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Int. J. Technology Management, Vol. 41, Nos. 1/2, 2008 111 2 3 4 5 6 7 8 9 1011 1 2 3 4 5 6 7 8 9 2011 1 2 3 4 5 6 7 8 9 30 1 2 3 4 5 6 7 8 9 40 1 2 3 4 5 6 711 8

Flexibility in innovation through external learning: exploring two models for enhanced industry–university collaboration Sigvald Harryson* Visiting Associate Professor, Copenhagen Business School Program Director, Baltic Business School of Kalmar University, The Growth Through Innovation Program, SE-391 82 Kalmar, Sweden Fax: +46-480-497110 E-mail: sh.ivs@cbs.dk *Corresponding author

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Sandra Kliknaite Baltic Business School and Lund University, The Growth Through Innovation Program, SE-391 82 Kalmar, Sweden Fax: +46-480-49710 E-mail: Sandra.kliknaite@hik.se

Rafal Dudkowski Baltic Business School and St. Gallen University, The Growth Through Innovation Program, SE-391 82 Kalmar, Sweden Fax: +46-480-49710 E-mail: Rafal.dudkowski@hik.se Abstract: This paper draws on extensive theoretical research and literature reviews, and presents two cases to illustrate practical applications. It addresses the problem of how learning both from extracorporate sources, like universities, as well as across internal corporate functions, like R&D and manufacturing, can enhance company flexibility and performance in innovation.

This paper aims at delivering a new theoretical rationale for industry–university (I-U) learning alliances as a natural way out from the managerial problem of trying to perform both exploration and exploitation within the same company boundaries. Through our theoretical framework, the academic science domain becomes a logical partner to handle the full phase of exploration and support the process of exploitation. The presented cases of Packman and HiFiPower offer new insight into how to perform this act in practice.

Keywords: exploration and exploitation; flexibility; industry–university collaboration; innovation management; organisational learning; networks; weak ties.

Reference to this paper should be made as folows: Harryson, S., .

Kliknaite, S. and Dudkowski, R. (2008) ‘Flexibility in innovation through external learning: exploring two models for enhanced industry–university collaboration’, Int. J. Technology Management, Vol. 41, Nos. 1/2, pp.109–137.

Biographical notes: Dr Sigvald Harryson is a Visiting Associate Professor at Copenhagen Business School, and the Program Director of Managing Growth Through Innovation at the Baltic Business School in Sweden. Prior to his academic career, he spent 9 years in management consulting including a

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S. Harryson, S. Kliknaite and R. Dudkowski position as Partner and Leader of the Technology and Innovation Management (TIM) Practice within Arthur D. Little. He received a magna cum laude doctoral degree in Japanese R&D Management from the St. Gallen University in 1995, and completed a PhD in Knowledge and Innovation Management at the Göteborg School of Economics in October 2002. He has published four books and in journals such as Harvard Business Review, Journal of Product Innovation Management and Strategy+Business. .

Sandra Kliknaite is a post graduate research associate in the field of managing growth through innovation in general and strategic intelligence in particular. Her research position centres around the Baltic Business School in Sweden, and she is pursuing the PhD Program in Business Administration at the University of Lund. She holds a Master’s degree in International Management and Leadership (2003) from the Baltic Business School in Sweden; and a Master’s degree in International Trade from Vilnius University, International Business School.

Rafal Dudkowski is a post graduate research associate in the field of managing growth through innovation of the Baltic Business School in Sweden, while pursuing a PhD Program in Business Administration at the University of St. Gallen in Switzerland. He studied in Poland (Warsaw), Switzerland (St. Gallen) and Austria (Vienna) and holds a Master’s Degree (MSc Econ) in International Management and International Economic and Political Relations from the Warsaw School of Economics (CEMS). Prior to re-joining the academia, Mr Dudkowski worked for five years as a management consultant within the strategy consulting company Booz Allen Hamilton.

1

Introducing the dilemma of technological leadership

For long-term prosperity, companies must not only meet the needs and wants of today’s customers, but must simultaneously innovate to ensure the creation of new customers and the means of satisfying their future needs and wants – an ability that has been termed ‘organisational ambidexterity’ (Duncan, 1976; He and Wong, 2004; Tushman and O’Reilly, 1996). The emergence of new customers and creating the value for them is determined by the ability of the company to flexibly accumulate and deploy its tangible and intangible resources in innovative ways. An innovative organisation is characterised by the capacity to question and review itself in order to achieve a relationship of fit with the marketplace, creating its own competitive identity as a projection of the evolving environment (Llorens Montes et al., 2005; Verdú-Jover et al., 2005).

When the knowledge base of an industry is both complex and expanding, with widely dispersed sources of expertise, the locus of innovation will be found in networks of learning rather than in individual large firms (Powell et al., 1996).

Networked innovation (Tuomi, 2002), openness to innovation (Berthon et al., 1999), or open innovation (Chesbrough, 2003a,b) implies that companies have more permeable boundaries. Ideas can be spun-out from one organisation and spun-in to another organisation where they offer better complementarity with the innovation strategy and have a greater likelihood of reaching exploitation (Teresko, 2004).

The greater ability to identify and bring in external ideas and technologies enhances a company’s flexibility to respond to changing customer needs.

This well-known context of open innovation, coupled with accelerating technological complexity and shrinking product lives,1 creates an intractable dilemma for companies

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that rely entirely on internal technological development, which may cause internal ‘competence traps’.2 To aim for a radical, new solution means destroying, or at least neglecting, part of the accumulated technological knowledge. This – as well as any kind of unlearning3 – tends to be more difficult the more specialised the researchers, the more advanced the technology development process and the deeper the organisational architecture and legacy business model.4 Accordingly, the further the technology development process advances, the more it becomes irreversibly self-driven and closed to the dynamic factors that ought to guide it. In addition, due to the lacking networking abilities, the new knowledge generated by internal specialists is not well dissipated across the organisation and, therefore, causes further specialisation in isolation instead of transfer to design and manufacturing for transformation into innovation. The so critical technological and mainly tacit technological knowledge remains stuck in research as suggested by Figure 1.

Figure 1 The dilemma of technological leadership

Source: Own adoptions from Harryson (2002)

Our conclusion is that many MNCs have reached an inner limit in terms of flexibility and innovation ability due to excessive internal technological development. We call this the dilemma of technological leadership:

The dilemma of technological leadership is that successful pursuit of such tends to focus firms on intracorporate activities. This decreases their sensitivity and responsiveness to external technological and market factors that ought to guide innovation.

