Sousa, C., Videira, P. e Fontes, M. (2011), “The role of entrepreneurs’ social networks in the...

Int. J. Entrepreneurship and Small Business, Vol. 12, No. 2, 2011 227

Copyright © 2011 Inderscience Enterprises Ltd.

The role of entrepreneurs’ social networks in the creation and early development of biotechnology companies

Cristina Sousa* and Margarida Fontes INETI & DINAMIA, INETI – Departamento de Modelação e Simulação, Estrada da Portela – Zambujal – Apartado 7586, 2721-866 Alfragide, Lisboa, Portugal E-mail: [email protected] E-mail: [email protected] *Corresponding author

Pedro Videira DINAMIA, Edifício ISCTE, Avenida das Forças Armadas, 1649-026 Lisboa, Portugal E-mail: [email protected]

Abstract: This paper addresses the influence of entrepreneurs’ social networks on early access and mobilisation of resources in a science-based field. Combining contributions from the technological entrepreneurship and the social network literature, it is proposed that different network configurations – in terms of origin, composition and structure – are associated with the mobilisation of different types of key resources. A new methodology is developed that permits to assemble a vast array of data capturing the nature and contents of key relationships. It is applied in an exploratory way, to the case of Portuguese biotechnology, enabling to (re)construct, analyse and compare the networks mobilised by firms to obtain: tangible resources; scientific and technological knowledge; credibility and mediation. The results confirm the methodology usefulness to gain an in-depth understanding on the process of network building and network mobilisation, as well as on the contribution of different types of networks to the entrepreneurial process.

Keywords: technological entrepreneurship; social networks; biotechnology firms.

Reference to this paper should be made as follows: Sousa, C. and Fontes, M. and Videira, P. (2011) ‘The role of entrepreneurs’ social networks in the creation and early development of biotechnology companies’, Int. J. Entrepreneurship and Small Business, Vol. 12, No. 2, pp.227–244.

Biographical notes: Cristina Sousa is a Researcher at LNEG (National Laboratory for Energy and Geology) and DINAMIA (Research Centre on Socioeconomic Change) and an Assistant Professor at ISLA (Lisbon). She received her PhD in Economics at ISEG (Technical University of Lisbon). Her main research interests include innovation systems, knowledge diffusion, networks and collaboration processes.

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Margarida Fontes is a Researcher at LNEG (National Laboratory for Energy and Geology) and a member of DINAMIA (Research Centre on Socioeconomic Change). She received her PhD in Management of Innovation from the University of Manchester. Her main research interests are management of technological innovation, knowledge dynamics and technological entrepreneurship. She has been conducting research on the process of knowledge production and dissemination in emerging fields with particular emphasis on the role of new technology-intensive firms and on the mobility of scientists. She has written several papers and participated in several national and international research projects in these fields.

Pedro Videira is currently a Researcher at DINAMIA/ISCTE where he has been collaborating since 2006 in numerous projects in the area of innovation and technology-based entrepreneurship. He has a degree in Sociology at ISCTE, a Master in Sociology of Economic and Social Development at the Sorbonne and has been developing his PhD research at the ISCTE doctoral programme in sociology under the supervision of Margarida Fontes.

1 Introduction

Research has demonstrated the continued relevance of new entrepreneurial firms for biotechnology development (McKelvey and Orsenigo, 2006; Ebers and Powell, 2007). This role can be particularly important in intermediate developed countries where a good science base and highly skilled human resources are available but where large biotechnology firms are often absent. In this type of context, scientific entrepreneurship can be seen as a mechanism of knowledge transfer and transformation in market value through human capital mobility, emerging as a critical factor in the development of a biotechnology industry (Feldman et al, 2005; Fontes, 2007).

