How human capital and social networks may

doi:10.1111/j.1435-5957.2011.00363.x

How human capital and social networks may influence the patterns of international learning among academic spin-off firms* Mozhdeh Taheri1, Marina van Geenhuizen1 1

Faculty of Technology, Policy and Management, Delft University of Technology, Jaffalaan 5, NL-2628 BX Delft, The Netherlands (e-mail: [email protected], [email protected])

Received: 16 July 2010 / Accepted: 20 March 2011

Abstract. The extent and background of establishing international knowledge relations among young academic spin-off firms are explored in this paper. Drawing on survey data of 100 of such firms, the influence of human capital and social networks of these firms is examined, alongside their innovation level. International learning is measured in two ways, adoption of the strategy and spatial reach related to this adoption namely, from Europe to worldwide. The paper fits into a stream of research in which it is recognized that new technology-based firms interact both in local knowledge networks and knowledge networks abroad to remain competitive. A majority of the spin-off firms were found to be engaged in international networks and the most powerful influences tended to be the presence of PhD experience and size of the starting team. Social capital released through social networks is a relatively strong influence only in the spatial reach of knowledge relations, supporting the idea that strong social networks form a solid base from which global learning can be undertaken. The implications of the results of this work and future research steps are discussed. JEL classification: M13, D83, D85 Key words: Academic spin-off firms, international knowledge relations, human capital, social networks, innovation level

1 Introduction The production and utilization of new knowledge is a major distinctive factor between regions and their economies today. In the development of the regional knowledge-based economy, universities are recognized as employers and purchasers and as a main source of new knowledge and innovations (Drucker and Goldstein 2007). Introducing commercialization of knowledge among others through spin-off firms, university-business collaboration and licensing of patents, * We acknowledge the input from the published work of Dr. Danny Soetanto. © 2011 the author(s). Papers in Regional Science © 2011 RSAI. Published by Blackwell Publishing, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden MA 02148, USA. Papers in Regional Science, Volume 90 Number 2 June 2011.

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universities started to be recognized as important players and nodes in knowledge flows in regions’ economies in the early 1980s (e.g., Charles and Howells 1992). In the US this has been particularly noticeable since the passage of the Bayh-Dole act (1984) which gives universities the right to patent inventions and requires them to license patents resulting from federally sponsored research to the private sector (e.g., Shane 2004). The recognition of universities as a major source of knowledge, inventions and entrepreneurship in a region has increased since the early 2000s (Huggins and Johnston 2009; Kitson and Hughes 2009). Other academic institutions have also been recognized, such as research centres, but universities often have the additional advantage of offering courses in entrepreneurship and employing incubation centres. Two developments are worth mentioning in this context. Universities in many European countries today face, aside from research and education, the commercialization, or valorization, of research results as their third mission. At technical universities, which have an emphasis on applied research and engineering activity, this seems to be better developed compared with general research universities. A second important development is the increased collaboration of universities, general or technical ones, with universities of applied science namely, higher educational institutes, with the latter more often involved in collaboration with small and medium-sized enterprises in the region. Thus, given these developments, entrepreneurship at universities will certainly increase in importance in newly build networks with other institutes in the near future and serve as a powerful force in the regional economy. Regional policy-makers perceive universities today as having a pivotal role in open innovation networks with various firms, including regional ‘testbeds’ that enable co-design, validation and testing of new products and processes on their way to market. It remains to be seen whether a tight knowledge relationship between the university and the region is a realistic model. Rather, it seems that knowledge flows are bundled within a certain bounded space with related institutes (region) while at the same time following patterns of a network-based space, in which the region is a node among a set of sites across the globe which are dependent upon shifting alignments of economic and institutional actors (e.g., Amin and Cohendet 2006). The network-based model of the learning firm also indicates various advantages of distant interaction in acquisition and commercialization of new knowledge, for example, it increases the chance for a better match with specialized knowledge needs and it gives access to diversity in knowledge that may prevent processes of negative lock-in in the region (e.g., Boschma 2005). One particular mode in knowledge commercialization has attracted strong attention among researchers and policy-makers: academic spin-off firms, particularly since a shift from closed to more open innovation systems has become visible (Chesbrough et al. 2003). Academic spin-off firms are acknowledged in the literature as one of the key drivers of economic change and growth. Spin-off firms develop university inventions towards application in the market and they are perceived by policy-makers as contributing to a wider diffusion of university knowledge into the business community, to the improvement of infrastructures supporting high-tech entrepreneurship, and, if quickly growing and in need for new knowledge, these firms in their turn may finance specific research projects at university (e.g., Shane 2004; Clarysse et al. 2005; Bercovitz and Feldman 2006; Wright et al. 2008). Today, most advanced regional and national economies strive to generate economic wealth by exploiting and diffusing public research results using academic spin-off firms. Moreover, if spin-off firms actively connect with sources of knowledge abroad, ‘global pipelines’, and act as ‘gatekeepers’ (Bathelt et al. 2004; Graf 2011), they help or trigger the introduction of global knowledge to the region thereby increasing diversity and opportunities for innovation. If firms connect with global sources, they also provide better learning opportunities for themselves, particularly if there is diversity in international relationships (Zahra et al. 2000; Frenz et al. 2005; Frenz and Letto-Gillies 2007). There is, however, not much insight into how academic spin-off firms gain knowledge when crossing borders. The bridging function between the Papers in Regional Science, Volume 90 Number 2 June 2011.

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university with its local firms and global knowledge nodes has not yet received much attention. Thus, there is a lack of understanding as to what extent academic spin-off firms have adopted international knowledge acquisition and learning, and which factors influence this adoption. In this paper, a model that clarifies the adoption of international knowledge relationships and the spatial reach involved is proposed and explored, drawing mainly on human capital and social network theory and on innovation theory. The comprehensive approach taken to international knowledge interactions in this paper, including human capital, social networks and the innovation context, is the key contribution of the paper to the literature. We use a given sample of 100 spin-off firms from two universities, Delft University of Technology in Delft, the Netherlands, and National Technical University of Norway in Trondheim, Norway (Soetanto 2009). The paper is structured as follows. Relevant theory and concepts concerning knowledge relations and model construction are examined in Sections 2 and 3. The methodology features of the empirical study are discussed in Section 4, including measurement of key concepts through variables, data collection, and descriptive analysis. The outcomes of the model exploration are presented and discussed in Section 5. The paper closes with a summary and indication of implications of the outcomes.

2 Setting the scene: Internationalization There is a long tradition of research into internationalization of new technology-based firms in a broad sense. Internationalization of firms can be described as the process of increasing involvement in international operations (Welch and Luostarinen 1988). This rather broad process is seen to encompass the adaption of firms’ operations like strategy, structure, resources, etc., to fit (perfectly) the international environment, and therefore also includes the extension of knowledge relationships across national borders.1 Most theoretical models and empirical research on internationalization of new technologybased firms refer to exports. Therefore two contrasting models in this area need to be mentioned: the Uppsala model and ‘born-global’ thesis. The first assumes an interplay between gradual acquisition of knowledge and commitment to international operations. Knowledge is primarily gained from experience related to specific markets and is gradually embedded in the activities and capabilities of firms. This model acknowledges that international operations, including learning and collaboration, require resources that newly founded ventures find difficult to identify and acquire (Johanson and Vahlne 1977; Forsgren 2002). These resources may include the ability to connect with foreign partners and overcome language and institutional barriers, like regulation and intellectual property rights rules, and barriers in business culture. A shortage in this ability may particularly hold true for university spin-offs firms (Lockett et al. 2005; Soetanto 2009). In contrast, the born-global thesis contradicts the influence of constraints from lack of resources and assumes that new ventures start to act and trade in foreign markets immediately or soon after their start (e.g., Madsen and Servais 1997; Andersson and Wictor 2003; Rialp et al. 2005). Using personal networks and international contacts, and experience gained in education of the management team, ‘born globals’ overcome constraints more easily. Recently, an increased role has been ascribed to benefits from social networks in overcoming constraints (Bruneel et al. 2010). Accordingly, the differences in extent of internationalization may reside in human capital and in social capital released through networks, meaning that the two contrasting models may occur in one and the same population of young firms. 1 The concept of globalization of economic activity is qualitatively different. It is a more advanced and complex form of internationalization, which implies a degree of functional integration between internationally dispersed economic activities (Dicken 1992).

