Turbocharging the competitor: Unintended spillovers of personnel on-road insurance implementation in ride-sourcing industry

• 成果类型: Article; Early Access
• 署名作者: Pal, Sukrit; Hoang, Thu Trang
• 署名单位: Iowa State University; University of Texas System; University of Texas Arlington
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1177/10591478261441928
• 发表日期: 2026
• 关键词: work; uber

摘要

In response to social protests, transportation network companies introduced incentive programs to improve hourly utilization and retain drivers. While such initiatives assume increased driver commitment, their impact on labor allocation across competing gig economy platforms remains unexplored. This study uses a quasi-experimental design and analyzes 20 weeks of data comprising over 75 million trips to examine spillover effects of an East Asian platform's comprehensive personnel on-road insurance (PI) policy on a competitor platform. The research uncovers significant changes in labor allocation patterns by identifying multihoming and dedicated drivers through license plate matches. A difference-in-difference analysis provides evidence of cross-platform spillovers of PI: multihoming drivers allocated 16.4% more hours to food delivery and reduced ride-hailing hours by 10.69% on the competitor platform, while dedicated drivers reduced food-delivery hours by 34.6% but added 12.81% more ride-hailing hours. These shifts in hours corresponded to changes in earnings. To explore the mechanisms of such spillover effects, the study observes multihoming drivers' labor allocation patterns on the implementing platform: Multihoming drivers shifted toward food delivery, increasing hours by 17.3% and decreasing ride-hailing hours by 11.2% on the platform. The study further links such cross-platform shifts in drivers' labor allocation by estimating job aggregation among multihoming drivers and provides multiple robustness analyses to validate these effects. The study highlights the interconnectedness of gig economy stakeholders by empirically linking policy changes on one platform to cross-platform labor dynamics. Practical insights are provided on how the PI policy influenced platform utilization, driver behavior, and productivity, emphasizing that gig-economy platforms operate within a highly interdependent ecosystem rather than in isolation.