IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v85y2025ipbs1544612325012425.html

Artificial intelligence and resilience of supply chain in China’s listed firms: Insights into vertical spillover effects

Author

Listed:
  • Yang, Lu
  • Huang, Manyu
  • Wang, Haoyang

Abstract

Based on data from China’s A-share listed firms from 2012 to 2022, this study employs advanced big data text recognition technology to examine the impact and mechanisms of artificial intelligence (AI) development on supply chain resilience (SCR). The findings indicate that AI development significantly enhances SCR. The primary mechanisms involve promoting diversified supply chain configurations, reducing inefficient investment, and improving operational efficiency. The effect is more pronounced in non-state-owned enterprises compared to state-owned enterprises. Moreover, AI development exerts a stronger positive influence on SCR when firm executives have overseas experience. Additionally, it generates vertical spillover effects, particularly benefiting suppliers. This study fills the gap in quantitative research on the relationship between AI and SCR by employing text mining and patent identification to measure AI development, identifying key mechanisms, and revealing vertical spillover effects.

Suggested Citation

  • Yang, Lu & Huang, Manyu & Wang, Haoyang, 2025. "Artificial intelligence and resilience of supply chain in China’s listed firms: Insights into vertical spillover effects," Finance Research Letters, Elsevier, vol. 85(PB).
    • Handle: RePEc:eee:finlet:v:85:y:2025:i:pb:s1544612325012425
    DOI: 10.1016/j.frl.2025.107984
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612325012425
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.frl.2025.107984?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Zhang, Xinyu & Liu, Yixuan & Zhu, Jiawei & Teng, Yifei & Chen, Zedong & You, Jiansheng & Wang, Xiaohui, 2025. "The outgoing audit of natural resources assets and green governance: New evidence from causal inference based on double machine learning," Economic Analysis and Policy, Elsevier, vol. 87(C), pages 1113-1129.
    2. Pournader, Mehrdokht & Ghaderi, Hadi & Hassanzadegan, Amir & Fahimnia, Behnam, 2021. "Artificial intelligence applications in supply chain management," International Journal of Production Economics, Elsevier, vol. 241(C).
    3. Ron Martin, 2012. "Regional economic resilience, hysteresis and recessionary shocks," Journal of Economic Geography, Oxford University Press, vol. 12(1), pages 1-32, January.
    4. Qi, Ruijuan & Ma, Guowei & Liu, Chang & Zhang, Qiwen & Wang, Qianyi, 2024. "Enterprise digital transformation and supply chain resilience," Finance Research Letters, Elsevier, vol. 66(C).
    5. Cull, Robert & Xu, Lixin Colin & Zhu, Tian, 2009. "Formal finance and trade credit during China's transition," Journal of Financial Intermediation, Elsevier, vol. 18(2), pages 173-192, April.
    6. Fu, Tong & Qiu, Zhaoxuan & Yang, Xiangyang & Li, Zijun, 2024. "The impact of artificial intelligence on green technology cycles in China," Technological Forecasting and Social Change, Elsevier, vol. 209(C).
    7. Shi, Hongyu & Liu, Yu & Yang, Yaru, 2024. "Enterprise digital transformation and supply chain stability," Finance Research Letters, Elsevier, vol. 63(C).
    8. Li, Yuanyuan & Zhang, Yindan & Geng, Liping, 2024. "Digital finance, financing constraints and supply chain resilience," International Review of Economics & Finance, Elsevier, vol. 96(PA).
    9. James Levinsohn & Amil Petrin, 2003. "Estimating Production Functions Using Inputs to Control for Unobservables," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 70(2), pages 317-341.
    10. Scott Richardson, 2006. "Over-investment of free cash flow," Review of Accounting Studies, Springer, vol. 11(2), pages 159-189, September.
    11. Li, Zongru & Zhang, Xiaohan & Tao, Zhe & Wang, Binbin, 2024. "Enterprise digital transformation and supply chain management," Finance Research Letters, Elsevier, vol. 60(C).
    12. Hugo Bodory & Martin Huber & Lukáš Lafférs, 2022. "Evaluating (weighted) dynamic treatment effects by double machine learning [Identification of causal effects using instrumental variables]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 628-648.
    13. Cheng, Dongshu & Wei, Ying & Zhang, Mengchen & Wang, Chang, 2025. "The smart city pilot policy and corporate supply chain resilience," International Review of Economics & Finance, Elsevier, vol. 102(C).
    14. Dmitry Ivanov & Alexandre Dolgui, 2020. "Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak," International Journal of Production Research, Taylor & Francis Journals, vol. 58(10), pages 2904-2915, May.
    15. Tsan‐Ming Choi & Stein W. Wallace & Yulan Wang, 2018. "Big Data Analytics in Operations Management," Production and Operations Management, Production and Operations Management Society, vol. 27(10), pages 1868-1883, October.
