78
Int. J. Technoentrepreneurship, Vol. 1, No. 1, 2007
Network-centrality versus network-position in regional networks: what matters most? – a study of a French high-tech cluster Christian Lechner* and Christophe Leyronas ESC Toulouse, Research Center for Entrepreneurship and Growth Strategies, 20, Bd. Lacrosses, Toulouse Cedex 31068, France E-mail:
[email protected] E-mail:
[email protected] *Corresponding author Abstract: This research is concerned with the role of regional inter-firm networks and the position of a firm within a regional inter-firm network for entrepreneurial firm performance. Our results confirm that regional network size is a rough explanatory variable of firm performance, but it is one that hides more important network properties. More important than network size are favourable network positions such as structural holes. It seems that for entrepreneurial firms it is more important to build strong network positions by developing exclusive alliance networks instead of simply collaborating with the greatest number of firms within the region. The implications are important for both entrepreneurs in high-tech clusters as well as for policy-makers promoting cluster development. Keywords: networks; clusters; firm performance; network structure; high-tech; france. Reference to this paper should be made as follows: Lechner, C. and Leyronas, C. (2007) ‘Network-centrality versus network-position in regional networks: what matters most? – a study of a French high-tech cluster’, Int. J. Technoentrepreneurship, Vol. 1, No. 1, pp.78–91. Biographical notes: Christian Lechner is a Full-time Professor in Entrepreneurship and Strategic Management at the Toulouse Business School (ESC Toulouse), France and Director of the Specialised Master in Entrepreneurship at the Toulouse Business School. He has visiting appointments in Germany, Switzerland, Italy and the USA. His research interests include growth strategies of entrepreneurial firms, inter-firm networks, clusters, the impact of organisational forms on entrepreneurial firm performance and the RBV. Christophe Leyronas is a Professor in Entrepreneurship and Strategic Management at the Toulouse Business School (ESC Toulouse), France. His interests are in inter-firm networks, familial groups and clusters dynamics.
Copyright © 2007 Inderscience Enterprises Ltd.
Network-centrality versus network-position in regional networks
1
79
Introduction
The question ‘how does (co)-location make a difference for firms’ is at the heart of cluster research. Despite global markets and global competition, economic success is regionally concentrated. In other words, as Porter has stated: Location matters (Porter, 1998). Increasingly, domestic cooperation rather than domestic competition seems to be a source of global competitive advantage (Lazonick, 1993). It has been suggested that regional clusters are an important element in understanding firms’ competitiveness (Porter, 1998). Clusters can be understood as an agglomeration of firms within one industry in a specific geographical area (Prevezer, 1998). The importance of regional concentration of firms, industrial districts and regional and strategic networks has been rediscovered in order to contribute to explanations about competitive advantages (Henderson, 1975; Piore and Sabel, 1984; Sydow, 1992). The idea of cluster advantage implies therefore that firms within clusters can benefit from proximity. Proximity advantages though colocation include agglomeration and transaction cost effects. Agglomeration effects mean that a critical mass of firms will benefit from dedicated and shared infrastructure, access to a specialised supplier and work force pool, political support and access to capital. Locations close to transaction partners will eventually also reduce transaction costs (Staber, 1998). However, proximity is not magic1 and other research suggests that – besides advantages of proximity – networks, that is, the regional networks, are the key to understanding clusters (Boari and Lipparini, 2000; Corno et al., 2000; Grandori and Soda, 1995). In this sense, successful clusters can be characterised by the regional networks that constitute the cluster (Lechner and Dowling, 2000). From a firm pespective, inter-firm networks are considered an alternative model of organisational development (Richardson, 1972) for entrepreneurial firms (Freel, 2000; Jarillo, 1988; Johannisson, 1998; Lorenzoni and Ornati, 1988; Nohria, 1992) that are constrainted by liability of newness and smallness (Baum et al., 2000; Stinchcombe, 1965). Firms that lack critical resources can access these complimentary resources through relationships with other firms. Empirical research has shown an association between networking activity and growth (Chell and Baines, 2000; Huggins, 2000; Jarillo, 1989). In general, research on clusters assumes that all firms benefit from their cluster location and that performance differences between firms within the cluster are less important (Tallman et al., 2004). There is a lack of studies investigating performance differences within clusters (Cooper and Folta, 2000). This research is concerned with the role of regional inter-firm networks and the impact of the position of a firm in a regional inter-firm network on firm performance: are there strong performance differences and what drives performance differences within clusters? Our research problem is: Is it more important to be a very central player within a regional network by maximising the number of direct collaborative inter-firm relations or is it better to build selective and exclusive (sub-) networks? Research on inter-firm networking has highlighted the benefits, costs and risk associated with direct cooperative relations with other firms (Lechner and Dowling, 2003), while research on network embeddedness has shown the importance of a privileged position within networks (Burt, 1992). From these research streams emerge the following questions: Firstly, is regional network size a sufficient predictor of entrepreneurial firm performance? Secondly, does a firm’s privileged position within a regional network lead to superior performance? The primary empirical research is based on an in-depth study of a high-tech cluster in France.