Moreover, the rigidity of typical technology problem-solving processes impedes cross-departmental collaboration, learning and knowledge transfer, which are vital enablers for flexible exploration and exploitation of innovation (Leonard-Barton, 1992; Lorange and Nelson, 1987). The technology development process becomes increasingly self-driven and irreversible (O’Connor and Rice, 2001; O’Connor et al., 2002; Kusunoki, 1992).

The tacit knowledge-base might increase at the level of specific individuals, but without systematically transforming into organisational knowledge that can drive speed and flexibility in innovation (Harryson, 1997; Hedlund, 1992).

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Clearly, this dilemma provides several rationales for external sourcing of specialised technologies and skills. In this context, it seems that new forms of organisational flexibility and learning alliances are required to adapt to substantial and uncertain changes in the environments (Lorange, 1996). Many firms turn to external sourcing strategies and learning partnerships to acquire new knowledge and reduce uncertainty in R&D. According to leading literature,5 the key factors contributing to this trend include:

the growth in cross-technology and field interdisciplinarity

the globalisation of technology and proliferation of sources

the necessity for rapid commercialisation at reduced risk and cost.

While these are perfectly valid arguments, they miss one perhaps equally important point, which is to use learning through external networking not only to acquire new knowledge, but also to improve the ways in which people learn from each other and transfer knowledge within the company.

An important finding from our research on a selection of technology innovation leadership companies in Japan6 and Europe7 is that external sourcing of technologies and skills does not have to result in a hollowing out of internal R&D capabilities.

In contrast, it seems to energise and create a unique flexibility to network tacit knowledge and disruptive technologies into innovation. This unique flexibility does not seem to have been revealed so far in the current literature on the topic – nor how to leverage learning alliances with universities across both extra- and intra-corporate levels to support both exploration and exploitation of innovation.

1.1

Industry–University collaboration to support learning both in exploration and exploitation

If innovation is viewed as the ability of organisations to adopt new ideas, processes or products, learning alliances between industry and academia can enhance both flexibility and speed of innovation. Indeed, Industry–University (I-U) collaboration is recognised as a critical form of learning alliance, and an essential instrument to gain speed and flexibility in technology innovation, while reducing cost in R&D. As stated by Etzkowitz and Leydesdorff: “Students are also potential inventors. They represent a dynamic flow-through of ‘human capital’ in academic research groups, as opposed to more static industrial laboratories and research institutes. Although they are sometimes considered a necessary distraction, the turnover of students insures the primacy of the university as a source of innovation.” (2000, pp.117–118)

As a consequence, we see the emergence of new and more flexible ‘triple-helix’ models of knowledge generation with an increased orientation toward the exploitation of publicly funded research (Etzkowitz, 2003; Leydesdorff and Etzkowitz, 1996).

However, while the degree of I-U collaboration is increasing, it is still widely recognised that such collaborations are exposed to significant learning barriers – summarised in Table 1.

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Table 1 Summary of learning barriers in I-U collaboration Main barriers

Accounts by authors

Lack of adequate resources on Industry and University side

Schartinger et al. (2001)

Cultural differences between Industry and University counterparts

Kruecken (2003)

Inflexible academic research timetables

Liyanage and Mitchell (1994)

Long-term orientation of academic research, tending to remain in exploration – vs. focus on short- and medium-term exploitation-oriented research by companies

Beise and Stahl (1999), Chiesa and Piccaluga (2000), Gonnard (1999), Howells and Nedeva (2003), Liyanage and Mitchell (1994), Mansfield and Lee (1996), Meyer-Kramer and Schmoch (1998)

Incompatible reward systems with focus on publishing vs. protecting results

Howells et al. (1998), Santoro and Chakrabarti (1999)

Risk related to obtaining control over University inventions through IP rights

Graf et al. (2002), Rappert et al. (1999)

I-U collaborations struggling with exploitation of too premature technologies

Thursby and Thursby (2003)

Many of these barriers relate to a fundamental difference between corporate and academic research: Scientific knowledge produced by companies is usually claimed to be short- and medium-term oriented, aiming at exploitation, whereas the strength of academic research is claimed to prevail in exploration, but seldom comes up with results ready for commercialisation. As a consequence, I-U collaborations are often struggling with exploitation of too premature technologies.

Addressing these learning barriers, we propose two distinct I-U collaboration models – both aimed at effective learning between academic and corporate research, but with a different balance between exploration and exploitation and a different focus on weak and strong ties across open and closed networks. To start with, we will introduce the different types of networks required for exploration and exploitation. We then link this to the learning dimension of knowledge creation in theory, before we move to practice with the two learning-models.

1.2

The strength of weak ties

Granovetter (1973, p.106) is the pioneer in highlighting and exemplifying the importance of weak ties in linking otherwise unconnected networks. He argues that individuals with few weak ties have difficulties in being up-to-date with information from distant parts of the social system, and that ‘social systems lacking in weak ties will be fragmented and incoherent’. In the context of innovation, he argues that new ideas more often emanate through weak ties from the margins of a specific network rather than through strong ties from its core or its nucleus. Accordingly, the relative strength of weak ties can transform

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marginal idea-creating networks into a new nucleus of innovation. This argument poses new challenges to the science of innovation management: if the idea creation process is centred within and around marginal networks and their relatively unstructured weak ties, it becomes difficult to manage the main source of innovation and hence also difficult to control the innovation process as a whole – at least if attempted to do it all within one and the same company. It may be that innovation requires management of both weak and strong ties cutting across both peripheral and core networks with a strong focus on developing and managing relationships for transfer and transformation of information into innovation.

Hansen (1999) uses a network study to explore how weak inter-unit ties help a new product development team with purposeful knowledge-sharing. His findings are that while weak ties help the team find new knowledge located in other units, the weak ties are not useful in supporting the actual transfer of complex knowledge. The more complex the knowledge, the stronger the ties required to support its transfer. If these findings are correct, it is also reasonable to assume that weak ties will accelerate development speed in the early phases of exploration when the required knowledge is not complex.

This argument is substantiated by the observation that exploration leading to the internalisation of knowledge is characterised by relatively higher levels of individual autonomy (Johnson and Johnston, 2004). Conversely, weak ties will not be supportive, or even slow down the speed, in situations of high knowledge complexity where strong ties will support the required learning to drive exploitation of innovation.

1.3

Open and closed networks

Along the connectivity dimension of the social network, we can also distinguish between open and closed networks. Having no social capital on which to rely, the open network is mainly about resource exchange of information, while the closed network focuses on social exchange, trust and shared norms (Walker et al., 1997). An example of an open network is one in which firms have direct social contacts with all their partners, but these partners do not have any direct contacts with each other. A high number of such non-connected parties, or structural holes, means that the network consists of few redundant contacts and is information rich, since people on either side of the hole have access to different flows of information (Burt, 1992, 1993). This implies that the structure of an open network is suitable when the purpose of the network is knowledge creation by maximising the number of contacts gathering, processing and screening new sources of information. This kind of a more exploration-oriented network then stresses the indirect linkage, has mainly weak relationships and is loosely coupled.