However, the transformation of a technological opportunity into a marketable technology, product or service and its commercialisation, can be a complex process, requiring the combination of a variety of technological and non-technological competences and resources (Autio, 1997; Mustar et al, 2006). Thus, success in firm formation is determined, not only by the entrepreneurs’ capacity to identify an opportunity, but also by their ability to mobilise these resources and competences (Vohora et al, 2004). The entrepreneurship literature sustains that firm creation is a social process and that entrepreneurs’ social networks, built along their academic, professional and personal life, have an important role in this mobilisation process (Walker et al., 1997). Our argument is that different types of networks may be required to mobilise different types of resources and that, therefore, entrepreneurial teams with diverse backgrounds and experiences – which result in networks with distinct compositions and structures – will face different resource access problems and will develop different strategies to mobilise the set of actors who can assist in their solution.

These differences concern on one hand the ease of access to specific resources. In the case of science-based firms, knowledge resources are generally easier to access than business-related resources, because knowledge networks are likely to be more developed than business networks (Ensley and Hmieleski, 2005). But dissimilarities in terms of backgrounds and personal experiences may still exist, with impact upon entrepreneurs’ ability to encompass the whole range of needed resources (Mangematin et al, 2002). The

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differences also concern the mode of access to resources. In fact the channels used to gain access to specific assets, and the strategies deployed to mobilise the actors that can support that access, can vary strongly, being influenced by the composition and also by the structure of the entrepreneurs’ social networks (Powell et al, 1996). In conducting these processes entrepreneurs will rely on their existing ties, but will also strive to build new relationships with actors they regard as key for the firm (Lin, 1999). The members of the existing network can also be instrumental at that level, assisting in the identification of such actors and acting as mediators or credibilisers (Moensted, 2007).

Considering the above, the main research question examined in this paper is: how do entrepreneurs’ social networks affect the opportunity identification and the access and mobilisation of resources in a science-based field, facilitating the founding of new firms? To address this question we have reconstructed the social networks of a set of biotechnology entrepreneurial teams, and assessed the way these networks were mobilised to solve key problems in the process of firm formation and early development:

a opportunity identification and exploration through the acquisition of tangible resources (capital, human, physical)

b acquisition of scientific and technological knowledge

c achieving credibility and obtaining mediation to critical sources.

The paper is organised as follows. Section 2 presents some contributions from the literature on entrepreneurship and social networks to the development of an analytical framework. Section 3 presents the methodology, providing a detailed analysis of the methods used to reconstruct the social networks and to analyse their composition and structure. Section 4 presents some preliminary results of the empirical analysis conducted for a group of Portuguese biotechnology (molecular biology) firms. The last section summarises and discusses the findings.

2 Analytical framework

In order to investigate the role of scientific entrepreneurs’ social capital in the process of firm creation and growth, we combine literature on social networks and literature on technological entrepreneurship.

2.1 The influence of social networks on entrepreneurship

Recent research has shown the importance of the entrepreneurs’ social capital in the access to several tangible and intangible resources, needed for the formation and growth of new firms (Greve and Salaff, 2003; Singh, 2000). Entrepreneurship is described as a social process, embedded in social structures and thus, in order to fully understand the nature of the entrepreneurial process, it is necessary to take into consideration the social networks built by the entrepreneurs along their academic, professional and personal life, as well as the ways entrepreneurs mobilise them to achieve their goals (Johannisson, 1998; Murray, 2004).

The literature agrees on the fact that a variety of resources are important in the entrepreneurial process (Brush et al., 2001; Alvarez, 2001), being equally recognised that

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social networks can be relevant to access and mobilise several of them. For instance: knowledge and information (Gittelman, 2007; Powell et al., 1996; Stuart and Sorenson, 2003, Uzzi, 1997, Van Geenhuizen, 2008); qualified human resources (Castilla et al., 2000; Granovetter, 1973; Saxenian, 1994); financial capital (Burton et al., 2002; Shane and Stuart, 2002); power and influence (Castilla et al., 2000; Lin, 1999); credibility and reputation (Lin, 1999; Moensted, 2007); and emotional support (Brüderl and Preisendörfer, 1998). Social networks are also described as instrumental in the identification of an opportunity (Ardichvili et al, 2003; Singh, 2000).

