Performance gains and losses from network centrality in cluster located firms: a longitudinal study^*

* We gratefully acknowledge financial assistance from the EU FRIDA project (7th FRAMEWORK PROGRAMME Grant nr: 225546) and the MIUR-PRIN Funding Scheme (Project: Performance drivers and evolutionary dynamics of geographical clusters). We are deeply grateful to Carlo Boschetti and Gianni Lorenzoni for their support. We also wish to extend our thanks to Paul Allison, Petra Andries, Charles Baden-Fuller, Cristina Boari, Emilio Castilla, Raffaele Corrado, Giovanni Battista Dagnino, Fabio Fonti, Alfonso Gambardella, Elizabeth Garnsey, Federico Signorini, Maurizio Sobrero, AnnaLee Saxenian, Mattheus Urwyler and all the participants in the workshop series of the Sol C. Snider Entrepreneurial Research Center for fruitful comments. In addition, we thank the Sociology Department at the University of Pennsylvania, where the first author spent a visiting period during the completion of this manuscript.

Abstract

This paper develops and tests theoretically derived arguments on the performance trade-offs that arise when firms located inside geographical clusters broaden their cluster networks and increase their centrality. Using three-year longitudinal data gathered on a sample of 89 small media firms located in a geographical cluster of Northern Italy, we model growth in revenues and in employees as a function of their centrality in different types of networks. We find an inverted U-shaped effect of centrality across all types of networks. We also find strong evidence of negative interactivity between network types in predicting sales and employee growth. This result not only concurs with the view that centrality brings tangible and intangible benefits, but also provides empirical support for the contention that centrality fosters dispositions and disturbances that undermine performance.

Keywords:

• Geographical clusters
• interorganizational networks
• firm performance
• growth
• network centrality

Notes

* We gratefully acknowledge financial assistance from the EU FRIDA project (7th FRAMEWORK PROGRAMME Grant nr: 225546) and the MIUR-PRIN Funding Scheme (Project: Performance drivers and evolutionary dynamics of geographical clusters). We are deeply grateful to Carlo Boschetti and Gianni Lorenzoni for their support. We also wish to extend our thanks to Paul Allison, Petra Andries, Charles Baden-Fuller, Cristina Boari, Emilio Castilla, Raffaele Corrado, Giovanni Battista Dagnino, Fabio Fonti, Alfonso Gambardella, Elizabeth Garnsey, Federico Signorini, Maurizio Sobrero, AnnaLee Saxenian, Mattheus Urwyler and all the participants in the workshop series of the Sol C. Snider Entrepreneurial Research Center for fruitful comments. In addition, we thank the Sociology Department at the University of Pennsylvania, where the first author spent a visiting period during the completion of this manuscript.

1. Companies that refused to be involved in the study appeared randomly mixed between those not interested in the research and those without time to devote to the interview. Possible non-response bias was analyzed by comparing respondents and non-respondents in terms of industry segments, founding year, and employees. We collected data on industry segment and founding years for all non-responding firms from the InfoImprese database and employee data for only a sub-sample of non-respondents for which data were publicly available from the Chambers of Commerce. T-tests revealed no significant differences between respondents and non-respondents across these variables, hence suggesting the representativeness of our sample.

2. The six excluded firms had been founded later than 1999. They were considered ineligible since they couldn’t provide retrospective network data for 1999, our starting data point.

3. Four companies contributed only one interview having ceased their activity after 2001.

4. The questionnaires also included a free recall area (Wasserman & Faust, 1994), in which respondents could add other company names that had not been included in the list. These data, which we did not include in the analysis, allowed us to assess the degree of firms’ internal vs. external connectedness. External connectedness accounted for about 30% of the total connectedness of the sampled firms, suggesting that most of the firms’ network activity was taking place within the boundaries defined by our population list. This is not surprising given that the cluster is still in an early stage of development (on the localized nature of cluster networks see, in particular, Sorenson, 2005).

5. This measure appears particularly well suited to our purposes as it does not embody so stringent assumptions as other equally popular measures. In particular, unlike centrality measures that only count geodesic paths like closeness and betweenness, the eigenvector measure does not imply that whatever moves through the network will only follow the shortest path, which is surely not the case with information (firms may share information along multiple paths, and not just the shortest path). Nor does it assume that the traffic moves from node to node (as it is implied by betweenness centrality) rather than being broadcast from a node, like the spread of information. Instead, the eigenvector measure assumes ‘that traffic is able to move via unrestricted walks rather than being constrained by trails, paths or geodesics. In addition, the measure […] is consistent with a mechanism in which each node affects all of its neighbors simultaneously’ (Borgatti, 2005, p. 62). These properties make this measure well suited to the type of connectionist argument advanced in this study.

6. For methodological details, see Snijders (1996) and Snijders et al. (2005). For recent sociological applications of SIENA to the organizational study of network dynamics over time see Schulte, Cohen, and Klein (2012).