Employees' Trust in AI and Innovative Behavior: A JD-R Model Perspective

With the rapid advancement of technology, whether to cultivate employees' trust in artificial intelligence (AI) has emerged as a practical issue that managers must address to drive innovation. In this study, we explore how employees' trust in AI affects their innovative behavior drawing on Job Demands-Resources (JD-R) theory with job autonomy and concentration of work-related flow as parallel mediators, and job complexity as a boundary condition. Using two-wave survey (with a two-week interval) data from 254 participants and structural equation modeling, we find that employees' trust in AI positively relates to innovative behavior and this relationship is fully mediated by job autonomy and concentration of work-related flow. Furthermore, job complexity negatively moderates the trust in AI-mediator links and weakens the indirect effect on innovation. Based on the findings that enrich the literature on trust in AI and extend its boundary conditions, this study advises managers to cultivate employees' trust in AI, leverage the resource-gaining and demand-enabling pathways, and adopt differentiated strategies tailored to job complexity to maximize innovation-enhancing effects of trust in AI.