IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v67y2024ipbs1544612324009589.html
   My bibliography  Save this article

Enterprise digital transformation and employment: Spillover effect within supply chains

Author

Listed:
  • Lan, Sen
  • Cui, Maosheng

Abstract

This study investigates the spillover effect of enterprise digital transformation on employment within supply chains using Chinese A-share listed enterprises spanning from 2009 to 2022. The results suggest a positive impact of customers’ digital transformation on suppliers’ employment. We identify three potential mechanisms underlying it: customers’ digital transformation drives the digital transformation process, alleviates financial constraints, and enhances sales of suppliers. Heterogeneity analysis reveals that this effect is particularly pronounced among state-owned suppliers and within the manufacturing industries. Overall, our study contributes to the research field concerning the economic impacts of digital transformation in enterprises and its spillover effects.

Suggested Citation

  • Lan, Sen & Cui, Maosheng, 2024. "Enterprise digital transformation and employment: Spillover effect within supply chains," Finance Research Letters, Elsevier, vol. 67(PB).
    • Handle: RePEc:eee:finlet:v:67:y:2024:i:pb:s1544612324009589
    DOI: 10.1016/j.frl.2024.105928
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612324009589
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.frl.2024.105928?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Daron Acemoglu & Pascual Restrepo, 2020. "The wrong kind of AI? Artificial intelligence and the future of labour demand," Cambridge Journal of Regions, Economy and Society, Cambridge Political Economy Society, vol. 13(1), pages 25-35.
    2. Juan He & Xiaodong Du & Wei Tu, 2024. "Can corporate digital transformation alleviate financing constraints?," Applied Economics, Taylor & Francis Journals, vol. 56(20), pages 2434-2450, April.
    3. Han, Yue & Yang, Jie & Ying, Limeng & Niu, Yanfang, 2024. "The impact of corporate digital transformation on labor employment," Finance Research Letters, Elsevier, vol. 60(C).
    4. Wang, Di & Shao, Xuefeng, 2024. "Research on the impact of digital transformation on the production efficiency of manufacturing enterprises: Institution-based analysis of the threshold effect," International Review of Economics & Finance, Elsevier, vol. 91(C), pages 883-897.
    5. Rob Handfield, 2016. "Preparing for the Era of the Digitally Transparent Supply Chain: A Call to Research in a New Kind of Journal," Logistics, MDPI, vol. 1(1), pages 1-15, November.
    6. Charles J. Hadlock & Joshua R. Pierce, 2010. "New Evidence on Measuring Financial Constraints: Moving Beyond the KZ Index," The Review of Financial Studies, Society for Financial Studies, vol. 23(5), pages 1909-1940.
    7. Arntz, Melanie & Gregory, Terry & Zierahn, Ulrich, 2017. "Revisiting the risk of automation," Economics Letters, Elsevier, vol. 159(C), pages 157-160.
    8. Daron Acemoglu & Pascual Restrepo, 2020. "Robots and Jobs: Evidence from US Labor Markets," Journal of Political Economy, University of Chicago Press, vol. 128(6), pages 2188-2244.
    9. Charles I. Jones & Christopher Tonetti, 2020. "Nonrivalry and the Economics of Data," American Economic Review, American Economic Association, vol. 110(9), pages 2819-2858, September.
    10. Guo, Chenhao & Ke, Yun & Zhang, Jinkang, 2023. "Digital transformation along the supply chain," Pacific-Basin Finance Journal, Elsevier, vol. 80(C).
    11. Daron Acemoglu & Pascual Restrepo, 2018. "The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment," American Economic Review, American Economic Association, vol. 108(6), pages 1488-1542, June.
    12. Dengler, Katharina & Matthes, Britta, 2018. "The impacts of digital transformation on the labour market: Substitution potentials of occupations in Germany," Technological Forecasting and Social Change, Elsevier, vol. 137(C), pages 304-316.
    13. Zhang, Dongyang & Bai, Dingchuan & Wang, Cao & He, Yurun, 2024. "Distribution dynamics and quantile dynamic convergence of the digital economy: Prefecture-level evidence in China," International Review of Financial Analysis, Elsevier, vol. 95(PA).
    14. Yang, Miying & Fu, Mingtao & Zhang, Zihan, 2021. "The adoption of digital technologies in supply chains: Drivers, process and impact," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    15. Anna M. Costello, 2020. "Credit Market Disruptions and Liquidity Spillover Effects in the Supply Chain," Journal of Political Economy, University of Chicago Press, vol. 128(9), pages 3434-3468.
