Why people need privacy? The role of privacy fatigue in app users' intention to disclose privacy: based on personality traits

A machine learning model to predict privacy fatigued users from social media personalized advertisements

The increasing use of social media platforms as personalized advertising channels is a double-edged sword. A high level of personalization on these platforms increases users' sense of losing control over personal data: This could trigger the privacy fatigue phenomenon manifested in emotional exhaustion and cynicism toward privacy, which leads to a lack of privacy-protective behavior. Machine learning has shown its effectiveness in the early prediction of people's psychological state to avoid such consequences. Therefore, this study aims to classify users with low and medium-to-high levels of privacy fatigue, based on their information privacy awareness and big-five personality traits. A dataset was collected from 538 participants via an online questionnaire. The prediction models were built using the Support Vector Machine, Naïve Bayes, K-Nearest Neighbors, Decision Tree, and Random Forest classifiers, based on the literature. The results showed that awareness and conscientiousness trait have a significant relationship with privacy fatigue. Support Vector Machine and Naïve Bayes classifiers outperformed the other classifiers by attaining a classification accuracy of 78%, F1 of 87%, recall of 100% and 98%, and precision of 78% and 79% respectively, using five-fold cross-validation.

Understanding the adoption context of China's digital currency electronic payment

Central banks worldwide have started researching and developing central bank digital currencies (CBDCs). In the digital economy context, concerns regarding the integrity, competition, and privacy of CBDC systems have also gradually emerged. Against this backdrop, this study aims to evaluate users' willingness to use China's digital currency electronic payment (DCEP) system, a digital payment and processing network, and its influencing factors by comprehensively considering and comparing the characteristics of cash and third-party payment services. Combining the push-pull-mooring framework (PPM) and task-technology fit (TTF) theory, we discuss the scenarios and mechanisms that may inspire users' DCEP adoption intention through an empirical study. The results reveal that privacy concerns regarding the original payment methods and technology-task fitting level of DCEP positively impact users' willingness to adopt DCEP. The technical characteristics of DCEP, users' payment requirements, and government support positively affect users' adoption intention by influencing the task-technology fitting degree of DCEP. Switching cost significantly and negatively impacts adoption intention, whereas relative advantage exhibits no significant effect. This research contributes to a better understanding of the factors that influence switching intentions and the actual use of DCEP, and provides policy guidance on promoting the efficiency and effectiveness of DCEP.

Research on the influence mechanism of privacy invasion experiences with privacy protection intentions in social media contexts: Regulatory focus as the moderator

Introduction

In recent years, there have been numerous online privacy violation incidents caused by the leakage of personal information of social media users, yet there seems to be a tendency for users to burn out when it comes to privacy protection, which leads to more privacy invasions and forms a vicious circle. Few studies have examined the impact of social media users' privacy invasion experiences on their privacy protection intention. Protection motivation theory has often been applied to privacy protection research. However, it has been suggested that the theory could be improved by introducing individual emotional factors, and empirical research in this area is lacking.

Methods

To fill these gaps, the current study constructs a moderated chain mediation model based on protection motivation theory and regulatory focus theory, and introduces privacy fatigue as an emotional variable.

Results and discussion

An analysis of a sample of 4800 from China finds that: (1) Social media users' previous privacy invasion experiences can increase their privacy protection intention. This process is mediated by response costs and privacy fatigue. (2) Privacy fatigue plays a masking effect, i.e., increased privacy invasion experiences and response costs will raise individuals' privacy fatigue, and the feeling of privacy fatigue significantly reduces individuals' willingness to protect their privacy. (3) Promotion-focus individuals are less likely to experience privacy fatigue than those with prevention-focus. In summary, this trend of "lie flat" on social media users' privacy protection is caused by the key factor of "privacy fatigue", and the psychological trait of regulatory focus can be used to interfere with the development of privacy fatigue. This study extends the scope of research on privacy protection and regulatory focus theory, refines the theory of protection motivation, and expands the empirical study of privacy fatigue; the findings also inform the practical governance of social network privacy.

Evolutionary Game—Theoretic Approach for Analyzing User Privacy Disclosure Behavior in Online Health Communities

Privacy disclosure is one of the most common user information behaviors in online health communities. Under the premise of implementing privacy protection strategies in online health communities, promoting user privacy disclosure behavior can result in a “win–win” scenario for users and online health communities. Combining the real situation and evolutionary game theory, in this study, we first constructed an evolutionary game model of privacy disclosure behavior with users and online health communities as the main participants. Then, we solved the replication dynamic equations for both parties and analyzed the evolutionary stable strategies (ESSs) in different scenarios. Finally, we adopted MATLAB for numerical simulations to verify the accuracy of the model. Studies show that: (1) factors such as medical service support and community rewards that users receive after disclosing their private personal information affect user game strategy; and (2) the additional costs of the online health communities implementing the “positive protection” strategy and the expected loss related to the privacy leakage risk affect the online health communities’ game strategy. In this regard, this paper puts forward the following suggestions in order to optimize the benefits of both sets of participants: the explicit benefits of users should be improved, the internal environment of the communities should be optimized, the additional costs of the “positive protection” strategy should be reduced, and penalties for privacy leakages should be increased.