Abstract

As artificial intelligence (AI) becomes increasingly embedded in everyday work processes, organizations still face a paradox: despite considerable investments and widely recognized productivity gains, AI often fails to translate into sustained employee-level innovation. This study examines this gap by examining how individual and organizational resources jointly shape Innovative Work Behavior (IWB) in AI-enabled workplaces. Drawing on the Job Demands–Resources (JD–R) model, we conceptualize AI as a job characteristic that simultaneously introduces new demands and enables new resources, with its ultimate impact contingent on employee perceptions and contextual conditions. We developed a theoretical model that integrates personal resources (Adaptive Disposition and Self-Efficacy), a challenge demand (job complexity), and a key job resource (perceived organizational support) to explain variation in employees’ innovative responses to AI. Adaptive Disposition is theorized as a persistent tendency to adjust to change and learn in the presence of uncertainty, enabling employees to view AI as an opportunity rather than a threat. Job Complexity is framed as a stimulating demand that stimulates experimentation and analytical reasoning when adequate resources are available. Perceived organizational support reflects the extent to which employees feel encouraged and psychologically safe to explore and experiment with AI-based tools. In addition, self-efficacy is proposed as a critical moderator that strengthens the relationship between adaptive disposition and innovative work behavior by enabling employees not only to adapt to AI-enabled tasks but also to extend their use in novel and creative ways. Extending the JD–R framework to AI-enabled work, this study contributes to theory in two ways. First, it reframes AI as a dual-purpose job characteristic whose influence on innovation depends on the relationship of job demands, job resources, and personal resources. Second, it positions employee adaptation as the key pathway through which AI adoption leads to innovative work behavior. In practice, the findings show that AI-driven innovation requires more than investment in technology: organizations need to foster supportive environments and strengthen employees’ adaptive dispositions and self-efficacy to realize AI’s innovative potential. Overall, the study offers a human-centered account of innovation in the AI era, noting that AI’s value depends as much on people and organizational context as on the technology itself.

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