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

The generation of innovation is well known to be a social process depending on mutual interactions, aiming at accessing and exchanging knowledge in order to generate novel goods and services. Accordingly, interest in interfirm innovation networks has increased sharply over the last decade. Preceding research indicates that the structural dynamics of networks is driven both by endogenous and exogenous forces. In particular, we focus on the role of the endogenous determinants of the network evolution of interfirm networks - a category of often underestimated forces. We employ a longitudinal dataset that comprises German automotive firms' performance between 2002 and 2006 and apply a stochastic actor-oriented model (SAOM) designed to analyse both the endogenous and exogenous determinants of network change. Our results show that endogenous determinants - approximated by measures for local and global clustering - exhibit greater explanatory power than exogenous firm characteristics such as age, size, and R%D activity.