Service Quality on Online Platforms: Empirical Evidence About Driving Quality at Uber

Online marketplaces have adopted new quality control mechanisms that can accommodate a flexible pool of providers. In the context of ride-hailing, we measure the effectiveness of these mechanisms, which include ratings, incentives, and behavioral nudges. Using telemetry data as an objective measure of quality, we find that drivers not only respond to user preferences but also improve their behavior after receiving warnings about their low ratings. Furthermore, we use data from a randomized experiment to show that informing drivers about their past behavior improves quality, especially for low-performing drivers. Lastly, we find that UberX drivers exhibit behavior comparable to that of UberTaxi drivers, suggesting that Uber’s new quality control mechanisms successfully maintain a high level of service quality.

This paper was accepted by Anindya Ghose, information systems.

Funding: The authors are grateful for funding from the Sloan Foundation and the Stanford Cyber Initiative. The conclusions of this paper are those of the authors and do not represent the views of any corporation or institution.

Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2023.01043.