Abstract
Social belonging is a fundamental human need, which people experience to varying degrees in the workplace. Interventions to boost belonging have typically focused on changing individuals’ mindsets. We instead develop a structural intervention that seeks to foster belonging by exposing people to unfamiliar colleagues—ones they are not in regular contact with. We consider two forms of such exposure: convergent, to colleagues from the same network community as the focal actor; and divergent, to colleagues from different network communities. Participants in a non-profit organization (N=213) engaged in a facilitated professional development program with unfamiliar colleagues and were randomly assigned to either convergent or divergent groups. Consistent with pre-registered expectations, convergent-condition participants reported more group solidarity and—three months post-intervention—more persistent ties to fellow group members and greater social belonging. Using computational linguistics and machine learning techniques to impute survey responses, we further show that convergent-condition participants experienced greater belonging than did a synthetic control group. Yet, pointing to the tradeoffs of the two forms of exposure, divergentexposure participants experienced steeper declines in network constraint and greater increases in betweenness and closeness centrality, independent of fellow-group-member ties. We discuss implications for research on social networks, workplace belonging, and organizational interventions.