Value of Information Sharing via Ride-Hailing Apps: An Empirical Analysis

A ride-hailing platform is an app-based, two-sided platform that matches riders with vehicles via information technology (IT). In 2015, the Shanghai government introduced a policy to restrict taxi drivers' access to and acceptance of ride requests via ride-hailing apps during certain hours. We conceptualize this policy shock as the restricting of information sharing enabled by IT and collect comprehensive data on various uses of transportation to gauge the economic benefits of this information sharing for existing capacity and its subsequent externalities on other transportation. Through a time series analysis, we identify significant decreases in the ridership of an affected taxi fleet during times of enforcement but significant increases at some times of nonenforcement postlaunch. Furthermore, the traffic on public transportation, via the city's transportation cards, and the congestion on the surface streets and expressways significantly increase after launching the policy during both enforcement and most nonenforcement times. These suggest that information sharing via ride-hailing apps can improve the utilization of existing taxi capacity, which further alleviates traffic during alternative times and the burden placed on alternative transportation modes. Interestingly, our mechanism analysis shows decreased profitability after the restriction, which supports the notion that information sharing via ride-hailing apps reduces drivers' search cost and thus enables them to match not only with more orders but also with those of higher marginal profit. This study contributes to the literature on ride-hailing platforms' impact and the economic value of information sharing and IT by dissecting the compound ride-hailing's impact to extract the value of information sharing enabled by IT and to reveal the underlying mechanism. Practically, we evaluate the policy studied, make postrevision suggestions for general contexts, and provide managerial insights on precise policy making that best extracts the economic value of information sharing in ride-hailing and general forms of online two-sided platforms.