Putting Teams into the Gig Economy: A Field Experiment at a Ride-Sharing Platform - Ai, Wei; Chen, Yan; Mei, Qiaozhu; Ye, Jieping; Zhang, Lingyu (Management Science, 2023)
The gig economy provides workers with the benefits of autonomy and flexibility but at the expense of work identity and coworker bonds. Among the many reasons why gig workers leave their platforms, one unexplored aspect is the lack of an organization identity. In this study, we develop a team formation and interteam contest field experiment at a ride-sharing platform. We assign drivers to teams either randomly or based on similarity in age, hometown location, or productivity. Having these teams compete for cash prizes, we find that (1) compared with those in the control condition, treated drivers work longer hours and earn 12% higher revenue during the contest; (2) the treatment effect persists two weeks postcontest, albeit with half of the effect size; and (3) drivers in hometown-similar teams are more likely to communicate with each other, whereas those in age-similar teams continue to work longer hours and earn higher revenue during the two weeks after the contest ends. Together, our results show that platform designers can leverage team identity and team contests to increase revenue and worker engagement in a gig economy. This paper was accepted by David Simchi-Levi, behavioral economics and decision analysis. Funding: Financial support from the platform through the Michigan Institute for Data Science is gratefully acknowledged. Supplemental Material: The e-companion are data are available at https://doi.org/10.1287/mnsc.2022.4624 .