To Survive and Succeed as a Virtual Team: How Team Social Capital Affects Teams' Survival, Growth, and Performance

Yiqi Li, Nathan Bartley, Jingyi Sun, Dmitri Williams


Virtual teams, a unique and omnipresent organizing form, have been increasingly attracting scholarly attention. Although there is ample research on team social capital, research on virtual teams is scarce. This study investigates how various types of team social capital affect team performance and team size. Our results offer novel insights on not only factors associated with team performance, but also teams’ evolutionary patterns, and chances to grow and survive. Theoretically, this study adds considerations of team size into virtual team social capital research. It also provides important practical implications for team leaders. Our research drew data from a Massively Multiplayer Online Game (MMOG) spanning across 32-time points (September 2016 to April 2019). Out of 45,319 teams on the entire North America server, we extracted 5,000 teams by stratified sampling. Following Oh et al. (2006)’s framework, we approach group social capital on two levels: group internal social capital and external social capital. This research applies network measurements to operationalize social capital. In order to fully capture bridging and bonding team social capital within and across teams, on the individual level, we constructed 5,000 co-play social networks within teams, and on the team level, a co-play social network across teams and a network indicating membership flow across teams were constructed. We propose that, for virtual groups that are often self-organized, joined and left at members’ will, and sustained by having enough membership, size is an essential outcome variable that reflects teams’ propensity to survive and grow. First, to explore teams’ evolutionary patterns, Hidden Markov Models were fit. We found that young teams (< 893 days) are mostly small (<=10 members), and with relatively low probability to grow. For older teams (>=893 days), there weren’t any with the size of below ten members. Middle-sized older teams most-often stay middle-sized over time. Larger teams, both young and old, tend to experience churn and fall into the middle-sized range. In conclusion, younger teams face challenges to grow, and older teams, are often larger and more sustainable. Then, Longitudinal Multilevel Models on two dependent variables: team size and team performance, were fit and validated. Within-team bonding social capital, measured by network density, negatively predict team size and team performance. Inter-team bonding social capital, overall, positively influences team size. It also has an inverted U-shaped relationship with team size, indicating that intensive inter-team bonding activities are not optimal for team growth. However, inter-team bonding social capital negatively predicts team performance. Inter-team bridging social capital positively affects both team size and team performance. Diverse teams featuring members of distinct levels of win rate, have more members and perform better. However, diverse teams with members of various rankings do not perform well. Since players also bring information and knowledge when they jump from teams to teams, we found teams that attracted people from many other groups (high in-degree centrality in the membership flow network), perform better. An event history model will be added to provide more insights on the relationship between team social capital and teams’ likelihood to dissolve.

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