PurposeDrawing 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/approachThe data were collected from 78 R&D teams, and hierarchical linear model and bootstrapping analysis outlined by Hayes were used to test the hypotheses.FindingsSelf-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 implicationsOrganizations 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/valueThis 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 The aim of this work is to investigate the influence of leaders' innovation expectation on nurses' innovation behaviour in conjunction with artificial intelligence, as well as explore the chain mediating effect of job control and creative self‐efficacy between leaders' innovation expectation and nurses' innovation behaviour. Background The nurses' innovation behaviour is crucial in promoting medical artificial intelligence. Thus, clarifying the influencing factors of nurses' innovation behaviour has become a priority. Methods A cross‐sectional survey was conducted with 263 Chinese nurses from tertiary hospitals and secondary hospitals in Hefei, Anhui province. Results Leaders' innovation expectation was positively related to nurses' innovation behaviour. Creative self‐efficacy and job control respectively mediated the relationship between leaders' innovation expectation and nurses' innovation behaviour. Furthermore, creative self‐efficacy and job control played a chain mediation role between leaders' innovation expectation and nurses' innovation behaviour. Conclusion Leaders' innovation expectation helps to enhance nurses' creative self‐efficacy and job control, thereby enhancing nurses' enthusiasm for innovation. Implications for Nursing Management Hospital managers and leaders formulate intervention measures to increase leaders' innovation expectation, nurses' creative self‐efficacy and job control, and encourage nurses' innovation behaviour.

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.