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Human capital can be seen as created by changes in persons which leads to skills and capabilities that enable them to act in new ways (Coleman 1988; Becker 1993). The main sources of human capital are education and experience. Firms are better able, using human capital, to adapt continuously to changing circumstances in the external environment, to perceive new opportunities and threats, and to gain competitive edge. Social networks are important because achieving new skills and capabilities may be facilitated by interaction in social networks, and enhance a person’s knowledge capture and understanding. Social capital can be perceived as the sum of actual and potential resources a person/organization can access or derive through membership in networks (Kogut and Zander 1992; Nahapiet and Ghoshal 1998). Preferential knowledge access is one such resource (e.g., Inkpen and Tsang 2005), and may facilitate international learning, however, social networks are not always producing benefits in terms of resources (Elfring and Hulsink 2003; Hughes et al. 2007). Networks may, for example, be too tight with all partners connected to each other, or too homogeneous regarding the social background of the partners, thereby missing the virtues of social capital. There are some contradictory results in the empirical literature on the influence of human capital and social networks on firm performance (e.g., Florin et al. 2003), and this is a reason why it seems necessary to broaden the scope with the innovation level of firms. The support gained from human and social capital may be highly diverse for firms that have chosen to be a first mover or a late follower in their industry sector, or to hold a position in-between, because their need for resources is different (Lieberman and Montgomery 1988; Finney et al. 2008). What may also make a difference is the development stage of the product/process and whether the firm already has a solid market position or is still engaged in development activities (e.g., Gilsing and Duysters 2008). In addition, patenting behaviour may also differentiate in knowledge interaction abroad. Owning patented knowledge may enhance building international knowledge exchange in two ways; (i) having patented knowledge increases the status and reputation of firms, and their attractiveness as an international partner abroad; and (ii) firms owning patented knowledge may feel less reluctant to establish knowledge relations and exchange knowledge abroad because ownership of their knowledge is protected (Andersen 2006). Few studies so far have investigated the influence of human and social capital on internationalization, in particular on international knowledge relations. Many studies deal with social capital or human capital and new ventures’ performance, but these are much broader in perspective than merely internationalization and building international knowledge relations (for human capital see, Wright et al. 2007; for social capital see, Burt 2005; Stuart and Sorenson 2008, and specific for local networks see, Keeble et al. 1998). More recently, research attention has increased for absorptive capacity, with overlap into human capital, and international learning and collaboration (e.g., Sedoglavich et al. 2009; de Jong and Freel 2010), but analysis of the influence of human capital along with social networks on adoption of international knowledge relations, while also accounting for different levels of innovation, is new.

3 Conceptual framework 3.1 Academic spin-off firms For many decades, new technology-based firms have been at the centre of policies for regional innovation and employment growth; this has to be seen in the context of the, at the time, decline of Fordist production and the recognition of small high-tech firms as engines of economic growth. New technology-based firms are new, independent firms whose technology is based on exploitation of an invention or technological innovation which implies certain technological Papers in Regional Science, Volume 90 Number 2 June 2011.


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risks (Tether and Storey 1998; Kirby and Cox 2006). Butchart (1987) delineates the high technology sectors as sectors which face higher than average expenditures on R&D as a proportion of sales, or which employ more ‘qualified scientists and engineers’ than other sectors, and by this definition, academic spin-offs can be seen as a sub-category of new technologybased firms. What basically distinguishes academic spin-offs from other new technology-based firms is that they draw on knowledge from a particular university or other academic institute, and are created to bring it to market (Pirnay et al. 2003; Shane 2004). In some studies, the definition also emphasizes the status of the persons involved, thereby excluding external entrepreneurs. In most cases, however, spin-offs have a connection with the university through the education or employment (history) of the entrepreneurs. The latter are often graduates from the university or (former) employees. In this paper, the delineation of Pirnay is followed. Being created to exploit or commercialize knowledge from the university, spin-off firms may be different, particularly in the amount of resources available at start-up and later on. Although generally academic spin-offs are short of resources, there may be differences derived from, for example, pre-start experience of the entrepreneur and from type of start, a team start or single start. In both cases, the differences are mainly concerned with accumulated knowledge within the firm(s), as a part of human capital, (e.g., Druilhe and Garnsey 2004; Lockett et al. 2005). In addition, spin-offs may also face differences in participation in social networks, the types of networks involved and connected social capital (Soetanto 2009).

3.2 Knowledge acquisition and innovation New technology-based firms need to acquire new knowledge from external sources and to produce new knowledge by themselves to survive and grow; because of the tacit character of part of the knowledge and the benefit of local context (culture) necessary to understand this knowledge, many regional innovation studies have focused on localized knowledge spillovers as an important source of competitiveness. In addition, the idea that trust is also necessary in sharing tacit knowledge and that trust is partially based on personal relations in the region has contributed to the important role ascribed to localized knowledge spillovers (e.g., Maskell and Malmberg 1999). In contrast to the emphasis on benefits from knowledge spillovers in local networks, various authors in spatial innovation studies have forwarded the idea of interaction and learning through knowledge sources belonging to the regional (local) innovation system and to global innovation systems (Bathelt et al. 2004; Amin and Cohendet 2006; Gertler 2008). Young spin-off firms in technology fields often have little market-related and management related knowledge (Van Geenhuizen and Soetanto 2009) or supplementary technical knowledge in the context of application. Due to the high levels of specialization in these firms, it is less likely that the last type of knowledge is found in local networks (Torre 2008; Drejer and Vinding 2005). For example, in Germany cooperative relationships between firms and public research institutes are geographically widespread (Fritsch and Schwirten 1999). Thus, in searching for specific knowledge, high-tech firms may connect with different sources around the globe (Best 2001; Britton 2004) and this situation is facilitated by the increased speed of transportation and telecommunication. A firm’s links with partners abroad have been named ‘global pipelines’, because of their role in feeding the learning in the regional economy with global knowledge (Owen-Smith and Powell 2004; Bathelt et al. 2004; Bathelt 2007). The establishment of such pipelines is very complex and expensive, and requires partners at both ends to develop a joint framework to engage in interaction. The partners need to overcome physical and possibly cultural distances, and cognitive distances (Boschma 2005; Gertler 2008). Where cognitive distance refers to the Papers in Regional Science, Volume 90 Number 2 June 2011.

