Exploratory Research of Integrating Large Language Models and Grounded Theory: Scientific and Technological Intelligence Case Study

This study addresses the need to transform enterprise scientific and technological intelligence (STI) services from a discrete resource-supply model toward a more systematic value-creation approach, an important challenge in the digital transformation of knowledge-intensive industries. As an exploratory qualitative inquiry, this work combines large language model-assisted analysis with grounded theory to examine the construction logic and operational mechanisms of an embedded intelligent STI service system. Drawing on in-depth interviews with STI professionals, a qualitative corpus was analyzed using human–machine collaborative coding to systematically derive and organize key constructs. The findings yield a preliminary three-layer conceptual framework: “supply-demand interactive matching, organizational embedded services, and digital-intelligent platform support.” Specifically, the supply–demand matching layer facilitates targeted alignment through demand insight, dynamic response, and quality closed-loop management; the organizational embedded service layer delivers intelligence through scenario integration, process integration, and responsibility–authority integration; and the digital-intelligent platform support layer enables core capabilities via data element induction, intelligent diffusion, and tacit knowledge conversion. The proposed framework offers an initial, structured perspective on how embedded intelligent STI services may operate, providing a foundational reference for both research and practice in this emerging domain.