In the case of technological entrepreneurship, key resources include: scientific and technological knowledge; capital; highly qualified human resources; reputation and credibility; information about markets, financing, regulatory processes, intellectual property; as well as counselling in these same fields (Mangematin et al, 2002; Chesbrough and Rosenbloom, 2002; Mustar et al, 2006, Roberts, 1991). Given the high uncertainty (technology and market) often confronted by these firms and the level and nature of their resource requirements, social networks are presented as playing a particularly important role during their formation and early development (Yli-Renko et al, 2001; Elfring and Hulsink, 2003).

2.2 The impact of network characteristics on resource mobilisation

Castilla et al. (2000) define social network as a set of nodes or actors (persons or organisations) connected by a social relationship (or tie) of a specified type. Network configurations can differ, according to the type of actors and the type of relations they encompass. Relations can be characterised by the type of interaction (e.g., formal vs. informal), the intensity of the tie and the content of the relation (e.g., the type of resource(s) that circulate through it).

It is argued that different networks configurations will be associated with the access and mobilisation of different types of resources. Differences in network configuration can be induced by the nature of the resource or by the firm stage in the entrepreneurial process (Casson and Giusta, 2007; Elfring and Hulsink, 2003; Hite and Hesterly, 2001; Larson and Starr, 1993). In this research we will focus on the nature of resources, since we are only considering the early entrepreneurial stages. For this purpose, we consider the network types mentioned by Castilla et al. (2000): networks of access and opportunity (related to opportunity identification and exploration and obtaining tangible resources); networks of power and influence; and networks of production and innovation (where knowledge is the main resource that circulates).

Network characterisation as conducted in the social network literature usually involves three aspects: the position occupied by different actors in the network, the network structure and the tie content. Regarding network position, it is considered that different positions, usually measured by network centrality measures, offer different opportunities to access the relevant sources of resources (Powell et al, 1996).

Network structure is frequently analysed using density measures. There is some debate over the effects of different network configurations, i.e., more densely embedded or ‘closed’ networks with many strong ties (Coleman, 1988), vs. more ‘open’ networks with many weak ties (Granovetter, 1973) and structural holes (Burt, 1992). Some authors suggest that firm creation will require a mix of strong and weak ties (Uzzi, 1997), the former enabling the exchange of fine-grained information and tacit knowledge,


trust-based governance, and resource cooptation (Ahuja, 2000; Gulati, 1998), the latter providing access to novel (non-redundant) information (Burt, 1992).

Tie content concerns the substance of the relation. In the case of firm creation such content are the resources and the activities performed to access and mobilise them. The concept of multiplex ties was introduced to take into account the fact that the same tie can be used to access more than one type of resource (Degenne and Forsé, 1994), but this theoretical concept has rarely been applied at an empirical level. In this research, we use the concept of multiplexity as a basis to understand what type of network(s) – involving different types of actors, relations, structures – are more appropriate for obtaining different resources/ combinations of resources.

An additional dimension is the mode of construction of the network. Here we can distinguish between intentional and non-intentional networks. The former are purposefully created to achieve a given goal – as Lin’s (1999) ‘instrumental actions related with contact resources’, or Hite and Hesterly (2001) ‘calculative networks’. The latter are by-products of the activity or trajectory of the actor and do not have a particular motivation behind them, as is the case with Lin’s (1999) ‘expressive actions related with accessible resources’, or the ‘identity-based networks’ proposed by Hite and Hesterly (2001).

