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How Unbecoming of You: Gender Biases in Perceptions of Ridesharing Performance

RSM MSc BIM CCDC – Group 11

This blog post aims to provide a review of the research paper “How Unbecoming of You: Gender Biases in Perceptions of Ridesharing Performance” published by the researchers Greenwood, Adjerid, and Angst in 2018. The post concludes with a business case of Uber, which relates closely to the paper’s topic of the perceived gender biases in ridesharing performance.

Paper Review

The main objective of this research is to unravel significant biases that exist when consumers place a review online. More specifically, the researchers decided to focus on the gender biases that might take place on ridesharing platforms. While aspects of the rating process have a role to play, the characteristics of the rater and ratee have been found to have an effect on the willingness to transact. That is, the ex-ante evaluation of quality, meaning how an individual assesses a product or service before actually consuming it. These ex-ante quality perceptions were examined against the post transaction perceptions of quality. A few papers have previously addressed gender as a possible factor that can affect service quality evaluation. However, this paper delivers novel and valuable insights by taking into consideration gender as a plausible factor that could affect user post-consumption evaluation.

The researchers measured the perception of quality both before and after the service and developed three hypotheses to test, namely;

  • (H1) “Female gender status will correlate with lower ex ante perceived quality of service, as compared with men, all else equal.”
  • (H2) “Female drivers will be penalized to a greater degree, as compared with male drivers, for performance shortfalls, all else equal.”
  • (H3) “Female drivers will be penalized to a greater degree, as compared with male drivers, for performance shortfalls when performing highly gendered tasks, all else equal.”

To test the hypotheses, the paper uses an experiment with a 2 (gender) x 2 (race) x 2 (Historical Quality) x 2 (Experience Quality), between-subjects research design. The researchers informed the participants that they represented a new ride sharing service, called Agile Rides. Agile Rides was in the process of being launched and participants’ assistance was required to understand what makes a good rider experience, bringing the experiment closer to a real world setting.

One of the main findings of the paper was that with historic quality being available, gender bias does not penalize women drivers before the service is rendered in a ridesharing context (H1). However, if the service provided by a woman is of a lower quality, worse ratings accrue for females relative to males with the similar performance (H2). Furthermore, when the tasks were considered highly gendered (either feminine or masculine), these penalties were intensified when performed by female drivers than by male drivers with the same performance (H3).

Strenghts & Weaknesses

Although there is no question regarding the relevancy of the paper, multiple strengths and weaknesses do exist. First, one strength of the paper is that gender and quality manipulations are extensively tested in the pre-studies. This allows the researchers to make accurate comparisons of perceived quality before and after the experiment has taken place. Second, the paper delivers high practical implications for services that work with online rating systems. These services can now identify which steps they must take in order to limit how gender bias is affecting the perceived quality of services offered.

However, one weakness of the paper is that the researchers did not account for previous ride sharing experiences of participants. These experiences, either positively or negatively, could have influenced their quality perceptions. One suggestion would be that the researchers should inquire about the respondents’ previous ride sharing experiences. By doing so, the researchers could examine and compare the responses of the respondents with positive, negative, or no previous ride sharing experiences, in order to find out if pre-ride sharing experiences could yield different results. Second, participants were asked to imagine a hypothetical situation, which creates the risk that riders’ behaviour in real life could differ from what they have indicated. According to Ajzen et al. (2004), bias in hypothetical situations exist because individuals imagine that they will act according to social norms and expectations, which is not always the case in real life. The results in a real world setting could therefore differ from the results found in the research. A possible solution to this problem is to implement Virtual Reality (VR) when measuring the quality perceptions of the participants. Instead of only relying on text to imagine a situation, participants can now also experience it with visuals. Situations closer to real life settings can be created and bias can be reduced.

Business Case Uber

An example of a company that outsources driver ratings and has experienced gender bias in their evaluation system is Uber.

Generally, after a ride, a passenger is asked, through an Uber app, to rate the driver anonymously using “1- to 5-star scale” (Rosenblat et al., 2016, p. 3). Leveraging anonymous consumer-sourced ratings, Uber outsources driver evaluation to consumers. Nevertheless, as consumers enter their ratings into the system, algorithms also record the consumers’ implicit biases. A case study on Uber reveals that driver ratings are highly likely to be biased by factors such as race, ethnicity and gender (Rosenblat et al., 2016). In the world, there are laws that protects consumers from direct discrimination such as The Equality Act 2010. However, there is currently no law that handles indirect bias like those generated from consumer-sourced ratings. As such, the authors of the Uber case study propose following ten interventions to limit bias in the consumer-sourced ratings (Rosenblat et al., 2016).

