smaller, as it were, such as transport technology (ever-faster planes and efficient logistic solutions) and information and communications technology (for instance e-mail, internet and i-mode). These technological developments are said to have done away with the role played by distance and proximity, and thus the requirement that knowledge workers should be positioned at a particular physical place. In the view of the pessimists the place where you happen to be is no longer of importance: all the world citizen needs is a good cable connection that puts the entire world within easy reach. The consequence of this ‘death of distance’ is said to be that the city of streets, squares, stations, shops and restaurants will be replaced by a ‘city of bits’, a virtual city with a street pattern consisting of digital ‘information highways’ (Mitchell, 1995). Other writers are less pessimistic and see globalization as an exceptional opportunity for cities. In order to develop new knowledge and the innovations it leads to, they believe that face-to-face contacts between people at a certain place remain of crucial importance. New ideas and innovative solutions, in fact, come into being by intensive communication and exchange of knowledge with others. The proximity of people is a condition here, as the Silicon Valley success story demonstrates: it makes more sense for knowledge workers to pop into a colleague’s office than to work via e-mail on a new project with an unknown person on the other side of the world (Saxenian, 1994). In addition, people still have the need for physical contact with others not only in their work but also in their free time. And it is precisely the city, with its vibrancy and range of pubs, cinemas and shopping centres that offers all the space required for this. How can we explain otherwise the fact that it is precisely innovative cities such as Stockholm, Barcelona, Munich, Toulouse, Dublin and Louvain that have blossomed in the world of the knowledge economy? The optimists then reply by saying that knowledge development, globalization and vital cities do not need to be © 2008 The Authors Journal compilation © 2008 Blackwell Publishing
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mutually exclusive. On the contrary: for the cities the knowledge economy means ‘localization’ – the increasing importance of the local level and thus the city – rather than globalization (Cooke & Morgan, 1998). We can propose equally valid arguments for the views both of pessimists and of optimists. In practice, however, we can observe an apparent contradiction between cities and globalization. In other words, we may be dealing here with a ‘global–local paradox’: it is precisely in a world that is becoming increasingly integrated that cities must lean more and more heavily on their specific local characteristics. These unique characteristics, indeed, determine that in which a city excels and in which it can distinguish itself in the competition with other cities in the knowledge economy. The European knowledge economy and the related global–local paradox mean that cities, more than in the past, compete for the favours of inhabitants, companies and visitors. Here, every city derives benefits by drawing in and binding to itself knowledge workers and knowledge-intensive activities. This is something from which a city can derive competitive advantage. And the battle for knowledge is being hard fought in Europe, a process caused partly by the advancing process of European integration: every city that wishes to have a high profile has its own university or institute of higher education, high-quality shops, a music centre or a renowned theatre. In this respect, Landry (2006) even coins the term ‘clone cities’. The similarity in the form of cities, demonstrated especially in a comparable range of facilities, knowledge institutions and cultural provisions, is seen in Europe particularly in the region known – because of its shape – as the ‘Blue Banana’ (Delamaide, 1994). In this homogeneous and prosperous region between Manchester and Milan the cities have come more and more to resemble one another over time. European convergence of this nature has major consequences. In fact, it means that small details, such as the city’s image, can be decisive in decisions taken by companies or individuals looking for a place to settle or to visit. In order to maintain and increase their attractiveness to knowledge workers and other target groups, cities must reflect on what sort of profile they should have. For this a clear competitive strategy is required. If someone is free to choose, in the end it is the most attractive city that will win. The local parties involved in this process have to deal with a wide variety of questions. On which target groups should they focus? What sorts of activities (culture, economy and/or leisure) should be employed in the strategy? How do they want their city to be known to the outside world? Providing © 2008 The Authors Journal compilation © 2008 Blackwell Publishing
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answers to such questions requires a great deal of creativity on the part of city authorities, the local population and business community. Cities can hope to distinguish themselves from others only by finding creative solutions and in this way hope to beat the competition. In other words, the hefty inter-city competition for knowledge requires that they become so-called ‘creative cities’.
The Concept of Creative Cities Though the world-wide knowledge economy may lead to a ‘global village’, we have just seen that this does not necessarily mean that the city is on its last legs. Paradoxically enough, in an ever integrating world, there is still room for vital and innovative cities. But cities – especially in Europe – will certainly have to defend and strengthen their competitiveness in order to ensure that they are not wiped off the map by their rivals. Clever and original strategies on a local scale are required for this. Cities that succeed in developing such strategies have the opportunity to grow to become competitive, creative cities. But what, in fact, are creative cities – and how can we recognize them? It should be stated from the outset that it is no simple task to indicate precisely what a creative city is (Simmie, 2001; Hemel, 2002; Landry, 2006).1 This can be seen, for instance, in the book Cities in Civilization (1998) written by the famous English professor Sir Peter Hall (Hall, 1998; Florida, 2002). He shows that the creative city is a phenomenon that belongs to every era, but that no single city is always creative. In the course of history various types of creative cities existed: technological-innovative, cultural-intellectual, cultural-technological and technologicalorganizational cities. We will deal with them briefly in order to find out what the cities in the current European knowledge economy might be able to learn from their earlier colleagues.
Technological-Innovative Cities For a start, we can find examples of technological-innovative cities in the past. 1
As an anonymous referee rightly noted, it is of course questionable whether ‘creative cities’ is an appropriate term. As is argued later in the paper, in the end it is only human beings that can be creative, and not a city as such. However, we see a creative city as a place with a relatively high percentage of creative people. Proxies of this are the number of creative professions in a city, its innovativeness and other measures that are used in research on creative cities (Revilla Diez et al., 2001; Florida, 2002; Van Oort, 2003).
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Such places functioned as birthplaces for new technological developments or sometimes even for real technological revolutions. Generally, only a few innovative entrepreneurs – ‘new men’, as Schumpeter calls them – were capable of causing the city to bloom by creating an atmosphere of collaboration, specialization and innovation (Schumpeter, 1912). A classic example of this type of technologicalinnovative city was Detroit, where Henry Ford and his Model T laid the foundations of the American automobile industry around 1900. Other examples are 19th-century Manchester (textiles), Glasgow (shipbuilding), the cities of the Ruhr (coal and steel) and Berlin (electricity). Technological-innovative cities of more recent date are to be found particularly in America’s Silicon Valley (San Francisco and Palo Alto) and Cambridge, both of them Meccas of information technology. Currently, such ‘technopoles’ are the target to be aimed at for many European areas: simply, names such as Dommel Valley (Eindhoven), Silicon Glen (Scotland), Silicon Saxony (Dresden) and Bavaria Valley (Bayern) betray how much public officials hope to imitate the technological success of Silicon Valley.
Cultural-Intellectual Cities Creativity in cultural-intellectual cities is of a totally different order from that found in technological-innovative cities. History shows that in ‘soft’ cities of this type, culture (e.g., the figurative and performing arts) and science bloomed in a period of tension between the established conservative order and a small group of innovation-minded radicals. It is precisely that generation gap that produced creative reactions on the part of artists, philosophers and intellectuals. In its turn, this ‘creative revolution’ again acted on outsiders as a magnet, outsiders who saw the cities as places where they could give free rein to their talents. By way of illustration, we could call to mind the Athens of classical antiquity – the cradle of democracy – and Florence during the Renaissance. But also 17th-century London (theatre) and Paris (painting), Vienna (science and art) and Berlin (theatre) in the early 20th century are examples of cultural-intellectual cities. With a little goodwill we could also regard lively university cities such as Dublin, Heidelberg, Toulouse, Amsterdam and Louvain as contemporary European representatives of the cultural-intellectual city.
Cultural-Technological Cities The third type of creative city is represented by the cultural-technological cities. In essence this
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type of city is a merger of the major characteristics of the two already referred to. In cultural-technological cities, in fact, technology and culture go hand in hand. In the past this has resulted in so-called ‘cultural industries’, such as the film industry in Hollywood (1920) and its Indian variant (Bollywood) in Bombay, the music branch in Memphis and the fashion (haute couture) industry in Paris and Milan. Examples of this sort of city in the 1990s are Manchester (New Wave music) and Leipzig after the fall of the Berlin Wall (multimedia). Moreover, we encounter cultural-technological elements in Amsterdam, not only during the city’s Golden Age but also today (Amsterdam Osdorp), and in Rotterdam, predominantly because of its architecture and film festival. Hall (1998) expects a great deal from this type in the 21st century. In particular, he envisages a golden future for places that combine internet and multimedia in an intelligent manner with culture, for instance in the form of virtual museum visits.
Technological-Organizational Cities The last category is that of the technologicalorganizational cities. Such cities are creative to the extent that local actors have found original solutions to problems stemming from largescale urban life. Here we can think of the supply of water for the population, the need for infrastructure, transport and housing. Examples of cities that shine in this type of ‘urban innovation’ are Rome under Caesar (aquaducts), 19th-century London and Paris (underground rail system), New York around 1900 (skyscrapers), post-war Stockholm (durable housing) and London in the 1980s (the re-structuring of the Docklands). Currently some European cities have shown that they have technological-organizational creativity at their disposal: here we are thinking of Tilburg (running the city as a company) and Rotterdam (revitalization of the docks area with the Kop van Zuid). In contrast to the other types of creative city, in the technological-organizational cities it is mainly the government that goes to work in a creative fashion in collaboration with the local business community. In such cases we can speak of public–private partnership (PPP).
Conditions for a Creative City If history from the time of the ancient Greeks up to the present makes one thing clear, it is that the creative city does not exist. At first sight, the Athens of Pericles, Manchester during the Industrial Revolution, film city © 2008 The Authors Journal compilation © 2008 Blackwell Publishing
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Hollywood and the Rotterdam of the 1990s have little in common. But on closer inspection these cities can be seen to agree on one point: they were without exception breeding places of creativity, whether on the technological, cultural, intellectual or organizational level. It is impossible to predict where and when a creative city of this sort will come into existence. That is related to the essence of creativity: the capacity to think up original solutions to dayto-day problems and challenges. The creative mind sees what others see but thinks and does something different. The result is that existing ideas not previously linked together lead to an innovation. In the words of Schumpeter: creativity leads to ‘Neue Kombinationen’ (new combinations) (Schumpeter, 1912). An illustration of how creativity works is the invention of the bra (Jacobs, 1969; Desrochers, 2001). Mrs Ida Rosenthal, a seamstress in a small shop in New York, did not like the way the dresses she made hung on her customers. In an attempt to improve the dresses’ fit, she made improvements to underclothing. The result of that was the first brassiere. The customers liked the tailor-made bra with each dress they bought and soon came to the shop to ask for bras as such. Gradually, Mrs Rosenthal dropped dress making, opened a workroom and devoted herself entirely to producing and selling bras. This example shows that creativity is not only the result of human work but is surrounded by coincidence and unexpected circumstances. So it is an illusion to think that one can force creativity or ‘construct’ a knowledgeintensive city. And yet there are a few factors that can increase the chances of urban creativity developing and that can thus contribute to an urban knowledge economy. In general terms these factors are: concentration, diversity and instability. The three elements are elaborated below.
Concentration Urban creativity is first stimulated by the presence of a substantial number of people at a certain location. Concentration leads to the critical mass required for sufficient human interaction and communication. In the end, indeed, creativity, knowledge development and innovation are human work: not a city in itself but only its population can be innovative. The actual number of inhabitants in a city is, incidentally, a limited rule of thumb for defining concentration (Jacobs, 1961; Landry, 2000). Although in a city housing a large number of people the chances of creative ideas emerging are greater, a large population is definitely not a requirement © 2008 The Authors Journal compilation © 2008 Blackwell Publishing
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for creativity. The Athens of classical times contained at its peak something like 200,000 people, including slaves. And indeed, that is more people than live in a standard provincial town, but it hardly represents the character of metropolis with which creative cities are often associated. Concentration is not so much a matter of the number of people but rather of the density of interaction. A dense concentration of people at a certain location favours frequent meetings and happenstance contact between individuals and thus makes new ideas and innovations more likely. As far as this is concerned, we have no cause for complaint in Western Europe. The Netherlands, for example, is small and densely populated: meetings can be arranged easily, which fosters the chance for creativity.
Diversity Diversity is the second factor that encourages urban creativity. Here we are talking about diversity in the widest meaning of the word: not just variation between the citizens, their knowledge and skills and the activities they pursue, but also variation in the image the city projects as far as buildings are concerned. Nobody has been as enthusiastic as the American publicist Jane Jacobs in propagating the notion of diversity as the fertile soil for creativity of cities (Jacobs, 1961, 1969). In her eyes, a city with a diverse population (families, entrepreneurs, artists, migrants, old people, students) can benefit from an equally varied set of skills and demands. In a city of this nature there is every possible opportunity for the inhabitants to meet one another on the street, swap knowledge, pick up new ideas and bring about innovations. The built-up environment can give an extra helping hand here: in a street with ‘function mixing’ – that is, a mix of buildings with differing functions (old buildings, new dwellings, offices, shops, churches, pubs and restaurants) – there is always something happening, day and night, and the chances are greater of accidental encounters and Schumpeterian ‘new combinations’. In this way a city can, says Jacobs, develop into a real breeding place for entrepreneurship, creativity and innovation. In short: diversity leads to dynamism and thus to a flourishing city life.
Instability Concentration and diversity of people at a certain location are not, however, sufficient to allow us to speak of a creative city. Some cities possess these essential ingredients and yet they are not creative. If we dip back into
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the past we notice that it is precisely in a period of crisis, confrontation and chaos that cities show the greatest creativity. Amsterdam around 1600, 19th-century Vienna, London and Paris, also Berlin between the two World Wars – they were all far from stable. Some see ‘instability’ as an extra condition for urban creativity. To clarify this vague and unpredictable factor – often referred to as ‘bifurcation’ – we can think in metaphorical terms of a river running off a mountain: if the river’s fall is steep, the direction of flow is clearly defined (stable); but when the fall levels out the river’s situation becomes unstable – with the river ‘hesitating’, as it were, as to which direction to take (Buttimer, 1983). It then takes very little to determine the further progress of the river. Like a river, a city can also find itself in a vulnerable situation and invite creativity. Small, chance events, such as the meeting between a few creative and enterprising persons, can then be of major influence on the way the city is to develop in the near future. A telling example is the Austrian capital of Vienna during the ‘fin de siècle’. To be honest, Vienna today does not make a particularly strong impression of creativity on the unsuspecting tourist. A century ago things were different: the Austrian capital was the intellectual and artistic focus of Europe – in other words, the centre of the then knowledge economy (Francis, 1985). In a relatively brief period (1890–1930) countless learned people and artists with a reputation, such as Wittgenstein (philosophy), Freud (psychology), Hertz (physics), Schumpeter (economics), Loos (architecture), Klimt (painting) and Kraus (political ideology) were present in the city. In the Vienna of the time we find all three conditions for creativity. The city was coloured by over-population, a rich public life and tight networks. All the academic institutes were within walking distance of one another, something that fostered communication and interaction between intellectuals working in a wide variety of disciplines. In addition, the city was in a state of permanent political instability: the crumbling of the Austro-Hungarian Empire and the First World War were widely opposed by the population and provoked lively discussions and every type of creative expression (philosophical treatises, writings, works of art). But perhaps the most important background to Vienna’s creativity around and after 1900 was the ‘café factor’: the countless Kaffeehäuser in the city, open from early in the morning until late at night, served as the meeting place of creative minds. In these cafés many ‘Neue Kombinationen’ were born over a cup of Wiener mélange.
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The Role of Spatial Cognition Above we saw that a creative city with opportunities in the knowledge economy is, whatever else, a densely populated and diverse city with sufficient opportunity for the happenstance to occur. A reasonably large number of cities in Europe match this profile. And yet not every city has an equal chance of growing into a creative knowledge city. Even if a particular location possesses the basic ingredients for creativity, in the end the place is creative only if recognized as such. This has everything to do with what psychologists call ‘perception’. Because people – whether they be citizens, entrepreneurs or tourists – do not know everything when they take decisions, they use whatever knowledge they may happen to possess. That knowledge is always selective and is formed out of experiences from the past and by outside sources, by information gleaned from the media, for instance. Using this perception, people construct for themselves an image of reality. The view we have of the world is therefore always coloured. And the image we have of a particular human settlement is also formed in this way. In this context geographers speak of ‘spatial cognition’: the knowledge people have of spatial unities such as regions and cities (Pred, 1967; Gold & Ward, 1994). That image is of major importance for the choices people make when deciding where to work, live or spend their free time. Such decisions are not made on the basis of the objective characteristics of an area but on subjective grounds such as the perception people have of the area. The image summoned up in people by a particular region – in brief, its ‘image’ – has, in other words, a great deal of influence on the choice of a place to settle down.
Positive Image That which applies to areas in general also applies to cities in particular: unconsciously we all have a more or less well-defined image of certain cities, whether based on correct information or prejudices. Research shows that a city’s image is influenced in a positive manner by the extent to which the city is known, or ‘unknown, unloved’ and ‘known, loved’ (Anholt, 2007). It would also seem that Einstein’s famous statement (‘It is easier to split an atom than a prejudice’) applies to the image forming of cities in the knowledge economy. This explains why metropolises such as New York and London – but also smaller cities, such as in the Dutch Randstad – are often seen by outsiders as more creative and innovative than they really are. At the © 2008 The Authors Journal compilation © 2008 Blackwell Publishing
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same time, cities that are relatively unknown to the wider public, such as places in the German Ruhrgebiet and in the Dutch regions of Twente and Zealand, have a traditional image, though all the ingredients necessary for creativity are present there. Here the past history of such regions often plays a decisive role, which means that they have been burdened for years with a rural, traditional and dull – even negative – image. In promoting such urban areas as knowledge regions, they will always lose out to cities in the Randstad that are already seen as ‘cool’. Thus, creative cities such as London, Paris, Berlin and Amsterdam can rest for years on the laurels gained in their creative past. Here we see a clear example of the ‘Matthew effect’, a phenomenon named after the old biblical principle: ‘For whosoever hath, to him shall be given . . . but whosoever hath not, from him shall be taken away even that he hath’ (Matthew 13:12). Most cities in Europe realize that apparently minor details such as the city’s image can be decisive for (knowledge-intensive) companies who may wish to settle in the city and for people looking for a place to live or spend their holidays. A bad image perceived by one or more of these target groups can drive them away and mean a loss of income for the city. More and more cities are therefore finding it insufficient merely to invest in the provision of urban facilities: they make efforts to communicate their attractiveness and creativity inside and outside the city. This strategy of positive image-forming is known as ‘city marketing’ or ‘branding’. Currently it is a popular instrument which, it is hoped, will contribute to making the city known and to improving its reputation. Cities make extensive use of headline-grabbing slogans and promotion campaigns to put themselves on the map. Though the effect of this city marketing is difficult to measure, it would seem that some cities really have succeeded in developing a ‘strong brand’ (Anholt, 2007). There are examples of this throughout Europe, such as Hull, Birmingham, Glasgow, Dublin, Munich, Lille and Seville. It is remarkable how little trouble cities throughout Europe take to distinguish themselves from their rivals (Hospers, 2006a). For instance the Dutch cities of Delft, Enschede and Eindhoven have all adopted the profile of technology and knowledge cities – therefore qualifying themselves as ‘creative cities’ of the technological-innovative type – without placing any emphasis on their own uniqueness. The result of this herd behaviour can be easily guessed at: vague slogans imparting little information, such as ‘Eindhoven: © 2008 The Authors Journal compilation © 2008 Blackwell Publishing
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Leading in Technology’ and ‘Knowledge City’ (Delft and Enschede). By giving themselves this sort of profile, none of the three cities make it clear how they differ from one another, nor do they give any idea of what they have to offer to the knowledge worker looking for a place to work and live. In this way the three university cities undermine their own competitiveness: in fact, real competitive edge can be gained from building on and emphasizing the local conditions – in other words, a strategy of ‘trend through tradition’. That it is still possible for a relatively unknown city to gain a reputation is shown by the example of ‘city marketing’ in the Dutch city of Almere (Bulthuis & Padmos, 1999). The ‘geographical market research’ commissioned by the city of Almere in the mid-1990s showed that the average Dutch citizen had little idea of what Almere was like. Most people came up with vague descriptions such as ‘dull new town’ and ‘city in the polder’. The city authorities then decided that a large-scale marketing campaign was needed to tackle the image problem. Their particular aim was to attract new commercial activity to the city, since as far as population was concerned, Almere had already reached the position of fastest growing city in the country. In order to establish the city’s image as a centre of business activity, the city council, aided by substantial financial support from the local business community, set up the Stichting Stadspromotie Almere (Foundation for the Promotion of the City of Almere). Under the slogan ‘It’s Really Possible in Almere’ (which, admittedly, is a rather nondescript and generic phrase because it does not emphasize any factor of local uniqueness), the organization launched a promotion campaign which, apart from adverts placed in the national press, had an advertising spot on TV showing Almere inhabitants singing an urban anthem. Since then the foundation has held large-scale events and projects within the city limits such as Holland Sand Sculpture and a branch of the World Trade Centre. It is hard to assess the effectiveness of Almere’s branding strategy, but the growth of the city’s inhabitants at the cost of surrounding towns suggests that the city promotion might have played a supporting role.
The Role of Local Economic Policy If anything has become clear from the above, it is that there is no recipe for cities in the European knowledge economy. There are various types of creative city, and even cities of the same type, such as technologicalinnovative and cultural-intellectual cities,
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show enormous differences. The Detroit of Henry Ford is, at first sight, difficult to compare with today’s Palo Alto, and 14thcentury Florence would appear to have little in common with the Dublin of today. Despite their differences, however, generally speaking one could say that creative cities possess a number of basic ingredients: a high concentration of people, a dose of happenstance and luck and – definitely not unimportant – a positive image familiar to the outside world.2 Local authority policy as an essential condition for urban creativity does not appear in this list because history shows that policy makers have played scarcely any part in the history of the birth of creative cities. It was only when a city had grown and problems were occurring, for instance in transport and housing, that the city authorities sometimes proposed creative solutions on the technological-organisational level. London and Paris, Stockholm and Rotterdam, for example, can thank the local authorities for their underground train systems and original housing projects, respectively. At present, our cities are facing totally different problems, such as how to cope with maintaining their momentum on a global level in the inter-city knowledge race. In principle, it ought to be possible for the authorities to come up with creative solutions in this case – even if the question of urban competitiveness is rather less tangible than the more fundamental problems that cities are used to wrestling with. When making the city more attractive in the knowledge economy the local authorities can invest in the creativity of their own population. But a word of warning: creative cities cannot be constructed from the ground up. The roots of creativity, in fact, always lie in the existing, historically developed urban environment. In their enthusiasm, local authorities sometimes tend to forget this. Inspired by success stories such as Silicon Valley, they hope to be able to make of their city a technopolis of similar stature. Terms such as Silicon Saxony (Dresden), Silicon Kashba (Istanbul) and Food Valley (Wageningen) speak volumes in this regard. That sort of 2
Obviously, much more rigorous research is needed to validate this hypothesis empirically. This paper must be read as a hypothesis on creative cities rather than a generalized vision on urban creativity. Our statements on creative cities are based on some historical examples and need further exploration to say anything conclusive beyond that. We hope, however, that the paper invokes some inspiration in a way the anecdotal work of Jane Jacobs on urban diversity has done for the research of economists such as Lucas, Glaeser and Van Oort on ‘Jacobian externalities’ (see more on this point in Desrochers & Hospers, 2007).
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copycat behaviour is, however, far from creative (Hospers, 2006a). The local authority would do better to proceed from the city’s specific characteristics, using them as a basis in the search for urban creativity (‘localization’). This is not the same thing as blueprint planning: local authorities will have to be content with measures designed to create conditions whereby they do no more than increase the chances of creative powers coming into existence. To start with, authorities can contribute to increasing the critical mass of their city by seeking collaboration with a neighbouring city in the fields of infrastructural, educational and cultural facilities (inter-urban networking). It is also possible to increase the diversity of the city with targeted policies, for instance by mixing residential and working locations (function mixing) and removing obstacles to migrant entrepreneurs (ethnic entrepreneurship). Finally, the city government can consider holding a major event or organizing a new project, for instance a competition for the population or for the business community with the winner submitting the most creative proposal. Although this type of measure does not lead directly to urban creativity, it does increase the chances of it appearing. In addition to creating conditions, the local authority can fulfil a useful role in promoting the city with a targeted ‘branding strategy’ (Anholt, 2007). A particular place may fulfil all the conditions for creativity but it is a creative city only when perceived as such by the outside world. Because the ‘unknown, unloved’ principle also applies to cities, local authorities would do well to invest in making the name of their city known and improving its reputation. It is of major importance that the authorities put out a realistic image of the city when branding it – in other words, project an image derived from and matching up with the specific context of the city in question. A small, sleepy, rural town that presents itself to the outside world as a cool technopolis tests credibility and is only treated as an object of derision. City marketing, it should also be said, is not a matter for the local authorities on their own. Work on a positive urban image requires collaboration on the part of the entire city, particularly entrepreneurs, of whom it can be expected that they have wide-ranging experience of marketing products to the people. Moreover, local authorities and the business community have a common interest, namely that the city should remain attractive in the inter-city competition. One conurbation where a result-targeted and broadly supported branding strategy has borne fruit is the German Ruhr Area (Hospers, 2004). In this © 2008 The Authors Journal compilation © 2008 Blackwell Publishing
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‘Rust Belt’ of coal and steel, local parties have invested heavily in the integration of new technologies and trends into the existing local economic structure. Young, technologically high-value companies (‘technostarters’) are housed in former factories and warehouses. And the industrial heritage is being recycled as exhibition halls, concert halls or restaurants. These symbols underpin the Ruhr Area brand as a place where trend and tradition are not mutually exclusive but get along fine together. With campaigns such as ‘The Ruhr Area: a Strong Piece of Germany’ and ‘The Ruhr Area is Hard to Beat’ the local authorities and entrepreneurs have succeeded in dragging the traditional industrial area into the era of the modern knowledge economy.
Creative Cities: Two Case Study Examples There are various interesting examples of cities where urban management has contributed to the rise of a local knowledge economy. Different authorities have had varying degrees of success in, for example, transplanting the success of the Silicon Valley towns into their own city. Sometimes it has succeeded, but the beckoning future of this Californian ‘hot spot’ has also led regularly to disappointments. The doom-laden example in this context is Akademgorodok in Russia (Castells & Hall, 1994). This ‘city of science’ built in Siberia and based on the Silicon Valley model was, from its very beginnings in the 1950s, anything but knowledge-intensive and has been languishing for decades. The lesson to be learnt from ‘great planning disasters’ of this sort is that a local knowledge economy cannot be produced ex nihilo. Knowledge-intensive activity must always have a basis in the existing local economic structure or at least be able to find some sort of link-up there. In addition, clear vision, collaboration, an eye for practical details and good marketing are indispensable ingredients for the successful development of knowledge cities. At least, those are the most important lessons that we can draw from successful examples of local knowledge policy. By way of illustration, below we examine experience with ‘creative city’ policy in two European urban areas: the Scandinavian Øresund (Copenhagen/Malmö) and the English city of Manchester. These cases not only illustrate the factors leading to local economic success: they also make clear that urban policies to support the development of a local knowledge economy can have several faces. © 2008 The Authors Journal compilation © 2008 Blackwell Publishing
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Øresund: The Human Capital The Øresund is a cross-border (Euregional) ‘twin city’, linking Copenhagen (Denmark) and Malmö (Sweden) together via a large bridge, the Øresund Link. Historically, the Øresund was the core of the Danish Kingdom, but since the peace of Roskilde (1660) that followed the Swedish-Danish war, the Swedish part came under Swedish rule. Despite occasional negotiations to foster Danish–Swedish contacts, it was only in the 1990s that more and more policy makers in the Øresund realized that increased cross-border co-operation could be beneficial for the region as a whole. Obviously, one of the aims was to create the critical mass needed for more urban creativity (Landry, 2000). Although, with more than 3 million inhabitants, the Øresund is a region rather than a city as regards surface area, it can be regarded as a single urban knowledge area. It is the most densely populated agglomeration in Scandinavia. Since the 1990s the location grew from a relatively traditional industrial area to become a true ‘creative hub’. The Øresund excels in ‘health’, i.e., all activities to do with health care (e.g., medical technology and life sciences). Next to London and Paris, the Øresund has already gained recognition as one of the top three ‘hot spots’ in Europe in this youthful branch of the knowledge economy (Hospers, 2006b). Collaboration in medical matters has been practised on both sides of the border since the late 1980s, collaboration that was sealed in 1997 by the establishment of the Medicon Valley Academy, a joint venture between local medical technology companies, universities and hospitals. The project received extensive support from the EU because of its innovative character. Employment in the health sector in the Øresund has shown vigorous growth in the last few years, especially as regards technically high-flying jobs. This is partly because the conurbation has shown itself able to draw in an increasing number of knowledgeintensive foreign companies, particularly from the United States. What is the background to the excellent economic achievements of the Øresund? In the few studies carried out to explain the development of the region, at least two success factors are identified: effective collaboration between local parties and a clear branding strategy. Indeed, there are few places in Europe where government, education and commerce have operated so effectively in a united manner as in the Øresund. The Øresund Committee, with representatives from all the social parties, opted for
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the theme ‘man and his needs’ as regional spearhead. Under this banner the committee has invested in local economic diversity, in particular in a variety of facilities related to human needs, such as health (medical technology), contact with others (the Øresund Bridge) and recreation (varied supply of culture). The local parties realized that the presence of these elements was insufficient to place the region properly on the map – the Øresund did not yet have a real image. So they also worked on making the name of the Øresund familiar in Europe through a targeted branding strategy (Øresundsbro Konsortiet, 2007), partly by creating a web page and producing marketing brochures. To carry out this branding, a special organization was formed: the Øresund Network. The network owns the rights to the trademark of the Øresund area comprising a graphic profile, logotype (the typical Scandinavian ‘Ø’) and a number of messages that can be used in the marketing of the area. Meanwhile, many Danish and Swedish companies and public organizations have become members of the Øresund Identity Network. Thus, they have free access to the regional logotype that can be used for their own marketing efforts. Moreover, in the media the region has been actively promoted as ‘The Human Capital’ – note the double meaning – where it is good to live, work and take recreation. And even though it may be difficult to measure the effect of the branding strategy, one gets the impression that this localized approach to the Øresund has not left its creators emptyhanded (Hospers, 2006b). After almost 350 years it seems that the Øresund is slowly but surely re-uniting itself.
Manchester: Original and Modern Manchester, an agglomeration of about 3 million people in the North-West of England, can be proud of its glorious past: it was the cradle of the Industrial Revolution. In the 18th century it was the textiles capital of the world and as such the city can be called an example of a ‘classic industrial metropolis’ (Zimmermann, 2000). However, the first industrial city, in which half of the workforce used to work in manufacturing, was also the first place to experience massive de-industrialization. Since the 1960s onwards, factories closed, workers were fired and the city entered into a heavy economic crisis. A lack of sectoral diversity meant that it was hard to find local Schumpeterian ‘new combinations’ of trend and tradition that could help to rejuvenate the Mancunian economy. With the establishment of the Central Manchester Development Corpora-
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tion in 1986, however, a new strategic tool was employed in an attempt to restructure the economy, namely public–private partnership (PPP). Thanks to this co-operative strategy in which the private and public sector join forces, major redevelopment projects in the city could be realized relatively rapidly. Examples of such successful PPP projects were the Central Station, the first phase of Metrolink, the creation of Manchester’s inward investment agency MIDAS and the opening of the Urbis museum. Ironically, the explosion of an IRA bomb in 1996, which devastated the Victorian city centre, paved the way for the local authorities to rebuild Manchester’s heart as well and to develop a new image of the city from scratch (Peck & Ward, 2002). Over the years, the economy of Manchester has improved. Less than one-fifth of the city’s workers are employed in factories now; most of them work in service occupations, although these are often relatively low-paid retail, personal service and clerical jobs. However, business in Manchester is doing well: it is the fastest growing municipality outside London (Manchester City Council, 2007). In the 1990s the City Council also established Marketing Manchester to promote the ‘new’ Manchester. The branding campaign this body developed, around the slogan ‘We’re up and going’, was heavily criticized, mainly by a group calling itself ‘The McEnroe Group’ (after the wellknown tennis player John McEnroe who was famous for saying ‘You cannot be serious’). One of the dissenters was the Mancunian designer Peter Saville, who ultimately took up the challenge to try better: the Manchester City Council appointed him in 2004 as Manchester’s city brander. Recently, Peter Saville – officially titled ‘Manchester’s Creative Director’ – has launched a new branding campaign for Manchester with ‘original modern’ as the organizing concept (Manchester City Council, 2007). This concept, which is meant to be more than a marketing slogan, draws on the industrial tradition of the city, but simultaneously tries to make clear that Manchester has shifted from an industrial city to an industrial city. According to Saville, in an interview with researcher O’Connor (2006): ‘Manchester is a city concerned with the now. It knows it has a history, but it’s not historically minded. Originality and modernity are values characteristic of Manchester, values which the city has epitomised. Original and modern thinking built it. My vision for the brand was the pursuit of the original and modern in this century’ (Saville, cited in O’Connor, 2006). The relevant city authorities have greeted the new branding strategy of Manchester with open © 2008 The Authors Journal compilation © 2008 Blackwell Publishing
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arms. For example, Manchester Knowledge Capital, a public–private partnership aimed at the promotion of the local knowledge economy, now makes use of the branding concept to stimulate creativity and innovation in Mancunian neighbourhoods. In its policy the platform puts emphasis on ‘real world science’ rather than ‘rocket science’: for a local knowledge economy to develop, it is important that many people are involved rather than a small group of scientists. To stir up enthusiasm among the public, Manchester Knowledge Capital has started the Innovation Investment Fund that supports each Mancunian citizen with a realistic and feasible innovative idea. In this way, Manchester’s authorities hope to attain the critical mass needed for the whole city to become creative.