    16. Marta Negri & Enrico Cagno & Claudia Colicchia & Joseph Sarkis, 2021. "Integrating sustainability and resilience in the supply chain: A systematic literature review and a research agenda," Business Strategy and the Environment, Wiley Blackwell, vol. 30(7), pages 2858-2886, November.
    17. Sube Singh & Ramesh Kumar & Rohit Panchal & Manoj Kumar Tiwari, 2021. "Impact of COVID-19 on logistics systems and disruptions in food supply chain," International Journal of Production Research, Taylor & Francis Journals, vol. 59(7), pages 1993-2008, April.
    18. Jiang, Wenjuan & Wang, Xin, 2024. "Enterprise digital transformation empowers supply Chain stability," Finance Research Letters, Elsevier, vol. 66(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yang, Lian-Xing & Zhang, Yin-Ting & Chen, Mo-Lei, 2025. "Impact of temperature variability on supply chain resilience in China: Mechanisms and insights," Finance Research Letters, Elsevier, vol. 85(PA).
    2. An, Qiguang & Wang, Yongkai & Liu, Feini & Wang, Ruoyu, 2025. "Does the integration of digital and real economies enhance corporate supply chain resilience? Evidence from China’s listed firms," Finance Research Letters, Elsevier, vol. 85(PA).
    3. Chen, Guangren & Yu, Zuwei, 2024. "Digital finance, value cocreation, and supply chain resilience: Evidence from listed manufacturing firms in China," Finance Research Letters, Elsevier, vol. 70(C).
    4. Zheng, Mengze & Wang, Rui & Ye, Jing & Li, Te, 2025. "How does supply chain finance enhance firms' supply chain resilience?," International Review of Economics & Finance, Elsevier, vol. 102(C).
    5. Xu, Xinkuo & Liu, Qianyu, 2025. "Transformation of innovation in heavily polluting enterprises under resource constraints: The role of green finance policy," International Review of Economics & Finance, Elsevier, vol. 103(C).
    6. Guo, Xiao & Li, Rongxin, 2025. "Climate physical risks and corporate supply chain resilience: How do investor sentiment and risk-taking behaviors influence supply chain operations?," Finance Research Letters, Elsevier, vol. 85(PA).
    7. Chen, Xiaofei & Li, Qing, 2025. "Digital finance penetration, new media-driven development, and enhancement of industrial chain resilience," Finance Research Letters, Elsevier, vol. 85(PD).
    8. Zhou, Wenxiao & Song, Yi & Xu, Deyi & Zhang, Yijun, 2025. "Government subsidies and industrial chain resilience: Evidence from Chinese resource-based enterprises," Resources Policy, Elsevier, vol. 102(C).
    9. Dong, Feng & Zhao, Xu & Mangla, Sachin Kumar & Song, Malin, 2025. "Enhanced supply chain resilience under geopolitical risks: The role of artificial intelligence," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 202(C).
    10. Zhang, Juan & Miao, Xiaoming & Shang, Tiantian, 2025. "Impact mechanism of digital transformation on supply chain resilience," Finance Research Letters, Elsevier, vol. 76(C).
    11. Yingjie Xu & Bingchao Zheng & Baojie Guo, 2025. "How Do Industrial Robots Affect the Total Factor Productivity in Manufacturing Enterprises?," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(4), pages 14057-14093, October.
    12. Zeng, Heqi & Wen, Li, 2025. "How does intelligent transformation enhance the security of industrial and supply chains?," Finance Research Letters, Elsevier, vol. 84(C).
    13. Zhen, Xiaofei & Zhou, Yiyang, 2025. "Digital transformation and corporate creditworthiness," Finance Research Letters, Elsevier, vol. 74(C).
    14. Xu, Xinkuo & Liu, Qianyu, 2025. "Green credit policy and the innovation dilemma: Evidence from China," Finance Research Letters, Elsevier, vol. 83(C).
    15. Xia, Jia & Wei, Wei, 2025. "Government procurement and corporate investment: Evidence from China's A-share listed companies," Economic Modelling, Elsevier, vol. 152(C).
    16. Cao, Fuzhen & An, Yi & Xie, Wei, 2025. "Chinese exchange rate policy uncertainty, foreign exchange hedging, and corporate resilience," Finance Research Letters, Elsevier, vol. 86(PG).
    17. Liao, Wenqi & Zhuo, Ling, 2025. "The impact of public data openness on corporate supply chain efficiency: A quasi-natural experiment based on a local government data openness platform," Economic Analysis and Policy, Elsevier, vol. 87(C), pages 1557-1574.
    18. Zhang, Hengfeng & Wu, Jing, 2025. "How ESG performance aspiration gap affect the supply chain stability?," Finance Research Letters, Elsevier, vol. 86(PA).
    19. Li, Shuquan & Kong, Xiangcai & Ianenko, Marina & Wang, Wentao, 2025. "The impact of digital transformation on enterprise supply chain finance," Finance Research Letters, Elsevier, vol. 85(PA).
    20. Emilia Vann Yaroson & Soumyadeb Chowdhury & Sachin Kumar Mangla & Prasanta Kumar Dey, 2024. "Unearthing the interplay between organisational resources, knowledge and industry 4.0 analytical decision support tools to achieve sustainability and supply chain wellbeing," Annals of Operations Research, Springer, vol. 342(2), pages 1321-1368, November.

    More about this item

    Keywords

    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:finlet:v:85:y:2025:i:pb:s1544612325012425. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/frl .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.