Understanding user engagement in mobile health applications from a privacy management perspective.

Mobile health applications (mHealth apps) have surged in popularity for their role in promoting knowledge exchange and providing emotional support among health consumers. However, this enhanced social connectivity via these apps has led to an escalation in privacy breaches, potentially hindering user engagement. Drawing upon the communication privacy management theory, this study proposes a moderated mediation model to link social privacy concerns to user engagement in mHealth apps. An online survey involving 1149 mHealth app users was conducted in China to empirically validate the proposed model. Results indicated that social privacy concerns were negatively related to user engagement in mHealth apps, and perceived privacy of the app partially mediated this relationship. Moreover, perceived control positively moderated the indirect relationship between social privacy concerns and user engagement via perceived privacy. Specifically, the negative impact of social privacy concerns on perceived privacy was mitigated for users who reported higher levels of perceived control, indicating that when users feel more in control of their personal data, they are less affected by concerns over social privacy. Theoretically, this study has the potential to help scholars understand user engagement in mHealth apps from a privacy management perspective. Practically, the results of this study could assist mobile app providers and health professionals in devising evidence-based strategies to enhance social engagement and promote effective and sustainable use of mHealth apps among health consumers.

Achieving a successful e-government: Determinants of behavioral intention from Thai citizens' perspective.

From the COVID-19 pandemic, e-government is a crucial tool in managing crisis and coping with change through communication and collaboration between the government, private, and civil sectors. The objective of this study was to develop an e-government development success model from the perspective of Thai citizens using integrated multiple concepts and to examine factors affecting the behavioral intention of citizens in e-government. A sample is Thai people in all regions of Thailand (n = 540) and analyzes by Structural Equation Model (SEM). The hypothesis testing found that factors directly influencing behavioral intention were information quality, system quality, service quality, citizen satisfaction, perceived usefulness, computer self-efficacy, and trust in government. Trust in government was the most direct influencing factor and was the mediating variable between perceived privacy and perceived security leading to behavioral intention. The results will benefit governments in developing e-government to drive the digital economy and society further.

Social Q&A communities: A multi-factor study of the influence of users' knowledge sharing behaviors.

Recently, social Q&A communities have grown increasingly popular, serving as a primary platform for people to learn and share information. Nonetheless, fewer knowledge producers in these communities are significant than knowledge consumers. Thus, promoting users' participation in knowledge sharing is a challenge for managers of social Q&A communities. Even though many scholars have studied factors influencing willingness to share knowledge, they tend to start with one theory and ignore the impact of several factors on behaviors. Thus, this manuscript presents a multi-factor model based on three dimensions of technology, cognition, and security to explore the effects of the six factors of perceived ease of use and perceived usefulness, perceived behavioral control and subjective norms, perceived security, and perceived privacy in terms of the three knowledge sharing methods of browsing including like and favorite, publishing and replying, and to compare users' willingness to use the three knowledge sharing methods. A total of 482 questionnaires were collected online, and the hypotheses were tested and analyzed using structural equation modeling (SEM). According to the results, the factors affecting different sharing methods are not the same. Perceived behavioral control and perceived security can have a significant influence on their willingness to browse, users' willingness to publish and reply to posts is significantly influenced by their perception of behavioral control and subjective norms, while perceived usefulness also affects their willingness to respond, it can be seen that cognition is the most important factor affecting users' knowledge sharing among the three dimensions. In addition, users' willingness to browse is significantly greater than their willingness to reply, and their willingness to post is the lowest.

Predicting the Mass Adoption of eDoctor Apps During COVID-19 in China Using Hybrid SEM-Neural Network Analysis.

Technology plays an increasingly important role in our daily lives. The use of technology-based healthcare apps facilitates and empowers users to use such apps and saves the burden on the public healthcare system during COVID-19. Through technology-based healthcare apps, patients can be virtually connected to doctors for medical services. This study explored users' intention and adoption of eDoctor apps in relation to their health behaviors and healthcare technology attributes among Chinese adults. Cross-sectional data were collected through social media, resulting in a total of 961 valid responses for analysis. The hybrid analysis technique of partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) analysis was applied. The obtained results revealed the significant influence of eDoctor apps in terms of usefulness, compatibility, accuracy, and privacy on users' intention to use eDoctor apps. Intention and product value were also found to suggestively promote the adoption of eDoctor apps. This study offered practical recommendations for the suppliers and developers of eHealth apps to make every attempt of informing and building awareness to nurture users' intention and usage of healthcare technology. Users' weak health consciousness and motivation are notable barriers that restrict their intention and adoption of the apps. Mass adoption of eDoctor apps can also be achieved through the integration of the right technology features that build the product value and adoption of eDoctor apps. The limitations of the current study and recommendations for future research are presented at the end of this paper.

When Big Data Backfires: The Impact of a Perceived Privacy Breach by Pharmaceutical E-Retailers on Customer Boycott Intention in China.

The objective of this study was to explore the impact of a perceived privacy breach by pharmaceutical e-retailers on customer boycott intention, especially the mediating role of emotional violation and the moderating effect of customer previous trust. Data were collected via a questionnaire survey of 335 customers of pharmaceutical e-retailers from China. Our research results showed that a perceived privacy breach by a pharmaceutical e-retailer had no direct effect on customer boycott intention; a perceived privacy breach positively affected emotional violation; emotional violation led to customer boycott intention; emotional violation played a mediating role in the relationship between a perceived privacy breach and customer boycott intention; and customer previous trust positively moderated the mediating effect of emotional violation.

Working from home: Cognitive irritation as mediator of the link between perceived privacy and sleep problems.

Since the COVID-19 pandemic began, many employees have been required to work full or part-time at home. This paper investigates the impact of perceived privacy on cognitive irritation and sleep problems among employees who worked from home during the pandemic. Additionally, we analyzed the role of cognitive irritation as a mediator between privacy and sleep problems. We created a cross-sectional questionnaire, which was completed by 293 employees who performed home-based telework in German-speaking Switzerland. A mediation analysis was then conducted using a multiple regression analysis. A test of the indirect effect showed a significant mediation path from perceived privacy via cognitive irritation to sleep problems. Hence, the negative indirect effect indicates that perceived privacy is an important job resource that may prevent sleep problems. Further research is needed regarding home-based telework and recovery strategies to prevent sleep problems.

Perspectives to social media usage of depressed patients and caregivers affecting to change the health behavior of patients in terms of information and perceived privacy risks.

It has been confirmed that the use of social media (SM) can affect the mental health of users. However, there is no explanation for its impact on health behavior. This study focuses on the perspectives of depressed patients and caregivers on social media usage and how it can change their health behavior. A questionnaire designed according to the framework of the I-Change Model (ICM) is used to collect data from a sample group. This group consists of 214 patients diagnosed with major depressive disorders, and 110 caregivers. The data is used to analyze causal relationships with the help of structural equation modeling. The results showed that from the patient's perspective it is essential to be aware of the content and volume of social media usage. An awareness of the perceived risk to privacy is also essentially a motivating factor in patients' decisions to use social media. The views of caregivers suggest that content valence has an essential role to play in their use of social media. After viewing content on social media patients change their behavior. The perceived privacy risk also plays a critical role in patients' decisions to use social media.