Jing Ji, Lingyan Hu
Purpose Based on resource dependence theory, this study aims to explore the dual-path impact of artificial intelligence (AI) knowledge creation on the innovativeness of new product development (NPD). Specifically, it examines the mediating roles of knowledge sabotage and cognitive load, the moderating effects of AI decision-making transparency, as well as the moderated effects that AI decision-making transparency exerts on the mediating effects of knowledge sabotage and cognitive load, respectively. Design/methodology/approach A three-stage filed survey of 428 managers was conducted to validate the proposed theoretical model. Findings The findings indicate that: (1) AI knowledge creation positively affects NPD innovativeness; (2) AI knowledge creation negatively affects knowledge sabotage; (3) AI knowledge creation positively affects cognitive load; (4) knowledge sabotage mediates the relationship between AI knowledge creation and NPD innovativeness; (5) cognitive load mediates the relationship between AI knowledge creation and NPD innovativeness; (6) AI decision-making transparency enhances the negative relationship between AI knowledge creation and knowledge sabotage; (7) AI decision-making transparency weakens the positive relationship between AI knowledge creation and cognitive load; (8) AI decision-making transparency enhances the mediating effect of knowledge sabotage; (9) AI decision-making transparency weakens the mediating effect of cognitive load. Originality/value This study fills a significant gap by providing a comprehensive analysis of how AI knowledge creation influences NPD innovativeness through dual pathways: organizational knowledge sabotage and organizational cognitive load. It further reveals the mediating roles of knowledge sabotage and cognitive load, as well as the moderating effects and the moderated mediation effects of AI decision-making transparency on these relationships. By doing so, this research not only advances theoretical understanding but also offers practical insights for optimizing AI knowledge management practices and enhancing NPD innovativeness, moving beyond the scope of existing literature.