Support for technology clusters is an important element of innovation policy, with an increasing number of national and regional plans promoting Research and Development projects in collaborative networks. This promotes the need to evaluate the effectiveness and efficiency of these policies. Several methods and techniques have been proposed, but a standardised approach has not yet emerged. It is important to assess the role of non-economic effects and, in this paper, we propose a methodology, which uses a social network approach, to assess the social capital outcomes of a technology district policy. Building on social capital theory, we construct a set of indicators to account for structural, relational and cognitive components, as well as bonding, bridging and linking kinds. This approach is not costly, in terms of data and information collection and can be combined with other methods that measure policy performance via physical, as well as economic, outputs and impacts.

Evaluating social dynamics within technology clusters: A methodological approach to assess social capital

Alessandro Muscio
Writing – Original Draft Preparation
;
Antonio Lopolito
Methodology
;
Gianluca Nardone
Writing – Review & Editing
2019-01-01

Abstract

Support for technology clusters is an important element of innovation policy, with an increasing number of national and regional plans promoting Research and Development projects in collaborative networks. This promotes the need to evaluate the effectiveness and efficiency of these policies. Several methods and techniques have been proposed, but a standardised approach has not yet emerged. It is important to assess the role of non-economic effects and, in this paper, we propose a methodology, which uses a social network approach, to assess the social capital outcomes of a technology district policy. Building on social capital theory, we construct a set of indicators to account for structural, relational and cognitive components, as well as bonding, bridging and linking kinds. This approach is not costly, in terms of data and information collection and can be combined with other methods that measure policy performance via physical, as well as economic, outputs and impacts.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11369/380385
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