The opposite is the tightly coupled closed network, where all partners have direct and strong ties with each other. This network is centred on social capital, which is built through trust and shared norms and behaviour (Coleman, 1988). Embeddedness in dense networks supports effective knowledge transfer and interfirm cooperation (Granovetter, 1985; Walker et al., 1997). We believe that this type of network is required for exploitation, but not suited for exploration. Ahuja (2000) highlights the contradiction between open and closed networks and proposes that the larger the number of structural holes spanned by a firm, the greater its innovation output. There seems to be a trade-off between a large loosely coupled network that maximises information benefits and a smaller tightly coupled network promoting trust building and more reliable information.

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This contraction is studied in the context of project teams by Soda et al. (2004), who argue that the best performing teams are those with strong ties among the project members based on past joint-experience, but with a multitude of current weak ties to complementary, non-redundant resources.

If we summarise the complementary networking theories in a model, it would seem that the network structure required for exploration is the absolute opposite of the one required to support exploitation. We can also conclude from the literature that a project team should have its nucleus in the north-eastern corner of Figure 2, but also have a multitude of current weak ties into the open networks – as represented by the opposing south-western corner. However, we have not found any advice in the literature on how to secure learning within, or how to transfer the results from one network structure into another.

Figure 2 Conflicting network structures required for exploration vs. exploitation of innovation

This paper will illustrate and analyse how two companies have established distinct models for collaboration with universities that enable learning and knowledge creation within and across both opposing corners of Figure 2.

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Introducing the learning dimension of knowledge creation

Know-how – often referred to as behavioural, or intuitive, knowledge – is acquired by ‘persistent habitual experience.’ (Barnard, 1938, p.291) Polanyi (1966) refers to this type of knowledge as tacit, i.e. deeply rooted in action and hard to formalise or communicate. According to Polanyi, the acquisition, or learning, of tacit knowledge requires close interaction and readiness to imitate: “By watching the master and emulating his effort in the presence of his example, the apprentice learns unconsciously, picks up the rules of the art, including those which are not explicitly known to the master himself.” (1948, p.53)

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Cyert and March (1963) see the organisation as an adaptively rational system that basically learns from experience. Kim (1993) defines organisational learning as a process through which individual learning becomes shared and transferred into an organisation’s memory and structure. In this sense, organisational learning is both a function of access to knowledge and the capability to utilise and build on such knowledge (Powell et al., 1996).

A learning organisation is one that learns from every experience and not just about the experience. It manages to build bridges from individual 8 to organisational learning and thereby create significant business impact. To accomplish this ambitious goal, it:

pulls in outside expertise

provides a context for creative individuals to create knowledge

organisationally amplifies this knowledge through transfer and cross-fertilisation

crystallises the knowledge created as part of an organisational knowledge network so that prior actions and outcomes interact to explain subsequent action.9

The focus of organisational learning is primarily on the development, or creation of knowledge through specific mental models and levels of learning, while knowledge management is more focused on its transfer, crystallisation and application.10 Amin and Cohendet (2004) draw on earlier works (Bateson, 1972; Dewey, 1916/1997; Lakoff and Johnson, 1999; Polanyi, 1966; Varela, 1999) when they propose a concept of knowledge as an embodied social practice, highlighting the crucial role of daily actions in social interaction. For them knowledge is a derivative of individual and group practices, generated by social interaction, influenced by factors such as trust, reciprocity, communication, and socialisation. Building on the contributions of Lave and Wenger (1991), and Brown and Duguid (1991), Amin and Cohendet stress the important role played by working communities in which knowing and learning occur through the daily social interactions and activities of its members, being bundled together by a joint interest, ambition, and purpose.

There is a broadly shared view of a positive relation between organisational learning and innovation, as organisational learning fosters creativity,11 and facilitates the implementation of new ideas, being crucial for the value growth of an organisation (Llorens Montes et al., 2005). In a similar vein, Lee and Tsai (2005) argue that the interrelationship between market orientation, learning orientation and innovativeness has been recognised as a major factor of business performance. Indeed, organisations can develop the ability to manage new technological opportunities effectively if they invest in learning abilities for flexibility in innovation such as ‘integrative capabilities’ (Henderson, 1994), or, ‘combinative capabilities’ (Kogut and Zander, 1992). In order to build the base for future competitive advantage, companies also need a visionary ability to imagine markets that do not presently exist, and subsequently invest in their development ahead of the competition (Kanter, 1983). Accordingly, it is important for the managers to extend their view beyond current operating practice to imagine how an industry may develop and look at the potential discontinuity in the context of possible new industrial scenarios, which may require fundamentally new value networks (Christensen, 1997).

This type of foresight, flexibility and adaptability are supported by double-loop learning – explained below.

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Single-loop and double-loop learning

In its very essence, organisational learning requires inquiry, such as the examination of past experience of success or failure, the learning from which should lead to improvements in behaviour, procedures, or processes and be stored in the corporate genes for retrieval also by individuals not related to the initial inquiry. This type of learning, called single-loop learning, thus results in a behavioural or organisational improvement, but the way in which the learning was initiated, i.e. how the inquiry was made, remains the same. Double-loop learning implies not only the improvement and storage of single-loop learning, but also changes the values of organisational theory-in-use (Argyris and Schön, 1996).

As long as the main concern is to keep organisational performance within the range specified by existing values and competitive conditions, single-loop learning is sufficient with a focus on exploitation. Double-loop learning goes beyond the correction of error in organisational behaviour and processes to inquire and correct errors in the organisational values and norms themselves. Double-loop learning thus implies that an organisation learns how to learn, by processing both the experience and the way in which the organisation experiences it. This kind of meta-learning contributes to exploration and the proactive anticipation of surprise that characterises know-who based companies (Harryson, 2002) and prepares them for corporate renewal whenever required by the changes in the competitive and time-paced business markets.

2.2

Relationship driven know-who based innovation

More than a decade ago, Akio Morita, Chairman of the Board of Sony Corporation, strongly inspired the development of a more relationship driven approach to innovation through the following statement: “The driving force of our rapid innovation is the conviction that if we lose money we can always recover, but if we lose time we can’t. Therefore, time has always been a critical issue at Sony. The best way to gain time is to communicate a lot and establish as many personal relationships as possible...

