Collaboration networks within a French cluster: Do partners really interact with each other?

Abstract

International audienceWe discuss the common hypothesis of complete graph representation according to which, in collaborative projects, all partners interact with each other in homogeneous ways.More precisely, this research aims to determine the heterogeneity in terms of existence and frequency of interactions between dyads of organizations that jointly participated in collaborative projects. From a survey of participants involved in innovation projects approved by a French cluster, we collect information about 754 collaboration ties. Using an ordered probit with selection equation, we then test the impact of several determinants, including geographical proximity, on the existence and frequency of the observed interactions

Effects of Cluster Policies on Regional Innovation Networks: Evidence From France

“…The decrease of local collaboration in this type of departments could be the result of the involvement of local innovative actors in partnership with regional or national actors. This result is consistent with the observation made by Bernela and Levy (2017) on French clusters. While focusing on publicly-supported R&D projects, they find no impact of the geographical proximity on the likelihood that two partners interact within a collaborative project.…”

The visible hand of cluster policy makers: An analysis of Aerospace Valley (2006-2015) using a place-based network methodology

“…To give an example of these misinterpretation risks, let us consider an organization that is affiliated to one 15-members consortium, and only to this one. It will have a high degree centrality, while, as shown by Bernela & Levy (2017), its influence and involvement in the innovation system can be very weak, in particular if this organization does not actually interact with all the other consortium members. Let us now consider another organization involved in 3 collaborative projects affiliating each one only 3 partners.…”

Proximity dynamics and the geography of innovation: diminishing returns or renewal?

“…Accordingly, "various kinds of data have been used to indicate knowledge networks" (Balland, 2014), including knowledge sharing relations (Giuliani and Bell, 2005;Giuliani, 2007;Morrison, 2008;Broekel and Boschma 2012), patent citations (Agrawal et al, 2006;Breschi and Lissoni, 2009), joint patents (Cantner and Graf, 2006;Hoekman et al 2009), joint publications (Ponds et al 2007(Ponds et al , 2010Frenken et al 2009;Scherngell and Hu, 2011;Hardeman et al 2012) and joint participation in R&D projects (Hagedoorn, 2002, Autant-Bernard et al 2007Maggioni et al 2007;Scherngell and Barber 2009;Balland, 2012). Nevertheless, authors sometimes mention limitations associated with their data (Ter Wal and Boschma, 2011), pointing out that although interview data provide the most information, because interviews themselves are so time-consuming, it is impossible to obtain enough data extending over time and space (for more details on the limitations of data used to analyze a network, see Bernela and Levy (2015) discussing the virtually systematic hypothesis of complete graphs). The proliferation of empirical studies applying the same model and the same methodology to different databases, yet which were accepted by peer-review, was a factor of enrichment but also one of saturation.…”