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.
Access the Research publicationHow unbecoming of you: gender biases in perceptions of ridesharing performance
- Research publication
- client characteristics, rating, worker demographics
- Academy of Management Proceedings (Publisher)
- Qualitative research