EAGER: Social Dynamics of Organizational Behavior in Temporary Virtual Teams

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This research leverages the large-scale, detailed, international, and unobtrusive data logged by online electronic sports (e-sports) to understand the organizational behavior of temporary virtual teams. Teamwork and collaboration are essential for success in contemporary organizations, and as teams become increasingly distributed, virtual, self-assembled, cross-functional, and temporary, existing frameworks for supporting effective teamwork need to be revised. Multiplayer online e-sports are models for developing new and more effective frameworks for technologically-mediated teamwork: clear and consistent performance metrics, detailed and public behavioral data for quantitative analysis, a large and international user base, and extensive randomization, repeated observations, and matching for making strong causal inferences. The inferences made from detailed behavioral records of high-tempo, naturalistic decision making can be extended to many other settings such as disaster response or breaking news. The findings from this research could provide the empirical basis for identifying under-utilized expertise in noisy social systems, optimizing team assembly algorithms to improve performance, improving decision making in temporary virtual teams, and using online e-sports as diagnostic tools for existing teams. The findings from this project will also inform the design and governance of e-sports that already attract tens of millions of users around the world. The project pursues two initiatives to understand how to improve the performance of temporary virtual teams. The first initiative examines how team assembly decision-making in high-tempo contexts influences team performance. The second initiative examines how software and database patches disrupt mental models and decision-making. This research will triangulate between (1) existing organizational theories and constructs about team processes, (2) quantitative methods from data mining, machine learning, and econometrics for analyzing unobtrusive observational data about user behavior, and (3) the unique affordances of several popular e-sports to examine the variables and mechanisms that influence team performance in a naturalistic setting. The results of this research project will (1) provide comparative empirical insights into a rapidly growing cultural and economic phenomenon; (2) develop frameworks and models to increase engagement in sociotechnical systems; and (3) provide generalizable recommendations for improving the performance of temporary virtual teams. This project will create free software libraries, data collections, and supporting tutorials and documentation enabling other researchers to develop and evaluate organizational theories using data drawn from e-sports.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.