IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i19p8576-d1757217.html

Research on the Impact of Enterprise Artificial Intelligence on Supply Chain Resilience: Empirical Evidence from Chinese Listed Companies

Author

Listed:
  • Lijie Lin

    (School of Economics and Management, Yantai University, Yantai 264005, China)

  • Xiangyu Zhang

    (School of Economics and Management, Yantai University, Yantai 264005, China)

Abstract

Artificial intelligence (AI), as a strategic technology leading the current technological revolution and industrial transformation, functions as a pivotal catalyst for enhancing high-quality supply chain development and as the primary engine driving supply chains towards environmentally sustainable, low-carbon models. This study seeks to clarify how AI bolsters supply chain resilience through enhanced information transparency and dynamic capabilities, while examining the moderating influence of digital government in this context. Based on this, this study selected A-share listed companies from 2012 to 2023 as research samples. An entropy-based approach was utilized to develop a supply chain resilience indicator system. A two-way fixed-effects model was employed to analyze the mechanism by which business AI impacts supply chain resilience. Studies demonstrate that company artificial intelligence can markedly improve supply chain resilience. In this process, information transparency, innovative capacity, and absorptive capacity partially mediate the effect, while digital governance exerts a positive moderating influence. Heterogeneity studies indicate that artificial intelligence has a significantly greater favorable effect on supply chain resilience for high-tech corporations, manufacturing firms, growth-stage companies, mature-stage businesses, and chain master enterprises. The research findings not only reveal the impact and underlying mechanisms of enterprise artificial intelligence on supply chain resilience, offering a new perspective for systematically understanding the relationship between enterprise AI and supply chain resilience, but also provide key pathways and empirical evidence for leveraging digital technologies to build sustainable supply chains.

