Health Privacy Information Self-Disclosure in Online Health Community

Determining factors affecting the user's intention to disclose privacy in online health communities: a dual-calculus model

Background

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.

Methods

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.

Results

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.

Conclusion

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.

Research on user's highly sensitive privacy disclosure intention in home intelligent health service system: A perspective from trust enhancement mechanism

Objective

The aim is to investigate the determinants and mechanisms that influence user's highly sensitive privacy disclosure intention (HSPDI) in home intelligent health service system (HIHSS).

Methods

This study improves the privacy calculus theory by considering the influence of service providers' trust enhancement mechanism besides benefit and risk factors and investigates their impact on users' HSPDIs. This study takes perceived valence and perceived security as the trade-off result among perceived benefits, perceived risks, financial trust enhancement mechanism, and the technical trust enhancement mechanism and suggests that perceived valence and perceived security further affect users' HSPDI in HIHSS. Moreover, the common and differential effects of the perceived justice of privacy violation compensation (PJOPVC) and the perceived effectiveness of privacy protection technologies (PEOPPTs) are studied. The structural equation model is used to analyze 204 valid samples to test the proposed model.

Results

The results show that perceived benefits and perceived risks are important predictors of perceived valence and perceived security, and further affect users' HSPDI. We find PJOPVC has a greater impact on perceived valence while PEOPPT has a greater impact on perceived security.

Conclusions

We recommend that the HSPDI of users with low perceived valence can be improved by providing privacy violation compensation while the HSPDI of users with low perceived security can be enhanced by popularizing relevant knowledge of privacy protection technologies.

Online health community-An empirical analysis based on grounded theory and entropy weight TOPSIS method to evaluate the service quality

Introduction

With the enhancement of people's health awareness and the impact of the coronavirus pandemic in recent years, people's demand for online health information continues to expand, and online health communities (OHCs) have developed rapidly. However, the service quality of OHCs is uneven, and problems such as content quality, privacy disclosure are increasingly prominent. It's of great significance to establish normalized OHC service quality evaluation standards and develop effective evaluation tools and methods for the improvement of OHC service quality.

Material and Methods

Based on the grounded theory, the raw materials obtained from semi-structured interviews were coded in three stages to construct a service quality evaluation system for OHC. Through empirical analysis, the rationality and effectiveness of the evaluation system were verified. Then six representative Chinese OHCs were selected and their service quality was evaluated by the entropy weight TOPSIS method.

Results

The service quality evaluation system of OHC was constructed which includes 4 first-level indicators and 16 second-level indicators. The weights of the first-level indicators from large to small are content quality, emotional experience quality, interaction quality and function quality. Among the second-level indicator weights, the top three are perceived cost reasonableness, content professionalism and effectiveness of interactive content.

Conclusions

The indicator system is reasonable and effective and the evaluation method has strong applicability and operability. This study will provide theoretical guidance for community platform operators and relevant departments to design effective evaluation mechanism of OHC service quality, offering a reference for decisions and policymakers.

Research on the influencing factors of users’ information processing in online health communities based on heuristic-systematic model

With the rapid development of the Internet and the normalization of COVID-19 epidemic prevention and control, Online health communities (OHCs) have gradually become one of the important ways for people to obtain health information, and users have to go through a series of information processing when facing the massive amount of data. Understanding the factors influencing user information processing is necessary to promote users’ health literacy, health knowledge popularization and health behavior shaping. Based on the Heuristic-Systematic Model (HSM), Information Ecology Theory, Privacy Trade-Off and Self-Efficacy Theory, we constructed a model of factors influencing user information processing in online health communities. We found that information quality and emotional support had indirect effects on heuristic and systematic information processing, and these effects were mediated by privacy concerns and self-efficacy. In our research model, systematic information processing was most positively influenced directly by self-efficacy. Privacy concerns had a direct negative correlation with both dual information processing pathways. Therefore, OHCs managers should develop relevant regulations to ensure the information quality in OHCs and improve privacy protection services to promote user information processing by improving users’ self-efficacy and reducing their privacy concerns. Providing a user-friendly and interactive environment for users is also recommended to create more emotional support, thus facilitating more systematic information processing.

"I" Am Willing to Disclose, but "We" are Unwilling: The Impact of Self-Construal on Individuals' Willingness to Disclose

Purpose

In the big data era, many institutions (ie, hospitals) and firms use various methods to encourage people to disclose more personal information to gain competitive advantages in many businesses, such as healthcare and the Internet of Things (IoT) devices. Discussions on antecedents of individuals’ willingness to reveal private data from individual differences perspective are limited. Drawing on information boundary theory, we examine how self-construal prompts a different regulatory focus (promotion focus versus prevention focus), thus, affects individuals’ willingness to disclose private data.

Methods

A mixed-method approach was used to examine our hypothesis. Study 1 (N = 93, participants in China) manipulated self-construal in lab experiments and examined participants’ actual disclosure behavior in the emerging IoT context of connected cars. Study 2 (an online survey, N = 200, participants in US) measured chronic self-construal in another disclosure context (healthcare app), replicating the preliminary effect and examined the mediating effect of the regulatory focus. Study 3 (an online experiment, N = 284, participants in US) tested the moderating effect of message framing.

Results

Study 1 showed that participants primed an independent self-construal were more willing to share private information, whether it is real driving data or private identity information. Study 2 showed that independent (interdependent) self-construal individuals tend to have promotion focus (prevention focus), thus leading to higher (lower) willingness to disclose personal health information. Study 3 demonstrated that independent (interdependent) self-construal individuals are more willing to share information when presented with gain-framing (loss-framing) information.

Conclusion

Independent (interdependent) self-construal positively (negatively) affects individuals’ willingness to disclose and these effects will be mediated by regulatory focus and moderated by message farming. Our study provides a theoretical paradigm that is new to the willingness to disclose literature, and offers an effective, actionable strategy on how institutions and firms can facilitate individuals’ personal information disclosure.