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idea that partners need to be sufficiently interesting to make it worth the effort to cross borders. This may imply that firms owning patented knowledge will have better chances for knowledge networking abroad because their research and development has a recognized status (Andersen 2006). Entrepreneurship is about the deployment of resources in pursuit of opportunities, independent of origin or ownership (Johannisson 1998). Studies on entrepreneurship performance have put an emphasize on the influence of entrepreneur-associated factors, characteristics of the firm, and the geographical environment (e.g., Tamasy 2006 and Tamasy and Le Heron 2008, for Germany and New Zealand respectively). In seizing opportunities, in particular product-markets with particular levels of newness, entrepreneurs develop strategies to define their competitive edge. Different strategies lead to diverse needs for resources. For example, a biotechnology research firm developing new drugs will require much larger amounts of investment capital, and over longer periods of time compared with a service firm in the same sector running routine measurements and determinations. The first will also employ more specialized labour and require access to global knowledge networks (Van Geenhuizen 2008). A main distinction in innovation among new technology-based firms is the position they aim to hold their respective industry, for example, as first movers or followers, or positions in-between. This strategy may be reflected in the R&D expenditure of a firm that allow it to produce new knowledge and/or gain it from external sources, but also to increase its ability to learn through external knowledge (Cohen and Levinthal 1990). So we may assume that a high level of R&D expenditure within technology-based firms facilitates knowledge collaboration abroad to capture highly specialized knowledge (Drejer and Vinding 2005). At one extreme in sectors, we find firms aiming at a first mover position, taking the risk of the high costs of not yet perfect technology and of building the market, by producing a breakthrough that is new for the sector and/or for the world they may yield high profits, profits that are eventually based on patent protection (Lieberman and Montgomery 1988; Kerin et al. 1992). Conversely, followers take less risk and tend to learn from first movers but they may also gain less profit. It is plausible that the higher the level of newness, the larger the chance that the best knowledge is not available locally, but just in a few places across the globe. Young and financially weak firms, however, do not often patent their inventions due to high costs, namely, of maintaining the patent, a situation that my prevent them from learning and sharing knowledge internationally (e.g., Andersen, 2006). In addition, firms may be more different in newness and learning habits than a simple first mover-follower division suggests. In fact, there are many positions in-between first mover at one extreme and last follower at the other. It is plausible that the knowledge and learning needed in these diverse positions are different and cause a different need for building international knowledge relations per sector. What may also differentiate the need for building such relations is the firm’s stage in product development, for example, still engaged in development or already with a position in the market (Gilsing and Duysters 2008). Firms that are active in the market and engaged in exports may easily build knowledge relations with customers abroad, in contrast to firms still mainly engaged in development activity.

3.3 Human capital Apart from knowledge on the technology itself, new technology-based firms need market knowledge and skills, business skills, including knowledge on firms’ daily management and long-term strategy, accounting and other financial knowledge, and financial resources. Human capital comprises the stock of knowledge and skills that resides within individuals (Becker 1993). Human capital can be developed over time and transferred between individuals and Papers in Regional Science, Volume 90 Number 2 June 2011.


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between organizations. Many theoretical and empirical studies in economics have addressed the importance of human capital, namely, knowledge and experience in enabling firms to adapt successfully to changes in technology and markets, based on knowing how to adapt in particular situations and on a more generic understanding of when adaptation is necessary and in which direction (e.g., Bartel and Lichtenberg 1991; Dodgson 1993; Siegel 1999). These studies share in common an underlying assumption, based on human capital theory, that employees with more human capital, through education and experience, are more productive than comparable employees in high-technology firms, for example, they have a greater ability to solve problems in a timely fashion and to adapt to changes fluidly. With respect to start-up firms, the human capital is that of the founder or founding team. For example, Shane and Stuart (2002) observed a positive influence of the founders’ experience in the industry on the start-up and performance of their firm. Human capital encompasses general and specific human capital (Becker 1993). The dimensions of general human capital are level of education, past experience, disciplinary mix and job training (e.g., Pennings et al. 1998; Bosma et al. 2004, Florin et al. 2003) (Table 1). Further, the size of the founding team at start can also be seen as an indicator of general human capital, but this may work in different directions with regard to knowledge capture abroad: more accumulated knowledge due to a larger team size may provide more capability for international learning, however, the more knowledge that is actively available in-house, the less there is impetus for gaining external knowledge, including that from abroad. Specific human capital in relation to internationalization is concerned with skills in foreign languages and an ability to

Table 1. Conceptualization in the study Main concepts

Dimensions in the literature

Human capital

Team (size) at start or in current situation, derived from absorptive capacity concept (Zahra and George 2002) Schooling/education (level) Becker 1993; Pennings et al. 1998; Siegel 1999; Cooper 2002; Florin et al. 2003; Bosma et al. 2004; Link and Siegel 2007 Specific education Becker 1993; Inkpen and Tsang 2005; Shane and Venkataraman 2000 Diversity in team, including education Becker 1993; Pennings et al. 1998; Ucbasaran et al. 2003; Colombo and Grilli 2006 Experience Bartel and Lichtenberg 1991; Becker 1993; Siegel 1999; Shane and Stuart 2002; Florin et al. 2003; Bosma et al. 2004 Job training Becker 1993; Pennings et al. 1998; Florin et al. 2003 Density of networks Borgatti et al. 1998; Burt 2005 Centrality of node (ego) Freeman et al. 1979; Borgatti et al. 1998, 2006 Strength of ties Granovetter 1983, 1995; Larson and Starr 1993; Hansen 1999; Gilsing and Duysters 2008

Social networks

Indicators in the current study (given data)

Heterogeneity of partners (social background) Marsden 1987; Pennings et al. 1998; Zheng et al. 2010

Size of founding team Almost no differentiation in founding team (all academic level) PhD experience in founding team Mix of disciplines in founding team Work experience of founding team (mainly in the same industry) Data not available in given data set Density of networks Data not available in given data set Frequency of face-to-face contact; duration of relations; familiarity level of ego with partners Heterogeneity of partners (social background)


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perform in different cultures and circumstances abroad. This may be derived from previous experience in international studies or through PhD study contact networks. Accordingly, if entrepreneurs feel confident and perceive no large risk using this experience they are more inclined to establish international relations (e.g., Shane and Venkataraman 2000). The empirical support for the core idea that more human capital leads to a higher level of performance, has been found to be mixed in various performance areas, including internationalization (e.g., Wright et al. 2007). For example, support for the effect of a founding team’s prior starting experience on venture performance is absent in some studies (Florin et al. 2003). These mixed results may be due to the fact that different kinds of experience are related to entrepreneurship performance and the relevance of a specific experience varies in different contexts, such as in diverse industry sectors and development stages namely, exploration and exploitation. In addition, human capital may influence firm performance indirectly, for example, through the fit between the firm’s strategy and team experience (Shrader and Siegel 2007).

3.4 Social networks Social networks are crucial for new technology-based firms as these are a source of social capital, in other words a source of access to scarce resources, they also support the reputation and self-esteem of the entrepreneur (e.g., Granovetter 1983; Coleman 1990; Putnam 2000; Lin et al. 2001). Social networks are defined as social structures made up of persons or organizations (nodes) connected by one or more specific type of interdependency, like friendship, kinship, a business goal (e.g., Uzzi 1996). Unlike exchanges in markets, social networks support exchange without using competitive pricing or legal contracting. The shared norms of the partners of social networks alone will ensure that the outcomes are fair. This situation is the one of socially embedded relationships, and these sharply contrast with arm’s length relationships, that are established and modelled to avoid conflicts of interest between partners (e.g., Uzzi 1996). We may also distinguish between social networks originating from the personal circles of the founder, like family, friends and former colleagues at university, and social networks originating in business circles that have become personal and trustworthy as with a customer or a venture capitalist. Another relevant distinction is based on what flows through the networks, like investment capital, goods in the case of trade relations, and knowledge. In this paper, we will focus on social networks in which shared norms and trust are a major asset, with origins both in the smaller circle of the entrepreneur and in business circles, and with knowledge and information as the major flow. Despite broad consensus on the importance of such social networks in the creation of social capital, there is a debate in the literature on the mechanisms through which social networks impact on firm performance (e.g., Moran 2005). From the perspective of a person or organization in the centre of a network (ego), emphasis is put on four concepts: the structural position, namely, dense vs. loose, strength of the ties, namely, strong vs. weak, centrality of position, namely, more or less central, and the social background of the network partners, namely, homogeneous vs. heterogeneous (Table 1). These concepts will be briefly discussed in an entrepreneurial learning context in the remaining section. Due to data limitations, it was not possible to include the item on centrality in networks. This means that our study could not include an analysis of resource advantages gained through a central position in the social network.2 2 Centrality in ego-networks is a dimension of the structural position of ego in the entire network (e.g., Freeman et al. 1979; Borgatti et al. 2006). It is generally assumed that the stronger the centrality of a firm in a network the better the access to external resources may be. To analyse centrality an overview of the entire network of the focal person/ organization is needed, which is not available in the current dataset.



