The Application of Artificial Intelligence and the Resilience of Manufacturing Enterprises: Mechanisms of Action and Heterogeneity Boundaries
DOI:
https://doi.org/10.54097/xkyz6a93Keywords:
artificial intelligence, manufacturing enterprises, corporate resilience, mechanism of action, heterogeneity, technological innovation.Abstract
Against the backdrop of increasing external environmental uncertainties and accelerated intelligent transformation in the manufacturing sector, enhancing the resilience of manufacturing enterprises has become a critical issue for ensuring the security of industrial and supply chains and promoting high-quality development in manufacturing. This study uses China A-share listed manufacturing companies from 2015 to 2024 as research samples, constructs an indicator of corporate AI application levels based on annual report text mining methods, and measures corporate resilience across three dimensions: resistance capacity, recovery capacity, and innovation capacity, empirically examining the impact of AI application on manufacturing enterprise resilience and its underlying mechanisms. The findings reveal that AI application significantly enhances the resilience of manufacturing enterprises, with conclusions remaining robust even after substituting core variables, adjusting model specifications, controlling for cluster standard errors at the firm level, incorporating provincial fixed effects, and employing instrumental variable methods. Mechanism tests indicate that AI primarily strengthens corporate resilience by improving talent incentives, fostering technological innovation, and reducing management costs, with technological innovation serving as the primary transmission pathway; while internal control is partially influenced by AI, its mediating effect fails the Bootstrap robustness test. Further heterogeneity analysis demonstrates that the impact of AI on corporate resilience varies across ownership structures, firm size, regional differences, and industry attributes. This study enriches research on factors influencing corporate resilience in the digital economy context and provides empirical evidence for manufacturing enterprises to rationally advance AI adoption and enhance risk resistance and recovery capabilities.
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[1] Qiongwen Cheng, Jingli Zhu. How can artificial intelligence applications enhance the resilience of manufacturing enterprises? [J]. Economic and Management Research, 2025,46(8):56–75. DOI: 10.13502/j.cnki.issn 1000-7636.2025.08.004.
[2] Chu Suru, Long Suying. The impact of R&D innovation on corporate resilience: the mediating role of human capital structure upgrading [J]. Business Observation, 2026,12(5):92–96+107.
[3] Deng Feng, Wang Jindan. A Study on the Multidimensional Effects of Artificial Intelligence Technology in Enhancing Corporate Resilience: From the Perspective of Industrial Chain Collaboration [J]. Science and Technology Progress & Strategies, 2026,43(7):11–21.
[4] Fang Haochao. The Impact of Artificial Intelligence Applications on Corporate Innovation and Its Mechanisms: Evidence from China A-share Listed Companies [J]. Science and Technology Management Research, 2024,44(15):157-167.
[5] Ministry of Industry and Information Technology, National Development and Reform Commission, Ministry of Education, Ministry of Science and Technology, Ministry of Finance, Ministry of Human Resources and Social Security, State Administration for Market Regulation, State-owned Assets Supervision and Administration Commission of the State Council. "14th Five-Year Plan for Intelligent Manufacturing Development [Z]." 2021.
[6] Gong Tingting, Wang Jitian. Application of Digital and Intelligent Technologies and the Quality of Corporate Information Disclosure: The Mediating Effect of Internal Control and Dynamic Adjustment by Marketization Process [J]. Industrial Technology Economics, 2025,44(11):114–123.
[7] State Council. Opinions on Further Implementing the "Artificial Intelligence+" Initiative [Z]. 2025.
[8] State Council. New Generation Artificial Intelligence Development Plan [Z]. 2017.
[9] Hua Junguo, Su Yiming, Cui Yu. The Impact of Digital Transformation on the Quality of Corporate Internal Control: A Study Based on Analysis of Shanghai and Shenzhen A-share Listed Companies (2011–2021) [J]. Journal of Zhengzhou University of Light Industry (Social Sciences Edition), 2025,26(4):66–73.
[10] Li Bin, Xue Ximeng, Zhang Qirui, et al. The impact of artificial intelligence on corporate innovation resilience: from the perspective of technological capability adaptability [J]. Research and Development Management, 2025,37(6):53–65. DOI: 10.13581/j.cnki.rdm. 20241839.
[11] Li Rongrong, Yang Rui. Application of Artificial Intelligence Technology Enhances Organizational Resilience in Enterprises: A Perspective on the Co-evolution of Technological Affordance and Dynamic Capabilities [J]. Journal of Shanxi University of Finance and Economics, 2026,48(3):102–114. DOI: 10.13781/j.cnki.1007-9556.2026.03.009.
[12] Li Changying, Zhang Shuai. How Digital Technology Innovation Enhances Corporate Resilience [J]. Financial Issues Research, 2026, (4):88–102. DOI: 10.19654/j.cnki.cjwtyj. 2026.04.007.
[13] Liu Shuchun, Yan Jincheng, Zhang Sixue, et al. Can digital transformation in enterprise management improve input-output efficiency? [J]. Management World, 2021,37(5):170–190+13. DOI: 10.19744/j.cnki.11-1235/f.2021.0072.
