Purpose: Online health information-seeking behavior is undergoing a transformation worldwide. The development of generative artificial intelligence (GAI) has prompted users to switch from traditional health information platforms (THIPs) to GAI. In this study, we examine factors influencing users’ switching intentions so that THIPs can optimize services, enhance user experience and improve retention. Design/methodology/approach: Using the push–pull–mooring (PPM) model, we analyzed data from 834 valid samples through a mixed-method approach combining covariance-based structural equation modeling (CB-SEM) and fuzzy-set qualitative comparative analysis (fsQCA). Findings: We found that push factors (health anxiety, usage fatigue and privacy concerns), pull factors (perceived convenience, perceived mind and information quality) and the mooring factor (social influence) significantly and positively influenced switching intention. Further, social influence was found to moderate the effects of health anxiety, usage fatigue, perceived convenience and information quality on switching intention. Three paths to switching intention were identified through fsQCA. Practical implications: From the study findings, it is recommended that THIPs can optimize services, boost user experience and improve retention by enhancing information filtering, strengthening privacy protection, integrating GAI technology and leveraging social influence and reputation management. Originality/value: The PPM model has been used for the first time to explore health information switching intentions in the context of GAI. The mixed-method approach combining CB-SEM and fsQCA ensures robust findings, contributing to research on health information behaviors.

Privacy disclosure is one of the most common user information behaviors in online health communities. Under the premise of implementing privacy protection strategies in online health communities, promoting user privacy disclosure behavior can result in a “win–win” scenario for users and online health communities. Combining the real situation and evolutionary game theory, in this study, we first constructed an evolutionary game model of privacy disclosure behavior with users and online health communities as the main participants. Then, we solved the replication dynamic equations for both parties and analyzed the evolutionary stable strategies (ESSs) in different scenarios. Finally, we adopted MATLAB for numerical simulations to verify the accuracy of the model. Studies show that: (1) factors such as medical service support and community rewards that users receive after disclosing their private personal information affect user game strategy; and (2) the additional costs of the online health communities implementing the “positive protection” strategy and the expected loss related to the privacy leakage risk affect the online health communities’ game strategy. In this regard, this paper puts forward the following suggestions in order to optimize the benefits of both sets of participants: the explicit benefits of users should be improved, the internal environment of the communities should be optimized, the additional costs of the “positive protection” strategy should be reduced, and penalties for privacy leakages should be increased.

Purpose: Establishing and developing digital science and education evaluation platforms (DSEEPs) have several practical implications for the development of China's science, technology and education. Identifying and analyzing the key factors influencing DSEEP user experience (UX) can improve the users' willingness to use the platform and effectively promote its sustainable development.Design/methodology/approach: First, a literature survey, a five-element model of UX and semi-structured interviews were used in this study to develop a DSEEP UX-influencing factor model, which included five dimensions and 22 influencing factors. Second, the model validity was verified using questionnaire data. Finally, the key influencing factors were identified and analyzed using a fuzzy decision-making trial and evaluation laboratory (fuzzy-DEMATEL) method.Findings: Fourteen influencing factors, including diverse information forms and comprehensive information content, are crucial for the DSEEP UX. Its optimization path is “‘Function Services’ → ‘Information Resources’ → ‘Interaction Design’ → ‘Interface Design’ and ‘Visual Design’.” In this regard, platform managers can take the following measures to optimize UX: strengthening functional services, improving information resources, enhancing the interactive experience and considering interface effects.Originality/value: This study uses a combination of qualitative and quantitative research methods to determine the key influencing factors and optimization path of DSEEP UX. Optimization suggestions for UX are proposed from the perspective of platform managers, who provide an effective theoretical reference for innovating and developing a DSEEP.

Purpose: Based on the social interaction theory and trust theory, this study investigates the switching of users on social question and answer (Q&A) platforms from knowledge seekers to knowledge contributors. Design/methodology/approach: We used Python to gather data from Zhihu, performed hypothesis testing on the models using Poisson regression and finally conducted a mediation effect analysis. Findings: The findings reveal that knowledge seeking impacts users' motivation for information interaction, emotional interaction and trust. Notably, information interaction and trust exhibit a chained mediation effect that subsequently influences knowledge contribution. Originality/value: Current studies on user knowledge behavior typically examine individual actions, rarely connecting knowledge seeking and knowledge contribution. However, the balance of knowledge inflow and outflow is crucial for social Q&A platforms. To cover this gap, this paper empirically investigates the switching between knowledge seeking and knowledge contribution based on the social interaction theory and trust theory.

Scientific data sharing (SDS) has become essential for scientific progress, technological innovation and socioeconomic development. Identifying the key influencing factors of SDS can effectively promote SDS programmes and give full play to the critical role of scientific data. This study used grounded theory and information ecology theory to construct an SDS influencing factor model that encompassed five dimensions and 28 influencing factors and followed the fuzzy decision-making trial and evaluation laboratory (fuzzy-DEMATEL) approach to measure and analyse the degree of influence of each influencing factor and identify the key factors. The results show that (1) there are interactions and mutual interactions between the various influencing factors of SDS, which can form a complex network system. (2) 16 influencing factors, such as data-sharing policies, data-sharing regulations and data-sharing standards, comprise the key influencing factors in SDS. (3) The optimisation path of SDS is 'Scientific Researchers' → 'Scientific Data' → 'Policy Environment' → 'Research Organisations → 'Information Technologies'. In this regard, we proposed the following management suggestions to promote the development of SDS programmes in China: focusing on researchers' subjective willingness to share, enhancing the integrated governance of scientific data, fulfilling the role of policy support and guidance, strengthening the support of research organisations and improving SDS platforms with information technology.

Purpose: China’s healthcare system is being burdened by the increasing prevalence of chronic diseases. Therefore, this study investigated the health service requirements of mobile health applications (mHealth apps) users in Hangzhou, China. This study aimed to propose suggestions and theoretical references to improve mHealth apps and promote their development, thereby meeting public medical and health needs and creating an efficient medical service system.Design/methodology/approach: We constructed a model of health service demands using a literature review, network survey, and semi-structured interviews. We analyzed the demand attributes using the Kano model and Better-Worse index and obtained the priority ranking of demands.Findings: The results revealed 25 demand elements in four dimensions: must-be (M), one-dimensional (O), attractive (A), and indifferent (I) requirements. The findings suggest that mHealth app developers can optimize health services by categorizing and managing health services, focusing on middle-aged users, enhancing the professionalism of health service providers, and improving the feedback mechanism.Originality/value: Studies on mHealth apps user demands, particularly on health service needs, remain scarce. This study employed a mixed-methods approach, integrating both qualitative and quantitative research techniques, to establish a priority ranking of user health service needs for mHealth apps. The study offers recommendations and theoretical references to optimize and improve mHealth app services.Highlights: Construct a better health service requirements model for mHealth app users. Obtain the prioritization of demand elements in the model. Propose some management suggestions to improve mHealth apps.