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Dense networks are described as networks in which all partners are connected, and because all partners interact they are familiar with each other’s interests and build mutual trust and credibility. Such networks are beneficial for the transfer of complex and tacit knowledge as they reduce the costs of knowledge exchange (Coleman 1990; Uzzi 1996). Contrary to the previous arguments, Granovetter (1983) suggests that persons connected in loose networks will enjoy more advantages than those connected in dense networks, because a loose structure provides benefits from the diversity of knowledge and brokerage opportunities. Persons who hold brokerage positions between separate parts of the networks have better access to new knowledge. In particular, structural holes tend to separate non-redundant sources of knowledge and provide sources that are more additive than overlapping (Burt 2005; Stuart and Sorenson 2008). Overall, studies of the role of structural characteristics of networks on new firms’ performance have yielded rather inconclusive results (see also, Greve 1995; McEvily and Zaheer 1999). The strength of the ties in networks refers to the intensity of relationships. Usually, strong relationships develop as a result of long-term and intense interactions, as in personal friendships and family ties. Trust in these relations may facilitate the transfer of new knowledge, however, the theory of social networks presents a contradictory argument (e.g., Granovetter 1983; Larson and Starr 1993; Slotte-Kock and Coviello 2009), pointing to the ‘strength of weak ties’. Novel knowledge is obtained through casual meetings and relationships rather than through strong personal ties. Since strongly connected persons are likely to interact frequently, much of the knowledge that circulates in strong networks tends to be the same (Jack 2005). With regard to social heterogeneity of partners, it is commonly agreed that a network of partners from a diverse social background, integrating several spheres of society, enables a more beneficial interaction than partners from a similar background (Marsden 1987). Accordingly, concerning university spin-offs, a set of partners originating from different social environments, namely, university, large and small firms, public institutes, etc., will produce a larger variety in perceptions and ideas than a set of partners with a common origin, like local university staff and student friends with whom the entrepreneurs are familiar from their recent past (Rodan and Galunic 2004). All in all, for most of the above characteristics of social networks there are some contradictory viewpoints in the literature reviewed. Soetanto (2009) drawing on the same sample of academic spin-off firms as we use in the current study, observes that heterogeneity and looseness provide the strongest influence on firm performance in terms of job growth.3 There is however a potential limitation here. Social networks among spin-off firms may become important only after some years of existence, because building networks takes some time (Soetanto and Van Geenhuizen 2010). In some other studies, the characteristics of firm relationships have been found to be dependent on the stage of development of new products/processes, namely, exploration/exploitation, within the firm and its industry context (e.g., Rowley et al. 2000), this points to the innovation level of the firm as an influencing factor.

3.5 Model components We explored the above issues by focusing on three main component of the explanatory model: (i) innovation position; (ii) human capital; and (iii) social networks. In addition, we take two other factors of firm size and firm location into consideration, which may influence firm performance, including international learning.

3 All the models, for example, different development stage of the spin-off firms and different location, indicated a positive influence of relatively heterogeneous networks. In addition, almost all the models indicated a positive influence of relatively loose networks (Soetanto and Van Geenhuizen 2008, 2010; Soetanto 2009).


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In general, a difference in firm size causes differences in capabilities and internal resources, as well as a difference in gaining access to resources outside the firm, leading to different performance patterns. Larger firms in general have overcome the liability of smallness and face better conditions to grow (Barney and Clark 2007). Further, in spatial innovation studies, much attention is paid to available knowledge in urban places for example, localized knowledge spillovers. New technology-based firms thus tend to benefit from being in large cities in metropolitan areas due to a large availability of new and diverse knowledge and the availability of a large pool of specialized workers and talented people (e.g., Audretsch and Feldman 1996; Capello 2006); this is in contrast to small cities and rural and peripheral places where new technology-based firms may feel urged to move more quickly abroad, and probably have a need to bridge larger distances than those required by firms based in large metropolitan areas. Yet, there is a contradictory argument here as well. Large cities in metropolitan areas are better connected to global knowledge centres, through air and rail connections, and through the corporate networks of multinationals, thus facilitating building knowledge networks abroad. Based upon the previous discussion we also included firm size and location in the model, as control variables.

4 Methodological aspects of the study 4.1 From concepts to indicators and variables In this section, we will discuss the steps taken from core concepts of human capital and social networks to variables, and how the variables were measured. A large number of dimensions in the context of new technology-based firms are shown in Table 1. Human capital in these firms contains the following dimensions: size of founding or management team, general schooling/ education, specific education, diversity in founding or management team, experience, namely, with starting a firm, work in the same industry/technology sector, and job training. These dimensions mainly refer to amount and diversity of accumulated knowledge, and particularly to the ability to bridge cultural barriers in learning. The available data allowed us to select: size of founding team, and to focus on the founding team in terms of presence of a PhD experience, diversity in education, namely, disciplinary mix, and work experience. All the previous dimensions refer to the founding team at firm start-up. There were no data available on job training since the firm start-up, meaning that our analysis lacked more recent information on the firm’s accumulation of knowledge/skills in the years between the founding and the time of the survey. With regard to social networks, four dimensions have been addressed in the literature: density, centrality, strength of ties and heterogeneity in background of partners. We included: density of networks to clarify the structural position, three indicators of strength of relationships, and heterogeneity of partners.

4.2 Data collection and measurement The given dataset was connected to firms in two cities selected from a worldwide meta-analysis of factors contributing to growth of university-related incubators: the firms were based in Delft in the Netherlands and in Trondheim in Norway (Soetanto and Van Geenhuizen 2008). The incubators in Delft and Trondheim were found to have grown differently under the influence of the different locations, metropolitan vs. non-metropolitan, and a different model of stakeholder involvement in the incubator at the time (2005), namely, single stakeholder involvement (Delft) and multiple stakeholder involvement (Trondheim). Taking two cities in different countries Papers in Regional Science, Volume 90 Number 2 June 2011.


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introduced the danger of two different national innovation systems into the comparison, particularly a diverse valuation of risk-taking in entrepreneurship. This danger was found to be negligible because the Netherlands and Norway share a similar, rather risk-avoiding, culture in entrepreneurship (GEM 2007), meaning that we could compare the two cities. In addition, both the Dutch and the Norwegian domestic markets are relatively small, causing the firms to have a similar export-orientation. With regard to urban characteristics, it should be mentioned that aside from a metropolitan/non-metropolitan location, Delft can be qualified as a small town, low in the urban hierarchy, and a location close to the European core. In contrast, Trondheim is a medium-sized town, high in the urban hierarchy, but with a location far from the Norwegian and European core. The current paper is based on a survey of spin-off firms from Delft University of Technology and from Norwegian University of Science and Technology (NTNU) (Soetanto 2009). The population of spin-offs from the two universities was delineated following two criteria: (i) commercializing knowledge created at the university; and (ii) the firm’s survival to 2006,4 not older than 10 years, and the firm having enjoyed at least one type of support from the incubation organization/university. All firms in this population (150) were approached, and the overall response rate was 67 percent (100 firms). Data were collected using a semi-structured questionnaire in personal face-to-face interviews with the principal manager, often the main founder/ owner. Aside from the commonly used characteristics age, size, product/technology, export orientation, etc., the interviews focused on innovation indicators, such as R&D expenditure, newness of the invention, stage in product development, and patenting, profile of the founding team, namely, human capital regarding education and work experience, social network profile of the principal manager, that is, ego-network with a maximum of five ties, and characteristics such as network structure, strength of ties and heterogeneity in the firm’s partners’ social background. The measurement of variables was as follows: the dependent variable, international knowledge networks, was measured in two ways: one as a dichotomous variable indicating whether the spin-off had established international knowledge networks using more formal knowledge sources (yes/no); and two as an ordinal variable indicating the spatial reach in the knowledge relations, namely, no international knowledge relations, relations in Europe only, relations in Europe and US or Europe and Asia, and truly worldwide relations covering a number of continents. Knowledge relations using formal sources were identified by asking the respondents for the main relationships with knowledge sources that were important for the firms’ growth. Fifteen different knowledge sources were presented in the questionnaire including researchrelated and market-related sources and one ‘open’ category. In addition, the importance of these sources for growth for a firm could be graded (five point scale), enabling us to select the important ones. The spatial reach of the relationships was identified by determining the cities of the locations of the main sources, however, in some cases the respondents were reluctant to mention the city, due to the sensitivity of the information, and gave only the name of the country. Interview data were supplemented by website analysis, allowing the knowledge relationships to be better linked with activities of the firms. The two control variables were measured as follows.5 Firm size was measured in full time equivalents (log). Location was measured as a binary dummy. Delft represents a city in a large metropolitan area close to the urban heartland of Europe, whereas Trondheim represents a city in a non-metropolitan area, that is, an isolated region on the periphery of Europe, but with a quite high position in the national urban hierarchy. We did not include separate variables for differences like urbanization level or position in urban hierarchy. The core indicators/variables were measured as follows. 4 In a previous study it was found, using simulation experiments, that non-survived firms do not differ significantly from the ones that survived in main respects (Van Geenhuizen and Soetanto 2009). 5 In a round of experiments preceding the current model design, we also included the age of the firms as control variable, but it was found not to be significant.


