Purpose Although the technological potential of AI to support employee innovation has received extensive attention in both theoretical and empirical research, how employees can leverage AI to unlock this potential remains underexplored. Therefore, this study aims to explore how AI use breadth and AI use depth can contribute to digital innovation performance by influencing AI crafting. Furthermore, this study examines the impact of the balance/imbalance between AI use breadth and depth, as well as the moderating effect of experience inertia on the relationships between AI use and AI crafting. Design/methodology/approach Based on the job demands-resources model, this study constructs theoretical models to explore how and when AI use affects digital innovation performance. This study used structural equation modeling, polynomial regression, and response surface analysis to analyze questionnaire data from 193 employees. Findings The results show that AI use breadth and AI use depth positively affect digital innovation performance through AI crafting. The more balanced the breadth and depth of AI use, the stronger the AI crafting. The degree of AI crafting is lower as the imbalance between AI use breadth and depth increases in either direction. Experience inertia strengthens the positive impact of AI use breadth on AI crafting while weakening the positive impact of AI use depth on AI crafting. Originality/value This study advances research on AI use and employee innovation by distinguishing between AI use breadth and depth and examining both their individual effects and the influence of their balance/imbalance on digital innovation performance. It further identifies AI crafting as a key mediating mechanism linking AI use to innovation outcomes. In addition, the results show that experience inertia moderates the effects of AI use breadth and AI use depth on AI crafting in different ways. These insights provide practical guidance for helping employees leverage AI more effectively to unlock its innovative potential.
Zhang et al. (Thu,) studied this question.