Due to the rapid growth of social media and mobile devices, social apps have become deeply integrated into people's lives. Extensive adoption of social apps entails the collection of massive amounts of users' private information, causing serious privacy issues. To protect their privacy, in practice, social app users are quite likely to disclose false information (i.e., to engage in misrepresentation behavior). Although misrepresentation behavior both influences the regular involvement of fellow users and reduces the efficiency of the marketing strategies designed by the social app operator, this phenomenon has received limited attention from academics. Accordingly, we use privacy calculus theory to investigate user misrepresentation behavior in the context of social apps. Our empirical analysis shows that both privacy concerns and social rewards motivate users to engage in misrepresentation behavior, while personalized benefits discourage users from doing so. We verify the interdependence of privacy concerns and perceived benefits by confirming that users with a high degree of privacy concerns tend to discount the perceived benefits of information disclosure. Moreover, we find that, as antecedents, perceived privacy control and app permission sensitivity influence privacy concerns significantly, while disposition to value privacy and perceived effectiveness of privacy policies have nonsignificant effects on privacy concerns. We also conduct various mediation analyses to extend our basic model. This study enriches the literature on privacy calculus theory and information disclosure behavior. Additionally, our findings shed light on the strategies that the social app operator can adopt to incentivize users to refrain from disclosing false information.

由于社交媒体和移动设备的快速发展，社交应用已经深深融入人们的生活。社交应用程序的广泛采用需要收集大量用户的隐私信息，从而导致严重的隐私问题。为了保护他们的隐私，在实践中，社交应用程序用户很可能会披露虚假信息（即从事虚假陈述行为）。尽管虚假陈述行为既影响了其他用户的定期参与，又降低了社交应用运营商设计的营销策略的效率，但这种现象并未受到学术界的关注。因此，我们使用隐私演算理论来调查社交应用程序背景下的用户虚假陈述行为。我们的实证分析表明，隐私问题和社会奖励都会促使用户进行虚假陈述行为，而个性化利益会阻止用户这样做。我们通过确认具有高度隐私关注的用户倾向于低估信息披露的感知利益来验证隐私关注和感知利益的相互依赖性。此外，我们发现，作为前因，感知隐私控制和应用程序权限敏感性显着影响隐私问题，而重视隐私的倾向和隐私政策的感知有效性对隐私问题没有显着影响。我们还进行各种中介分析以扩展我们的基本模型。本研究丰富了隐私演算理论和信息披露行为方面的文献。此外，