Towards a Hypothesis: Giving Coincidence a Hand In the European knowledge economy cities still hold the future. History suggests that cities are the places par excellence where knowledge, creativity and innovation reach full maturity. But at the same time, it is true that not every city has unquestionably good prospects in the knowledge economy. In our view, the cities that will win the inter-city knowledge race are the ‘creative cities’. Although more rigorous research is needed to test our hypothesis, we assume that such creative cities possess not only sufficient concentration, diversity and instability, but also project a matching image based on innovation and modernity. For the rest, we suppose, the success of cities in the knowledge economy remains a question of human effort and happenstance. This somewhat fatalistic hypothesis does not mean that cities can simply rely on fate and afford to adopt a passive attitude. On the contrary: certainly in the current inter-city competitive race it will be precisely its creative powers that a city will need to bring into play. But the unpredictability surrounding creativity and innovation means that a tailor-made, unambiguous creative competitive strategy for cities in the knowledge economy is simply not available. The only thing the authorities can do, in collaboration with local parties, might be to increase the chances of creativity coming into being. In principle this is possible if certain conditions are created and investments are made to make the city’s name known to outsiders. But success here is not assured. Local authorities wishing to give their city a place in the European knowledge economy will have to be content with the fact that they can only give chance a helping hand. Perhaps © 2008 The Authors Journal compilation © 2008 Blackwell Publishing
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the famous chemist Louis Pasteur, an unrivalled ‘knowledge worker’, best expressed what a realistic urban knowledge strategy should be. When he was asked how he arrived at his creative discoveries and innovations, he said: ‘Chance favours only the prepared mind’ (cited in Florida, 2002). Similarly, the English writer and free spirit William Shakespeare was right when he asserted ‘What is the city but the people?’ (Shakespeare, cited in Halliday, 1964). Both statements suggest what ultimately might make up a creative city: a fertile ground for happenstance and human creativity. If policy makers take this notion into account, we think, cities indeed may have a large role to play in the European knowledge economy. Then creative cities are not just hype, but structural drivers for the economic future of our continent.
References Anholt, S. (2007) Competitive Identity: The New Brand Management of Nations, Cities and Regions. Palgrave, New York. Bulthuis, J. and Padmos, J. (1999) Regionale Beeldvorming (in Dutch). Meulenhoff, Amsterdam. Buttimer, A. (1983) Creativity and Context. University of Lund, Lund. Castells, M. and Hall, P. (1994) Technopoles of the World: The Making of Twenty-First Industrial Complexes. Routledge, London. Cooke, P. (2002) Knowledge Economies: Clusters, Learning and Cooperative Advantage. Routledge, London. Cooke, P. and Morgan, K. (1998) The Associational Economy: Firms, Regions and Innovation. Oxford University Press, Oxford. Delamaide, D. (1994) The New Superregions of Europe. Dutton, New York. Desrochers, P. (2001) Local Diversity, Human Creativity, and Technological Innovation. Growth and Change, 32, 369–94. Desrochers, P. and Hospers, G.-J. (2007) Cities and the Economic Development of Nations: An Essay on Jane Jacobs’ Contribution to Economic Theory. Canadian Journal of Regional Science, 30, 1–15. Di Cicco, P. (2007) Municipal Mind: Manifestos for the Creative City. Mansfield Press, Toronto. Dicken, P. (2003) Global Shift: Reshaping the Global Economic Map in the 21st Centrury, 4th edn. Chapman, London. Drucker, P. (1999) Management Challenges for the 21st Century. Harper Business Books, New York. Florida, R. (2002) The Rise of the Creative Class: And How it’s Transforming Work, Leisure, Community and Everyday Life. Basic Books, New York. Florida, R. (2005) Cities and the Creative Class. Routledge, New York. Florida, R. (2008) Who’s Your City? How the Creative Economy is Making Where to Live the Most Important Decision of Your Life. Basic Books, New York.
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Francis, M. (ed.) (1985) The Viennese Enlightenment. Croom Helm, London. Gold, J. and Ward, S. (1994) Place Promotion: The Use of Publicity and Marketing to Sell Towns and Regions. John Wiley, Chichester. Hall, P. (1998) Cities in Civilization. Phoenix, London. Halliday, F. (1964) A Shakespeare Companion 1564– 1964. Penguin, Baltimore. Hemel, Z. (2002) Creative Cities! Vereniging Deltametropool, The Hague. Hospers, G.-J. (2004) Restructuring Europe’s Rustbelt: The Case of the German Ruhrgebiet. Intereconomics: Review of European Economic Policy, 39, 147–56. Hospers, G.-J. (2006a) Silicon Somewhere? Assessing the Usefulness of Best Practices in Regional Policy. Policy Studies, 27, 1–15. Hospers, G.-J. (2006b) Borders, Bridges and Branding: The Transformation of the Øresund Region into an Imagined Space. European Planning Studies, 14, 1023–41. Jacobs, J. (1961) The Death and Life of Great American Cities. Random House, New York. Jacobs, J. (1969) The Economy of Cities. Random House, New York. Krugman, P. and Obstfeld, M. (2003) International Economics: Theory and Policy, 6th edn. Addison Wesley, Boston, MA. Landry, C. (2000) The Creative City: A Toolkit for Urban Innovators. Earthscan, London. Landry, C. (2006) The Art of City Making. Earthscan, London. Manchester City Council (2007) Manchester Edition. Manchester City Council, Manchester. Mitchell, W. (1995) City of Bits: Space, Place and the Infobahn. MIT Press, Cambridge, MA. O’Connor, J. (2006) Creative Cities: The Role of Creative Industries in Regeneration. Power Point Presentation, University of Leeds. Øresundsbro Konsortiet (2007) Facts Worth Knowing about the Øresund. Øresundsbro Konsortiet, Copenhagen. Peck, J. (2005) Struggling with the Creative Class. International Journal of Urban and Regional Research, 29, 740–70.
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Peck, J. and Ward, K. (2002) City of Revolution: Restructuring Manchester. Manchester University Press, Manchester. Pred, A. (1967) Behaviour and Location: Foundations for a Geographic and Dynamic Location Theory: Part 1. University of Lund, Lund. Revilla Diez, J., Fisher, M., Snickars, F. and Varga, A. (2001) Metropolitan Systems of Innovation: Theory and Evidence from Three Metropolitan Regions in Europe. Springer, Berlin. Rutten, R. (2003) Knowledge and Innovation in Regional Industry: An Entrepreneurial Coalition. Routledge, London. Saxenian, A.-L. (1994) Regional Advantage: Culture and Competition in Silicon Valley and Route 128. Harvard University Press, Cambridge, MA. Schumpeter, J. (1912) Die Theorie der Wirtschaftlichen Entwicklung (in German). Duncker & Humblot, Leipzig. Schumpeter, J. (1943) Capitalism, Socialism and Democracy. Allen & Unwin, London. Simmie, J. (ed.) (2001) Innovative Cities. Spon Press, London. Van Oort, F. (2003) Urban Growth and Innovation: Analysis of Spatially Bounded Externalities in the Netherlands. Ashgate, Aldershot. Zimmermann, C. (2000) Die Zeit der Metropolen: Urbanisierung und Grossstadtentwicklung. Fischer, Frankfurt am Main.
Dr Gert-Jan Hospers is a lecturer in economic geography at the University of Twente and associate researcher at Nicis Institute (The Netherlands). Dr Cees-Jan Pen graduated in economic geography and acts as a senior policy advisor and researcher at Nicis Institute (The Netherlands).
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How Should a Small Company Interact in Its Business Network to Sustain Its Exchange Effectiveness? Ariane von Raesfeld and Kaspar Roos In this paper we followed the dynamic alignment between networking approaches and business development of a small firm in the printing industry, with the aim of investigating how a small firm embedded in its network can deal with networking paradoxes when developing its business, such that it efficiently maintains its existing business and keeps its flexibility to develop new business. The empirical contribution of this paper lies in the use of a longitudinal case study that enables us to show the intermediary functions of counterparts along the path of a small firm’s business and network development. Three mediating functions of counterparts are introduced: the relating, joining and insulating functions. In the study we saw that joining functions of intermediaries were especially used in opportunity creation, while insulating functions of intermediaries were especially used in opportunity exploitation. The case study indicates an evolution into an efficient sales network with an important partner who is not willing to develop new business with Atlas. An explanation for the unwillingness to see the focal firm as a development partner might be found in its network position, reflected in the moderate technological and business integration of their product in customer applications, the weak contact of focal firm with end users, and the insulating function of distributors and original equipment manufacturers, who isolated the focal firm from end users. Managerial implications are drawn and will be investigated in further research.
Introduction
I
n essence, business development refers to the entrepreneurial process of discovering, creating and exploiting opportunities (Shane & Venkataraman, 2000; Groen, 2005). As such, the business development process parallels the business market management process of understanding, creating and delivering value (Anderson & Narus, 2004). To be effective in business development, companies face a twofold challenge. On the one hand, companies have to run their existing business by exploiting opportunities and delivering value. On the other hand, they need to develop new business by creating new opportunities and value. Several scholars have referred to this as the challenge of balancing exploration and exploitation (March, 1991; Benner & Tushman, 2003; Lee et al., 2003). Where exploitation requires a company to focus on efficiently delivering value to its existing customer, exploration requires it to © 2008 The Authors Journal compilation © 2008 Blackwell Publishing
remain flexible in developing new business solutions. Business development is a process that builds on the efforts of many (Anderson & Narus, 2004; Groen, 2005). The success and failure of companies depends to a large extent on their business relationships, which are connected in business networks (Håkansson, 1989; Håkansson & Waluszewski, 2002; Gadde et al., 2003; Hoang & Antoncic, 2003). Companies face a wide range of opportunities which they often cannot realize on their own. By means of outsourcing relationships and alliances, but also by more informal forms of co-operation, companies, though, are able to expand both their explorative and exploitative actions (Koza & Lewin, 1998; Soh & Roberts, 2003; Faems et al., 2005; Vlaar et al., 2007). Yet, network benefits come with their costs as well (Rothaermel & Deeds, 2004). In particular, the setting up and maintenance of strong relationships can require tremendous effort and time (Granovetter, 1973, 1982). Based on this duality
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of networks, Håkansson and Ford (2002) have formulated three network paradoxes. When we consider the business development process in the light of these three paradoxes, we can get some notion of the complexity that companies face in their everyday operation: • ‘Strong relationships are the heart of a company’s survival and of its growth and development. But a well-developed network of relationships also ties a company into its current ways of operating and restricts its abilities to change’ (p. 250). • ‘A company’s relationships are the outcomes of its strategy and its actions. But the paradox is that the company is itself the outcome of those relationships and of what has happened in them. Thus a network is both a way to influence and to be influenced’ (p. 252). • ‘Companies try to control the network that surrounds them and to manage their relationships to achieve their own aims . . . the paradox is that the more a company achieves this ambition of control, the less effective and innovative will be the network’ (p. 254). While large companies also face these paradoxes, they apply to small companies much more strongly for two reasons (cf. Rothwell & Dodgson, 1994; Vossen, 1998). First, networks are more important to small companies than to large companies. Small companies are more dependent on others in their network because they have fewer resources themselves. Second, small firms are less able to affect others exactly because they are small and typically have less power than large companies. Hence, small firms both face a higher dependency on their network and a lower ability to control that network. The success of small firms in their network is not entirely manageable nor is it entirely determined by luck and external factors. Management can make a difference, as Van de Ven et al. (1989) argued, by ‘managing the odds’, fostering the chances of success in times of change. Therefore, the guiding question for this study is: Facing the three paradoxes, how can a small company develop and utilize its network relations along the path of business development, such that it efficiently maintains its existing business and keeps its flexibility to develop new business? To answer this question, we will analyse the networking approaches of a small software company operating in the printing industry. Over the years company sales have grown exponentially. This growth was achieved through different intermediary sales partners. However, the company was less successful in exploring new opportunities with partners. In
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the case study we looked at the different networking approaches the company used to develop their business. Our examination of the business development of this firm shows that marketing and networking approaches drive the degree to which the firm is able to run its existing business in combination with developing new business.
Theoretical Background The Challenge of Managing Network Relations Especially when one views relationships as interconnected, they are not necessarily positive to change. This is reflected in the industrial marketing and purchasing perspective on networks. By relying on social exchange theory, this perspective defines a business network as a set of two ore more connected business relationships. ‘Connected’ in this sense means the extent to which ‘exchange in one relation is contingent upon exchange (or non-exchange) in the other relations. The connection is positive if exchange in one is contingent upon exchange in the other. The connection is negative if exchange in one is contingent upon non-exchange in the other’ (Cook & Emerson, 1978, p. 725). Viewing networks in this way shows that interconnection generates positive and negative effects. Anderson et al. (1994) further elaborated on network effect by distinguishing between primary functions as the positive and negative effect on two partners in a dyad and secondary network functions as the indirect positive and negative effects of a relationship due to its connection to other relationships. Positive secondary effects refer to the extent to which resources, activities and actor relations in a relationship between two firms can be transferred to other relationships. When, however, the transfer of these three elements to other relationships is hindered, due to lock-ins in the focal relationship, the authors refer to it as a ‘negative effect’. Following this line of argument, Håkansson and Ford (2002) highlighted the three network paradoxes that were mentioned in the introduction. Based on these three paradoxes, Gadde et al. (2003) drew implications for strategizing. According to these authors, even when one looks at strategizing from a single firm point of view, the heterogeneity of resources and interdependencies between activities across company boundaries, as well as co-operation among firms involved, must be considered simultaneously. A consequence of this interconnectedness is that the firm’s freedom of action is restricted. On the other © 2008 The Authors Journal compilation © 2008 Blackwell Publishing
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hand, interconnection makes it possible to mobilize relationships in change processes (Gadde et al., 2003, p. 362).
Possible Networking Strategies In this paper we lay out the network strategies of a small firm in the printing industry in detail, with the aim of understanding how a small firm embedded in its network can deal with networking paradoxes when developing its business. Gadde et al. (2003) provide two general strategies, a reactive one and a proactive one. They suggest that, on the one hand, firms who are really locked in in their relationships can develop their business if they build on what is proposed or implemented by the counterparts. Firms in such situations act reactively to what is initiated by their relations. On the other hand, depending on the nature of its relationships, a firm can activate counterparts in the development, which is a more pro-active approach. The occupation of an informationrich position in the network is seen as an opportunity to do so. Suggestions about what an information-rich position might imply are given by Holmen and Pedersen (2003). They state that through the mediating functions of counterparts, a firm can get a broader view of the network. Three mediating functions are introduced: a joining function enabling direct co-ordination; a relating function enabling co-ordination between the focal firm and a third party via the counterpart; and an insulating function enabling co-ordination between the focal firm and a third party via the counterpart, while the focal firm and the third party have no knowledge of each other. In fact, two ideal types of intermediary functions can be distinguished, one in which integration between parties is co-ordinated and the other where separation between parties is maintained (see also Obstfeld, 2005). The joining and relating functions refer to integration of parties, where the degree of integration is stronger in the case of joining. The match-maker who facilitates the exchange of goods by relating or joining a buyer with a seller is an example of an integrating intermediary. The insulating function refers to separation of parties by a counterpart, such as the market-maker who sells and buys goods and where sellers and buyers do not need to know each other. Holmen and Pedersen claim that in order to support a firm’s strategizing, managers need to analyse and influence counterparts’ mediating functions. However, no suggestions are given about what this might imply for business development. Obstfeld’s identification of the integrating intermediary function as a pre© 2008 The Authors Journal compilation © 2008 Blackwell Publishing
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dictor of innovation provides further explanations for business development in a network context. Following the line of the debate about the benefits for innovation of open versus closed networks (Ahuja, 2000), Obstfeld (2005) indicates that open networks with many structural holes can generate new ideas but create a co-ordination problem, while closed networks with dense ties provide more opportunities for co-ordination but might hinder the generation of new ideas. Based on this observation, Obstfeld holds that integrating intermediaries are especially involved in innovation. A similar argument is made from an economic perspective on intermediaries. Yavas (1992) found that if demand variance is low, products are homogeneous and search is relatively efficient and inexpensive, market-making is preferable to match-making. In contrast, if demand variance is high, products are heterogeneous and search is relatively inefficient and costly, match-making is preferable to market-making. The literature discussed implies that a firm who aims to be both efficient and flexible in its network, needs to have strong ties and at the same time have a broad view of its network. A broad view can be established through a variety in actor bonds, activity links and resource ties. But small firms are not in the position to connect to a large number of actors, activities and resources and thus really need the intermediary functions of partners. Therefore, in this paper we try to identify how a small firm uses intermediary functions of counterparts in different phases of business development. The study distinguishes three phases of business development: (1) opportunity discovery, (2) opportunity creation and (3) opportunity exploitation, which are characterized by the degree of both demand and supply uncertainty (see Table 1). Further, we expect that each phase will require a preferred type of intermediary function. Combining the arguments of Holmen and Pedersen, Obstfeld and Yavas, we expect that firms who are in the phase of opportunity discovery are looking for new business ideas through a variety of weak ties. We think that a firm can establish weak ties through relating intermediary functions of counterparts. Firms who are in the phase of opportunity creation have to share resources and develop activities with different others to shape offerings which can solve customer problems. As this phase requires close co-operation between different actors, we expect that joining intermediaries will facilitate such close co-operation. In the phase of opportunity exploitation in which an offering has to be delivered to its customers, efficiency is generated via loose
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Table 1. Characteristics of the Three Phases of Business Development and the Expected Intermediary Function Needed
Demand uncertainty Supply uncertainty Intermediary function needed
Opportunity discovery
Opportunity creation
Opportunity exploitation
High High Relating
Medium Medium Joining
Low Low Insulating
co-operation. This, however, only works if the shape and technology of the offering is clear, and transformation and transaction activities are transparent. If this transparency exists, we expect that insulating intermediaries will facilitate such loose co-operation. The approach of Holmen and Pedersen (2003), which suggests firms use intermediary functions to gain an information-rich position, is considered here as a way to solve relationship constraints. Therefore, by describing the intermediary functions of counterparts along the path of business development, we try to analyse the extent to which these functions provide the small firm the efficiency to run its existing business and flexibility to develop business in new directions.
Research Approach The analysis draws on a case study (Yin, 1989) of Atlas Software B.V., a small software development comany in the printing industry. Atlas Software B.V. is followed from 1992 up to the takeover by Objectif Lune in 2004. By 2004 Atlas had 30 employees and produced software that optimizes printing workflows. Its main product was PrintShop Mail, a software package that is used for variable data printing (VDP). This product is still sold by Objectif Lune.
Data Collection Data on the relationships of Atlas were mainly collected in a retrospective way. For triangulation purposes we applied multiple data collection techniques, including interviews, desk research, participation in seminars and workshops and attending tradeshows. As suggested by Miles and Huberman (1994), we also cross-examined our findings with the people involved when the data or the results were ambiguous. In the first stage, we studied literature on the printing industry (Interquest, 2001; Rose
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et al., 2002; Matthyssens et al., 2004; European Commission, 2005) and relation-specific documents such as contracts, financial overviews and websites. In the second stage, we conducted several semi-structured interviews with Harry Raaphorst, managing director of Atlas. In these interviews the director was asked how the business had developed, which companies have been relevant for the development and who had linked them to each other. In addition to the managing director, we also interviewed other employees, partners and industry experts. Based on the interviews, we were able to visualize the network development of the company (see Figure 1) and to characterize the business development.
Research Context: the Digital Printing Industry Technological Developments During the 1970s and 1980s laser printer technology developed and the first form of digital printing became available. The type of printing then was mainly black and white transactional printing, and laser printers were directly attached to mainframe computers. In the 1980s, the rise of personal computers led to a need for a standardized printing language, and PostScript turned out to be the de facto industry standard, at least for promotional printing. Around 1990, Xerox launched its revolutionary DocuTech 135, a machine that contained the properties of a copier, but that could also be connected to a computer network so that multiple users were able to share the same printer. Since then, desktop publishing took off, and virtually everyone could become a publisher; something that had always been the preserve of printing professionals. As a result, commercial printing faced a decline in demand. Looking for opportunities for growth, commercial printers realized they needed to search for value-added services and productivity gains by means of new technologies, faster make-ready and network © 2008 The Authors Journal compilation © 2008 Blackwell Publishing
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Legend • • •
OEMs Distributors RIP vendors
Figure 1. Development of Atlas’ Actor Network digital workflows. However, digital (promotional) printing was initially quite expensive. VDP and printing on demand (PoD) were needed as a means to add value to paper and in that way to form a justification for the high printing costs. For short print runs, on-demand jobs, or personalized jobs, commercial printers choose to print digital, because digital is more cost-effective. Moreover, on offset presses personalization is not feasible and short and on-demand runs have too long turnaround times.
Central Actors A variety of companies are doing business in the digital printing network. To start with © 2008 The Authors Journal compilation © 2008 Blackwell Publishing
there are the large original equipment manufacturers (OEMs) who produce digital printers, such as Xerox, Oce, Canon, HP and Xeikon. OEMs co-operate in technological platforms to develop technology in which information and communications technology (ICT) plays a central role. OEMs change their focus from a product orientation to a demand orientation, combined with a development to full-service contract related to document management. A second group is the regional distributors who sell OEM printers. Their margins are under pressure because of the forward integration of OEMs. In the search for added value they change to value added resellers (VARs). VARs sell OEM printers under their own brand and provide additional
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services such as consulting and installation. Danka, Ricoh, Nashuatec are examples of VARs. A third group is formed by the printing companies, who face high price competition, consolidation and increasing production scale. New players with an ICT background enter the domain. In order to avoid price competition, printing firms seek new solutions for customer problems, such as offering digital solutions; becoming a logistic partner or focusing on database and document management. The firm investigated is supplying software and services for different actors in the digital printing network. When, in 1992, Atlas developed PrintShop Mail, it was to solve the problems that printing house De Klomp had with VDP. As we will outline in more detail, later on OEMs were interested in PrintShop Mail as they argued that adding production software to their hardware was needed to increase printing volume. In the following we lay out the business and network development of Atlas in more detail.
Analysis and Results Business and Network Development of Atlas Over the years Atlas’s business developed from providing customized software towards supplying specialized software for digital printing manufacturers and printshops. In alignment with this business development, Atlas’s network developed in a worldwide sales network. The following section describes these developments. Figure 1 visualizes the
development of Atlas’s network. Table 2 summarizes the intermediary functions at different phases of business development. The stage of opportunity discovery started in 1991 when Drukkerij de Klomp, a Dutch printing firm, asked Atlas to solve their problems with the Xerox printer. Their newly bought Xerox printer was slow due to network congestion. De Klomp lacked IT experience to solve the problem and Atlas had software experience to develop a solution. As, at that time, Atlas made customized software for a diversity of customers, it operated in a heterogeneous network of weak ties. Harry Raaphorst, managing director of Atlas, expected that there would be more printing companies with the same problem, and saw an opportunity for new business development. Raaphorst decided to focus his business on this particular problem. In this initial stage, in contrast to what we expected, no intermediary functions of counterparts were used. Based on De Klomp’s specifications and requirements, Atlas developed the product PrintShop Mail and established a joint venture with De Klomp to develop a customer base for this new product. So the new business idea generated from a close customer–supplier interaction with no intermediary involved. In the stage of opportunity creation, Drukkerij de Klomp joined Atlas to Xerox Netherlands, who was willing to bundle PrintShop Mail with newly sold printers, Xerox expected an increase in print volume when customers used such a VDP program. In this manner Xerox related Atlas’s product to customers of Xerox printers. As Xerox was responsible for
Table 2. Focal Relationships and Intermediary Functions along Atlas’ Path of Business Development Opportunity discovery
Opportunity exploitation
Time line
1991–1992
1992–2000
2000–2005
Direct counterpart
Drukkerij de Klomp
Drukkerij de Klomp till 1995 Xerox RIP vendors Other OEMs
Xerox Distributors Other OEMs
De Klomp joins Xerox to Atlas Xerox relates Atlas to its customers/ end-users Rip vendors join Atlas to other OEMs
Xerox, the other OEMs and the distributors insulate Atlas from their end-users/customers
Intermediary function of the direct counterpart
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the sales of the product, a strong relationship with Xerox developed. This relationship was focused on one product and one activity: the selling of PrinShop Mail. However, Atlas was also working with RIP (raster imaging processing) vendors, which are companies that develop technologies that convert the output instructions from programs such as PrintShop Mail into a bitmap for every document that has to be printed. Each RIP vendor has its own converting technique called the printing technology. Through the co-operation with different RIP vendors, Printshop Mail became an interface for various printing technologies and could be applied to both the Macintosh and the Windows platforms. These technological developments and the reputation of Xerox, their most important partner, created opportunities to bundle PrintShop Mail with the printing machines of other OEMs as well. In this way, the RIP vendors and Xerox provided joining functions connecting Atlas to several other OEMs. So basically, Atlas was involved in two types of activities: organizing the sales with the OEMs and developing the technological aspects of their product with the RIP vendors. In 1995 Drukkerij de Klomp was bought out; they realized that software development was not their competence. In the stage of opportunity exploitation, the OEMs transferred PrintShop Mail to large clients, which provided access to extensive marketing infrastructures and possibilities to test software; however, local users could not be reached with these partners. Therefore, Atlas further developed a worldwide distribution network, which transferred PrintShop Mail to local users all over the world. Atlas established strong relationships with OEMs and distributors for the purpose of selling its product, leading to a high similarity of partners and a focus on sales. In fact, OEMs and distributors fulfilled only an insulating intermediary function. Furthermore, due to the increasing amount of users served via many OEMs and distributors, Atlas became isolated from end users. An additional problem was that Xerox, Atlas’s most important partner, did not seem to be willing to relate or join Atlas in new business development. For example, in 2004 with the rise of the Internet, Atlas developed a web version of PrintShop Mail. Xerox, who developed their own workflow systems used in web applications, did not support the web development. It is probably for this reason that Atlas’s resulting web application was much less successful than the original PrintShop Mail. The examination of intermediary functions and the visualization of Atlas’s network development in Figure 1 indicate a development in © 2008 The Authors Journal compilation © 2008 Blackwell Publishing
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which the amount of relationships with OEMs and distributors increased. The strong relationship with Drukkerij de Klomp disappeared over time, thus losing a close relationship with a lead user. Being disconnected from users eliminates opportunities to learn about problems for which Atlas might develop new solutions. Also, one sees that over time the ratio between cross links and direct links decreased. The network began to look like a portfolio of direct relationships. The description of Atlas’s business development indicates an evolution into an efficient sales network with an important partner who is not willing to develop new business with Atlas. A further analysis of Atlas’s use of intermediary functions offers a view of the network mechanisms of business development. Atlas developed an efficient network, which generated sales volume through a growth of new partnerships by which sales increased exponential. For Atlas, close co-operation with Xerox had positive reputational effects; due to its relationship with Xerox, other OEMs and distributors easily started supplying PrintShop Mail. Despite many technological opportunities, Xerox was not willing to co-develop new solutions and share knowledge about new development. An explanation for Xerox’s unwillingness to see Atlas as a development partner might be found in Atlas’s network position, reflected in the moderate technological and business integration of PrintShop Mail in customer applications, the weak contact of Atlas with end users, and the insulating function of distributors and OEMs. Due to this network position, Atlas had not been able to provide Xerox with interesting knowledge about adding value for customers and end users. Table 2 indicates the direct counterparts, and the intermediary functions they fulfil in different phases of Atlas’s business development. A look at Table 2 shows that in the case study, relating intermediaries are not observed in the opportunity discovery phase; in fact the new idea developed from a strong customer relationship and there was no third party who brought Atlas and Drukkerij de Klomp together. In the phase of opportunity creation, Drukkerij de Klomp and the RIP vendors fulfilled a joining function for Atlas by connecting them to Xerox and other OEMs. Xerox fulfilled a relating function by connecting Atlas to its customers. In the opportunity exploitation phase, Atlas used insulating functions of distributors and OEMs to reach the final users (see Table 2). In this way Atlas could develop its existing business in an efficient way, but became iso-
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lated from the problems of end users and thus from new opportunity discovery. The analysis suggests that different networking approaches facilitate and hinder business development. For successful business development, both strong and varied ties as well as the existence of different intermediary functions of partners are necessary.
