How to govern the big data discriminatory pricing behavior in the platform service supply chain?An examination with a three-party evolutionary game model
Introduction
With the support of technologies such as digitization and artificial intelligence, service platform has developed rapidly as a new business model, and the platform service supply chain has become the mainstream of economic development. In the two-tiered platform service supply chain composed of service platforms and consumers, some platforms use their own big data to discriminate against consumers. This kind of discriminatory pricing is called big data discriminatory pricing (BDDP), which is considered as a scandal that is irresponsible to the consumers. There is a precedent for the use of BDDP in the United States. In September 2000, Amazon used big data technology to set discriminatory pricing for the DVDs of the movie “Titus”. Consumers later discovered that their personal information was used by Amazon to charge some customers an increased price; consequently, Amazon was not viewed as a responsible company, and this perception had a major negative impact on Amazon (Washington Post, n.d.; Rayna et al., 2015). With the development of technology, BDDP is becoming increasingly common. In 2012, it was discovered that the US travel site Orbitz adjusted its prices for customers who used Apple Mac computers, as Orbitz found that these customers spent 30% more on hotel rooms than other customers (Theguardian, 2017). In China, there are also many cases of BDDP. In 2018, many service platforms (e.g. “flying pig”) dynamically increased prices based on the user's search behavior (Sinatech, 2018); Other service platforms, such as the platforms for the Didi event and the Tencent video event in 2018, charge different prices depending on clients, which has aroused strong consumer dissatisfaction (CSDNnews, 2018).
Since the platform has a stronger monopoly than an average company (Alex et al., 2016), in recent years, governments around the world have paid close attention to the service platforms' use of BDDP and have enacted regulations, such as the General Data Protection Regulation (GDPR) adopted by the EU in April 2016, to control its use. In January 2019, the “Electronic Commerce Law of the People's Republic of China” promulgated by the Chinese government also included relevant regulations on the prevention of BDDP. In terms of specific measures, the Ministry of Industry and Information Technology of People's Republic of China (MIIT-CHINA) is regularly rectifying apps that infringe on the rights and interests of users, For example, on November 6, 2019, the MIIT-CHINA announced that it would carry out a two-month special rectification work of apps. (MIIT-CHINA, 2019). Even with the high level of attention focused by consumers and relevant government departments, BDDP has been a recurring phenomenon. In March 2019, the online travel platform Ctrip was once again accused of using BDDP. The ticket price becomes more expensive when the consumer does more online searching (Communemaker, 2019). Governments have introduced relevant policies, but they have not resulted in effective solutions. Many scholars in the Chinese legal field believe that BDDP constitutes price fraud (Zou and Liu, 2018) and hope that the government can in time intervene. It is a common method for the government to directly use penalties to enforce supervision. With the development of the market economy, for supervising the industry, increasingly more scholars have advocated the use of other methods, such as tax rates that have been used to control pollution (Pizer et al., 2019). A study has shown that low platform taxes make the platform set low online prices (Ward and Sipior, 2011) rather than high prices based on the consumers' willingness-to-pay and the low taxation policy may directly reduce the probability of service platforms to use BDDP. However, the taxation system in many countries is still controversial (Onu and Oats, 2016). Currently, there are no countries that impose a high penalty on service platforms that use BDDP; Consequently, the service platforms continue to engage in BDDP behavior. As mentioned above, it is worthwhile to study what kind of strategy the government should choose under different conditions to effectively prevent platforms from using BDDP, and it is worthwhile to determine whether there is a reasonable taxation system that can enable the government to achieve this goal without practical supervision.
Theoretically, there is a major gap in the theoretical research on the prevention of BDDP in online platforms. On the one hand, there are many “private data” and “discrimination pricing” studies, which mostly focus on how companies make consumers more willing to disclose their private information in order to achieve mutually beneficial price discrimination (Rayna et al., 2015) or mostly consider the issue of price discrimination supervision and personal data privacy protection from a theoretical perspective (Richard, 2017). These studies do not consider the recurrence of BDDP or put forward a specific supervision strategy. On the other hand, service platform risk aversion has not been considered in research. The use of new technologies, such as big data technology for price discrimination, represents a risk behavior of the platform because the platform may face collective opposition from consumers and even strong penalties from government departments. Such concerns will promote the platform risk aversion behavior. For example, in 2000, Amazon's discriminatory pricing experiment ended in complete failure. This incident has made Amazon more cautious about using private data for pricing; engaging in strong risk aversion behavior, the company has promised not to carry out price discrimination in the future (Washington Post, n.d.). Therefore, in the context of the risk aversion behavior of service platforms, there is an urgent need for a special study on the governance strategy of BDDP.