Suggested Citation

  • Lijie Lin & Xiangyu Zhang, 2025. "Research on the Impact of Enterprise Artificial Intelligence on Supply Chain Resilience: Empirical Evidence from Chinese Listed Companies," Sustainability, MDPI, vol. 17(19), pages 1-24, September.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:19:p:8576-:d:1757217
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/19/8576/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/17/19/8576/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tang, Haodan & Wu, Kaiwen & Zhou, Jing, 2025. "Smarter supply chains, stronger resilience? The impact of AI on preparation, response, and recovery," Economics Letters, Elsevier, vol. 254(C).
    2. Toorajipour, Reza & Sohrabpour, Vahid & Nazarpour, Ali & Oghazi, Pejvak & Fischl, Maria, 2021. "Artificial intelligence in supply chain management: A systematic literature review," Journal of Business Research, Elsevier, vol. 122(C), pages 502-517.
    3. Liu, Xukang & Ma, Chao-Qun & Ren, Yi-Shuai, 2025. "ESG reactions to fintech: The role of cross-border capital flows," Research in International Business and Finance, Elsevier, vol. 76(C).
    4. Brusset, Xavier, 2016. "Does supply chain visibility enhance agility?," International Journal of Production Economics, Elsevier, vol. 171(P1), pages 46-59.
    5. Mengmeng Wang & Xiaoming Pan, 2022. "Drivers of Artificial Intelligence and Their Effects on Supply Chain Resilience and Performance: An Empirical Analysis on an Emerging Market," Sustainability, MDPI, vol. 14(24), pages 1-16, December.
    6. Safinaz H. Abourokbah & Reem M. Mashat & Mohammad Asif Salam, 2023. "Role of Absorptive Capacity, Digital Capability, Agility, and Resilience in Supply Chain Innovation Performance," Sustainability, MDPI, vol. 15(4), pages 1-25, February.
    7. Michael Trusov & Liye Ma & Zainab Jamal, 2016. "Crumbs of the Cookie: User Profiling in Customer-Base Analysis and Behavioral Targeting," Marketing Science, INFORMS, vol. 35(3), pages 405-426, May.
    8. Liu, Yingji & Shen, Fangbing & Guo, Ju & Hu, Guoheng & Song, Yuegang, 2025. "Can artificial intelligence technology improve companies' capacity for green innovation? Evidence from listed companies in China," Energy Economics, Elsevier, vol. 143(C).
    9. Xun Zhang & Yamei Wei, 2025. "The Impact Mechanism of AI Technology on Enterprise Innovation Resilience," Sustainability, MDPI, vol. 17(11), pages 1-19, June.
    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. Xun Zhang & Yamei Wei, 2025. "The Impact Mechanism of AI Technology on Enterprise Innovation Resilience," Sustainability, MDPI, vol. 17(11), pages 1-19, June.
    2. Logožar, Klavdij, 2025. "The role of Artificial Intelligence in Supply Chain Management: A systematic Literature Review," Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference (2024), Hybrid Conference, Dubrovnik, Croatia, in: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Hybrid Conference, Dubrovnik, Croatia, 5-7 September, 2024, pages 328-337, IRENET - Society for Advancing Innovation and Research in Economy, Zagreb.
    3. Rao, Amar & Sharma, Gagan Deep & Tiwari, Aviral Kumar & Hossain, Mohammad Razib & Dev, Dhairya, 2025. "Crude oil Price forecasting: Leveraging machine learning for global economic stability," Technological Forecasting and Social Change, Elsevier, vol. 216(C).
    4. Abdullah Alhamad & Hashed Mabkhot, 2023. "Determinants of Product Innovation Performance in Aviation Industry in Saudi Arabia," Economies, MDPI, vol. 11(2), pages 1-18, February.
    5. Sujan Piya & Ahm Shamsuzzoha & Mohammad Khadem & Nasr Al-Hinai, 2020. "Identification of Critical Factors and Their Interrelationships to Design Agile Supply Chain: Special Focus to Oil and Gas Industries," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 21(3), pages 263-281, September.
    6. Prajogo, Daniel & Toy, Jordan & Bhattacharya, Ananya & Oke, Adegoke & Cheng, T.C.E., 2018. "The relationships between information management, process management and operational performance: Internal and external contexts," International Journal of Production Economics, Elsevier, vol. 199(C), pages 95-103.
    7. Toorajipour, Reza & Oghazi, Pejvak & Sohrabpour, Vahid & Patel, Pankaj C. & Mostaghel, Rana, 2022. "Block by block: A blockchain-based peer-to-peer business transaction for international trade," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    8. Ifeyinwa Juliet Orji & Chukwuebuka Martinjoe U-Dominic, 2026. "The effect of supply chain learning in mitigating the challenges to adopting artificial intelligence for supply chain sustainability," Annals of Operations Research, Springer, vol. 358(3), pages 1561-1602, March.
    9. EuiBeom Jeong & DonHee Lee, 2025. "AI–human collaboration in services: an integrative framework to uncover the key success factors," Service Business, Springer;Pan-Pacific Business Association, vol. 19(3), pages 1-45, September.
    10. Vincenzo Varriale & Antonello Cammarano & Francesca Michelino & Mauro Caputo, 2025. "Critical analysis of the impact of artificial intelligence integration with cutting-edge technologies for production systems," Journal of Intelligent Manufacturing, Springer, vol. 36(1), pages 61-93, January.
    11. Luka Tomat, 2023. "Artificial Intelligence in Higher Education Research: Insights From Co-Occurrence Analysis," Economic, Social and Environmental Sustainability; The Role of Technology and Political Dialogue,, ToKnowPress.
    12. Fang, Xia & Zhang, Yun & Lv, Shuqing & Tan, Longxin, 2025. "Does digital finance spatial correlation drive income convergence?," Economic Modelling, Elsevier, vol. 153(C).
    13. Samadhiya, Ashutosh & Yadav, Sanjeev & Kumar, Anil & Majumdar, Abhijit & Luthra, Sunil & Garza-Reyes, Jose Arturo & Upadhyay, Arvind, 2023. "The influence of artificial intelligence techniques on disruption management: Does supply chain dynamism matter?," Technology in Society, Elsevier, vol. 75(C).
    14. Viachaslau Filimonau & Mark Ashton & Belén Derqui & Gilda Hernandez‐Maskivker, 2025. "Exploring How Artificial Intelligence (AI) Can Enable Sustainability in the Hospitality Industry," Sustainable Development, John Wiley & Sons, Ltd., vol. 33(6), pages 9123-9143, December.
    15. Guilherme Francisco Frederico, 2023. "ChatGPT in Supply Chains: Initial Evidence of Applications and Potential Research Agenda," Logistics, MDPI, vol. 7(2), pages 1-9, April.
    16. Gunasekaran, Angappa & Subramanian, Nachiappan & Papadopoulos, Thanos, 2017. "Information technology for competitive advantage within logistics and supply chains: A review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 99(C), pages 14-33.
    17. Loske, Dominic & Klumpp, Matthias, 2021. "Human-AI collaboration in route planning: An empirical efficiency-based analysis in retail logistics," International Journal of Production Economics, Elsevier, vol. 241(C).
    18. Mohammed Alquraish, 2025. "Digital Transformation, Supply Chain Resilience, and Sustainability: A Comprehensive Review with Implications for Saudi Arabian Manufacturing," Sustainability, MDPI, vol. 17(10), pages 1-33, May.
    19. Shaheer, Noman & Kim, Kijong & Li, Sali, 2022. "Internationalization of Digital Innovations: A Rapidly Evolving Research Stream," Journal of International Management, Elsevier, vol. 28(4).
    20. Azadi, Majid & Yousefi, Saeed & Farzipoor Saen, Reza & Shabanpour, Hadi & Jabeen, Fauzia, 2023. "Forecasting sustainability of healthcare supply chains using deep learning and network data envelopment analysis," Journal of Business Research, Elsevier, vol. 154(C).

    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:gam:jsusta:v:17:y:2025:i:19:p:8576-:d:1757217. 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: MDPI Indexing Manager The email address of this maintainer does not seem to be valid anymore. Please ask MDPI Indexing Manager to update the entry or send us the correct address (email available below). General contact details of provider: https://www.mdpi.com .

    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.