This paper aims to contribute to the empirical identification of clusters by proposing methodological issues based on network analysis. We start with the detection of a composite knowledge process rather than a territorial one stricto sensu. Such a consideration allows us to avoid the overestimation of the role played by geographical proximity between agents, and grasp its ambivalence in knowledge relations. Networks and clusters correspond to the complex aggregation process of bi or n-lateral relations in which agents can play heterogeneous structural roles. Their empirical reconstitution requires thus to gather located relational data, whereas their structural properties analysis requires to compute a set of indexes developed in the field of the social network analysis. Our theoretical considerations are tested in the technological field of GNSS (Global Satellite Navigation Systems). We propose a sample of knowledge relations based on collaborative R&D projects and discuss how this sample is shaped and why we can assume its representativeness. The network we obtain allows us to show how the composite knowledge process gives rise to a structure with a peculiar combination of local and distant relations. Descriptive statistics and structural properties show the influence or the centrality of certain agents in the aggregate structure, and permit to discuss the complementarities between their heterogeneous knowledge profiles. Quantitative results are completed and confirmed by an interpretative discussion based on a run of semi-structured interviews. Concluding remarks provide theoretical feedbacks.
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Nooteboom, B., 2004.
"Innovation, learning and cluster dynamics,"
Research Paper
ERS-2004-006-ORG Revision, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus Uni.
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