Conclusions In this paper we followed the dynamic alignment between networking approaches and business development of a small firm in the printing industry, with the aim of investigating how a small firm embedded in its network can deal with networking paradoxes when developing its business, such that it efficiently maintains its existing business and keeps its flexibility to develop new business. The empirical contribution of this paper lies in the use of a longitudinal case study that enables us to show the intermediary functions of counterparts along the path of a small firm’s business and network development. Freeman (1991) proposed such a research approach as the most appropriate strategy for investigating networks aimed at new product development. Little empirical confirmation exists for a theory of network development (De Man & Duysters, 2005), although recently Dittrich et al. (2007) conducted two longitudinal studies investigating the role of strategic alliance networks in the process of strategic repositioning. However, these were studies on network development of two large firms (IBM and Nokia), while we have focused on a small firm’s network development. In our theoretical introduction we suggested that especially small firms have to use intermediary functions of counterparts to deal with network paradoxes. The literature discussed implies that a firm can keep its exchange effectiveness through strong ties and a broad view of its network. A large firm can establish a broad view through a variety in actor bonds, activity links and resource ties, whereas a small firm is not in a position to connect to a large number of actors, activities and resources, and thus really needs the intermediary functions of partners. A lot is written in the economics literature about intermediary roles (for an overview see Rose, 1999) and in social network analysis on the benefits of intermediary positions (Burt, 1992). The point of view in these studies is basically from the intermediary itself, while we have looked at how others can benefit from the intermediary. So far, with the exception of Holmen and Pedersen (2003) and Obstfeld (2005), little
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research has been conducted on how a focal firm can use intermediaries. Atlas has successfully developed its business from a supplier of customized software to a provider of specialized software for digital printing manufacturers. Over the years sales grew exponentially, and its existing business was efficiently maintained. However, as it turned out, it was less successful in maintaining its flexibility to develop new business. The case study illustrates that the networking approach, that is the use of intermediary functions and the resulting network paradoxes, can facilitate and hinder business development. In the study we saw that joining functions of intermediaries were especially used in opportunity creation, while insulating functions of intermediaries were especially used in opportunity exploitation. These findings are in line with previous research on the importance of closed networks in innovation. However, seen from a more dynamic perspective, this approach seemed to generate an efficient business development but hindered the flexibility to develop new business. Further, in contrast to what we expected, in the case study no relating intermediary functions were observed in the phase of opportunity discovery. Instead opportunities were discovered in a direct customer–supplier relationship. An explanation for this observation is that Atlas did not need relating functions as, at that time, they served a broad range of different customers, and thus already had a broad view of their network. On the other hand, this finding could also support the assumption of Håkansson and Waluszewski (2002) that most change comes from within relationships. Along the path of business development, Atlas was, in particular, confronted with the first and second network paradox. The first network paradox became apparent in the relationship with Xerox, which formed the basis of Atlas’s operations and development, but also tied them to their current way of operations and restricted their ability to change. As Atlas in its relationship with Xerox moved towards selling PrintShop Mail and moved away from developing custom-made software, options to develop new technology disappeared. Atlas also encountered the second network paradox. Atlas chose to develop relationships with Xerox to reach users for PrintShop Mail and relationships with the RIP vendors to link up their product with the technological standards in use. As a consequence, both Atlas and its partners regarded Atlas as a provider of a standard component used in the machines of the printing manufacturers. Further, the characteristics of their relationships with the OEMs and the fact that they had very little direct © 2008 The Authors Journal compilation © 2008 Blackwell Publishing
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contact with printing firms and other users meant that they were unable to spot, or respond to changes in the in the broader network. In reference to the third network paradox, Atlas really did not try to control the network; moreover, a small firm such as Atlas is not in a position to do so. In the study we saw how intermediary functions were used along the path of business development. We could see network paradoxes arising. However, we could not observe how intermediary functions were used to solve network paradoxes, though from the case study we draw some managerial implications, which could be investigated in further research. By using the insulating functions of its counterparts (e.g., OEMs and distributors) Atlas developed a high level of network efficiency. And by using the relating and joining functions of their counterparts (e.g., RIP vendors) network flexibility was created. But over time they lost flexibility. Atlas could probably have avoided this rigidity if it had used its counterparts’ joining and relating functions, in particular those of the OEMs to get in closer contact with end users. This would have broadened the relationship pattern with new actors and deepened the relationship with the existing counterpart. Then if joint with an important end user, it makes sense to develop added value in the offering provided, through either technological integration or integration into the customer’s business process. This might decrease flexibility but increases the interdependence between Atlas and the end user and might help to be seen as an important development partner. It seems relevant to further research these managerial implications in a comparative case study. Preferably we would like to compare the development process of Atlas with that of another small firm who has been able to maintain its flexibility along the path of business development. Then we could investigate whether the other firm is more successful because they make better use of intermediary functions to broaden their network and at the same time deepen their network by providing added value to end users. From the case study we concluded that awareness and understanding of intermediary functions helps a firm to position itself within its network in such a way that it can develop its business in new directions. Similar dynamics might be discerned when investigating resource interfaces (Araujo et al., 1999; Dubois and Araujo, 2005). As technological development is often driving new business development, further research into the role of resource interfaces seems relevant. © 2008 The Authors Journal compilation © 2008 Blackwell Publishing
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References Ahuja, G. (2000) Collaborating Networks, Structural Holes, and Innovation: A Longitudinal Study. Administrative Quarterly, 45, 425–55. Anderson, J.C. and Narus, J.A. (2004) Business Market Management: Understanding, Creating and Delivering Value, 2nd edn. Uppr Saddle River, NJ: Pearson Prentice Hall. Anderson, J.C., Håkansson, H. and Johanson, J. (1994) Dyadic Business Relationships within a Business Network Context. Journal of Marketing, 58, 1–15. Araujo, L., Dubois, A. and Gadde, L.E. (1999) Managing Interfaces with Suppliers. Industrial Marketing Management, 28, 497–506. Benner, M.J. and Tushman, M.L. (2003) Exploitation, Exploration, and Process Management: The Productivity Dilemma Revisited. Academy of Management Review, 28, 238–56. Burt, R.S. (1992) Structural Holes: The Social Structure of Competition. Harvard University Press, Cambridge, MA. Cook, K.S. and Emerson, R.M. (1978) Power, Equity, Commitment in Exchange Networks. American Sociological Review, 43, 721–38. De Man, A. and Duysters, D. (2005) Collabortion and Innovation: A Review of the Effects of Mergers, Acquisitions and Alliances on Innovation. Technovation, 25, 1377–87. Dittrich, K., Duysters, G. and De Man, A. (2007) Strategic Repositioning by Means of Alliance Networks: The Case of IBM. Research Policy, 36, 1496–511. Dubois, A. and Araujo, L. (2005) The Relationship between Technical and Organizational Interfaces in Product Development. The IMP Journal, 1, 28–51. European Commission (2005) ICT and Electronic Business in the Publishing & Printing Industry. Sector Report No 03-11. Faems, D., Van Looy, B. and Debackere, K. (2005) Interorganizational Collaboration and Innovation: Toward a Portfolio Approach. Journal of Product Innovation Management, 22, 238–50. Freeman, C. (1991) Networks of Innovators: A Synthesis of Research Issues. Research Policy, 20, 499–514. Gadde, L.E., Huemer, L. and Håkansson, H. (2003) Strategizing in Industrial Networks. Industrial Marketing Management, 32, 357–64. Granovetter, M.S. (1973) The Strength of Weak Ties. American Journal of Sociology, 78, 1360–80. Granovetter, M.S. (1982) The Strength of Weak Ties: A Network Theory Revisited. In Marsden, P.V. and Lin, N. (eds.), Social Structure and Network Analysis. Sage, Beverly Hills, CA, pp. 105–30. Groen, A.J. (2005) Knowledge Intensive Entrepreneurship in Networks: Towards a Multilevel/ Multidimensional Approach. Journal of Enterprising Culture, 13, 69–88. Håkansson, H. (1989) Corporate Technological Behaviour: Co-operation in Networks. Routledge, London. Håkansson, H. and Ford, D. (2002) How Should Companies Interact in Business Networks? In Ford, D. (ed.), Understanding Business Marketing and Purchasing. Thomson Learning, London.
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Van de Ven, A.H., Angle, H.I. and Poole, M.S. (1989) Research on the Management of Innovation: The Minnesota Studies. Harper & Row, New York. Venkataraman, S. (1997) The Distinctive Domain of Entrepreneurship Research. In: Katz, J. (ed.), Advances in Entrepreneurship: Firm Emergence and Growth. JAI Press, Greenwich, CT. Vlaar, P.W.L., Van Den Bosch, F.A.J. and Volberda, H.W. (2007) Towards a Dialectic Perspective on Formalization in Interorganizational Relationships: How Alliance Managers Capitalize on the Duality Inherent in Contacts, Rules and Procedures. Organization Studies, 28, 437–66. Vossen, R.W. (1998) Relative Strengths and Weaknesses of Small Firms in Innovation. International Small Business Journal, 16, 88–94. Yavas, A. (1992) Marketmaker vs Matchmaker. Journal of Financial Intermediation, 2, 33–58. Yin, R.K. (1989) Case Study Research – Design and Methods. Sage, London.
Ariane von Raesfeld (a.m.
[email protected]) currently works as assistant professor at the School of Management and Governance at the University of Twente, The Netherlands. Her research topics studied include social and industrial networks, technological development and organizational cognitions. She received a PhD in Management Studies from the University of Twente, an MBA from the Erasmus University, and a degree in Biochemistry from the University of Amsterdam. Kaspar Roos is a Senior Consultant for InfoTrends’ European On Demand Printing & Publishing and Production Workflow & Custom Communications Solutions Services. Mr Roos holds a Master of Science degree in Industrial Engineering and Management from Twente University, the Netherlands, with a specialization in Business Marketing/Corporate Strategy and Information Systems Management. He is also author and co-author of several scientific publications, all related to business development in dynamic environments.
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Universities in Evolutionary Systems of Innovation Marianne van der Steen and Jurgen Enders This paper criticizes the current narrow view on the role of universities in knowledge-based economies. We propose to extend the current policy framework of universities in national innovation systems (NIS) to a more dynamic one, based on evolutionary economic principles. The main reason is that this dynamic view fits better with the practice of innovation processes. We contribute on ontological and methodological levels to the literature and policy discussions on the effectiveness of university-industry knowledge transfer and the third mission of universities. We conclude with a discussion of the policy implications for the main stakeholders.
1. Introduction
U
niversities have always played a major role in the economic and cultural development of countries. However, their role and expected contribution has changed substantially over the years. Whereas, since 1945, universities in Europe were expected to contribute to ‘basic’ research, which could be freely used by society, in recent decades they are expected to contribute more substantially and directly to the competitiveness of firms and societies (Jaffe, 2008). Examples are the Bayh–Dole Act (1982) in the United States and in Europe the Lisbon Agenda (2000–2010) which marked an era of a changing and more substantial role for universities. However, it seems that this ‘new’ role of universities is a sort of universal given one (ex post), instead of an ex ante changing one in a dynamic institutional environment. Many universities are expected nowadays to stimulate a limited number of knowledge transfer activities such as university spin-offs and university patenting and licensing to demonstrate that they are actively engaged in knowledge transfer. It is questioned in the literature if this onesize-fits-all approach improves the usefulness and the applicability of university knowledge in industry and society as a whole (e.g., Litan et al., 2007). Moreover, the various national or regional economic systems have idiosyncratic characteristics that in principle pose different (changing) demands towards universities. Instead of assuming that there is only one ‘optimal’ gov-
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ernance mode for universities, there may be multiple ways of organizing the role of universities in innovation processes. In addition, we assume that this can change over time. Recently, more attention in the literature has focused on diversity across technologies (e.g., King, 2004; Malerba, 2005; Dosi et al., 2006; Van der Steen et al., 2008) and diversity of formal and informal knowledge interactions between universities and industry (e.g., Cohen et al., 1998). So far, there has been less attention paid to the dynamics of the changing role of universities in economic systems: how do the roles of universities vary over time and why? Therefore, this article focuses on the ontological premises of the functioning of universities in innovation systems from a dynamic, evolutionary perspective. In order to do so, we analyse the role of universities from the perspective of an evolutionary system of innovation to understand the embeddedness of universities in a dynamic (national) system of science and innovation. The article is structured as follows. In Section 2 we describe the changing role of universities from the static perspective of a national innovation system (NIS), whereas Section 3 analyses the dynamic perspective of universities based on evolutionary principles. Based on this evolutionary perspective, Section 4 introduces the characteristics of a Learning University in a dynamic innovation system, summarizing an alternative perception to the static view of universities in dynamic economic systems in Section 5. Finally, the concluding
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section discusses policy recommendations for more effective policy instruments from our dynamic perspective.
2. Static View of Universities in NIS 2.1 The Emergence of the Role of Universities in NIS First we start with a discussion of the literature and policy reports on national innovation system (NIS). The literature on national innovation systems (NIS) is a relatively new and rapidly growing field of research and widely used by policy-makers worldwide (Fagerberg, 2003; Balzat & Hanusch, 2004; Sharif, 2006). The NIS approach was initiated in the late 1980s by Freeman (1987), Dosi et al. (1988) and Lundvall (1992) and followed by Nelson (1993), Edquist (1997), and many others. Balzat and Hanusch (2004, p. 196) describe a NIS as ‘a historically grown subsystem of the national economy in which various organizations and institutions interact with and influence one another in the carrying out of innovative activity’. It is about a systemic approach to innovation, in which the interaction between technology, institutions and organizations is central. With the introduction of the notion of a national innovation system, universities were formally on the agenda of many innovation policymakers worldwide. Clearly, the NIS demonstrated that universities and their interactions with industry matter for innovation processes in economic systems. Indeed, since a decade most governments acknowledge that interactions between university and industry add to better utilization of scientific knowledge and herewith increase the innovation performance of nations. One of the central notions of the innovation system approach is that universities play an important role in the development of commercial useful knowledge (Edquist, 1997; Sharif, 2006). This contrasts with the linear model innovation that dominated the thinking of science and industry policy makers during the last century. The linear innovation model perceives innovation as an industry activity that ‘only’ utilizes fundamental scientific knowledge of universities as an input factor for their innovative activities. The emergence of the non-linear approach led to a renewed vision on the role – and expectations – of universities in society. Some authors have referred to a new social contract between science and society (e.g., Neave, 2000). The Triple Helix (e.g., Etzkowitz & Leydesdorff, 1997) and the innovation
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system approach (e.g., Lundvall, 1988) and more recently, the model of Open Innovation (Chesbrough, 2003) demonstrated that innovation in a knowledge-based economy is an interactive process involving many different innovation actors that interact in a system of overlapping organizational fields (science, technology, government) with many interfaces.
2.2 Static Policy View of Universities in NIS Since the late 1990s, the new role of universities in NIS thinking emerged in a growing number of policy studies (e.g., OECD, 1999, 2002; European Commission, 2000). The contributions of the NIS literature had a large impact on policy makers’ perception of the role of universities in the national innovation performance (e.g., European Commission, 2006). The NIS approach gradually replaced linear thinking about innovation by a more holistic system perspective on innovations, focusing on the interdependencies among the various agents, organizations and institutions. NIS thinking led to a structurally different view of how governments can stimulate the innovation performance of a country. The OECD report of the national innovation system (OECD, 1999) clearly incorporated these new economic principles of innovation system theory. This report emphasized this new role and interfaces of universities in knowledgebased economies. This created a new policy rationale and new awareness for technology transfer policy in many countries. The NIS report (1999) was followed by more attention for the diversity of technology transfer mechanisms employed in university-industry relations (OECD, 2002) and the (need for new) emerging governance structures for the ‘third mission’ of universities in society, i.e., patenting, licensing and spin-offs, of public research organizations (OECD, 2003). The various policy studies have in common that they try to describe and compare the most important institutions, organizations, activities and interactions of public and private actors that take part in or influence the innovation performance of a country. Figure 1 provides an illustration. The figure demonstrates the major building blocks of a NIS in a practical policy setting. It includes firms, universities and other public research organizations (PROs) involved in (higher) education and training, science and technology. These organizations embody the science and technology capabilities and knowledge fund of a country. The interaction is represented by the arrows which refer to interactive learning and diffusion of knowledge (Lundvall, © 2008 The Authors Journal compilation © 2008 Blackwell Publishing
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Figure 1. The Benchmark NIS Model Source: Bemer et al. (2001). 1992).1 The building block ‘Demand’ refers to the level and quality of demand that can be a pull factor for firms to innovate. Finally, institutions are represented in the building blocks ‘Framework conditions’ and ‘Infrastructure’, including various laws, policies and regulations related to science, technology and entrepreneurship. It includes a very broad array of policy issues from intellectual property rights laws to fiscal instruments that stimulate labour mobility between universities and firms. The figure demonstrates that, in order to improve the innovation performance of a country, the NIS as a whole should be conducive for innovative activities in a
1
These organizations that interact with each other sometimes co-operate and sometimes compete with each other. For instance, firms sometimes co-operate in certain pre-competitive research projects but can be competitors as well. This is often the case as well with universities.
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country. Since the late 1990s, the conceptual framework as represented in Figure 1 serves as a dominant design for many comparative studies of national innovation systems (Polt et al., 2001; OECD, 2002). The typical policy benchmark exercise is to compare a number of innovation indicators related to the role of university-industry interactions. Effective performance of universities in the NIS is judged on a number of standardized indicators such as the number of spin-offs, patents and licensing. Policy has especially focused on ‘getting the incentives right’ to create a generic, good innovative enhancing context for firms. Moreover, policy has also influenced the use of specific ‘formal’ transfer mechanisms, such as university patents and university spin-offs, to facilitate this collaboration. In this way best practice policies are identified and policy recommendations are derived: the so-called one-size-fits-allapproach. The focus is on determining the ingredients of an efficient benchmark NIS, downplaying institutional diversity and
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variety in the roles of universities in enhancing innovation performance. The theoretical contributions to the NIS literature have outlined the importance of institutions and institutional change. However, a further theoretical development of the elements of NIS is necessary in order to be useful for policy makers; they need better systemic NIS benchmarks, taking systematically into account the variety of ‘national idiosyncrasies’. Edquist (1997) argues that most NIS contributions are more focused on firms and technology, sometimes reducing the analysis of the (national) institutions to a left-over category (Geels, 2005). Following Hodgson (2000), Nelson (2002), Malerba (2005) and Groenewegen and Van der Steen (2006), more attention should be paid to the institutional idiosyncrasies of the various systems and their evolution over time. This creates variety and evolving demands towards universities over time where the functioning of universities and their interactions with the other part of the NIS do evolve as well. We suggest to conceptualize the dynamics of innovation systems from an evolutionary perspective in order to develop a more subtle and dynamic vision on the role of universities in innovation systems. We emphasize our focus on ‘evolutionary systems’ instead of national innovation systems because for many universities, in particular some science-based disciplinary fields such as biotechnology and nanotechnology, the national institutional environment is less relevant than the institutional and technical characteristics of the technological regimes, which is in fact a ‘subsystem’ of the national innovation system.
3. Evolutionary Systems of Innovation as an Alternative Concept 3.1 Evolutionary Theory on Economic Change and Innovation Charles Darwin’s The Origin of Species (1859) is the foundation of modern thinking about change and evolution (Luria et al., 1981, pp. 584–7; Gould, 1987). Darwin’s theory of natural selection has had the most important consequences for our perception of change. His view of evolution refers to a continuous and gradual adaptation of species to changes in the environment. The idea of ‘survival of the fittest’ means that the most adaptive organisms in a population will survive. This occurs through a process of ‘natural selection’ in which the most adaptive ‘species’ (organisms) will survive. This is a gradual process
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taking place in a relatively stable environment, working slowly over long periods of time necessary for the distinctive characteristics of species to show their superiority in the ‘survival contest’. Based on Darwin, evolutionary biology identifies three levels of aggregation. These three levels are the unit of variation, unit of selection and unit of evolution. The unit of variation concerns the entity which contains the genetic information and which mutates following specific rules, namely the genes. Genes contain the hereditary information which is preserved in the DNA. This does not alter significantly throughout the reproductive lifetime of an organism. Genes are passed on from an organism to its successors. The gene pool, i.e., the total stock of genetic structures of a species, only changes in the reproduction process as individuals die and are born. Particular genes contribute to distinctive characteristics and behaviour of species which are more or less conducive to survival. The gene pool constitutes the mechanism to transmit the characteristics of surviving organisms from one generation to the next. The unit of selection is the expression of those genes in the entities which live and die as individual specimens, namely (individual) organisms. These organisms, in their turn, are subjected to a process of natural selection in the environment. ‘Fit’ organisms endowed with a relatively ‘successful’ gene pool, are more likely to pass them on to their progeny. As genes contain information to form and program the organisms, it can be expected that in a stable environment genes aiding survival will tend to become more prominent in succeeding generations. ‘Natural selection’, thus, is a gradual process selecting the ‘fittest’ organisms. Finally, there is the unit of evolution, or that which changes over time as the gene pool changes, namely populations. Natural selection produces changes at the level of the population by ‘trimming’ the set of genetic structures in a population. We would like to point out two central principles of Darwinian evolution. First, its profound indeterminacy since the process of development, for instance the development of DNA, is dominated by time at which highly improbable events happen (Boulding, 1991, p. 12). Secondly, the process of natural selection eliminates poorly adapted variants in a compulsory manner, since individuals who are ‘unfit’ are supposed to have no way of escaping the consequences of selection.2 2
We acknowledge that within evolutionary thinking, the theory of Jean Baptiste Lamarck, which acknowledges in essence that acquired characteristics can be transmitted (instead of hereditary © 2008 The Authors Journal compilation © 2008 Blackwell Publishing
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These three levels of aggregation express the differences between ‘what is changing’ (genes), ‘what is being selected’ (organisms), and ‘what changes over time’ (populations) in an evolutionary process (Luria et al., 1981, p. 625). According to Nelson (see for instance Nelson, 1995): ‘Technical change is clearly an evolutionary process; the innovation generator keeps on producing entities superior to those earlier in existence, and adjustment forces work slowly’. Technological change and innovation processes are thus ‘evolutionary’ because of its characteristics of non-optimality and of an open-ended and path-dependent process. Nelson and Winter (1982) introduced the idea of technical change as an evolutionary process in capitalist economies. Routines in firms function as the relatively durable ‘genes’. Economic competition leads to the selection of certain ‘successful’ routines and these can be transferred to other firms by imitation, through buy-outs, training, labour mobility, and so on. Innovation processes involving interactions between universities and industry are central in the NIS approach. Therefore, it seems logical that evolutionary theory would be useful to grasp the role of universities in innovation processes within the NIS framework.
3.2 Evolutionary Underpinnings of Innovation Systems Based on the central evolutionary notions as discussed above, we discuss in this section how the existing NIS approaches have already incorporated notions in their NIS frameworks. Moreover, we investigate to what extent these notions can be better incorporated in an evolutionary innovation system to improve our understanding of universities in dynamic innovation processes. We focus on non-optimality, novelty, the anti-reductionist methodology, gradualism and the evolutionary metaphor. Non-optimality (and Bounded Rationality) Based on institutional diversity, the notion of optimality is absent in most NIS approaches. characteristics as in the theory of Darwin), is acknowledged to fit better with socio-economic processes of technical change and innovation (e.g., Nelson & Winter, 1982; Hodgson, 2000). Therefore, our theory is based on Lamarckian evolutionary theory. However, for the purpose of this article, we will not discuss the differences between these theories at greater length and limit our analysis to the fundamental evolutionary building blocks that are present in both theories. © 2008 The Authors Journal compilation © 2008 Blackwell Publishing
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We cannot define an optimal system of innovation because evolutionary learning processes are important in such systems and thus are subject to continuous change. The system never achieves an equilibrium since the evolutionary processes are open-ended and path dependent. In Nelson’s work (e.g., 1993, 1995) he has emphasized the presence of contingent outcomes of innovation processes and thus of NIS: ‘At any time, there are feasible entities not present in the prevailing system that have a chance of being introduced’. This continuing existence of feasible alternative developments means that the system never reaches a state of equilibrium or finality. The process always remains dynamic and never reaches an optimum. Nelson argues further that diversity exists because technical change is an openended multi-path process where no best solution to a technical problem can be identified ex post. As a consequence technical change can be seen as a very wasteful process in capitalist economies with many duplications and dead-ends. Institutional variety is closely linked to nonoptimality. In other words, we cannot define the optimal innovation system because the evolutionary learning processes that take place in a particular system make it subject to continuous change. Therefore, comparisons between an existing system and an ideal system are not possible. Hence, in the absence of any notion of optimality, a method of comparing existing systems is necessary. According to Edquist (1997), comparisons between systems were more explicit and systematic than they had been using the NIS approaches.
Novelty: Innovations Central Novelty is already a central notion in the current NIS approaches. Learning is interpreted in a broad way. Technological innovations are defined as combining existing knowledge in new ways or producing new knowledge (generation), and transforming this into economically significant products and processes (absorption). Learning is the most important process behind technological innovations. Learning can be formal in the form of education and searching through research and development. However, in many cases, innovations are the consequence of several kinds of learning processes involving many different kinds of economic agents. According to Lundvall (1992, p. 9): ‘those activities involve learning-by-doing, increasing the efficiency of production operations, learning-
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by-using, increasing the efficiency of the use of complex systems, and learning-by-interacting, involving users and producers in an interaction resulting in product innovations’. In this sense, learning is part of daily routines and activities in an economy. In his Learning Economy concept, Lundvall makes learning more explicit, emphasizing further that ‘knowledge is assumed as the most fundamental resource and learning the most important process’ (1992, p. 10).
Anti-reductionist Approach: Systems and Subsystems of Innovation So far, NIS approaches are not yet clear and systematic in their analysis of the dynamics and change in innovation systems. Lundvall’s (1992) distinction between subsystem and system level based on the work of Boulding implicitly incorporates both the actor (who can undertake innovative activities) as well as the structure (institutional selection environment) in innovation processes of a nation. Moreover, most NIS approaches acknowledge that within the national system, there are different institutional subsystems (e.g., sectors, regions) that all influence each other again in processes of change. However, an explicit analysis of the structured environment is still missing (Edquist, 1997). In accordance with the basic principles of evolutionary theory as discussed in Section 3.1, institutional evolutionary theory has developed a very explicit systemic methodology to investigate the continuous interaction of actors and institutional structures in the evolution of economic systems. The so-called ‘methodological interactionism’ can be perceived as a methodology that combines a structural perspective and an actor approach to understand processes of economic evolution. Whereas the structural perspective emphasizes the existence of independent institutional layers and processes which determine individual actions, the actor approach emphasizes the free will of individuals. The latter has been referred to as methodological individualism, as we have seen in neoclassical approaches. Methodological individualism will explain phenomena in terms of the rational individual (showing fixed preferences and having one rational response to any fully specified decision problem (Hodgson, 2000)). The interactionist approach recognizes a level of analysis above the individual or firm level. NIS approaches recognize that national differences exist in terms of national institutions, socio-economic factors, industries and networks, and so on.
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So, an explicit methodological interactionist approach, explicitly recognizing various institutional layers in the system and subsystem in interaction with the learning agents, can improve our understanding of the evolution of innovation. Gradualism: Learning Processes and Path-Dependency Path-dependency in biology can be translated in an economic context in the form of (sometimes very large) time lags between a technical invention, its transformation into an economic innovation, and the widespread diffusion. Clearly, in many of the empirical case studies of NIS, the historical dimension has been stressed. For instance, in the study of Denmark and Sweden, it has been shown that the natural resource base (for Denmark fertile land, and for Sweden minerals) and economic history, from the period of the Industrial Revolution onwards, has strongly influenced present specialization patterns (Edquist & Lundvall, 1993, pp. 269–82). Hence, history matters in processes of innovation as the innovation processes are influenced by many institutions and economic agents. In addition, they are often pathdependent as small events are reinforced and become crucially important through processes of positive feedback, in line with evolutionary processes as discussed in Section 3.1. Evolutionary Metaphor Finally, most NIS approaches do not explicitly use the biological metaphor. Nevertheless, many of the approaches are based on innovation theories in which they do use an explicit evolutionary metaphor (e.g., the work of Nelson). To summarize, the current (policy) NIS approaches have already implicitly incorporated some evolutionary notions such as nonoptimality, novelty and gradualism. However, what is missing is a more explicit analysis of the different institutional levels of the economic system and innovation subsystems (their inertia and evolution) and how they change over time in interaction with the various learning activities of economic agents. These economic agents reside at established firms, start-up firms, universities, governments, undertaking learning and innovation activities or strategic actions. The explicit use of the biological metaphor and an explicit use of the methodological interactionst approach may increase our understanding of the evolution of innovation systems. © 2008 The Authors Journal compilation © 2008 Blackwell Publishing
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4. Towards a Dynamic View of Universities 4.1 The Logic of an Endogenous ‘Learning’ University If we translate the methodological interactionist approach to the changing role of universities in an evolutionary innovation system, it follows that universities not only respond to changes of the institutional environment (government policies, business demands or changes in scientific paradigms) but universities also influence the institutions of the selection environment by their strategic, scientific and entrepreneurial actions. Moreover, these actions influence – and are influenced by – the actions of other economic agents as well. So, instead of a one-way rational response by universities to changes (as in reductionist approach), they are intertwined in those processes of change. So, universities actually function as an endogenous source of change in the evolution of the innovation system. This is (on an ontological level) a fundamental different view on the role of universities in innovation systems from the existing policy NIS frameworks. In earlier empirical research, we observed that universities already effectively function endogenously in evolutionary innovation system frameworks; universities as actors (already) develop new knowledge, innovate and have their own internal capacity to change, adapt and influence the institutional development of the economic system (e.g., Van der Steen et al., 2009). Moreover, universities consist of a network of various actors, i.e., the scientists, administrators at technology transfer offices (TTO) as well as the university boards, interacting in various ways with industry and governments and embedded in various ways in the regional, national or international environment. So, universities behave in an at least partly endogenous manner because they depend in complex and often unpredictable ways on the decision making of a substantial number of non-collusive agents. Agents at universities react in continuous interaction with the learning activities of firms and governments and other universities. Furthermore, the endogenous processes of technical and institutional learning of universities are entangled in the co-evolution of institutional and technical change of the evolutionary innovation system at large. We propose to treat the learning of universities as an inseparable endogenous variable in the innovation processes of the economic system. In order to structure the endogenization in the system of innovation analysis, the concept of © 2008 The Authors Journal compilation © 2008 Blackwell Publishing
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the Learning University is introduced. In the next subsection we discuss the main characteristics of the Learning University and Section 5 discusses the learning university in a dynamic, evolutionary innovation system. An evolutionary metaphor may be helpful to make the university factor more transparent in the co-evolution of technical and institutional change, as we try to understand how various economic agents interact in learning processes.
4.2 Characteristics of the Learning University The evolution of the involvement of universities in innovation processes is a learning process, because (we assume that) university public agents have their ‘own agenda’.Various incentives in the environment of universities such as government regulations and technology transfer policies as well as the innovative behaviour of economic agents, compel policy makers at universities to constantly respond by adapting and improving their strategies and policies, whereas the university scientists are partly steered by these strategies and partly influenced by their own scientific peers and partly by their historically grown interactions with industry. During this process, university boards try to be forward-looking and to behave strategically in the knowledge that their actions ‘influence the world’ (also referred to earlier as ‘intentional variety’; see, for instance, Dosi et al., 1988). ‘Intentional variety’ presupposes that technical and institutional development of universities is a learning process. University agents undertake purposeful action for change, they learn from experience and anticipate future states of the selective environment. Furthermore, university agents take initiatives to improve and develop learning paths. An example of these learning agents is provided in Box 1. We consider technological and institutional development of universities as a process that involves many knowledge-seeking activities where public and private agents’ perceptions and actions are translated into practice.3 The institutional changes are the result of interactions among economic agents defined by Lundvall (1992) as interactive learning. These interactions result in an evolutionary pattern 3
Using a theory developed in one scientific discipline as a metaphor in a different discipline may result, in a worst-case scenario, in misleading analogies. In the best case, however, it can be a source of creativity. As Hodgson (2000) pointed out, the evolutionary metaphor is useful for understanding processes of technical and institutional change, that can help to identify new events, characteristics and phenomena.
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of technology and institutional adjustments of universities. Therefore, we perceive technical and institutional development of universities as an evolutionary pattern of change consisting of four evolutionary principles: (1) the generation of novelty that leads to diversity (mutations); (2) the retention and transmission of characteristics over time (interactive learning and technical and policy trajectories) within the innovating population; (3) social selection (social embeddedness in the institutional environment); and (4) the assumption of non-optimization of the evolution. These principles are now discussed in more detail. First, the principle that generation of novelty may lead to diversity. This functions as a mechanism that creates diversity. Randomness is an aspect, but the process may also produce predictable novelties, such as purposeoriented development work. In this study, novelty will be interpreted in terms of technological and institutional mutations. Second, the principle of transmission and retention of knowledge. This mechanism is closely related to learning processes and the underlying behavioural assumptions of policy makers and scientists at universities. The literature often emphasizes ‘cumulativeness’ of knowledge in learning, e.g., in the form of technological learning trajectories. Moreover, we include the behavioural assumptions of the ‘adaptive learning university (see, for instance, Dosi et al., 1988). In our analysis this term refers to the bounded rational members of university boards, TTO officials as well as scientist at universities who have the intention to improve their respective policies, strategies and scientific work while building on their accumulated experience, knowledge, or knowledge network. At the same time these actors interact together within the setting of the university system as a whole and interactively learn along the way. The third characteristic is that of selection among the entities present in the system. In the literature, selection mechanisms include both market and non-market selection. Selection processes tend to reduce diversity. The selection process has been explicitly defined as social, i.e., the creation of a social environment. In the literature this process has also been called the ‘social embeddedness of technical change’. We define the selection environment as the institutional framework of the economic system for the university policy makers as well as the scientific and industrial system for scientists at universities. Finally, we assume that the outcome of processes of policy and scientific adjustments do not automatically lead to improvement. Instead, these are a dynamic process undergo-
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Box 1. Endogenous Learning Agents at Universities In earlier research we have demonstrated how academic Life Sciences spin-off companies (Van der Steen et al., 2009), i.e., a new firm based on innovative technological knowledge developed at a university, develop along an evolutionary process. The project starts with a scientific research team, consisting of academic scientists mainly interested in scientific findings and publication who learn, adapt and eventually develop towards a businessoriented business firm with a market orientation. Our analysis shows that the learning of academic scientists in the spin-off teams was never a linear process with well-defined phases of development. For instance, the business model and company focus were often strongly modified because competing products can enter the market or the denial of the patent application resulted in the decision to position the product in the cosmetic market, instead of the drugs market, which required the expensive FDA approval track. This illustrates that spin-off success is difficult to define. The learning of the scientist teams, whether successful or not in starting a Life Sciences spin-off company, was an iterative and dynamic process, reacting to all sorts of events that occurred along the way and incentives in the institutional environment such as the spin-off programmes of the TTO, national governments and industry demands.
ing continuous change, never reaching an optimum. To sum up, we acknowledge three evolutionary characteristics of learning universities. First, universities produce institutional and scientific mutations based on purposeful and unintentional learning processes of the scientists and other agents at universities. A mutation will be defined as an institutional or scientific adaptation. The adaptation can be either responsive or innovative. Secondly, diversity is a central characteristic of the Learning University. Diversity refers to the dynamics of different learning trajectories, differential learning behaviour and asymmetries of knowledge and information. University agents adapt and learn in continuous © 2008 The Authors Journal compilation © 2008 Blackwell Publishing
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interaction with their environment, placing interactive learning of the university actors at the centre of analysis. It follows that the characteristic of diversity puts the complex interactions of knowledge seeking activities of private and public agents at the core. Diversity is closely related to intentional variation (Dosi et al., 1988; Hodgson, 2000). We propose to attribute (the property of) intentional learning behaviour to university agents. The background for this is that the ‘evolutionary’ agent learns from his experience and will anticipate future states of the selective environment. This is an extension of the microfoundation of interactive learning processes. In the next section we introduce the Learning University in the broader evolutionary framework of an evolutionary innovation system.
5. The Learning University in an Evolutionary Innovation System According to the evolutionary principles discussed above, selection is a central component of the Learning University in an evolutionary innovation system. The selection mechanism is closely related to the institutional set-up of the university as a whole as well as the scientific and economic system, since it represents the (selective) environment in which public and private agents interact, learn and innovate. Institutions explain to a great extent why and how innovations occur. Most universities worldwide are governed by national regulation, higher education, science and innovation policy instruments and funding. Universities function in a multiple selection environment. It consists of the business environment (demands of firms and industries towards universities or scientists), scientific environment (peer review competition) as well as the regulatory institutional environment of the nation or a region (requirements and demands of governments towards universities such as third mission, excellence, etc.). These selection environments are not stable but instead change over time and sometimes at a rather quick pace. We will give some examples of the newly developed selection environments of business (Box 2) and governments (Box 3).
