Artificial intelligence (AI)-assisted diagnosis and treatment could expand the medical scenarios and augment work efficiency and accuracy. However, factors influencing healthcare workers’ adoption intention of AI-assisted diagnosis and treatment are not well-understood. This study conducted a cross-sectional study of 343 dental healthcare workers from tertiary hospitals and secondary hospitals in Anhui Province. The obtained data were analyzed using structural equation modeling. The results showed that performance expectancy and effort expectancy were both positively related to healthcare workers’ adoption intention of AI-assisted diagnosis and treatment. Social influence and human–computer trust, respectively, mediated the relationship between expectancy (performance expectancy and effort expectancy) and healthcare workers’ adoption intention of AI-assisted diagnosis and treatment. Furthermore, social influence and human–computer trust played a chain mediation role between expectancy and healthcare workers’ adoption intention of AI-assisted diagnosis and treatment. Our study provided novel insights into the path mechanism of healthcare workers’ adoption intention of AI-assisted diagnosis and treatment.

Purpose: Drawing on the social exchange theory, this study examines the impact of self-sacrificial leadership on employee creativity and explores the mediating role of employees' creative process engagement and shared vision, along with the multilevel moderating role of shared vision.Design/methodology/approach: The data were collected from 78 R&D teams, and hierarchical linear model and bootstrapping analysis outlined by Hayes were used to test the hypotheses.Findings: Self-sacrificial leadership exerted a significantly positive impact on employee creativity, employees' creative process engagement and shared vision, whereas employees' creative process engagement and shared vision mediated the relationship between self-sacrificial leadership and employee creativity, respectively. Moreover, shared vision significantly moderated the relationship between employees' creative process engagement and employee creativity, such that the higher the shared vision, the stronger the relationship between employees' creative process engagement and employee creativity.Practical implications: Organizations and managers should provide leadership development training programs, such as leadership skills and abilities, to exhibit their self-sacrificing behaviors. Besides, while team leaders set an example for employees, in turn, spurring employees' creative process engagement, they should also elucidate the team/organization mission, amplify the publicity and employees' understanding of the mission.Originality/value: This study advances the self-sacrificial leadership and creativity literature by establishing employees' creative process engagement and shared vision as crucial mediators and shared vision as boundary condition in the relationship between employees' creative process engagement and employee creativity, as well as stressing the theoretical research of leadership and creativity.

Aim:This study aims to investigate the influence of medical explainable artificial intelligence (XAI) on the innovation behaviour of nurses, as well as explore the dual‐pathway mediating effect of AI self‐efficacy and AI anxiety and organizational ethical climate as the moderating effect.Background:To address the practical application of medical AI technology, alleviate the scarcity of medical resources and fulfil the medical and health demands of the public, it is crucial to improve the innovation behaviour of nurses through the use of medical XAI.Methods:A cross‐sectional survey was conducted involving 368 Chinese nurses working at tertiary and secondary hospitals in Anhui Province, Jiangsu Province, Zhejiang Province and Shanghai.Results:Implementing medical XAI significantly enhanced the innovation behaviour of nurses. Anxiety and self‐efficacy regarding AI mediated the connection between medical XAI and the innovation behaviour of nurses. Furthermore, the organizational ethical climate positively moderated the relationship between medical XAI and AI self‐efficacy.Conclusion:Medical XAI helps to enhance nurses’ AI self‐efficacy and reduce AI anxiety, thereby enhancing nurses’ innovation behaviour. An organizational ethical climate enhances the positive relationship between medical XAI and AI self‐efficacy.Implications for Nursing Management:Organizations and technology developers must augment the study about XAI and the system design of human‐centred AI technology. The organizations aim to enhance the education and training of nurses in AI, specifically focussing on boosting nurses’ self‐efficacy in utilizing AI technology. Moreover, they want to alleviate nurses’ fear of new technological advancements. Hospital administrators and leaders develop strategies to address the ethical atmosphere inside their organization.