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How Generative Artificial Intelligence Adoption Enhances Firm-Level Supply Chain Resilience: Empirical Evidence from Chinese A-Share Listed Firms

Shi Jun, Yijun Chen, Wenli Hu
Vol.10, No.6, pp.14-32, 2026 DOI: 10.62051/ijgem.v10n6.03 June 29, 2026
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Abstract: Escalating global supply chain risks and rapid AI diffusion have elevated supply chain resilience (SCR) to a critical strategic imperative, yet whether and how generative artificial intelligence (GAI) adoption enhances firm-level SCR remains underexplored. Drawing on a panel dataset of Chinese A-share listed firms spanning 2017–2024, we quantify GAI adoption through systematic text analysis of annual reports, construct a composite SCR index via the entropy weight method, and estimate a two-way fixed effects model incorporating Bartik shift-share instrument and lagged province-by-industry mean instrumental variables to address endogeneity concerns. We find that GAI adoption significantly enhances firm-level SCR. Mechanism analysis reveals three positive transmission pathways: technological innovation, organizational investment intensification, and downstream customer structure optimization. It also uncovers a suppression effect operating through upstream supplier concentration, exposing an inherent trade-off between flexibility gains and coordination stability losses. Collectively, these findings indicate that GAI's SCR-enhancing effect is not automatic, but depends critically on the alignment between technological deployment and organizational adaptation, offering actionable implications for firms' GAI investment strategies in supply chain management.
Keywords: Generative artificial intelligence, Supply chain resilience, Technological innovation, Supply chain concentration, Organizational reconfiguration
APA Citation: Shi Jun, Yijun Chen, Wenli Hu (2026). How Generative Artificial Intelligence Adoption Enhances Firm-Level Supply Chain Resilience: Empirical Evidence from Chinese A-Share Listed Firms. International Journal of Global Economics and Management, 10(6), 14-32. https://doi.org/10.62051/ijgem.v10n6.03

References

  1. Ponomarov, S. Y., & Holcomb, M. C. (2009). Understanding the concept of supply chain resilience. International Journal of Logistics Management, 20(1), 124–143. https://doi.org/10.1108/09600030910964916
  2. Sheffi, Y., & Rice, J. B., Jr. (2005). A supply chain view of the resilient enterprise. MIT Sloan Management Review, 47(1), 41–48.
  3. Craighead, C. W., Blackhurst, J., Rungtusanatham, M. J., & Handfield, R. B. (2007). The severity of supply chain disruptions: Design characteristics and mitigation capabilities. Decision Sciences, 38(1), 131–156. https://doi.org/10.1111/j.1540-5915.2007.00154.x
  4. Ivanov, D. (2020). Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transportation Research Part E: Logistics and Transportation Review, 136, 101922. https://doi.org/10.1016/j.tre.2020.101922
  5. Ivanov, D., Dolgui, A., Blackhurst, J. V., & Choi, T. M. (2023). Toward supply chain viability theory: From lessons learned through COVID-19 pandemic to viable ecosystems. International Journal of Production Research, 61(8), 2402–2415. https://doi.org/10.1080/00207543.2022.2108637
  6. Jackson, I., Ivanov, D., Dolgui, A., & Namdar, J. (2024). Generative artificial intelligence in supply chain and operations management: A capability-based framework for analysis and implementation. International Journal of Production Research, 62(17), 6120–6145. https://doi.org/10.1080/00207543.2023.2294214
  7. Wu, H., Li, G., & Ivanov, D. (2025). The transformative power of generative AI for supply chain management: Theoretical framework and agenda. Frontiers of Engineering Management, 12(2), 425–433. https://doi.org/10.1016/j.fem.2024.11.004
  8. Boone, T., Fahimnia, B., Ganeshan, R., Herold, D. M., & Sanders, N. R. (2025). Generative AI: Opportunities, challenges, and research directions for supply chain resilience. Transportation Research Part E: Logistics and Transportation Review, 199, 104135. https://doi.org/10.1016/j.tre.2025.104135
  9. Li, L., Liu, Y., Jin, Y., Cheng, T. C. E., & Zhang, Q. (2024). Generative AI-enabled supply chain management: The critical role of coordination and dynamism. International Journal of Production Economics, 277, 109388. https://doi.org/10.1016/j.ijpe.2024.109388
  10. Park, M. J. (2026). Generative AI and responsible supply chains: When sustainability gains undermine social legitimacy. Technology in Society, 85, 103240. https://doi.org/10.1016/j.techsoc.2026.103240
  11. Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210. https://doi.org/10.5465/amr.2018.0172
