Abstract
Informal financing methods like trade credit have emerged as a key approach to ease corporate financial constraints. Based on the data of A-share listed manufacturing companies from 2011 to 2019, we empirically investigate the influence and mechanism of industrial robot adoption on a firm’s trade credit. The findings show that the industrial robot adoption enhances a firm’s trade credit, specifically functioning through strengthening the firm’s supply chain resilience, enhancing operational efficiency, and easing financing constraints. Further analysis shows that the impact is more pronounced in firms with a higher degree of technical matching, in non-SOEs, and in firms facing fierce competition. Our study broadens the understanding of how artificial intelligence is reshaping corporate financial behavior. It supplements the literature on the firm-level economic consequences of industrial robot adoption and the influencing factors of trade credit. The study also holds important practical implications for cultivating high-quality productivity and empowering corporate high-quality development.
Similar content being viewed by others
Data availability
The data that support the findings of this study are available on request from the corresponding author.
References
Abdulla Y, Dang VA, Khurshed A (2017) Stock market listing and the use of trade credit: evidence from public and private firms. J Corp Financ 46:391–410. https://doi.org/10.1016/j.jcorpfin.2017.08.004
Acemoglu D, Restrepo P (2020) Robots and jobs: evidence from US labor markets. J Political Econ 128(6):2188–2244. https://doi.org/10.1086/705716
Aktas N, De Bodt E, Lobez F, Statnik JC (2012) The information content of trade credit. J Bank Financ 36(5):1402–1413. https://doi.org/10.1016/j.jbankfin.2011.12.001
Allen F, Qian Y, Tu G, Yu F (2019) Entrusted loans: a close look at China’s shadow banking system. J Financ Econ 133(1):18–41. https://doi.org/10.1016/j.jfineco.2019.01.006
Ang JS, Cole RA, Lin JW (2007) Agency costs and ownership structure. J Financ 55(1):81–106. https://doi.org/10.1111/0022-1082.00201
Bai C, Yao D, Xue Q (2025) Does artificial intelligence suppress firms’ greenwashing behavior? Evidence from robot adoption in China. Energy Econ 142: 108168. https://doi.org/10.1016/j.eneco.2024.108168
Ballestar MT, García-Lazaro A, Sainz J, Sanz I (2022) Why is your company not robotic? The technology and human capital needed by firms to become robotic. J Bus Res 142:328–343. https://doi.org/10.1016/j.jbusres.2021.12.061
Bentley KA, Omer TC, Sharp NY (2013) Business strategy, financial reporting irregularities, and audit effort. Contemp Account Res 30(2):780–817. https://doi.org/10.1111/j.1911-3846.2012.01174.x
Campbell JT, Bilgili H, Crossland C, Ajay B (2023) The background on executive background: an integrative review. J Manag 49(1):7–51. https://doi.org/10.1177/01492063221120392
Chen S, Ma H, Wu Q (2019) Bank credit and trade credit: evidence from natural experiments. J Bank Financ 108: 105616. https://doi.org/10.1016/j.jbankfin.2019.105616
Chen S, Mu S, He X, Han J, Tan Z (2024) Does industrial robot adoption affect green total factor productivity? Evidence from China. Ecol Indic 161: 111958. https://doi.org/10.1016/j.ecolind.2024.111958
Cheng C, Yang L (2022) What drives the credit constraints faced by Chinese small and micro enterprises? Econ. Model 113:105898. https://doi.org/10.1016/j.econmod.2022.105898
Chung H (2021) Adoption and development of the fourth industrial revolution technology: features and determinants. Sustainability 13(2):871. https://doi.org/10.3390/su13020871
Cohen Y, Naseraldin H, Chaudhuri A, Pilati F (2019) Assembly systems in Industry 4.0 era: a road map to understand Assembly 4.0. Int J Adv Manuf Technol 105:4037–4054. https://doi.org/10.1007/s00170-019-04203-1
Cui H, Liang S, Xu C, Junli Y (2024) Robots and analyst forecast precision: evidence from Chinese manufacturing. Int Rev Financ Anal 94: 103197. https://doi.org/10.1016/j.irfa.2024.103197
De Vries GJ, Gentile E, Miroudot S, Wacker KM (2020) The rise of robots and the fall of routine jobs. Labour Econ 66: 101885. https://doi.org/10.1016/j.labeco.2020.101885
Diamond DW (1984) Financial intermediation and delegated monitoring. Rev Econ Stud 51(3):393–414. https://doi.org/10.2307/2297430
Ding F, Liu Q, Shi H, Wang W, Wu S (2023) Firms’ access to informal financing: the role of shared managers in trade credit access. J Corp Financ 79: 102388. https://doi.org/10.1016/j.jcorpfin.2023.102388
Dixon J, Hong B, Wu L (2021) The robot revolution: managerial and employment consequences for firms. Manag Sci 67(9):5586–5605. https://doi.org/10.1287/mnsc.2020.3812
