Innovation networks are undoubtedly complex and dynamic phenomena. This is particularly true in services networks, as discussed by Djellal and Gallouj and Windrum (in Chs 2 and 4). As a result, empirical comparison of different innovation networks requires a taxonomy that is sufficiently flexible to facilitate a common analytical basis. Creation of such a taxonomy, however, is not an easy task. This is because innovation networks evolve permanently along most of their characteristic dimensions (including, for example, composition, structure, research directions and goals). The process of change across dimensions is highly interconnected and co-evolutionary: it involves, in the main, micro and incremental changes that are endogenous to the network and that occur frequently (but not continuously) throughout network evolution. This complicates the identification, distinction and comparison of different types of innovation networks since observed differences might be related simply to different stages in respective network development. Thus, a typology of innovation networks must be sensitive to both network dynamics and evolutionary factors. A theoretically well-founded and empirically testable concept that incorporates dynamic aspects, and at the same time allows for a clear-cut distinction between different stages, is the theory of life cycles. This concept has been successfully applied and tested in the context of manufacturing industries and their underlying products and technologies (Abernathy and Utterback, 1978; Jovanovic and MacDonald, 1994; Klepper, 1996, 1997).
You are not authenticated to view the full text of this chapter or article.
Get access to the full article by using one of the access options below.