[14] Liu Xiaoguang, Li Mengxue. Generative Artificial Intelligence and Corporate Innovation [J]. Journal of Shanxi University (Philosophy and Social Sciences Edition), 2026,49(3):131–143. DOI: 10.13451/j.cnki.shanxi.univ(phil.soc.), 2026.03.01.
[15] Luo Jun, Yang Hiqin, Zhang Panpan. Digital Talent Allocation and Resilience of the Digital Industry Chain [J]. China Science and Technology Forum, 2026, (2):73-82. DOI: 10.13580/j.cnki.fstc. 2026.02.007.
[16] Ronnie, Tian Yue. The Impact of Artificial Intelligence on the Resilience of "Specialized, Sophisticated, Unique, and Innovative" Enterprises: The Moderating Effect of Executive Team Heterogeneity [J]. Journal of Yunnan University of Finance and Economics, 2024,40(11):98–110. DOI: 10.16537/j.cnki.jynufe.000989.
[17] Niu Chuanyun. The Impact of Artificial Intelligence on the High-Quality Development of Private Enterprises: A Study from the Perspectives of Breakthrough Innovation and Management Efficiency [J]. Journal of Yunnan Minzu University (Philosophy and Social Sciences Edition), 2025,42(5):103–113. DOI: 10.13727/j.cnki.53-1191/c.20250820.001.
[18] Niu Hua, Yu Zhenyue, Chen Junhong. Digital Transformation and High-Quality Export Development of Enterprises: A Perspective Based on Export Technology Complexity [J]. Foreign Economics and Management, 2024,46(7):53–68. DOI: 10.16538/j.cnki.fem. 20240209.203.
[19] Qi Yongxing, Cheng Junqi. The impact of artificial intelligence on total factor productivity in manufacturing enterprises [J]. Scientific Decision-Making, 2025, (9):22–45.
[20] Qiao Penghua, Du Xin, Han Xianfeng. How does generative artificial intelligence enhance the resilience of manufacturing enterprises? [J/OL]. Science Studies and Science and Technology Management, 1–22 [2026-06-01]. https://doi.org/10.20201/j.cnki.ssstm.20250827.001.
[21] Wang Lu, Dong Zhiqing. Building a New Form of Intelligent Economy: Theoretical Framework and Practical Approaches for Artificial Intelligence to Empower Enterprise Growth [J]. Academic Exchange, 2026, (3):33–50.
[22] Wang Peng, Chen Diexin. Application of Artificial Intelligence Technology and Corporate Resilience: Empirical Evidence from Machine Learning-Based AI Dictionary Development [J]. Economics Dynamics, 2025, (9):98–117.
[23] Wang Shuo, Xiong Kaijun, Deng Guoying. Artificial Intelligence and Enterprise Specialization in Division of Labor: A Study Based on Text Analysis Methods [J]. Journal of Zhongnan University of Economics and Law, 2024, (5):97–109. DOI: 10.19639/j.cnki.issn1003-5230.2024.0058.
[24] Wang Yueying, Chen Shaofeng. Mechanistic Study on How Artificial Intelligence Facilitates Breakthrough Green Innovation in Enterprises [J/OL]. Science Studies, 1–20 [2026-06-02]. https://doi.org/10.16192/j.cnki.1003-2053.20260510.002.
[25] Xiao Xingzhi, Xie Weimin. Artificial Intelligence and Corporate Resilience: Empirical Evidence Based on Industrial Robot Applications [J]. System Engineering Theory and Practice, 2024,44(8):2456–2474.
[26] Xie Weihong, Lin Man, Zheng Diwen, et al. How does artificial intelligence technology promote the transformation and upgrading of manufacturing enterprises? — Based on the chain-mediated effects of data elements and internal control [J]. Technology Economics, 2025,44(4):72–88.
[27] Yang Yang, Liu Shuwen, Deng Xiangying. Danger and Opportunity: A Study on the Dual Effects of Artificial Intelligence on Corporate Resilience [J/OL]. Nankai Management Review, 1–39 [2026-06-02]. https://link.cnki.netUrlid/12.1288.F.20251124.1701.010
[28] Yao Chen. How Digital Technology Innovation Enhances Corporate Organizational Resilience: Empirical Evidence from China A-share Listed Companies [J]. International Business (Journal of University of International Business and Economics), 2026, (2):19-37. DOI: 10.13509/j.cnki.ib. 2026.02.002.
[29] Zhang Ruichen, Yang Jinghan, Wen Lei. Can digital transformation promote high-quality enterprise development? — From the dual perspectives of internal control and social responsibility [J]. Accounting Research, 2023, (10):129–142.
[30] Zhou Hongguang. Research on Pathways for Empowering the Development of High-Quality Talent Teams in State-Owned Enterprises through Artificial Intelligence [J]. Small and Medium Enterprise Management and Technology, 2025, (23):101–103.
