How Unbecoming of You: Gender Biases in Perceptions of Ridesharing Performance
Open accessAbstract
It has been suggested that the gig-economy’s elimination of traditional arm’s-length transactions may 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 research to consider not simply willingness to transact, but post transaction perceptions of quality. We also 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, penalties for women accrue faster, notably when poorly performed tasks are perceived to be highly gendered.
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Customer satisfaction ratings are questioned for their volatility, reliability, and validity.
“The strong impact of these ratings, largely questioned about their volatility, the reliability and validity (Gerber & Krzywdzinski, 2019;Greenwood, Adjerid, & Angst, 2019;Luca & Zervas, 2016;Orlikowski & Scott, 2013;Rosenblat, Levy, Barocas, & Hwang, 2017), has been associated with workers' perception of job insecurity (Curchod et al., 2019;Veen et al., 2019).”
From Algorithms as work designers: How algorithmic management influences the design of jobs (2022) · cites this paper as Greenwood, Adjerid, & Angst, 2019
The Related Literature section (page unavailable) discusses selection and exaggeration biases in online ratings, and the Results section finds that female drivers can receive a larger rating penalty than male drivers for a low-quality experience. These findings support questioning whether ratings accurately reflect performance, but the paper does not establish that customer satisfaction ratings are volatile or generally unreliable. (LLM, conf=0.84)
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Algorithms as work designers: How algorithmic management influences the design of jobs
Parent-Rocheleau, X., & Parker, S. K. (2022) · Human Resource Management Review · doi:10.1016/j.hrmr.2021.100838 · Cited by 512 · see citing works
- Authors
- Greenwood, B., Adjerid, I., & Angst, C.
- Affiliations
- University of Notre Dame (US)
- Venue
- Proceedings of the Annual Hawaii International Conference on System Sciences
- Published
- 2019
- ISSN
- 1530-1605 (ISSN-L)
- 2572-6862
- OpenAlex
- W2783642693
- Cited by
- 8
- References
- 0
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 1 · 2022: 1 · 2021: 1 · 2020: 2 · 2019: 1
- FWCI
- 3.56
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Hawaii International Conference on System Sciences (HICSS) (publishedVersion, cc-by-nc-nd)
- Journal of the Association for Information Systems (submittedVersion)
- ScholarSpace (University of Hawaii at Manoa) (submittedVersion, cc-by-nc-nd)
- Topics
- Social Sciences › Sociology and Political Science › Digital Economy and Work Transformation
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Sociology and Political Science › Names, Identity, and Discrimination Research
- Keywords
- gender bias, ride-sharing, gig economy, driver performance evaluation, perceived service quality, women drivers
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