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• Innovation position: measured as a composed variable including newness of new products/ processes etc., and R&D expenditure. Newness of the invention was measured in three categories, based on whether the invention at the time of the survey was a breakthrough and/or new to the sector, and on whether a patent application or granted patent(s) were involved. We had to limit ourselves to three categories (two dummies) since our given data were not sufficiently detailed to construct more categories. R&D expenditure was measured as percentage of turnover/other income of a firm. • Human capital: measured by four variables: (1) size of founding team using number of team members (2) work experience at start as the average number of years of experience among the first three founders; (3) disciplinary background of the founding team in two categories, i.e., single technology, or multiple technology or multidisciplinary; (4) PhD experience using the number of doctorate degrees held by the founding team. • Social network profile (see also Appendix 1): we used given data on social networks as an egocentric (firm) network consisting of a maximum of five direct and personal dyadic ties and relations between these ties, and five variables to measure their character: (1) density of networks, as the proportion of partners of ego that are mutually connected; (2) heterogeneity of partners, social background, as the proportion of heterogeneous partners, using a heterogeneity index; (3) frequency of face-to-face contact of ego with partners, average times per month; (4) duration of relationships, number of years ego knows the partners; and (5) familiarity of ego with partners, how well ego knows them. Variables (3) to (5) were used to indicate the strength of relationships.

4.3 Descriptive statistics: Knowledge relations abroad A small majority of the spin-off firms in our database (61%) was active in international knowledge relationships. In terms of spatial reach in these relationships, 11.1 percent were focused on Europe (Table 2), a pattern that mainly occurred among spin-offs in Delft and was exceptional among spin-offs in Trondheim. Overall, the spin-off firms were found to be quite strongly internationalized in terms of bridging physical and cultural distances, given the fact that the firms active outside Europe clearly outnumbered the ones active within Europe, 36 percent vs. 23 percent. Firms with worldwide knowledge relations accounted for 23 percent.

Table 2. Knowledge relations International relations and spatial reacha

N

(%)

Not internationalized Neighboring Europe only Far Europeb USc Asiac Europe/US/Asia and worldwide Worldwide Missing Total

39 11 12 7 6 11 12 1 99

39.4 11.1 12.1 7.1 6.1 11.1 12.1 1.0 100

Notes: a To use these data in the model, aggregation of categories was necessary for statistical reasons: within Europe (23.2%), US and Asia (13.2%) and worldwide (23.2%). b Often also Neighboring Europe included. c Often also Europe included. Papers in Regional Science, Volume 90 Number 2 June 2011.


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The last category encompassed spin-offs that were already at the stage of sales of products, and employed sales offices abroad thereby learning from interaction with customers, and spin-offs that worked on a project basis in many countries on the spot, such as civil engineering works, construction in the oil sector, software projects and consultancy, and adapted their work on request to meet the needs of local customers like governments. Among the most important sources in knowledge relations, market-related sources, mainly customers and suppliers, occurred the most often (41%), with exhibitions and fairs as the next important set of relationships (22%). Relations with universities and research institutes occur less often (6%). The classification of knowledge relations (Table 2) distinguishes between physical distance in broad terms. The differences between Neighbouring and Far Europe, and between Europe and US, Europe and Asia, and between Europe, US and Asia and Worldwide also include elements of increasing cultural distances.

4.4 Remaining descriptive statistics The average age of the spin-offs in the sample was 5.1 years (in a range of 1 to 10 years) and the average size was 7.4 full time equivalent (in a range of 0.5 to 51). With regard to sectors and technology, the spin-offs were mainly involved in research-intensive services (79%) and a minority (21%) was involved in research in manufacturing. Manufacturing encompassed new materials (nanotechnology), sensor technology and control systems (hardware). Biotechnology (medical) formed a very small part of this minority. The largest segments in services were software design (28%) and engineering, testing and optimization services and consultancy (27%), the last connected to sea transport, oil and gas production, wind energy production at sea, and to civil engineering projects in river management and seaport development. The firms in our study showed strong differences in innovation (Table 3): 44 percent were working with novel inventions and involved with patents, while 36 percent were active at a medium level and not involved with patents. A smaller group (21%) employed less novel approaches. The last group were mostly firms performing in the market, partly exports, and had done so for several years with products/processes or services that were no longer novel. With regard to R&D expenditure, on average, the firms spent 39 percent of their income on R&D but there was considerable variation. With regard to human capital, most spin-offs had taken off as a team: average team size being 2.3. Regarding work experience at start, the picture was as follows: the average work experience among the three first founders counted for 2.5 years with a high standard deviation (3.9). With regard to the founding team’s disciplinary background, most spin-offs had a technical background in one field (65%) whereas 35 percent had a background in more than one technical field or a combination with a non-technical field like management. In addition, spin-offs at start include on average less than one member with PhD experience (0.57), 38 percent of firms teams had PhD experience. The social networks were delineated by the respondent, and included mostly personal relationships with friends and colleagues, and research/business relationships that had turned into personal relations, with researchers, consultants, advisors, co-designers, etc. The density of networks on average was at a medium level (0.49) and the heterogeneity of network partners was on average relatively low (0.15) given a theoretical range for both variables between 0 and 1 (see Appendix 1). Further, the principal manager of the spin-offs met partners face-to-face on average once per month, and he/she had known them on average for 4 years. The principal manager valued familiarity with partners on average at a medium level Papers in Regional Science, Volume 90 Number 2 June 2011.