The more people you know, the better it is.” (Interview, 19.07.1993)

Reflections on the highly relationship-driven approach of Sony and other large but flexibly networked companies led to conclusions like: “ultimately, the knowledge-creating R&D process is no longer limited to individual know-how, but draws instead on know-who – and unlimited global sources of invention that continually nurture internal learning and improve R&D performance.” (Harryson, 1996, p.37)

The ‘who’ in know-who based companies knows who has the know-how, has the active empathy to rapidly establish the trustful relationship required to acquire that know-how, and has the multiple competencies required to transform and apply it in a new context so that innovation can occur. Know-who based engineers have the relationship-ability to ‘develop the knowledge networks and know-how acquisition skills they need for their own situations.’ (Harryson, 1998, p.2) Referring to the increasing trend towards a composite knowledge economy, Lundvall (1998, p.417) states that ‘know-who involves information about who knows

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what and who knows to do what’. He also confirms that know-who ‘involves the social capability to cooperate and communicate with different kinds of people and experts’ (ibid.). George Stalk (1998, p.xiii) endorses the concept through his argument that ‘moving from know-how to know-who is not just a powerful tool to increase innovation capacity, it is the sine qua non to manage the continuously increasing complexity of most industries’. Hedberg et al. (2000) introduce the value-star as a complement to Porters’ value-chain and use the analogy of a know-who based company acting as node in a network of know-how required to create new knowledge and customer value.

The greatest challenge seems to reside in providing relationship management principles and procedures that both foster invention – by stimulating diversity for new ideas to spark – and furthers this invention into value-creating innovation for business implementation. The latter requires network structures and processes too rigid and orchestrated to enable the former, but the former requires the latter to turn into reality (Seely et al., 2000). Are there any ways of overcoming this fundamental dilemma?

In response to our main-hypothesis, the research outlined in this paper suggests that the best approach for a large company to overcome the dilemma of technological leadership is to find new models to combine diversity in exploration and integrative capabilities for exploitation. We believe this is possible for a company that is able to: focus on global scale and process disciplines where it can build competitive advantage; rely on an external network with universities and academic entrepreneurs for inventive technology; and develop the socially competent individuals skilled at networking who use their superior relationship-ability to link the external and internal networks for integration of (mainly external) specialised (often tacit) complementary knowledge and skills to better profit from innovation (cf. Teece, 1986). The main challenge emerging in this context is not to build a multitude of relationships as such, but how to build relationships that secure effective transfer of the specialised complementary knowledge and skills and how to achieve joint double-loop learning to transform the results into innovation (cf. Powell et al., 1996).

Our main-hypothesis is that flexible exploration and exploitation of innovation are based on a clear distinction between and yet a seamless interconnection of the south-western and north-eastern corners of Figure 2. This flexible interchange will allow for optimal use of the most appropriate network structure to support learning within and across exploration and exploitation. Critical in this context is to secure good transfer and transformation of knowledge in the interchange between the two opposing network structures.

2.3

Transfer and transformation of knowledge

Gunnar Hedlund (1991, 1992, 1994) proposes a model for knowledge management, with significant improvements in the evolutionary theory developed a decade earlier by Nelson and Winter (1982). All these theorists use the tacit nature of knowledge and skills as their starting point, but while this is given prominence in Nelson and Winter, Hedlund (1992) deals both with tacit and articulated knowledge, as well as the interaction between the two. Instead of focusing mainly on the interaction between individual knowledge, Hedlund distinguishes between, and looks at how, four different agents of knowledge at different organisational levels – the individual, the small group, the organisation and the interorganisational domain – interact. More important still,

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while Nelson and Winter look primarily at the storage12 and transfer of information, Hedlund (1991, 1992, 1994) also stresses the critical activity of transforming information and knowledge. Whereas transfer is usually dominant in the exploitation of given knowledge, transformation13 through its combination with other knowledge within or beyond the firm, is more critical to support exploration and radical change.

For effective knowledge creation through assimilation of extracorporate knowledge, such as links to universities and research institutions, Hedlund (1992) recommends high quality of involvement with deep understanding of the tacit components and the local context. These knowledge creation processes are closed into continual cycles through the activity of acquiring and assimilating extracorporate knowledge, as this activity both is driven by, and is a driver of, the development of a broad portfolio of different competencies – multicompetency in our terminology. The imperative to develop multicompetency for effective integration of knowledge resonates well with the argument by Zhang et al. (2004) claiming that companies involved in incremental innovation rely predominantly on individual team members learning skills, whereas those engaged in radical innovation must integrate both individual level as well as project-team level learning skills.

Multicompetency supports the creation of redundancy by sharing extra information.

This, in turn, helps individuals to recognise their part and location in the larger context of the organisation, by clarifying the meaning of ‘the specific requisite information held by distinct individuals and groups’. (Nonaka, 1990, p.28) Accordingly, individuals’ sense of control is increased and provides purposeful direction to their individual thoughts and behaviour, which enrich and accelerate the collective knowledge creation. With respect to Senge’s (1990) theory and terminology, one could also argue that multicompetency and redundancy enable the transition from discussion to dialogue and support the transformation of invention into innovation. Thanks to their broader frames of reference, team members can jointly climb the ladder of inference using both advocacy and inquiry to support collaborative learning .

Similarly, Cohen and Levinthal (1990) define absorptive capacity as the ability to recognise the value of new, external information, to assimilate it, and finally to apply it for the purpose of generating economic rents. The authors argue that a company’s potential to acquire and deploy outside knowledge is driven by the acquired level of prior related knowledge – just like Powell et al. (1996) hold that knowledge facilitates the use of other knowledge, or, that what can be learned is crucially affected by what is already known.

Based on Cohen and Levinthal, we can assume that the organisation’s absorptive capacity is the sum of the absorptive capacities of its individual members and, as such, it can only emerge through effective communication. This requires, first, a certain level of shared knowledge and common expertise throughout the organisation and, second, an awareness of the capabilities and the knowledge of others (Brown and Duguid, 2000), which echoes the concept of multicompetency in know-who based companies.

Figure 3 summarises the different enablers of learning that were reviewed in this section. Without making any claim of precision in the positioning, we think that the different enablers thrive, or can be best created, within or around the areas where we have plotted them in the network structure matrix. An important hypothesis in our theoretical framework is that the development of multicompetency can act as meta-enabler in the transformation from exploration to exploitation across this matrix.

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Figure 3 Enablers of learning in exploration and exploitation

After introducing our methodology, the following sections will review how companies can obtain flexibility in innovation by performing both exploration and exploitation in different types of learning relationships with universities and their academic brainpower.

Two cases will be introduced in this context – a leading packaging company and a high-end HiFi company. Both companies build learning alliances with leading-edge academic institutions, but through fundamentally different models of collaboration in terms of the locus of the interaction and focus on exploration vs. exploitation.