    16. Zhang, Dongyang, 2024. "The pathway to curb greenwashing in sustainable growth: The role of artificial intelligence," Energy Economics, Elsevier, vol. 133(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. Zhou, Zhongsheng & Li, Zhuo & Du, Shanzhong & Cao, June, 2024. "Robot adoption and enterprise R&D manipulation: Evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    2. Jasmine Mondolo, 2022. "The composite link between technological change and employment: A survey of the literature," Journal of Economic Surveys, Wiley Blackwell, vol. 36(4), pages 1027-1068, September.
    3. Arntz, Melanie & Gregory, Terry & Zierahn-Weilage, Ulrich, 2019. "Digitalization and the Future of Work: Macroeconomic Consequences," IZA Discussion Papers 12428, Institute of Labor Economics (IZA).
    4. Wang, Linhui & Cao, Zhanglu & Dong, Zhiqing, 2023. "Are artificial intelligence dividends evenly distributed between profits and wages? Evidence from the private enterprise survey data in China," Structural Change and Economic Dynamics, Elsevier, vol. 66(C), pages 342-356.
    5. Laura Abrardi & Carlo Cambini & Laura Rondi, 2022. "Artificial intelligence, firms and consumer behavior: A survey," Journal of Economic Surveys, Wiley Blackwell, vol. 36(4), pages 969-991, September.
    6. Ke An & Yike Shan & Sheng Shi, 2022. "Impact of Industrial Intelligence on Total Factor Productivity," Sustainability, MDPI, vol. 14(21), pages 1-21, November.
    7. Camiña, Ester & Díaz-Chao, Ángel & Torrent-Sellens, Joan, 2020. "Automation technologies: Long-term effects for Spanish industrial firms," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    8. Wang, Jiaxin & Zhao, Mu & Huang, Xiang & Song, Zilong & Sun, Di, 2024. "Supply chain diffusion mechanisms for AI applications: A perspective on audit pricing," International Review of Financial Analysis, Elsevier, vol. 93(C).
    9. Zhang, Dongyang & Bai, Dingchuan & He, Yurun & Sun, Qiaobing, 2024. "Synergistic abatement effects of Broadband China and environmental regulation: Firm-level evidence," International Review of Financial Analysis, Elsevier, vol. 96(PB).
    10. Fedorets Alexandra & Kirchner Stefan & Adriaans Jule & Giering Oliver, 2022. "Data on Digital Transformation in the German Socio-Economic Panel," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 242(5-6), pages 691-705, December.
    11. Gries, Thomas & Naudé, Wim, 2022. "Modelling artificial intelligence in economics," Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 56, pages 1-12.
    12. Prettner, Klaus & Strulik, Holger, 2020. "Innovation, automation, and inequality: Policy challenges in the race against the machine," Journal of Monetary Economics, Elsevier, vol. 116(C), pages 249-265.
    13. Zhihui Dai & Yue Niu & Hongru Zhang & Xiaodi Niu, 2022. "Impact of the Transforming and Upgrading of China’s Labor-Intensive Manufacturing Industry on the Labor Market," Sustainability, MDPI, vol. 14(21), pages 1-25, October.
    14. Li, Chengming & Huo, Peng & Wang, Zeyu & Zhang, Weiguang & Liang, Feiyan & Mardani, Abbas, 2023. "Digitalization generates equality? Enterprises’ digital transformation, financing constraints, and labor share in China," Journal of Business Research, Elsevier, vol. 163(C).
    15. Yuan, Sai & Zhou, Ran & Li, Mengna & Lv, Chengchao, 2023. "Investigating the influence of digital technology application on employee compensation," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    16. Ana L. ABELIANSKY & Eda ALGUR & David E. BLOOM & Klaus PRETTNER, 2020. "The future of work: Meeting the global challenges of demographic change and automation," International Labour Review, International Labour Organization, vol. 159(3), pages 285-306, September.
    17. Ana L. Abeliansky & Klaus Prettner & Roman Stoellinger, 2023. "Infection Risk at Work, Automatability, and Employment," Department of Economics Working Papers wuwp352, Vienna University of Economics and Business, Department of Economics.
    18. Liang, Peng & Liang, Lin & Tang, Xinhui, 2024. "The impact of digital-oriented mergers and acquisitions on enterprise labor demand," International Review of Financial Analysis, Elsevier, vol. 96(PB).
    19. Damioli, G. & Van Roy, V. & Vertesy, D. & Vivarelli, M., 2021. "May AI revolution be labour-friendly? Some micro evidence from the supply side," GLO Discussion Paper Series 823, Global Labor Organization (GLO).
    20. David Autor & Caroline Chin & Anna Salomons & Bryan Seegmiller, 2024. "New Frontiers: The Origins and Content of New Work, 1940–2018," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 139(3), pages 1399-1465.

    More about this item

    Keywords

    Enterprise digital transformation; Labor employment; Supply chain spillover;
    All these keywords.

    JEL classification:

    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • J2 - Labor and Demographic Economics - - Demand and Supply of Labor

    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:67:y:2024:i:pb:s1544612324009589. 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.