Purpose: The Internet of Things (IoT), which enables smart objects to collect and exchange data, has a variety of application domains used in everyday life including healthcare. As a set of promising next-generation technologies in the healthcare domain, Healthcare-related Internet of Things (H-IoT) promises to facilitate better healthcare by offering data-driven insights. While effective in practice at large, emerging data concerns arise because of the inscrutable black-box systems. Inspired by the notion of human data interaction, this paper seeks to understand how people engage with the H-IoT data that is about and produced by themselves and to elucidate the main data issues and challenges involved in the development of H-IoT.Design/methodology/approach: This work conducted a comprehensive survey and integrated the method of content analysis by systematically review the recently published H-IoT research work in the healthcare domain.Findings: This study thoroughly surveyed more than 300 research studies published in the last decades and classified seven H-IoT end-user groups, and three H-IoT data types that are important to H-IoT comprehension. Attention to human data interaction, our study also highlights several critical issues associated with this notion in the context of H-IoT.Originality/value: This study will support H-IoT research by characterizing the data issues and challenges exist in the context of H-IoT user and data interaction. The findings will provide insights in designing for effective interactions with data in the H-IoT.

Purpose: The proliferation of Artificial Intelligence of Things (AIoT) devices has introduced notable privacy concerns, influencing user adoption and trust. This study integrates privacy calculus theory with the technology acceptance model to analyze how privacy risk perception affects users’ intentions to adopt and continue using AIoT devices.Design/methodology/approach: A research model was developed and validated using data from 313 AIoT users. Findings indicate that perceived usefulness and ease of use significantly enhance users’ trust in AIoT devices. Additionally, prior privacy experiences and privacy knowledge amplify users’ privacy concerns.Findings: Privacy risk perception and concerns reduce trust in AIoT devices but do not significantly deter continued usage intentions, highlighting a “privacy paradox” where functionality and convenience outweigh privacy concerns. Future research is encouraged to examine user attitudes across diverse demographics and controlled settings to gain deeper insights into privacy perceptions and behaviors toward AIoT.Originality/value: These findings contribute to a comprehensive understanding of AIoT adoption dynamics and offer practical implications for designing privacy-conscious AIoT applications.

With the rapid development of mobile applications (apps), various types of mobile apps have become the main channels for smartphone interaction. The user’s app switching behavior in mobile search tasks has also received attention from academia. This article uses the push–pull–mooring (PPM) theoretical model to determine the three influencing factors of push, pull, and mooring that affect user’s app switching behavior in mobile search. Data were collected from 374 respondents using a structural equation model. This study can deepen the understanding of app switching in user mobile search from the perspectives of information source selection, user information search path, etc. This study found that in terms of pushing factors, the complexity of search tasks positively affects users’ willingness to switch apps. In terms of pulling factors, the attractiveness of alternative products and users’ follow-up activities will positively affect their switching willingness. Meanwhile, inertia serves as a mooring variable to regulate the relationship between push-pull factors and user switching intentions. This research highlights key insights on user behavior, follow-up activities, and the role of switching costs and inertia, contributing to the broader literature on information-seeking behavior. It also provides actionable recommendations for app developers to enhance search experiences and retain users by integrating personalized, multi-modal features.

Purpose: This paper aims to explore the users' cross-app behavior characteristics in mobile search and to predict users' cross-app behavior using multi-dimensional information.Design/methodology/approach: This paper presents a longitudinal user experiment in 15 days. This paper recruited 30 participants and collected their mobile phone log data in the whole experiment. The structured diary method was also used to collect contextual information in mobile search.Findings: This study focused on the users' cross-app behavior in mobile search and described cross-app behavior's basic characteristics. Usage of communication app and tool apps could trigger more cross-app behavior in mobile search. The method of cross-app behavior prediction in the mobile search was proposed. Collecting users' more contextual information, such as search tasks, search motivation and other environmental information, can effectively improve the prediction accuracy of cross-app behavior in mobile search.Practical implications: The future research on cross-app behavior prediction should focus on context information in mobile search. Better prediction of cross-app behavior can reduce the users' interaction burden.Originality/value: This paper contributes to research into cross-app behavior, especially in the mobile search research domain.

Purpose: Not all apps can be favored on a smartphone. Users are bound to abandon some if they lose value. Analyzing the reasons behind this phenomenon can help develop strategies for avoiding app abandonment, which is the focus of this study. Design/methodology/approach: This study conducted in-depth open interviews with 47 respondents who had abandoned or deleted mobile apps before and used the qualitative research method grounded theory to analyze the interview content, followed by open coding, axial coding and selective coding. Findings: Seven main reasons are summarized after the relationship between every statement and concept is found and constructed, namely functional value, information expectation, emotional value, platform service, user trust, cost, and social influence. Originality/value: Based on these findings, this study provided suggestions for mobile app developers to help them effectively improve the user experience and retain their customers.

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.