6. Conclusions The main objective of this paper is to develop a dynamic, evolutionary perspective of the role of universities in dynamic innovation systems. We have focused our analysis on the ontological principles of a Learning University that adapts to a continuously changing institu© 2008 The Authors Journal compilation © 2008 Blackwell Publishing
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Box 2. Evolving Business Selection Environment Open Innovation and New Business Demands Placed on Universities The demands placed on universities by the business community have changed over the last few decades. This is related to internationalization and globalization of business R&D and the changed nature of innovation processes from a linear towards open models of innovation. Traditionally, the industry innovation process required ‘only’ to utilize fundamental scientific knowledge of universities as an input factor for their innovative activities. This perception is associated with closed innovation or Mode 1 type of innovation (Gibbons, Limoges and Nowothy, 1994). In this old-fashioned type of innovation, knowledge production is organized along the traditional basic versus applied knowledge division, where the universities are responsible for the former and business for the latter. Basic scientific knowledge is produced by universities in relative isolation from society, it is organized along the disciplinary structures and the quality guaranteed by peer reviews. This means that universities are involved with the production of basic knowledge and disseminate their knowledge using traditional academic transfer channels such as publications in journals and at conferences. Firms can use this scientific knowledge and conduct additional applied research, which can eventually lead to innovations in those firms. In the open innovation model (Chesbrough, 2003), the firm R&D organization is run in a more open way. Firms try to use the best technology worldwide to stay competitive in an open R&D environment. This requires that firms find a new mixture of internal and (absorption of) external innovations. Inherently, this also forces firms to find a new balance of internal and external communications, amongst others with universities in all parts of the world (depending on their scientific excellence and the expertise of universities in a certain field). Firms’ demands on universities become more diverse and universities play different roles: the application can play a role in the production of scientific knowledge,
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multidisciplinary research and heterogeneity of motivations of scientists exists, and finally social and commercial accountability of scientists instead of the traditional peer-reviewed quality assurance. Here, university-industry networks matter, and a large array of all sorts of relations and knowledge exchanges between scientists and industry depend on contextual variables such as the type of technology (science-based versus developmentbased technologies). So, clearly, these trends of internationalization and restructuring of industrial R&D and firm innovation processes put new demands on university research. However, there are differences in degree, forms and speed of change between different sectors and thus different firm demands of universities and even different demands placed on different faculties at universities. So, the selection environment ofuniversities is changing.
tional, scientific and technical environment of an innovation system. On a methodological level, we have emphasized the interactionist methodology in which universities learn and change because of purposeful intention and learning activities and, on the other hand, respond to external changes in the institutional selection environment of an innovation system. We also demonstrated that various agents purposefully learn, act and interact within the university: policy makers, administrative agents (such as the TTOs) and scientists. Their learning paths, their interests and their selection environments differ and thus provide different selection environments and different incentives to agents within the same university.
Policy Implications The theoretical findings can have a number of policy implications. The acknowledgement of these dynamic evolutionary principles provides a profoundly (on an ontological level) different perspective on the role of universities in innovation systems in four ways. First, differences – and stratification processes – between universities are natural and should be nurtured because they serve a function; they are the result of adaptation and learning processes of universities to the (local) innovation system. So the (teaching, scientific and entrepreneurial) focus of universities can
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Box 3. Evolving Policy Selection Environment Evolution of Regulatory Environments: The Lisbon Process In 2000, European leaders agreed in Lisbon to boost the European Union’s competitiveness and growth. This target, which concerned reforms at several levels, has definitely been a turning point in the development of research, higher education and innovation policies. Since the Lisbon agenda of 2000, universities are key players in the debate about policy measures to meet the target by 2010 of turning the European economy into the most dynamic knowledge-based economy in the world (European Commission, 2000). In this context, higher education institutions are required to interact intensively with industry to overcome the ‘European paradox’ (assuming that knowledge interactions between universities and business are weak in Europe compared to the US; see, for instance, Dosi et al., 2006). To overcome the so-called European paradox, European governments are rapidly changing their regulatory environments, focusing more on the commercialization of university knowledge, in particular in the form of university patenting and university spin-offs and formal research collaboration with industry. Some authors have referred to a new social contract between science and society (Neave, 2000). The new policy regime constitutes a new selection environment of universities with new pressure for structural adaptations of universities. The newly evolving regulatory environment pressures universities to give a stronger focus on the usefulness of scientific knowledge for industry and society (Van der Steen & Vieira, 2008). However, at the same time the scientific selection environment of peer-reviewed publications is still in place and has evolved towards stronger pressures on the scientific excellence of university research based on reputational rewards. In earlier research we have demonstrated that in some academic disciplines these are contradictory pressures.
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differ and should not be forced into one optimal benchmark model. Some universities will develop into excellent scientific institutions that attract firm investments, often focused in a certain technological field, for instance biotechnology, nanotechnology or engineering. Other universities may serve a more regional function of teaching and/or local entrepreneurship. Bottom-up learning processes that often respond better to local needs are important and policy makers should avoid enforcing a top-down view on the ideal university. Second, there can be profound differences across the disciplines within one university. Different firms (in terms of sector, region, size) have different demands of universities to enhance their innovation processes. Local university administrators should be aware of these differences and should not be overly focused on a few university-industry knowledge interaction channels such as spin-offs or university patenting and licensing. So far, the attention in many technology transfer programmes of universities is often limited to a few transfer activities such as university spin-offs and patenting and licensing. It follows from our paper that there are multiple ways in which universities can be involved in firm innovation processes and their role can be different across sectors and across regions and countries. University and government policies should take into account this diversity in order to be effective. Third, governments should be aware that their policy incentives have an impact on the strategic behaviour of universities. There is a risk that scientists at the level of the research unit, who can respond quite effectively and flexibly to firm demands, are forced to change their behaviour because of enforced strategies of university administration as a reaction to changed national or federal polices. These university policies can have counterproductive effects. Learning policy makers with an open vision are a prerequisite in an evolutionary (open) innovation system because their behaviour is also endogenously intertwined with the learning and innovation activities of firms and universities. Finally, it is important that governments acknowledge that universities consist of many actors that purposefully learn and adapt to changes in the institutional environment. We have demonstrated that these multiple selection environments can differ quite profoundly. Governments should realize that this is at the cost of effectiveness of their outcomes on a specific domain, for instance policy instruments to stimulate collaboration with industry. These incentives have to ‘compete’ with pres© 2008 The Authors Journal compilation © 2008 Blackwell Publishing
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sures from the other selection environments such as peer pressure to publish in academic mono-disciplinary journals.
References Balzat, M. and Hanusch, H. (2004) Recent Trends in the Research on National Innovation Systems. Journal of Evolutionary Economics, 14, 197–210. Bemer, R., Gilsing, V. and Roelandt, T. (2001) Grondslagen voor de Vernieuwing van het Innovatie-beleid. In Gradus, R.H.J.M., Kremers, J.J.M. and van Sinderen, J. (eds.), Nederland Kennisland? Stenfert Kroese, Groningen. Boulding, K. (1991) What is Evolutionary Economics? Journal of Evolutionary Economics, 1, 9–17. Chesbrough, H. (2003) Open Innovation. The New Imperative for Creating and Profiting from Technology. Harvard Business School Press, Boston, MA. Cohen, W., Florida, R., Randazzese, L. and Walsh, J. (1998) Industry and the Academy: Uneasy Partners in the Cause of Technological Advance. In Noll, R. (ed.), Challenges to Research Universities. Brookings Institution Press, Washington DC. Dosi, G., Freeman, C., Nelson, R., Silverberg, G. and Soete, L. (eds.) (1988) Technical Change and Economic Theory. Pinter Publishers, London. Dosi, G., Llerena, P. and Labini, M. (2006) The Relationships between Science, Technologies and their Industrial Exploitation: An Illustration through the Myths and Realities of the So-called ‘European Paradox’. Research Policy, 35, 1450–64. Edquist, C. (ed.) (1997) Systems of Innovation – Technologies, Institutions, and Organizations. Pinter Publishers, London. Edquist, C. and Lundvall, B.-A. (1993) Comparing the Danish and Swedish Systems of Innovation. In Nelson, R. (ed.), National Innovation Systems – A Comparative Analysis. Oxford University Press, New York. Etzkowitz, H. and Leydesdorff, L. (1997) Universities in a Global Economy: A Triple Helix of AcademicIndustry and Government Relations. Croom-Helm, London. European Commission (2000) Toward a European Research Area. COM (2000) 6, Brussels. European Commission (2006) Delivering on the Modernisation Agenda for Universities: Education, Research and Innovation. COM (2006) 208, Brussels. Fagerberg, J. (2003) Schumpeter and the Revival of Evolutionary Economics: An Appraisal of the Literature, Journal of Evolutionary Economics, 13, 125–59. Freeman, C. (1987) Technology Policy and Economic Performance: Lessons from Japan. Pinter Publishers, London. Geels, F.W. (2005) The Dynamics of Transitions in Socio-Technical Systems. Research Policy, 31, 1257–74. Gibbons, M., Limoges, C. and Nowothy, H. (1994) The New Production of Knowledge. Sage, London. Gould, S. (1987) The Panda’s Thumb of Technology. Natural History, 1, 14–23. Groenewegen, J. and van der Steen, M. (2006) The Evolution of National Systems of Innovation. Journal of Economic Issues, XL, 277–85.
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Hodgson, G. (2000) What is the Essence of Institutional Economics? Journal of Economic Issues, 34, 317–29. Jaffe (2008) The Science of Science Policy: Reflections on the Important Questions and Challenges They Present. Journal of Technology Transfer, 33, 131–9. King, D.A. (2004) The Scientific Impact of Nations. Nature, 430, 311–16. Litan, R., Mitchell, L. and Reedy, E. (2007) Commercializing University Innovations: Alternative Approaches, NBER Working Paper, May. Lundvall, B.-A. (1992) National Systems of Innovation: Towards a Theory of Innovation and Interactive Learning. Pinter Publishers, London. Luria, S., Gould, S. and Singer, S. (1981) A View of Life. Benjamin Cummings Publishing Co., Menlo Park, CA. Malerba, F. (2005) Sectoral Systems of Innovation: A Framework for Linking Innovation to the Knowledge Base, Structure, and Dynamics of Sectors. Economics of Innovation New Technology, 14, 63–82. Neave, G. (2000) The Social Responsibility of the University. Elsevier Science, Oxford. Nelson, R. (1993) National Innovation Systems – A Comparative Analysis. Oxford University Press, New York. Nelson, R. (1995) Recent Evolutionary Theorizing about Economic Change. Journal of Economic Literature, XXXIII, 48–90. Nelson, R. (2002) Bringing Institutions into Evolutionary Growth Theory. Journal of Evolutionary Economics, 12, 17–28. Nelson, R. and Winter, S. (1982) An Evolutionary Theory of Economic Change. Belknap Press, London. OECD (1999) Managing National Innovation Systems. OECD, Paris. OECD (2002) Benchmarking Industry-Science Relations. OECD, Paris. OECD (2003) Turning Science into Business: Patenting and Licensing at Public Research Organisations. OECD, Paris. Polt, W., Rammer, C., Gassler, H., Schibany, A. and Schartinger, D. (2001) Benchmarking Industry Science Relations: The Role of Framework Conditions. Science and Public Policy, 28, 247–58. Sharif, N. (2006) Emergence and Development of the National Innovation Systems Concept. Research Policy, 35, 745–66. Van der Steen, M. and Vieira, M. (2008) The Emergence of European Technology Transfer Policy: Co-evolution of Theories, Rationales and Policy Governance, Working paper, May. Van der Steen, M., Bekkers, R., Bodas Freitas, I. and Gilsing, V. (2008) Beyond the Demand-Side Perspective of Technology Transfer Policies: An Empirical Analysis of the Netherlands, DIME conference paper presented at BETA, Louis Pasteur, Strasbourg, April 2008. Van der Steen, M., Ortt, R. and Scholten, V. (2009) Exploring Success Determinants of Spin-off Creation: Empirical Evidence from the Life Sciences in the Netherlands, to be published in International Journal of Entrepreneurship and Small Business, December 2009 and presented at the IECER Conference, 5–7 March 2008.
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Marianne van der Steen (m.vandersteen@ utwent.nl) joined CHEPS as a senior research associate on a tenure track for Associate Professor of Universities and Innovation in 2007. In 2005 she received a research grant from the Netherlands Organisation for Scientific Research (NWO) to conduct research on the diversity of knowledge transfer across universityindustry knowledge networks. She was also awarded the US Vistor’s Fellowship and conducted research on academic entrepreneurship and technology transfer of universities in the United States. From 1999 to 2004, Marianne van der Steen was a senior policy advisor at the Ministry of Economic Affairs in the Netherlands, responsible for national and EU innovation policy related to university-industry knowledge interaction. She received her PhD in innovation economics in 1999 and from 1995 until 1999 she was a lecturer in International Economics at Twente University. During that period she received her post-doctoral training at several renowned innovation institutes in Europe. She served on several advisory boards at the OECD, European Commission, as advisor to the EU Presidency Conference on Investing in Research and Innovation, The Dutch Innovation Platform and speaker at expert hearings of the European Parliament. Jürgen Enders joined CHEPS as Full Professor of Higher Education Policy Studies in 2002. He was previously Assistant Professor and Executive Director at the Centre for Research on Higher Education and Work at the University of Kassel, Germany, where he received his doctorate in political science in 1995 and specialized in the field of research on higher education. He has been Director of CHEPS since 2004. He also serves as a member of the management team of the spearhead research programme on ‘Institutional Change’ of the Institute for Governance Studies at the University of Twente. He served as a member of the board and Secretary of the Consortium of Higher Education Researchers (CHER) and is member of the editorial board of the book series ‘Higher Education Dynamics’ and the journal Higher Education. He is member of an Advisory Council for the Swiss Science and Technology Council and the Dutch Association of Universities. He serves as a reviewer to the Dutch and the German Research Council and as a reviewer to the German Excellence Initiative. He has published approximately 80 articles in scientific journals and multiauthored books, (co-)authored or (co-) edited eight books and has given about 80 keynote and paper presentations at conferences.
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Public Policy Systems Dealing with Ethically Contested Medical Technological Innovations Rob Hoppe The questions tackled in this paper are: How do we deal with ethically contested medical innovations?, and Can we do better? First, I analyse how we deal with these problems by a division of labour and competitive boundary work between the medical R&D system’s research and technological imperative, the medical profession’s claim to self-regulation and health policy-makers’ claim to political primacy and an incrementalist style of policy making. Second, turning to the normative question, I propose that policy-makers shift to a primacy of problems. Different types of problems demand different types of policy-making systems and styles. Thus, policy-makers could commence designing a health policy-making system robust enough to adequately deal with non-incremental but ethically contested medical innovations. I argue for medical innovation which also takes ethical, social and legal issues into account. This may be achieved by turning political competition through venue shopping into metagovernance through deliberate venue choice. This requires deliberative and participatory design elements in procedures and spaces for health technology assessment.
Medical Technological Developments, Health Policy Innovation and Politics: A Complex Affair
H
aving your own biological child, even though you are an infertile couple, or single parent? Bearing your child irrespective of age? No more premature deaths due to shortages in donor organs? Celebrating your 100th birthday in good health and excellent spirits? These are just some of the promises offered by innovations in medical science and technology. Biomedical technology, genetics, genomics, nano- and neuroscience have already led to and will continue to generate wide-ranging medical technological innovations. Because they are so tangible for most men and women as (potential) parents, this is especially true for technologies in human reproduction (Kirejczyk et al., 2001), pre-natal diagnostics and predictive medicine. Pre-implantation genetic diagnostics, therapeutic cloning (Swierstra, 2000) and preventive embryo selection are good examples. Journal compilation © 2008 Blackwell Publishing No claims to works in the public domain
Yet, is being informed about carrying a foetus infected by Down’s syndrome or neural tube defects (potentially leading to anencephaly or spina bifida) inherently desirable? Is being informed about your own genetic profile with its vulnerabilities to future (hereditary) diseases desirable or not? Is there a right not-toknow? How about the rights of relatives and loved ones to such knowledge? What is the use of such knowledge when no effective prevention and/or treatments are (yet) available? Are such ethical dilemmas to be solved on a personal basis; or is there a role for government regulation? The development from genetics to genomics may even affect the entire health care system. Genetics is about rare hereditary diseases, whereas genomics is about the genetic profile leading to widespread common diseases. Genetics-based innovations still fit within a treatment-based system in which the security and privacy of the doctor–patient relation are paramount. Yet, genomics-based innovations become cost-effective only in a preventionbased public health care system where political and social pressure for healthy lifestyles may easily threaten personal responsibility
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and individual patient empowerment (Van Rijswoud et al., 2008). How have we dealt with such ethically contested medical innovations so far? Can we do better? These are the questions tackled in this article on the admittedly limited basis of examples and illustrations from the Dutch health care system. First, I analyse how we reluctantly deal with these problems at present by a division of labour and competitive boundary work between the medical R&D system’s research and technological imperative, the medical profession’s claim to selfregulation and health policy-makers’ claim to political primacy and an incrementalist style of policy making. Second, turning to the normative question, I propose that policy-makers shift to a primacy of problems. Acknowledging that different types of problems demand different types of policy-making systems and styles, policy-makers could commence designing a health policy-making system robust enough to adequately deal with nonincremental but ethically contested medical innovation. I argue for more societally responsible medical innovation which, next to scientific and economic aspects, takes ethical, social and legal issues into account. This may be achieved by turning political competition through venue shopping into meta-governance through deliberate venue choice. This implies introducing deliberative and participatory design elements in procedures and spaces for health technology assessment.
Boundary Work and Policy Politics of Medical Technological Innovation One way of looking at political systems is as ‘federations’ of policy sectors, or issue domains. These are components of the political system organized around stable, substantive policy problems. Health policy is one of the oldest policy sectors in any political system. Usually, too, policy domains develop their own style of policy politics. This is the specific mode or style of policy making among the set of players – politicians in parliaments or as ministers in executive positions, policy advisers in- or outside bureaucracy, interest groups or stakeholders, implementing agencies in the public or private sector, non-governmental organizations, target groups, media personalities and commentators – involved on a continuous basis in processing a particular policy issue. Again, health policy is no exception. Its policy politics is a close but clumsy kind of ‘boundary work’ between players in the medical research and professional camp,
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and those directly engaged in health policy making. Boundary work is, like a living apart together relationship, simultaneously about keeping distance by demarcation of your own domain, and staying close enough to co-ordinate your activities (Jasanoff, 1990; Halffman, 2003). In the case of health care, the boundary involved is the institutional demarcation line between science and politics/administration; while co-ordination involves necessary transactions and resultant co-operative schemes between science-based experts and advisors and political executives and their policy-analytic staff. Currently, the professional medical players produce a constant stream of ethically sensitive and politically contestable and contested new medical technologies. The issue for the policy-making players – private, like health insurers, and public, like policy analysts in the Department of Health – is whether or not medical treatments using these technologies ought to be included in the set of medical treatments applied in clinics and other health services institutions; and whether or not such innovative treatments will be reimbursed to patients with health care cost insurance. For the policymakers, the problem is exacerbated because, at present, the entire health care system is in transition. It is envisaged to change from a need- and professional- or producer-driven and state-financed health care regime to a new system of ‘managed competition’, which will be more demand- or health-consumer or patient-driven and market-financed. In this system of boundary work, the medical research and professional camp is largely concerned about policy for medical science. This advocacy coalition is driven by the operational codes of the technologicalcum-research imperative and professional selfregulation. The technological ‘imperative’ is the tendency to believe that once technologies exist, they ought to be used and most of the time actually will be used. The research ‘imperative’ is the conviction that research either for its own sake, or as a means to achieve individual or social ends, such as the possible prevention or relief of suffering, is obligatory. Both ‘imperatives’ imply that medical research and technology cease to be psychologically, morally and politically optional (Callahan, 2003, p. 3).1 Although technically distinct from the research-cum-technology imperative, it is reinforced by the principle of self regulation of 1
On the same page Callahan quotes Joshua Lederberg, a Nobel prize winner for work in genetics, as a particularly strong advocate of the research imperative: ‘The blood of those who will die if biomedical research is not pursued, will be upon the hands of those who don’t do it’. Journal compilation © 2008 Blackwell Publishing No claims to works in the public domain
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an independent professional medical community at large. In the case of ethically contestable medical technological innovations, it means that ethical debate ought to be depoliticized by leaving it to professional peers. The public and private policy-makers are mainly concerned about medical science for (public) health policy, i.e., with priority setting between society’s politically articulated health needs and cost control. Policy-makers also have their own operational codes. First, by far the most important operational code of public policy-makers is the rule of the primacy of politics. Applied to medical technological innovation it may be expressed in the maxim: ‘Politics on top, medical experts on tap’. It is clear that the primacy of politics may conflict with the research imperative and the principle of professional self-regulation – unless there is prudent and clever boundary work on both sides. The trick here is ‘acquired’ regulation (Evett, 2002) in the form of disguised selfregulation by the profession itself, but ‘in the shadow’ of state hierarchy (Scharpf, 1993): ‘Regulation is an object of professional concern and professional bodies are working to develop their own regulatory systems in order to prevent intervention by the state. The result is a form of externally required but internally devised and operated regulation which might be termed acquired regulation’ (Evett, 2002). This acquired regulation usually is the result of consultation and bargaining between professional associations’ leaders and high-level state officials. The political constraints on professional self-regulation result from two other operational codes of policy-makers. For these players, health policy is a legacy, i.e., a path-dependent stream of commitments of resources (money, personnel, buildings, equipment, existing divisions of labour, regulatory regimes, etc.) by vested interests and known stakeholders. Hence, first, the constant pressure to weave medical innovations as new threads into an already laid out regulatory matrix. Hence, second, the incrementalist reflexes dominating the puzzling and powering in most policy formation. The status quo is the rock-like benchmark against which all proposals are measured as marginal improvements or deteriorations of stakeholder positions. Although incrementalism slows down policy reform and innovation, it is defended as one manifestation of a sort of precaution against political hubris or policy overreach (Popper, 1957; Lindblom, 1979, 1999; Scott, 1999): Deal prudently with (cognitive) uncertainty and (ethical) ambiguity by small experimental steps, avoiding irreversible collective tragedies. By taking only a few incremental and reversible steps at a time, politicians avoid Journal compilation © 2008 Blackwell Publishing No claims to works in the public domain
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the hubris of large-scale reforms and the irreversible damage they may cause for ordinary citizens. Applied to (medical) technological innovations, incrementalism is particularly prone to the Collingridge dilemma (Collingridge, 1980). In the early stages of innovation, incrementalist prudence suggests government non-interference for the sake of experimental learning. In the later stages an innovation may already be so technically, economically and socially entrenched that an incrementalist regulatory approach is ‘too little, too late’. It should be clear that, in this way, the medical profession may exploit the Collingridge dilemma as a resource in boundary work. Professionals have much earlier and deeper knowledge of new medical technological developments than politicians and other policy-makers. Using this information asymmetry strategically, the profession may aim for ‘too little, too late’ regulation. This is one important reason why the boundary work is frequently more about demarcation than co-ordination. I will illuminate this through a brief analysis of venue shopping by some parties in the Dutch debate on the issue of pre-natal screening.
Venue Shopping in Ethical Medical Issues, or the Art of Political Manipulation Policy politics and boundary work are not without political manipulation for the sake of achieving one’s substantive policy goals. The art of political manipulation (Riker, 1986) consists mainly in combining three strategies: (1) agenda control through agenda shaping (Rochefort & Cobb, 1994) and agenda denial strategies (e.g. Cobb & Ross, 1997); (2) controlling the nature and number of policy alternatives; and (3) selection of (comparative) decision dimensions or criteria under active political consideration. Usually, these partial strategies jointly make up a venue2 shopping 2
Webster’s Dictionary defines a ‘venue’ juridically as ‘the locality in which a jury is drawn and a case tried’. By analogy, political and policy scientists use the term to mean the institutional and/or procedural landscapes in which an issue is up for political decision making: ‘Depending on the issue and how it is understood by those potentially involved it may be assigned to an agency of the federal government, to private market mechanism, to state or local authorities, to the family, or to any of a number of institutions. We term this the venue problem. Each venue carries with it a decisional bias, because both participants and decision-making routines differ’ (Baumgartner & Jones, 1991, p. 1047).
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strategy, i.e., the deliberate political effort to find or create a decision arena or setting that offers the best prospects for achieving one’s policy preferences (Baumgartner & Jones, 1991, 1993). Venue shopping, however, is more than political strategy or tactics. It can be experimental, instead of instrumental and calculated; or it may serve organizational needs and identities, instead of best advancing substantive policy goals (Pralle, 2003). The political strategy of venue shopping in medical-ethical issues may be illustrated by the example of the Dutch debate on pre-natal screening. Given the players and their operational codes in the policy domain, there appear to be three major venues for decision making on innovative but ethically contested medical technology: (1) let the politicians decide; (2) let the doctors decide; and (3) let the citizens decide. The boundary work practice of acquired regulation described above is, in fact, a venue shopping strategy of modulation and compromise between politicians and medical professionals. Still, this long-term strategy is not a manifestation of peaceful and prudential consensus, but comes about as a result of continuous political strife. Christian-Democratic MPs and politicians, recently supported (and surpassed in ethical zeal) by their new coalition partner, the Christian Union, oppose many new medical technologies on ethical and religious grounds. Instead of a previous Cabinet’s3 policy of generously introducing and making available new medical technologies, Christian politicians tacitly endorse and implement a policy of restraint, if not stand-still. For a time, they have opted for a political strategy of deliberate debate avoidance. Says Kees Klop, ChristianDemocrat politician and the party’s former think-tank director (quoted in Trappenburg, 2005:19): ‘It is deliberate that Christian Democrats do not make lots of noise about the shift in policy. That is clever when you know that the greater part of the population disagrees with cabinet decision making on pre-natal screening. One should not start a national political debate on the issue’. The ChristianDemocrats’ political strategy, in fact, leaves medical-ethical issues arising from medical innovations to spontaneous and chaotic processes of opinion formation in civil society and the news industry, hoping that ‘political silence is policy gold’. This low-cost agenda denial strategy (Cobb & Ross, 1997) of a politics of silence and debate avoidance is challenged by the Rathenau 3
The so-called ‘purple’ cabinet made up of socialdemocrats (PvdA or Labour Party), progressive liberals (Democrats ’66), and conservative liberals (VVD, or Party for Freedom and Democracy).
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Institute, a knowledge institute and advisory body which informs and advises the Dutch parliament on societal and ethical impacts of technological developments in general. On the basis of previous studies on ethical discourse about medical technologies they concluded that political debate usually reflects orthodox, academically fashionable, and ‘rational’ ethical paradigms, but disregards the ‘life ethics’ found to be used among sizable parts of ordinary citizens and patients (Kirejczyk et al., 2001). Concluding that ethical debate was insufficiently pluralistic to capture a major strand in public beliefs and discourse on issues of human reproduction and pre-natal screening, the Rathenau Institute advocates a more citizen-oriented participatory approach to medical ethical decision making. In medical technology assessment, the voice of patients and citizens should be strengthened relative to the chorus of the ‘usual suspects’, such as the medical professions and health insurers in the public health policy networks. Representatives of the Labour Party, finally, prefer to keep the system of medical provision on medical indication intact; and generally stress equality or solidarity between younger and older, and poor and rich patients, as a value in health care. For them this implies that clinics and other health services organizations should not have the discretion to vary access and treatment regimes on the basis of ideological or religious world views or ‘patient-as-client’ willingness to pay. Hence, they advocate more stress in health policy making on ‘consent on the level of the medical profession’, as a ‘third way’ between debate avoidance and debate stimulation through interactive and participatory exercises in medical technology assessment. Defending this option, they explicitly state that the plausible outcome of public debate would be undermining the medical provision on medical indication model. They fear that public debate would reveal a consensus among citizens for more patient selfmanagement, more diversified demand and more diversified supply at individual cost. Hence, the Labour Party defends the status quo, leaving decision making on the acceptability of new medical innovations, ethically contestable or not, largely in the hands of the medical profession.
Towards the Primacy of Problems One implication of the primacy of politics is venue shopping as described in the previous section: politicians ought to think about the possible course and potential outcomes for political decision making of debating issues Journal compilation © 2008 Blackwell Publishing No claims to works in the public domain
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for particular audiences in particular institutional settings. If, for example, the potential outcomes of deliberative and participatory policy making on ethically contestable medical-technical issues involving ordinary citizens are perceived as threats to your preferred party-political preferences, you look for other modes of regulation. Although debate and argumentation is said to be at the heart of democratic governance, this type of political manipulation may lead politicians to debate avoidance, and bans on certain topics for policy analysis. However, the political and political science ‘truth’ that party politics determines the content of debate and the substance of policy is not self-evident. One may substitute the primacy of politics maxim for a primacy of problems principle. In this view, the nature of the policy problem determines the properties of the political and policy-making process in and through which the problem is addressed: ‘perceived attributes of the policy determine the attributes of the political process that makes the policy’ (McCool, 1995, p. 175). From a social-constructivist and discursive view of politics and policy, the meanings people attribute to policy proposals are inherently multi-vocal, ambiguous, and thus politically contested. Therefore, politics is the verbal and symbolic struggle over the frames and key terminology used in the definition of the meaning of policies (Hoppe, 1993; Stone, 1997). From this perspective Hoppe (1989) and Hisschemöller and Hoppe (1996, 2001) developed a typology of possible problem types and their concomitant political and policy-
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making processes. The starting point is that in judging a particular situation as problematic, people distinguish between ‘facts’ and ‘norms and values’, if only for pragmatic reasons. Every problem is socially constructed as a claim about ‘facts’ deviating from a ‘norm’ or ‘standard’ or ‘ideal’; a deviation which ought not to exist. In the case of political or policy problems, addressing the problem is not an individual concern, but a matter of public, collective action; where usually government is claimed to have an initiating and leading role. In claims making about policy problems, policy actors and public authorities face different kinds of situations. Regarding moral or ethical standards, they may differentiate between problem claims whose standards, norms and values carry more or less consent. Similarly, concerning the perception of ‘facts’ about present and future conditions, and the conversion of the former into the latter, they may differentiate between problems in which there is more or less certainty about available and usable knowledge. Using these two dimensions – degree of agreement on normative claims at stake, and degree of certainty on relevant and available knowledge – one may construct the following typology of the politico-cognitive status of problems for policy-makers (see Figure 1). These four policy problem types may also be interpreted as different task fields. In individual cognition and problem processing, different task environments require different methods and heuristics for successful problem solving. Similarly, in addressing public policy problems, the different politico-cognitive status of
Figure 1. Types of Policy Problems and Concomitant Policy-Making Styles (Hoppe, 1989; Hisschemöller & Hoppe, 1996) Journal compilation © 2008 Blackwell Publishing No claims to works in the public domain
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problems generates its concomitant variable styles of policy making.
Structured and Unstructured, ‘Wicked’ Problems When we are very close to full agreement on values and norms, and if we are close to certainty on knowledge instrumental for achieving our concrete objectives, we are in the top-left quadrant of fully structured problems. A structured problem is like a puzzle. However complex, the pieces of the puzzle are given, and for every puzzle there is a configuration of pieces representing an adequate solution (Mason & Mitroff, 1981; Dery, 1984). It is the type of problem politicians and civil servants like and desire to create and maintain – at least, in principle, and as long as it delivers satisfactory results (Hisschemöller & Hoppe, 1996). For structured problems may be delegated to professional experts – a kind of ‘invited’ technocracy.4 Cost control and some re-allocation of resources is the only administrative task left for non-expert administrators. Bureaucratic managers and professionals rule in closed professional communities. Through (scientific) learning by analysis-cuminstruction, policy regulation may gradually avoid errors, qualitatively improve or become more efficient. The history of the eventual political acceptance of pre-natal screening for Down’s syndrome and neural tube defects provides a good example (Meijer, 2008). For a long time, politicians resisted the expansion of pre-natal screening to women under the age of 36.5 In 1996, the deputy minister of health ruled that if pre-natal screening was to be offered to all women (and reimbursed), screening centres would need official permits under the Bill on Medical Population Screening (Wet Bevolking-
4
Pellizoni (2003, p. 330) speaks of this traditional model of the politics of expertise as enacting a tacit co-operative scheme: ‘This scheme assumes that an essentially technical definition of policy issues is possible, so that they can be settled by relying on specialized knowledge. . . . In this scheme, lay citizens are disabled because they lack the ability to speak pertinently and appropriately. . . . Moreover, a co-operative scheme focused on specialized knowledge implies the tendency to narrow the definition of the relevant abilities involved in a policy issue, i.e., to increase the specialization of usable expertise’. 5 Since the beginning of the 20th century it has been known that the probability of having a baby with Down’s syndrome increases with age.
