How AI Information Quality Predicts Innovation Through Knowledge Quality: The Mediation and Moderation Mechanisms

In the era of digitalization, innovative activities have received increasing attention from scholars, analysis of the mechanisms by which AI information quality shapes knowledge quality, ultimately enhancing innovative outcomes, remains limited. Drawing on Knowledge‐Based View (KBV) theory and integrating Conservation of Resources (COR) theory, this study empirically examines how and through which mechanisms AI information quality enhances firms' innovation capabilities and employees' innovative performance. It also investigates the boundary condition of AI trust in the linkage between AI information quality and knowledge quality. The three‐wave, multi‐source dataset comprises 71 supervisors and 408 employees working in Pakistan's Information Technology (IT) sector. We analyzed the data using AMOS software and employed covariance‐based structural equation modeling (CB‐SEM). In addition, Hayes PROCESS was used for mediation analysis. Our findings showed that AI information quality shapes knowledge quality, and knowledge quality simultaneously fosters firms' innovation capabilities and employees' innovative performance. Data analysis further showed that knowledge quality partially mediates the relationships among AI information quality, innovation capabilities, and innovative performance. Moreover, AI trust serves as a crucial boundary condition, amplifying the resource‐gain pathway and attenuating the resource‐loss pathway. The study offers practical implications beyond the IT sector.