Can technologies help organizations assemble more effective and diverse teams? This study seeks to understand how recommender systems can help individuals assemble functionally diverse teams. We run a 2×2 between-subject laboratory experiment with 384 participants to test the effect of a recommender system’s algorithms on individuals’ teaming experience, team processes, and team performance. This experiment controls two main factors: personal agency (i.e., when users can choose their teammates), and algorithmic diversity (i.e., an algorithm that maximizes diversity in teammate recommendations). Preliminary results show that personal agency alone negatively affects teams’ performance and functional diversity. In contrast, combining personal agency with algorithmic diversity helps participants assemble teams with high functional diversity levels and achieve better task performance. This study provides an example of how shepherding users by combining their agency with diversity criteria helps them assemble more effective teams.
Diego Gómez-Zará is a postdoctoral fellow at Kellogg School of Management and an incoming assistant professor in Computer Science at the University of Notre Dame. He studies how people assemble groups and work together using online systems. He is interested in developing a theoretical understanding of the role that socio-technical systems play in supporting team formation and team dynamics. A key component of his work is the application of social network analysis to leverage users’ decisions. He received his Ph.D. in Technology & Social Behavior from Northwestern University in 2021. His dissertation research was awarded the 2020 Microsoft Research Dissertation Grant and the National Science Foundation SES-2021117. He served as a graduate student representative on the ACM CSCW Steering committee.