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

Abstract

Online marketplaces have adopted new mechanisms for quality control that can accommodate a flexible pool of providers, with unclear effects on overall service quality. We focus on ride-hailing: pre-screening, which prevailed in taxi markets, has been diminished in favor of automated quality measurement, incentives, and nudges. Using telemetry data, an objective measure of quality, we show that UberX drivers perform better than UberTaxi drivers in multiple dimensions, including according to a score of quality that reflects the preferences of UberX riders. We then explore a variety of mechanisms that affect driver behavior, establishing that UberX drivers respond to user preferences, nudges, and information about driving quality. We use data from a randomized experiment to show that informing drivers about their past behavior improves quality, especially for low-performing drivers.

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

JEL Classification: L91, D83, O33