Using Digital Trace Data to Explore How Firm Interaction Shapes Online Community User Networks: An Exponential Random Graph Approach

Organizations increasingly acknowledge online communities’ importance for innovation. In addition to developing proprietary platforms that host and support such communities, firms hire employees as community managers, moderators, or supporters to engage with users and practice implicit control to achieve their strategic goals. However, the effect of such direct interactions with users on the social structure of online communities is not yet well understood. In this chapter, we explore how employee involvement with users can create direct and indirect changes to how users connect with peers. As interactions in online communities are recorded as they occur, they offer a rich repository of trace data that can be used for analysis. We use trace data in this chapter to study the changes in the social structure of an online community as employees interact with users. After developing three hypotheses on how firm interaction with a user influences the social connections of surrounding peers, we use trace data to create social networks and test our hypotheses using exponential random graph models (ERGM). Our findings suggest that users who interact with the firm receive more attention from peers, and those who receive collaborative help from the firm support connections between peers more than those who receive firm directions. Finally, we discuss research challenges for researchers wishing to study similar contexts or use similar methods.