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M. Taheri, M. van Geenhuizen Table 3. Variables and descriptive statistics

Variables Dependent variables Knowledge relations abroad. Binary variable (yes/no) Reach in knowledge relations. Variable in four rank categories: not internationalized; internationalized within Europe, US or Asia, and Worldwide Control variables Firm size. Number of full time equivalent (as log) Location. Dummy variable: Delft = 0; Trondheim = 1 Innovation position Newness. Variable in three categories based on type of innovation (breakthrough and/or new to sector or not) and number of patents applied for and/or granted (two dummy variables) R&D expenditure. Percentage of turnover (income) over the last three years Human capital (founder/founding team) Size of team. Number of team members Work experience (at start). Average work experience in years of first three founders Disciplinary background. Variable in two categories: single technology and multiple technology/ multidisciplinary (dummy variable) PhD experience. Number of doctorate degrees Social network (ego) Density of network. Proportion of partners that are mutually tied (see Appendix 1) Heterogeneity of partners. Proportion of heterogeneous partners among all partners (see Appendix 1) Frequency of face-to-face contact. Average number of meetings with partners per month Duration of relationships. Average number of years ego interacts with partners Familiarity with partners. Average score by ego on a three class interval scale (1–3)

Descriptive statistics Yes (60.6%); No (39.4%) Not internationalized (39.4%) Internationalized within Europe (23.2%) Internationalized in US or Asia (13.2%) Worldwide (23.2%)a Average: 7.43; standard deviation: 7.06; Min-max: 0.5–51 Delft (58.5%) Trondheim (41.5%) Low level (21.0%) Medium level without patents (35.5%) High level with patents (43.5%) Average: 38.6; standard deviation: 21.2; min-max range: 0–100 Average: 2.35; standard deviation: 1.00; Min-max: 1–5 Average: 2.51; standard deviation: 3.9; Min-max: 0–20.6 Single technology (64.6%) Multiple studies (35.4%) Average: 0.57; standard deviation: 0.08; Min-max: 0–3 Average: 0.49; standard deviation: 0.3; Min-max: 0.1–1 Average: 0.15; standard deviation: 0.02; Min-max: 0.06–0.184 Average: 1.03; standard deviation: 0.60; Min-max: 0.2–4 Average: 4.25; standard deviation: 2.13; Min-max: 1–13 Average: 1.5; standard deviation: 0.55; Min-max: 0.4–2.8

Notes: a Missing value: 1%. N (firms) = 99.

(1.5) given a theoretical range of 0 to 3. The last three outcomes suggest a relatively modest strength for ties.

4.5 Multiple regression analysis Since the first dependent variable, establishment of international knowledge relations, is a dichotomous variable, we used a logistic regression analysis. Accordingly, our regression model predicts the logit that is the (natural) log of the odds of having one or the other outcome, here, the international knowledge relation. Thus, in a simple logistic regression with one predictor Papers in Regional Science, Volume 90 Number 2 June 2011.


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variable (X): Ln (ODDS) = Ln (Y/1 - Y) = a + bX where Y is the predicted probability of the event and X is a predictor variable. Given the second dependent variable, spatial reach in knowledge networks, was measured as a rank variable, we used ordered logistic regression. We included variables at the highest level of measurement and transformed them if necessary.6 With the aim to check for multicollinearity, correlation between the independent variables was determined (see Appendix 2). The strongest single correlations were between density of networks and heterogeneity of partners (-0.78). Familiarity level with network partners was also relatively strongly correlated with other social network characteristics for example, density of networks and heterogeneity of partners. Given these results, we decided to remove two variables from the model: heterogeneity and familiarity with partners. The reported correlations for the remaining independent variables (most are below 0.50), did not indicate serious concern for multicollinearity (Hair et al. 1995). As a final point we also tested for the endogeneity using the Wald test.7

5 Model exploration We will now discuss the results obtained from the exploration of influences on building international knowledge networks. We used a hierarchical procedure (Table 4). In Model 1 we only include control variables leading to a very weak model. This was followed by adding variables on innovation level (Model 2), variables on human capital (Model 3) and social network variables (Model 4). In Model 2, adding an innovation level caused the coefficients of two dummy variables of newness to be significant, that is a high level and, at the other end, a low level of newness. Further, adding human capital characteristics in Model 3 increased the model fitness in pseudo R2 considerably, that is by 0.095 compared to Model 2. Including all variables in Model 4, the model fitness increased by only 0.014 in pseudo R2, indicating a small influence of social network characteristics such as frequency of face-to-face meetings and duration of relationships, referring to strength of relationships. With regard to the sign of the coefficients in the full model (Model 4) almost all showed a positive one. This result complies with the idea that more human capital and more social capital from networks produce a better situation for accessing resources to adopt international knowledge relations and to co-ordinate them. The exceptions are size of founding team and density of social networks: a smaller founding team tends to promote international knowledge networks, not a larger team. Apparently, a smaller founding team faces shortages in essential knowledge that are compensated by knowledge relations abroad. In addition, the result that firms in loose social networks were more likely to establish international knowledge relations, refers to the larger and more diverse access to resource opportunities for firms in network brokerage positions (Burt 2005; Stuart and Sorenson 2008). The result is also in agreement with the outcomes of Soetanto (2009) where firms engaged in loose social networks tend to perform better in job growth, however, most of the previously mentioned results yielded coefficients that were not significant. Only the coefficients of size of the founding team and of PhD experience were significant, aside from the ones of firm size and low newness. Firm size was found to be significant in the full model (Model 4) reflecting a firm’s stronger capacity to adopt international knowledge relations when employing more personnel. The result 6 To obtain normally distributed variables we used log transformation for firm size and square root transformation for R&D expenditure, work experience and PhD experience at start. Transformation was also applied to the variables frequency of face-to-face contact and duration of relationships. 7 In using the IV Wald test of exogeneity, we assumed that variable X, international knowledge relations, was endogenous, and we chose as the instrument for it the variable Z, R&D expenditure. The Wald Test is used to check whether X is endogenous or not, on the basis of whether the error terms in the structural equation and the reduced-form equation for the endogenous variable are correlated. The outcome of the test Chi2 (1) = 6.37 (Prob > Chi2 = 0.011) indicated that X was exogeneous.


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M. Taheri, M. van Geenhuizen Table 4. Logistic regression analysis of international learning (yes/no)

Variables

Control variables Log firm size Firm location (1 = Trondheim) Innovation position High newness (1 = yes) Low newness (1 = yes) R&D expenditure Human capital (founder/ founding team) Size of team Working experience Disciplinary background (1 = multiple studies) PhD experience Social network (ego) Density of network Frequency of face-to-face contact Duration of relationships Intercept N LR Chi square Pseudo R Square Log likelihood

Model 1

Model 2

Model 3

Model 4

Logit coef. (s.e.)

Logit coef.(s.e.)

Logit coef.(s.e.)

Logit coef. (s.e.)

0.29 (0.25) 0.46 (0.46)

0.49 (0.31) 0.50 (0.53)

0.56 (0.34)* 0.91 (0.64)

1.26 (0.54)** 1.12 (0.61)* 0.03 (0.14)

0.84 (0.60) 1.28 (0.64)** 0.14 (0.15)

0.92 (0.62) 1.32 (0.65)** 0.12 (0.16)

0.25 (0.24) 0.27 (0.42)

– – –

– – –

– – –

-0.63 (0.26)** 0.05 (0.23) 0.38 (0.59)

-0.65 (0.27)** 0.12 (0.24) 0.46 (0.60)

–

–

0.90 (0.44)**

0.82 (0.45)*

–

–

-0.37 (0.80) 99 1.31 0.009 -65.72

-1.67 (1.26) 99 9.4* 0.07 -61.67

–

-1.99 (1.40) 99 21.91*** 0.165 -55.42

-0.85 (1.10) 0.55 (0.53) 0.46 (0.61) -2.84 (1.69) 99 23.87** 0.179 -54.44

Notes: * P < 0.1, ** P < 0.05, *** P < 0.01.

on low newness may be explained as follows. The majority of the firms facing low newness (21%) held (established) positions in customer markets worldwide with products and services that had lost novelty but remain somewhat innovative through local adjustment and participation in knowledge networks on site. In the remaining section we will focus on the results from exploring the spatial reach of firms with respect to international knowledge relationships using ordered logistic regression (Table 5). In Model 1 we only include control variables in which both firm size and location were found to be positive and significant in a rather weak model (a pseudo R square of 0.03). In Model 2, innovation variables were added, this gave one more coefficient that was significant, namely, that of high newness. In Model 3 human capital indicators were added causing a considerable improvement in model fitness indicated using pseudo R square, an improvement of 0.061. In Model 4 model fitness was somewhat improved by adding social network variables. In the full model, two human capital indicators, PhD experience and size of founding team; two social network characteristics, density of networks and frequency of face-to-face contact; and both indicators of newness had significant coefficients, aside from both control variables. The full model results suggested that larger spin-off firms, spin-offs in Trondheim (Norway), a low newness and a high newness including a patent profile, small founding teams, PhD experience, loose social networks and stronger ties, evidenced by more face-to-face contact, were more likely to be involved in large distances in knowledge relations. Overall, three more coefficients were significant compared to the full model of international knowledge relations: firm location, high newness, and frequency of face-to-face contact. For the remaining variables, the trends are largely similar. Papers in Regional Science, Volume 90 Number 2 June 2011.


