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Does regional AI development improve corporate supply chain efficiency? Evidence from China

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  • Wang, Wei
  • Huang, Hui
  • Fu, Xuan

Abstract

Based on data from Chinese listed firms, this study examines how regional artificial intelligence (AI) development affects supply chain efficiency. The results show that regional AI development significantly improves efficiency by promoting digital transformation and reducing information asymmetry, with stronger effects observed in labor-intensive industries and inland regions. This study makes three key contributions. First, it extends the regional innovation systems perspective to the field of supply chain management by highlighting the spillover effects of regional AI ecosystems. Second, it supports the “latecomer advantage” in technology diffusion by analyzing industry and regional heterogeneity. Third, it reveals how external digital infrastructure and internal supply chain collaboration interact through the dual mechanisms of digitalization and information transparency.

Suggested Citation

  • Wang, Wei & Huang, Hui & Fu, Xuan, 2025. "Does regional AI development improve corporate supply chain efficiency? Evidence from China," International Review of Economics & Finance, Elsevier, vol. 103(C).
    • Handle: RePEc:eee:reveco:v:103:y:2025:i:c:s1059056025007609
    DOI: 10.1016/j.iref.2025.104597
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    1. Feldman, Maryann P. & Audretsch, David B., 1999. "Innovation in cities:: Science-based diversity, specialization and localized competition," European Economic Review, Elsevier, vol. 43(2), pages 409-429, February.
    2. Geng, Haipeng & Su, Zhongfeng & Wang, Junkai, 2025. "The impact of management equity incentive on firms' risk-taking under different monitoring conditions," International Review of Financial Analysis, Elsevier, vol. 97(C).
    3. Feng Dong & Shengnan Zhang & Jiao Zhu & Jiaojiao Sun, 2021. "The Impact of the Integrated Development of AI and Energy Industry on Regional Energy Industry: A Case of China," IJERPH, MDPI, vol. 18(17), pages 1-24, August.
    4. Yijia Chen & Kangmin Wu, 2025. "Integrating Artificial Intelligence into Regional Technological Domains. The Role of Intra- and Extra-Regional AI Relatedness," Papers in Evolutionary Economic Geography (PEEG) 2507, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Mar 2025.
    5. Müller, Julian Marius & Buliga, Oana & Voigt, Kai-Ingo, 2018. "Fortune favors the prepared: How SMEs approach business model innovations in Industry 4.0," Technological Forecasting and Social Change, Elsevier, vol. 132(C), pages 2-17.
    6. Yijia Chen & Kangmin Wu, 2025. "Integrating artificial intelligence into regional technological domains: the role of intra- and extra-regional AI relatedness," Cambridge Journal of Regions, Economy and Society, Cambridge Political Economy Society, vol. 18(1), pages 111-130.
    7. Sheng, Margaret L. & Saide, Saide, 2021. "Supply chain survivability in crisis times through a viable system perspective: Big data, knowledge ambidexterity, and the mediating role of virtual enterprise," Journal of Business Research, Elsevier, vol. 137(C), pages 567-578.
    8. Michael G. Jacobides & Stefano Brusoni & Francois Candelon, 2021. "The Evolutionary Dynamics of the Artificial Intelligence Ecosystem," Strategy Science, INFORMS, vol. 6(4), pages 412-435, December.
    9. Daron Acemoglu & Pascual Restrepo, 2020. "Robots and Jobs: Evidence from US Labor Markets," Journal of Political Economy, University of Chicago Press, vol. 128(6), pages 2188-2244.
    10. Zhen Yu & Zheng Liang & Lan Xue, 2022. "A data-driven global innovation system approach and the rise of China’s artificial intelligence industry," Regional Studies, Taylor & Francis Journals, vol. 56(4), pages 619-629, April.
    11. Lei Wang & Shibo Liu & Wanfang Xiong, 2022. "The Impact of Digital Transformation on Corporate Environment Performance: Evidence from China," IJERPH, MDPI, vol. 19(19), pages 1-19, October.
    12. S. J. Liebowitz & Stephen E. Margolis, 1994. "Network Externality: An Uncommon Tragedy," Journal of Economic Perspectives, American Economic Association, vol. 8(2), pages 133-150, Spring.
    13. Dmitry Ivanov & Alexandre Dolgui & Boris Sokolov, 2019. "The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics," International Journal of Production Research, Taylor & Francis Journals, vol. 57(3), pages 829-846, February.
    14. Katz, Michael L & Shapiro, Carl, 1985. "Network Externalities, Competition, and Compatibility," American Economic Review, American Economic Association, vol. 75(3), pages 424-440, June.
    15. Chien-Yin Chen & Shiue-Hung Lin & Li-Chen Chou & Kun-Dang Chen, 2018. "A comparative study of production efficiency in coastal region and non-coastal region in Mainland China: An application of metafrontier model," The Journal of International Trade & Economic Development, Taylor & Francis Journals, vol. 27(8), pages 901-916, November.
    16. Liang Liang & Feng Yang & Wade Cook & Joe Zhu, 2006. "DEA models for supply chain efficiency evaluation," Annals of Operations Research, Springer, vol. 145(1), pages 35-49, July.
    17. Shahidul Islam & S.T. Syed Shazali, 2011. "Determinants of manufacturing productivity: pilot study on labor‐intensive industries," International Journal of Productivity and Performance Management, Emerald Group Publishing Limited, vol. 60(6), pages 567-582, July.
    18. Shahidul Islam & S.T. Syed Shazali, 2011. "Determinants of manufacturing productivity: pilot study on labor‐intensive industries," International Journal of Productivity and Performance Management, Emerald Group Publishing Limited, vol. 60(6), pages 567-582, July.
    19. Giacomo Damioli & Vincent Van Roy & Daniel Vertesy, 2021. "The impact of artificial intelligence on labor productivity," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 11(1), pages 1-25, March.
    20. Mohammadali Vosooghidizaji & Atour Taghipour & Béatrice Canel-Depitre, 2020. "Supply chain coordination under information asymmetry: a review," International Journal of Production Research, Taylor & Francis Journals, vol. 58(6), pages 1805-1834, March.
    21. Enrique, Daisy Valle & Lerman, Laura Visintainer & Sousa, Paulo Renato de & Benitez, Guilherme Brittes & Bigares Charrua Santos, Fernando M. & Frank, Alejandro G., 2022. "Being digital and flexible to navigate the storm: How digital transformation enhances supply chain flexibility in turbulent environments," International Journal of Production Economics, Elsevier, vol. 250(C).
    22. Rohit Sharma & Anjali Shishodia & Angappa Gunasekaran & Hokey Min & Ziaul Haque Munim, 2022. "The role of artificial intelligence in supply chain management: mapping the territory," International Journal of Production Research, Taylor & Francis Journals, vol. 60(24), pages 7527-7550, December.
    23. Timothy F. Bresnahan & Erik Brynjolfsson & Lorin M. Hitt, 2002. "Information Technology, Workplace Organization, and the Demand for Skilled Labor: Firm-Level Evidence," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 117(1), pages 339-376.
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    Cited by:

    1. Xie, Liufang & Peng, Zhengbo & Tong, Xiaoge, 2026. "AI strategy, earnings management, and corporate fraud: Evidence from listed firms in China," Economic Modelling, Elsevier, vol. 156(C).

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