80
C. Lechner and C. Leyronas
Regression models of the influence of regional networks and regional network position on firm performance are tested. The remainder of this paper is organised in four sections: literature review and development of the research questions, method, results and discussion. In the following sections, relevant literature is presented and the two key questions are presented: the role of network size and of structural holes for the performance of firms within regional networks. The method section illustrates the research context, that is, the regional network being studied, explains the research approach and the measures and analytical models. Finally, the results are presented, followed by a discussion of the results and their implications.
2
Literature review and development of research questions: networks as an entrepreneurial growth and development strategy
Analytically, networks can be understood as nodes (actors) and connections between nodes. The basic idea of network analysis is that the connections of an actor can be interpreted in order to better understand the action potential of the concerned actor (Fombrun, 1982). The insufficient resource position of the entrepreneurial firm is expressed in the theoretical constructs of liability of newness (Stinchcombe, 1965) and liability of smallness (Baum, 1996). Therefore, the strategic use of external resources through inter-firm networks (Jarillo, 1989; Lorenzoni and Ornati, 1988) that are often embedded in regions (Boari and Lipparini, 2000; Lechner and Dowling, 2000) is regarded as a natural development and growth alternative.2 The survival and development of an entrepreneurial firm depend on its ability to maintain and extend its network of inter-firm relationships (Venkataraman and Van de Ven, 1998) in order to access complimentary resources (Deeds and Hill, 1996).
2.1 Network size – entrepreneurial networking in regions Networking has been found to be important for entrepreneurial firms. Most research in this setting has analysed egocentric networks, that is, the relationships of one focal actor with other actors (Johannisson, 1998; Wassermann and Faust, 1994). Because of the new ventures’ liabilities, entrepreneurs need to mobilise social relationships (Starr and MacMillan, 1990) to access external resources. An entrepreneur’s personal networks are all the relationships between the entrepreneur and other individuals (Dubini and Aldrich, 1991). Research has shown that the personal and social networks of the entrepreneur are perhaps his/her most important strategic resources, especially in the case of the start-up firm and that these relationships are mainly regionally concentrated (Aldrich, 1991, 1999; Ardichvilli et al., 2003; Dubini and Aldrich, 1991; Johannisson, 1995, 1998, 2000; Lipparini and Sobrero, 1997; Ostgaard and Birley, 1994). A simple way of measuring the results of entrepreneurial networking is to count the number of direct network ties. Different studies have proposed a simple relationship between network size (as a centrality measure) and firm performance, that is, the more direct partners an entrepreneur’s firm has, the better it is (Johannisson, 2000). Case study research has suggested that network size is related to the growth of firms (Zhao and Aram, 1995). Inter-firm networks can provide access to complementary resources to develop, produce and market products (Deeds and Hill, 1996). Entering into inter-firm
Network-centrality versus network-position in regional networks
81
relationships has costs, risks and benefits. Costs include both financial resources and time. The marginal benefits of alliances can decline while costs increase (Deeds and Hill, 1996). It has been argued that the relational capability, that is, the capability to enter and maintain relationships, is limited (Pihkala, 1999) but path-dependent. Firms learn to better manage more relationships over time (Deeds and Hill, 1996; Lechner and Dowling, 2003; Lechner et al., 2006). A start-up’s initial performance has been shown to increase with the size of the alliance network of firms at foundation (Baum et al., 2000). Overall, previous research has led to the general assumption that network size is a good indicator for explaining the performance of young firms, especially if the focus is on regional networks. Research question 1: regional network size, that is, the total number of direct network ties in the entrepreneurial firm’s home region, increases firm performance.