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sonderzoek).6 Technological improvements in probability-based screening methods enabled the Health Council in 2001 to advise the conditional introduction of screening for all. Good quality control and good counselling in order to realize informed consent and rational patient choice were two of the strictest requirements. In 2004 the cabinet resolved the issue. Screening was offered to all women irrespective of age, but reimbursement was limited to women of 36 and over. Immediately, implementation was delegated to a small professional community of relevant (para)medical professionals, coordinated by the National Institute for Public Health (and the Environment – RIVM). The situation causing unease among most politicians, and nervousness among their civil service staff, is thoroughly unstructured, or messy or wicked problems (in the lower, righthand quadrant). Policy-makers perceive persistent high uncertainty about relevant knowledge claims, and high preference volatility in mass and elite opinions; or strong and divisive, even community- or regimethreatening discord and conflict over values at stake. Also, they have no fixed and reliable set of ‘partners in governance’. Unstructured or ‘wicked’ problems frequently come in issue networks in flux, open to many different social groups next to the ‘usual suspects’ of bureaucrats, politicians and representatives of vested interests. Unstructured problems are difficult to disentangle ‘webs’ of interrelated problems; they resist decomposition in (quasi)independent, separately solvable problem clusters or parts. There is dissent and conflict over which pieces belong to the ‘puzzle’, and over which arrangement of the pieces means ‘solving’ the puzzle. Sometimes the negative side effects of entrenched technologies cause a U-turn from structured to unstructured problem, like the car mobility problem. Sometimes, as in the case of medical science and technology as argued above, it is the unbridled research and innovation drive that leads to new, unstructured problems. Contrary to structured problems that are almost politically uninteresting, unstructured problems occasionally are in the political spotlight, and may even generate sustained, intractable political controversies (Schön & Rein, 1994). Part of the predicament for policy-makers is that even the recognized experts and scientists continue to quarrel over problem causes 6
This Bill regulates medical screening among people who are in principle free of complaints for cancer and other diseases for which no prevention and/or treatment methods are (yet) available. Journal compilation © 2008 Blackwell Publishing No claims to works in the public domain
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and future perfectability of technologies. However, this does not mean that learning about unstructured problems is impossible. Rather, the orderly forms of analysis-cuminstruction learning have to give way to ‘wilder’, more destructive (Schumpeter), but sometimes more creative modes of innovation through variety-cum-selection learning. These learning modes are characteristic for (quasi-)markets or ad-hoc experimental policy making, and for the kind of society-wide debates triggered by participatory and deliberative technology assessments such as advocated and sometimes staged by the Rathenau Institute (Van Eijndhoven & Van Est, 2000; Hoppe & Grin, 2000).
Two Types of Moderately Structured Problems Moving away from unstructured problems is only possible by generating a transition to moderately structured problems in scientific, social, media and political debates. One possibility is that instrumental knowledge is increasing over time, and the problematic situation moves from unstructured to moderately structured, with means consensus (lower, left-hand quadrant). This problem type occurs when relevant and required knowledge tends to high levels of certainty, but there is ongoing dissent on the normative claims at stake. The key characteristic of this type of policy problem is not knowledge certainty, but the valuative ambiguity, and frequently the contested and divisive nature of the ethics of the problem. Under such conditions, some policy actors may decide to bring together a new network of a selected, restricted number of policy-makers, some of them as representatives of groups outside normal venues of policy making. Contrary to open issue networks’ spontaneous processes of garbage cans and variety/ selection learning, institutional design is the catchword here. The design is for building of discourse coalitions between stakeholders with different, sometimes diametrically opposed belief systems. The design is for interactive learning aiming for synthesis, or some other means for turning divergent views and mutual criticism into opportunities for policy change (Roe, 1994; Van Eeten, 1999). Or, in case synthesis and real change are a ‘bridge too far’, design of other means for deliberative and procedural accommodation of conflicting values, principles and goals; finding means for credible conflict management and pacification; gaining time to avoid solving the problem immediately, without losing trust and legitiJournal compilation © 2008 Blackwell Publishing No claims to works in the public domain
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macy among citizens. Clearly, designed networks for discourse coalition formation need strong network management, both in their creation and maintenance. The Dutch debate on abortion provides an excellent example. When the issue arrived at the political agenda, a new, fully safe abortion technique had recently been introduced. The early debate focused on the moral permissibility of abortion in principle; later phases concentrated on the conditions under which abortion might be permissible; and on alternative procedures of consultation for establishing such conditions (Outshoorn, 1986). A very recent case involves embryo selection after in vitro fertilization. Until 2008 embryo selection was allowed only for defective genes leading to lethal diseases such as Huntington’s. The deputy minister for health announced a ‘technical’ expansion to serious forms of hereditary breast and intestinal cancer. However, she was temporarily opposed by the Christian Union, a coalition partner in the present cabinet. This political party feared a ‘slippery slope’ if the list of diseases for allowed embryo selection was politically determined. The resulting compromise was to allow screening for such genetic defects and embryo selection for IVF patients on a case-by-case basis, after approval in a specially created ethical commission. Another possible scenario is the move to moderately structured problems, with goal consensus (upper, right-hand quadrant). In this type of case, the policy-making arena usually consists of a number of advocacy coalitions in well-delineated (Sabatier & Weible, 2007) or institutionalized policy sub-systems, like health policy. Coalitions come about because policy actors are aware of basic congruencies in their policy belief systems and policy core values; and on this basis decide to pool resources and co-ordinate strategic policy influence. Advocacy coalitions attempt to influence the goals, instruments, budgets and personnel for government policy making in their own direction. Usually there is one advocacy coalition which dominates the others, sometimes for considerable periods of time. Given this dominance, policy-makers observe a great deal of agreement on the norms, principles, ends and goals of defining a desirable future state; but simultaneously considerable levels of uncertainty about the relevance and/or reliability of knowledge claims about how to bring it about. This kind of problem typically leads to disputes of what kind of research might deliver more certain knowledge for solving the problem. Given uncertain knowledge, and thus uncertain effectiveness and efficiency of interventions, this problem type also frequently raises issues of bargaining
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about who will be responsible for expenditures in financing or otherwise enabling certain interventions; and for risks in case of ineffectiveness or negative side effects. Players in and around the national advisory systems and implementation agencies in health care policy act in this way when they decide on the long list of treatments that belong to ‘medically required care’, or negotiate hospital budget allocations. Also an issue like tackling obesity seems a good example (Council for Public Health and Health Care – Raad voor de Volksgezondheid en Zorg [RVGZ], 2002).
Primacy of Problems Applied to Health Care The big issue facing players in the health policy sub-system for quite some time to come is whether or not, and to what extent, medical treatments using innovative but ethically contestable technologies ought to be included in the set of medical treatments routinely applied in clinics and reimbursed to patients with health care cost insurance, but partly also from the government budget. So far, government and health insurance companies rely on a system of medical provision on medical indication. Decision making seeks consent on the cost-effectiveness of the technology and treatment methods derived from it. This is the task of a rather closed, corporatist policy sub-system of stakeholders and medical professionals. On the one hand, there are the medical professional groups, such as specialists, general practitioners, medical researchers, the many types of paramedical professionals, the pharmaceutical and biotechnology researchers and engineers in their industrial laboratory complexes. Their views count especially to establish sufficient certainty on the laboratory, clinical and real-life effectiveness of standardized medical treatments. On the other hand, there are stakeholders, such as health insurance companies, hospital and other health care institution managers, representatives of the medical-pharmaceuticalbiotechnological commercial complex, trade unions of health care workers and patient organizations. They are supposed to discuss matters of resource efficiency, implementation feasibility, patient acceptability, etc. In fact, the policy-making system is a hybrid between professional self-regulation and corporatist interest articulation. The historical core of the system is shaped by defining the problem of medical technology and its impact on health policy as a moderately structured problem with knowledge certainty. Medical peer review took care of the knowledge
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dimension; the ethical dimension was left to politics. Politicians would have the role of linesmen: doctors should not mix up medical treatment with implicit, but constraining conditions that implicitly or explicitly reflect religious, ethical and political judgements on ‘good parenting’, or on the role of medical technology in a ‘good society’. Once new technologies were admitted, the system shifted to a rule-mode fit for structured problems, where everything was left to the medical profession. Owing to strong consensus on the goals for health policy – equal access to equal medical treatment for all Dutch citizens at reasonable costs – the health sector was allowed to grow and grow, seemingly under medical self-regulation. But the ever increasing macro-economic importance of healthrelated technological, industrial and service activities gradually led to more deliberate efforts at cost control. This took the shape, in essence, of bargaining about health care costs in an ‘iron triangle’ between medical professionals, insurance companies and the state. In order to curb and complement the interests of the medical professionals, in the 1980s and 1990s, politicians facilitated the interests of patient groups as a kind of countervailing public power. It is a clear case of policy subsystem restructuring. Politicians feared that the existing ‘iron triangle’ network, without a representative ‘voice’ for patient interests, threatened effective policy deliberation and the legitimacy of decisions on far-reaching reforms for managed competition in health care. In order to widen the deliberative capacity and save the legitimacy of political decision making, they recognized patient groups as legitimate ‘players’, and used public funding of patient interest groups and organizations to expand the composition of the network. Hence, the problem definition shifted to moderately structured problems with considerable goal consent; and the policy sub-system acquired the corresponding traits of a neocorporatist sub-system for stakeholder interest articulation – but as an overlay on top, not as a substitute for the older institutional arrangements. However, next to the transition to managed competition, this hybrid, opaque and difficult to steer policy sub-system is also challenged by ethically contested new medical technologies. These innovations breed unstructured problems, because frequently neither their cost-effectiveness, nor their ethical dimensions have crystallized into clear, publicly defensible, and dominant policy views or beliefs. The contemporary policy system evolved from efforts to cope with both forms of moderately structured and fully structured problems. Yet, Journal compilation © 2008 Blackwell Publishing No claims to works in the public domain
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now it appears to have serious difficulties in dealing with the new type of unstructured problems. Thus, one may speak of a potential structural mismatch: a policy network, designed to cope with structured and moderately structured problems is now ‘bombarded’ with ‘un-processable’ unstructured problems as a result of rapid technological developments. The system was capable of relatively harmonious ‘gear shifts’ between accommodation, negotiation and rule as modes of problem coping. But, in spite of the presence of patient organizations and forums as a modest shift to a more pluralist, participatory and deliberative style of policy making, the system is straining to add a learning style of coping with unstructured problems to its repertoire. This is reflected in the political responses to the problematic situation. They typically show how different players differ in their political judgements on the nature of the ‘same’ problem. They also clearly demonstrate that politicians consider it their task to initiate or suppress and avoid political and public debate, depending on their political judgement on the course and potential outcome of such debates. They keep seeing patients and patient interest organizations only as countervailing power in the new bargaining system for managed competition in health care; and probably the patient organizations have come to share this definition of their identity and role. This is a regularly occurring co-operative scheme in network restructuring towards more ‘participatory’ modes: These often seek to reveal a ‘public opinion’ on an issue by throwing light on opinions and ideas, principles and values, and by comparing to the ‘facts’ provided by experts . . . . However, the abilities attributed to citizens [in this case, patients] are carefully circumscribed. They . . . have an ethical competence, they can discuss what is to be inferred by looking at facts from their own principled viewpoint, but they do not have a say on the facts themselves – how they are constructed, selected and presented (Pellizoni, 2003, pp. 335–6).
Societally Responsible Innovation through Meta-governance It looks like competitive venue shopping is an inevitable part of normal politics. Yet, sometimes venue choice is shaped by policy learning. Far-sighted policy intellectuals and policy entrepreneurs may arrive at a new understanding of the nature of the policy problem (Pralle, 2003, pp. 242–4). This may cause them Journal compilation © 2008 Blackwell Publishing No claims to works in the public domain
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to choose different venues, and sometimes even to create new venues that did not exist before. In other words, they may redesign the boundaries, composition of players (adding patient organizations is a case in point), rules and policy-making styles of the existing policy sub-system – or even create new ones. In health care, the social, legal and ethical aspects of medical technological innovations ought to be as intelligently and seriously debated as the more common scientific, technical and economic aspects. On the basis of the principle of the primacy or good governance of policy problems, the health policy system ought to be sufficiently robust and flexible to accommodate all policy-making styles for all types of problems. In the previous section, I showed this not to be the case; the system is less able to deal with unstructured problems. In the present system of health care politics, such ‘wicked’ problems are supposed to gradually acquire more structured formats in unpredictable or erratic processes of mass communication and opinion formation. If this is considered unsatisfactory, politicians and policy-makers should create more opportunities or ‘spaces’ for more disciplined deliberation and debate among all involved stakeholders (e.g. Funtowicz et al., 2000). What would be necessary, then, is to find institutional resources to inject more participant and substantive pluralism in more seriously and creatively dealing with unstructured problems through more public spaces for participation, deliberation and learning. How is this to be achieved? The short answer to this question is by gently nudging the policy politics of the health policy network to more deliberation and/or participation through meta-governance. Metagovernance means letting the primacy of politics be influenced by the primacy of problems. It constitutes the endeavour by politicians, policy intellectuals or some other policy entrepreneurs to influence the discourse, the composition and participation modes of players, the rules of the game, and the interdependencies between players in governance networks (Sørensen & Torfing, 2005, pp. 202–5) so that they are continuously aligned to the problem type involved. Frankly, it would be difficult to draw the line between meta-governance and political tinkering or bricolage. The health system is simply too complex and selfcontrolled to be amenable to meta-governance as top-down steering from a central control unit. The only way to ‘steer’ such complex systems is by using the forces and drivers already effective in it; to ‘interpolate’ small doses of change in such ways that the balance of forces is changed in the desired direction
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(Dunsire, 1986; Hood, 1986). One would have to fine-tune the relative weight of the different, partially opposing governance modes (rule, negotiation, accommodation, learning) present in the overall constellation. In health care, some developments go in this direction already. Under managed competition, the role of (potential) patients as clients will gain in weight and influence in policy implementation as health service delivery. Patient self-management, case management, more choice in health service delivery, patient and user rankings of health service organizations will all become more important. Yet, bolder steps are required in dealing prudently with ethically contestable medical innovation. Under present conditions, patient organizations and platforms function as interest groups. Valuable as this may be, they have become part of the ‘usual suspects’. The voices (in the plural) of ordinary people and citizens as potential patients, and as relatives, friends or care-takers of patients, are only faintly audible through the normal modes of political participation in representative democracy. To really inject more pluralism in creatively dealing with and collectively learning about unstructured problems of medical technological innovations, a good start would be to introduce more participatory and deliberative design elements in health technology assessments (Van der Wilt, 1995; Grin, 2004; Reuzel, 2004; MoretHartman, 2008).
References Baumgartner, F.R. and Jones, B.D. (1991) Agenda Dynamics and Policy Subsystems. The Journal of Politics, 53, 1044–74. Baumgartner, F.R. and Jones, B.D. (1993) Agendas and Instability in American Politics. Chicago University Press, Chicago. Callahan, D. (2003) What Price Better Health? Hazards of the Research Imperative. University of California Press and The Millbank Memorial Fund, Berkeley, CA. Cobb, R.W. and Ross, M.H. (1997) Cultural Strategies of Agenda Denial. Avoidance, Attack, Redefinition. University Press of Kansas, Lawrence, KS. Collingridge, D. (1980) The Social Control of Technology. Open University Press, Milton Keynes. Dery, D. (1984) Problem Definition in Policy Analysis. University of Kansas Press, Lawrence, KS. Dunsire, A. (1986) A Cybernetic View of Guidance, Control and Evaluation in the Public Sector. In Kaufmann, F.X., Majone, G. and Ostrom, C. (eds.), Guidance, Control and Evaluation in the Public Sector. Walter de Gruyter, Berlin, pp. 327– 48. Evett, J. (2002) New Directions in State and International Professional Occupations: Discretio-
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nary Decision-Making and Acquired Regulation. Work, Employment and Society, 16, 341–53. Funtowicz, S., Shepherd, I., Wilkinson, D. and Ravetz, J.R. (2000) Science and Governance in the European Union: A Contribution to the Debate. Science and Public Policy, 27, 327–36. Grin, J. (2004) Health Technology Assessment between Our Health Care System and Our Health. Poièsis and Praxis, 2, 157–74. Halffman, W. (2003) Boundaries of Regulatory Science. Dissertation, University of Amsterdam, Amsterdam. Hisschemöller, M. and Hoppe, R. (1996) Coping with Intractable Problems: The Case for Problem Structuring in Policy Design and Analysis. Knowledge for Policy, 4, 40–60. Hisschemöller, M., Hoppe, R., Dunn, W.N. and Ravetz, J.R. (2001) Knowledge, Power, and Participation in Environmental Policy Analysis. Policy Studies Review, Annual Volume 12. Transaction Publishers, New Brunswick, NJ. Hood, Chr. (1986) Concepts of Control over Bureaucracies: ‘Comptrol’ and ‘Interpolable Balance’. In Kaufmann, F.X., Majone, G. and Ostrom, C. (eds.), Guidance, Control and Evaluation in the Public Sector. Walter de Gruyter, Berlin, pp. 765–86. Hoppe, R. (1989) Het Beleidsprobleem Geproblematiseerd. Over Beleid Ontwerpen. Coutinho, Muiderberg. Hoppe, R. (1993) Political Judgment in the Policy Cycle: The Case of Ethnicity Policy Arguments in The Netherlands. In Fischer, F. and Forester, J. (eds.), The Argumentative Turn in Policy Analysis and Planning. Duke University Press, Durham, NC, pp. 77–100. Hoppe, R. and Grin, J. (2000) Traffic Problems Go Through the Technology Assessment Machine. In Vig, N. and Paschen, H. (eds.), Parliaments and Technology. The Development of Technology Assessment in Europe. SUNY Press, Albany, NY, pp. 273– 324. Jasanoff, S. (1990) The Fifth Branch. Science Advisers as Policy Makers. Harvard University Press, Cambridge, MA. Kirejczyk, M., Van Berkel, D. and Swierstra, T. (2001) Nieuwe Voortplanting: Afscheid van de Ooievaar. Rathenau Instituut, The Hague. Lindblom, Ch.E. (1979) Still Muddling, Not Yet Through. Public Administration Review, 39, 517– 26. Lindblom, Ch.E. (1999) A Century of Planning. In Kenny, M. and Meadowcroft, J. (eds.), Planning Sustainability, Routledge, London, pp. 39–65. McCool, D.C. (ed.) (1995) Public Policy Theories, Models, and Concepts. An Anthology. Prentice Hall, Upper Saddle River, NJ. Mason, R.O. and Mitroff, I.I. (1981) Challenging Strategic Planning Assumptions. Theory, Cases, and Techniques. John Wiley & Sons, New York. Meijer, H.S. (2008) Discussie en Aanpak van het Beleidsvraagstuk Rondom Prenatale Screening op Downsyndroom en Neuralebuisdefecten. Bachelor thesis, Enschede, Twente University. Moret-Hartman, M. (2008) Problem Structuring in Health Technology Assessment. An Argumentative Approach to Increase its Usefulness. Dissertation, Radboud University, Nijmegen. Journal compilation © 2008 Blackwell Publishing No claims to works in the public domain
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Outshoorn, J. (1986) De Politieke Strijd Rond de Abortuswetgeving in Nederland, 1964–1984. DSWO-Press, Leiden. Pellizoni, L. (2003) Knowledge, Uncertainty and the Transformation of the Public Sphere. European Journal of Social Theory, 6, 327–55. Popper, K.R. (1957) The Poverty of Historicism. Routledge and Kegan Paul, London. Pralle, S.B. (2003) Venue Shopping, Political Strategy, and Policy Change: The Internationalization of Canadian Forest Advocacy. Journal of Public Policy, 23, 233–60. Reuzel, R. (2004) Interactive Technology Assessment of Paediatric Cochlear Implantation. Poièsis and Praxis, 2, 119–32. Riker, W.H. (1986) The Art of Political Manipulation. Yale University Press, New Haven, CT. Rochefort, D.A. and Cobb, R.W. (eds.) (1994) The Politics of Problem Definition. Shaping the Policy Agenda. University Press of Kansas, Lawrence, KS. Roe, E. (1994) Narrative Policy Analysis. Theory and Practice. Duke University Press, Durham, NC. RVGZ (2002) Gezondheid en Gedrag, Zoetermeer. Sabatier, P. and Weible, Chr. (2007) The Advocacy Coalition Framework – Innovations and Clarifications. In Sabatier, P. (ed.), Theories of the Policy Process, 2nd edn. Westview Press, Boulder, CO, pp. 189–220. Scharpf, F.W. (1993) Games Real Actors Play: Actor-Centered Institutionalism in Policy Research. Westview Press, Boulder, CO. Schön, D.A. and Rein, M. (1994) Frame Reflection: Towards the Resolution of Intractable Policy Controversies. Basic Books, New York. Scott, J.C. (1999) Seeing Like A State. Yale University Press, New Haven, CT. Sørensen, E. and Torfing, J. (2005) The Democratic Anchorage of Policy Neworks. Scandinavian Political Studies, 28, 195–218.
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Stone, D. (1997) Policy Paradox: The Art of Political Decision Making. W.W. Norton, New York. Swierstra, T. (2000) Kloneren in de Polder. Rathenau Instituut, The Hague. Trappenburg, M. et al. (eds.) (2005) Debat ter Discussie. Wie Mag Meepraten Over Medische Technologie. Rathenau Instituut, The Hague. Van Eeten, M. (1999) Dialogues of the Deaf. Defining New Agendas for Environmental Deadlocks. Eburon, Delft. Van Eijndhoven, J. and Van Est, R. (2000) The Choice of Participatory Technology Assessment Methods. In Joss, S. and Belucci, S. (eds.), Participatory Technology Assessment. European Perspectives. Athenaeum Press, Gateshead, pp. 209–34. Van Rijswoud, E., Stemerding, D. and Swierstra, T. (2008) Genetica, Genomics en Gezondheidszorg. Een Toekomstverkenning. Centre for Society and Genomics, Nijmegen. Van der Wilt, G.J. (1995) Alternative Ways of Framing Parkinson’s Disease: Implications for Priorities in Health Care and Biomedical Research. Organization and Environment, 9, 13–48.
Rob Hoppe is professor of Policy and Knowledge at the Science, Technology and Health and Policy Studies Group (STeHPS) in the Faculty of Management and Governance, University of Twente. He is a senior fellow of the Institute of Governance Studies, the Netherlands Institute of Government, and the Institute for Science, Technology and Modern Culture. His fields of interest are long-term policy dynamics, (deliberative) policy analysis, evidencebased policy, knowledge use and the governance of expertise.
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Aerospace Supply Chains as Evolutionary Networks of Activities: Innovation via Risk-Sharing Partnerships Christen Rose-Anderssen, James S. Baldwin, Keith Ridgway, Peter M. Allen and Liz Varga In the aerospace industry competitive advantage is searched through product innovation. This paper sets out to explore the effects that relationship development in the commercial aerospace supply chains have on innovation and competitive advantage. A perspective of supply chains as complex activity networks is used for data analysis based on in-depth interviews in a global setting. Applying these concepts of supply chains as the interaction of multiple work activities assists in comprehending the forces of change. The processes of change are characterized by expansive learning processes of creating instruments for initializing, developing and sustaining these relationships. These processes take place in a terrain of complex power exercises. The long-term effects are totally dependent on nurturing the relationships. The findings may be useful to practitioners in understanding how implementation of successful supply chain changes may come about. It promotes risk-sharing partnerships as instruments for innovation. The paper provides evidence of changing relationships in commercial aerospace supply chains.
Introduction
I
n response to fiercer competition in the global marketplace, there has been a call for innovative solutions in terms of products, technologies and practices at the same time as reducing lead-time and costs (Rose-Anderssen et al., 2005; Goffin et al., 2006). The obvious approach to reduce lead time and costs has been to adopt the lean practices of the automotive industries. The key principles of lean practices are open dependencies between business partners, just-in-time deliveries and no wastage of resources and materials (Womack et al., 1990). Creating innovative solutions, however, requires going beyond the improvement of adopting practices used within other industries and by competitors. Competitive advantage can be enhanced through introducing radically new products into the market. Veryzer (1998) refers to these new products as discontinuous innovations, where the products have been designed
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beyond customers’ imagination. In that sense creative performance and innovation can be positively influenced by what Kratzer et al. (2006) refer to as team polarity. Team polarity is defined as the difference in opinions and perspectives among members of innovative teams. In this paper, Kratzer et al.’s polarity is referred to as diversity. The case study presented has its focus on the global competition in the market for large commercial aircraft. Risk-sharing partnerships are used as instruments for enhancing innovation and competitive advantage for a new aircraft model; the tenet being that risk-sharing partnerships allows for exploratory processes and financial strength that go beyond the limited creative capacity of a firm working alone. The paper then presents supply chains as evolutionary networks of multiple work activities. These work activities are discussed in terms of the evolutionary transformation of relationships at boundaries. Within this discussion a model of the © 2008 The Authors Journal compilation © 2008 Blackwell Publishing
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instrumentality of relationships as complex evolutionary processes is introduced. Following this, a method of data collection is proposed and justified. The case is then discussed within the framewprk of the model in the specific context of one airframe manufacturer and its suppliers. A combination of an activity theoretical approach and complex systems thinking is applied for analysing these processes of change. Work activities are characterized by the multi-voiced interaction created within them, and their potential for expansive transformation (Engeström, 2001). In this communicative interaction, the diversity of opinions and perspectives create contradictions that play a central role as sources of change and development. Contradictions are historically accumulated tensions within and between activity networks (Engeström, 2001). In these activities there are continuous processes for creation of new instruments for change (Engeström, 1987).
Risk-Sharing Partnerships as Instruments for Innovation A supply chain can be defined as the integration of the flow of information and materials between customers, manufacturers and suppliers (Samaranayake, 2005). There are different aerospace supply chain forms. These are characterized by changing practices in the evolutionary adaptation to market realities and to proactive responses to increased competition (Rose-Anderssen et al., 2009). The adaptation can be exemplified in lean practices, where complex networks of suppliers and customers are closely integrated into long-term relationships for reducing costs and ensuring high quality (Cagliano et al., 2004). Risk-sharing partnerships, on the other hand, are proactive responses to increased competition.
Traditionally airframe manufacturers handled most of the risk associated with innovation, development and production (Williams et al., 2002). Risk-sharing partnerships, however, spread this risk at the same time as influence and revenue is shared between the partners. Risk-sharing partnerships used as instruments for enhancing creative capacity is based on the intention of integrating a diverse division of labour and expertise held by each partner company. It is the synergetic effects caused by collaboration on diversity of perspectives through constructive dialogue while creating a shared voice and vision (John-Steiner, 2000) that is sought when bringing firms into collective partnerships. From the point of division of expertise, Gemünden et al. (2007) have studied the roles of innovators in highly innovative ventures. In this so-called German approach, four types of promoters of innovation are identified. First, there is the power promoter who has the necessary hierarchical power to drive a project. Second, there is the expert promoter who has the specific technical knowledge for the innovation process. Third, the process promoter has the expertise of organizational know-how and network building. Fourth, the relationship promoter has the strong personal ties outside the organization. Although this paper is not about individuals in the single firm, our findings regarding individual firms engaged in radical innovations in aircraft production fit the role models of the German approach (Table 1). As will be seen in this paper, the instruments used in these roles are important for large-scale commercial aircraft innovation. Pritchard and MacPherson (2004, 2007) are concerned about the substantial technology transfer from the prime contractor to globallylocated risk-sharing partners and the loss of jobs among skilled workers and designers in the Western commercial aircraft sector.
Table 1. Innovation Process Role
The German single firm approach
1 2 3 4
Power promoter Expert promoter Process promoter Relationship promoter
© 2008 The Authors Journal compilation © 2008 Blackwell Publishing
The aerospace supply chain approach
Airframe manufacturer Expert risk-sharing partners Large scale integrators; risk-sharing partners Airframe manufacturer towards airlines and risk-sharing partners Risk-sharing partners towards suppliers, their local governments and airlines
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Nonetheless, Romano (2003) argues that supply network partners, in realizing their interdependencies, seek to improve the competitiveness of the network as a whole. Complex global networks of one airframe manufacturer and its suppliers are formed to reduce financial, technological and market access barriers (Esposito, 2004). The reality shift from the traditional local subcontractor base is also enforced by the fact that there is a growth demand in the key emerging markets (Lefebvre & Lefebvre, 1998). Changing the form of relationship from one type to another is not only a matter of choice but also a matter of capacity to do so. The realistic point of departure here is to assess the reality of power regimes within the supply network and adapt and work according to these (Cox, 2004). The lack of general appropriateness of supplier development models and supply chain managed models is that they both become effective in situations of buyer dominance and interdependence only. Risk-sharing partnerships emphasize the importance of the latter. In this regime of relationships, technological innovation depends particularly on the co-ordination and integration activities ensuring durable relationships between customers and suppliers (Lefebvre & Lefebvre, 1998).
Activity Networks and Boundary Crossing Aerospace supply chains are complex systems of power relationships. Complex systems thinking, pioneered in the natural sciences by I. Prigogine and his colleagues in the 1970s, is attaining increasing recognition for understanding change, adaptation and evolutionary processes within social and industrial settings. Cultural-historical Activity Theory was founded by L. Vygotsky, A.N. Leont’ev and A.R. Luria in Soviet-Russia in the 1920–1930s. This theory is a philosophical framework for studying different forms of human work practices and their transformation. The theoretical concepts in this section will be applied to the evolutionary framework of Figure 1. A work activity is a complex network of individuals and their human artefacts (RoseAnderssen & Allen, 2008). As such it is a developmental process connecting the individual and the social levels through their human artefacts and their object orientation. In these complex networks the physical reality is governed by the complex set of invisible effects of individuals interacting by using a diversity
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Identification of new potential opportunities
Instruments for initializing new relationships
Instruments for developing close relationships
Instruments for sustaining close relationships
Risk-sharing partnerships
Figure 1. Instrumentality of Relationships as Complex Evolutionary Processes
of opinions and experiences (Rose-Anderssen et al., 2005). The network as a whole may therefore potentially adapt and respond to the environment in multiple and unpredictable ways (Allen, 2001a). It is the excess diversity that fuels adaptation, exploration and change. Innovation is therefore restricted when diversity is reduced and standardization is increased (McCarthy, 2004). As an activity is already a network, the supply chain becomes a complex network of multiple work activities. Here individuals interact within and across boundaries between companies. The human artefacts are similarly used within and across company boundaries.
The Evolution of a Work Activity and the Significance of History The elements of the activity are represented by the individual subjects of consideration, their activity community, the object and the mediating human artefacts. There are three mediating artefacts. The instruments mediate between the individual and the object. The social rules mediate between the individual and the activity community. And the division of labour and expertise mediates between the community and the object. It is important to make a distinction between object and objectives. Objectives are rigidly independent of individual conceptions, personal bias, thoughts and feelings. An object, however, is the emerging vision that integrates the elements of the activity (Figure 2). Qualitative change can occur when there are disturbances between the elements. That is, change occurs at moments of instability when some new aspects or elements grow in the system, restructure it, and invade new dimensions. Thus new properties of the elements continually emerge (Rose-Anderssen et al., 2005), making the outcome unpredictable in © 2008 The Authors Journal compilation © 2008 Blackwell Publishing
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Instruments: Concepts, language, technologies, strategies
Individual, group Emerging object
Social rules
Community
Division of labour
Figure 2. Complex Activity Network (Engeström, 1987) detail. The emerging changes to supply chains beyond individual human control must therefore always be of concern through collective efforts. Under these dynamic circumstances, the relationships between the elements are therefore in continual processes of modification. And they take multiple and diverse forms within the time of the activity (Foucault, 1972). In other words, as people try to change and develop an activity, they are themselves changed by their adaptation to these changes. The object is central for integrating individual action into a collective activity. The point of departure is the identification of a problem or an opportunity. In the case and results section, the airframe manufacturer identifies a potential opportunity to gain a competitive advantage through closer integration of key suppliers. This is the initiation of an object formation process with the basis in risksharing partnerships.
The Mediating Artefacts of Work Activities Vygotsky (1978) argues that an instrument’s function is to serve as the individuals’ influence on the object of the activity. The instruments are therefore the tools and signs individuals are jointly applying in developing the object (Kerosuo & Engeström, 2003). Essential signs are the languages individuals use to co-ordinate their actions with others (Blackler, 1993). Burns and Flam (1987) argue that social rules have a collective purpose. In that sense they are means for co-ordination within an activity for meeting mutually desirable ends. They serve as means for producing clearer communication and thereby reduce social uncertainty. Successful coordination of supply chains are dependent on some shared rules © 2008 The Authors Journal compilation © 2008 Blackwell Publishing
of comprehension of how to interact in the supply chain. However, sharing social rules does not necessarily mean the same as consensus. The core set of the organizing principles or rules are the contradictions and they are also outcomes of unintended actions. The division of labour in the supply chain comes about as a mutual need for bringing together a diversity of experience and ability with an intention to use this excess knowledge capacity to develop a new object that can give directions to innovative solutions. There is a choice of instruments for initializing new relationships within the network of airframe manufacturer and its first-tier suppliers. The most obvious choice is language. The initiative would normally start off by getting into a dialogue on mutual needs to see how these could be shared, and then reaching some consensus on the form of the new relationships.