3

Methodology

In our study, we have used a qualitative in-depth case study method, since a large extent of information was to be collected from a limited number of research units.

The goal was to gain a deeper understanding and knowledge of ‘how’ a selected few companies in Europe14 – that can be seen as highly flexible innovation leaders in their respective businesses – manage their external I-U collaborations to enhance impact and speed of cost-efficient innovation. The primary instruments in the data collection have been interviews with audio recording and transcribing, including several types of documentation. There has been a continuous interchange between empirical data and theory, as empirical findings initiated the search for further theories. The theoretical model is a further development of earlier work by Harryson (1998, 2002). To enhance the internal validity of our cases, we have always had the cases reviewed by the companies in several iterations, and also organised four seminars in which we have presented the empirical research to all the European benchmarking companies for a group-wide dialogue on best practices regarding steering and knowledge transfer in I-U collaborations. While our total sample amounts to 10 high-tech companies, this paper presents the two cases that best represent

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the scope of this IJTM issue. Out of the total 120 interviews that were conducted between 2002 and early 2006, 36 were made with Packman and HiFi.

4

The case of Packman

Packman makes extensive use of university research. Over the past 10 years, Packman engaged in 39 university projects out of a total of 60 external projects. The total external cost of the university-related projects was less than €2,000,000, but the results can be directly related to revenues of €700,000,000 through products or services in which the university collaboration results played a significant role.

Based on the intensifying cooperation with universities and the need for someone to manage the different academic collaboration projects, the Head of Future Technologies agreed that the professor from the Technical University of Q (called TUQ in this case), with whom Packman had already collaborated for more than 10 years, would found a private research company working exclusively for Packman and getting funding exclusively from Packman as well, but without any formal ownership of the company.

This company was named Reseach Center (RC), and was located in a science-park-oriented office-centre called GWT (Gesellschaft für Wissentransfer), which was a technology-commercialisation organisation owned by the university (TUQ).

The company has an average of three full-time employees. Prior to recruitment into the RC, these packaging experts have all been widely exposed to Packman through internships, diploma thesis work and sponsored PhD work. In addition to the small pool of internal employees, the RC will regularly utilise highly qualified experts from the TUQ, or from other universities, who are recruited on a temporary basis (usually professors or docents employed as ‘Nebentätigkeit’). Through his guest professorship at TUQ and through his formal professorship at another university in a neighbouring country, the professor has an extensive network from which he can select good students for Master’s and PhD theses. An average of 10 students are doing their Master’s or Doctoral theses for Packman at any given time – mostly through RC coordination with the owning professor as academic Thesis Director. The RC can easily find experts willing to dedicate their brainpower to packaging. In 3 years time, the IC has reviewed and analysed the last 20 years of research relevant to packaging. In addition, the RC is also often called in as a neutral party to give recommendations on important technology investment decisions.

In order to learn from their academic collaboration, Packman also enhances the degree of knowledge transfer by focusing on understanding the factors behind the research results through direct interaction with the research team by asking not only for documented results, but also by having personal meetings and a dialogue with the RC/university team to understand how they came up with the proposed solutions, finding out what research steps the researchers took to utilise the knowledge that was created through the RC collaboration and if/how they used this knowledge in the other tasks. Additionally, Packman uses the interaction to identify new networks, or research units, that can be leveraged to gain further knowledge in the desired area. Finally, Packman employs some of the researchers that were working on the project – either through fixed employment into RC, or directly into Packman’s R&D. This outsourced solution gave Packman permanent access to a very skilled team with deep packaging knowledge and a good understanding of the specific business

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needs of Packman. The cost level of the services doubled compared to similar services previously provided by the university TUQ. The doubling of the cost-level is compensated by the more extensive and deeper reach into academic knowledge networks and the exclusivity of the results. Moreover, the success-rate of the collaboration projects increased to almost 100%, while the time required by Packman employees to establish and steer the projects almost fell to zero. Students and researchers get a bonus for successful results.

The GWT handles all personnel management, administration, and back-office support including work contracts and agreements with customers of the RC. This set-up allows the owning professor to focus fully on his interface with Packman and on knowledge development for this client. In return, GWT gets 10% of the total revenues of RC, and all emerging patents belong to Packman. Over the 3 years of operation, the RC has received yearly revenues of approximately EUR 200,000. The RC has Packman Future Technologies as the main interface within Packman, partly based on the strong personal link and partly due to the focus of the collaboration on exploratory research.

5

The case of HiFi

As a bold move towards continued innovation leadership, HiFi decided to spin-off their absolute core technology into a separate company – called HiFiPower in this case – to Copenhagen. HiFi had 15 audio power conversion specialists who joined the spin-off.

The main-reason to move to Copenhagen was to leverage Master’s students to test all the crazy ideas so as to enhance the innovation power of the company. From this stage onwards, HiFiPower was having some 20 Master’s students per year. Today, in early 2006, HiFiPower consists of 35 internal employees and a similar amount of students who rotate in and out per year. The decision was based on the conviction that creative exploration for breakthrough innovation can be better run outside of the main-company – in particular when a strong network of academic research can be leveraged. By spinning out technology development and its dedicated specialists, HiFi can now focus all their R&D resources on concept development and unique design.

As a consequence, HiFi no longer has any research centre or in-house technology development, but sources this from its spin-off company, which in turn relies entirely on universities for cost-efficient exploratory research. The cost-benefit when cooperating with universities – compared to internal research – is estimated to be a factor of four.

The overall intention of HiFiPower is to offer student projects that are closely related to the everyday work of experienced engineers. The student interaction is coordinated by the research technology access (RTA) office, and the results mainly go to the technology platform development (TPD). Generally, the different projects are designed based on a technology platform (TP) roadmap where parallel activities in several technical areas are visualised and described – so that they become modularised and flexibly combinable Lego pieces going into future innovations. A student plan for the students interested in HiFiPower is made to match the TP roadmap and RTA priorities with last semester’s student projects. This is designed to give the student a deep insight in the technical project content and enable him/her to get the required overview to perform the work on his/her thesis.

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In general, for professors in Denmark, the driver to cooperate with HiFiPower is not the potential financial contribution, but the possibility of investigating new research areas and consequently to attract more students to write their thesis with him/her. This is due to the evaluation system of professors in Denmark, which is based on:

the amount of students (s)he attracts to his/her institute or course

the number of publications and papers (s)he writes

the number of patents (s)he generates.

By working very closely with the students and putting more focus on Master’s students than on PhD students, HiFiPower can avoid the drawbacks of senior researchers’ focus on publishing and patenting results.

