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Discrimination on online platforms: a call for regulation

March 15, 2019 417754 Leave a comment

Context

In the housing and rental market, anti-discrimination laws in the US gradually reduced discrimination through the legal system for the past two decades. However, academic scholars (Edelman, Luca & Svirsky, 2017) argue that the emergence of online platforms facilitate discrimination, as these laws do not reach smaller property owners using online platforms. Airbnb, the world largest online platform for short-term rental and housing, adopts a design choice that enables discrimination on its platform. Hosts decide whether or not to accept a guest after seeing the name and profile picture of the guest.

Methodology and experiment

In order to test whether Airbnb facilitated discrimination through its design choice, the authors (Edelman, Luca & Svirsky, 2017) conducted a field experiment across five different cities, including: Baltimore, Dallas, Los Angeles, St. Louis and Washing DC between 7 July 2015 and 30 July 2015 (see Figure 1). Originally, the experiment aimed to gather data from the top 20 cities in the US, but the experiment was halted due to Airbnb’s systems detected and blocked the automated tools used to gather the data.

The experiment gathered a wide range of information about hosts and their listings (see Figure 2). Information of hosts include but are not limited to profile image, gender, age, number of properties listed and previous guests that visited the host. Information on listings include price, number of rooms, cancellation policy, cleaning fee, rating and whether the room was shared or not to control for interaction between the guest and the host.

After gathering data, the experiment sent 6,400 messages with 20 Airbnb accounts. Hosts who offered multiple listings on the platform were contacted for one of their listings to prevent the host from receiving identical e-mails and to reduce the imposed burden. The accounts used to send messages are identical except for two variables: i) race and ii) gender. Race and gender were indirectly embedded in the profiles through the use of names based on Bertrand and Mullainathan (2004). Additionally, to alleviate confounds that would arise from using profile pictures, accounts did not include a profile picture. Finally, the experiment tracked the response over 30 days after the message was sent.

Results

The authors found that guests with distinctive White American sounding names were accepted ±50 of the time, while guests with African American sounding names were accepted at ±42 of the time. The ±8% gap persists across characteristics of the hosts and listings as control variables. More important, the results infer that the discrimination effect occurs in differences of a simple “Yes” or “No” response and not because of intermediate response and non-response. The authors further found that the discrimination effect disappears when hosts previously accepted African American guests. Control variables including homophily concerning race, age categories, price of the listing and demographics of the vicinity are however of no significant influence on the discrimination effect. Discrimination further cause financial consequences, as host incur costs when rejecting guests causes a unit to remain unrented.

Discussion: strengths and weaknesses

This paper provides clear evidence of the presence of discrimination in online platforms. The relevance of this paper is also strengthened by the way it emphasizes discrimination in the online channels, while in the past the focus was primarily on discrimination in offline channels. The results are consistent with other studies on discrimination in the online rental and housing market. Ge, Knittel, MacKenzie and Zoepf (2016) found a similar pattern of discrimination in peer transportation companies such as Uber and Lyft; African American passengers face longer waiting times and more frequent cancellations compared to their White-American counterparts.

The research also has a few flaws. First, the research is not able to detect the type of discrimination that occurs (e.g. statistical discrimination and taste-based discrimination) and whether discrimination is based on socioeconomic status or race that is associated with the name. Second, the paper suggests that the discrimination effect occurs when users of these platforms gain the choice to accept or to reject guests and passengers, which suggests that the problem lies in the platform’s design choice. The suggestions to alleviate discrimination by limiting design choice such as removing information of guests and passengers such as concealing names and profile photos or to eliminate the screening procedures by introducing instant book options as the only option, may harm the user experience for both (hosts and guests) sides. For hosts it is desirable if they can maintain control on who they allow to stay at their place, while for guests the platform is attractive if they can choose the place and host of their liking. When choosing to reduce discrimination by lowering the user experience for either party, online platforms run the risk of becoming less attractive than their competitors and jeopardizing their own competitiveness. Ultimately, discrimination will continue to occur on competing platforms that do not change their design in benefit of combatting discrimination and the non-discriminating company will lose its competitive edge and fail. Third, the inferences made by the paper are to a certain extent limited to the US. A recent study found that racial discrimination is more prominent in the US than in Europe (Pitner, 2018). The focus on metropolitan areas also questions whether the same effect will occur in rural areas. On the assumption that metropolitan areas are more globally connected and face higher exposure to other races, one can logically assume that metropolitan areas are more tolerant and discriminate less against other races.

