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

Abstract

The paper focuses on cluster policies with particular attention to the role of R&D collaborative incentives in the structuring of knowledge networks in clusters. We disentangle the main network failures in regional innovation systems, and discuss the selection procedures designed by policy makers to enhance the production of innovation outputs. We draw evidence from the French Aerospace Valley cluster from 2006 to 2015. The empirical analysis relies on a dataset of 248 granted research consortia, from which we build 4-cohorts knowledge networks enable us evidencing the evolving structural properties of the cluster over time. We suggest avoiding the bias and limitations of 1 and 2-mode network analysis by developing an original placebased network methodology that emphasizes on structural equivalence and groups behaviors. We discuss the results focusing on the convergence degree between the network statistical findings and the policy makers' objectives. Finally, the methodology allows us identifying who are the agents of the structural and technological changes observed during the period.

Still in the shadow of the wall? The case of the Berlin biotechnology cluster

“…Alternatively, less experienced organisations might tend to establish ties with more experienced ones (older and more successful in getting grants). They thereby seek to tap into these organisations’ skills and know-how bases (Lucena-Piquero and Vicente, 2019). A similar effect is not observed for the coefficients of PROJ on the persistence of ties.…”

Evaluating clusters: Where theory collides with practice

“…There are also clear opportunities from engaging further with some of the new approaches to evaluating cluster policies that are emerging, such as analysis of social media networks (Etxabe, 2018), or a stronger combination of case‐oriented and quantitative analysis through the adaption of fuzzy‐set qualitative comparative analysis for cluster evaluation (Lu & Chang, 2016; Ragin, 2000). Leveraging and connecting the different types of data collected by policy‐makers over sustained periods of time could also support more widespread and consistent analysis of the types of behavioural elements of cluster networks that are starting to be explored (Felzensztein et al, 2018; Graf & Broekel, 2020; Lucena‐Piquero & Vicente, 2019). Indeed, perhaps the key result of the process of academic‐policy‐practice engagement analysed here is the emergence of an exciting research agenda which brings together the interests and capabilities to act of the different participants.…”

“…Most studies in this emerging literature are fundamentally concerned with understanding the relational dynamics fostered by cluster policies and exploring their impacts. Felzensztein, Gimmon, and Deans (2018), for example, examine the long‐term evolution of strategic inter‐firm co‐operation, Choi, Sang‐Hyun, and Cha (2013) relate the structural and behavioral characteristics of clusters to their learning performance, and Lucena‐Piquero and Vicente (2019) and Graf and Broekel (2020) both focus on the behavioural impacts of cluster policies as reflected in the development of knowledge networks.…”

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

“…Questions regarding additionality at the network and/or regional level are tackled in a third strand of the cluster evaluation literature (e.g. Bellandi and Caloffi, 2010;He et al, 2013;Ter Wal, 2013;Rothgang et al 2017;Töpfer et al, 2017;Lucena-Piquero and Vicente, 2019). Studies from this strand of research start from the premise that innovation network policies are primarily designed to strengthen innovation networks and build on the theoretical work on the link between network structures and innovation performance.…”

“…Despite the relevance of this type of evaluation, systemic additionality is rarely addressed in the literature. The most recent and relevant works in this area are essentially based on descriptive analyses of social networks 2 (Rothgang et al 2017;Töpfer et al, 2017;Lucena-Piquero and Vicente, 2019) or on qualitative analyses (He et al, 2013). Moreover, most of these studies rely on information about the collaborative projects supported by clusters.…”