Finally, it is also relevant to take into consideration the debates about proximity and its impact upon access to key resources and competences (Feldman, 1994). While accepting that geographical proximity is an important facilitator in the establishment and mobilisation of key relations (Stuart and Sorenson, 2003), it can be argued that it is a necessary but not sufficient condition. Indeed, recent contributions propose other modes of proximity (social, organisational, technological) (Boschma, 2005), that can also be instrumental in accessing scarce or specialised resources and whose operation can take place at a distance (Breschi and Lissoni, 2001). However, distant relations can be more difficult to build and to maintain (Bathelt et al, 2004) and therefore there may be limits on the number of such relations that young small firms are able to manage.

3 Research methodology

Building on the contributions of the entrepreneurship and social network literature described above, we developed a methodology to analyse the role of social networks on the creation of the new firm that enables us to identify the key networks, uncover the way they were built and achieve an understanding of the contribution of different types of ties to the identification of an opportunity and to the mobilisation of a set of resources critical for the formation of a science-based firm.

A central aspect of this research was the (re)construction of the firm’s social networks, encompassing both the entrepreneurial personal networks, built along their academic and professional trajectories, and the intentional networks purposefully built along the formation period. This reconstruction raised several methodological challenges. The ways we addressed those challenges are presented in this section, covering data collection, network (re)construction methods and network measures.

The methodology was applied to the most science-based subset of the Portuguese biotechnology industry: the molecular biology firms, involving 23 firms and a total of 61 entrepreneurs. This group of firms belongs to the younger generation of biotechnology

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firms (80% were created from 2003 onwards) and thus several of them are still in an embryonic stage of development and only a few have fully developed their technologies/products and started introducing them into the market. Given the nature of the technologies being exploited, their activities concentrate in the health sector, with a predominance of clinical applications. All the firms were created by entrepreneurs originating from universities or research organisations – although several teams also included non-academic individuals with managerial competences and/or industrial experience – and are located in the major cities or their metropolitan areas, where the main research centres in this field are equally installed. The teams were mostly composed of young entrepreneurs (although some also involved one senior researcher) and in almost all firms there was at least one individual with some type of international background.

3.1 Data collection

Data about the biotechnology firms and about their entrepreneurs was collected using a novel combination of complementary methods, involving both documentary information and in-depth face-to-face interviews with the founders. The former included: the curriculum vitae (CV) of the entrepreneurs, publication and patent data, published data about formal collaborative projects and information on firms’ formation histories. The interviews, conducted in 2008, were based on a semi-structured questionnaire that focused on the entrepreneurs’ personal networks and their importance for the creation process. They elicited systematic and fine grained information about the people who were important during the formation period (defined as the pre-start-up period and the first three years), including the origin of the relationships and the type, nature and relevance of their respective contributions. They also gathered data on firm activities, strategy and performance.

3.2 Network (re)construction

The (re)construction of the social networks took several steps. The first was the identification of ego-centred networks at personal (entrepreneur) level – the ‘trajectory networks’. It involved the (re)construction of the academic and professional paths of all members of the founding team, in order to identify the organisations where they had developed training or professional activities, and thus where personal relationships might have been established (Burton et al, 2002). It combined methods usually applied independently (e.g., Balconi et al, 2004; Casper, 2007; Dietz et al, 2000; Murray, 2004): CV, project, patent and bibliometric analysis.

The next step was to (re)construct the networks at firm level. We combined, for each firm, the personal networks of all the members of the founding team, based on the assumption that when firms are being created their social networks are the sum of those of their entrepreneurs (Hite and Hesterly, 2001). Since we were now adopting an organisational perspective, a correspondence was made between individuals and the respective organisation(s). The resulting networks were labelled ‘potential networks’.

The final step was to build the networks that were effectively mobilised by the entrepreneurs during the creation and early development of the firm. For this purpose, we used the information, elicited trough the interviews, about the actors described as important to identify the opportunity and to obtain the critical resources, and about their


contributions. This information was combined with data on formal relationships established by the firm up to its third year of activity.

