How does explainable AI affect service innovation of frontline employees? A moderated chain mediation model

Purpose This study aims to explore the mechanism through which explainable artificial intelligence (XAI) influences employees’ service innovation in the hospitality industry. Grounded in social cognitive theory (SCT), it develops a cognition-behavior-driven chain mediation model to uncover how XAI indirectly impacts employees’ service innovation through learning behavior, while examining the moderating role of growth mindset in this process. Design/methodology/approach Data were collected from 423 hospitality employees and 15 direct supervisors in China using a multiphase survey. Hypotheses were tested through structural equation modeling. Findings The study reveals that XAI indirectly enhances service innovation performance by improving employees’ AI technology self-efficacy and reducing AI-related anxiety, which in turn stimulates their learning behavior. Furthermore, growth mindset significantly moderates the impact of XAI on AI anxiety. Practical implications This study offers strategic recommendations for hospitality managers to promote service innovation among frontline employees. These strategies include fostering employees’ understanding and trust in XAI creating an organizational environment supportive of innovation, and addressing individual differences by nurturing growth mindsets. Originality/value This study distinguishing it from previous studies that primarily emphasized the functional attributes of artificial intelligence (AI) technologies. Grounded in SCT, this study revealed the impact of XAI on service innovation through cognition and behavior of XAI. Moreover, it incorporated growth mindset as a moderating variable, thereby identifying the boundary conditions for its application within the hospitality industry. Overall, this study extends the application of SCT and offers a novel theoretical framework for understanding the dynamic interplay among technology, employees and innovation.