IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i8p6871-d1127294.html
   My bibliography  Save this article

Does the Opening of High-Speed Railway Promote Corporate Digital Transformation?

Author

Listed:
  • Xiao-Hui Xin

    (School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China)

  • Guo-Li Ou

    (School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China)

  • Ruo-Yu Zhu

    (School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China)

Abstract

As China’s economy enters the digital era, guiding enterprises to conduct better digital transformations has become an urgent problem to be solved. In this regard, this paper builds a multiperiod DID model to empirically examine the impact of opening a high-speed railway (HSR) on corporate digital transformation by matching the data of HSR with the data of Shanghai and Shenzhen A-share listed companies from 2008 to 2019. It was found that (1) the opening of an HSR can significantly improve corporate digital transformation, and this finding still held after considering endogeneity issues and various robustness tests. (2) A heterogeneity analysis showed that the promoting effect of opening an HSR on corporate digital transformation was mainly found in nonstate enterprises, high-tech enterprises, and enterprises located in cities with low initial transportation endowment. (3) A mechanistic analysis found that opening an HSR can promote corporate digital transformation by promoting senior staff mobility, increasing industry competition, and enhancing financial agglomeration. This paper not only enriches the research related to the economic consequences of opening an HSR, but it also has important implications for guiding enterprises to successfully conduct corporate digital transformations.

