While the end of the global financial crisis is still debated in most economies, many companies and institutions have pursued a search for new inter- and intra-organizational forms in order to improve their efficiency and the effectiveness of their actions. In such a scenario, the paper focuses on innovation performance and 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 inter-cluster 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, trying to explain how cluster dynamics could be seen as response to the crisis.

Getting Together During and After the Crisis: Inter- and Intra-Cluster Dynamics in Localized Networks in the Life-Science Sector / Chiara, D'Alise; Giustiniano, Luca. - (2014), pp. 25-38.

Getting Together During and After the Crisis: Inter- and Intra-Cluster Dynamics in Localized Networks in the Life-Science Sector

GIUSTINIANO, LUCA
2014

Abstract

While the end of the global financial crisis is still debated in most economies, many companies and institutions have pursued a search for new inter- and intra-organizational forms in order to improve their efficiency and the effectiveness of their actions. In such a scenario, the paper focuses on innovation performance and 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 inter-cluster 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, trying to explain how cluster dynamics could be seen as response to the crisis.
Clusters; Life-science sector; Innovation; Social Network Analysis
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11385/86981
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