Page - (000177) - in Knowledge and Networks

171 knowledge bases for the common goal of fostering exploratory research on complex polymers. An ideal core–periphery configuration is taken as a benchmark against which to compare the evolution of the observed patterns of collaborations within the IMAST project network. We conduct a prespecified blockmodel analysis of IMAST’s col- laboration network for research and development (R&D) in each of the 7 years from 2006 to 2013 to determine how far its observed topology was from a core–periphery model configuration at each time point (Doreian, Batagelj, & Ferligoj, 2005). The core–periphery model used as a benchmark was defined as a multiple-core model with a bridging core connecting all the others (Kronegger, Ferligoj, & Dorein, 2011). Blockmodeling can provide a synthetic and effective visualization of the evolu- tionary trajectory of the cluster. It is thus possible to use prespecified blockmodeling to assess the extent to which the observed trajectory was path dependent. From an evolutionary perspective, we operationalize the concept of structural variations introduced by Glückler (2007) to explain how a developmental trajectory can be shifted away from its path. Lastly, we examine what type of actors occupied key structural positions and what degree of positional mobility there was in the period examined. The available theories on cluster configuration and evolution are reviewed in the first section, with particular attention to the core–periphery model. The second sec- tion introduces the case study characteristics and the data used for the analysis. In the third section we illustrate the method of prespecified blockmodeling and opera- tionalize key indicators of path reproduction and variation. The fourth and fifth sections contain the results of our prespecified blockmodeling analysis, which shows the evolutionary trajectory of IMAST. This part of the chapter also has a detailed discussion of the organizations’ attributes. The final section presents our conclusions and recommendations. Cluster Topologies in the Literature: Generative Processes and Configurations Innovation in a knowledge economy is conceptualized as an interactive process of learning that involves different actors in a system (Cooke & Morgan, 1999). This learning process is shaped by a variety of institutional routines and practices, defined as organizational patterns of behavior. The delicate role of managing collaborations and harmonizing practices and routines is particularly sensitive when technology- and science-based organizations are called on to coexist within the same cluster (Autio, 1998). In these cases a dedicated administration can be instituted as a knowledge integrator in the network-administered organizations (NAO) (Provan & Kenis, 2008). The literature on innovative clusters does not directly examine top- down management strategies of collaborations, probably because top-down man- agement is not an ideal model that fits all cases. However, several economic 9 Topology and Evolution of Collaboration Networks