How unbecoming of you: gender biases in perceptions of ridesharing performance

The advent of the Internet and the digitization of commerce have provided both a mechanism by which goods and services can be exchanged, as well as an efficient way for consumers to voice their opinions about retailers, i.e. online rating systems. Yet, recent work has begun to uncover significant biases that manifest during the review process. In particular, it has suggested that the gig- economy’s elimination of arm’s-length transactions may further introduce bias into perceptions of quality. In this work, we build upon research that has identified biases based on ascriptive characteristics in rating systems, and examine gender biases in ridesharing platforms. In doing so, we extend extant research to consider not simply willingness to transact, but post transaction perceptions of quality. Further, we examine which types of tasks may yield more biased ratings for female drivers. We find no differences in ratings across gender in the presence of a high quality experience. However, when there is a lower quality experience, markedly worse ratings accrue for females. These penalties are exacerbated when female drivers are performing tasks which are perceived to be highly gendered.

Greenwood, B., Adjerid, I. and Angst, C. (2018), 'How unbecoming of you: gender biases in perceptions of ridesharing performance', Academy of Management Proceedings, 1.


  • Research publication
  • Other
  • Yes
  • transport
  • client characteristics, rating, worker demographics
  • English
  • Academy of Management Proceedings (Publisher)
  • Qualitative research
  • 2018
  • Subscription
Disclaimer  —  Eurofound aims to keep the information in this database up to date and accurate. If errors are brought to our attention, we will try to correct them. However, Eurofound bears no responsibility or liability whatsoever with regard to the accuracy or content of this database or of external links over which Eurofound services have no control and for which Eurofound assumes no responsibility.