Suggested Citation

  • Xiao-Hui Xin & Guo-Li Ou & Ruo-Yu Zhu, 2023. "Does the Opening of High-Speed Railway Promote Corporate Digital Transformation?," Sustainability, MDPI, vol. 15(8), pages 1-24, April.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:8:p:6871-:d:1127294
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/8/6871/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/8/6871/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ge Zhang & Yuxiang Gao & Gaoyong Li, 2023. "Research on Digital Transformation and Green Technology Innovation—Evidence from China’s Listed Manufacturing Enterprises," Sustainability, MDPI, vol. 15(8), pages 1-23, April.
    2. Lolli, Francesco & Coruzzolo, Antonio Maria & Peron, Mirco & Sgarbossa, Fabio, 2022. "Age-based preventive maintenance with multiple printing options," International Journal of Production Economics, Elsevier, vol. 243(C).
    3. Malcolm Baker & Jeffrey Wurgler, 2007. "Investor Sentiment in the Stock Market," Journal of Economic Perspectives, American Economic Association, vol. 21(2), pages 129-152, Spring.
    4. Yu Qin, 2017. "‘No county left behind?’ The distributional impact of high-speed rail upgrades in China," Journal of Economic Geography, Oxford University Press, vol. 17(3), pages 489-520.
    5. Wu, Keping & Fu, Yumei & Kong, Dongmin, 2022. "Does the digital transformation of enterprises affect stock price crash risk?," Finance Research Letters, Elsevier, vol. 48(C).
    6. Xiaomin Wang & Jingyu Liu & Wenxin Zhang, 2022. "Impact of High-Speed Rail on Spatial Structure in Prefecture-Level Cities: Evidence from the Central Plains Urban Agglomeration, China," Sustainability, MDPI, vol. 14(23), pages 1-17, December.
    7. Ardito, Lorenzo & Raby, Simon & Albino, Vito & Bertoldi, Bernardo, 2021. "The duality of digital and environmental orientations in the context of SMEs: Implications for innovation performance," Journal of Business Research, Elsevier, vol. 123(C), pages 44-56.
    8. Matarazzo, Michela & Penco, Lara & Profumo, Giorgia & Quaglia, Roberto, 2021. "Digital transformation and customer value creation in Made in Italy SMEs: A dynamic capabilities perspective," Journal of Business Research, Elsevier, vol. 123(C), pages 642-656.
    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, 2023. "Corporate digital transformation and trade credit financing," Journal of Business Research, Elsevier, vol. 160(C).
    2. Kun Liu & Xuemin Liu & Zihao Wu, 2024. "Nexus between Corporate Digital Transformation and Green Technological Innovation Performance: The Mediating Role of Optimizing Resource Allocation," Sustainability, MDPI, vol. 16(3), pages 1-21, February.
    3. Liu, Guangqiang & Liu, Boyang, 2023. "How digital technology improves the high-quality development of enterprises and capital markets: A liquidity perspective," Finance Research Letters, Elsevier, vol. 53(C).
    4. Zhong, Xi & Ren, Ge, 2023. "Independent and joint effects of CSR and CSI on the effectiveness of digital transformation for transition economy firms," Journal of Business Research, Elsevier, vol. 156(C).
    5. Khurana, Indu & Dutta, Dev K. & Singh Ghura, Amarpreet, 2022. "SMEs and digital transformation during a crisis: The emergence of resilience as a second-order dynamic capability in an entrepreneurial ecosystem," Journal of Business Research, Elsevier, vol. 150(C), pages 623-641.
    6. Peiyan Zhou & Shuya Zhou & Ming Zhang & Shujuan Miao, 2022. "Executive Overconfidence, Digital Transformation and Environmental Innovation: The Role of Moderated Mediator," IJERPH, MDPI, vol. 19(10), pages 1-28, May.
    7. Wei, Lang & Zhang, Yiling, 2023. "Nonfinancial indicators in identifying stock price crash risk," Finance Research Letters, Elsevier, vol. 52(C).
    8. David Hirshleifer & Danling Jiang, 2010. "A Financing-Based Misvaluation Factor and the Cross-Section of Expected Returns," The Review of Financial Studies, Society for Financial Studies, vol. 23(9), pages 3401-3436.
    9. Lepori, Gabriele M., 2015. "Investor mood and demand for stocks: Evidence from popular TV series finales," Journal of Economic Psychology, Elsevier, vol. 48(C), pages 33-47.
    10. Wang, Peipei & Wen, Yuanji & Singh, Harminder, 2017. "The high-volume return premium: Does it exist in the Chinese stock market?," Pacific-Basin Finance Journal, Elsevier, vol. 46(PB), pages 323-336.
    11. Tianjiao Zhao & Xiang Xiao & Qinghui Dai, 2021. "Transportation Infrastructure Construction and High-Quality Development of Enterprises: Evidence from the Quasi-Natural Experiment of High-Speed Railway Opening in China," Sustainability, MDPI, vol. 13(23), pages 1-23, December.
    12. Caginalp, Gunduz & DeSantis, Mark, 2017. "Does price efficiency increase with trading volume? Evidence of nonlinearity and power laws in ETFs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 436-452.
    13. Liu, Xueli & Jiang, Chunxia & Wang, Feng & Yao, Shujie, 2021. "The impact of high-speed railway on urban housing prices in China: A network accessibility perspective," Transportation Research Part A: Policy and Practice, Elsevier, vol. 152(C), pages 84-99.
    14. Constantinos Antoniou & John A. Doukas & Avanidhar Subrahmanyam, 2016. "Investor Sentiment, Beta, and the Cost of Equity Capital," Management Science, INFORMS, vol. 62(2), pages 347-367, February.
    15. Papapostolou, Nikos C. & Pouliasis, Panos K. & Nomikos, Nikos K. & Kyriakou, Ioannis, 2016. "Shipping investor sentiment and international stock return predictability," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 96(C), pages 81-94.
    16. Fang, Mingyue & Nie, Huihua & Shen, Xinyi, 2023. "Can enterprise digitization improve ESG performance?," Economic Modelling, Elsevier, vol. 118(C).
    17. Kubin, Ingrid & Zörner, Thomas O. & Gardini, Laura & Commendatore, Pasquale, 2019. "A credit cycle model with market sentiments," Structural Change and Economic Dynamics, Elsevier, vol. 50(C), pages 159-174.
    18. Meng, Xuechen & Lin, Shanlang & Zhu, Xiaochuan, 2018. "The resource redistribution effect of high-speed rail stations on the economic growth of neighbouring regions: Evidence from China," Transport Policy, Elsevier, vol. 68(C), pages 178-191.
    19. Ben-Rephael, Azi & Kandel, Shmuel & Wohl, Avi, 2012. "Measuring investor sentiment with mutual fund flows," Journal of Financial Economics, Elsevier, vol. 104(2), pages 363-382.
    20. Yousaf, Imran & Youssef, Manel & Goodell, John W., 2022. "Quantile connectedness between sentiment and financial markets: Evidence from the S&P 500 twitter sentiment index," International Review of Financial Analysis, Elsevier, vol. 83(C).

    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:gam:jsusta:v:15:y:2023:i:8:p:6871-:d:1127294. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.