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Table 5. Ordered logistic regression of spatial reach in knowledge relations Variables

Control variables Log firm size Firm location (1 = Trondheim) Innovation position High newness (1 = yes) Low newness (1 = yes) R&D expenditure Human capital (founder/ founding team) Size of team Work experience Disciplinary background (1 = multiple studies) PhD experience Social network (ego) Density of network Frequency of face-to-face contact Duration of relationships N LR Chi square Pseudo R Square Log likelihood Notes:

†

Model 1

Model 2

Model 3

Model 4

Ologit coef.(s.e.)

Ologit coef.(s.e.)

Ologit coef.(s.e.)

Ologit coef.(s.e.)

0.46 (0.21)** 0.88 (0.39)**

0.52 (0.22)** 1.10 (0.41)***

0.72 (0.26)*** 1.37 (0.47)†

0.98 (0.31)† 2.33 (0.62)†

– – –

1.19 (0.50)** 0.79 (0.55) -0.02 (0.12)

0.74 (0.54) 0.93 (0.57) 0.05 (0.13)

1.07 (0.58)* 1.08 (0.58)* 0.02 (0.14)

– –

– –

-0.56 (0.22)** 0.15 (0.19) 0.40 (0.48)

-0.67 (0.24)*** -0.06 (0.2) 0.60 (0.50)

–

–

1.16 (0.37)†

1.01 (0.38)***

– –

– –

– –

– 98 8.54** 0.03 -124.59

– 98 15.22** 0.059 -121.25

– 98 31.15† 0.120 -113.29

-2.04 (1.05)* 1.28 (0.51)** 0.63 (0.54) 98 39.89† 0.154 -108.92

P < 0.005, * P < 0.1, ** P < 0.05, *** P < 0.01.

With regard to location, firms in Trondheim were more likely to bridge larger distances in capturing knowledge than firms in Delft (the Netherlands). This can be explained by the formers’ rather remote location in Norway and peripheral location in Europe, giving rise to a need to establish knowledge relations outside Scandinavia. Further, the spatial reach in international knowledge relations was positively influenced by a high newness of product/process aside from low newness. This result can be explained by the trend that spin-offs facing high newness are involved in patenting, a situation which makes them a more solid and trustworthy partner (reputation) in countries at large spatial, and probably also cultural distances, it can also be explained by the fact that the highly specialized knowledge needs of these firms can only be satisfied in a few places globally, in parts of Europe, the US and in Japan. Social network indicators showed similar behaviour in both models, namely, international relations and spatial reach, but two of the indicators give significant results in the model on spatial reach. The result on frequency of face-to-face contact suggests that strong informal ties facilitate large-distance knowledge relations, supporting the idea of a gradual and balanced development of a firm in which the building of more formal international relations is preceded, or accompanied by, building of, in some ways, strong social networks at the home base (city/region). In addition, as our result on network density suggests, networks that act positively to bridge large distances tend to be relatively open. Note that one result was different between the two models, that of work experience in the founding team. In the full model of spatial reach, while adding social network variables, the coefficient was negative, though not significant. This change in sign is not easy to understand in Papers in Regional Science, Volume 90 Number 2 June 2011.

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a situation in which work experience is highly varied, that is in industry sector, in starting a firm, etc., while the given dataset was only able to grasp work experience in general. Summarizing the main results of the two models, the following becomes clear. First, the models did not show considerable differences in influence of human capital: both full models indicated a positive influence of PhD experience and negative influence of founding team size. Work experience and disciplinary background tended to be relatively unimportant. Second, the models indicated some difference in the influence of social networks. Only the model of spatial reach suggested an important negative influence of network density and positive influence of frequency of face-to-face contact. Third, with regard to innovation, low newness tended positively to influence establishing knowledge relations abroad and bridging larger distances, while a high newness tended positively to influence only bridging larger distances.

6 Concluding remarks The primary objective of this research was to explore the extent and background of established knowledge relations abroad among academic spin-off firms. Such study is relevant because the high levels of specialization required for technology inventions today call for highly specialized knowledge, most probably not (all) available in regional clusters of firms. Clusters of high-technology firms may benefit from an infusion of global knowledge from abroad increasing local diversity in knowledge supply and learning. This is all more important because research universities, research institutes and universities of applied science are increasingly collaborating with firms on a regional basis in their mission to commercialize new knowledge and support the regional economy while gaining some knowledge input or other resources for themselves. As theory on human capital and social networks indicates, new technology-based firms may be active in international knowledge relationships to different degrees. More human capital leads to a better performance while particular profiles of social networks cause social capital to be released, equally leading to better performance. However, empirical research to date reflects some contradictory trends, which may be caused by the innovation position of a firm, caused by sector characteristics or the development stage of a firm, for example, exploration vs. exploitation. The contribution of this paper to the literature resides mainly in the exploration of a more complete model of international knowledge relations, including human and social capital and the innovation level of firms. In addition, the model was explored for a specific category of new technology-based firms, namely, academic spin-offs, a type of firm for which our understanding is still limited. Academic spin-offs in Delft, the Netherlands and Trondheim, Norway were found to be involved in international knowledge relations for a small majority (61%). Among them, however, spin-off firms that were active outside Europe outnumbered the ones active only within Europe, suggesting an important role for spin-offs in creating truly global pipelines. In line with theory on benefits from human capital, the study confirmed the positive influence of international experience (through PhD research), on both international knowledge relationships and the large spatial reach in these relationships. The work experience and disciplinary background of the entrepreneurs, in contrast, seemed to be less important. The last result most probably emerged because the measurement was not sufficiently detailed thus causing a lack of match with specific needs in different product/service development stages and different industry sectors. In addition, the influence of social capital indicators tended to be weak in the adoption of international knowledge relations, but less so in the distances involved: a negative influence of dense social networks and positive influence of strength of ties was derived from face-to-face contact. These results support the idea of gradual developments in internationalization of new Papers in Regional Science, Volume 90 Number 2 June 2011.