In order to proactively manoeuvre academic knowledge creation towards the business needs of HiFiPower, a so-called ‘student education roadmap’ has been introduced at two selected universities to be considered by students interested in the science of Audio Power Conversion. HiFiPower first invests time in giving introductory courses at the beginning of the students’ education (typically a 4-year programme). Later, specific courses are offered where practical research problems are presented to enhance their awareness of applied R&D in areas relevant to HiFiPower. As a last teaching interaction with one and the same class of students, an advanced course is given on the R&D issues of HiFiPower at the end of their Master’s programme. The student interaction is monitored for the duration so as to spot the best candidates early on and offer them interesting thesis projects – based on the R&D priorities of the aforementioned technology map.

A bonus for outstanding results and the aspiration to obtain permanent employment further drive the students’ work towards business needs. In a handful of cases per year, the best students are hired into the company upon completion of a fruitful cooperation.

The training and student selection process is visualised in Figure 4.

Figure 4 Training and student selection process of HiFiPower

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HiFiPower prefers to keep the selected students in-house in order to give them inspiration, steer the results, and get the output and at the same time internalise the results from externally acquired university research. HiFiPower is using only two internal gatekeepers dedicated to manage and steer the collaborative university relationships. The rest of the team, at any given point of time, will consist of two to three PhD students and 10–15 Master’s and Bachelor’s students doing their thesis work. A recent law change in Denmark (in favour of the American Bath Doyle act) caused HiFiPower to stop collaborating with PhD students from Danish universities, but Swedish universities still allow for ownership of research results by the individual researchers.

In total, HiFiPower has allocated 0.7 man-years for doing university-oriented marketing. This covers lecturing, coaching student projects, generating ideas for the projects, writing research proposals and finding the right candidates. In addition, eight employees are devoting 20–30% of their time to be the daily coach of two or three students each. According to the HiFiPower CTO (interview, 24.09.04), ‘they devote up to a third of their time to manage the students, but the students triple their manpower in return’.

In this sense, employees are motivated to cooperate with students by clearly communicating and demonstrating that the coaching will widen the area of their research attack, while saving 3–4 months of their work that students will do in their place as a fully integrated partner in their project.

Another important reason for the spin off was to enhance the leverage of HiFiPower technology by offering it to the entire audio market instead of serving only one important player in this market. HiFi allowed for the spin-off company to address a customer-base far beyond the mother-company. HiFiPower is a separate unit that can act as a commercialisation vehicle deploying HiFiPower technology to fundamentally new markets and driving innovation in completely new areas that would not have been reached from the mother company. In this context, HiFiPower is actively helping their new clients to learn how to apply their technology for optimal design and functionality by sending their engineers – and selected students – to the clients.

In terms of financial performance, HiFiPower has enjoyed a steady revenue growth since its establishment in year 2000. In 2004, the important break-even milestone was passed and 80% of the revenues came from new customers – beyond the captive market of HiFi. It follows that by spinning-off its core technology development, HiFi did not only gain access to cost-efficient breakthrough innovations that would not have been discovered through internal development, but also acquired the majority of a fast growing business.

6

Analysis and discussion

The two I-U collaboration models both aim at effective learning between academic and corporate research and have several elements in common, but they also display some significant differences with respect to foci on exploration vs. exploitation across open or closed networks. To start with, Table 2 outlines how the two learning-models address the learning barriers that were summarised in Table 1.

Solutions in common Both companies use dedicated academic brainpower and have clear organisational setups dedicated to coordinate collaboration Dedicated ‘semi-academic’ organisations acting as interface with the academic community Both companies have clearly defined projects that are run within an average timeframe of 6 months Linking all collaboration projects to areas of clear business needs, or showing a clearly established business potential Both companies focus stronger on collaboration with master students than with (more publication-oriented) PhD students Both companies always own the IP rights By giving projects based on clearly defined company needs

Lack of adequate resources on both sides

Cultural differences

Inflexible academic research timetables

Long-term exploration vs. short term exploitation-oriented research

Incompatible reward systems – publication vs. protection of results

Risk related to obtaining IP rights

Exploitation of premature technologies

By allowing experienced RC team to coach and manage projects

Packman does not start collaboration if they can not own IP rights

Packman avoids working with publication-oriented professors and gains direct access to the researchers through the RC and pays a bonus for successful results

Most collaboration projects are spun off from concrete customer problems that require rapid pragmatic solutions by leveraging new academic expertise

University collaboration projects always based on milestones and clearly defined deliverables

RC as ‘outsourced’ partner to be Packman’s interface with the academic world

Packman finds dedicated students through RC, which also provides dedicated resources for coordination

Packman specific solutions

By giving projects that are strictly based on the TP roadmap

Avoiding research projects with PhD students in Denmark

Allowing selected professors to use the brand name of HiFi to attract more students. Paying students for results that lead to commercialisation

Student projects are defined as spin offs from the TP roadmap and often linked to exploitation by temporarily transferring the student to a lead-customer

Clearly defined master’s thesis projects performed by students working like internal employees

Preparation of students through university courses, followed by temporary integration of selected students into HiFiPower

Master’s students are always brought directly in-house with internal employees dedicated to their coordination

HiFiPower specific solutions

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Main barriers

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Table 2 Summary of how the case companies address the learning barriers

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6.1

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Spin-off a university collaboration core technology unit for innovation flexibility

In order to gain more flexibility in innovation, HiFi decided to spin-off its core technology into a separate company dedicated to university collaboration. HiFiPower enhanced their flexibility in terms of exploring further concepts to enrich the core technology platform, and by using students to support its application to new segments like mobile phones. In other words, having low-cost Master’s students allows HiFiPower to pursue a bolder and far more flexible innovation strategy, which also benefits the mother company in terms of enhanced core technology and additional revenues.

6.2

Establishing a dedicated university collaboration centre for exploration flexibility

Packman enhanced their flexibility and speed of innovation by expanding the search area to the whole of Germany and beyond, as opposed to limiting their interaction to a few German universities. The outsourced unit (RC) gives Packman a clear interface to the ‘fuzzy’ world of academic exploratory research, and allows for long term-exploration of technologies going into new product generations required for future success. In addition, it extends the flexibility to attack Packman’s problems with a vast pool of scientific knowledge to find new solutions. By outsourcing collaborations with universities to RC, Packman accelerates the speed of external learning: In three years, the RC reviewed and analysed and transferred to Packman the last 20 years of research relevant to Packman.

This mechanism also allows for joint learning by getting assistance in moving from exploratory research to explanatory results, with RC offering practical explanations as to why only some theoretical approaches work in practice.

6.3

Linking university collaboration to the core technology roadmap for joint learning

HiFiPower applies the practice of internal brainstorming to spin off approximately 30 concepts and ideas from the TP roadmap to pursue through exploration, validation and possibly exploitation. The practice of lead users in the innovation process also calls for great flexibility as the lead user’s needs may change in the course of the project.