The information was collected for different types of activities, enabling us to reconstruct the three ‘mobilisation networks’ suggested by Castilla et al. (2000). The network of access and opportunity is composed of all the actors/relationships used to identify the opportunity and to access and acquire the tangible resources (capital, human resources and facilities) necessary to explore it. The network of innovation includes actors/relationships used to obtain scientific and technological knowledge. The network of power and influence is related with the process of achieving credibility and/or using well positioned and influential individuals as mediators in granting access to critical actors that are not part of the potential network and could not be mobilised without proper references.

3.3 Network analysis

A detailed analysis of the composition and structure of the potential and mobilisation networks was conducted using the methods of social network analysis (Wasserman and Faust, 1994) and supported by the UCINET software. Network characterisation involves determining the actors’ position, the network structure and the tie content. Given the nature of the networks being analysed at this stage (ego-networks with only direct ties) we only calculated measures to characterise the networks in terms of structure and content, which are described below.

1 Network size: According to Burt (2000), other things being equal, bigger networks mean that an entrepreneur can receive a more diverse and complete set of resources from his network. To have bigger networks entrepreneurs must have richer academic and/or career paths or direct more resources to network construction. Since networks are made of actors and relations, network size can be analysed at those two levels. We have considered the following measures: • total number of actors in a network • total number of ties in a network • mean (nodal) degree: mean of the number of nodes adjacent to each ego, that

gives the mean ‘activity’ of the network (Wasserman and Faust, 1994).

2 Network density: In a network not all relations have the same intensity: we can have strong and weak relations that take different amounts of time, energy and money to develop and maintain. Networks with a bigger proportion of strong ties are denser. To characterise network density we have considered the following measures: • network density (Wasserman and Faust, 1994): ratio between the number of ties

that are present in the network and the maximum ties possible • average strength of connection between actors (Burt, 2000): ratio between the

sum of all ties considering the respective strength and the total number of ties (represented in networks by thickness of line).

3 Actor composition: Networks are composed of different types of actors and relationships with them can have different origins. In this case we have considered (represented in networks by different symbols): biotechnology firms (square), firms from other sectors (diamond), venture capital organisations (up triangle), universities

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and research centres (circle), science & technology parks (down triangle) and professional and trade associations (circle-in-box). We measured: • importance of each type of actor, measured through the respective proportion in

the total number of ties • proportion of actors that were part of entrepreneurs’ trajectory networks and of

new actors intentionally mobilised.

4 Spatial distribution: The analysis of spatial distribution enables us to assess the level of geographical proximity/distance between the firm and the actors that compose their networks. We distinguished between three spatial levels: (represented in networks with different colours) local/ regional (white), national non-local (grey) and international (black), and measured: • weight of local/regional, national non-local and international relations, measured

through the share of relations in each location, for each network.

Globally this methodology enables us to obtain a rich set of network information for each firm, to reconstruct their potential and mobilised networks and to compute the network measures in order to analyse and compare the structure and composition of these networks, both across resources for each firm and across firms.

4 Preliminary results

In this section we present some preliminary results of the application of the methodology described above, based on the analysis of four firms, out of our sample of 23 molecular biology companies, whose main characteristics are briefly described in Table 1. Table 1 Characterisation of firms in case studies

A B C D

Year creation 2006 2003 2007 2000 Location Coimbra Coimbra Braga Lisbon Field Health (clinical) Health (clinical) Health

(clinical) Health (clinical) and food (diagnostics)

Activity Technology to deliver service

Service Product Technology + product + service

Number of entrepreneurs

2 5 2 3

Origin of entrepreneurs

Young scientists (foreign univ.)

Young scientists (local +

foreign univ.) + practitioners

Young + senior

scientists (local univ.)

