Endogenous dynamics of innovation networks in the German automotive industry: Analysing structural network evolution using a stochastic actor-oriented approach

Innovation is, above all, a social process depending on mutual interactions of individuals aiming at accessing and exchanging external inputs in order to generate novel good and services. Accordingly, the interest in – and research on – interfirm innovation networks has sharply increased over the last decade. The structural dynamics of networks are driven by endogenous and exogenous forces. In this paper, we particularly stress the role of endogenous determinants of network evolution of interfirm networks – a category of often underestimated forces. We employ a longitudinal dataset that compromises German automotive firms between 2002 and 2006 and apply a stochastic actor-oriented model (SAOM) designed to analyze the importance of endogenous and exogenous determinants. Our results show that endogenous determinants – proxied by measures for local and global clustering – have a higher explanatory power than commonly used exogenous firm characteristics such as age, size, and R&D activity.