[31] Zhou Shitong, Wang Xiaodan, Shi Yutang. Application of Artificial Intelligence Technology and Enhancement of Corporate Industrial Chain Resilience: A Study Based on Dual Machine Learning for Causal Inference [J]. Modern Finance (Journal of Tianjin University of Finance and Economics), 2025,45(11):3–20. DOI: 10.19559/j.cnki.12-1387.2025.11.001.
[32] Zhu Ling, Liu Kun. Enterprise Artificial Intelligence, Internal Control Quality, and Accounting Prudence [J]. Accounting Communications, 2025, (23):44–48. DOI: 10.16144/j.cnki.issn1002-8072.2025.23.008.
[33] A. T W ,A. D G ,M. K S , A. T W ,A. D G ,M. K S , et al. Organizational Response to Adversity: Fusing Crisis Management and Resilience Research Streams[J].Academy of Management Annals,2017,11(2):733-769.DOI:10.5465/annals.2015.0134.
[34] BOH W, CONSTANTINIDES P, PADMANABHAN B, VISWANATHAN S. Building digital resilience against major shocks[J]. MIS Quarterly, 2023, 47(1): 343-360.
[35] BRESNAHAN T F, BRYNJOLFSSON E, HITT L M. Information technology, workplace organization, and the demand for skilled labor: Firm-level evidence[J]. The Quarterly Journal of Economics, 2002, 117(1): 339-376.
[36] BRYNJOLFSSON E, ROCK D, SYVERSON C. Artificial intelligence and the modern productivity paradox: A clash of expectations and statistics[M]//AGRAWAL A, GANS J, GOLDFARB A. The economics of artificial intelligence: An agenda. Chicago: University of Chicago Press, 2019: 23-57.
[37] C A S, L B K, Sal M, C A S, L B K, Sal M, et al. BOUNCING BACK TOGETHER: TOWARD A THEORETICAL MODEL OF WORK TEAM RESILIENCE [J]. Academy of Management Review, 2018, DOI:10.5465/amr.2017.0005.
[38] COCKBURN I M, HENDERSON R, STERN S. The impact of artificial intelligence on innovation: An exploratory analysis[M]//AGRAWAL A, GANS J, GOLDFARB A. The economics of artificial intelligence: An agenda. Chicago: University of Chicago Press, 2019: 115-146.
[39] COHEN W M, LEVINTHAL D A. Absorptive capacity: A new perspective on learning and innovation[J]. Administrative Science Quarterly, 1990, 35(1): 128-152.
[40] DUCHEK S. Organizational resilience: A capability-based conceptualization[J]. Business Research, 2020, 13(1): 215-246. DOI: 10.1007/s40685-019-0085-7.
[41] HAN M, SHEN H, WU J, ZHANG X. Artificial intelligence and firm resilience: Empirical evidence from natural disaster shocks[J]. Information Systems Research, 2025, 36(4): 2116-2133.
[42] HAYES A F. Introduction to mediation, moderation, and conditional process analysis: A regression-based approach[M]. New York: Guilford Press, 2013.
[43] Liudmila A, José A, Mireia G, et al. The Demand for AI Skills in the Labor Market [J]. Labour Economics, 2021, (prepublish): 102002-. DOI:10.1016/J.LABECO.2021.102002.
[44] MEYER A D. Adapting to environmental jolts[J]. Administrative Science Quarterly, 1982, 27(4): 515-537. DOI: 10.2307/2392528.
[45] MITHAS S, RAMASUBBU N, SAMBAMURTHY V. How information management capability influences firm performance[J]. MIS Quarterly, 2011, 35(1): 237-256.
[46] NAMBISAN S, LYYTINEN K, MAJCHRZAK A, SONG M. Digital innovation management: Reinventing innovation management research in a digital world[J]. MIS Quarterly, 2017, 41(1): 223-238.
[47] PREACHER K J, HAYES A F. Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models[J]. Behavior Research Methods, 2008, 40(3): 879-891.
[48] Sahebjamnia N, Torabi A S, Mansouri A S. Building organizational resilience in the face of multiple disruptions [J]. International Journal of Production Economics, 2018, 19763-83. DOI: 10.1016/j.ijpe.2017.12.009.
[49] Tania B, Anastassia F, Alex H, Tania B, Anastassia F, Alex H, et al. Artificial intelligence, firm growth, and product innovation [J]. Journal of Financial Economics, 2024, 151 DOI: 10.1016/J.JFINECO.2023.103745.
[50] TEECE D J, PISANO G, SHUEN A. Dynamic capabilities and strategic management[J]. Strategic Management Journal, 1997, 18(7): 509-533.
[51] YOO Y, HENFRIDSSON O, LYYTINEN K. The new organizing logic of digital innovation: An agenda for information systems research[J]. Information Systems Research, 2010, 21(4): 724-735.
[52] ZHAO X, LYNCH J G, CHEN Q. Reconsidering Baron and Kenny: Myths and truths about mediation analysis[J]. Journal of Consumer Research, 2010, 37(2): 197-206.
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