Background: The swift shift toward internet hospitals has relied on the willingness of medical practitioners to embrace new systems and workflows. Low engagement or acceptance by medical practitioners leads to difficulties in patient access. However, few investigations have focused on barriers and facilitators of adoption of internet hospitals from the perspective of medical practitioners. Objective: This study aims to identify both enabling and inhibiting predictors associated with resistance and behavioral intentions of medical practitioners to use internet hospitals by combining the conservation of resources theory with the Unified Theory of Acceptance and Use of Technology and technostress framework. Methods: A mixed methods research design was conducted to qualitatively identify the factors that enable and inhibit resistance and behavioral intention to use internet hospitals, followed by a quantitative survey-based study that empirically tested the effects of the identified factors. The qualitative phase involved conducting in-depth interviews with 16 experts in China from June to August 2022. Thematic analysis was performed using the qualitative data analysis software NVivo version 10 (QSR International). On the basis of the findings and conceptual framework gained from the qualitative interviews, a cross-sectional, anonymous, web-based survey of 593 medical practitioners in 28 provincial administrative regions of China was conducted. The data collected were analyzed using the partial least squares method, with the assistance of SPSS 27.0 (IBM Corp) and Mplus 7.0 (Muthen and Muthen), to measure and validate the proposed model. Results: On the basis of qualitative results, this study identified 4 facilitators and inhibitors, namely performance expectancy, social influence, work overload, and role ambiguity. Of the 593 medical practitioners surveyed in the quantitative research, most were female (n=364, 61.4%), had a middle title (n=211, 35.6%) or primary title (n=212, 35.8%), and had an average use experience of 6 months every year. By conducting structural equation modeling, we found that performance expectancy (β=−.55; P<.001) and work overload (β=.16; P=.005) had the most significant impact on resistance to change. Resistance to change fully mediated the influence of performance expectancy and partially mediated the influences of social influence (variance accounted for [VAF]=43.3%; P=.002), work overload (VAF=37.2%; P=.03), and role ambiguity (VAF=12.2%; P<.001) on behavioral intentions to use internet hospitals. In addition, this study found that the sex, age, professional title, and use experience of medical practitioners significantly moderated the aforementioned influencing mechanisms. Conclusions: This study investigated the factors that facilitate or hinder medical practitioners’ resistance to change and their behavioral intentions to use internet hospitals. The findings suggest that policy makers avoid the resistance and further promote the adoption of internet hospitals by ensuring performance expectancy and social influence and eliminating work overload and role ambiguity.

Background: The emergence of the COVID-19 pandemic towards the end of 2019 triggered a relentless spread of online misinformation, which significantly impacted societal stability, public perception, and the effectiveness of measures to prevent and control the epidemic. Understanding the complex dynamics and characteristics that determine the duration of rumors is crucial for their effective management. In response to this urgent requirement, our study takes survival analysis method to analyze COVID-19 rumors comprehensively and rigorously. Our primary aim is to clarify the distribution patterns and key determinants of their persistence. Through this exploration, we aim to contribute to the development of robust rumor management strategies, thereby reducing the adverse effects of misinformation during the ongoing pandemic. Methods: The dataset utilized in this research was sourced from Tencent's “Jiao Zhen” Verification Platform's “Real-Time Debunking of Novel Coronavirus Pneumonia” system. We gathered a total of 754 instances of rumors from January 18, 2020, to January 17, 2023. The duration of each rumor was ascertained using the Baidu search engine. To analyze these rumors, survival analysis techniques were applied. The study focused on examining various factors that might influence the rumors' longevity, including the theme of the content, emotional appeal, the credibility of the source, and the mode of presentation. Results: Our study's results indicate that a rumor's lifecycle post-emergence typically progresses through three distinct phases: an initial rapid decline phase (0–25 days), followed by a stable phase (25–1000 days), and ultimately, an extinction phase (beyond 1000 days). It is observed that half of the rumors fade within the first 25 days, with an average duration of approximately 260.15 days. When compared to the baseline category of prevention and treatment rumors, the risk of dissipation is markedly higher in other categories: policy measures rumors are 3.58 times more likely to perish, virus information rumors have a 0.52 times higher risk, epidemic situation rumors are 4.86 times more likely to die out, and social current affairs rumors face a 2.02 times increased risk. Additionally, in comparison to wish rumors, bogie rumors and aggression rumors have 0.26 and 0.27 times higher risks of dying, respectively. In terms of presentation, graphical and video rumors share similar dissolution risks, whereas textual rumors tend to have a longer survival time. Interestingly, the credibility of the rumor's source does not significantly impact its longevity. Conclusion: The survival time of rumors is strongly linked to their content theme and emotional appeal, whereas the credibility of the source and the format of presentation have a more auxiliary influence. This study recommends that government agencies should adopt specific strategies to counter rumors. Experts and scholars are encouraged to take an active role in spreading health knowledge. It's important for the public to proactively seek trustworthy sources for accurate information. Media platforms are advised to maintain journalistic integrity, verify the accuracy of information, and guide the public towards improved media literacy. These actions, collectively, can foster a collaborative alliance between the government and the media, effectively combating misinformation.