  • First, it is important to track consumer-sourced ratings which enables identification of potential driver bias patterns.
  • Second, it is equally important to disclose the identified patterns to the public in order to propel solutions within Uber. 
  • Third, ratings should be validated in conjunction with behavioural data. For example, if a driver receives a low rating, the speed with which the driver drove should also be assessed in order to justify the low performance.
  • Fourth, each rating should have a different weight to account for potential biased raters, which are found upon statistics. 
  • Fifth, Uber should increase feedback criteria for consumers who provide low ratings, for example elaboration on certain dimensions they were dissatisfied with. 
  • Sixth way to eliminate consumer-sourced biases is to keep the consumer-sourced ratings only for internal uses rather than driver evaluation. 
  • Seventh, Uber can also increase in-person assessments of low-rated drivers. 
  • Eight suggestion is about opening the platform fully to both drivers and consumers. With an open policy, both parties would be able to join the platform, get to know each other and receive the option to select or approve upon each other requests.

The last two interventions prompt to alter the legal aspect of ride-sharing platforms: 

  • Ninth plausible solution could be to turn self-employed drivers into law-protected employees.  
  • Finally, the authors suggest legal bodies to “lower the pleading requirements for claims” that are brought against ride-sharing platforms (Rosenblat et al., 2016, p. 16).

In conclusion, the paper presents novel findings that serve to inform ridesharing platforms, such as Uber, about biases in their evaluation services. Furthermore, this blog post provides ridesharing platforms with ten interventions to limit possible biases in their consumer-sourced ratings.


Ajzen, I., Brown, T. C., and Carvajal, F. 2004. “Explaining the Discrepancy Between Intentions and Actions: The case of hypothetical bias in contingent valuation,” Personality and Social Psychology Bulletin (30:9), pp. 1108-1121.

Greenwood, B. N., Adjerid, I., & Angst, C. M. (2017). How Unbecoming of You: Gender Biases in Perceptions of Ridesharing Performance.

The sharing economy: already saving lives for a decade!

Nowadays, if the sharing economy and its benefits are discussed, most will raise the matter of an increased socialisation and community growth, a greater focus on sustainability, and the improved utilitarian and cost aspects (Habib et al., 2017). However, a beneficial consequence of the sharing economy that is not discussed often enough is the actual social welfare opportunities it offers in many situations. Take as an example Uber, the ride-sharing service industry leader (MacMillan & Demos 2015). The company has been studied various times in the past in order to establish a link between its service and a decrease in motor vehicle accidents, specifically, fatalities. This relationship has recently also been successfully evaluated by Greenwood and Wattal, whose’ paper “Show Me the Way to Go Home: An Empirical Investigation of Ride-Sharing and Alcohol Related Motor Vehicle Fatalities” will be reviewed in this blog post.

What did they intend to analyse?

Overall, the researchers assessed what the exact impact is of ride-sharing services on alcohol related motor vehicle fatalities over time and the mechanics behind this specific impact. As a result, they split their research question into two hypotheses. The first was influenced by the platform theory, which states that consumers are willing to pay a premium for ride-sharing services, since customers are provided with the certainty of getting a car and knowing when this car will arrive, over the cost of searching for a random taxi and not even knowing whether they will find a taxi. Consequently, the first hypothesis was generated:

Implementation of a premium ride-sharing service will be associated with a negative and significant effect on the rate of alcohol related motor vehicle fatalities.

The second hypothesis followed the rational choice theory, where it is believed that if there is a high availability of ride-sharing vehicles, the inebriated individual might still consider their own car as a better option because the premium of the ride-sharing service is too high. In other words, the cost of hiring a driver might be too steep in their minds compared to the potential criminal cost of driving their own cars. Thus, the effect of a discounted service, which simultaneously increases the ease of use of transportation and decreases the gap between the cost of being punished for DUI and the cost of booking a driver, had to be assessed:

Implementation of a discount ride-sharing service will be associated with a negative and significant effect on the rate of alcohol related motor vehicle fatalities.

Which method was applied?

As a representation of the ride-sharing service, Greenwood and Wattal made use of information on Uber. More specifically, they analyzed Uber’s information between 2009 and 2014 in California and chose Uber X and Uber Black as representative services of a discount- (20%-30% under traditional taxi fares) and premium-service (20%-30% over traditional taxi fares), respectively. With regard to the dataset on motor vehicle fatalities, the researchers took advantage of the sources in the California Highway Patrol’s Statewide Integrated Traffic Report System (SWITRS). After combining both the Uber information with the SWITRS sources, a dataset of 12,420 observations spanning between January 2009 and September 2014 over 540 townships in the state of California, was able to be produced.

A difference in difference estimation was employed on the dataset, since it allowed for an imitation of an experimental design while using observational data, which led to the comparison of the number of fatalities changes after the application of the treatment over time.

What was found?

The results confirm that a discount ride-sharing service (e.g. Uber X) has a significant negative effect on the rate of alcohol related driving fatalities, while premium services (e.g. Uber Black) do not. Consequently, it can be determined that the cause for the decrease in DUI deaths lies in the combination of cost, availability, and ease of use, since consumers are not willing to pay a price premium. Moreover, it was found that the effect was significantly enhanced in larger cities and did not affect the overall fatalities rate. The latter finding disconfirms the common belief that by having introduced Uber and, therefore, more cars on the road, an increase in fatal accidents might have been caused.

