从赋能到驱动：AI如何影响科技型员工创新？

Against the backdrop of AI empowerment, high-tech employees’ innovation behavior is jointly shaped by individual, organizational, and technological factors. Based on the Motivation–Opportunity–Ability (MOA) theory, this paper constructs an integrated analytical framework encompassing multiple antecedent factors, takes 231 samples as cases, and employs the fsQCA method to explore the driving effects of different factor configurations on innovation behavior. The results show that: (1) Six core factors affect high-tech employees’ innovation behavior (innovative motivation, work motivation, organizational innovation climate, work characteristics, innovative self-efficacy, and AI literacy), which can be categorized into three dimensions: intrinsic motivation, external opportunities, and individual abilities. (2) No single antecedent can constitute a necessary condition for high-level innovation behavior, which instead emerges from the matching and interplay of multiple factors. (3) High-level innovation behavior is driven by three configurations: The motivation-confidence driving type indicates that employees’ individual psychological resources can form stable endogenous motivation and drive high-level innovation independently; the “environment-AI literacy empowering type” reflects that employees can form a strong support system under the organizational innovation climate and AI literacy support; and the “motivation–environment–confidence synergy type” relies on the interaction of work motivation, organizational innovation climate, and innovative self-efficacy to achieve the coordinated matching of internal and external factors to drive innovation. This paper deepens the understanding of the causal complexity in the driving mechanism of high-tech employees’ innovation behavior, and provides practical references for enterprises to formulate differentiated management strategies to enhance innovation performance in the AI era.