El Ghoul S, Zheng X (2016) Trade credit provision and national culture. J Corp Financ 41:475–501. https://doi.org/10.1016/j.jcorpfin.2016.07.002
Fabbri D, Klapper LF (2016) Bargaining power and trade credit. J Corp Financ 41:66–80. https://doi.org/10.1016/j.jcorpfin.2016.07.001
Fare R, Grosskopf S, Margaritis D (eds) (2024) Market power, economic efficiency and the Lerner Index (Vol. 19). World Scientific
Fisman R, Love I (2003) Trade credit, financial intermediary development, and industry growth. J Financ 58(1):353–374. https://doi.org/10.1111/1540-6261.00527
Fisman R, Raturi M (2004) Does competition encourage credit provision? Evidence from African trade credit relationships. Rev Econ Stat 86(1):345–352. https://doi.org/10.1162/003465304323023859
Gan J, Liu L, Qiao G, Zhang Q (2023) The role of robot adoption in green innovation: evidence from China. Econ Model 119: 106128. https://doi.org/10.1016/j.econmod.2022.106128
Hoang CH, Ly KC, Xiao Q, Zhang X (2023) Does national culture impact trade credit provision of SMEs? Econ Model 124: 106288. https://doi.org/10.1016/j.econmod.2023.106288
Huang Y, Mukherjee T, Wang W (2024) Labor protection and financing decisions of firms: the case of China. Int Rev Econ Financ 94: 103396. https://doi.org/10.1016/j.iref.2024.103396
Impullitti G, Licandro O, Rendahl P (2022) Technology, market structure and the gains from trade. J Int Econ 135: 103557. https://doi.org/10.1016/j.jinteco.2021.103557
Jung JH, Lim D-G (2020) Industrial robots, employment growth, and labor cost: a simultaneous equation analysis. Technol Forecast Soc Change 159: 120202. https://doi.org/10.1016/j.techfore.2020.120202
Lee CC, Qin S, Li Y (2022) Does industrial robot adoption promote green technology innovation in the manufacturing industry? Technol Forecast Soc Change 183: 121893. https://doi.org/10.1016/j.techfore.2022.121893
Leng Y, Shi X, Hiroatsu F, Kalachev A, Wan D (2023) Automated construction for human–robot interaction in wooden buildings: Integrated robotic construction and digital design of iSMART wooden arches. J Field Robot 40(4):810–827. https://doi.org/10.1002/rob.22154
Li C, Wang Y (2024) Digital transformation and enterprise resilience: Enabling or burdening? PLoS ONE 19(7):e0305615. https://doi.org/10.1371/journal.pone.0305615
Li J, Wu Z, Yu K, Zhao W (2024) The effect of industrial robot adoption on firm value: evidence from China. Financ Res Lett 60: 104907. https://doi.org/10.1016/j.frl.2023.104907
Li X, Ng J, Saffar W (2021) Financial reporting and trade credit: evidence from mandatory IFRS adoption. Contemp Account Res 38(1):96–128. https://doi.org/10.1111/1911-3846.12611
Li Y, Wang Y, Ma R, Wang R (2025) Research on the impact of financialization of high-tech manufacturing listed companies on real investment. Appl Econ 57(9):1010–1023. https://doi.org/10.1080/00036846.2024.2311060
Liang L, Lu L, Su L (2023) The impact of industrial robot adoption on corporate green innovation in China. Sci Rep 13(1):18695. https://doi.org/10.1038/s41598-023-46037-8
Liu X, Cifuentes-Faura J, Yang X, Pan J (2025) The green innovation effect of industrial robot adoptions: evidence from Chinese manufacturing companies. Technol Forecast Soc Change 210: 123904. https://doi.org/10.1016/j.techfore.2024.123904
Makridakis S (2017) The forthcoming artificial intelligence (AI) revolution: its impact on society and firms. Futures 90:46–60. https://doi.org/10.1016/j.futures.2017.03.006
Mao H, Zhang Q, Chen G (2024) Impact of industrial robot adoption on trade credit financing in manufacturing firms. Acad J Business Manag 6(2). https://doi.org/10.25236/AJBM.2024.060202
Marcucci G, Antomarioni S, Ciarapica FE, Bevilacqua M (2022) The impact of Operations and IT-related Industry 4.0 key technologies on organizational resilience. Prod Plan Control 33(15):1417–1431. https://doi.org/10.1080/09537287.2021.1874702
McMullin J, Schonberger B (2022) When good balance goes bad: a discussion of common pitfalls when using entropy balancing. J Financ Rep 7(1):167–196. https://doi.org/10.2308/JFR-2021-007
Petersen MA, Rajan RG (1997) Trade credit: Theories and evidence. Rev Financ Stud 10(3):661–691. https://doi.org/10.1093/rfs/10.3.661
Ralston P, Blackhurst J (2020) Industry 4.0 and resilience in the supply chain: a driver of capability enhancement or capability loss? Int J Prod Res 58(16):5006–5019. https://doi.org/10.1080/00207543.2020.1736724
Rüßmann M, Lorenz M, Gerbert P, Waldner M, Justus J, Engel P, Harnisch M (2015) Industry 4.0: the future of productivity and growth in manufacturing industries. Boston Consult Group 9(1):54–89
Soni U, Jain V, Kumar S (2014) Measuring supply chain resilience using a deterministic modeling approach. Comput Ind Eng 74:11–25. https://doi.org/10.1016/j.cie.2014.04.019
