The Impact of Medical Explainable Artificial Intelligence on Nurses’ Innovation Behaviour: A Structural Equation Modelling Approach

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.