Contemporary teams are self-assembling with increasing frequency, meaning the component members are choosing to join forces with some degree of agency rather than being assigned to work with one another. However, the majority of the teams literature up until this point has focused on randomly assigned or staffed teams. Thus, the purpose of this dissertation was to investigate how people do form into teams and how people should form into teams. First, I evaluated the bases for and performance implications of team self-assembly using a sample of digital traces from the Chinese version of the massively multiplayer online role-playing game Dragon Nest. The final sample included 1,568 players who played on 1,744 teams. Second, I conducted 51 semi-structured interviews (26 with American participants and 25 with Chinese participants) in order to assess the extent to which teaming behaviors enacted in virtual worlds can be generalized to the real world. The results of the digital trace data analyses and semi-structured interviews both indicated that self-assembled teams form via three mechanisms: homophily, familiarity, and propinquity. However, certain patterns emerged from the trace data analyses that did not surface during the structured interviews—such as self-assembly based on closure—while interviewees highlighted other attraction mechanisms that were not confirmed by the results of trace data analyses—such as preferential attachment, functional diversity, and geographic dispersion. Moreover, results of the digital trace data analyses indicated that unsuccessful teams were more homogenous in terms of certain deep-level characteristics than successful teams were, and successful teams formed based on friendship more often than unsuccessful teams did. Overall, the findings from this dissertation shed new light on the attraction mechanisms that drive the formation of high- and low-performing self-assembled teams.
|Date||08 June 2015|
|Contributors||DeChurch, Leslie A.|
|Publisher||Georgia Institute of Technology|
|Source Sets||Georgia Tech Electronic Thesis and Dissertation Archive|
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