Anmerkungen:
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments November 29, 2022 erstellt
Beschreibung:
Ride-sourcing companies have been widely using threshold-based incentive programs to encourage drivers to extend their working hours. In such programs, a driver receives a certain amount of monetary reward if he/she completes a given supply task within a predetermined time window. However, despite the popularity of these incentives, little is known about how drivers respond to them in practice, and currently, there is no means to evaluate and optimize their designs systematically. To fill the void, we develop a dynamic discrete choice model that formulates ride-sourcing drivers' working decisions influenced by threshold-based incentives and then calibrate it using real-world data from a ride-sourcing company. Our results provide fresh insights into the market and welfare effects of the threshold-based incentive and its various designs. It is found that the threshold-based incentive could increase welfare significantly for full-time drivers but marginally for part-time drivers. In contrast, involving part-time drivers in the incentive programs generally can yield higher profits for the platform, while incentivizing full-time drivers is mostly unprofitable. On incentive design, the incentive threshold and reward must be closely matched for different driver groups to avoid inferior consequences for the platform and drivers. For certain full-time drivers, switching from threshold-based incentives to direct wage increments may benefit both the drivers and the ride-sourcing company