Artificial intelligence-driven transformation in healthcare: the mediating role of technostress in the impact of openness to organizational change and attitude toward AI on innovative behavior.

The Impact of AI-Powered Tools on Employee Well-Being and Mental Health: Opportunities and Challenges

Purpose: This paper discusses the impact of AI-based technologies on the workplace in terms of well-being and mental health. The paper will also involve the discussion of the possibilities and challenges of using AI, namely in the framework of the automation of jobs powered by AI, workplace surveillance, mental health support offered by AI, and the introduction of organizational support. Methodology: A sequential mixed method was employed to be explained in an explanatory manner. Quantitative data on 100 employees of the organizations which use AI technologies and qualitative data on the same issue were collected with the help of the survey method and semi-structured interviews respectively. The outcomes were summarized, synthesized to derive conclusions and a better understanding of what employees can do with AI-powered tools. Findings: The study revealed the beneficial effects of AI on employee welfare in terms of productivity, reduction of unnecessary work, enhancement of flexibility in the workplace and employee satisfaction. However, problems related to employment insecurity, surveillance, loss of privacy, loss of autonomy and continuous adaption to new technologies cannot be underestimated. The findings as well revealed that the organizational environment is a fundamental component in determining the positive experiences of AI technologies among the workers, by way of excellent organizational support, training initiatives, and furnishing entry to psychological support systems. Unique Contribution to Theory, Practice, and Policy: It is appreciated and highly acknowledged as providing extended JD-R Theory and TAM theories, which continues the insights into AI and employee well-being. In order to restrain any negative outcomes, organisations need to adopt human-focused strategies towards AI, improve employee training, establish ethical governance of AI, and offer mental health assistance to employees. It provides practical recommendations to organizations and human resources and policy advice on how AI can be used ethically, protect the employees and develop their workforce.

Patients’ Perception and Attitude Toward Artificial Intelligence: A Cross-Sectional Study

Background: Artificial Intelligence (AI) is rapidly transforming healthcare. However, its impact on the core of medical practice, the doctor-patient relationship, remains a subject of debate, particularly regarding patients' trust. Methods: A cross-sectional study was conducted to assess patients’ perceptions of the integration of Artificial Intelligence (AI) in healthcare and its impact on the doctor–patient relationship and participants’ trust, comfort, and concerns toward AI use. The study was carried out on 258 participants recruited from outpatient clinics at Mutah University Health Center and Al-Karak Hospital from December 2025 to March 2026. Data were collected through a structured questionnaire by face-to-face interview. Statistical analysis was performed using SPSS version 27 and relationships were analyzed using non-parametric tests including the Mann-Whitney U test and the Kruskal-Wallis test. Results: An overall of 258 participants were enrolled in the study, with the majority of them aged between 18 and 25 years, and a higher proportion of females (73.3%). 76.7% of the participants had university degrees. Students’ knowledge of AI varied significantly by age (p < 0.001) and occupation (p < 0.001) with younger students having higher proficiency. Education level was a significant predictor of attitudes toward the role of AI in enhancing the doctor-patient relationship (p = 0.038). Conclusion: Participants generally express optimism about AI's efficiency in healthcare, especially regarding time savings. However, comfort and trust in AI are significantly affected by factors such as gender and digital literacy.    

DETERMINANTS OF ARTIFICIAL INTELLIGENCE-BASED CLINICAL DECISION SUPPORT SYSTEMS ADOPTION AMONG MENTAL HEALTH PROFESSIONALS: THE ROLES OF EMPATHY, ACCOUNTABILITY AND TRUST

Human–AI Collaboration and Innovative Work Behavior: Examining the Mediating Role of Job Satisfaction

