Abstract: We investigate the relationship between managers’ and researchers’ embeddedness in interpersonal knowledge networks and their organizations’ involvement in R&D collaborations in the context of regional clusters. Social mechanisms are supposed to give rise to patterns of cross-level assortativity and cross-level closure characterizing multilevel knowledge networks. To find out which patterns actually determine knowledge sharing and cooperation in regional high-tech clusters we apply exponential random graph models for multilevel networks to relational data collected in two clusters in Germany. Our results show a positive interaction between individuals’ popularity as sources for knowledge and their organizations’ centrality in the formal collaboration network as well as a negative interaction between individuals’ activity seeking knowledge from others and their organizations’ centrality. We also find a tendency for cross-level closure.

Abstract: In this paper we investigate the micro-mechanisms governing the structural evolution and performance of scientific collaboration. Using a dataset consisting of full bibliometric coverage from a Large Scale Research Facility, we utilize a stochastic actor oriented model (SAOM) to analyze both network endogeneous mechanisms and individual agency driving the collaboration network and further if being a Big Ego in Big Science translates to increasing performance. Our preliminary results suggest that the selection of collaborators, are not based on preferential attachment, but more of an assortativity effect creating not merely a rich-gets-richer effect but an elitist network with high entry barriers. In this acclaimed democratic and collaborative environment of Big Science, the elite closes in on itself, thus potentially restricting innovation and knowledge creation.

Abstract: It is well established that brokerage creates private benefits for the broker. How brokers reap private benefits and the extent to which other network members benefit from brokerage remains unclear. Acknowledging a direct relationship between knowledge flows and innovation, we use a 3SLS model to elucidate the individual and simultaneous links among brokerage, innovation and knowledge flows. Based on subunit network data of 145 subunits belonging to three multinational enterprises (MNEs), we find that broker subunits do not exhibit any definite patterns of knowledge flow behaviors. We show, however, that facilitating knowledge flows in the network enhances the brokers’ innovation. As facilitators of knowledge flows, brokers optimize both their private as well as the benefits of their respective networks.

Abstract: In this proposal, we argue that the structure of an inter-organizational network of clinical trials affects rates of knowledge production. Moreover, the position of individual organizations in the network largely maps onto different incentives for knowledge generation between pharmaceutical companies and research institutions. Using a co-sponsorship network of clinical trials for depression, we identify a core-periphery structure, calculate the sponsors’ coreness and betweenness centrality; and evaluate their effect on publication rates in medical journals. Our analysis indicates network position does not affect significantly publication rates. Nonetheless, dividing the sample into funding subsamples, our preliminary results suggest the effect of network position is contingent to sponsor’s organizational type. We propose that different incentives combine with different network positions to yield different publication rates.