These findings were also quantified, in order to increase the paper’s managerial relevance. It was determined that with only Uber X there was already a 3.6% to 5.6% decrease in alcohol related motor vehicle deaths per quarter in California. These percentages represent around 500 saved lives on an annual basis, which creates an additional public welfare of over $1.3 billion for Americans. Consequently, the paper truly confirms the social benefits ride-sharing services and the sharing economy in general generate.


These findings have implications for various professionals. First of all, it has direct implications for regulators and policy makers, who are currently assessing the legality of ride-sharing services. These results provide the necessary evidence of the sharing economy’s nontrivial effect, such as decreased mortality. This effect also impacts the second group of professionals, namely venture capitalists who can more easily be convinced by the idea of this social benefit and how it can be marketed. Finally, a specific group of professionals who gain a new strategic insight due to this paper’s specific results are restaurateurs, event planners, and nightlife managers. These professionals can use ride-sharing partnerships in order to promote themselves as safe environments after their clients leave their local’s inebriated. Furthermore, it is considered by many to be a sign of prestige to have a ride-sharing partnership, since it comes close to the traditional idea of a chauffeured service.


The paper poses a considerable strength when compared to past studies that analysed the link between ride-sharing services and their negative effect on DUI scenarios. Most of these studies were considered as invalid sources of information because they either involved the ride-sharing companies in the data analysis, their studies’ methodological rigor was invalid, or did not solve the presence of cofounding factors. In contrast, Greenwood and Wattal were able to solve these different issues in their paper. A great example of how they went above and beyond in order to validate their methodological rigor was through their several robustness checks, such as count models (e.g. OLS and QMLE), introduction of information of other ride-sharing providers, a coarsened exact match, a different data generation process, and a diagnosis of standard errors. And as if this would not be enough, the researchers covered most of their cofounding factors by performing a empirical extensions section that covered topics from the effect of local populations to a comparison of the alcohol related motor vehicle fatalities to the non-alcoholic ones.


A considerable weakness of the paper that was observed is its inability to account for random external causes for the accidents. While the reports of the SWITRS base cover what the cause of the accident was and what type of weather took place during the time of the accident, a lot of other factors were not able to be accounted for during the paper’s analysis. Furthermore, all analyses were not performed in a randomised manner, which decreased measurement validity and measurement reliability considerably.


Greenwood, B. N. and Wattal, S. (2017) “Show Me the Way to Go Home: An Empirical Investigation of Ride-Sharing and Alcohol Related Motor Vehicle Fatalities”, MIS Quarterly, 41(1), pp. 163–189.

Habibi, M.R., Davidson, A. and Laroche, M., 2017. What managers should know about the sharing economy. Business Horizons, 60(1), 113-121. Links to an external site.

MacMillan, D. and Demos, T., 2015. “Uber Eyes $50 Billion Valuation in New Funding,” The Wall Street Journal, May 9.

The Insurance Industry Is Taking Advantage of the Sharing Economy

The so-called ‘sharing economy’ has benefited numerous consumers through the value it has added to their lives. Companies such as Uber, Airbnb and Lyft, to name just a few, have taken advantage of the digital technologies humans have developed over the years. However, consumers are not the only benefactors of the sharing economy, the insurance industry has developed products and services specifically catered to its unique characteristics, most notably in the ride-sharing sector, where insurance providers have taken advantage of liability concerns occurring in such ‘sharing’ activities (Traum, Vol. 14:511).

One of the first products developed, the “Metronome”, came from a collaboration between Uber and MetroMile. The device tracks the vehicle of a Transport Network Company (TNC) driver, and is embedded in the Uber application (Traum, Vol. 14:511). It only turns on and activates the required insurance plan when drivers are engaged in TNC services. When the driver is not carrying a passenger, or hasn’t accepted a ride, any liabilities arising from an accident are covered by his own insurance. This product considers both the professional and personal roles of Uber drivers. In a similar fashion, a new plan from Farmers Insurance, on offer since May 2015, supplements a TNC driver’s personal plan with a premium of eight percent (Traum, Vol. 14:511). Many insurances providers have begun to offer similar services to the ride-sharing industry.

Furthermore, the use of such digital technologies has expanded to mainstream customers’ insurance plans. Some companies have developed a chip to be installed on the vehicle during production. Similarly to the Metronome, this device tracks if a vehicle is in use and offers full coverage, to the extent of the customer’s plan, in the case of an incident. However, when the vehicle is parked and the engine is off, the insurance company provides a more limited plan. This enables insurance firms to offer their customer with a more suited, and personalised service.

In the case of Airbnb and other home-sharing services, the lack of legislative development with regards to the coverages of issues common to such activities (Traum, Vol. 14:511). However, insurance providers are aware of the risks that may arise but have yet to adapt and respond to liability issues specific to the home-sharing industry. Together with national governments and sharing economy companies, insurance providers have to strive towards addressing consumer needs; such as protection issues. Furthermore, innovations in this industry can be translated to insurance plans for the mainstream customer, taking the advantage of newly available digital technologies.

Traum, Vol. 14:511. Sharing Risk in the Sharing Economy: Insurance Regulation in the Age of Uber. Cardozo Pub. Law, Policy & Ethics J.