KIBS and the Dynamics of Industrial Clusters: a Complex Adaptive Systems Approach

Abstract

An important and highly debated question in economic geography is how to explain the dynamics of industrial clusters, i.e. their emergence and evolution through time. Two main theories are generally explored, without being confronted: the cluster life cycle theory-which mainly adopts an aggregate point of view-and the network-based approach. Although KIBS are an important actor of industrial clusters, these two theories pay little attention to them as a potential driver of clusters' dynamics. We show in this paper that properly taking KIBS into account requires considering an alternative and integrative approach that conciliates these two theories. In particular, we argue that complex adaptive systems (CAS) constitute a promising basis for such a synthesis. We then operationalize the CAS approach by studying an existing industrial cluster-Skywin (aeronautics in Wallonia region, Belgium)-within this framework. For this purpose, we use an exhaustive list of the innovation projects undertaken within this cluster between 2006 and 2014 and we build temporal innovation networks linking the agents of the cluster. It appears that Skywin's innovation networks exhibit a small-world effect. This implies that any agent who takes part into an innovation project of this cluster can easily benefit from knowledge and information generated within another ongoing project. We argue that this effect is an interesting proxy of a cluster's attractiveness and an appropriate aggregate variable for studying clusters' dynamics as it shows cluster's potential for further growth. We also demonstrate that KIBS are the main responsible for the emergence of this small-world effect in Skywin's innovation networks.

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