Riadh Brini’s research while affiliated with Majmaah University and other places

Objective: This study explores ChatGPT adoption in higher education, highlighting both its benefits and perceived risks. Method: This research adopts an exploratory and qualitative approach to deeply analyse instructors’ perceptions regarding ChatGPT adoption in higher education. This approach is implemented via semi-structured interviews with 25 faculty members from different nationalities. Result: The findings reveal that ChatGPT improves academic performance, research activities, and training, but also raises concerns about technology dependency, ethical implications, and diminished human interaction. Conclusion: Faculty members emphasize the need for thoughtful and balanced integration of this technology to maximize its benefits while minimizing its potential risks.

Like many other sectors, women in higher education have had negative experiences with sexual harassment. This study examines the coping mechanisms used by female lecturer/researchers and their impact on burnout. Based on Occupational Stress Theory, this research specifically analyzes the mediating role of three coping strategies, i.e., problem-focused coping, emotion-focused coping, and avoidant coping. A quantitative survey was conducted among 800 Tunisian women teacher-researchers, with 613 complete responses that are valid for analysis. The results revealed that under the influence of sexual harassment, women in Tunisian higher education institutions suffer increased burnout. Structural equation analysis shows that emotion-focused coping has a partial mediation effect, while avoidant coping fully mediates the link between harassment and burnout. This research adds to the literature on sexual harassment and suggests implications for the prevention and support of victims in higher education institutions.

This study examines the structural relationship between a destination’s image (DI), social influence (SI), and tourists’ brand trust (BT) and brand loyalty (BL) in the destination of Tozeur, a Tunisian town located at the gateway to the Sahara and rooted in the Atlas Mountains, where George Lucas set scenes for the Star Wars saga. The structural correlations between the variables in the model were tested through structural equation modeling (SEM). Data from 1405 tourists, who had visited Tozeur, were analyzed through SEM using AMOS software (version 25). The results showed that DI significantly affects BT (β = 0.924, p < 0.001) and significantly affects BL (β = 0.481, p < 0.01). Additionally, SI significantly affects BT (β = 0.274, p < 0.001) and significantly affects BL (β = 0.234, p < 0.001). Furthermore, BT significantly affects the BL (β = 0.461, p < 0.01). Tourist’s trust in a brand was found to act as a partial mediator on the link between destination image and brand loyalty and between social influence and brand loyalty. The findings demonstrate the importance of the tourism destination as well as social influence in boosting tourism trust and increasing destination loyalty among tourists. The results have many practical implications for destination marketers.

Artificial intelligence (AI) has transformed healthcare, yet patients’ acceptance of AI-driven medical services remains constrained. Despite its significant potential, patients exhibit reluctance towards this technology. A notable lack of comprehensive research exists that examines the variables driving patients’ resistance to AI. This study explores the variables influencing patients’ resistance to adopt AI technology in healthcare by applying an extended Ram and Sheth Model. More specifically, this research examines the roles of the need for personal contact (NPC), perceived technological dependence (PTD), and general skepticism toward AI (GSAI) in shaping patient resistance to AI integration. For this reason, a sequential mixed-method approach was employed, beginning with semi-structured interviews to identify adaptable factors in healthcare. It then followed with a survey to validate the qualitative findings through Structural Equation Modeling (SEM) via AMOS (version 24). The findings confirm that NPC, PTD, and GSAI significantly contribute to patient resistance to AI in healthcare. Precisely, patients who prefer personal interaction, feel dependent on AI, or are skeptical of AI’s promises are more likely to resist its adoption. The findings highlight the psychological factors driving patient reluctance toward AI in healthcare, offering valuable insights for healthcare administrators. Strategies to balance AI’s efficiency with human interaction, mitigate technological dependence, and foster trust are recommended for successful implementation of AI. This research adds to the theoretical understanding of Innovation Resistance Theory, providing both conceptual insights and practical implications for the effective incorporation of AI in healthcare.