Medical institutions face a variety of challenges as they seek to enhance their reputation and increase the influence of their social media accounts. Becoming a social media influencer in the health field in today’s complex online environment requires integrated social and technical systems. However, rather than holistically investigating the mechanism of account influence, studies have focused on a narrow subset of social and technical conditions that drive online influence. We attribute this to the mismatch between complex causality problems and traditional symmetric regression methods. In this study, we adopted an asymmetric configurational perspective that allowed us to test a causally complex model of the conditions that create strong and not-strong account influence. We used fuzzy-set qualitative comparative analysis (fsQCA) to detect the effects of varying configurations of three social system characteristics (i.e., an oncology-related attribute, a public attribute, and comment interaction) and two technical system characteristics (i.e., telepresence and video collection) on the TikTok accounts of 63 elderly Chinese doctors (60 to 92 years old). Our results revealed two pathways associated with distinct sociotechnical configurations to strong account influence and three pathways associated with distinct sociotechnical configurations to not-strong account influence. Furthermore, the results confirmed that a single antecedent condition cannot, on its own, produce an outcome, i.e., account influence. Multiple inter-related conditions are required to produce an influential account. These results offer a more holistic picture of how health science communication accounts operate and reconcile the scattered results in the literature. We also demonstrate how configurational theory and methods can be used to analyze the complexities of social media platforms.

In the face of the profound impacts of global digitization, aging, and major public health emergencies on work patterns, occupational health in the workplace is of inescapable importance for the achievement of the Sustainable Development Goals

Purpose: This study aims to investigate the complex relationship between artificial intelligence (AI)-induced job insecurity and employee innovative behavior. It examines a potential U-shaped relationship between these variables, with knowledge hiding behavior as a mediator and trait resilience as a moderator. The research specifically explores whether trait resilience might amplify rather than mitigate the positive effect of AI-induced job insecurity on knowledge hiding behavior, revealing its potential dark side in organizational contexts. Design/methodology/approach: A two-wave survey was conducted through the Credamo platform, collecting data from 485 Chinese office employees across multiple industries. The study used established scales to measure AI-induced job insecurity, knowledge hiding behavior, innovative behavior and trait resilience. Data analysis was performed using SPSS 27.0 and Amos 27.0, using hierarchical regression and bootstrap methods to test the hypothesized relationships. Findings: Results confirmed a significant positive U-shaped relationship between AI-induced job insecurity and innovative behavior. Knowledge hiding behavior partially mediated this relationship. Notably, trait resilience positively moderated the connection between AI-induced job insecurity and knowledge hiding behavior. Employees with higher trait resilience demonstrated a stronger tendency to engage in knowledge hiding when experiencing AI-induced job insecurity, revealing an unexpected negative aspect of this typically positive trait. Originality/value: This study breaks new ground by identifying a U-shaped relationship between AI-induced job insecurity and innovative behavior. It reveals the dark side of trait resilience, demonstrating its potential to strengthen counterproductive knowledge behaviors. These findings provide novel theoretical insights into employee adaptation to AI technologies and offer important practical implications for organizational management in the digital transformation era.

Objective: We sought to understand the status of promotion pressure among university teachers in China. This study explored the promotion duration and influencing factors among teachers in different disciplines of the social sciences. Methods: Using event history analysis, this study collected data regarding university teachers of China. The sample included 536 teachers who had been promoted from assistant to associate professor and 243 teachers promoted from associate to full professor. Our results revealed that the overall time required for promotion in the social sciences is relatively long. For those promoted from assistant to associate professor, the mean time for promotion was 14.155 years, with a median of 11 years, while for the transition from associate to full professor, the mean was 13.904 years with a median of nine years. Furthermore, in the survival function of the promotion duration, there is a stage pattern for both assistant to associate professor and associate to full professor. In addition, the Kaplan–Meier results showed that the mean promotion time in economics was the shortest. The Cox regression results indicated that males had a higher chance of promotion than females, and faculty members with doctoral degrees had a higher likelihood of promotion than those without. For those advancing from assistant to associate professor, the university of employment had significant positive effects on promotion. This paper provides empirical support for the current societal concerns regarding promotion pressure among university teachers.