Young scientists (local univ. +

foreign back.) + entrepreneurs

(small biotech) Institutional partners

N Y (small firm) Y (PNPI) Y (small biotech)

Venture capital Y N Negotiating Y Patent applications

N N Y (filed by university)

Y


Table 2 Firm A: network measures

Measure Potential network

Network of access and opportunity

Network of innovation

Network of power and influence

1.1 No of actors 5 7 5 10

% of new actors - 83 25 78

1.2 No of ties 4 10 5 13

1.3 Mean degree 1.6 2.57 2 2.60

2.1 Network density 0.40 0.48 0.25 0.24

2.2 Average strength connection 3.0 1.77 0.92 1.27

3.1 % biotech firms 0 0 0 11

3.2 % other firms 0 0 0 0

3.3 % VC institutions 0 50 0 33

3.4 % universities 100 17 75 33

3.5 % S&T parks 0 17 25 11

3.6 % associations 0 17 0 11

4.1 % local actors 25 50 50 44

4.2 % national actors 50 50 25 56

4.3 % international actors 25 0 25 0

Table 3 Firm B: network measures





1.1 No of actors 12 5 32 9 % of new actors - 50 90 75 1.2 No of ties 11 4 308 10 1.3 Mean degree 1.91 1.60 19.25 2.22 2.1 Network density 0.17 0.4 0.62 0.28

2.2 Average strength connection 1.64 1.75 1 1

3.1 % biotech firms 9 0 23 38

3.2 % other firms 18 50 48 12

3.3 % VC institutions 0 0 0 0 3.4 % universities 36 50 19 38 3.5 % S&T parks 0 0 3 0 3.6 % associations 36 0 7 12 4.1 % local actors 46 75 61 24 4.2 % national actors 36 0 32 38 4.3 % international actors 18 25 7 38

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Table 4 Firm C: network measures





1.1 No of actors 62 3 2 - % of new actors - 0 0 - 1.2 No of ties 332 2 1 - 1.3 Mean degree 5.35 1.33 1 - 2.1 Network density 0.18 0.66 1 - 2.2 Average strength connection 1.14 2.5 1 - 3.1 % biotech firms 7 0 0 - 3.2 % other firms 20 0 0 - 3.3 % VC institutions 0 0 0 - 3.4 % universities 62 100 100 - 3.5 % S&T parks 0 0 0 - 3.6 % associations 11 0 0 - 4.1 % local actors 2 50 100 - 4.2 % national actors 15 50 0 - 4.3 % international actors 83 0 0 -

Table 5 Firm D: network measures





1.1 No of actors 23 12 76 7 % of new actors - 82 96 67 1.2 No of ties 43 11 940 13 1.3 Mean degree 3.74 1.83 23.64 4.57 2.1 Network density 0.17 0.17 0.17 0.62 2.2 Average strength connection 1.16 1.18 1.01 1.15 3.1 % biotech firms 23 18 13 0 3.2 % other firms 9 27 23 0 3.3 % VC institutions 0 18 0 0 3.4 % universities 55 27 59 33 3.5 % S&T parks 0 0 0 0 3.6 % associations 14 10 5 67 4.1 % local actors 50 45 9 67 4.2 % national actors 0 0 3 0 4.3 % international actors 50 55 88 33

This preliminary analysis, was based on cases expected to display substantially different characteristics and enabled us to test the viability and robustness of the methodology, to fine-tune some of the methods and to evaluate its effectiveness in answering to our


research question. The results are therefore still exploratory, being particularly focused on regularities that could be identified on the composition and structure of the networks and that might reflect modes of behaviour shared by this category of firms.

For each firm we built four networks – the ‘potential network’ and the three ‘mobilisation networks’: access and opportunity, innovation, power and influence – and computed the networks measures described above, which are presented in Tables 2 to 5. In Appendix we present, for illustrative purposes, the graphical representation of those networks, for the case of one firm (Firm D).

In order to assess whether the nature of different types of resources had implications for network configuration, we examined the composition and structure of the three different ‘mobilisation networks’ – that is, networks that are effectively used by the firms in their search for resources – and compared them among themselves and also with the ‘potential networks’. The analysis of the network representations and respective measures was expected to provide some preliminary insights regarding the following questions:

• What types of actors are present in each mobilisation network?