Airbnb adjusted its non-discrimination policy in 2018. Hosts are no longer allowed to request a guest’s photo before accepting a booking agreement (Thinkprogress, 2018). Based on the research (Edelman, Luca and Svirsky, 2017), the adjustment will not help as hosts can still view names prior to the selection procedure. A potential solution is to increase the prevalence of reviews in the selection procedure. Cui, Li and Zhang (2016) found that encouraging credible peer-generated reviews mitigates the discrimination effect of guests with African American-sounding names on Airbnb. However, we argued that the action of one platform may not suffice as a solution to stop discrimination and call for more regulation on online platforms from authorities.

Sources

Bertrand, M. & Mullainathan, S. (2004). “Are Emily and Greg More Employable Than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination.” American Economic review 94 (4): 991–1013.

Cui, R., Li, J., & Zhang, D. (2016). Discrimination with incomplete information in the sharing economy: Evidence from field experiments on Airbnb.

Edelman, B., Luca, M., & Svirsky, D. (2017). Racial discrimination in the sharing economy: Evidence from a field experiment. American Economic Journal: Applied Economics, 9(2), 1-22.

Ge, Y., Knittel, C. R., MacKenzie, D., & Zoepf, S. (2016). Racial and gender discrimination in transportation network companies (No. w22776). National Bureau of Economic Research.

Pitner, B. H. (2018, May 17). Viewpoint: Why racism in US is worse than in Europe. Retrieved March 5, 2019, from https://www.bbc.com/news/world-us-canada-4415809

Thinkprogress. (2018). Airbnb announces booking policy change to head off outcry over persistent racial discrimination. Retrieved fromhttps://thinkprogress.org/airbnb-changes-photo-policy-combat-racial-discrimination-4f71c375553a/

Business Case

Pinduoduo – “Chinese Groupon”: the fastest growing challenger in China’s e-commerce market

February 23, 2019 417754 Leave a comment

Context

The Chinese e-commerce market is dominated by large players such as Alibaba and JD.com who hold nearly 75% of the total market share. Newer players often struggle to enter the market due to intense competition, but a new player called Pinduoduo has managed to claim the third spot in the e-commerce market and is the fastest growing e-commerce app in China (see figure 1 and 2). In a short period of time, Pinduoduo acquired a market share of roughly 5% (Financial Times, 2018) and achieved 100 billion RMB annual merchandise sold in two years after launch (Graziani, 2018). Data from December 2017 indicate that 50% of all users that uninstalled Taobao (owned by Alibaba) moved to Pinduoduo (Dailypanda, 2018), marking Pinduoduo is a potential threat if left unchecked.

Figure 1. Average Monthly Active Users (MAU) from March 2017 until June 2018. Retrieved from: Financial Times (2018).

Figure 2. Largest Chinese e-commerce App by Monthly Active Users (MAU) in January 2018. Retrieved from: Graziani, T. (2018)

Introduction – Pinduoduo and its business model

Pinduoduo was found in September 2015 in Shanghai by former Google engineer Zheng Huang and is a third-party social commerce platform that focusses on connecting manufacturers, suppliers and retailers with end-consumers in the B2C market. The platform earns revenue from collecting commission fees and online marketing services including advertising. Pinduoduo’s platform distinguishes itself from its competitors by providing users the option to conduct “team purchases”. The concept of team purchase is similar to Groupon’s “group buy” (see blogpost of Hsuchiachenjenny (2014) for a brief introduction). Users can invite friends through other social media platforms to create a “shopping team” and order discounted items together in bulk (see figure 3 and 4). Team purchases allow consumers to receive discounts as much as 90% off on products ranging from T-shirts to smartphones. The platform sold more than 4.8 million umbrellas at 10.3 RMB (1.51 USD) per piece and 6.4 million units of tissue paper at 1.29 RMB (0.19 USD) per box (Lee, 2018). Users mainly benefit from Pinduoduo due to getting products at a lower price, while suppliers are enabled to benefit from reducing inventories and generating revenue from aggregation of demand.

Figure 3. Steps to conduct team purchasing at Pinduoduo’s platform. Retrieved from: Fung Business Intelligence (2018)

Figure 4. Pinduoduo’s interface. Source: Fung Business Intelligence (2018)

Pinduoduo’s unique value: user engagement – more than just financial stimuli!

