PBESProceedings of Business and Economic Studies2209-26412209-265XBio-Byword Scientific Publishing Pty. Ltd.10.26689/pbes.v9i4.14962ArticleLarge Model-Driven Technology Transfer: Value Conduction, Policy Optimization and Empirical Explorationhttps://artdesignp.com/journal/PBES/9/4/10.26689/pbes.v9i4.14962PanYiqun2026942026-06-03Addressing the challenges in the commercialization of traditional scientific and technological achievements, this study explores the empowerment mechanisms and value conduction pathways of large model technologies, and proposes policy optimization directions through empirical validation. First, a three-dimensional empowerment framework of “technology-subject-ecosystem” is constructed to elucidate how large models address traditional challenges through four key value conduction pathways. Subsequent empirical analysis using data from 2021–2023 demonstrates a positive correlation between large model adoption levels and technology transfer success rates, with particularly pronounced effects observed in high-tech enterprises, and R&D investment intensity playing a significant moderating role. Finally, based on the analytical framework and case studies, policy recommendations are formulated across four dimensions, providing actionable insights for overcoming technology transfer challenges.

State Council. Guidelines on Further Improving the Evaluation Mechanism for Scientific and Technological Achievements, August 23, 2021, http://www.chinadaily.com.cn/regional/bda/2016-05/23/content_25460538.htm

Floridi L, Chiriatti M, 2023, GPT-4 and Artificial Intelligence in Research: Opportunities and Challenges for Knowledge Translation. Research Policy, 52(8): 104689.

Auerswald P, Branscomb L, 2022, Bridging the Valley of Death: University Technology Transfer and the Journey of New Technologies. Journal of Technology Transfer, 47(3): 987–1012.

Keyi Network, 2023, White Paper on Upgrade of Large Model-Driven Technology Trading Platform, Xiamen, http://en.cnki.com.cn/Article_en/CJFDTotal-ZTKB201707003.htm

Liu F, Zhao C, 2024, Research on Pathways and Efficiency of Technology Transfer Driven by Artificial Intelligence. Management Review, 36(2): 102–111.

Hangzhou Technology Transfer Center. Digital and Intelligent Empowerment of Regional Technology Transfer Practice Report, 2023, Hangzhou, 2024, http://en.cnki.com.cn/Article_en/CJFDTOTAL-ZGKT201511025.htm

Ministry of Science and Technology, 2024, Action Plan for Empowering Technology Transfer with Artificial Intelligence (2024–2026), January 15, 2024, http://www.1010jiajiao.com/czyy/shiti_id_69c5d8de7358c2f63ba9945587f20c44