  12. Yang, M., Fu, M., & Zhang, Z. (2021). The adoption of digital technologies in supply chains: Drivers, process and impact. Technological Forecasting and Social Change, 169, 120795. https://doi.org/10.1016/j.techfore.2021.120795
  13. Sodhi, M. M. S., Seyedghorban, Z., Tahernejad, H., & Samson, D. (2022). Why emerging supply chain technologies initially disappoint: Blockchain, IoT, and AI. Production and Operations Management, 31(6), 2517–2537. https://doi.org/10.1111/poms.13693
  14. Guo, J., Jia, F., & Chen, L. (2026). How generative AI adoption affects supply chain resilience: An operations and supply chain management perspective. Technological Forecasting and Social Change, 224, 124446. https://doi.org/10.1016/j.techfore.2026.124446
  15. Li, L., Zhu, W., Chen, L., & Liu, Y. (2024). Generative AI usage and sustainable supply chain performance: A practice-based view. Transportation Research Part E: Logistics and Transportation Review, 192, 103761. https://doi.org/10.1016/j.tre.2024.103761
  16. Pettit, T. J., Croxton, K. L., & Fiksel, J. (2013). Ensuring supply chain resilience: Development and implementation of an assessment tool. Journal of Business Logistics, 34(1), 46–76. https://doi.org/10.1111/jbl.12007
  17. Hohenstein, N. O., Feisel, E., Hartmann, E., & Giunipero, L. (2015). Research on the phenomenon of supply chain resilience: A systematic review and paths for further investigation. International Journal of Physical Distribution & Logistics Management, 45(1/2), 90–117. https://doi.org/10.1108/IJPDLM-06-2013-0186
  18. Ivanov, D., & Dolgui, A. (2020). A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. Production Planning & Control, 32(9), 775–788. https://doi.org/10.1080/09537287.2020.1768450
  19. Dubey, R., Gunasekaran, A., Childe, S. J., Fosso Wamba, S., Roubaud, D., & Foropon, C. (2021). Empirical investigation of data analytics capability and organizational flexibility as complements to supply chain resilience. International Journal of Production Research, 59(1), 110–128. https://doi.org/10.1080/00207543.2019.1690640
  20. Jiang, S., Yeung, A. C., Han, Z., & Huo, B. (2023). The effect of customer and supplier concentrations on firm resilience during the COVID-19 pandemic: Resource dependence and power balancing. Journal of Operations Management, 69(3), 497–518. https://doi.org/10.1002/joom.1132
  21. Pan, H., Zou, N., Wang, R., Ma, J., & Liu, D. (2025). Artificial Intelligence Usage and Supply Chain Resilience: An Organizational Information Processing Theory Perspective. Sustainability, 13(9), 724. https://doi.org/10.3390/su13090724
  22. Guo, X., Chen, Y., Xie, J., Wang, H., & Lei, X. (2025). Research on supply chain resilience mechanism of AI-Enabled manufacturing enterprises -based on organizational change perspective. Scientific Reports, 15(1), 31177. https://doi.org/10.1038/s41598-025-94162-7
  23. Zamani, E. D., Smyth, C., Gupta, S., & Dennehy, D. (2023). Artificial intelligence and big data analytics for supply chain resilience: a systematic literature review. Annals of Operations Research, 327(2), 605–632. https://doi.org/10.1007/s10479-022-04773-8
  24. Galbraith, J. R. (1974). Organization design: An information processing view. Interfaces, 4(3), 28–36. https://doi.org/10.1287/inte.4.3.28
  25. Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533. https://doi.org/10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z
  26. Teece, D. J. (2007). Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance. Strategic Management Journal, 28(13), 1319–1350. https://doi.org/10.1002/smj.640
  27. Fosso Wamba, S., Guthrie, C., Queiroz, M. M., & Minner, S. (2024). ChatGPT and generative artificial intelligence: An exploratory study of key benefits and challenges in operations and supply chain management. International Journal of Production Research, 62(16), 5676–5696. https://doi.org/10.1080/00207543.2023.2291174
  28. Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128–152. https://doi.org/10.2307/2393553
  29. Chowdhury, M. M. H., & Quaddus, M. (2017). Supply chain resilience: Conceptualization and scale development using dynamic capability theory. International Journal of Production Economics, 188, 185–204. https://doi.org/10.1016/j.ijpe.2017.03.017
  30. Feng, B., & Jalali, M. (2024). The Influence of Technological Innovation Capability on Sustainable Supply Chain Management Implementation—Evidence from Patent-Intensive Industries. Sustainability, 16(12), 5183. https://doi.org/10.3390/su16125183
  31. Brynjolfsson, E., Li, D., & Raymond, L. (2025). Generative AI at work. The Quarterly Journal of Economics, 140(2), 889–942. https://doi.org/10.1093/qje/qjae009
  32. Cockburn, I. M., Henderson, R., & Stern, S. (2018). The impact of artificial intelligence on innovation: An exploratory analysis (pp. 115–146). University of Chicago Press.