The Complex Dynamics of Work Activities The Object Formation Process and Instruments for Change Central to activities is the object formation process. In that respect Hasu and Engeström (2000) make the following distinction between the goals individuals have for their actions, and the collective purpose and direction the object gives to an activity. The object becomes the visual target or focus collectively being created by the community of a particular activity. Because of individuals’ different interpretation of the object, it is continuously subject to changing influences. The dynamics of the influences sustain the object’s evolutionary capacity. As an emerging collective property, the object becomes clearer and more
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meaningful (Lektorsky, 1984) as people coexplore their work activity. An object can occupy two different roles. First, it functions as an object and then it may function as an instrument (Foot, 2002). This paper explores the instruments used for the object of developing relationships in supply chains. Simultaneously the developing relationships are used for enhancing innovation. Knowledge and Routines versus Expansive Learning Miettinen and Virkunnen (2005) argue that notions of routine and rule-governed action do not explain the emergence of new practices. Traditionally, routines have been conceptualized as carriers of regular and predictable practices. From that perspective routines are seen as carriers of an organization’s skills and knowledge. In supply chains this becomes more difficult as the chain is a meeting place of several companies’ different routines. Going beyond the limitations of routines in a rapidly changing environment, communities with the ability to learn will prevail over communities with optimal, but fixed behaviour (Allen, 2001b). In that sense Blackler (1995) argues that the notion of knowledge should rather be seen as a dynamic process of social construction, the knowing in doing. That is, at moments of instability, during which knowledge is transformed, qualitative and structural change can be created (Rose-Anderssen et al., 2005). In other words, it is the transformation of knowledge in order to adapt the supply chain to the future that is interesting. This means releasing the creative capacity within the supply chain into these unstable situations. Here, individuals start questioning present practices and suggest new models of practice. When these challenged models descend to the practical level of implementation, and are tested out in practice, this collective movement has become an expansive learning process (Engeström, 1987). These expansive learning processes, Engeström argues, should be viewed as partially destructive rejection of old perspectives and practices. As collective practices, collaboration between firms on developing radically new products also becomes practice in developing close relationships. The learning in doing therefore transforms both knowledge and relationships. Dilemma Situations and Boundaries Dilemma situations occur at boundaries (Kerosuo & Engeström, 2003). A boundary is crossed as an individual tries out new instruments in interaction with another individual
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in order to inquire or negotiate a common object for creation of alternative practices. At the social level essential dilemmas cannot be resolved through individual actions alone. Here it is the collective creation of new instruments that resolves the dilemma. In that sense, evolving inner contradictions are the chief sources of movement and change in the activity network. For the supply chain as such the inner contradictions are, as Kratzer et al. (2006) argue, the potential for creative performance. In supply chains, boundaries are therefore experienced when old practices do not work. This may occur when involving suppliers from low cost economy (LCE) countries or when trying to search for more innovative solutions. Developing close relationships are collective processes. These processes are characterized by dismantling those old routines that would inhibit relationships across company boundaries. The instrument risk-sharing partnerships can be used as a crowbar for negotiating how to collectively proceed. And that is a continuous dialogical process of questioning present practices and how to change these. Power, Distance, Trust and Relationships Power differences create boundaries. In that sense traditional supply chain forms of buyer dominance create boundaries. Power is really shifting from action to action (Engeström, 2000), and it is therefore shifting from actor to actor (Foucault, 1980; Giddens, 1982). Therefore the ability of any individual to construct reality is completely dependent on their place in the activity process, mediated by the division of labour (Engeström, 2000). Power and distance issues are a challenge to master in human relationships. Scollon and Scollon (1983) argue that the way we speak or the way our discourse systems work are governed by both the way individuals or groups value or assume a relationship when an imposition is put upon them in terms of power relationships, and the distance between self and the other (in terms of closeness of relationship). In that respect, meaningful interactive relationships between people are facilitated by trust as it produces mutual expectations (Bachmann, 2003). Mutual expectations are developed through the object formation process. Trust is associated with the risk of things going wrong (Nooteboom & Six, 2003). However, as people interact and share experiences, they may learn about their potential partner’s needs, which may change their assumptions, and may eventually create a mutual platform of trust (Rose-Anderssen & Allen, 2008). Due to the issue of risk, people © 2008 The Authors Journal compilation © 2008 Blackwell Publishing
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may also substitute trust with control. Control as a trust substitute is characterized by formal, contractual control with an imposition of procedures for monitoring (Nooteboom, 2003). As a substitute for control, rational trust would be based on evidence of the partner’s competence and intentions to conform. In practice, rational trust becomes the criteria used for supplier selection. However, sustaining close relationships is a continuous effort of nurturing mutual expectations. The Dynamic Connection between Multiple Work Activities Supply chains as evolutionary networks of multiple work activities can be presented in its simplest form as the interaction between two companies in Figure 3 (see Engeström, 2001). Dilemma situations are experienced at boundaries between firms. The object formation process in the diffuse boundary zone between Company A and Company B in Figure 3 is as follows. First, Objects A and B are the objects Company A and Company B respectively start off forming within each company. These are the visions or purposes each company is collectively developing for their role in the supply chain. Secondly, Objects A2 and B2 are the emerging objects the companies bring into the boundary zone of collaboration between them. Object C, therefore, is Company A’s and Company B’s collaborative understanding of their collective work activity. The difference between A2 and B2 makes the boundary zone, Object C, emerge. The risk-sharing partnership can be characterized by a continuous object formation process in sometimes multiple and diffuse boundary zones. In this context, as Lektorsky (1984) argues, the object becomes clearer as people co-explore this boundary zone. In the case results and discussion section, the evolutionary transformations of relationships in boundary zones are explored within the framework of Figure 1.
Method of Data Collection This paper is a part outcome of a three-year research project on the evolution of commercial aerospace supply chains. Data was collected in two steps: a literature research and interviews. The research started off by conducting a literature research on: supply chains in general; on aerospace supply chains specifically; historical accounts of aircraft production; literature on evolution, learning, knowledge and change; and data from company internet sources. The treatment of data involved systematic coding of categories identified in the texts. It meant a process of continuous comparison and recoding of categories. The data was used to create an evolutionary classification scheme of commercial aerospace supply chains. It concluded the first phase of the project. The second phase of the project was concerned with the perspectives of key players of one specific supply chain regarding the evolution of large aircraft supply chains. Therefore, based on the interpretation of the literature research data, an evolutionary form of semi-structured interview was chosen to obtain rich data. Richness of data would be less by questionnaire survey or structured interviews. Such instruments would produce data that was easier to compare but would be unlikely to have revealed things unfamiliar to the researcher. At the start of the interview sessions, the interviewees were presented with a list of issues identified from the secondary data: How is the evolution of your company’s supply chains represented by changes of key supply chain practices and supply chain systems? How have key practices changed and been introduced along the life cycle of an aircraft model and from aircraft model to aircraft model? These issues were to be discussed in terms of the issues of: types of supplier selection, types of relationships with customers and suppliers, coordination and integration, training, learning and development, i.e., how do people learn and
Instruments
Instruments Object A2
Subject
Social rules
Object B2
Object A
Object B
Division of labour/expertise
Subject
Division of Community B labour/expertise Object C
Figure 3. Interaction between Two Companies in a Supply Chain © 2008 The Authors Journal compilation © 2008 Blackwell Publishing
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therefore change practices, quality assurance, risk, lean/agile, costs, offsets, technology and future scenarios. This paper focuses on the relationship issues elicited from the interviews. Nineteen interviews took place in Europe, Japan and the USA at the levels of airframe manufacturer and first-tier suppliers in the second half of 2006. The interviews were conducted in seven companies with senior managers and directors in each company. Each session took from 11/2 hour to 2 hours. Every interview session was recorded on tape for later transcription. The interviews were conducted either as individual interviews or as focus group interviews. The first seven interviews took place as individual interviews. To enhance the richness of data, it was agreed with three companies in Japan and the USA to conduct focus group interviews within their companies. The idea was that each member of the focus group community would bring unique experiences and perspectives into the interview session. In the focus group interviews conducted in this research, open-ended questioning took place within the framework of the semistructured interview session. This occurred as interviewees would reflect on what others were saying and discuss that. In that sense, the interviews became interactive conversations. Again each of the interview sessions became a foundation for the sessions to follow. Although the interviewees were given the same semi-structured questions at the start of each session, an iterative practice took place in that the researcher would intervene in the conversation based on the data retrieved during previous interviews and also ask for elaboration on what was said in the present interview. In that sense, he became an active member of the focus group community. The advantage of individual interviews over focus group interviews, however, is the control the interviewer has with closer communication with the interviewee (Morgan, 1997). Our experience, however, is that our interviews of focus groups became close conversations between the interviewees. This peer interaction helped expand on the issues to be explored. The interviewees collaborated in an activity where the object was to explore and make sense of specific issues. Therefore due to the interviewees’ complementary insights, the synergies produced were an expansion of what the individuals could have produced alone. The atmosphere also became more relaxed than in the individual interviews where the individual would be under constant pressure to perform.
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Case Results and Discussions In the following sections, a developmental process of changing supply chain relationships is presented. The success of one supply chain form of highly collaborative relationships between airframe manufacturer, airlines and first-tier suppliers for one aircraft model is developed into risk-sharing partnerships for a new aircraft model.
The Instrumentality of Relationships in Aerospace Supply Chains Identification of Competitive Advantage – Relationship Promotion When the airframe manufacturer was losing market share, an initial dilemma occurred. They chose to involve more strongly the needs of airlines. This was the point of departure of the collaborative supply chain form. A decade ago really, probably, [we] felt like it applied more to working together with our airline customers as a stronger connection. In driving the product design the airlines were embodied in the model [xxx] plane, which has got a huge part of the market compared to the competition. (Airframe manufacturer, interviewee 1) Bringing the customer into the activity of aircraft design increased the diversity of perspectives influencing an object for user-friendly aircraft design. The positive experience with close relationships to airlines also encouraged close relationships to be developed with suppliers. These relationships were used as instruments for bringing together latent expertise for producing more customer desirable aircraft. And I think we have learned, we have been learning that aspect of collaboration applies in more than just as [with] the customer. So we have been moving in a direction to be more collaborative with our partners. Sharing more, earlier, involving them earlier and more deeply in our design. So that collectively we can achieve something better than the old producer system. (Airframe manufacturer, Interviewee 1) The identification of a relationship form that potentially could facilitate innovation and competitive advantage was the point of departure of forming a new object (Object A in Figure 3). Expanding on imagining the future happened through the entrepreneurial step where the airframe manufacturer brought selected suppliers into the boundary zone for developing a shared object (Object C in Figure 3). © 2008 The Authors Journal compilation © 2008 Blackwell Publishing
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Instruments for Initializing New Relationships – Power and Expert Promotion The aircraft manufacturer was the company that had to face the airlines in the market directly. Thus their interpretation of market demand was essential for the success of the supply chain as a whole. And in that sense, the airframe manufacturer as a major customer of the suppliers was in a position of power to impose a reflection of the airlines’ demand. Although the supply chain hierarchy had not changed at the initialization stage, a mutual need situation had been created. It was in the interests of both the airframe manufacturer and the suppliers to sustain their presence in the market. Bringing the airlines and its suppliers into an object formation process of user-friendly design and adopting the practices used by competitors, gave the airframe manufacturer competitive ability with the plane xxx. But it did not give them competitive advantage as they had only made marginal improvements to existing airplane concepts. In other words, they were listening to market needs but they did not create and present something radically new to the market. I think that was taking an existing plane and making point solutions. It was taking low risk and a conservative approach. We made improvements with the cockpit, the navigation system, the seating and storage. They were what I call point solutions integrated. (Airframe manufacturer, interviewee 2) A more proactive response to the fiercer global competition was made when the airframe manufacturer considered a relationship form that could give them competitive advantage. Therefore to be able to move beyond the marginal improvements based on the imagination of the customer (Veryzer, 1998), they needed to cross boundaries into the unknown. This meant moving totally away from old concepts of aircraft design. The airframe manufacturer realized that this could not be resolved through their own actions alone (Kerosuo & Engeström, 2003). Risk-sharing partnership was the relationship form that could connect the diverse capacity needed for radical change. The selection criteria were based on rational trust. This was built on long-term relationships, and upfront issues of financial and technological capabilities. You almost have a different requirement of a supplier upfront capabilities. But those capabilities now quickly move into a sustaining load. They are a very different set of capabilities. The upfront capabilities © 2008 The Authors Journal compilation © 2008 Blackwell Publishing
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are characterized by financial and technological capacities. (Airframe manufacturer, interviewee 3) Changing well-established relationships meant divorcing old routines and practices. The airframe manufacturer was in power to do so. Therefore change could be initiated by the airframe manufacturer withdrawing existing items such as long-term contracts and replacing them with new risk-sharing contracts. And if you look at the main contracts, for example, for the [new airplane], they have called back the terms we have with them, pretty much straight through. And it is a partnership you know. They have become really a part of our business in the way their contract reflects. There is risk associated with that obviously. (European mechanical systems supplier, interviewee 1) The power of expertise gave the selected supplier an advantage towards the airframe manufacturer. This contractual interdependency between the airframe manufacturer and supplier empowered the supplier as long as he complied with the contract. However, once contracts had been signed, the power of national airlines to demand use of suppliers from their own country was part of that complex system. This power exercise was balanced through the airframe manufacturer and risk-sharing partners’ choice of suppliers in these countries to promote sales of planes and secure local maintenance for the aftermarket of equipment and systems. Thus the supply chain became a complex system of power relationships (Engeström, 2000). We essentially take into account market access. We take into account access to capital market, access to technology. (Airframe manufacturer, interviewee 3) We also approached one country to develop engineering skills because we know that when we introduce commercial aircraft into a commercial market, those airlines would need engineers help them maintain those airplanes. We have network relationships and an infrastructure that supports those sales. (Airframe manufacturer, interviewee, 2) We have global relationships with suppliers of the new aircraft model and these are different. These are actually risk-sharing partners. We ask them to take on the design, the certification and the fully integrated work for key elements of that aeroplane. (Airframe manufacturer, interviewee 4) We need other companies to be risk-sharing partners that could be financially, but also
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be from a sales standpoint. So it also mitigates our risk investing billions of dollars on our own. We now have partners with us who help look at this. (Airframe manufacturer, interviewee 2) Although the relationships evolved from a mutual need situation, the selection of partners was based on rational trust (Nooteboom, 2003). That is, the tier below had to show competence and intentions to conform to the impositions put upon them from above. Only we are healthy enough to invest a huge amount of money for this project, and [have the] human resources, [and the] technical background. Those three factors. Our process itself is quite lean in its design. It was designed that way. (Japanese materials supplier, interviewee 1) We have invested [a] very huge [amount of] money to build our new facilities for the [new aircraft model]. [The airframe manufacturer] is applying lean [systems]. They are directing us to build under their instructions, [and] learning lean concepts. Our general manager and executive have changed their mind. We must be lean. So we are trying to be lean. Our suppliers stay in previous situation. First, we must learn, then we will teach them. (Japanese structural supplier, interviewee 1) First-tier suppliers with a long history of compliance with the airframe manufacturer satisfied the initial criteria for rational trust. Another one is [our] accumulated role of history with the airframe manufacturer [of] more than 10 years. So this activity, the reliability of us has been increased. This is quite [an] important factor [for] why we are selected. (Japanese materials supplier, interviewee 1) Conclusively, a complex system of interacting instruments was used for initializing new relationships. First, the airframe manufacturer used the mutual need situation as an instrument for proposing change. Simultaneously, the key suppliers could use the rational trust they had built up with the airframe manufacturer as an instrument whereby they would be selected as risk-sharing partners. Second, the airframe manufacturer used their power as an instrument to change contracts. Simultaneously, the key suppliers used their power of unique expertise, financial resources and access to their local markets to negotiate their position in the supply chain.
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Instruments for Developing Close Relationships – Process Promotion The interviewees realized that it is beneficial to develop relationships based on more trust and transparency. One approach that could potentially develop into mutual expectations and into long-term relationships occurred when a supplier was encouraged to develop their products for their own future benefits. The supplier thereby became more innovative at the same time as the customer received more innovative systems and units of systems. By developing their expertise these lower tier suppliers changed their relationships by becoming suppliers of whole units. In these hierarchical regimes, first-tier suppliers actually empowered low cost economy (LCE) suppliers to become more trusted by letting them make simple parts first; thereby letting them gradually learn, develop competence and thus develop closer relationships to their customer. However, creative initiatives are not advantageous for an aircraft model if they are not introduced based on mutual expectations. These mutual expectations must come about through a collective object formation process on what kinds of innovations fit airline demands at the same time as they fit the production systems of the aircraft model. To develop relationships takes a lot of that informal conversation in trust and creditability through your words. (Airframe manufacturer, interviewee 2) And developing a relationship we try to get more trust in that relationship and more transparency in that relationship. Relationships are a very cyclic thing. Partly because we haven’t got clear commodity strategies, we tend to be very dependent on the chemistry between CEO of a suppliers and the appropriate senior person in-house. I think the relationship side of it and supplier selection is really based on who you know. (Engine supplier, interviewee 1) If we identify something we want to work at together with a supplier then we will have an Early Suppliers Engagement contract. And that contract clarifies who owns the intellectual property. And we use that value of the IP as part of the benefit of working with the suppliers. [It] gives them the opportunity to exploit that technology. (Engine supplier, interviewee 2) We give the LCE suppliers a development plan. Start them [off] with fairly simple stuff. You are building the relationship on maybe some machine part, and then you start to step up to complete design and © 2008 The Authors Journal compilation © 2008 Blackwell Publishing
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manufacture of an actuator or an entire gearbox or something like that. (European mechanical systems supplier, interviewee 2) So we have a supplier that comes up with a really good idea. But our customer says: we are not going to accept that. They want a stable thing. Also when a supplier comes up with a new idea and technology, getting it on an existing programme is very difficult in terms of incorporation. We are learning that we have to get better co-ordination on where they are spending their R&D money. (Airframe manufacturer, interviewee 3) Triangular relationships between a first-tier supplier, western prototype suppliers and LCE mass producers of a unit illustrate a collective approach to a global production dilemma situation. As far as supplier relationships are concerned, we are trying to involve some of our traditional suppliers in co-operative arrangements with [suppliers in] low cost economies. So that means, the machined [parts] suppliers would be involved at the prototyping stage. The parts in the volume production phase may be offloaded to a low cost country area. (Electrical systems supplier, interviewee 1) Developing supply chain relationships, as the case shows, is not a straightforward process of implementation. Despite the point of departure, there is an intention to collaborate. Beyond the point of the contract agreement, the developmental process relies on very individual interactions in the boundary zone (Figure 3) between the companies. Distinct cultural boundaries create another dilemma in yet another diffuse boundary zone (Figure 3) between companies in the supply chain. Although the situation is between risksharing partners, this is most typically exemplified by the difference between Japanese and American practices (Cristiano et al., 2000). The Japanese individual assumes collective obedience to the system and the level above, and therefore assumes power difference and distance to strangers. On contrast, the American individual would relate to a more individualistic regime of low power difference and small distance to strangers and more freedom of action for the individual. You literally have to watch the Japanese all the way to get them to [perform], handholding, walking through [the process at] every level of their organization to get them to go at the risk level and speed we need to go ahead. (Airframe manufacturer, interviewee 5) © 2008 The Authors Journal compilation © 2008 Blackwell Publishing
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The boundary zone therefore becomes even more diffuse when one partner company has to guide another partner due to different cultural performance expectations. These problems are therefore not specific to risk-sharing partnerships, but are due to mismatch of collaborative skills across cultural boundaries. However, collaboration across company interfaces is not only a challenge between Asian and Western companies. As the case shows, integrating the global network of airplane structure risk-sharing partners also needs substantial developmental attention. The airplane structures suppliers are difficult to integrate. The co-ordination and integration is maybe one of the keys to successful companies. We see ourselves as large-scale integrators. (Airframe manufacturer, interviewee 5) As the risk-sharing relationships’ intended collective practices are not fully implemented, it means that the airframe manufacturer as a large-scale integrator still has to make impositions on suppliers. Thus in line with Cox’s (2004) argument, the interdependence between risk-sharing partners and the dominance by the airframe manufacturer is necessary for the development of these suppliers and for making the supply chain effective. Top-down or bottom-up innovations are realities in any kind of customer/supplier relationship. However, neither encourages collaboration as mutual expectations are not met. Whether it is in relationships between risk-sharing partners or between different tiers in the supply chain, identifying something together takes place in a boundary zone of mutual expectations. This collective object formation for identifying the ‘new’ together can thus be made to fit the process of production as well as customer needs. The main thing is that those affected by constructing something new are involved in decision-making processes. Although risk-sharing partnerships are supposed to enhance innovation through bringing together a diversity of expertise and the financial resources to invest in exploration and innovative solutions, in this case these relationships are still at a developmental stage. It seems that different and established company routines of fixed behaviour (Allen, 2001) are inhibiting the ability to learn outside existing boundaries and thereby to participate in an object formation across company boundaries. Instruments for Sustaining Close Relationships and Innovation As important as it was to destroy obsolete artefacts, it was important to establish new
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enduring artefacts to support new forms of relationships. The question will therefore always be whether new enduring artefacts such as contracts, e-portals, design and engineering structure adjustment programmes and product planning tools can be effectively used across company interfaces not only at present but also are flexible enough for the supply chain to sustain into the future. [For] the integration of communication and data transfer, we have developed portals in our systems of design and engineering that enable us to work with our suppliers and researchers and partners at a completely virtual basis. (Airframe manufacturer, interviewee 4) Sustaining close supply chain relationships requires effort beyond establishing enduring artefacts. Compared to sustaining relationships within a single firm, sustaining relationships between firms is more complex. This is because the distance and power issues may be more pronounced in a supply chain than within an individual firm. First, people are separated physically by interacting from different localities, making it more difficult to build up the trust that naturally evolves between people in daily face-to-face contact. Second, there is an inherent power present in customer/supplier relationships. The risksharing partnership, however, invites at least a decrease in power difference between partners due to the strong interdependence the unique diverse expertise held by each partner gives. Therefore to take advantage of this diversity, it is realized that face-to-face and other more direct personal interaction across company boundaries are paramount for nurturing relationships. Multi-voicedness is represented through personal relationships. These serve as nodes between firms, and echoes Engelstad and Gustavsen’s (1993, p. 244) findings. They claim that the network can be seen as consisting not of the participating organizations but of groups of actors linked to each other where the groups operate according to the principle of a democratic dialogue internally as well as in relation to each other. Today it is much more of an open communication, working together, and [with] continuous improvement. The sustaining part is characterized by close relationships and collaboration. (Airframe manufacturer, interviewee 5) Things like schedules, purchase orders, the transactional parts of the relationship happen via e-portals through [the] internet now with all our suppliers. It is an excellent
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system. Suppliers like it and it works very well. What we lost was the interaction between people. But we now have material controllers who own relationships with suppliers. And they know the person, and that relationship is better. (Electrical systems supplier, interviewee 2) The types of relationships with customers and suppliers tend to be very individual kind of responses. It depends upon the company you have relationships with, and what their philosophy is. (Japanese materials supplier interviewee 2) In this case, practising development and sustainability of the risk-sharing partnerships are done in the context of a new aircraft model development. This context changes due to the disturbances between the connected activities (Rose-Anderssen et al., 2005). As the case shows, these disturbances come about as there are imperfect actions promoting corrective actions in a dynamic context of transformation. Although the relationships are immature, the collective investment in unique expertise and financial resources are producing very promising outcomes in terms of a conceptually new aircraft. Thus, even if the boundary zone between firms regarding details may sometimes be diffuse, due to the open space for negotiations and peer guidance the risksharing partnership gives, successful innovation is achieved. Throughout the world, our new airplane is recognized as an extraordinarily high technology advanced aircraft. People believed that we have stepped out a very innovative solution. We have stretched the boundaries of technology, offered a unique solution that has never been offered to the industry before. We are an extremely risk aversive company. And I think this is an indication we have started to accept risk and be innovative. Quite honestly we have to be global. We have those relationships. So globally, I think those relationships are really important. (Airframe manufacturer, interviewee 2) So we go into production of [the new airplane] around, well it starts next year. And it wraps up quite quickly because they have been very successful [with that] aircraft. (European mechanical systems supplier, interviewee 2) In conclusion, shared ownership to building an aircraft, multi-voicedness and successful innovation of new aircraft became instruments for sustaining relationships. A successful outcome in terms of an innovative © 2008 The Authors Journal compilation © 2008 Blackwell Publishing
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product can be argued to be the strongest ingredient for nurturing risk-sharing partnerships. And this helps develop the relationship further.
Risk-Sharing Partnerships as Instruments for Innovation The model of the instrumentality of relationships (Figure 1) has been used to mould the text in these subsections into an interactive model of supply chain relationship evolution. This complex evolutionary model confirms risk-sharing partnerships as strong instruments for innovation (Figure 4). The aim of risk-sharing partnerships is to go beyond simple technology transfer. It is about knowledge and technology transformation as a collective process for creating entirely new concepts of aircraft technology. As such, it
is interesting as it expands the capacities to go beyond present capabilities to achieve competitive advantage in the global space. The collective investment into this expansion is what risk-sharing is about. Western suppliers as well as their global competitors were initially invited to present their up-front capabilities to engage in risksharing partnerships. Sadly, many Western suppliers are not prepared to do that. We need to find suppliers who can grow and develop, and contribute to the innovation process. I think, by and large, most Western suppliers have not woken up to this reality. They can’t develop. They can’t be our suppliers as we need to go forward. (European mechanical systems supplier, interviewee 2) That meaning we need to take more of a risk letting them get on with it. The question is; there are not many suppliers
Identification of competitive advantage potential •
Positive experience with close relationships
Instruments for initializing new relationships • • • • •
Mutual need situation Rational trust based on history Power to change contracts Power of supplier expertise and financial resources Access to markets
Instruments for developing close relationships
Instruments for sustaining close relationships
• •
• •
• • •
Transparency and trust Replacing individual benefits by mutual benefits Low power difference and small distance Improving communication skills Power to guide change
•
Shared ownership to building aircraft Multi-voicedness through democratic dialogue Successful innovation of products encourage relationships – the new aircraft model
Risk-sharing partnerships • • •
Strong instrument for product innovation Constant process of development On-going collective learning process
Figure 4. Risk-Sharing Partnerships as Instruments for Innovation © 2008 The Authors Journal compilation © 2008 Blackwell Publishing
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that are big enough to carry on a mass project development. (Engine supplier, interviewee 2) However, risk-sharing partnerships are open and transparent relationships for the sharing of experiences. Knowledge created by partners during the processes of aircraft development is shared. They are therefore gaining knowledge they would not necessarily have developed alone. To enhance competitive advantage, the airframe manufacturer invites the best expertise available from the global space into risk-sharing partnerships. Innovation is therefore not outsourced or transferred; it is created within the risk-sharing partnership. Although knowledge is shared, early supplier engagement contracts may protect intellectual property rights. Changing practices to support risk-sharing partnerships is a major step in the evolution of commercial aerospace supply chains. Risksharing partnerships facilitate going beyond the limitations of incremental innovation. It is about the collective approach to airplane technology transformation. Advances in new concepts in aircraft design based on advances in material technology help facilitate that. Pritchard and MacPherson’s (2004, 2007) concern about substantial technology transfer from US prime contractors to non-Western companies, thus, does not hold. Western companies need to evolve with the ever-changing global market realities to be considered as risksharing partners to survive. Risk-sharing partnerships have in this case shown to be a strong instrument for the creation of a complete innovative solution for a new aircraft model. The new aircraft represents a totally new aircraft concept whilst its predecessor was based on innovations to various part of an existing aircraft type. The risk-sharing partnership has been shown to facilitate diversity of expertise. The power of expertise by suppliers is a move away from the tradition of one dominant voice towards the multi-voiced approach of this activity network. The combination of financial strength and expertise held by each partner brought together by their co-developed object has facilitated exploration and learning with the outcome radical innovations. Although some actions in the case were characterized as imperfect, the strength of the risk-sharing partnership is that it brings expert firms into an initially diffuse boundary zone where they can co-develop a shared object (Figure 3). It is this facilitation of strong interdependence between partners that is the strength of risk-sharing partnerships compared to more loosely
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collaborative or single-voiced supply chain forms.
dominated
Concluding Remarks: Theoretical and Practical Implications The paper set out to explore the effect of risk-sharing partnership on innovation and competitive advantage in the market for large commercial aircraft. This was done using an evolutionary model of the transformation of relationship practices. Within the framework of the model, a case of one supply chain was analysed using a combination of activity theory and complex systems thinking. Through the perspectives of activity theory and complex systems thinking, the transformation of a supply chain relationship form from the level of collaborative practices to the collective practices of risk-sharing partnerships was discussed. It is the strong collective effort by the risk-sharing partners to develop common objects for their shared activities that connects and develops the diversity of expertise and opinions into a dynamic process that could otherwise have led to pulling the supply chain in multiple directions. Since this supply chain is still immature in terms of object comprehension for some parts, guidance is needed to make the object clearer. Kratzer et al.’s (2006) argument that creative performance and innovation can be positively affected by a diversity of influences is supported by this paper. In previous collaborative supply chain relationships, the airframe manufacturer took most of the risk associated with innovation, development and production (Williams et al., 2002). This domination by the airframe manufacturer is in this case gradually transformed into a more multi-voiced relationship of diverse company capabilities. The expert partners are given the responsibility to develop their high technology products within the context of the common object. This is neither top-down nor bottom-up innovation but an individual contribution in a collective setting. The case discussed in this paper indicates the synergetic effects of bringing together a diversity of high technology expert companies to produce radical innovation. Implementing global risk-sharing partnerships is a complex developmental process as they have to be adapted across different cultural boundaries. The practical learning is therefore that there are no standard solutions to the implementation processes. They are subject to trial and error in diffuse boundary zones of collective object formation processes of trust-building and mutual expectation development. © 2008 The Authors Journal compilation © 2008 Blackwell Publishing
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References Allen, P.M. (2001a) A Complex Systems Approach to Learning in Adaptive Networks. International Journal of Innovation Management, 5, 149–80. Allen, P.M. (2001b) Knowledge, Ignorance and the Evolution of Complex Systems. In Foster, J. and Metcalf, S. (eds.), Frontier of Evolutionary Economics: Competition, Self-Organization and Innovative Policy. Edward Elgar, Cheltenham. Bachmann, R. (2003) Trust and Power as Means of Coordinating the Internal Relations of the Organization: A Conceptual Framework. In Nooteboom, B. and Six, F. (eds.), The Trust Process in Organizations – Empirical Studies of the Determinants and the Process of Trust Development. Edward Elgar, Cheltenham. Blackler, F. (1993) Knowledge and the Theory of Organizations: Organizations as Activity Systems and the Reframing of Management Studies. Journal of Management Studies, 30, 863–84. Blackler, F. (1995) Knowledge, Knowledge Work and Organizations: An Overview and Interpretation. Organization Studies, 16, 1021–46. Burns, T.R. and Flam, H. (1987) The Shaping of Social Organization – Social Rule System Theory with Applications. Sage Publications, London. Cagliano, R., Caniato, F. and Spina, G. (2004) Lean, Agile and Traditional Supply; How Do They Impact Manufacturing Performance? Journal of Purchasing and Supply Management, 10, 151–64. Cox, A. (2004) The Art of the Possible: Relationship Management in Power Regimes and Supply Chains. Supply Chain Management: An International Journal, 9, 346–56. Cristiano, J.J., Liker, J.K. and White, C.C. III (2000) Customer-Driven Product Development through Quality Function Deployment in the U.S. and Japan. Journal of Product Innovation Management, 17, 286–308. Engelstad, P.M. and Gustavsen, B. (1993) Swedish Network Development for Implementing National Work Reform Strategy. Human Relations, 46, 219–48. Engeström, Y. (1987) Learning by Expanding: An Activity-Theoretical Approach to Developmental Research. Orienta-Konsultit, Helsinki. Engeström, Y. (2000) Comment on Blackler et al. Activity Theory and Social Construction of Knowledge: A Story of Four Umpires. Organization, 7, 301–10. Engeström, Y. (2001) Expansive Learning at Work: Toward an Activity Theoretical Reconceptualization. Journal of Education and Work, 14, 133–56. Esposito, E. (2004) Strategic Alliances and Internationalisation in the Aircraft Manufacturing Industry. Technical Forecasting and Social Change, 71, 443–68. Foot, K. (2002) Pursuing an Evolving Object: A Case Study in Object Formation and Identification. Mind, Culture and Activity, 9, 132–49. Foucault, M. (1972) The Archaeology of Knowledge. Harper & Row, New York. Foucault, M. (1980) Power/Knowledge, Selected Interviews, and Other Writings, 1972–1977. Harvester Wheatsheaf, Hemel Hempstead.