The use of academic brainpower involvement is highly complementary to both innovation practices. Without the flexible access to academic resources, HiFiPower would be forced to kill two thirds of the ideas without further exploration. Also, if lead users call for sudden changes, the rapid mobilisation of academic resources makes it possible to respond to these changes. In other words, HiFiPower takes a learning lead by helping students and clients to unlearn the old standards and restrictions and then learn how to find new applications of the unique technology. In this way HiFiPower carries out external reversed learning to later be able to perform joint learning both in the exploration and exploitation phases.

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Using co-location to build learning enablers

In its joint academic innovation practices, Packman has developed routines for knowledge transfer, which support learning from the academic partners – both about the innovation and the process behind it. To amplify and speed up the utilisation of knowledge, Packman has frequent and regular interaction with academic partners throughout the project. Thanks to the co-location or frequent meetings between Packman and the students, the students can learn about the changing needs of Packman and are more flexible to adapt to the emerging requirements. As a final step of the knowledge transfer and utilisation of the resulting technology, Packman can hire the students responsible for the project, or encourage the RC to recruit them. HiFiPower has a yet stronger emphasis on co-location. In this sense, both cases offer practical illustrations of how to build mutual trust and social capital with a shared vision, joint language and a mutual research culture, joint deployment of existing innovative networks and personal contacts, frequent face-to-face meetings and on-site demonstrations. Table 3 summarises how the two collaboration models make use of the different enablers of learning outlined in Figure 3.

Table 3 Summary of the ways in which the case companies create learning enablers Learning enablers

Packman approach

HiFiPower approach

Learning of tacit knowledge through close interaction and persistent habitual experience

Frequent meetings with RC coordinators and co-location of some of the students

Mandatory co-location of the students, who are integrated as regular employees

Pull in outside expertise for combination of new bodies of knowledge

Using RC for temporary recruitment of researchers from various universities

Co-location of master students during the whole thesis projects

Provide a context for creative individuals to create new knowledge

GWT handles all administration, which allows RC to fully focus on cooperation with Packman

HiFiPower keeps the students in-house to give them inspiration from an innovation house

Transfer and cross-fertilise the results

Through direct interaction with the research team and by asking questions to understanding the factors behind the research results

Projects are designed based on TP roadmap and the results are modularised for future use as combinable Lego pieces

Crystallise the knowledge created into an organisational knowledge network

Monthly research progress Patent applications, the thesis report meetings with the itself and integration of the students and the RC coordinator results into the technology roadmap

Socialisation through daily interaction

Co-location of some of the students at Packman

Free daily lunches with students at HiFiPower

Joint interest, ambition and purpose towards a shared vision

Only hiring people into RC who already worked with Packman

Five university courses held over 5 years to create interest and ambition to join

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Table 3 Summary of the ways in which the case companies create learning enablers (continued) Learning enablers

Packman approach

HiFiPower approach

Imagine markets that do not presently exist

Not applied – I-U relationships focused on supporting core business innovation through new know-how and technologies

Allowing students to explore the wildest ideas from the technology roadmap and apply successful results to new segments (like mobile phones)

Know who as social ability to access know how

Through RC professor’s wide network

Building a know-who network through the student education

Stimulate diversity

Bringing some students into the company

Bringing many students into the company

Transform invention into value creating innovation

Through RC project management and a focus on finding new solutions to customer problems

Transforming technology roadmap priorities into student projects and sometimes transferring the student to the customer for implementation

Deep understanding of the local context

Using RC employees who have good understanding of Packman’s business needs through past collaboration

Students preparation through education programme and by mandatory co-location

In conclusion, our paper provides a new theoretical rationale for I-U learning alliances as a possibility to avoid the dilemma of technological leadership and the ‘mission impossible’ of trying to perform both exploration and exploitation within the same company boundaries. Through our theoretical framework that proposes clearly distinguished network structures for exploration and exploitation, the world of academic science can become a logical partner to handle the full phase of exploration, but also support the process of exploitation. This latter step requires an act of transformation that deserves further attention, in particular regarding how to secure seamless interconnection between the two opposing network structures outlined in Figure 2. In this context, both cases illustrate the important individual dimension of organisational learning, and show how the knowledge of skilled external individuals can be integrated into a corporate innovation project through co-location – following a migration path from weak ties in open networks to strong ties in more closed networks as the innovation process progresses from exploration to exploitation.

It is reasonable to assume that the development of the models has been at least partly sparked and supported by the increasingly entrepreneurial role taken by universities15 in contributing to the knowledge economy as they can flexibly integrate teaching and research. In this context, both models build a certain institutional overlay as the two HiFiPower I-U coordinators and the combined RC owner and professor can bring the practical problems of the companies they serve into their teaching and thereby shorten the distance between academic exploration and industrial exploitation.

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Developing multicompetency as meta-enabler for acquisition of invention and transformation into innovation

By outsourcing administration and back office support to GWT, the RC can focus fully on the interface with Packman and keep updated on the business needs of Packman.

Through his wide network, the owning professor is able to identify the best candidates for the required exploration, which in turn allows Packman to devote full internal time and attention to exploitation. In this sense, Packman avoids the dilemma of technological leadership by reducing the need for internal specialists. Similarly, HiFi ‘got rid of’ 15 specialised audio power conversion researchers, who transferred to the new spin-off where they enjoyed a better network structure for exploration, while HiFi could focus more extensively on design and exploitation.

Accordingly, we conclude that both collaboration models help their mother companies to reduce, or possibly even fully avoid, the dilemma of technical leadership.

We also consider the related development of multicompetent individuals and teams to act as meta-enablers both for the acquisition of new knowledge and invention and to support its transformation into innovation for implementation – as illustrated in Figure 5.

Figure 5 Developing multicompetency as meta-enabler for acquisition of invention and transformation into innovation

Although our paper draws on extensive theoretical research and some literature reviews, it presents only two cases to illustrate practical applications. Further research will be required to gain a more solid understanding of how learning both from extracorporate sources like universities, and across internal functions like R&D, M&S and D&M, can enhance flexibility and performance in innovation.

This limitation also applies to our managerial conclusions – outlined below:

6.6

Managerial conclusions

Both presented models provide practical solutions to the dilemma of technological leadership and the I-U learning barriers as they reduce the need for internal specialisation

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and serve as knowledge acquisition mechanisms to both transfer and transform external academic knowledge into innovation. Rather than building internal specialists in technology that run the risk of remaining ‘stuck’ in research, both Packman and HiFiPower rely on external academic brainpower that is temporarily internalised.

