The Chinese Social Credit System (SCS), known as the first national digitally-implemented credit rating system, consists of two parallel arms: a government-run and a commercial one. The government-run arm of the SCS, especially efforts to blacklist and redlist individuals and organizations, has attracted significant attention worldwide. In contrast, the commercial part has been less often in the public spotlight except for discussions about Zhima Credit.
The commercial arm of the SCS, also referred to as the Consumer Credit Reporting System (CCRS), has been under development for about two decades and took a major step forward in 2015 when 8 companies were granted permission to implement pilot consumer credit reporting programs. This development fundamentally increased the reach and impact of the SCS due to these companies’ sizable customer base and access to vast troves of consumer-related information.
In this paper, we first map the Chinese CCRS to understand the actors in the credit reporting ecosystem. Then, we study 13 consumer credit reporting companies to examine how they collect and use personal information. Based on the findings, we discuss the relationship between the CCRS and the SCS including the changes in the power relationships between the government, consumer credit reporting companies and Chinese citizens.
In the context of third-party social apps, the problem of interdependency of privacy refers to users making app adoption decisions which cause the collection and utilization of personal information of users’ friends. In contrast, users’ friends have typically little or no direct influence over these decision-making processes.
We conduct a conjoint analysis study with two treatment conditions which vary the app data collection context (i.e., to which degree the functionality of the app makes it necessary for the app developer to collect friends’ information). Analyzing the data, we are able to quantify the monetary value which app users place on their friends’ and their own personal information in each context. Combining these valuations with the responses to a comprehensive survey, we apply structural equation modeling (SEM) analysis to investigate the roles of privacy concern, its antecedents, as well as app data collection context to work towards a model of interdependent privacy for the scenario of third-party social app adoption.
We find that individuals’ past experiences regarding privacy invasions are negatively associated with their trust for third-party social apps’ proper handling of their personal information, which in turn influences their concerns for their own privacy associated with third-party social apps. In addition, positive effects of users’ privacy knowledge on concerns for their own privacy and concerns for friends’ privacy regarding app adoption are partially supported. These privacy concerns are further found to affect how users value their own and their friends’ personal information. However, we are unable to support an association between users’ online social capital and their concerns for friends’ privacy. Nor do we have enough evidence to show that treatment conditions moderate the association between the concern for friends’ personal information and the value of such information in app adoption contexts.