2.2 Network position – structural embeddedness in regional networks It has been argued that the size of the network hides more important network properties (Fombrun, 1982). A more fine-grained picture of network development should therefore be a better indicator for firm development than sheer network size (measured as total numbers of relations). One approach to more fine-grained measures can be found in the theory of structural embeddedness. According to the theory of structural embeddedness, network structure and a firm’s or a person’s network position are considered to be both opportunities and constraints (Aldrich and Zimmer, 1986). The role that network forms and structures play for firm performance is ambiguously discussed. There are two contrary approaches: network closure (Coleman, 1988) and structural holes (Burt, 1982). While some researchers argue for dense and closed networks that facilitate deep information access and the establishment of trustful relations (Coleman, 1988, 1992; Walker et al., 1997), others see advantages in less dense networks where the brokerage function enables the gaining of advantages (Burt, 1992). A structural hole is an opportunity, an un-served space in a network that can be exploited as a result of brokering connections between disconnected segments. If actor A is connected with actor B and actor C but B and C are not connected with each other, then actor A possesses a structural hole: actor A possesses two non-redundant ties. A network rich in structural holes is considered an effective network. An effective network will give a firm autonomy of action and power. Structural holes are an opportunity to coordinate action and to access valuable and exclusive resources. An absence of non-redundant ties, however, would mean that the autonomy of the firm is heavily restricted, since each decision taken by a firm is subject to the acceptance and influence of all the inter-connected firms or because resources are shared among many other firms (Burt, 1992). Networks that have predominantly redundant ties therefore restrict a firm’s autonomy and lead to a phenomenon that is called over-embeddedness: a firm is trapped in it own net (Gargiulo and Benassi, 2000). In essence, favourable positions are regarded as network resources (Burt, 1992; Easton, 1992; Granovetter, 1974, 1985; Gulati, 1999; Hakansson and Snehota, 1995; McEvily and Zaheer, 1999); over-embeddedness, however, can lead to inability to act (Gargiulo and Benassi, 2000; Uzzi, 1997). The structural embededness approach could show that the effectiveness of a network depends on the existence of structural holes, which gives the broker a privileged access to resources, strong bargaining power (Burt, 1992) and the autonomy to change (Gargiulo and Benassi, 2000). Differences of firm performance within regional networks
82
C. Lechner and C. Leyronas
could therefore be understood through the effectiveness of a firm’s own network that is embedded in the larger regional network. In this sense, the performance of a firm within regional networks depends on its structural position. Research question 2: the number of structural holes occupied by an entrepreneurial firm within a regional network increases firm performance.
3
Method
3.1 Sample and questionnaire The study setting is a high-tech cluster in South-West France. The cluster concerns the full population of the region’s firms working in the sector of geographical information systems, comprising 63 firms with a regional turnover of about $500 million and about 4000 employees. This fairly young sector witnessed the birth of the first firms some 15 years ago. Most firms are less than ten years old with increased entrepreneurship during the last years. The sector is almost entirely made up of small and mid-size firms with from 1 to 200 employees. This high-tech cluster is considered one of the most important in the world. A Geographic Information System (GIS) is a system for the management, analysis and display of geographic knowledge, which is represented using a series of information sets such as maps and globes, geographic data sets, processing and work flow models, data models and metadata. Using the base-technologies, a myriad of applications is possible. For example, if you have a photo of Toulouse, the computer can show the picture but the computer has no information on what is on the photo. Digitalisation of the geographical information means to translate the photo into data points that can be processed by a computer. With satellite geographical information, digitalisation is possible in three dimensions (not only surface information but also height, etc.) and the data can be enriched by additional information (the agricultural regions use the information to classify the land according to the crops planted). If one defines it from a client perspective, one realises that any activity that can incorporate geographical information can constitute a business opportunity. The most natural markets are those linked to transportation, city or regional planning, etc. However, completely new sectors have arisen: one of the latest trends is called geo-marketing (using geographical information to better market products). The identification of the cluster’s population was a major challenge. There are a few assumptions of what defines a cluster. According to Porter (1998), a cluster is characterised by the presence of related and supporting industries, demanding clients, particular factor conditions and a support infrastructure. The GIS sector corresponds to these minimum assumptions: related industries are the space and the aeronautics sector, which dominates the regional economy, as well as traditional cartography, electronics and software activities. Therefore, there are particular factor conditions for high-tech competences. For the GIS, there are demanding regional clients such as, for example, the French weather agency. In addition, there is a strong support infrastructure. There are engineering schools specialised in space and aeronautics engineering, in software development and there is even a specialised school for GIS. Moreover, the French space agency (CNES), a technology transfer driver, is present in the region. A part from the infrastructure, it is the economic weight of the cluster within the region
Network-centrality versus network-position in regional networks
83