Spence M (1973) Job market signaling. Q J Econ 87(3):355–374. https://doi.org/10.2307/1882010
Teece DJ, Pisano G, Shuen A (1997) Dynamic capabilities and strategic management. Strat Manag J 18(7):509–533. https://www.jstor.org/stable/3088148
Wang KL, Sun TT, Xu RY (2023) The impact of artificial intelligence on total factor productivity: empirical evidence from China’s manufacturing enterprises. Econ Change Restruct 56(2):1113–1146. https://doi.org/10.1007/s10644-022-09467-4
Wang K, Zhou J, Li G, Hu Y, Hu F (2024) Industrial automation and product quality: the role of robotic production transformation. Appl Econ 1–16. https://doi.org/10.1080/00036846.2024.2364120
Wang L, Liu Z, Liu A, Tao F (2021) Artificial intelligence in product lifecycle management. Int J Adv Manuf Technol 114:771–796. https://doi.org/10.1007/s00170-021-06882-1
Wang X, Han L, Huang X, Mi B (2021) The financial and operational impacts of European SMEs’ use of trade credit as a substitute for bank credit. Eur J Financ 27(8):796–825. https://doi.org/10.1080/1351847X.2020.1846576
Wang X, Liu M, Liu C, Ling L, Zhang X (2023) Data-driven and knowledge-based predictive maintenance method for industrial robots for the production stability of intelligent manufacturing. Expert Syst Appl 234: 121136. https://doi.org/10.1016/j.eswa.2023.121136
Whited TM, Wu G (2006) Financial constraints risk. Rev Financ Stud 19(2):531–559. https://doi.org/10.1093/rfs/hhj012
Wilson HJ, Daugherty PR (2018) Collaborative intelligence: Humans and AI are joining forces. Harv Bus Rev 96(4):114–123
Xiao Y, Ma D, Cheng Y, Wang L (2020) Effect of labor cost and industrial structure on the development mode transformation of China’s industrial economy. Emerg Mark Financ Trade 56(8):1677–1690. https://doi.org/10.1080/1540496X.2019.1694887
Xie W, Tian H (2023) The effect of the COVID-19 pandemic on firm’s trade credit financing. Econ Lett 232: 111339. https://doi.org/10.1016/j.econlet.2023.111339
Zhang K, Bu C (2024) Top managers with information technology backgrounds and digital transformation: evidence from small and medium companies. Econo Model 132:106629. https://doi.org/10.1016/j.econmod.2023.106629
Zhang Q, Zhang F, Mai Q (2023) Robot adoption and labor demand: a new interpretation from external competition. Technol Soc 74:102310. https://doi.org/10.1016/j.techsoc.2023.102310
Zhang Z (2023) The impact of the artificial intelligence industry on the number and structure of employments in the digital economy environment. Technol Forecast Soc Change 197:122881. https://doi.org/10.1016/j.techfore.2023.122881
Zhang Z, Wang L (2025) The driving effect of industrial robots on international industrial transfer: empirical evidence from China. Emerg Markets Finance Trade 1–15. https://doi.org/10.1080/1540496X.2025.2473491
Zhao Y, Said R, Ismail NW, Hamzah HZ (2022) Effect of industrial robots on employment in China: an industry level analysis. Comput Intell Neurosci 2022(1):2267237. https://doi.org/10.1155/2022/2267237
Zhou Z, Li Z, Du S, Cao J (2024) Robot adoption and enterprise R&D manipulation: evidence from China. Technol Forecast Soc Change 200:123134. https://doi.org/10.1016/j.techfore.2023.123134
Acknowledgements
This work is supported by the PhD Research Startup Fund of Jiangsu University of Science and Technology (Grant No. 1042932504) and the General Project of Philosophy and Social Science Research in Jiangsu Universities (Grant No. 2025SJYB1641).
Author information
Authors and Affiliations
Contributions
Yiyun Ge (First Author): Conceptualization, methodology, investigation, visualization, writing−original draft, writing−review and editing. Ruixuan Zhang (Corresponding Author): Methodology, supervision, software, formal analysis, data curation. Hanbin Zhu: Methodology, supervision, software, formal analysis, data curation. Qiaohe Wang(Corresponding Author): Methodology, supervision, software, formal analysis, data curation. We declare that this manuscript is original, has not been published before, and is not currently being considered for publication elsewhere. We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Ethical approval
This article does not contain any studies with human participants performed by any of the authors.
Informed consent
This article does not contain any studies with human participants performed by any of the authors.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Ge, Y., Zhang, R., Zhu, H. et al. The impact of industrial robot adoption on firm’s trade credit. Humanit Soc Sci Commun (2026). https://doi.org/10.1057/s41599-025-06476-2
Received:
Accepted:
Published:
DOI: https://doi.org/10.1057/s41599-025-06476-2
