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Virtual teams have become a ubiquitous form of organizing, but the impact of social structures within and between teams on group performance remains understudied. This paper uses the case study of a massively multiplayer online game and server log data from over 10,000 players to examine the connection between group social capital (operationalized through guild network structure measures) and team effectiveness, given a variety of in-game social networks. Three different networks, social, task, and exchange networks, are compared and contrasted while controlling for group size, group age, and player experience. Team effectiveness is maximized at a roughly moderate level of closure across the networks, suggesting that this is the optimal level of the group's network density. Guilds with high brokerage, meaning they have diverse connections with other groups, were more effective in achievement-oriented networks. In addition, guilds with central leaders were more effective when they teamed up with other guild leaders.

This study describes the structure of the international Facebook friendship network and its determinants using various predictors, including physical proximity, cultural homophily, and communication. Network analysis resulted in one group of nations, with countries that bridge geographic and linguistic clusters (France, Spain, United Kingdom, and United Arab Emirates) being the most central. Countries with international Facebook friendship ties tended to share borders, language, civilization, and migration. Physical distance, shared hyperlinks, use of common websites, telephone traffic, cultural similarity, and international student exchange were either weakly or not significantly related to international Facebook friendships.

While the online sphere is believed to expose individuals to a wider array of viewpoints, a worry about self-reinforcing political echo chambers also persists. We join this scholarly debate by focusing on individual motives for political discussion and dyadic- and structural-level mechanisms that can drive one’s message-selection decision in online discussion settings. Using unobtrusively logged behavioral data matched with panel survey responses, our temporal exponential random graph model (TERGM) analysis indicates that message selection in online discussion settings is largely driven by the similarity of one’s candidate evaluative criteria and various endogenous structural factors, whereas the impact of overt partisan preference in shaping message selection is much more limited than is often assumed.

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