You drive like a girl: Study shows gender bias in perceptions of ride-sharing performance
Gender discrimination continues to plague organizations, and "gig economy" businesses, which have thrived over the last decade, are not immune, according to new research from the University of Notre Dame.
Gig economy businesses, including Uber and Airbnb, offer temporary positions to independent workers while relying on consumer ratings and reviews as part of their advertising and marketing strategies.
But the system has its flaws. While digital brokerages provide a more efficient method for the exchange of goods and services and an improved way for consumers to voice their opinions about the quality of work they receive, bias and discrimination can emerge as part of the review process, according to "How unbecoming of you: Online experiments uncovering gender biases in perceptions of ridesharing performance," forthcoming in the Journal of Business Ethics from Nathan Meikle, postdoctoral research and teaching associate, and Corey Angst, professor of information technology, analytics and operations at Notre Dame's Mendoza College of Business.
For the study, the team created an ostensibly new ride-sharing service called Agile Rides with a publicly available mock website to reinforce its legitimacy. The team asked 919 participants from the crowdsourcing website Amazon MTurk to help them understand what makes a good rider experience and to imagine going through a detailed experience based on a recent customer experience with a driver. The vignette varied by gender and whether the rider had a good or bad experience. Participants then rated driver performance.
"In the online experiment, we examined participants' perceptions of the drivers," said Meikle, who specializes in social perception and its implications for organizations. "When driver performance was high-quality, participants rated female and male drivers equally. However, when driver performance was low-quality, participants rated female drivers significantly lower than male drivers."
In the gig economy, the traditional manager-subordinate relationship is absent and drivers receive their "performance evaluations" from customers.
The team points out that because digital platforms represent new, rapidly growing work environments capable of subjecting workers to bias and discrimination from a wide number of evaluators, companies should -- for moral, strategic and legal reasons -- consider algorithmic weightings based on gender to combat discrimination.
"If customers discriminate against female drivers, the female drivers may be dismissed from the platform, or at least punished financially, for performing equal quality work as men," Meikle said. "When employees' compensation and job security depend on the ratings of customers rather than on the ratings of managers, companies should examine whether customers are rating employees fairly and adjust the ratings accordingly."
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Co-authors of the study include Brad Greenwood from George Mason University and Idris Adjerid from Virginia Tech.
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