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technology-based firms in which supportive social networks are built first or simultaneously with more formal knowledge relationships abroad over large distances. In contrast to many studies on human and social capital in new technology-based firm performance, the current study also took the innovation position of firms into account, using R&D expenditure and newness of inventions. R&D expenditure was found to be not significant but it had a positive impact on building international knowledge relations and the spatial reach of these relations. Newness was measured in three categories, with firms facing low newness of products or services more likely to capture knowledge internationally and at further distances. These firms had established market (export) positions based on products/services with relatively low novelty while continuing to learn internationally, mainly through customers in projects on-site. An important influence could also be observed for patenting behaviour of firms, qualified as high newness; if inventions were protected by patents, establishing international knowledge relations was more likely and also more likely to involve larger distances, for example, the US and Japan. A more solid reputation and higher attractiveness as an international knowledge partner, and stronger need for highly specialized knowledge tended to play a role. These observations connect with the first mover–late follower discussion. Our findings indicated that followers are often active in engineering projects and software industry. These relatively low innovative firms are more likely to establish international knowledge relationships due to the shorter period required for development and market introduction of their products. Their partners, in our case abroad, tended to be partly oil (energy) production firms, utility firms and local/regional authorities. In contrast, first or early movers, partly active in science-driven fields, may lag behind since it usually takes longer for them to introduce their products to market, but their solid patent position makes them trustworthy partners for developing highly specialized knowledge over larger distances, both physical and cultural, particularly in relationships with knowledge institutes or large technology-based firms. The influence of innovation thus tended to be differentiated. Further, the type of city with regard to position in a metropolitan area and (inter)national core/periphery was explored as a control variable. Knowledge relations tended to be established over larger distances by firms in Trondheim compared to those for firms located in Delft. Despite some interesting results the current study suffers from some shortcomings. The overall model power is rather weak.8 However, this is quite common in studies on human capital and social networks because these are fine-grained attributes of firms that may work differently under diverse conditions. Nevertheless, the model and some measurements could be improved. We mention the rather broad measurement of work experience and disciplinary background of the founding team, excluding job training and aspects of network centrality due to lack of data, and the rather simple structure of the model. First, work experience and disciplinary background could be examined in more detail and the measurement could be more refined and related to situations in which they really matter. Second, the database could be extended with more indicators of human and social capital, particularly aspects of network centrality and job training. Insight into network centrality of ego would help us better to evaluate the potentials of network position in accessing resources, and to assess how important firms may be as central actors in global pipelines. Insight into job training would help us to understand changes in human capital since the take-off of the firms. The training of the team members in more recent years may have increased their abilities to bridge distances in knowledge relationships. It also needs to be mentioned that regional networks and their social capital are currently undergoing 8 In a previous round of experimentation, we achieved stronger results compared to those presented in Table 4 and Table 5, in terms of model power, Pseudo R2. This was done by dichotomizing various variables: a Pseudo R2 of 0.20 for adoption of international learning and of 0.23 for spatial reach in knowledge relations were achieved. However, to analyse the relatively richer data in this study we used the real value of the variables.


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a process of change given the influence of open innovation and the establishment of many regional networks designed to serve as ‘testbeds’ for inventions. This might influence the social capital captured by small technology firms in such networks. Third, the model outcomes could be improved by refining the model structure; namely, by using interaction effects and intermediary factors. Good candidates for interaction effects could be location and social network characteristics. In this study we worked with cross section data. In future research we could investigate the impact of human and social capital over time using longitudinal data to reveal causal relationships. A point of consideration is whether the study has wider implications, for example, for other European countries and/or North America. We believe that technical universities in a rather risk-avoiding entrepreneurial culture and small domestic market may face similar patterns of internationalization in knowledge relationships as evidenced in the current study. This situation means that the results may have implications for technical universities in a selected number of similar countries, such as Denmark and Sweden. The conditions of a risk-avoiding entrepreneurial culture and small domestic market, seem however not to be true for the US, whereas only the second, a small domestic market, seems to be true for Canada. In addition, our sample of mainly service firms, partly working in software, engineering, and consultancy projects worldwide on-site, is rather specific, thus limiting wider interpretation to other industries. Some of the stronger results of this study provide ground for policy recommendations aimed at enhancing knowledge relations abroad: policy recommendations that mainly need to be addressed to the management of incubation organizations and/or universities. We mention three recommendations: (1) to advise founding teams of small technology-based firms to include a member(s) who holds a PhD because they will have experience of performing in international networks and overcoming particular barriers; (2), small technology-based firms should patent their innovative products/processes to protect their invention or IP, they should also consider branding and copyright their products; and (3) small technology-based firms should monitor the quality of local/regional social networks to keep them open and strengthen them to a certain extent while enhancing a local/regional supportive base for building international knowledge relations over larger distances.

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Appendix 1. Social network variables Name

Formula

Density of networks

Formula introduced by Borgatti et al. (1998): 2t/(n(n - 1)) t: total number of network relations n: total number of partners

Heterogeneity of partners

Heterogeneity index (recently used by Zheng et al. 2010): Network Heterogeneity 2 it = [1 - Sij (PAijt) ]/ NAij PA ijt: proportion of all spin-off firm i’s contacts with partner type j at time t, NA ij: spin-off firm i’s total number of contacts at time t.


Results (example) For example, if a firm has 5 partners but 3 three of them connect to each other, then the density would be (2 ¥ 3)/(5 ¥ (5 - 1)) = 0.3. A high value indicates a relatively tight network; min: 0, max: 1. For example a firm with 5 contacts, 2 with universities, 2 with small businesses and 1 with family would score [1 - ((2/5)2 + (2/5)2 + (1/5) 2) ]/5 = 0.128. A high value indicates a high level of heterogeneity; min: 0; max: 1.

2

Notes: * P < 0.05, ** P < 0.01. a Spearman correlation coefficients. b N (spin-off firms) = 99.

1 International knowledge 1 relations 2 Spatial reach 0.88** 1 3 Log firm size 0.08 0.18 4 Firm location 0.05 0.18 5 High newness with patent 0.19* 0.17 protection 6 Low newness 0.06 -0.00 7 R&D expenditure 0.10 0.04 8 Work experience 0.17 0.12 9 Size of founding team -0.20* -0.11 10 Disciplinary background 0.07 0.18 11 PhD level 0.25* 0.28** 12 Density of network 0.12 0.13 13 Heterogeneity of partners -0.01 0.02 14 Frequency of face-to-face 0.03 0.07 contact 15 Duration of relationships 0.11 0.10 16 Familiarity with partners 0.09 0.08

1

Appendix 2. Correlation matrixa,b

1 0.04

4

1

5

6

7

0.11 0.16

-0.27** -0.29** 0.08 0.16

0.02 -0.05

0.02 0.18

0.12 -0.24* -0.45** 1 -0.25* 0.14 0.59** -0.40** 1 -0.11 0.26** 0.24* -0.17 0.21* 0.18 0.14 -0.04 -0.05 0.11 0.41** 0.12 0.04 -0.04 -0.01 -0.13 0.11 0.32** -0.21* 0.15 0.32** -0.19* 0.14 -0.00 0.09 0.00 0.30** -0.04 -0.03 -0.10 0.07 -0.44** 0.01 0.05 -0.00

1 -0.13 -0.10

3

9

10

11

12

13

0.007 -0.06

-0.15 0.04

0.01 0.12

0.04 0.01

14

15

0.50 -0.39** 0.39** 1 0.76** -0.64** 0.44** 0.57**

1 -0.17 1 -0.06 0.30** 1 0.40** -0.04 -0.03 1 -0.05 0.07 0.27** 0.02 1 0.07 -0.08 0.00 -0.07 -0.78** 1 -0.26** 0.10 0.04 -0.00 0.44 -0.44** 1

8

1

16

Knowledge relations among academic spin-off firms 311


doi:10.1111/j.1435-5957.2011.00363.x

Resumen. En este artículo se explora el alcance y el contexto a la hora de establecer relaciones de conocimiento internacionales entre empresas derivadas (spin-off) jóvenes de carácter académico. Se examina la influencia del capital humano y las redes sociales de este tipo de empresa, junto con su nivel de innovación, mediante la utilización de datos de encuestas de 100 de estas empresas. El aprendizaje a escala internacional se mide de dos maneras, que son la adopción de la estrategia y el alcance espacial en relación a dicha adopción, desde Europa al mundo entero. El artículo se suma a una corriente de investigación que reconoce que, para poder mantenerse competitivas, las nuevas empresas de carácter tecnológico interactúan tanto en redes de conocimiento locales como en redes de conocimiento en el extranjero. Se encontró que la mayoría de empresas derivadas forman parte de redes internacionales y que el tamaño inicial del equipo y el tener experiencia a nivel de doctorado son lo que influye en mayor medida. El capital social que se libera por medio de redes sociales es una influencia relativamente poderosa solamente en cuanto al alcance espacial de las relaciones de conocimiento, lo cual apoya la idea de que unas redes sociales fuertes forman una base sólida desde la que se puede acceder a un conocimiento global. Se discuten las implicaciones de los resultados de este trabajo y de futuras líneas de investigación.

© 2011 the author(s). Papers in Regional Science © 2011 RSAI. Published by Blackwell Publishing, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden MA 02148, USA. Papers in Regional Science, Volume 90 Number 2 June 2011.