Virtual teams in a gig economy

Area of Science:

• Economics
• Organizational Behavior
• Labor Studies

Background:

• The gig economy offers flexibility but suffers from low worker engagement and high attrition due to a lack of organizational identity and coworker bonds.
• Ride-sharing platforms face challenges in retaining drivers, impacting service reliability and operational efficiency.

Purpose of the Study:

• To investigate the impact of virtual teams on worker productivity and retention within the gig economy.
• To determine if team-based performance feedback enhances driver engagement compared to individual feedback.

Main Methods:

• A large-scale field experiment was conducted with 27,790 ride-sharing drivers.
• Drivers were randomly assigned to receive team rankings, individual rankings within a team, or individual performance information (control group).
• Machine learning was used to analyze factors influencing virtual team efficacy across 149 team contests in 86 cities.

Main Results:

• Drivers in treated groups (team ranking and individual ranking within team) worked longer hours and generated significantly higher revenue compared to the control group.
• Drivers receiving team rankings showed sustained higher engagement levels three months after the experiment concluded.
• Machine learning identified social comparison, driver experience, and within-team similarity as key predictors of virtual team effectiveness.

Conclusions:

• Implementing virtual teams and team-based performance feedback can significantly improve productivity and retention for gig economy workers.
• Social comparison and team dynamics play a crucial role in the efficacy of virtual teams in the gig economy.
• Findings suggest a scalable model for enhancing worker engagement and performance in platform-based work environments.