Connecting others: Does a tertius iungens orientation shape the relationship between research networks and innovation?

Abstract

Research on social networks and innovation emphasizes that individuals spanning structural holes and crossing institutional boundaries have more opportunities for knowledge recombination and innovation involvement. However, transforming the potential knowledge and resources available through personal networks to attain innovation can be difficult for the focal individual. Using an ego-network approach, this study examines whether and to what extent an individual strategic orientation to cooperation (i.e. tertius iungens) contributes to strengthening the relation between two personal network properties (structural and institutional separation) and involvement in innovation. Our analysis is conducted in the context of biomedicine, where research networks are particularly relevant for science and innovation achievements. Our findings advance social network theory by decoupling social network mechanisms from individual strategic networking behavior as factors influencing knowledge generation processes. Results also provide original evidence on an overlooked phenomenon, the moderating role of a tertius iungens orientation in the relationship between multiple social network properties and innovation. Finally, our research sheds new light on the distinct sources of knowledge recombination in networks and the role of individual networking strategies to facilitate mobilization of resources for innovation.

Publication
Research Policy

Highlights

  1. We explore how biomedical scientists’ social network is related to their involvement in medical innovation.
  2. We conceptually and empirically distinguish two social network properties: institutional separation and structural separation.
  3. We find that the individuals’ strategic orientation towards connecting others (“tertius iungens”) shapes the relationship between structural separation, institutional separation and innovation involvement.
  4. Our models are tested on a sample of 924 biomedical scientists