This study will make up for this gap and address the following three issues. (1) In the case of service platform risk neutrality, is there a game equilibrium point where the service platform does not carry out BDDP, and what kind of decision should the government make? (2) What is the impact of service platform risk aversion factor on game equilibrium? (3) How can a model be used to explain the phenomenon in which the service platform recurrently carries out BDDP?
In this study, due to the recurrence of service platform's BDDP behavior, the game between subjects needs to consider the time factor. This study will use time-related evolutionary game theory, which can effectively describe the game learning mechanism and strategy evolution of game subjects, build a three-party evolutionary game model, and explore the decision-making behavior of the government, the platform and consumers in the case in which a platform uses BDDP. At the same time, considering that decision-making will be influenced by behavioral factors such as the risk attitudes of decision makers (Liu and Wang, 2015), this study considers the risk aversion of the service platform and builds a corresponding expansion model. this study answers the above questions and gains many new conclusions.
First, when the service platform is risk-neutral, there are two ideal game equilibrium points, namely, the government non-supervision and the government supervision situation, where the service platform does not carry out BDDP. To prevent the service platform from using BDDP, under the non-supervision situation, the government will set a high tax rate; under the supervision situation, the tax rate will remain unchanged and the government will set a high penalty.
Second, the risk aversion factor does not affect the number of ideal game equilibrium points but changes the conditions for reaching equilibrium in each game. this study finds that when the government does not supervise and the benefit generated by the service platform's extra revenue from BDDP is small or extremely small compared with the risk aversion factor, using a low tax rate is as effective as using a high tax rate; when the government chooses to supervise, a high penalty should be set, and the penalty becomes smaller as the platform risk aversion factor becomes larger.
Third, under the condition in which the government has no incentive for long-term supervision, the service platform has an incentive to use BDDP and the consumers do not give the service platform a bad evaluation, there are cases where the service platform does not carry out BDDP. This situation stems from the deterrence of the penalty, but as the deterrence of the penalty cannot be maintained for a long time, there will be recurrent rather than continuous use of BDDP by the service platform.
The rest of this study is organized according to the following structure. Section 2 discusses related literature, Section 3 gives the problem description and assumptions, Section 4 builds the basic evolutionary game model (Model 1), and Section 5 builds the extended model based on the risk aversion theory (Model 2). Section 6 provides a numerical simulation, and conclusions and management implications are given in Section 7.
Section snippets
Literature review
This study designs the governance mechanism for the service platform's BDDP, builds the three-party evolutionary game model, and considers the risk aversion of the service platform. The studies related to this study include four aspects: studies on platform service supply chain, studies on big data discriminatory pricing, studies on evolutionary game models and studies on risk aversion.
Description of the problem
This study studies the design of the supervision mechanism for preventing the service platforms from carrying out BDDP in the platform service supply chain, and considers a two-tiered platform service supply chain composed of a service platform and consumers, and the government will supervise this service supply chain. Two main aspects need to be considered: the recurrence of BDDP and the impact of risk aversion on the service platform. To solve this problem, this study will use the
Model building
This section considers the risk-neutral situation of the platform. At this time, the risk aversion factor of the platform is 0, that is the consideration of the risk aversion of the platform is not necessary. A basic three-party evolutionary game model (Model 1) consisting of a government, a service platform and consumers is built. The government's revenue is mainly derived from the taxation of the platform. When customers do not give a bad evaluation of the platform, there is no consumer loss.
Three-party evolutionary game model based on the risk aversion of service platforms (model 2)
Carrying out BDDP is a risk decision for the service platform. In 2000, Amazon's discriminatory pricing experiment ended in complete failure. This incident made Amazon more cautious in using private data for pricing. In this section, we will consider the risk aversion of the platform and solve the model.
Although there are many methods for risk measurement, in the literature of the supply chain, the mean variance model is more commonly used (Wei and Choi, 2010; Chiu et al., 2015; Cui et al., 2016
Numerical simulation
This section will use a numerical simulation to characterize the evolutionary path and analyze the sensitivity of the models. To intuitively observe the dynamic evolution of the choices of the three subjects, the MATLAB system simulation tool is used to simulate the dynamic evolution trajectory from the initial state to the equilibrium state. According to the report of iiMedia Research (a Chinese Data Analysis Agency), this study sets the consumer loss rate as 10% (Iimedia, 2018). Referring to
Main conclusions
This work studies the design of the supervision mechanism for preventing the service platforms from carrying out BDDP in the platform service supply chain, and considers a two-tiered platform service supply chain composed of a service platform and consumers, and the government will supervise this service supply chain. Two key points need to be considered: the recurrence of BDDP and the impact of risk aversion on the service platform. This study uses the evolutionary game theory to build the
Declaration of competing interest
The authors declare that there is no conflict of interests regarding the publication of this article.
Acknowledgement
This research is supported by Major Program of the National Social Science Foundation of China (Grant No. 18ZDA060). The reviewers' comments are also highly appreciated.
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