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

Abstract

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.

Keywords: Online Platforms, Ride Hailing, Reputation Systems, Nudges, Driving Quality, Telemetry Data

JEL Classification: L91, D83, O33