基于场景驱动创新理论与任务技术匹配理论，以知识密集型企业为研究对象，采用混合研究设计，探究生成式AI技术、领域知识与应用场景的适配影响知识密集型企业创新绩效的机制。经由多案例研究构建生成式AI“技术-知识-场景-创新”理论框架，继而运用结构方程模型进行实证检验。研究发现：不同企业存在不同的“技术-知识-场景”匹配范式；生成式AI技术特征、领域知识特征及应用场景特征均显著正向影响“技术-知识-场景”匹配；“技术-知识-场景”匹配既可直接正向影响企业创新绩效，亦可通过提升生成式AI应用强度间接促进企业创新绩效提升。

Based on context-driven innovation theory and task-technology fit (TTF) theory, this study focuses on knowledge-intensive enterprises and adopts a mixed-method research design to explore the mechanism through which the fit among generative AI technology, domain knowledge, and application contexts influences enterprise innovation performance. Through multiple case studies, a “technology-knowledge-context-innovation” theoretical framework for generative AI is constructed, which is further empirically tested using structural equation modeling (SEM). The findings indicate that different enterprises present distinct “technology-knowledge-context” fit paradigms. Furthermore, generative AI technology characteristics, domain knowledge characteristics, and application context characteristics all exert significant positive effects on “technology-knowledge-context” fit. In addition, “technology-knowledge-context” fit not only directly enhances enterprise innovation performance, but also indirectly promotes innovation performance by increasing the application intensity of generative AI.