and compared to other regions plus the relationships among firms that really define a cluster. This information was not available before and was indeed an important part of this study: given the employee figures and the aggregate turn-over, the GIS cluster is an important activity in the region. In order to perform network analysis, the names of all firms constituting the cluster had to be known a-priori. With the help of experts of the French space agency, we defined the sector of GIS and developed a preliminary list of the most important firms. We then searched available databases and the internet to have a potential list of firms in the sector in the region. We refined this list through expert interviews with members of the French space agency and other space and aeronautics-related research institutes. We received from the engineering schools a list of regional firms where students had realised their internships. We then interviewed some GIS clients such as the French National weather agency, the regional government and the city council to further develop the list. We then interviewed a few firms to validate and extend the list of firms and finalised the list through a last round of expert interviews. The final list comprised 63 firms. In May and June another series of expert interviews were undertaken with a view to refining our understanding of the development of the cluster; other expert interviews were conducted for networking purposes: known figures of the cluster were contacted in order to get access to the key people in the key companies. In order to ensure a high proportion of valid answers, we used a key informant approach (Huber and Power, 1985). Interviews were conducted face-to-face by the same duo of researchers in order to assure the reliability of data collection. The interviews were based on the questionnaire. All information was transcribed and coded within 24 hr. The interviewees were chosen according to their level of competence: interviews were conducted at least at the vice president level in large firms; in smaller firms, either the founders or individuals at the top of the hierarchy were interviewed, that is in any case individuals with managerial responsibilities who had spent a sufficient time with the company and in relation with other firms in order to assess the relationships of the firm. For demographic data, we used as far as possible data triangulation techniques, that is, we controlled the information through other available sources (websites, company reports, annual reports, etc.). After having identified the relevant population, face-to-face interviews following a standardised questionnaire were administered. Data collection took place between June and December 2004: We were able to collect networking data for all 63 firms and sufficient financial data for 40 of them. The main part of the standardised questionnaire consisted of data matrices in order to collect information on the relationship between the firm interviewed and the other GIS firms in the region. To measure the cooperative network of a firm within the cluster, we presented the list of cluster firms to the interview partner and asked him with whom the firm had cooperative relations. The answers for each firm in the cluster were coded with 1 if there was a relationship or 0 if there was no relationship. The result of this interview process was 63 individual matrices that were then merged into one symmetrical relationship matrix of the size 63 × 63. The first line as well as the first column contained the names of all cluster firms. The advantage of a symmetrical matrix is that the validity of the responses can be controlled. An example: firm A indicated a relationship with firm B. In a symmetrical matrix, there is also the
84
C. Lechner and C. Leyronas
information if B indicated a relationship with A. A relationship was counted only if the two answers matched. The final matrix contained therefore the data to map and analyse the total regional networks. These matrices were than analysed with UCINET6 and visualised with PAJEK. UCINET6 is a commonly used software package for the analysis of network data and other proximity measures. The programme runs various network analysis routines and allows (among others) for the measurement of centrality measures and structural holes. Pajek is an add-on application for UCINET for the visualisation of network data. The chosen approach allowed us to have sufficient data on the complete regional networks on one hand and on the individual firms on the other hand. Therefore, we were able, firstly, to establish the number of direct inter-firm relationships for each individual firm. Secondly, on the basis of the full network representation, we were able to calculate properties of the structure of the network for each individual firm: we chose to concentrate on structure holes.
4
Measures
4.1 Dependent variables A difficult decision in entrepreneurship research is the choice of performance measures. There are no commonly accepted performance measures for new ventures (McGee and Dowling, 1994). We used sales as performance measures. We chose sales because of possible distortions due to minimal or non-existent sales at the beginning of the firm’s existence. Sales volume and sales growth are a common performance measure especially for small and young firms (Begley and Boyd, 1986; Cardozo et al., 1996; Lebrasseur et al., 2003; Robinson, 1999; Rue and Ibrahim, 1998; Weinzimmer et al., 1998) and are arguably a sufficient single indicator for firm performance (Venkatraman and Ramanujam, 1987). Sales are relatively insensitive to capital intensity and degree of integration and therefore an appropriate measure for studying networks even if they are sensitive to inflation (Weinzimmer et al., 1998). In addition, the relatively low inflation rate during the period under study for the countries concerned does not call for a particular control for inflation.
4.2 Control variables We used two control variables. We used age in months and number of employees as control variables since time of existence and number of employees are most likely correlated with sales level.
4.3 Independent variables In general, when we refer to network size, we measure the number of direct cooperative relationships. In this sense, regional network size is the number of direct cooperative network ties in the region. We used UCINET transformations to calculate, on the basis of the full network data, the number of structural holes per firm.
Network-centrality versus network-position in regional networks
85
4.4 Models We used OLS regression to test the research questions. We tested two models. Model 1 was a simple model with age and number of employees as controls and regional network size as the explanatory variable. In Model 2, we added structural holes as the explanatory variable.