Pinduoduo uses financial stimuli to encourage consumers to help them expand the user base. For instance, convincing one person to install the app and sign in with WeChat will be rewarded with a box of candy and convincing nine people will grant you 1.3 kg of nuts (Graziani, 2018). While financial incentives motivate people to act, academic research (Burtch, Hong, Bapna & Griskevicius, 2018) argue that including social norms are more effective at motivating intensive effort. The social norms refer to “the prevalence of a behavior in a relevant population, such as the number of individuals who already have written reviews” (Burtch, Hong, Bapna & Griskevicius, 2018). Their study pointed out that financial incentives encourage people to write product reviews, while social norms are better to stimulate people to write longer reviews. A combination of financial benefits and social norms are posed to be the best driver of quantity and quality of writing reviews.

Drawing the link with Pinduoduo, we see that the company incorporates the concept of social norms in its business model. Pinduoduo’s application is gamified and includes a public leaderboard that ranks people based on the money they have made out of inviting friends and displays the number of friends they invited. This aspect allows its users to compare themselves with other people and creates a social motivating factor that goes beyond a mere financial stimulus. The appeal of Pinduoduo lies not merely in its low prices but comes from the satisfaction and the pleasure one receives from getting a good deal (Pandadaily, 2018). Therefore, the inclusion of a social motivating factor alleviates its dependence on the constant input of money to incentivize its users. Instigating users to act as brand ambassadors motivated by both financial and more importantly social benefits is a major success factor that allowed Pinduoduo to establish a large user base in a short period of time.

Are Pinduoduo and Groupon the same?

At the time Groupon emerged in 2008, social media and mobile was less entangled with people’s daily lives than in the current situation. Desktop usage, email newsletters, and credit card payments posed limitations on Groupon’s social commerce potential. In 2013, Groupon has dropped its group buy feature and has lost its status as a social commerce platform. The main difference with Groupon’s business model is that Pinduoduo’s business model leverages the social ecosystem in a more effective way. Tencent has been a principal shareholder of Pinduoduo since February 2017 and facilitated the integration of Pinduoduo’s platform with its own social media. Integration with WeChat (an all-inclusive social media app sharing characteristic of Facebook, Twitter and Whatsapp with Paypal functionality) allows fast and real-time communication between users and enables users to make payments with little effort. At this point of time, it is evident that Pinduoduo has surpassed Groupon’s ability to leverage the social ecosystem to establish the consumer base that it has now.

Figure 5. Difference between Pinduoduo and Groupon in user engagement.

The challenges ahead

Viewing Pinduoduo’s success in motivating users to spread the word and persuade their friends in using the app, the company however reported an annual loss of 525 million RMB (77.5 million USD) in 2017 (Fung Business Intelligence, 2018). The current strategy is focused on a push strategy and faces high costs in building brand awareness of the platform. The platform is however plagued with fake products similar to its rival Taobao and JD.com (Lee, 2018). Other questions concern to what extend product suppliers are willing to tap into the platform, as the platform highly focuses on price and pays little attention to brand awareness of its suppliers (Fung Business Intelligence, 2018).

Summary

Pinduoduo raises the traditional e-commerce platform to the next level by incorporating social media. Users may benefit from getting lower prices for products, but in return need to find friends to join them. The platform also encompasses social norms (e.g. public ranking system) for users to expose themselves and improve community building, while simultaneously gamifying the concept and adding a fun aspect. While the ability of its business model is rather successful, there are challenges that it needs to overcome to guarantee the sustainability of its business model. This is however a topic for another discussion.

Sources

Burtch, G., Ghose, A. and Wattal, S. (2013). An empirical examination of the antecedents and consequences of contribution patterns in crowd-funded markets. Information Systems Research, 24(3), pp.499-519.

Financial Times (2018). Ex-Google engineer set for big payday after Pinduoduo IPO. Retrieved from https://www.ft.com/content/05408022-8a2b-11e8-b18d-0181731a0340

Fung Business Intelligence (2018) Group-buying platform – Pinduoduo. Retrieved from https://www.fbicgroup.com/sites/default/files/CNE_Pinduoduo.pdf

Graziani, T. (2018) Pinduoduo: a Close Look at the Fastest Growing E-commerce App in China. Retrieved from https://walkthechat.com/pinduoduo-close-look-fastest-growing-app-china/

Hsuchiachenjenny (2014) Business case Groupon. Retrieved from https://consumervaluecreation.com/2014/05/18/groupon/

Lee, E. (2018) https://techcrunch.com/2018/07/26/the-incredible-rise-of-pinduoduo/

Pandadaily (2018) Alibaba’s Worst Nightmare: Pinduoduo Becoming the No.1 E-commerce App in China. Retrieved from https://pandaily.com/alibabas-worst-nightmare-pinduoduo-becoming-the-no-1-e-commerce-app-in-china/