• What types of network configurations (in terms of frequency, intensity, distance, nature) predominate in each mobilisation network?

• What types of resources circulate between different types of actors and what is the importance of multiplex ties?

4.1 What types of actors are present in each mobilisation network?

The data obtained for the cases analysed, permitted to conclude that the entrepreneurs mobilise only a sub-set of the relations in their potential network, but, at the same time, build intentional relations with new actors, particularly to access some types of resources.

As would be expected, potential and mobilisation networks are different. The former are largely dominated by actors from universities and research centres, reflecting the academic trajectory of a substantial proportion of the entrepreneurs. Mobilisation networks tend to be more diversified, reflecting the additional requirements of transforming technological knowledge into a product or service and commercialising it. This is particularly visible in the network of access and opportunity, which includes several new actors, namely related with access to capital (venture capital firms) and facilities (science and technology parks), that were not present in the potential networks.

A more detailed analysis of the actor composition of each mobilisation network, shows that innovation networks are dominated by universities, which is in line with the nature of biotechnology firms’ knowledge base (particularly in early stages). Those networks are built around some key organisations, already present in the potential network, with which firms maintain strong relations. But, innovation networks tend to be more diversified than potential networks, also integrating other firms (biotech and non-biotech). Interestingly, universities also emerge as important actors in access and opportunity and in power and influence networks. In the first case, former academic colleges or professors are instrumental in opportunity identification and early decision making process, as well as in access to human resources and facilities. Regarding power and influence, university actors enhance firms’ credibility and mediate their access to people/organisations that later become part of the other mobilisation networks.

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4.2 What type of networks configurations predominate?

When we observe network configurations in terms of density and tie strength, we conclude that, as expected, for each firm, different mobilisation networks have distinct configurations. Innovation networks, which are based on knowledge flows, tend to have small average density and small average tie strength. This may be explained by the distributed nature of S&T knowledge that leads firms to interacting with a large number of organisations, but possibly only maintaining strong relations with a few of them. Conversely networks associated with opportunity identification and exploration and access to tangible resources seem to have higher levels of density and average tie strength, which suggests that these networks may be associated with higher levels of trust (Ahuja, 2000).

Regarding the spatial distribution of the networks, the data shows that, in all firms, the most intense relations tend to be with local/regional and national actors. But while all firms have international actors in their potential networks (reflecting the international mobility of their founders), these play different roles. One firm did not mobilise any international relations, while another mobilised exclusively from the potential network and into the innovation network. The remaining firms had international actors in all mobilisation networks, some of whom were new intentional relations. This variety of results can be related with the nature of the international background and/or with the type of activities that may require different inputs from international actors. The greater complexity of distant relationships (particularly new ones) may also be determinant. A closer analysis of this type of ties and their association with firms’ activities (e.g., type of markets, stage development technology, local availability of resources) may provide additional insights.

4.3 What is the importance of multiplex ties?

All firms display multiplex ties, that is, ties in which the same actor belongs to more than one mobilisation network, being associated with access to more than one type of resource. Universities emerge as the prime actors in multiplex relations. In all cases there is at least one university whose ties are mobilised to access the three types of resources: information to identify and explore the opportunity, tangible assets, knowledge and influence. These strong multiplex ties originate from the entrepreneurs trajectory and thus involve actors that are likely to have higher levels of social, organisational and cognitive proximity with the entrepreneurs.

Additionally, there seems to be an inverse relation between the size of the potential network and the number of multiplex ties. This means that firms that interact with a small number of actors use these relations in a more intense way, to access a diversity of resources. So we can detect two possible strategies: some firms choose to extend the number of actors, with whom they develop less intense and less multiplex relations; other firms choose to have smaller networks, with more intense and multiplex relations.