This study aims to explore the impact of Human–AI Collaboration on the Innovation Behavior of employees in the banking sector, focusing on the mediation effect of Job Satisfaction and the moderation effect of AI Self-Efficacy. The study draws its concept of Human–AI Collaboration from the Job Demands–Resources (JD–R) Theory, which considers HACC as a strategic organizational resource that can boost employees' motivation and innovative performance. Quantitative, Cross Sectional. A quantitative, cross-sectional research design was used. Structured questionnaires were used to gather data on 384 employees of commercial banks who have experienced the use of AI in their work. To ensure the respondents had the relevant work experience with AI, purposive sampling was used and explore the direct, mediating, and moderating relationships, the proposed conceptual model was analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique by SmartPLS 4. The results show that Human–AI Collaboration has a significant positive impact on Job Satisfaction and Innovation Behavior. Job Satisfaction positively impacts Innovation Behavior and partially mediates the link between Human–AI Collaboration and Innovation Behavior, suggesting that a collaborative environment with AI can foster innovation by enhancing employee work satisfaction. The findings of this study could help bank executives, human resource managers, and policymakers understand the importance of human-focused implementation of AI, ongoing AI skill-building, and favorable organizational practices that boost employee satisfaction and innovation. To truly unlock the strategic benefits of Human–AI Collaboration, organizations must include investment in employee capability development within their AI investments. This study adds to the growing increasingly relevant literature on Human–AI Collaboration by combining technological and psychological aspects in one framework. It expands on the JD–R Theory by clarifying the strategic role of Human–AI Collaboration as a job resource that fosters innovation by Job Satisfaction, and shows the contingent role of AI Self-Efficacy in AI-enabled workplaces.

Predicting nurses' behavioral intention to adopt artificial intelligence in a resource-limited setting: an extended technology acceptance model study in Iran.

BACKGROUND The integration of Artificial Intelligence (AI) into healthcare systems has the potential to substantially enhance clinical decision-making, workflow efficiency, and quality of patient care. However, the successful implementation of AI technologies largely depends on their acceptance by frontline healthcare providers, particularly nurses. While the Technology Acceptance Model (TAM) has been widely used to explain technology adoption, evidence remains limited regarding the role of nurses' cognitive and attitudinal factors in resource-limited settings. METHODS A cross-sectional analytical study was conducted in 2025 among nurses working in hospitals affiliated with Ilam University of Medical Sciences, Iran. Using a census-based approach, 340 nurses were invited to participate, of whom 199 completed a validated online questionnaire. Data were collected using Davis's Technology Acceptance Model scales (Perceived Usefulness and Perceived Ease of Use) alongside an adapted instrument measuring knowledge, attitude, behavioral intention, and practical use of AI. Non-parametric tests and multiple linear regression analysis were performed using SPSS version 26. RESULTS Most participants demonstrated low levels of AI-related knowledge (70.4%) and relatively unfavorable attitudes toward AI (66.8%). Behavioral intention to adopt AI was moderate (57.3%), while reported practical use was low (48.7%). Attitude emerged as the strongest predictor (β = 0.272, p < 0.001), followed by perceived ease of use (β = 0.198, p = 0.006), perceived usefulness (β = 0.174, p = 0.016), and knowledge (β = 0.127, p = 0.047). CONCLUSION This study demonstrates that extending the Technology Acceptance Model by incorporating nurses' knowledge and attitudes provides a meaningful framework for predicting AI adoption in resource-limited healthcare settings. Beyond technological considerations, fostering positive attitudes and foundational AI literacy among nurses is essential for successful implementation. Targeted educational and organizational interventions are urgently needed to prepare the nursing workforce for AI-enabled healthcare. CLINICAL TRIAL NUMBER Not applicable.

Artificial Intelligence Literacy, Trust, and Anxiety: Implications for Workforce Development and Productivity in the Era of Smart Mobility

The rapid advancement of Artificial Intelligence (AI) is transforming organizational workflows and requiring employees to adapt to emerging technologies. This study aims to analyse the impact of AI literacy and trust in AI on work productivity, while examining whether AI anxiety serves as a mediating variable. A quantitative approach was employed using a cross-sectional survey design. Data were collected from 245 respondents working in the food and beverage sector in Jabodetabek, Indonesia, who have experience using AI-based systems. The instrument utilized a five-point Likert scale, and data were analysed through multiple regression within a mediation analysis framework. Results indicate that AI literacy has a strong positive and significant effect on work productivity $(\beta=0.637; \mathrm{p}<0.001)$, emerging as the strongest predictor in the model). However, AI anxiety does not mediate the relationship be-tween AI literacy or trust and productivity, as its effect on productivity is not significant $(\beta=-0.095; \mathrm{p}=0.291)$. These findings highlight the critical role of strengthening AI literacy to enhance employee productivity in the digital era. Organizations are encouraged to design training programs that go beyond technical skills, focusing on strategic AI utilization to optimize performance. The findings contribute to workforce development by highlighting the importance of AI capability building and psychological readiness in AI-driven operational environments associated with smart mobility ecosystems. Limitations include the small sample size and single-industry context, which limits the generalizability of findings across industries and smart mobility contexts. Future research should adopt longitudinal designs, explore additional variables such as perceived usefulness and organizational support, and extend to diverse sectors to deepen understanding of human–AI interaction dynamics.