Abstract
While the gig economy provides flexible jobs for millions of workers globally, a lack of organization identity and coworker bonds contributes to their low engagement and high attrition rates. To test the impact of virtual teams on worker productivity and retention, we conduct a field experiment with 27,790 drivers on a ride-sharing platform. We organize drivers into teams that are randomly assigned to receiving their team ranking, or individual ranking within their team, or individual performance information (control). We find that treated drivers work longer hours and generate significantly higher revenue. Furthermore, drivers in the team-ranking treatment continue to be more engaged 3 mo after the end of the experiment. A machine-learning analysis of 149 team contests in 86 cities suggests that social comparison, driver experience, and within-team similarity are the key predictors of the virtual team efficacy.
| Original language | English (US) |
|---|---|
| Article number | e2206580119 |
| Journal | Proceedings of the National Academy of Sciences of the United States of America |
| Volume | 119 |
| Issue number | 51 |
| DOIs | |
| State | Published - Dec 20 2022 |
Bibliographical note
Funding Information:We are grateful to the editor, Matthew Jackson, and two anonymous referees for their constructive comments that significantly improve the paper. We thank Subhasish Chowdhury, Alain Cohn, Jim Cox, Glenn Harrison, John Ledyard, Steve Leider, Yuqing Ren, Tanya Rosenblat, and Katya Sherstyuk; seminar participants at Brown University, Columbia University, Georgia State University, Goethe University Frankfurt, University of Bath, University of California, Berkeley, University of California, Los Angeles, University of Innsbruck, University of Michigan, University of Minnesota, Shanghai Jiao Tong University, Tsinghua University, Management Information Systems Quarterly Scholarly Development Academy, and the 2019 North America Economic Science Association Meetings (Los Angeles, CA) for helpful discussions and comments; and Miao Liang, Tao Song, Quanjiang Wan, Guobin Wu, and Lulu Zhang for their help in implementing the experiment. The research has been approved by the University of Michigan IRB (HUM00153090) and preregistered at AEA RCT registry (AEARCTR-0003537). Portions of this work were included in the PhD thesis of “Improving Worker Performance with Human-Centered Data Science” by Dr. Teng Ye. Financial support from the ride-sharing platform through the Michigan Institute for Data Science is gratefully acknowledged.
Publisher Copyright:
Copyright © 2022 the Author(s). Published by PNAS.
Keywords
- field experiment
- gig economy
- social identity
- team leaderboard
- virtual teams
PubMed: MeSH publication types
- Journal Article