Exploring the Evolution of Innovation Networks in Science-driven and Scale-intensive Industries: New Evidence from a Stochastic Actor-based Approach
Our primary goal is to analyse the drivers of evolutionary network change processes by using a stochastic actor-based simulation approach. We contribute to the literature by combining two unique datasets, concerning the German laser and automotive industry, between 2002 and 2006 to explore whether geographical, network-related, and techno-logical determinants affect the evolution of networks, and if so, as to what extent these determinants systematically differ for science-driven industries compared to scale-intensive industries. Our results provide empirical evidence for the explanatory power of network-related determinants in both industries. The ‘experience effect’ as well as the ‘transitivity effects’ are significant for both industries but more pronounced for laser manufacturing firms. When it comes to ‘geographical effects’ and ‘technological ef-fects’ the picture changes considerably. While geographical proximity plays an important role in the automotive industry, firms in the laser industry seem to be less dependent on geographical closeness to cooperation partners; instead they rather search out for cooperation opportunities in distance. This might reflect the strong dependence of firms in science-driven industries to access diverse external knowledge, which cannot necessarily be found in the close geographical surrounding. Technological proximity negatively influences cooperation decisions for laser source manufacturers, yet has no impact for automotive firms. In other words, technological heterogeneity seems to ex-plain, at least in science-driven industries, the attractiveness of potential cooperation partners.