Tailoring Intelligence to Context: Configurational Pathways to Context-Oriented AI Innovation

Generative AI is often celebrated as a general-purpose technology, yet its business value emerges when models are tailored to the contexts in which users act, decide, and create. This study examines context-oriented AI innovation, defined as the development of AI enabled products, services, or processes that address user needs and create value under specific usage contexts. Drawing on the Technology-Organization-Environment (TOE) framework and a configurational view of innovation, we investigate how technological resources, organizational capabilities, and environmental pressures combine to drive context-oriented AI innovation. Using Chinese listed firms that have registered generative AI models, we plan to employ qualitative comparative analysis (QCA) to identify multiple pathways leading to the depth and breadth of context-oriented AI innovation. This paper intends to advance AI innovation research by foregrounding context, theorizing configurational pathways of context-oriented AI innovation, and revealing how distinct configurations produce different patterns of context-specific innovation.