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Gemünden, H.G., Salomo, S. and Hölze, K. (2007) Role Models for Radical Innovations in Times of Open Innovation. Creativity and Innovation Management, 16, 1–19. Giddens, A. (1982) Power, the Dialectic of Control and Class Structuration. Cambridge University Press, Cambridge. Goffin, K., Lemke, F. and Szwejczewski, M. (2006) An Exploratory Study of ‘Close’ SupplierManufacturer Relationships. Journal of Operations Management, 24, 189–209. Hasu, M. and Engeström, Y. (2000) Measurement in Action: An Activity-Theoretical Perspective on Producer-User Interaction. International Journal of Human-Computer Studies, 53, 61–89. John-Steiner, V. (2000) Creative Collaboration. Oxford University Press, Oxford. Kerosuo, H. and Engeström, Y. (2003) Boundary Crossing and Learning in Creation of New Work Practice. Journal of Workplace Learning, 15, 345– 51. Kratzer, J., Leenders, R.Th.A.J. and van Engelen, J.M.L. (2006) Team Polarity and Creative Performance in Innovation Teams. Creativity and Innovation Management, 15, 96–104. Lefebvre, E. and Lefebvre, L.A. (1998) Global Strategic Benchmarking, Critical Capabilities and Performance of Aerospace Subcontractors. Technovation, 18, 223–34. Lektorsky, V.A. (1984) The Dialectic of Subject and Object and Some Problems of the Methodology of Science. Progress Publishers, Moscow. McCarthy, I.P. (2004) Manufacturing Strategy: Understanding the Fitness Landscape. International Journal of Operations and Production Management, 24, 124–50. Miettinen, R. and Virkunnen, J. (2005) Epistemic Objects, Artefacts and Organizational Change. Organization, 12, 437–56. Morgan, D.L. (1997) Focus Groups as Qualitative Research, Qualitative Research Method Series, Vol. 16. Sage Publications, Thousand Oaks, CA. Nooteboom, B. (2003) The Trust Process. In Nooteboom, B. and Six, F. (eds.), The Trust Process in Organizations – Empirical Studies of the Determinants and the Process of Trust Development. Edward Elgar, Cheltenham. Nooteboom, B. and Six, F. (2003) Introduction. In Nooteboom, B. and Six, F. (eds.), The Trust Process in Organizations – Empirical Studies of the Determinants and the Process of Trust Development. Edward Elgar, Cheltenham. Pritchard, D. and MacPherson, A. (2004) Outsourcing US Commercial Aircraft Technology and Innovation: Implications for the Industry’s Long Term Design and Building Capability. Canada–United States Trade Center, Department of Geography, State University of New York. Pritchard, D. and MacPherson, A. (2007) Strategic Destruction of the Western Commercial Aircraft Sector: Implications of Systems Integration and International Risk-Sharing Business Models. Aeronautical Journal, 111, 327– 34. Romano, P. (2003) Co-ordination and Integration Mechanics to Manage Logistic Processes across
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Supply Networks. Journal of purchasing and supply Management, 9, 119–34. Rose-Anderssen, C. and Allen, P.M. (2008) Diversity and Learning for Innovation: Dialogue for Collaboration. Journal of Management Development, 27, 307–27. Rose-Anderssen, C., Allen, P.M., Tsinopolous, C. and McCarthy, I. (2005) Innovation in Manufacturing as an Evolutionary Complex System. Technovation, 25, 1093–105. Rose-Anderssen, C., Baldwin, J.S., Ridgway, K., Allen, P.M., Varga, L. and Strathern, M. (2009) A Cladistic Classification of Commercial Aerospace Supply Chain Evolution. Journal of Manufacturing Technology Management, 20 (forthcoming). Samaranayake, P. (2005) A Conceptual Framework for Supply Chain Management: A Structural Integration. Supply Chain Management: An International Journal, 10, 47–59. Scollon, R. and Scollon, S.B.K. (1983) Narrative, Literacy and Face in Interethnic Communication, Ablex Publishing Corporation, New York. Veryzer, R.W. (1998) Discontinuous Innovation and the New Product Development Process. Journal of Product Innovation Management, 15, 304– 21. Vygotsky, L.S. (1978) Mind in Society – The Development of Higher Psychological Processes. Harvard University Press, Cambridge, MA. Williams, T., Maul, R.S. and Ellis, B. (2002) Demand Chain Management Theory: Constraints and Development from Global Aerospace Supply Webs. Journal of Operations Management, 20, 691– 706. Womack, J.P., Jones, D.T., Roos, D. and Carpenter, D. (1990) The Machine that Changed the World – The Story of Lean Production. Rawson Associates, New York.
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C. Rose-Anderssen (c.rose-anderssen@ sheffield.ac.uk) is a Research Associate at the Advanced Manufacturing Research Centre with Boeing, University of Sheffield. He is currently engaged in the ESRC research project ‘Modelling the Evolution of the Aerospace Supply Chain’. He previously worked as a Research Officer in the project ‘New Product Development as a Complex System of Decisions’ at the Complex Systems Research Centre, Cranfield University. He worked as a naval architect and manager in the shipbuilding industry in Northern Europe for many years. He worked as a consultant in shipbuilding in Asia and as a manager in the Norwegian offshore engineering industry. Dr J.S. Baldwin is a Lecturer at the School of Management, University of Sheffield. He is currently engaged in the ESRC research project ‘Modelling the Evolution of the Aerospace Supply Chain’. He has previously conducted research into the sustainability of complex systems with an emphasis on manufacturing in South Yorkshire. He is also involved in research on the evolutionary classification and modelling of industrial ecosystems. Professor K. Ridgway, OBE, is head of the research institute, Advanced Manufacturing Research Centre with Boeing, University of Sheffield. He established the AMRC to carry out research in manufacturing technologies directly related to the aerospace industry. He previously held the position of Professor of Design and Manufacturing and was Director of the Ibberson Technology Transfer Centre, Department of Mechanical Engineering, University of Sheffield. He was awarded the OBE in 2005 for services to UK manufacturing industries. Professor P.M. Allen is head of the Complex Systems Research Centre, School of Management, Cranfield University. He worked for 20 years with the Nobel Laureate, Ilya Prigogine in Brussels. For almost 30 years he has been working on the mathematical modelling of change and innovation in social, economic, financial and ecological systems, and the development of integrated systems models linking the physical, ecological and socio-economic aspects of complex systems as a basis for improved decision support systems. L. Varga is a Research Officer at the Complex Systems Research Centre, School of Management, Cranfield University. She is currently engaged in the ESRC research project ‘Modelling the Evolution of the Aerospace Supply Chain’. She works parttime for the Society of Information Technology. She also provides management consultancy and programme management to a number of public sector organizations.
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Action Planning for New Product Development Projects Jan Buijs Two empirical studies are presented to show how experienced project leaders execute New Product Development (NPD) projects. In the first study we interviewed project leaders from four different design firms. We discovered that inside realistic NPD projects the NPD activities seldom occur in the same order as they are described in the NPD literature. Some activities are omitted, some activities are run in parallel and some even have a seemingly illogical timing. The reasons for these ‘strange’ patterns are usually project-specific. The NPD project leaders distinguish four types of NPD projects. On the one hand, familiar (client well known and/or standard technology and/or re-design) or non-familiar projects (new client and/or new technology and/or innovative design), and on the other hand, the complexity of the product (simple versus complex), and they plan their NPD projects differently according to those four types. For instance, within simple and familiar projects they omit more NPD activities than in projects with a more complex and new nature. In the second empirical study we did a matched pairing study (finding NPD projects which would match each of the four types). This time we interviewed experienced project leaders from different companies, because they are probably more familiar with only one type of NPD project. We found a minimal and a ‘regular’ NPD process. Projects on new products (the non-familiar type) contain the most activities in the total project. Complex projects execute more activities in the first stages, and also different activities than in non-complex projects. We also found that NPD project leaders adapt an opportunistic attitude towards carrying out activities in parallel in order to gain time.
Introduction
T
he New Product Development (NPD) process or Product Creation Process, or more generally the Product Innovation Process, has been subject to model builders since the early to mid 1950s. The complex process of opportunity finding, idea generation, concept building, prototype testing and the market introduction of a new product has been described in many ways. However, a newly appointed project leader gets little or no support from the theory on how to plan his or her specific NPD project. Some 24 years ago, Saren published an overview of the different types of models prescribing the different steps or activities within the product innovation process (Saren, 1984). He distinguished five different categories: 1. Departmental stage models; 2. Activity-based stage models; 3. Decision-based models; © 2008 The Author Journal compilation © 2008 Blackwell Publishing
4. Transformation models; 5. Stimulus response models. He did not qualify the one category to be better than any other category. He also did not provide any help in selecting a category for a specific NPD project. It is all up to the project leader to find his or her way through this jungle of different models. Since Saren’s paper, no new categories have been developed (for examples of the models, see for instance the Germans Pahl & Beitz (1984), the Danes Andreasen & Hein (1985), the Englishman Cross (1994), the Dutchmen Roozenburg & Eekels (1995) and Buijs & Valkenburg (2005) or Cooper’s Stage-Gate Model (1984) from Canada and Ulrich & Eppinger (1995) from the United States). Most of these models come down to a variation of a stagegate process. The total NPD process is divided into a series of stages (e.g., pre-development, development and market introduction), and inside each stage a couple of interrelated
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actions have to be executed. Each stage is completed if a pre-determined milestone is reached (e.g., ‘market feasibility’ or ‘prototype testing’). This milestone is called a ‘gate’. After getting permission (usually by higher management) to go through the gate, the next stage will be executed. If permission is not granted, usually an iteration takes place; in other words an earlier stage or stages is executed once more until the necessary quality to get through the gate has been reached. This continues until the new product has entered the market. Sometimes the results of a stage lead to the end of the NPD project. The academic debate on these models has been concentrated on the one hand on the number of stages, the number and quality of the gates, which activities belong to which stage, and on the other hand on where the innovation process starts (with a technological idea or with the recognition of a need in the market place, a so-called opportunity) and where it ends (the first product sold or with the satisfaction of a happy customer or consumer?). Now the most recent models have added the ‘fuzzy front end’ at the beginning of the process, and some have added the use of the product at the end. The most advanced ones have even tied together the product use stage and the fuzzy front end and come up with a circular innovation model (for an overview of this development, see Buijs, 2003). In conclusion, the theoretical innovation process has been made longer, both up front as well as downstream. All prescriptive models use their own jargon, use their own graphic representations and fail to show all the necessary iterations the real innovation process is famous for. All show more or less rational and logical sequences of actions. In reality, however, we rarely ever see these rational step-by-step sequences as shown in the theory books. As long ago as 1983, Cooper published an investigation about the empiricism of NPD, and discovered that seven different patterns can occur: 1. 2. 3. 4. 5. 6. 7.
The The The The The The The
market oriented process. design oriented process. balanced complete process. front end dominated process. minimum process. launch with prototype process. prototype dominated process.
His research was based on an inquiry among innovation managers. He gave them a list of 20 different NPD activities known from the theory books, and asked whether an activity had taken place in their NPD process, in which sequence and what the duration time was of
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the executed activities. He could not give any explanation about the reasons behind these patterns. Comparable results were found in a study in the Netherlands among about 150 innovating small and medium-size enterprises (SMEs) (Buijs, 1984, 1987). In this study, eight different patterns were observed, not based on a postfacto analysis as in the Cooper study, but based on real-time observations. But once again, here also there was no explanation for these differences. Now, nearly 25 years later, we felt the need to once again dive deep into the real world of NPD projects, and to redo some of these studies and now hopefully find some clues about the reasons behind these different patterns. We carried out two empirical studies, one with experienced project leaders from four design agencies, and a second one also with experienced project leaders but now from 11 different companies all with their own in-house design/development department. Based on the interviews with NPD project leaders from the design firms, we discovered not only that experienced project leaders recognize different NPD patterns, but that they even have reasons to distinguish NPD projects according to two different aspects. The first aspect is the complexity of the design task, and the second aspect is the familiarity of the design task. This leads to four different categories of NPD projects: simple and familiar, complex and familiar, simple and new and finally complex and new. In our second study, we searched for cases which matched these four categories. We interviewed experienced project leaders in 11 different firms about their NPD projects. We selected only projects which resulted in design award-winning products (to be certain of the performance of the NPD projects investigated). We asked the project leaders about the number of activities, what activities were executed in parallel and which had to be sequential, and how much time the activities took. We did indeed find different patterns of NPD processes for the four different categories of projects.
Preliminary Study: NPD Activities from the Literature In an analysis of the differences in modelling product innovation processes, a comparison between 90 different innovation models published in the NPD literature was made by Van der Zee (2003). The models were from all over the world and from the early 1950s to © 2008 The Author Journal compilation © 2008 Blackwell Publishing
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the present day. We discovered that, in total, more than 1,248 different terms were used to describe specific product innovation activities. We condensed these 1,248 into a set of 54 innovation activities. This proved to be a difficult task because none of the original authors is very explicit about the meaning of the terms used. For instance, what is a ‘product idea’? Just words on a piece of paper, a design sketch, or a fully documented idea for a new business activity including concrete market and technological information. Or take the notion of a ‘prototype’. For engineers and product designers, a prototype is a physical, tangible object you can perform tests with. According to Michael Schrage in his book Serious Play (2000), even Excel spreadsheets function as a prototype. Or, is the meaning of the German word ‘Grundanalyse’ exactly the same as ‘Feasibility study’ or has it more to do with ‘Scanning the competitive environment’? The way we did this convergence was to map all models against each other. The walls of our study room was covered with photocopies of all the models. All the models have at least two or three different terms for the same activity in real-life NPD. For instance, ‘product idea’ and ‘ideation’, ‘detailed design’ and ‘embodiment design’, ‘market introduction’ and ‘product launch’. We positioned all the models against each other, using these terms with more or less equal meanings as the linking pins. Some models start with a product idea, some with the corporate strategy; so the starting points of the models can differ greatly. Other models have different endings: some stop at the prototype, some include product use, and others include maintaining and recycling of the product. Recent models include the so-called fuzzy front end of innovation (Koen et al., 2001), the older models restrict themselves to a more limited engineering view on innovation (= product development). And depending on this length of the modelled innovation process, the models are detailed to a greater or lesser degree. ‘Long’ models tend to be more abstract, with fewer details; ‘short’ models tend to be more concrete with a lot of details. Some models distinguish only seven stages (VDI 2221, 1986), others nearly 50 (Archer, 1971). As already stated, the convergence from 1,248 different terms to 54 more or less shared terms was a difficult one, but nonetheless a very interesting process with many discussions. We used triangulation to come up with this list. Three researchers did the convergence independent of each other. Then the results were compared. When there were differences © 2008 The Author Journal compilation © 2008 Blackwell Publishing
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a discussion was held. This discussion was guided by a fourth researcher. At the end, all four researchers accepted the result. The complete list is shown in Table 1. The sequence of the 54 activities in our list is based on all the sequences seen in all the different models, and remains open for debate. For instance, after ‘appointing a project leader’ (which is activity no. 5 on our list), you can easily imagine that the formation of the project team will be the next activity. In our condensed list ‘forming the team’ is activity no. 11. This could imply that the project leader will execute all the in-between activities (nos 6–10) individually. But it could well be that those activities are carried out by the responsible functional departments of the company and that their results will be communicated to the team that is formed later. After its formation the innovation team can build on these results. The total list of 54 NPD activities can be divided into three groups: the pre-, core- and post-NPD activities. We noticed four activities at the very beginning of the fuzzy front end (or rather activities which should be executed even before starting a NPD project, such as doing basic technological research, hiring new staff or scanning the competitive environment). These activities are labelled the ‘preNPD activities’, our numbers 1–4. We see 42 activities as the core NPD activities (nos 5–46), and eight activities which have to do with the implementation of the results of the NPD project (activities such as manufacturing, distributing, maintenance or recycling, nos 47–54). These are labelled the ‘post-NPD activities’.
First Empirical Study: Exploring Categorizations of NPD Projects This convergence from theoretical models into 54 NPD activities does not say anything about duration time per activity or step, does not say anything about parallel processing of different activities in certain stages of the NPD process, nor does it say anything about the possibility of skipping certain activities. Based on the NPD theory, no activities can be skipped, but in real-life NPD we see that in some cases activities are deliberately skipped, accidentally neglected or stupidly just forgotten. And still new products enter the market after such ‘incomplete’ NPD processes. Some of them even turn into major market successes. Cooper already indicated in his research that different patterns of innovation processes do exist in real-life NPD (Cooper, 1983). Some of the innovation activities are carried out in parallel, others sequentially. And not always all theoretical NPD activities are executed.
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Table 1. The Total List of Acknowledged NPD Activities 1. Development of company’s competences by carrying out technological research, hiring new staff, etc. 2. Stimulating the generation of new ideas, or new applications of existing ideas, etc. 3. Carrying out explorative market research by scanning the competitive environment, looking for trends in the market, searching for needs, scientific developments, etc. 4. Adjusting the strategic plans of the company 5. Appointing a project leader 6. Preliminary description of the idea for a new product or a new challenge in the marketplace 7. Analysing present product portfolio of the company 8. Development of preliminary programme of requirements of the new product 9. Investigating the commercial feasibility of the new product 10. Generating principal solutions for the new product 11. Forming the project team 12. Determination and analysis of the target group 13. Building and testing of experimental prototypes 14. Generating and evaluating ideas for the new product 15. Preliminary planning for the total NPD process 16. Patent search 17. Carrying out the technical feasibility of the new product 18. Developing and testing concepts for the new product 19. Analysing competitive products 20. Developing the manufacturing plan for the new product (make-or-buy decision) 21. Developing the promotion plan for the new product 22. Detailed design of the new product 23. Carrying out user tests 24. Finding and selecting suppliers 25. Building prototype 26. Testing prototype 27. Building manufacturing facilities 28. Making technical drawings of the new product 29. Developing tooling 30. Testing new manufacturing facilities 31. Debugging manufacturing process 32. Sales forecasting of the new product 33. Debugging the new product 34. Coaching the first production runs 35. Developing maintenance plans for the new product 36. Training staff in assembling the new product 37. Testing promotion plan for the new product 38. Developing maintenance documents for the new product 39. Training staff in installing the product 40. Promoting the new product 41. Training staff in maintaining the new product 42. Developing the distribution plan for the new product 43. Training users 44. Evaluating the NPD process 45. Certifying the new product 46. Patenting the new technology 47. Manufacturing the new product 48. Assembling the new product 49. Distributing the new product 50. Selling the new product 51. Installing the new product 52. Maintenance and servicing the new product 53. Recycling the new product 54. Disposal of the new product
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ACTION PLANNING FOR NPD PROJECTS
According to Cooper’s empirical research, the minimal NPD process will have at least two different sequential activities (‘product design and development’ and ‘market launch’). Cooper’s maximum NPD process scored ‘only’ nine different activities. Keep in mind that the abstraction levels on which the different NPD activities are described determine the lengths of the different steps. Cooper’s ‘product design and development’ covers at least activities 8, 9, 10, 13, 14 and 17 in our list. And you can even add activities 17, 22, 25 and 26 if you want to. So what does a minimal process of only two steps mean? Comparing the different NPD models will remain tricky. In our first empirical study we interviewed four project leaders of NPD projects. All project leaders are experienced product designers (more than five years of professional practice) and are working at four different Dutch design agencies. They were chosen for interview because they had worked on many different NPD projects for a variety of clients. We asked them to look at the activities they had executed during their NPD projects, and to compare those against the list of the 54 theoretical activities. We had made separate cards for each the 54 activities and asked the project leaders if they (or their team) had performed this activity. Some extra cards were still blank, to allow the project leaders to add other NPD activities to the list of 54. All project leaders are working for different design firms. For competitive reasons, these firms use their own specific words and models to describe their ‘own’ NPD process. So we had the same semantic problems with them as we had encountered before during our own convergence from the NPD literature. But because we now had experience with the different terminologies, we could easily discuss with the project leaders the specific content of the NPD activities and were not restricted to the labels that the design firms use to describe their own way of executing a product innovation project for a client. In the end, no new NPD activities were discovered, and most of the project leaders accepted the terminology in the list of 54 NPD activities we had condensed from the literature. The research methodology was to ask the project leaders to place the NPD activities on a grid scorecard to find out the sequence of activities, the parallel execution of activities and more or less automatically they mentioned the duration time per activity. After the project leaders had placed all the relevant NPD activities on the grid, the yellow stickers were glued in place and the duration time was written down. © 2008 The Author Journal compilation © 2008 Blackwell Publishing
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We borrowed the idea from Cooper not to use the real duration time of the NPD projects to form patterns, but to use 100 per cent as the duration time and to place the activities within this 100 per cent frame. The reason for doing this is that otherwise the patterns will be categorized according to real duration time. So the shortest projects will all be categorized together, and similarly with all the projects with the longest duration time. The categorization would then be based on differences in total duration time and not on differences in patterns of NPD activities, which is what we are really interested in. In the past we have used this research method successfully (Buijs, 1984). In order to do this analysis properly, we developed a new version of the grid scorecard, adding five columns, each divided into 10 segments of 10 per cent of the duration time of the total NPD process. The columns are used to place more or less similar NPD activities together. The five columns are from left to right: 1. 2. 3. 4. 5.
Technology Product Market Organization Miscellaneous.
The grid scorecard is shown in Figure 1. This methodology is based on a specific model of the innovation process in which in each stage three groups of parallel NPD activities are distinguished (Buijs, 2003). Central are the product-related activities, such as developing the programme of requirements, or generating ideas. To the left, which in this circular model means the inside, are all technology-related NPD activities, such as manufacturing or building prototypes. To the right, on the outside of the model, are all market-oriented NPD activities, such as doing market research or making a promotion plan. It proved impossible to assign all 54 NPD activities to these three categories. For instance, appointing the project leader or forming the innovation team are of a different nature. Therefore, we added two extra columns: one for organizational NPD aspects and one category for the rest (= Miscellaneous). Now all 54 activities could be assigned to a relevant column. In this way, we could analyse the patterns of all different cases in an equal and comparable manner. To show the results of this kind of analysis we will show the patterns of two different NPD projects, one for Product A and one for Product B, each from a different design agency (see Figure 2). The NPD process of Product A has the following stream of activities. The reading starts from the top of the left figure (the start of the
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Grid scorecard 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Duration time
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Product
Market
Organization Miscellaneous
100
Technology
Product
42 Market
Organization Miscellaneous
Figure 2. Two Different NPD Patterns: Product A by Design Agency X (Left), and Product B by Design Agency Y (Right) Notes: The numbers correspond with the numbered NPD activities of Table 1. An interesting phenomenon did occur while scoring the different NPD activities on the scorecards. To put the numbers on the grid card, we used standard sheets with stick-on labels with numbers printed on. After sticking on the different NPD patterns on the grids, the unused stickers on the label sheets showed a kind of contrasting view in which the numbers left over more or less mirror the NPD patterns.
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NPD process) and from left to right over the five columns. It ends in the bottom right-hand corner. In brackets we will add the type of column the activity is assigned to. 3. Carrying out explorative market research by scanning the competitive environment, looking for trends in the market, searching for needs, scientific developments, etc. [M] 8. Development of preliminary programme of requirements of the new product [P] 6. Preliminary description of the idea for a new product or a new challenge in the marketplace [P] 15. Preliminary planning for the total NPD process [P] 5. Appointing a project leader [O] 19. Analysing competitive products [M] 9. Investigating the commercial feasibility of the new product [M] 11. Forming the project team [O] 14. Generating and evaluating ideas for the new product [P] 12. Determining and analysis of the target group [M] 10. Generating principle solutions for the new product [P] 13. Building and testing of experimental prototypes [T] 17. Carrying out the technical feasibility of the new product [T] 22. Detailed design of the new product [P] 25. Building prototype [T] 26. Testing prototype [T] 23. Carrying out user tests [M] 33. Debugging the new product [P] 24. Finding and selecting suppliers [T] 28. Making technical drawings of the new product [T] 37. Testing promotion plan for the new product [M] 20. Developing the manufacturing plan for the new product (make-or-buy decision) [T] 45. Certifying the new product [Mi] 46. Patenting the new technology [Mi] 29. Developing tooling [T] 27. Building manufacturing facilities [T] 30. Testing new manufacturing facilities [T] 31. Debugging manufacturing process [T] 48. Assembling the new product [T] 43. Training users [M] The NPD process for Product B (the right part of Figure 2) reads as follows: 6. Preliminary description of the idea for a new product or a new challenge in the marketplace [P] 15. Preliminary planning for the total NPD process [P] 7. Analysing present product portfolio of the company [Mi] © 2008 The Author Journal compilation © 2008 Blackwell Publishing
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5. Appointing a project leader [O] 11. Forming the project team [O] 8. Development of preliminary programme of requirements of the new product [P] 3. Carrying out explorative market research by scanning the competitive environment, looking for trends in the market, searching for needs, scientific developments, etc [M] 12. Determining and analysis of the target group [M] 10. Generating principle solutions for the new product [P] 17. Carrying out the technical feasibility of the new product [T] 19. Analysing competitive products [M] 18. Developing and testing concepts for the new product [P] 13. Building and testing of experimental prototypes [T] 9. Investigating the commercial feasibility of the new product [M] 20. Developing the manufacturing plan for the new product (make-or-buy decision) [T] 22. Detailed design of the new product [P] 24. Finding and selecting suppliers [T] 29. Developing tooling [T] 28. Making technical drawings of the new product [T] 25. Building prototype [T] 26. Testing prototype [T] 27. Building manufacturing facilities [T] 33. Debugging the new product [P] 30. Testing new manufacturing facilities [T] 31. Debugging manufacturing process [T] 34. Coaching the first production runs [O] 38. Developing maintenance documents for the new product [T] 43. Training users [M] 39. Training staff in installing the product [O] 44. Evaluating the NPD process [P] 42. Developing the distribution plan for the new product [M] Both processes show more or less normal steps in the NPD process (i.e., as published in the NPD literature), more or less normal sequences, such as building manufacturing facilities (no. 27), testing manufacturing facilities (no. 30) and debugging manufacturing process (no. 31). But both also show that certain activities are run in parallel, such as carrying out explorative market research by scanning the competitive environment, looking for trends in the market, searching for needs, scientific developments (no. 3), the development of preliminary programme of requirements of the new product (no. 8), the preliminary description of the idea for a new product or a new challenge in the marketplace (no. 6) and the preliminary planning for the total NPD process (no. 15). Both projects have used in total
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30 different NPD activities to get the new product on the market. But those 30 are not the same ones! Now we zoom out to all four cases. If we look at the 42 core NPD activities, 41 of them were executed at least once in those four cases. The only ‘theoretical’ activity that was not executed was activity no. 35 (developing maintenance plans for the new product). Only two out of the four pre-NPD activities were recorded, and only one out of the eight of the post-NPD activities was recognized (no. 48 assembling the new product). We discussed our results with the project leaders in individual feedback sessions and talked about the possible explications for the different patterns in the NPD projects. For their own projects they had very specific arguments (for each case/project more or less unique) why certain activities were omitted, run in parallel or had a special sequence. There were no general arguments used by all four to find reasons for specific patterns. But interestingly enough, when we asked about planning future NPD projects, all four used the same type of reasoning. They all had two arguments to plan a new project. One reason is the familiarity of the NPD project. Is it a redesign project, is it a well-known client they had worked for before or is the technology well known. The other reason is the complexity of the product. Is it a simple product, with very few components and with only one technology, or is it a complex product, with lots of different components, with a multitude of suppliers involved and with different or new technologies? Based on their work, these experienced project leaders distinguish four different categories of NPD projects (see Figure 3). The experienced project leaders indicated that,
The familiarity of the NPD project
according to their judgement of the category of a future NPD project, they adjusted their planning of the activities of the project. Going from type A to D they suggested that the number of activities is increasing, and that the number of parallel activities is decreasing. According to their insights, the total duration time is also increasing from type A to D. Planning NPD activities for the next project is dependent on the type of project in this familiarity/ complexity grid. And a simple product means a simple project plan; a complex product needs a complex project plan. We decided to start another study to test this reasoning.
Second Empirical Study: Investigating Patterns in NPD Projects Now, we know about the differences between the theoretical models of the NPD process, and we know about empirical evidence that real NPD projects are not executed according to these models. But what we do not know are the reasons behind these differences. Based on the interviews with the experienced project leaders, we have a hypothesis that for the four different categories of NPD projects different patterns of NPD activities might exist. Simple products have simple project structures and complex products have complex structures. Because these differences could be caused by differences in companies, markets or technologies, we decided to look for cases which are more or less equal in design quality. In research methodological terms, we are looking for matched pairings (the empirical case should fit the theoretical category). We have no real hypothesis about the content of the different patterns (simple
Type B: Simple product & New project
Type D: Complex product & New project
Type A: Simple product & Familiar project
Type C: Complex product & Familiar project
The complexity of the NPD product
Figure 3. The Four Categories of NPD Projects
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product implies simple project and complex product implies complex project, but there is no theoretical base for it). So we decided to do an exploratory study to test the four categories of NPD projects and whether these categories not only have different patterns, but also that, where these differences occur, they confirm the familiarity/complexity grid. In this second exploratory study, we interviewed 11 experienced NPD project leaders. This time we choose to interview project leaders inside companies. The reason is that project leaders of design agencies might have experiences with all four categories of NPD projects, which could confuse their memories. In this study we wanted to focus on differences between the project categories. We also hoped to find more of the pre- and post-NPD activities because a company is responsible for the whole NPD project whereas a design firm might focus on only a part (as we have seen in the exploratory study). All 11 project leaders come from 11 different companies. We were looking for projects in each of the four categories of NPD projects. We wanted, for comparison reasons, to guarantee a certain degree of design quality of each project and the resulting new product. Therefore, we choose to investigate NPD projects that had just won a Dutch design award for their product. All our 11 cases are prize-winning products, although in different industry sectors. With this careful selection of the cases, we tried to have NPD projects under investigation which are of the same high design quality and therefore comparable. The distribution of the cases over the four categories of NPD projects introduced yet another difficulty. The four types are based on differences on two axes: one on familiarity of the project and the other on the complexity of the product (remember Figure 2). The complexity of the NPD product is observable from the outside; you can see it by looking at the products that are the result of the NPD project. We did a small test to see whether design students were able to judge the complexity of products just by looking at them. For this test we used the products from our first study (at the design agencies), and we compared the students’ judgements with those of the original project leaders who designed the products. All the students had the same judgement as the professionals. We did the same experiment with the degree of novelty of the project, and no student was able the make the right judgement. The reason is quite simple: novelty is based on the knowledge and experience of the project leader who is going to design the new product. And outsiders are not able to judge this aspect. © 2008 The Author Journal compilation © 2008 Blackwell Publishing
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So in the case selection we had to rely on the information from the project leaders themselves to judge the scoring on the familiarity axis in order to categorize the case into the relevant category. For the complexity axis we made our own external judgement and compared that with those of the project leaders. As expected, there was a 100 per cent match. To get cases, we categorized the prizewinning products on complexity, phoned the companies who designed these products, and asked them if they were willing to join our research. After a positive reaction, we held a preliminary interview to find out about the familiarity scoring. After a couple of attempts, we finally had our cases matching the categories. For reasons of confidentiality, we are not allowed to show pictures of the products and/or to give the names of the companies. The research methodology used for getting the NPD activity patterns is the same as in our first exploratory study. We asked the project leaders about the number of activities, which activities were executed in parallel and which were carried out sequentially, and what the duration time of each different activity had been. We offered the interviewees our cards of the 54 NPD activities, asked them to locate the activities on the grid and to mention the duration time per activity. We formatted everything according to Cooper’s 100 per cent format and analysed the results. The interviews were executed at the offices of the project leaders. They had collected relevant data from their project files on starting dates of activities, number of participants, they showed us sketches and drawings and showed us the finished end result of their NPD work: the new prize-winning product. They proved to be very co-operative and open and were very interested in our study. The interviews usually took between 1.5 and 3 hours.
The Data Our raw data for each case is a large piece of paper with the 100 per cent grid, the yellow stickers with the (numbered) NPD activity glued in their positions. The duration time per activity was also written on the stickers. We also had some background information about the product involved, the composition of the design team and some information about the company. The 11 cases were evenly spread over the four categories (see Figure 4). Cases A and B executed 31 different NPD activities each. The Type B cases show a broader range: case C 29, case D only 22 (the least of all cases) and case E
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The familiarity of the NPD project
Type B: Simple product & New project 3 (case C, case D, case E)
Type D: Complex product & New project 3 (case I, case J, case K)
Type A: Simple product & Familiar project 2 (case A, case B)
Type C: Complex product & Familiar project 3 (case F, case G, case H)
The complexity of the NPD product
Figure 4. The 11 Cases Divided over the Four Categories
33. The Type C cases are more or less equal: cases F and G 28 and case H 30. The Type D cases are also diverse: case I 25, case J 34 (the case with the most NPD activities) and case K 29. The two cases from our first study (see Figure 2) scored 30 NPD activities each. The average number of NPD activities for all 11 cases is 29. Type A cases are above average, Type B cases are below average, Type C cases are average and Type D cases are just above average. An interesting finding is that in the category of the easier kind (Type A: simple and familiar), more NPD activities are executed than in the more complex ones, which contradicts the preliminary thoughts of the project leaders of the design agencies.
