Irem Demirkan

Network Characteristics and Patenting in Biotechnology, 1990-2006:

In this study, the authors focus on specifically which types of networks and what types of relationships matter most for the focal firm’s innovative performance in biotechnology patenting. They suggest that certain network characteristics, such as quality and the source of knowledge, and the quality of relationships among actors may have a profound effect on the knowledge exchange and hence the number of patents granted to the firm. The authors focus on the research coauthorship networks of 381 firms in the U.S. biotechnology industry over a span of 17 years, from 1990 to 2006. In the biotechnology industry, firms depend heavily on the social network of academic scientists for the exchange and production of knowledge, with firm-level patenting closely linked to it. Their findings largely support the hypotheses, suggesting the necessity to consider the characteristics of the actors and the qualitative nature of the relationships in the network while assessing the role of the firm’s networks on its patenting.

Exploring the Role of Network Characteristics, Knowledge Quality, and Inertia on the Evolution of Scientific Networks

Despite decades of network research, the crucial question, “How do networks evolve?” has not been sufficiently explored. The authors explore this question by analyzing the co-authorship networks in the U.S. biotechnology firms. Building on network management and network inertia perspectives, the authors build a model predicting that the structural changes in the firms’ co-authorship networks are dependent on the specific characteristics of firms’ initial networks, the firm’s age and size. The authors then extend the model by incorporating a measure of the impact of the quality of the knowledge produced by the network ties using the prominence and inertia perspectives, which lead to the incorporation of competing hypotheses and moderating relationships in the model of scientific network evolution. The authors then test the model using longitudinal analysis of 367 U.S. biotechnology firms over a span of 17 years. The authors find that firms’ existing tie-specific characteristics in the form of a firm’s existing network size, tie strength, and the knowledge quality are significant determinants of network evolution, but that this influence is tempered by organizational inertia.

Collaboration networks and innovation: does corporate lobbying matter?

Purpose – This study aims to investigate how corporate lobbying affects the relationship between collaboration networks and innovation.Design/methodology/approach – The study incorporates insights from the corporate political strategy perspective into the social network research to examine how firms utilize non‐market mechanisms as a way to manage uncertainty. In particular, using data from 291 US pharmaceutical firms, the authors study the moderating effects of corporate lobbying on the relationship between collaboration networks and firm innovativeness.Findings – The results show that corporate lobbying moderates the relationship between network centrality, structural holes, and network size, and firm innovativeness.Originality/value – The study integrates social network and corporate political strategy research in the case of collaboration networks. Integrating social network and corporate political strategy literatures provides us with new insights into what determines success of firm innovativeness. ...

Evolution of Research Collaboration Networks and Their Impact on Firm Innovation Output

Abstract How do ego-networks evolve? How does such evolution affect firms’ innovation output? This chapter uses a longitudinal sample of firms in the biotechnology industry to address these questions. We use social network theory to develop a model of the structure and dynamics of firms’ interorganizational research collaboration ego-networks. Using novel longitudinal methods, this chapter demonstrates how research collaboration ego-networks in the biotechnology industry change over time and how this evolution affects focal firms’ subsequent innovative output. The model is tested on a sample of 482 biotechnology firms over a span of 17 years (1990–2006). The results indicate the significant impacts of ego-network size, ego-network growth, and the inclusion of new members in the ego-network on the innovation output of biotechnology firms. Our results also suggest that enlarging ego-networks by adding new and diverse members presents significant management challenges.