  33. Cillo, P., & Rubera, G. (2025). Generative AI in innovation and marketing processes: A roadmap of research opportunities. Journal of the Academy of Marketing Science, 53(3), 684–701. https://doi.org/10.1007/s11747-024-00984-7
  34. Shinkle, G. A., Gujarati, C., & Sharry, P. (2026). Scenario analysis in the AI era: Redefining human involvement. Organizational Dynamics, 55(1), 101197. https://doi.org/10.1016/j.orgdyn.2026.101197
  35. Liu, W., He, Y., Dong, J., & Cao, Y. (2023). Disruptive technologies for advancing supply chain resilience. Frontiers of Engineering Management, 10(2), 360–366. https://doi.org/10.1016/j.fem.2023.04.003
  36. Pfeffer, J., & Salancik, G. R. (1978). The external control of organizations: A resource dependence perspective. Harper & Row.
  37. Hillman, A. J., Withers, M. C., & Collins, B. J. (2009). Resource dependence theory: A review. Journal of Management, 35(6), 1404–1427. https://doi.org/10.1177/0149206309343469
  38. Choi, T. Y., & Krause, D. R. (2006). The supply base and its complexity: Implications for transaction costs, risks, responsiveness, and innovation. Journal of Operations Management, 24(5), 637–652. https://doi.org/10.1016/j.jom.2005.09.003
  39. Handfield, R. B., Cousins, P. D., Lawson, B., & Petersen, K. J. (2015). How can supply management really improve performance? A knowledge-based model of alignment capabilities. Journal of Supply Chain Management, 51(3), 3–17. https://doi.org/10.1111/jscm.12076
  40. Li, J., Zhang, G., & Wang, X. (2026). Supply Chain Concentration and Enterprise Resilience: Evidence from China. Systems, 14(4), 386. https://doi.org/10.3390/systems14040386
  41. Dyer, J. H., & Singh, H. (1998). The relational view: Cooperative strategy and sources of interorganizational competitive advantage. Academy of Management Review, 23(4), 660–679. https://doi.org/10.5465/amr.1998.23707
  42. Wu, Z., Choi, T. Y., & Rungtusanatham, M. J. (2010). Supplier–supplier relationships in buyer–supplier–supplier triads: Implications for supplier performance. Journal of Operations Management, 28(2), 115–123. https://doi.org/10.1016/j.jom.2009.08.002
  43. Bode, C., & Wagner, S. M. (2015). Structural drivers of upstream supply chain complexity and the frequency of supply chain disruptions. Journal of Operations Management, 36, 215–228. https://doi.org/10.1016/j.jom.2015.04.001
  44. Du, C., & Zhang, Q. (2022). Supply network position, digital transformation and innovation performance: Evidence from listed Chinese manufacturing firms. PLOS ONE, 17(12), e0279133. https://doi.org/10.1371/journal.pone.0279133
  45. Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., et al. (2023). "So what if ChatGPT wrote it?" Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
  46. Zhong, W., Ma, Z., Tong, T. W., Zhang, Y., & Xie, L. (2021). Customer concentration, executive attention, and firm search behavior. Academy of Management Journal, 64(5), 1625–1647. https://doi.org/10.5465/amj.2019.0697
  47. Coase, R. H. (1993). The nature of the firm. In O. E. Williamson & S. G. Winter (Eds.), The Nature of the Firm: Origins, Evolution, and Development (pp. 18–33). Oxford University Press.