Results: the Existence of NPD Activities If we compare the 54 NPD activities from NPD theory and the NPD activities executed in the various cases, we see the following results (the score per activity is put in brackets in bold type after the description of the activity): 1. Development of company’s competences by carrying out technological research, hiring new staff, etc. (0) 2. Stimulating the generation of new ideas, or new applications of existing ideas, etc. (1) 3. Carrying out explorative market research by scanning the competitive environment, looking for trends in the market, searching for needs, scientific developments, etc. (9) 4. Adjusting the strategic plans of the company (0) 5. Appointing a project leader (10) 6. Preliminary description of the idea for a new product or a new challenge in the marketplace (8) 7. Analysing present product portfolio of the company (6)
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8. Development of preliminary programme of requirements of the new product (11) 9. Investigating the commercial feasibility of the new product (9) 10. Generating principle solutions for the new product (5) 11. Forming the project team (11) 12. Determining and analysis of the target group (9) 13. Building and testing of experimental prototypes (9) 14. Generating and evaluating ideas for the new product (9) 15. Preliminary planning for the total NPD process (11) 16. Patent search (7) 17. Carrying out the technical feasibility of the new product (11) 18. Developing and testing concepts for the new product (11) 19. Analysing competitive products (9) 20. Developing the manufacturing plan for the new product (make-or-buy decision) (8) 21. Developing the promotion plan for the new product (7) 22. Detailed design of the new product (11) 23. Carrying out user tests (6) 24. Finding and selecting suppliers (8) 25. Building prototype (10) 26. Testing prototype (10) 27. Building manufacturing facilities (8) 28. Making technical drawings of the new product (11) 29. Developing tooling (10) 30. Testing new manufacturing facilities (7) 31. Debugging manufacturing process (5) 32. Sales forecasting of the new product (6) 33. Debugging the new product (10) 34. Coaching the first production runs (8) 35. Developing maintenance plans for the new product (0) 36. Training staff in assembling the new product (5) © 2008 The Author Journal compilation © 2008 Blackwell Publishing
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37. Testing promotion plan for the new product (1) 38. Developing maintenance documents for the new product (7) 39. Training staff in installing the product (1) 40. Promoting the new product (1) 41. Training staff in maintaining the new product (0) 42. Developing the distribution plan for the new product (3) 43. Training users (7) 44. Evaluating the NPD process (10) 45. Certifying the new product (7) 46. Patenting the new technology (6) 47. Manufacturing the new product (0) 48. Assembling the new product (1) 49. Distributing the new product (0) 50. Selling the new product (0) 51. Installing the new product (0) 52. Maintenance and servicing the new product (0) 53. Recycling the new product (0) 54. Eliminating the new product (0) We see that seven different NPD activities are carried out in all 11 cases. NPD activities that are essential for all cases are: 8. Development of preliminary programme of requirements of the new product (11) 11. Forming the project team (11) 15. Preliminary planning for the total NPD process (11) 17. Carrying out the technical feasibility of the new product (11) 18. Developing and testing concepts for the new product (11) 22. Detailed design of the new product (11) 28. Making technical drawings of the new product (11) This seems to be a real minimal NPD process. You start with an NPD team, make a preliminary plan for the project, start thinking about the product requirements, generate concepts, check the technical feasibility of these concepts, start detailing the product design and finally record everything in the technical drawings. It is indeed simple, it has an engineering bias, but it could work! Probably because of the other level of abstraction, this minimal process has more steps than the minimal process Cooper reported (seven steps now compared with only two from Cooper’s study). Eleven NPD activities have not been carried out in any of the cases (nos 1, 4, 35, 41, 47, 49, 50, 51, 52, 53 and 54). This involves two pre-NPD activities (nos 1 and 4) and seven post-NPD activities (nos 47, 49, 50, 51, 52, 53 and 54). These post-NPD activities include activities for manufacturing, sales, © 2008 The Author Journal compilation © 2008 Blackwell Publishing
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maintenance and recycling, which are operational activities the NPD team has normally nothing to do with. These activities are included in the theoretical list of 54, because during design you have to be fully aware of these downstream aspects. Because the products of our second empirical study were available on the market (a must for being a participant in the Dutch design competition) these post-NPD activities must have been carried out by the company, but were apparently outside the scope of the interviewees (NPD project leaders). The two pre-NPD activities that were not carried out (nos 1 and 4) are related to corporate strategy, HRM and basic technological research. These are activities at a corporate level, which are indeed very influential for product innovation, but are also outside the scope of an NPD project leader. The two core NPD activities that have not been carried out in our cases are no. 35 The development of maintenance plans for the new product, and no. 41 Maintenance training. Apparently, as in our first exploratory study, in our 11 cases the subject of maintenance seems not so important or is ignored. Besides finding a minimalist NPD process, we were also able to find a kind of regular NPD process. We define regular here as the NPD process with the maximum number of activities which are executed in at least two cases in all four type categories (so the minimum score per activity should be 8). The regular NPD process looks like this: • Appointing a project leader (10) • Preliminary description of the idea for a new product or a new challenge in the marketplace (8) • Development of preliminary programme of requirements of the new product (11) • Investigating the commercial feasibility of the new product (9) • Forming the project team (11) • Determining and analysis of the target group (9) • Building and testing of experimental prototypes (9) • Generating and evaluating ideas for the new product (9) • Preliminary planning for the total NPD process (11) • Carrying out the technical feasibility of the new product (11) • Developing and testing concepts for the new product (11) • Analysing competitive products (9) • Detailed design of the new product (11) • Building prototype (10) • Testing prototype (10)
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• Building manufacturing facilities (8) • Making technical drawings of the new product (11) • Developing tooling (10) • Debugging new product (10)
for the original suggestion of the experienced project leaders of the design agencies that simple products require a simple NPD project?
This regular NPD process has 19 activities which represent a realistic product innovation process. There is a project leader and a team, a project plan, a product idea, requirements, generating of ideas and concepts, the target group and the competition is investigated, both commercial and technical feasibility studies are carried out, the product design is detailed, prototypes are built and tested, technical drawings are made, tooling and other manufacturing equipment is built and minor start-up problems in the new product are cured. It looks like a balanced NPD process. If we compare the results over the four categories of NPD projects, we see some differences (see Table 2). If we look at the number of activities that are carried out in all cases per type category, we see that for the Type A cases, 26 activities are executed in all cases, 10 activities are carried out in only one of the cases and 18 activities are not carried out. If we omit the 10 NPD activities that have not been carried out by any of the cases of all four types, you could say that for the Type A cases 59 per cent of all NPD activities is equal (26 of 44), and 41 per cent is different. In the Type B cases, 15 activities are carried out in all cases, five activities are carried out in only one of the cases, so equality here is 34 per cent. For the Type C cases, the number is 17 which give an equality of 39 per cent. For the Type D cases the number is 16, which is an equality of 36 per cent. The number of activities not carried out decreases from 18 for the Type As to 15 for the Type Cs and Ds. This could suggest that Type A is less demanding than the other three. If we also include the equality over the types, then it would suggest that Type A (Simple & familiar) is different compared to the other three types of NPD projects. Is this marginal evidence
Results: Patterns in NPD Projects We have now only analysed the existence of certain NPD activities (whether this activity was executed in one of the cases). But it is also interesting to see whether we can identify different patterns of activities. To do so, we have divided the NPD process into sub-stages of 20 per cent duration time each, and counted the activities which were carried out in each sub-stage. Activities which were present in more than one sub-stage are counted as separate activities in all the relevant stages. Compared to the analysis in the previous section, we have many more NPD activities now, because if an activity is executed in three sequential sub-stages, it is counted three times. An overview of all the sub-stages over the four types of NPD projects is shown in Table 3. The Type As are doing the fewest NPD activities and the Type Bs the most. Type Cs are average and the Ds are doing a little more. It also shows that the As do little in the last two sub-stages and relatively more in the substages 2 and 3. The Bs do a lot in the first and the last stages. The Cs are above average in sub-stage 1 and below average in sub-stages 2 and 3. The Ds are above average in sub-stage 2, and below in sub-stage 5. We also looked at the kind of sequences in which the NPD activities were carried out. Table 4 shows an overview. This table needs some extra explanation, because the score 3–19, for instance for Type A in sub-stage 1, does not mean that all possible NPD activities between 3 and 19 were executed, but it gives an indication which kind of NPD activities were done in such a sub-stage. These sequences are derived from the theoretical list of 54 NPD activities (see Table 1). The empiri-
Table 2. Number of NPD Activities per Category
In 3 cases In 2 cases In 1 case In 0 cases Total no. of NPD activities
Type A
Type B
Type C
Type D
Not relevant* 26 10 18 54
15 17 5 17 54
17 13 9 15 54
16 17 6 15 54
* In the Type A category, only two cases are present.
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Table 3. NPD Activities per Sub-stage for the Four Types of NPD Projects Sub-stage in %
1–20 (sub-stage 1) 20–40 (sub-stage 2) 40–60 (sub-stage 3) 60–80 (sub-stage 4) 80–100 (sub-stage 5) Total no. of NPD activities
Type A
Type B
Type C
Type D
Average
No.
%
No.
%
No.
%
No.
%
No.
%
18 17 14 7 8 64
29 25 22 11 13 100
32 12 12 14 22 92
35 13 13 15 24 100
29 13 8 14 19 83
34 16 10 17 23 100
26 20 12 16 15 89
29 23 13 18 17 100
26.25 15.50 11.50 12.75 16.00 82.00
31 19 14 16 20 100
Table 4. Sequences of NPD Activities Sub-stage in %
Type A
Type B
Type C
Type D
1–20
3–19
20–40
8–26
40–60
2–24 32* 10–28 45*, 46* 13–28
60–80
20–33 45*, 46* 27–33
3–19 32* 13–28 37*, 46* 9–32
80–100 Total no. of NPD activities
34–48 64
9–32 40* 16–46 92
20–29 43* 20–44 83
3–23 42* 8–24 32*, 37* 15–29 45* 21–38 45*, 46* 21–45 89
* indicate exceptional NPD activities for that sub-stage.
cal data, however, show that inside those sequences a lot of the activities are carried out in parallel. But no sub-patterns emerged. It seems as though project leaders use a very opportunistic attitude: they try to execute as many activities in parallel as possible. The opportunities are neither type-based nor typespecific, but are mainly the result of the availability of resources. Based on the raw data, we see that the Type As carried out 18 NPD activities in the first sub-stage. Those 18 range from activity no. 3 ‘Exploratory market research’ to no. 19 ‘Analysing competitive products’. Six activities were executed in all Type A cases (= 12) and six others (makes the total 18) were carried out only once, but not necessarily in one case. The exceptional scores are labelled exceptional because the activity was carried out at a time that was strange compared to its place on the original overall list, which is following more or less the logical order of the NPD process. An interesting result in this respect is, © 2008 The Author Journal compilation © 2008 Blackwell Publishing
for instance, activity no. 32 ‘Sales forecasting of the new product’, which on the original list is placed near the end of the NPD process, but in two of the cases it was carried out during the first sub-stage. The same is true for activity no. 42 ‘Developing the distribution plan’ which is also logical towards the end, but for one Type D case (New & complex product) it was found important to start thinking about the way to distribute this new product quite early, because that could influence the packaging design of the new product. This table is only meant to show different sequences of related NPD activities. A tentative conclusion could be that the two types which score high on the newness axis (Types B and D) execute more NPD activities than the other two. Looking to the NPD sequences shows that Types C and D (high on complexity) execute in the first sub-stage more and different activities than the other two types. While the others start detailing the design (activity no. 22) in sub-stage 2, the two
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complexity types start doing that already in sub-stage 1. Once again, does this represent some small support for the original suggestion, that complex products need a complex NPD project?
Conclusion and Discussion In the preliminary theoretical study we distinguished 54 different NPD activities that describe the commonalities of 90 different theoretical models on NPD. According to this summarized overview, the maximum NPD process can have 54 different activities. In the first exploratory empirical study, we analysed these activities. Most NPD projects do not use all theoretical activities to the full. Our four cases together covered nearly all activities, but the two detailed cases described used only 26 and 27 different activities. The theoretical NPD models are much broader than NPD reality requires. Our minimal NPD process shows only seven different activities, whilst our ‘regular’ process uses 19 different NPD activities. These are rather smaller numbers than the theoretical 54! We also discovered how experienced project leaders from design firms categorize their projects in a very specific way before they plan and organize them. They use the ‘complexity of the product’ and ‘the familiarity of the project’ as main drivers to guide their planning and timing of activities for future NPD projects. Doing so, there are four categories of NPD projects: Type A familiar + simple, Type B unfamiliar (new) + simple, Type C familiar + complex and Type D unfamiliar (new) + complex. Based on their experiences, the idea was that simple and familiar cases need fewer steps and less duration time than the complex and new cases. They used this idea to plan their future NPD projects. In the main empirical study we checked this planning idea and also looked for more details between the four categories of NPD projects. Analysis of NPD activities and their duration times of 11 detailed cases of prize-winning NPD projects executed within 11 different companies shows interesting differences and commonalities between the four categories. We can conclude that a familiar NPD project on a simple product (Type A) differs strongly in the number of NPD activities that are executed in comparison to the other types of NPD projects (Types B, C and D). In this respect it supports the original planning idea that simple projects require a simple process, but the differences between the other three categories are less clear and the results are inconclusive.
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We have found a minimal NPD process, where you start with an NPD team, make a preliminary plan for the project, start thinking about the product requirements, generate concepts, check the technical feasibility of these concepts, start detailing the product design and finally record everything in the technical drawings. We also found a process that we indicate as a ‘regular’ NPD process, that has 19 activities and looks very convincing as a realistic product innovation process. There is a project leader and an NPD team, a project plan, a product idea, requirements, generating of ideas and concepts, the target group and the competition is investigated, both commercial and technical feasibility studies are carried out, the product design is detailed, prototypes are built and tested, technical drawings are made, tooling and other manufacturing equipment is built and minor start-up problems in the new product are debugged. On the patterns of NPD projects we can conclude that in all types of NPD projects most activities are carried out at the early stages of the project. NPD projects on new products (Types B and D) contain the most activities in the total project, with a peak in the beginning and at the final stage of the project. Complex NPD projects (Types C and D) execute even more activities in the first stage of the project, and also different activities from projects that are not so complex. For instance, in complex projects we found that ‘detailing the design’ started already in the first stage of the project. We also found that NPD project leaders adopt an opportunistic attitude towards carrying out NPD activities in parallel in order to gain time. Their message is: if you can do activities in parallel, please do. The results from these two empirical studies provide extra ‘luggage’ for applying carefully the well-known theoretical models of the NPD process. The experience provided by 11 successful project leaders may help novices to build their own expertise in a more profound and reflective manner. Now the real action planning for NPD projects can begin! We hope this exploratory study will give fuel to other empirical studies on the NPD process in order to balance the prescriptive NPD theorists, which will help NPD practitioners with their difficult work.
Acknowledgements The author would like to thank Aernout van der Zee who executed the preliminary study and did the original interviewing. Rianne Valkenburg, Marc Tassoul as well as the © 2008 The Author Journal compilation © 2008 Blackwell Publishing
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anonymous reviewers of CIM made stimulating suggestions for improving the text. This research was carried out in the Netherlands during 2003 and 2004. Reporting the results was done in 2006 and 2007.
References Andreasen, M.M. and Hein, L. (1985) Integrated Product Development. IFS (Publications) Ltd/ Springer Verlag, Bedford/Heidelberg. Archer, B.L. (1971) Technological Innovation – A Methodology. Inforlink, London. Buijs, J.A. (1984) Innovatie en Interventie. Kluwer, Deventer. Buijs, J.A. (1987) Innovatie en Interventie. 2nd updated version. Kluwer, Deventer. Buijs, J.A. (2003) Modelling Product Innovation Processes, from Linear Logic to Circular Chaos. Creativity and Innovation Management, 12, 76–93. Buijs, J.A. and Valkenburg, R. (2005) Integrale Productontwikkeling. Lemma, Utrecht. Cooper, R.G. (1983) The New Product Process: An Empirically Based Classification Scheme. R & D Management, 13, 1–13. Cooper, R.G. (1984) The New Product Process: A Decision Guide for Management. Journal of Marketing Management, 3(3), 238–55. Cross, N. (1994) Engineering Methods, Strategies for Product Design, 2nd edn. John Wiley & Sons, Chichester. Koen, P., Ajamian, G., Burkart, R., Clamen, A., Davidson, J., D’Amore, R., Elkins, C., Herald, K., Incorvia, M., Johnson, A., Karol, R., Seibert, R., Slavejkov, A. and Wagner, K. (2001) Providing Clarity and a Common Language to the Fuzzy Front End. Industrial Research Institute, Washington. Pahl, G. and Beitz, W. (1984) Engineering Design: A Systematic Approach. Design Council, London.
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Roozenburg, N.F.M. and Eekels, J. (1995) Product Design: Fundamentals and Methods. John Wiley & Sons, Chichester. Saren, M.A. (1984) A Classification and Review of Models of the Intra-Firm Innovation Process. R&D Management, 14, 11–24. Schrage, M. (2000) Serious Play. MIT Press, Cambridge. Ulrich, K.T. and Eppinger, S.T. (1995) Product Design and Development. McGraw-Hill, New York. Van der Zee, A. (2003) Proceskeuze in de productontwikkeling. Unpublished masters thesis, Faculty of Industrial Design Engineering, University of Delft. VDI 2221 (1986) Methodik zur Entwicklung und Konstruiren technischer Systeme und Produkte. Verein Deutscher Ingenieure, Düsseldorf.
Jan Buijs (
[email protected]) is a professor in Product Innovation and Creativity at the Faculty of Industrial Design Engineering at the Delft University of Technology. He has been active for more than 30 years as a teacher, researcher and consultant. He has worked with many different organizations: from low-tech SMEs to high-tech multinationals, from hospitals to insurance companies, from consulting firms to universities. His work has ranged from helping start-ups to closing down mature industries. His current research interests include multidisciplinary innovation teams and the relationship between branding and NPD. From 2000 to 2007, he was chairman of the European Association for Creativity and Innovation (EACI), a not-for-profit organization which is bridging the gap between academics and professionals in the field of innovation and creativity (http://www.EACI.net).
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Book Review Chesbrough, H., Vanhaverbeke, W. and West, J. (2006) Open Innovation: Researching a New Paradigm, Oxford University Press, Oxford. 373 pp, ISBN 978-0199290727.
In his book Open Innovation: The New Imperative for Creating and Profiting from Technology, Chesbrough (2003) coined the term ‘open innovation’. Based on close observation of a small number of companies, Chesbrough (2003) described an innovation paradigm shift from a closed to an open innovation model. In a closed innovation model, firms internalize their firm-specific R&D activities, and commercialize them through internal development, manufacturing and distribution processes. In contrast, an open innovation model is characterized by the use of purposive inflows and outflows of knowledge to accelerate internal innovation, and expand the markets for external use of innovation, respectively. While the closed innovation model considers R&D as an inherent part of a vertically integrated system within firms, the open innovation model treats R&D as an open system in which external ideas and external paths to market are placed on the same level of importance as that reserved for internal ideas and paths to market. Recently, numerous companies (e.g., IBM, Intel, P&G) have started to adopt the concept of open innovation. Nowadays, some managers even argue that ‘open innovation is no longer a source of competitive advantage, it has become a competitive necessity’. At the same time, the concept of open innovation only slowly gained acceptance among academic researchers. As a result, the risk emerged that open innovation would become a managerial fad instead of a theoretically sound concept. In order to address this issue, Henry Chesbrough, Wim Vanhaverbeke and Joel West decided to write a new book on open innovation that aimed to engage the academic community in a dialogue about researching the processes of innovation. These efforts have resulted in Open Innovation: Researching a New Paradigm. This book consists of 14 chapters, encompassing both conceptual and empirical studies by 15 researchers. It needs to be stressed that this book is more than a simple collection of different studies on open innovation. In my opinion, the editors
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have successfully managed to integrate the different chapters into a coherent volume in which the ideas, results and implications of the different studies are explicitly linked to each other. This book contributes to clarifying and expanding the open innovation paradigm in four different ways. First, this book addresses some of the main misunderstandings that are present regarding the open innovation concept. While some people have considered open innovation to be a substitute for internal R&D activities, this book illuminates that internal R&D is an inherent part of the open innovation system. Moreover, this book illustrates that open innovation can even help firms in improving the effectiveness and efficiency of internal R&D efforts. In Chapter 3, for instance, Gina O’Connor provides case-based evidence that the relationship between the internal infrastructures to support longterm innovative activity and the mechanisms created to access external technologies is complementary. Another common misunderstanding is that open innovation is a synonym for open source. This book makes clear that, although both concepts are related, they are not the same. In Chapter 5, for instance, Joel West and Scott Gallagher try to connect the two concepts by identifying the conditions under which firms will choose to incorporate open source technologies into their overall innovation efforts, and develop open innovation business models. This book not only provides an excellent state of the art on open innovation, it also makes a commendable effort in expanding its application domain. For instance, while existing open innovation research has mainly applied a firm level perspective, this book provides first indications of the relevance of the open innovation paradigm on other levels of analysis such as the group level (see Chapter 2) or the inter-firm network level (see Chapters 10, 11, 12 and 13). Moreover, in the final chapter, Joel West, Wim Vanhaverbeke and Henry Chesbrough provide an extensive research agenda, providing numerous avenues © 2008 The Author Journal compilation © 2008 Blackwell Publishing
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for future research in order to further expand the open innovation paradigm. As a third contribution, this book provides new insights into the relationship between the open innovation paradigm and other important phenomena such as IP management and inter-firm networks. Several chapters are devoted to the impact of IP on open innovation. These chapters illustrate that, for some classes of innovations, IP plays a key role in providing appropriability, thus allowing open innovators to get returns on their internal innovations. At the same time, however, these chapters provide examples where IP laws seem to hamper the process of open innovation. In Chapter 7, for instance, Kira Fabrizio provides indications that increased university patenting has also increased the lag of citation to prior research, suggesting a slowing down of firm exploitation of existing technologies. Another phenomenon that increasingly gains attention is the inter-firm network. Although the open innovation concept and the phenomenon of inter-firm networks seem to be closely related, surprisingly limited research has tried to connect both concepts. In this book, first efforts are made in this respect. In Chapter 11, Caroline Simard and Joel West provide a conceptual framework that provides insights into how different kinds of inter-firm ties can enable open innovation. In Chapter 13, Wim Vanhaverbeke and Myriam Cloodt introduce the concept of value constellations in order to link inter-firm networks and open innovation strategies in the commercialization phase of business trajectories. Looking from an open innovation perspective, this book also identifies a number of new managerial challenges for innovating firms. In Chapter 2, Henry Chesbrough argues that firms should not only address the classical Not Invented Here (NIH) syndrome, but should also manage the risk of the Not Sold Here (NHS) syndrome. In Chapter 8, Timothy Simcoe identifies numerous managerial challenges for firms that participate in Standard Setting Organizations. In Chapter 12, Markku Maula, Thomas Keil and Jukka-Pekka Salmenkaita emphasize the need for network
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management in order to realize systemic innovations. It needs to be stressed that, in this book, these issues are illustrated with relevant and interesting examples, providing the readers with a clear picture of the managerial challenges at hand. At the same time, I also would like to make two critical remarks. In Chapter 10, Wim Vanhaverbeke explicitly mentions that ‘The challenge to relate Open Innovation to an integrated approach of the existing theoretical perspectives has just begun. This is a most promising area for future research.’ Despite this explicit recognition of the need to elaborate on the theoretical foundations of the open innovation paradigm, this book lacks such coherent theoretical framework. While several chapters refer to theoretical perspectives such as the transaction cost theory, the resourcebased view or social network theory, an explicit attempt to connect these different theories to the open innovation paradigm remains absent. Second, while this book provides valuable insights into how the open innovation paradigm can be expanded, it remains relatively silent on how it should be delineated. As a result, it was not always clear to me where open innovation began and where open innovation ended in this book. Nevertheless, if the open innovation paradigm wants to be more than a managerial fad, it also should become clear under which circumstances the open innovation paradigm is not a relevant perspective. Despite these critical remarks, I am convinced that this book substantially contributes to our understanding of the open innovation concept. In addition, this book clearly indicates a number of existing gaps in the open innovation literature and provides a lot of interesting suggestions to advance our knowledge in this respect. I therefore would like to recommend this book to all academics, practitioners and policy makers that are engaged in innovation management in general and open innovation in particular. Dries Faems University of Twente
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Book Review Charles Landry (2007) The Art of City Making, Earthscan, London. 462 pp, ISBN 978-1-84407245-3.
A More Positive City Debate At the beginning of this century, the urban economic debate described cities mainly in a negative or neutral sense. The debate focused on serious problems such as crime, pollution, traffic jams, concentration of low incomes and vulnerable groups, environmental problems, suburbanization, brain drain, unemployment, etc. It seemed as if the positive ideas of Jane Jacobs on how to become a dynamic and vibrant city had become outdated or forgotten. Recently, we have seen a paradigm shift in the way cities are analysed. Positive aspects of cities now dominate the debate. In the Netherlands, the regional and urban economic policy of the Ministry of Economic Affairs has the distinct title ‘Peaks in the delta’ and the Ministry of Spatial Planning and Environment focuses its city renewal policy on so-called power neighbourhoods (Krachtwijken). Cities are innovative and creative places, which strengthen the regional and national economy and where people feel at home, want to live and make a career. The city is characterized as vibrant, challenging, dynamic, open and tolerant, entrepreneurial and, of course, creative. This book review describes the important contribution of The Art of City Making by Charles Landry to the changing more positive city debate. Landry sees a new zeitgeist. He focuses on how cities become an imaginative city. Imaginative cities are vibrant but are also capable of finding a balance between economic growth and sustainable development.
Imaginative Cities instead of Creative Cities As an economic geographer, I welcome the increased scientific and societal interest in (economic) geographical city matters. The popularity of works by Richard Florida and Charles Landry have helped to place the urban and neighbourhood economy and, in particular, the importance of (innovative) entrepreneurship for a city high on the urban agenda.
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The extensive publications Who’s Your City by Florida (2008) and The Art of City Making by Landry (2007) are examples of this paradigm shift. Florida regards cities as peaks in an increasingly flat world, and Landry focuses on the (re)development process of cities in becoming an imaginative place. Landry’s book elaborates on the global analysis of Florida that cities matter and that they are peaks in the economic landscape. In a practical way, The Art of City Making describes the art of creating imaginative cities and of becoming places of passion and compassion. Innovation studies on cities should focus on how cities can be more appealing instead of focusing on how to become the most creative city. The Art of City Making appeals for an organic approach stimulating innovation. Landry criticizes the narrow-minded economic approach of the creative city. This is interesting because Landry and Richard Florida themselves are founding fathers of the actual creative cities hype. In the words of Landry, this hype leads to a simplistic competition between cities, who dream of achieving an attractive, creative image, thus helping to attract creative firms as far as possible. The creative city approach has lead to a tunnel vision and dominant economic belief in blueprints and best practices how to become a creative city. Landry rejects these economic laws of the creative city and focuses on mobilizing and exploiting the creative potential of all the inhabitants, entrepreneurs and visitors to a city. A creative city is not about the creative class but it is about the creative process to exploit the hard and software (civic creativity) of a city. The first sentence of his book is clear on this: ‘city-making is a complex art; it is not a formula’ (p. 1).
A New Zeitgeist The Art of City Making is a positive book in an insecure time in which the balance between localization and globalization is a continuous challenge. It is a broad overview of interesting and inspiring ideas and stories, which are © 2008 The Authors Journal compilation © 2008 Blackwell Publishing
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helpful in the drive to become a more imaginative city. Landry’s focus on imaginative cities is not solely driven by economics, but also by his humanistic idealism: inhabitants of a city should benefit, the city offers an open and tolerant atmosphere (ethical values) and the creativeness of all people is stimulated. On the one hand, The Art of City Making is a warm plea for social-economic prosperity of urban inhabitants. On the other hand, the book stresses the national importance of urban economics and a clear urban economic agenda: ‘it is cities that are the driving forces of national economies and it is relations and flows that reveal more about dynamics than quantities of attributes such as population’ (p. 200). A striking example is the social organization Katha, which works in Delhi’s largest slum. Their slogan is ‘uncommon creativities for a common good based on uncommon education’. Katha is famous for its endeavour to spread the joys of reading, knowing and living amongst adults and children, the experienced reader as well as the neo-literate (http://www.katha.org). Until 2010, Katha focused on wisdom, emotional, cultural and social capital of the participants. This was realized by life-long learning. If you visit the website, you feel the positive and creative approach of this project and the notion that poor people can also create imaginative neighbourhoods. Other inspiring examples in the book are the city safari in Rotterdam, which is an economic development project aiming to bring resources to local people rather than through intermediaries. Offices of Time in Italy reorganize time in more flexible ways to meet new needs, especially those of women, who often juggle their timetables at work and at home. Chapter 3 discusses the fundamental question by Jacobs regarding the impact of a shopping mall or outlet centre on the city economy and inner city areas in particular. Landry is very clear on this. He states that malls and outlet centres: ‘tore older cities apart, . . . drained the city of its lifeblood, [led to a] decline of local shopping [and] it has helped the process by which chains have become ever dominating’ (p. 129). I fully agree with his plea to stop the suburbanization of economic activities to the edges of cities and no longer listen only to market forces, short-term consumer needs, and the feeling that to be is to buy. This continuous suburbanization caused the loss of ‘the walkable place where living, working and having fun are in close proximity, with doctors and dentists nearby, schools accessible, park’ (p. 130). In the Netherlands, the building of a large mall near Tilburg led to a similar discussion. It is striking that despite the negative © 2008 The Authors Journal compilation © 2008 Blackwell Publishing
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impact on the inner city of Tilburg (and cities such as Den Bosch and Breda) and the carbased location on the edge of the city, the city council of Tilburg supports this new mall. The support for a new mall is based on market research to maximize consumer needs and regional labour market advantages. The first years a mall leads to hundreds of jobs, but the long-term effect is unclear. The ideas of the New Economics Foundation illustrate Landy’s criticism (http://www.neweconomics.org). Landry concludes that local shops close wherever large chains such as Tesco open. The New Economics Foundation typifies this process of homogenization of cities as ‘clone towns’. In their opinion, retail spaces once filled with a thriving mix of independent butchers, newsagents, tobacconists, pubs, bookshops, greengrocers and family-owned general stores are quickly filled with faceless supermarket retailers, fast-food chains, mobile phone shops and global fashion outlets. Chapter 5 offers an interesting description of city trends, which are useful for cities thinking about their future. Two hot political themes are described extensively and positively: the multicultural debate and the world climate challenge. Most interesting is Landry’s view on the multicultural debate. He strives for an intercultural city idea, which switches the focus: ‘instead of discussing diversity as a dilemma, it asks what is the diversity advantage of cities that can be achieved through intercultural exchange and innovation’ (p. 256). This refreshing view on the narrow-minded and controversial integration debate is more than welcome. For further reading on interculturalism, the book The Intercultural City: Planning for Diversity Advantage, written by Phil Wood and Charles Landry, is recommended (Earthscan, 2007). Landry also offers suggestions for climate neutral cities and a search for projects that add value economically and reinforce ethical values or balance between individual wants and collective and planetary needs. In this respect, Landry points to cities such as Freiburg – where car use remained stable despite growth in the population – and Vauban, where local job creation and local sourcing are important. In his opinion, sustainable development leads to imaginative cities.
Conclusion The book has three main problems. Firstly, the book is written from the perspective of a consultant and, therefore, the scientific framework has been neglected. The consultant perspective and the consultant language harm the
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inspiring and refreshing message of the book. The selection of inspiring cases/cities, examples and stories lacks a clear framework and is too much a summary of Landry’s own experience. In addition, parts of the book, such as Chapters 5 and 6, are more like a toolkit for successful process managers. The effort could have been put to better use by constructing a mental framework and a useful toolkit for cities to become more imaginative. Therefore, The Art of City Making sometimes feels like a best practice guide, which is a pity. Finally, Landry correctly criticizes the hype and the narrow-minded focus of aspiring creative cities on the creative industry and the creative
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class. The importance of creative city making is reiterated and popularized too frequently. After reading more than 400 pages, the word ‘creative’ causes irritation. As a researcher working at the Netherlands Institute of Cities Innovation Studies (Nicis Institute), I find The Art of City Making an inspiring handbook and reference book for further reading about city innovation, city diversity, the relationship between culture and economy and sustainable cities. Dr Cees-Jan Pen Nicis Institute The Hague
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