All posts by justinaaziz

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.

References

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.

Augmented Reality Finally Becoming Reality?


Though it has been under development since the 1950’s, Augmented Reality (AR) is only now becoming practically applicable (PwC, 2016). In the last decade, AR has been in the centre of attention among academics, resulting in numerous studies analysing the (dis)advantages derived from the many possible AR applications. AR offers an enhanced perception to help people to experience the environment in new and enriched ways that will benefit in the fields such as education, health, design, and retail (van Krevelen et al., 2007).

While different definitions of AR still exist, it’s objective is clear; enhancing the user’s perception of and interaction with the real world. The business opportunities of this novel technology seem promising, as people can look at operations from a combined view of digital and physical operations while externalizing the burden of the task (PwC, 2016). Since AR is one of the novel technologies that could support new business activities and generate new business opportunities, it is of great value to investigate AR’s business potential. AR is a technology that could revolutionize the way companies do business and consumers buy products (Azuma, 1997). Specifically, AR has the potential to reshape the world of retail with its’ influence on the shopping experience of consumers. Major benefits of AR in the retail industry are that users can virtually visualize the products they would like to purchase, explore virtual showrooms, and create a more appealing shopping experience (Guven et al., 2009). But how exactly does AR affect the retail user experience? Will it eventually result in higher user satisfaction or will the limitations of the technology outweigh the opportunities?

According to Poushneh et al. (2017), AR-enriched applications empower users to better perform their tasks and appreciate the functionality of the product more. This is because AR-enriched applications are more entertaining and it enables consumers to have endless interaction with virtual information (Poushneh et al., 2017). Not only does AR produce higher user satisfaction, but also higher user willingness to pay (Poushneh et al., 2017). However, these benefits of AR will only become available when the technology application is practical, easy to use, easy to learn, organized, symmetric, attractive, and pleasant, in order for it to provide relevant information to the users (Poushneh et al., 2017). If companies fail to do this, it could actually negatively affect the user experience after all.  Therefore, developing such applications is not feasible for every company. As can be seen from the figure below, only 3 percent of the retailers has already implemented a well-working AR application (Business Insider, 2019). Companies that have already implemented such applications in their business model are therefore considers the first movers, of which IKEA is one.

(Business Insider, 2019)

Business model

IKEA is a Swedish multinational group that designs and sells (ready-to-assemble) furniture kitchen appliances, and other useful home accessories. Launched in 2017, the IKEA Place app helps customers to visualize how over 2.000 furniture items would look like in their homes (IKEA, 2019).

(IKEA, 2019)

Currently available on the app are large furniture such as sofas, armchairs and storage units (IKEA, 2019). Other products are still in development to become available in the app. The main objective for IKEA according to Michael Valdsgaard, leader of digital transformation at IKEA, is to allow shoppers to make more reliable buying decisions. According to Michael, most people postpone a purchase because they are not entirely sure that the colour is going to match or fit the room (IKEA, 2019). With this app, he hopes that this insecurity to purchase gets replaced with entertainment and security. It saves the customer a lot of time and consideration before placing a purchase. Furthermore, IKEA (2019) automatically scales products based on a room’s dimensions and claims that is able to do so with up to 98 percent accuracy (IKEA, 2019). Even allowing customers to see the texture of a fabric and the interplay of lift and shadows on a specific location.

Efficiency & Limitations

As mentioned before, AR-applications must be practical, easy to use, easy to learn, organized, symmetric, attractive, and pleasant, in order for it to provide relevant information to the users (Poushneh et al., 2017). Looking at the IKEA Place app, it seems to fulfil all these criteria. With easy to follow guidelines that help people to use the app while offering 98 percent accuracy, the app is able to provide additional value to the shopping experience of the users.

From a joint profitability perspective, the IKEA Place app offers both additional value to IKEA as well as to its customers. IKEA is able to help the customer in their shopping journey in order to make it more enjoyable. Furthermore, it also helps the customer to make more correct purchasing decisions and thereby reducing the number of products being returned, lowering costs, and increasing customer satisfaction. With IKEA’s large customer base, the company is able to grab a large share of the online home furnishings market, boosting its sales and giving an advantage over its competition (Business Insider, 2019). On the other hand, app users are able to enjoy a 3D-visualization of potential furniture, reducing uncertainty and the time needed to make a purchase decision, since there is less hesitation regarding the colour, size, fabric, and overall fit.

From a feasibility point of view, the company is not facing any legal or political issues. Rather, IKEA’s biggest challenge would be the social aspect. While IKEA claims to offer 98 percent accuracy to the app users, there is a chance that the app might not visualize a piece of furniture correctly to the user. For example, when the user tries out a sofa in his/her living room and the app shows that the product should fit, however, once it arrives it results to be too big. The customer has then wasted time, money and effort while relying on the advice given from the company. In such a case, will the customer bear the costs or will IKEA be held responsible?

(IKEA, 2019)

Another drawback of the IKEA Place app could be linked to consumers’ cognitive limitations discussed during session two (Tsekouras, 2019). When customers are offered many options, they find it difficult to search through all of them. There is a fear of regretting the ‘wrong choice’. This could also be the case with IKEA, since the app allows the customers to compare thousands of products and have them visualize it in their room, making it hard t consider all relevant attributes when choosing between alternatives.

References

Azuma, R. T. (1997). A survey of augmented reality. Presence: Teleoperators & Virtual Environments6(4), 355-385.

Guven, S., Oda, O., Podlaseck, M., Stavropoulos, H., Kolluri, S., & Pingali, G. (2009, March). Social mobile augmented reality for retail. In 2009 IEEE International Conference on Pervasive Computing and Communications (pp. 1-3). IEEE.

IKEA (2019). A better Reality. Retrieved from Https://highlights.ikea.com/2017/ikea-place/.

Poushneh, A., & Vasquez-Parraga, A. Z. (2017). Discernible impact of augmented reality on retail customer’s experience, satisfaction and willingness to buy. Journal of Retailing and Consumer Services34, 229-234.

PwC. (2016). Augmented reality: the road ahead for augmented reality. Retrieved from

Tsekouras, D. (2019). Session 2: Consumers’ Cognitive Limitations. Erasmus University.

Van Krevelen, D., & Poelman, R. (2007). Augmented reality: Technologies, applications, and limitations. Vrije Univ. Amsterdam, Dep. Comput. Sci.