Generative AI in the workplace: how job demands and resources influence employee innovative performance – a JD-R theory perspective

Purpose Generative artificial intelligence (GenAI) is widely adopted in enterprises and supported by various policies; however, its usage and effectiveness vary significantly. This study, therefore, addresses two key questions: In the workplace, what critical factors determine employees' utilization of GenAI? And how can organizations transform the technological potential of GenAI into tangible innovative performance through systematic intervention strategies? Through a series of investigations, this research aims to provide theoretical support and practical guidance for enterprises implementing GenAI. Design/methodology/approach Drawing on a sample of 276 corporate research and development (R&D) employees and utilizing AMOS and SPSS software, this study employs empirical methods such as hierarchical regression analysis and 5,000-times bootstrap testing to systematically examine how job demands and job resources influence GenAI usage and, consequently, employee innovative performance. Findings Workload and organizational support positively influence GenAI usage, which in turn enhances employee innovative performance; Perceived technological affordances strengthens the relationships between workload, organizational support and GenAI usage, while prompt literacy facilitates the relationship between GenAI usage and innovative performance. Originality/value Systematically introducing the classic job demands-resources (JD-R) theory into workplace GenAI application scenarios, this study reveals how job demands and resources drive GenAI usage and translate it into innovative performance. Moreover, by incorporating perceived technology affordance and prompt literacy as boundary conditions, it extends the applicability of JD-R theory into the digital and artificial intelligence domains.