Evaluating Team Interdependence from the Perspective of Networks

Daniel Griffin, a dual PhD candidate at Michigan State University studying organizational psychology and computational mathematics science and engineering, offers a reflection on the recent article, “Evaluating Interdependence in Workgroups: A Network-Based Method,” he and co-authors Ajay V. Somaraju,  a PhD student in organizational psychology at Michigan State University, Christopher Dishop, a post-doctoral research fellow with the Consortium of Universities of the Washington Metropolitan Area and the U.S. Army Research Institute for the Behavioral and Social Sciences, and Richard P. DeShon, a professor of organizational psychology at Michigan State University saw published in Organizational Research Methods.

My research team and I are fascinated by the teams that the complex dynamic processes that define them. After various discussions, and a review of the literature, on such topics we came to recognize the central role of interdependence is one of the key factors defining teams and team processes. Despite the extent of the literature on team and group interdependence, the majority of this research was conducted using self-reports in a way that we felt was not set up to study the impact of interdependence on team processes well. Out of these early discussions, our focus on presenting a network perspective of team interdependence was born. 

While interdependence is a topic that easily fits within a network perspective, we have come to realize that this is an important step forward for the field. Not only does a network perspective of team interdependence elucidate the fundamental nature of interdependence and its impact on teams, but it highlights the way that interdependence is integrated into the complex and dynamic processes of teams. 

One thing that we wished we could have included was a look at non-index-based consideration when discussing the study of networks and interdependence. The present manuscript focuses almost exclusively on structural indices which do not account for individual differences or influence and selection processes often studied in network research.  

We hope readers will enjoy this research and find it helpful both methodological and in the development of more advanced theoretical perspectives of team processes.