  48. Williamson, O. E. (1975). Markets and hierarchies: analysis and antitrust implications: a study in the economics of internal organization. Free Press.
  49. Williamson, O. E. (1985). The economic institutions of capitalism: firms, markets, relational contracting. Free Press.
  50. Brynjolfsson, E., Rock, D., & Syverson, C. (2021). The productivity J-curve: How intangibles complement general purpose technologies. American Economic Journal: Macroeconomics, 13(1), 333–372. https://doi.org/10.1257/mac.20190350
  51. Klein, T., & Wieczorek, S. (2026). The Headless Firm: How AI Reshapes Enterprise Boundaries. arXiv preprint arXiv:2602.21401. https://doi.org/10.48550/arXiv.2602.21401
  52. Grewal, D., Satornino, C. B., Davenport, T. H., & Guha, A. (2025). How generative AI is shaping the future of marketing. Journal of the Academy of Marketing Science, 53(3), 702–722. https://doi.org/10.1007/s11747-024-00985-6
  53. Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24–42. https://doi.org/10.1007/s11747-019-00696-0
  54. AlJaloudi, O., Thiam, M., Abdel Qader, M., Al-Mhdawi, M. K. S., Qazi, A., & Dacre, N. (2024). Examining the integration of generative AI models for improved risk management practices in the financial sector. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5023563
  55. Zhang, Q., Zhang, Y., Chen, G., & Zheng, J. (2026). Generative AI for easing SMEs' financing constraints: Empirical evidence from E-commerce supply chain finance. International Review of Economics & Finance, 107, 105128. https://doi.org/10.1016/j.iref.2026.105128
  56. Brandon-Jones, E., Squire, B., Autry, C. W., & Petersen, K. J. (2014). A contingent resource-based perspective of supply chain resilience and robustness. Journal of Supply Chain Management, 50(3), 55–73. https://doi.org/10.1111/jscm.12040
  57. Ambulkar, S., Blackhurst, J., & Grawe, S. (2015). Firm's resilience to supply chain disruptions: Scale development and empirical examination. Journal of Operations Management, 33–34, 111–122. https://doi.org/10.1016/j.jom.2014.11.002
  58. Shi, J., Wu, S., & Cao, W. (2024). Slack Resources and Corporate Supply Chain Resilience: An Empirical Study Based on A-Share Listed Companies. In 2024 2nd International Conference on Digital Economy and Management Science (CDEMS 2024) (pp. 481–486). Atlantis Press.
  59. Su, Z. F., Wang, T. W., Bai, Y. L., & Li, J. Z. (2025). Digital finance and manufacturing enterprise supply chain resilience improvement. Economic Review, (1), 87–101. https://doi.org/10.19361/j.er.2025.01.06
  60. Yu, M. G., & Fan, R., Zhong, H. J. (2016). China's industrial policy and enterprise technological innovation. China Industrial Economics, (12), 5–22. https://doi.org/10.19581/j.cnki.ciejournal.2016.12.002
  61. Tang, Y. J. (2009). Bargaining power of suppliers and dealers and corporate performance: Empirical evidence from Chinese manufacturing listed companies (2005-2007). China Industrial Economics, (10), 67–76. https://doi.org/10.19581/j.cnki.ciejournal.2009.10.006
  62. Li, W. B., & Zhang, X. (2023). Digital transformation and enterprise transaction costs. Journal of Huazhong University of Science and Technology (Social Science Edition), 37(6), 8–17. https://doi.org/10.19648/j.cnki.jhustss1980.2023.06.02
  63. Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182. https://doi.org/10.1037/0022-3514.51.6.1173
  64. Wen, Z. L., & Ye, B. J. (2014). Mediation effect analysis: Methods and model development. Advances in Psychological Science, 22(5), 731–745.
  65. Wang, H., Chen, Y., Xie, J., & Liu, C. (2025). Research on digital empowerment, innovation vitality and manufacturing supply chain resilience mechanism. PLOS ONE, 20(2), e0316183. https://doi.org/10.1371/journal.pone.0316183