4.5 Descriptive statistics As given in Table 1, the average sales of the sample firms in 2003 was € 9.7 million, but with a large standard deviation. The average number of employees was about 60 and the firms were on average 12 years old. Figure 1 represents graphically the regional network of collaborative relationships. Table 1
Descriptive statistics
Variables
Mean
SD
Sales in € 1000’s
9740
26,535,801.04
Age of the firms in years
11.95
6.91
Number of employees
60.98
95.40
Regional network size
7.13
4.47
Number of firms = 40
Figure 1
The Toulouse network in 2004
86
5
C. Lechner and C. Leyronas
Results
5.1 The importance of regional networks on firm performance Table 2 presents OLS regressions for the model with size of the regional network as the independent variable, firm age and number of employees as the control variables and sales as the dependent variable. The overall model was significant but with a high explanatory power (adjusted R2 = 0.54, p < 0.001). The number of employees as a control variable had positive and significant influence on sales, but age was not significant. Network size was the most important explanatory variable with the largest beta: the variable had a positive and significant influence on sales (p < 0.01 for employees, p < 0.001 for network size). The model therefore showed a firm’s regional network size to have an influence on firm performance (measured in sales) research question 1 is therefore confirmed. Table 2
OLS regression – model 1 and 2 the influence of network size and structural holes model 1
Dependent variable: sales in € lagged by one year
Model 1: Network size
Model 2: network size and structural holes
(0.147)
(0.026)
Age
−0.165 (0.168)
−0.034 (0.669)
Employees
0.373 (0.007)
0.363 (0.000)
0.512 (0.000)
−0.806 (0.000)
Standardised regression coefficients (Significance) Intercept Controls
Research variables Regional network size Structural holes
1.403 (0.000)
Extroversity (external/internal relationships) Adjusted R2
0.503 (p < 0.001)
0.790 (p < 0.001)
F
14.156
37.752
df
3/36
4/35
Durbin-Watson
1.75
2.08
N=
40
40
5.2 The importance of structural holes on firm performance Table 2 gives the OLS regressions for model 2 which included additional structural holes as explanatory variable. The model is significant with a very high explanatory power, which might be partially explained by the fact that the models were tested almost on a full population. The adjusted R2 was 0.79 and significant (p < 0.001). This model was a much stronger explanatory model than model 1. Structural holes became the most important explanatory variable (standardised coefficient = 1.4, p < 0.001). More
Network-centrality versus network-position in regional networks
87
interestingly, while significant and important as an explanatory variable, regional network size changed the sign, that is, regional network size had a negative and significant influence on sales (standardised coefficient = 0.86, p < 0.001). Therefore, research question 2 was confirmed but research question 1 was rejected in this model. The results might indicate that firstly, there might be a curve-linear relationship between some of the independent variables and the dependent variables. Secondly, it might indicate that not all explanatory variables are non-independent from each other. While the correlations revealed some multicollinearity between network size and structural holes, an analysis of the Variance Inflation Factors (VIF’s) and further analysis indicated that multicollinearity would not significantly affect the interpretation of the results. The VIFs values never exceeded the critical limit of ten (Neter et al., 1989). Dubin-Watson values for autocorrelation were strong and close to two. We tested also for curve-linearity of the variable network size but the analysis could not support this assumption.
6
Discussion
6.1 Summary and interpretation of findings The objective of this study was to offer empirical evidence for the importance of regional networks, position in regional networks in combination with external networks on firm performance. We tested two models: one with (direct) regional network size as a rough indicator for the performance of firms located in the same cluster and a second model with the addition of structural holes as a measure of the structural properties of the firm’s networks in the region. All models were significant with a relative high explanatory power. The second model had, however, a higher explanatory power than regional network size alone. Research question 1 was confirmed in model 1 and rejected in model 2. What does this mean? In our view, it means that, yes, network size is a rough indicator for firm performance but that more fine-grained network-related measures are more appropriate to understand firm performance. This finding confirms the argument that network size hides other important network properties (Fombrun, 1982; Lechner and Dowling, 2003). Model 2 showed the overriding importance of structural holes for firm performance. This means that the structure of a firm’s networks is more important than sheer size. It means also that especially young firms with fewer relationships (age as a control was not significant for firm performance) have the possibility to build effective networks, by developing subnetworks rich in structural holes in the overall regional network as it happens to be already structured. From a statistical and theoretical point of view, it does not seem contradictory that the influence of network size changes in the presence of structural holes. The probability of networks with a maximum of structural holes should tend to decrease with the number of relations (Burt, 1982; Granovetter, 1985) and there might be a risk of over-embeddedness (Gargiulio and Benassi, 2000). This finding might, however, also indicate that there is a curve-linear relationship between network size and firm performance (Deeds and Hill, 1996), an argument that can be reinforced by the structural holes argument.
