School of Information Management, Zhengzhou University, Zhengzhou, China.
School of Politics and Public Administration, Zhengzhou University, Zhengzhou, China.
Front Public Health. 2023 Jul 19;11:1109093. doi: 10.3389/fpubh.2023.1109093. eCollection 2023.
As a new type of medical service application for doctor-patient interaction, online health communities (OHCs) have alleviated the imbalance between the supply and demand of medical resources in different regions and the problems of "difficult and expensive access to medical care", but also raised the concern of patients about the risk of disclosure of their health privacy information.
In this study, a dual-calculus model was developed to explore users' motivation and decision-making mechanism in disclosing privacy information in OHCs by combining risk calculus and privacy calculus theories.
In OHCs, users' trust in physicians and applications is a prerequisite for their willingness to disclose health information. Meanwhile, during the privacy calculation, users' perceived benefits in OHCs had a positive effect on both trust in doctors and trust in applications, while perceived risks had a negative effect on both trusts in doctors and trust in applications. Furthermore, in the risk calculation, the perceived threat assessment in OHCs had a significant positive effect on perceived risk, while the response assessment had a significant negative effect on perceived risk, and the effect of users' trust in physicians far exceeded the effect of trust in applications. Finally, users' trust in physicians/applications is a mediating effect between perceived benefits/risks and privacy disclosure intentions.
We combine risk calculus and privacy calculus theories to construct a dual-calculus model, which divides trust into trust in physicians and trust in applications, in order to explore the intrinsic motivation and decision-making mechanism of users' participation in privacy disclosure in OHCs. On the one hand, this theoretically compensates for the fact that privacy computing often underestimates perceived risk, complements the research on trust in OHCs, and reveals the influencing factors and decision transmission mechanisms of user privacy disclosure in OHCs. On the other hand, it also provides guidance for developing reasonable privacy policies and health information protection mechanisms for platform developers of OHCs.
在线健康社区（OHC）作为一种医患互动的新型医疗服务应用，缓解了不同地区间医疗资源供需不平衡和“看病难、看病贵”等问题，但也引发了患者对健康隐私信息泄露风险的担忧。
本研究结合风险计算和隐私计算理论，构建双计算模型，探索用户在 OHC 中披露隐私信息的动机和决策机制。
在 OHC 中，用户对医生和应用程序的信任是其披露健康信息意愿的前提。同时，在隐私计算中，用户在 OHC 中的感知收益对医生和应用程序的信任均有正向影响，感知风险对医生和应用程序的信任均有负向影响。此外，在风险计算中，OHC 中的感知威胁评估对感知风险有显著正向影响，而响应评估对感知风险有显著负向影响，用户对医生的信任影响大于对应用程序的信任。最后，用户对医生/应用程序的信任是感知收益/风险与隐私披露意愿之间的中介效应。
本研究结合风险计算和隐私计算理论，构建双计算模型，将信任分为对医生的信任和对应用程序的信任，以探讨用户参与 OHC 隐私披露的内在动机和决策机制。一方面，从理论上弥补了隐私计算往往低估感知风险的不足，补充了 OHC 中信任的研究，揭示了 OHC 中用户隐私披露的影响因素和决策传递机制。另一方面，为 OHC 平台开发者制定合理的隐私政策和健康信息保护机制提供了指导。
