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Enterprise digital transformation and employment: Spillover effect within supply chains

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  • 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
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    References listed on IDEAS

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    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).
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    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

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