Knowledge exchange and innovation in clusters: a dynamic social network analysis

Abstract

Innovation is deeply rooted in clusters and is strongly related to knowledge exchanges. In literature, scholars have started suggesting that innovation rates are expected to be higher in dynamic networks, where there is variation in roles and knowledge exchanges. This paper contributes to this debate by studying the correlation between knowledge exchange and innovation at the cluster level, with a dynamic view. Using dynamic social network analysis as a methodological approach and the literature on Collaborative Innovation Networks (COINs) and knowledge exchanges as the theoretical framework, we investigated how varying over time cluster members’ leadership role and contribution in the flow of knowledge benefit cluster-level innovation. We relied on data collected from a collaborative cluster-based platform, focusing on technological knowledge exchanges. Our findings indicate that there is a strong positive correlation between rotating leadership and innovation as well as between rotating contribution and innovation.