The paper investigates the impact of clusters, or localized networks involving industrial, academic and institutional players, in the life-science setting and aims to enrich the line of inquiry into cluster-based innovation by applying a social network analysis (SNA) approach. The cluster concept has been defined in ambiguous ways, corresponding to a large variety of spatial and organizational concrete configurations. We try to understand which of these configurations - i.e. what structural and nodal network characteristics of the cluster - are best suited to maximize the likelihood of clusters' innovation, from an intra-cluster and intercluster perspective. Quantitative methods are applied to relational and nodal data, using SNA and a regression model. The work sheds light on the factors that give rise to differential innovative outcomes across different clusters.

Localized networks and innovation in the life-science sector. Inter-cluster and intra-cluster dynamics / Chiara, D'Alise; Giustiniano, Luca. - Bridging Continents, Cultures and Worldviews, (2013), pp. - (29th EGOS (European Group for Organizational Studies) Colloquium, Montreal, Canada, 3-6 July 2013).

Localized networks and innovation in the life-science sector. Inter-cluster and intra-cluster dynamics

GIUSTINIANO, LUCA
2013

Abstract

The paper investigates the impact of clusters, or localized networks involving industrial, academic and institutional players, in the life-science setting and aims to enrich the line of inquiry into cluster-based innovation by applying a social network analysis (SNA) approach. The cluster concept has been defined in ambiguous ways, corresponding to a large variety of spatial and organizational concrete configurations. We try to understand which of these configurations - i.e. what structural and nodal network characteristics of the cluster - are best suited to maximize the likelihood of clusters' innovation, from an intra-cluster and intercluster perspective. Quantitative methods are applied to relational and nodal data, using SNA and a regression model. The work sheds light on the factors that give rise to differential innovative outcomes across different clusters.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11385/67660
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