88
C. Lechner and C. Leyronas
Overall, our models suggest, firstly, that network size is less important than network position. It is important for entrepreneurial firms to build regional networks rich in structural holes, which implies that these firms should try to connect with other companies that are less well connected in the region. Secondly, therefore, one could argue that the opportunity for newcomers to build effective networks depends on the density of the regional network or subnetworks within the regional network. Thirdly, as a consequence, entrepreneurial networking as a proactive task (connect unconnected firms before others do it) becomes an important factor for firm development. Finally, the latter could partially explain performance differentials within clusters that depend on entrepreneurial networking, as well as the resulting regional network positions of the firms.
6.2 Limitations of the study We acknowledge several limitations to our research. The present findings are based largely on entrepreneurs’ own reports of networking activity. Definition of networks is not reflected in elaborated measures but depends on the correct understanding of the network types described. We tried to assure the validity of the measures by conducting, on the one hand, face-to-face interviews and on the other hand by checking the data matrix for symmetry, since we collected networking data on all firms. Concerning the financial data, we cross-checked the data and used the available sources for data triangulation. This study was limited to firms located within one regional high-tech cluster. The high explanatory power of the models might be explained both by the limited number of firms and the fact that we captured almost the entire population. In addition, we used absolute sales as a performance measure. Sales volume and sales growth are a common performance measure especially for small and young firms (Begley and Boyd, 1986; Cardozo et al., 1996; Lebrasseur et al., 2003; Robinson, 1999; Rue and Ibrahim, 1998; Weinzimmer et al., 1998) and are arguably a sufficient single indicator for firm performance (Venkatraman and Ramanujam, 1987). Finally, we tested our research questions with quite a small sample; replication with a large sample is desirable.
7 Implications This study has both theoretical and practical implications. This study is a rare case where full regional network data were applied to explain individual firm performance, thereby allowing for the testing of the different perspectives. The study highlights the importance of rather exclusive networks for firm competitiveness. This means that entrepreneurs need to be aware of the relationships of firms with each other. It also means that a very dense network where a great number of firms are already connected with each other would offer little opportunities for newcomers. This argument is also important for policy makers. The idea that a successful cluster is characterised by a high-density (everybody is connected with everybody) is highly erroneous. In this sense, activities to promote collaboration in general might even be counter-productive. It would erode the performance of the top-firms within the cluster and therefore the overall performance of the cluster as well.
Network-centrality versus network-position in regional networks
89
Acknowledgement An earlier version of this paper was presented at the 25th Babson Kauffman Entrepreneurship Research Conference, June 2005, at Babson College, Boston, MA. This research was cofinanced by the European Community under the Pilot Action for Regions of Knowledge, KNRG-CT-2003 000032, NEKS.
References Aldrich, H. (1999) Organizations Evolving, London: Sage. Aldrich, H. and Zimmer, C. (1986) ‘Entrepreneurship trough social networks’, in D. Sexton and R. Smilor (Eds). The Art and Science of Entrepreneurship, New York: Balliner, pp.3–23. Ardichvilli, A., Cardozo, R. and Ray, S. (2003) ‘A theory of entrepreneurial opportunity identification and development’, Journal of Business Venturing, Vol. 18, No. 1, pp.105–123. Baum, J. (1996) ‘Organizational ecology’, in S. Clegg, C. Hardy and S. Nord (Eds). Handbook of Organization Studies, London: Sage. Baum, J., Calabrese, T. and Silverman, B. (2000) ‘Don’t go it alone: alliance network composition and startups’ performance in Canadian biotechnology’, Strategic Management Journal, Vol. 21, No. 3, pp.267–294. Begley, T. and Boyd, D. (1986) ‘Executive and corporate correlates of financial performance in smaller firms’, Journal of Small Business Management, April, pp.8–15. Boari, C. and Lipparini, A. (2000) ‘Networks within industrial districts: organising knowledge creation and transfer by means of moderate hierarchies’, Journal of Management and Governance, Vol. 99, No. 3, pp.339–360. Burt, R. (1992) Structural Holes, Cambridge MA: Harvard University Press. Burt, R. (1982) Toward a Structural Theory of Action, New York: Academic Press. Cardozo, R., Elder, T. and Harmon, B. (1996) ‘When does growth pay?’ in P. Reynolds, S. Birley, J. Butler, W. Bygrave, P. Davidsson, W. Gartner and P. McDougall (Eds). Frontiers of Entrepreneurship Research, Wellesley, MA: Babson College, pp.480–490. Chell, E. and Baines, S. (2000) ‘Networking, entrepreneurship and microbusiness behaviour’, Entrepreneurship and Regional Development, Vol. 12, pp.195–215. Coleman, J.S. (1992) ‘Norm-generating structures’, in K.S. Cook and M. Levi (Eds). The Limits of Rationality, Chicago: University Press. Coleman, J.S. (1988) ‘Social capital in the creation of human capital’, American Journal of Sociology, Vol. 94, pp.95–120. Cooper, A.C. and Folta, T.B. (Eds) (2000) Entrepreneurship and High-TechnologyClusters, Oxford: Blackwell Publishers, Inc. Corno, F., Reinmoeller, P. and Nonaka, I. (2000) ‘Knowledge creation within industrial systems’, Journal of Management and Governance, Vol. 3, No. 4, pp.379–394. Deeds, D. and Hill, C. (1996) ‘Strategic alliances and the rate of new product development: an empirical study of entrepreneurial biotechnology firms’, Journal of Business Venturing, Vol. 11, pp.41–55. Dubini, P. and Aldrich, H. (1991) ‘Personal and extended networks are central to the entrepreneurial process’, Journal of Business Venturing, Vol. 6, pp.305–313. Easton, G. (1992) ‘Industrial networks: a review’, in B. Axelsson and G. Easton (Eds). Industrial Networks: A New View of Reality, London: Routledge. Fombrun, C.J. (1982) ‘Strategies for network research in organizations’, Academy of Management Review, Vol. 7, pp.280–291. Freel, M. (2000) ‘External linkages and product innovation in small manufacturing firms’, Entrepreneurship and Regional Development, Vol. 12, pp.245–266.
90
C. Lechner and C. Leyronas
Gargiulo, M. and Benassi, M. (2000) ‘Trapped in your own net? Network cohesion, structural holes, and the adaptation of social capital’, Organization Science, Vol. 11, No. 2, pp.183–196. Grandori, A. and Soda, G. (1995) ‘Inter-firm networks: antecedents, mechanisms and forms’, Organization Studies, Vol. 16, No. 2, pp.183–214. Granovetter, M. (1974) Getting a Job: A Study of Contacts and Careers, Boston, MA: Harvard University Press. Granovetter, M. (1985) ‘Economic action and social structure: the problem of embeddedness’, American Journal of Sociology, Vol. 91, No. 3, pp.481–510. Gulati, R. (1999) ‘Network location and learning: the influence of network resources and firm capabilities on alliance formation’, Strategic Management Journal, Vol. 20, No. 5, pp.397–419. Hakansson, H. and Snehota, I. (1995) Developing Relationships in Business Networks, London: Routledge. Henderson, W. (1975) The Rise of German Industrial Power 1834 – 1914, London. Huber, G. and Power, J. (1985) ‘Retrospective reports of strategic level managers’, Strategic Management Journal, Vol. 6, pp.171–180. Huggins, R. (2000) ‘The success and failure of policy-implanted inter-firm network initiatives: motivations, processes and structure’, Entrepreneurship and Regional Development, Vol. 12, pp.111–135. Jarillo, C. (1988) ‘On strategic networks’, Strategic Management Journal, Vol. 9, pp.31–41. Jarillo, C. (1989) ‘Entrepreneurship and growth: the strategic use of external sources’, Journal of Business Venturing, Vol. 4, pp.133–147. Johannisson, B. (1995) ‘Paradigms and entrepreneurial networks: some methodological challenges’, Entrepreneurship and Regional Development, Vol. 7, pp.215–232. Johannisson, B. (1998) ‘Personal networks in emerging knowledge-based firms: Spatial and functional patterns’, Entrepreneurship and Regional Development, Vol. 10, pp.297–312. Johannisson, B. (2000) ‘Networking and entrepreneurial growth’, in D. Sexton and H. Landström (Eds). Handbook of Entrepreneurship, Oxford: Blackwell, pp.368–386. Lazonick, W. (1993) ‘Industry clusters versus global webs: organizational capabilities in the American economy’, Industry Clusters versus Global Webs: Organizational Capabilities in the American Economy. Industrial and Corporate Change, Vol. 2. Lebrasseur, R., Zanibbi, L. and Zinger, T. (2003) ‘Growth momentum in early stages of small business start-ups’, International Small Business Journal, Vol. 21, No. 3, pp.315–330. Lechner, C., Dowling, M. and Welpe, I. (2006) ‘Firm networks and firm development: the role of the relational mix’, Journal of Business Venturing, Special Issue on Alliances and Networks, Vol. 21, No. 4, pp.487–513. Lechner, C. and Dowling, M. (2003) ‘Firm networks: external relationships as sources for the growth and competitiveness of entrepreneurial firms’, Entrepreneurship and Regional Development, Vol. 1, pp.1–16. Lechner, C. and Dowling, M. (2000) ‘The evolution of industrial districts and regional networks: the case of the biotechnology region Munich/Martinsried’, Journal of Management and Governance, Vol. 99, No. 3, pp.309–338. Lipparini, A. and Sobrero, M. (1997) ‘Co-ordinating multi-firm innovative process: entrepreneur and catalyst in small firm network’, in M. Ebers (Ed). The Formation of Inter-Organisational Networks, New York: Oxford University Press. Lorenzoni, G. and Ornati, O. (1988) ‘Constellations of firms and new ventures’, Journal of Business Venturing, Vol. 3, pp.41–57. McEvily, B. and Zaheer, A. (1999) ‘Bridging ties: a source of firm heterogeneity in competitive capabilities’, Strategic Management Journal, Vol. 20, No. 12, pp.1133–1156.