4.4 Discussion

The comparison between the composition and structure of the ‘potential networks’ and the ‘mobilisation networks’ confirmed that entrepreneurs, in their search for resources and competences along the process of firm formation, effectively resort, more or less


extensively, to the members of networks that result from their academic and professional trajectory. But the research has also shown that they only select some of the members of these trajectory networks and, at the same time, add new members, purposefully chosen because they fulfil some important function in the new firm.

In addition, the analysis and comparison of the structure and composition of different ‘mobilisation networks’ – access and opportunity (access to tangible resources); innovation (access to scientific and technological knowledge); power and influence (achieving credibility and obtaining intermediation) – confirmed that access to different resources requires different types of actors and relations, thus being associated with different network configurations. But it also uncovered the presence of ‘multiplex ties’ that are present in different mobilisation networks, as well as the central role of some network actors.

Although the small number of cases still prevents a comparison between firms that would allow discovering differences and/or patterns along some dimensions, the analysis enabled us to identify some regularities of behaviour. At this level, they confirm some of our expectations and provide some insights into important features of the firms’ networks, which will be further explored in subsequent analysis, covering the larger sample.

These preliminary results suggest that our approach, by considering simultaneously a variety of resources and actors/relationships (which is rarely the case in this type of research), can contribute to a better understanding of the roles played by social networks on entrepreneurship, adding to ongoing theoretical and methodological debates on this field.

5 Conclusions

The main research question addressed in this paper concerns the way entrepreneurs’ social networks affect the opportunity identification and the access and mobilisation of resources in a science-based field – biotechnology – facilitating the founding of new firms. We adopted an analytical framework combining contributions from the technological entrepreneurship and the social network literature and proposed:

1 that entrepreneurs’ social networks, both associated with their academic and professional trajectories and intentionally build having the firm as a goal, are critical to access the wide range of resources required to successfully create a new firm

2 that different network configurations are associated with the access and mobilisation of different types of resources.

In order to investigate this question we developed a methodology that combines several methods usually applied separately and which permits to assemble of a vast array of data capturing the nature and contents of a wide range of relationships and the multiplicity of mechanisms through which the several resources flow into the new firms. It permits to (re)construct the social networks of the entrepreneurial team and to uncover the ways these networks are mobilised in the search for resources and competences, during firm formation.

This methodology is currently being applied to a subset of the Portuguese biotechnology industry – the molecular biology firms, encompassing 23 firms and a total

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of 61 entrepreneurs. In this paper we presented some preliminary results based on the network analysis of four firms out of our sample of 23, which enabled us to test the viability, robustness and effectiveness of the methodology. The results obtained, although still exploratory, already provide some indications concerning the formation and strategic use of social networks among these biotechnology entrepreneurs.

At this stage, our main contribution regards the development of a methodology that enables a comprehensive investigation of the networking behaviour of science-based entrepreneurs in their search for resources for firm formation and early development, and an assessment of the characteristics of the networks that support the access to different types of resources. But the promising results obtained so far, lead us to expect that the analysis of the remaining cases in our sample and the more in-depth exploration of the very rich data obtained, which is underway, will offer us important insights into the process of network mobilisation for biotechnology firm formation and early development. They are also expected to provide evidence regarding the network configurations that are more effective in the access to key resources in countries/regions that are peripheral to the major concentrations of biotechnology knowledge and business, contributing to the still scarce research on this type of contexts (Fontes, 2005; Gilding, 2008).

Acknowledgements

Research conducted in the context of the project ENTSOCNET ‘Entrepreneurs’ social networks and knowledge access’ (POCI/ESC/60500), funded by the Fundação para a Ciência e Tecnologia, whose support is gratefully acknowledged.

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Appendix

Representation of networks for Firm D

Figure 1 Potential network (see online version for colours)

Figure 2 Network of access and opportunity (Firm D) (see online version for colours)

244 C. Sousa et al.

Representation of networks for Firm D (continued)

Figure 3 Network of innovation (Firm D) (see online version for colours)

Figure 4 Network of power and influence (Firm D) (see online version for colours)

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