By applying these models, companies can enhance their ability to attack customer problems with a vast pool of scientific knowledge to find new solutions.

The models allow for repeated interaction and collaboration in a practice-related context between individuals in and across organisations for intensified creation and transfer of tacit knowledge. As such, both collaboration models can function as boundary-spanners in learning alliances between selected universities and the main-companies, combining exploratory research with practical experiments to assess the potential for exploitation. If the potential is there, both models can also actively support the exploitation. The models offer a possible solution for companies wishing to get a more clear interface to the ‘fuzzy’ world of academic exploratory research and to optimise the output of collaboration by focusing on clearly defined projects aligned with the innovation strategy of the company in order for academic results to be more easily implemented.

HiFiPower’s practice of teaching courses can be applied by other companies interested in influencing academic knowledge creation and identifying and selecting the best students for thesis collaboration. Both models offer pragmatic approaches to proactively steer the collaboration results towards business needs. They also illustrate how the internalisation of the students can support commercialisation of the results.

The outsourced model of Packman is more expensive, but requires less involvement of Packman employees to manage selection and coaching of students. This model seems to be particularly valuable when emerging customer problems call for new knowledge and a wide network of specialists to be solved. The spin-off model of HiFi may appear to be counterintuitive in the sense that companies rarely spin off, or outsource, their core technology. Yet, this model seems to have worked well for HiFi in terms of enhanced exploitation of the core technology, while also widening its scope of exploration both for perfection of the technology and for finding new areas of application through its spin-off.

The HiFiPower model delivers more exclusive and focused selection of university partners based on the need to establish a new technology standard, whereas the Packman model is driven by the need to reach a wider range of universities and disciplines to tap their existing pools of knowledge rather than influencing their knowledge creation activities. Depending on their specific knowledge needs, companies can choose which of the models to apply for more flexible exploration and exploitation of innovation.

Acknowledgements We would like to thank the three anonymous reviewers for their detailed and highly valuable feedback, which significantly improved the quality of this paper.

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Notes 1

2

3

The importance of time is well captured by Nelson and Winter (1982, p.279), who, in turn, refer to Schumpeter’s thinking, as they contend that ‘the payoff to an innovator may depend largely on his ability to exploit that innovation over a relatively short period of time.’ See also Stalk (1990) and Stalk et al. (1992) for more recent findings on time-based competition.

Argyris and Schön define competence traps as ‘situations in which an experience of perceived success leads an organization to persist in a familiar pattern of thought and action beyond the time and conditions within which it yields successful outcomes.’ (1996, p.19) In similar terms, Lorange and Nelson describe how ‘competitive success itself may trigger organizational decline by encouraging complacency’ (1987, p.42).

See, for example, Hedlund (1994, p.22, 1995, p.24), who argues that corporate forgetting is easier in global (networked) firms through their increased prospects for both internal and external benchmarking and conscious experimentation. In his words, ‘the future firm is a learning one, but also a systematically forgetting, de-learning one’ (1994, p.28).

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See McGill and Slocum (1993) for a detailed discussion on what it takes for organisations to unlearn. Takeuchi and Nonaka (1986, pp.144–145) give several examples of companies that unlearn to increase innovation performance. Kline et al. (1991, p.7) and Kusunoki (1992, p.75) argue in this direction. Leonard-Barton (1992, pp.111–125, 1995) argues that innovation requires core capabilities, but that these have a down side, called core rigidities, that paradoxically inhibit innovation.

See, for example, Badaracco (1991, pp.66–69); Bleeke and Ernst (1993); Contractor and Lorange (1988); Doz and Prahalad (1989); Fruin (1997); Hagedoorn and Schakenraad (1990, 1994); Hedlund (1994, 1995); Lamming (1993); Leonard-Barton (1995); Lewis (1990); Lorange (1996); Lorange et al. (1992); MacDonald (1987); Monck et al. (1988); Nimtz et al. (1995); NYFOR-Kommittén (1996); Pfeffer and Salancik (1978); Pisano (1991); Radnor (1991); Speir (1989); Turner (1992); Welch and Nayak (1992); Yoshino and Rangan (1995).

One hundred and fifty interviews were made in Japan between 1993 and 2002 – primarily with Canon, Sony, Toyota and Toshiba to explore how these companies build relationships to leverage external sources in general, and university research in particular, as the main source of creativity and exploration so as to put the internal focus on exploitation and commercialisation of the externally created invention. This research laid the foundation of the dilemma of technological leadership.

Between 2002 and early 2006, we have made approximately 120 in-depth interviews on industry–university alliances – with 10 European technology-intensive companies. Thrity-six of these interviews were with Packman and HiFi, which represent two seemingly unique models both aiming at achieving flexibility, innovation and learning, but also demonstrating certain differences that are analysed in this paper.

See, for example, Senge, who states that ‘organizations learn only through individuals who learn.

Individual learning does not guarantee organizational learning. But without it no organizational learning occurs’ (1990, p.139).

Crossan et al, (1999); Leonard-Barton (1995, p.8); McGill and Slocum (1993); Nonaka (1994); Pasternack and Viscio (1998); Van de Ven and Polley (1992, p.94).

See, for example, Argyris and Schön (1996); Harryson (1997, 2002); Roos et al. (1998, p.26); Senge (1990).

In this context, Senge (1990, p.14) stresses the ability to create knowledge: ‘for a learning organization, ‘adaptive learning’ must be joined by ‘generative learning, ‘ learning that enhances our capacity to create.’

To Nelson and Winter, routines play the role of genes in the evolutionary theory and act as server (in IT terminology) for all knowledge storage: ‘The notion of an organizational memory embedded in routines is as relevant to organizations with highly ambiguous objectives, such as universities, as it is to organizations with the modestly ambiguous objective of making money’ (1982, p.403).

Kogut and Zander use a different term to describe this phenomenon: ‘It is by recombining knowledge, resting upon what we have called a ‘combinative capability’ that a firm exploits its current knowledge for expansion into new markets’ (1991, p.16).

Between 2002 and early 2006, approximately 120 interviews were conducted on I-U collaboration – with European technology-intensive innovation-leaders in wireless high-end consumer goods; HiFi equipment, medical equipment, sports cars, packaging and mobile communication. A different model of collaboration, the ‘insourced model’, with a main-focus on exploitation of academic knowledge, was reported on in a recent publication (Harryson and Lorange, 2005).

See Etzkowitz (2003) and Etzkowitz and Leydesdorff (2000) for more details on how the ‘entrepreneurial university’ takes a more proactive role in the knowledge society. The authors of this paper currently explore how this trend affects Nordic universities in a VINNOVA project carrying the same title ‘The Entrepreneurial University’ (June 2005–February 2006).