Network-centrality versus network-position in regional networks
91
McGee, J. and Dowling, M. (1994) ‘Using R&D cooperative arrangements to leverage managerial experience: a study of technology-intensive ventures’, Journal of Business Venturing, Vol. 9, pp.33–48. Nohria, N. (1992) ‘Is a network perspective a useful way of studying organization?’ in R. Eccles (Ed). Networks and Organisations, Boston, MA: Harvard Business School Press. Ostgaard, T. and Birley, S. (1994) ‘Personal networks and firm competitive strategy – a strategic or coincidental match?’ Journal of Business Venturing, Vol. 9, pp.281–305. Pihkala, V.T. (1999) ‘Virtual organization and the SMEs: a review and model development’, Entrepreneurship and Regional Development, Vol. 11, No. 4, pp.335–349. Piore, M. and Sabel, C. (1984) The Second Industrial Divide, New York: Basic Books. Porter, M. (1998) ‘Clusters and the new economics of competition’, Harvard Business Review, Vol. 76, pp.77–90. Prevezer, M. (1998) ‘Clustering in biotechnology in the USA’, in P. Swann, M. Prevezer and D. Stout (Eds). The Dynamics of Industrial Clustering, Oxford, pp.124–193. Richardson, G. (1972) ‘The organization of industry’, Economy Journal, pp.883–896. Robinson, K. (1999) ‘An examination of the influence of industry structure on eight alternative measures of new venture performance for high potential independent new ventures’, Journal of Business Venturing, Vol. 14, pp.165–187. Rue, L. and Ibrahim, N. (1998) ‘The relationship between planning sophistication and performance in small businesses’, Journal of Small Business Management, Vol. 36, No. 4, pp.24–32. Staber, U. (1998) ‘Organizational survival in small-firm clusters’, Paper Presented at the Academy of Management, San Diego, CA. Starr, J. and MacMillan, I. (1990) ‘Resource cooptation via social contracting: resource acquisition strategies for new ventures’, Strategic Management Journal, Vol. 11, pp.79–92. Stinchcombe, A. (1965) ‘Social structure and organizations’, in J. March (Ed). Handbook of Organization, Chicago, IL: Rand McNally. Sydow, J. (1992) Strategische Netzwerke, Gabler: Wiesbaden. Tallman, S., Jenkins, M., Henry, M. and Pinch, S. (2004) ‘Knowledge, clusters, and competitive advantage’, Academy of Management Review, Vol. 29, No. 2, pp.258–271. Uzzi, B. (1997) ‘Social structure and competition in interfirm networks: the paradox of embeddedness’, Administrative Science Quarterly, Vol. 42, pp.35–67. Weinzimmer, L., Nystrom, P. and Freeman, S. (1998) ‘Measuring organizational growth: issues, consequences and guidelines’, Journal of Management, Vol. 24, No. 2, pp.235–262. Venkataraman, S. and Van de Ven, A. (1998) ‘Hostile environmental jolts, transaction set, and new business’, Journal of Business Venturing, Vol. 13, pp.231–255. Venkatraman, N. and Ramanujam, V. (1987) ‘Measurement of business economic performance: an examination of method convergence’, Journal of Management, Vol. 13, No. 1, pp.109–122. Walker, G. Kogut, B. and Shan, W. (1997) ‘Social capital, structural holes and the formation of an industry network’, Organizational Science, Vol. 8, pp.109–125. Wassermann, S. and Faust, K. (1994) Social Network Analysis. Methods and Applications, Cambridge University Press. Zhao, L. and Aram, J. (1995) ‘Networking and growth of young technology-intensive ventures in China’, Journal of Business Venturing, Vol. 10, pp.349–370.
Notes 1 2
I would like to thank Gianni Lorenzoni for this quote. We measure the growth of a firm in terms of sales. For a discussion for the problematic use of growth measures.
