Time and Space Dynamics in Networks: Do Network Perceptions Reflect Network Positions?

The network position of a company represents a pivotal concept in research on business networks within the tradition of the Industrial Network Approach. However, the existing literature has mostly been concerned with defining the term ‘network position’. Hence, the essence of the concept remains rather static, largely defined in terms of the constellation of relationships a company has with other actors. But networks are dynamic, and network positions change. The link between network position and network change relates to the interdependencies within a network, i.e. if one company’s position changes, this will in turn affect the position of other companies, resulting in a new configuration of the network. Therefore, issues around network position provide a conceptual link to the concept of network change. For the purpose of this research, network change is understood through actors’ perceptions, i.e. as cognitive schemata, in line with recent research on network pictures in the Industrial Network Approach. We specifically try to understand how actors’ perceptions of network dynamics (and their subsequent networking actions) will vary depending on the network position that their company holds. Our analysis suggests that although there are similarities between perceptions by actors holding similar positions in the network (i.e. those actors at periphery of the overall network have richer network pictures regarding change dynamics), network position alone cannot explain such differences in cognitive schemata. Rather, differences in actors’ interpretations and intentions in terms of their network role, as suggested by the role-and-position concept, are better at explaining differences in perceptions than the network position concept alone. Besides these findings which enrich the literature on network dynamics, a second contribution of this study is the use of a novel method for analysing and comparing actor perceptions in industrial networks. This method is applied to an extensive case study of the changing distribution structure for seafood in Norway and