Exploring the Relationship Between Interdisciplinary Ties and Linguistic Familiarity Using Multilevel Network Analysis

Abstract

Research shows that teams comprised of individuals with differing knowledge are increasingly important to enabling innovation in organizations. Beyond diverse connections, research also shows individuals must be familiar with their collaborators’ areas of expertise to effectively integrate knowledge. Despite growing recognition of the importance of familiarity for interdisciplinary collaboration, we argue that there is reason to suspect this form of relationship is likely to be particularly rare in organizations. We present an egocentric analysis of collaboration networks in a scientific organization, exploring factors associated with the copresence of interdisciplinary ties alongside familiarity with a collaborator’s area of expertise. Our results demonstrate pressures toward similarity of expertise that minimized connections to differing alters. Furthermore, those respondents who had diverse connections tended to be unfamiliar with their distant collaborators’ domains. Interaction counteracted this effect but participants reported pressures inhibiting interaction across knowledge boundaries. The findings demonstrate how network forces compound to inhibit what we call “different yet familiar” ties and, by doing so, offer conceptual and practical implications for contemporary organizations.