Spatial Information Sharing on On-Demand Service Platforms

We investigate how an on-demand service platform’s mechanism to share demand-supply mismatch information spatially affects drivers’ relocation decisions and the platform’s matching efficiency. We consider three mechanisms motivated by practice; the platform shares demand-supply mismatch information about either zones(s) with excess demand with all drivers (surge information sharing, common practice today), all zones with all drivers (full information sharing), or zone(s) with excess demand only with drivers sufficiently close by (local information sharing). We develop a game-theoretic model with three zones wherein drivers in two non-surge zones decide whether to relocate to the surge zone with excess demand. We incorporate two spatial aspects: drivers’ relocation costs and initial supply across different non-surge zones. Theoretically, full information sharing can hurt the platform’s matching efficiency compared with surge information sharing under low relocation costs because drivers in non-surge zones facing high demand locally do not chase the surge as much. Local information sharing is strictly dominated by other mechanisms in terms of matching efficiency when the supply of drivers near the surge zone is limited and weakly dominated otherwise by surge information sharing. We test these theory predictions in the laboratory with human participants as drivers in an environment where theoretical matching efficiency is highest with surge and lowest with local information sharing. Experimentally, the platform serves fewer customers than predicted with surge information sharing because drivers relocate too often, compromising efficiency in non-surge zones. In contrast, the platform serves more customers than predicted with full and local information sharing, and these mechanisms perform at least as well in matching efficiency as surge. Therefore, sharing demand-supply mismatch information either fully or in a targeted manner (as in local) can help to alleviate coordination problems on a platform. A behavioral equilibrium incorporating loss aversion through mental accounting and decision errors describes drivers’ behavior in our experiments better than the rational equilibrium. This paper was accepted by Elena Katok, operations management. Funding: This research was funded in part by the National Science Foundation [Award #2049872]. The authors also gratefully acknowledge partial support from the Business Analytics Center at Georgia Tech Scheller College of Business. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.03426 .