IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v58y2023ipcs1544612323009091.html
   My bibliography  Save this article

Digital transformation and labour investment efficiency: Heterogeneity across the enterprise life cycle

Author

Listed:
  • Liu, Shu
  • Wu, Yuting
  • Yin, Xiaobo
  • Wu, Bin

Abstract

This study delves into the influence of digital transformation on labour investment efficiency and its varied effects across different business phases. Utilizing panel regression and the instrumental variable model, the research analyses data from China's A-share listed companies spanning 2011–2021. Findings reveal that digital transformation considerably enhances labour investment efficiency, mitigating both overinvestment and underinvestment issues. Heterogeneity analysis further indicates that digital transformation fine-tunes resource distribution, especially aiding businesses in their growth and maturity phases. Contrarily, firms in a declining phase show no discernible impact from digital transformation.

Suggested Citation

  • Liu, Shu & Wu, Yuting & Yin, Xiaobo & Wu, Bin, 2023. "Digital transformation and labour investment efficiency: Heterogeneity across the enterprise life cycle," Finance Research Letters, Elsevier, vol. 58(PC).
  • Handle: RePEc:eee:finlet:v:58:y:2023:i:pc:s1544612323009091
    DOI: 10.1016/j.frl.2023.104537
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612323009091
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.frl.2023.104537?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. Lv, Panpan & Xiong, Hu, 2022. "Can FinTech improve corporate investment efficiency? Evidence from China," Research in International Business and Finance, Elsevier, vol. 60(C).
    2. Yalin Jiang & Chong Guo & Yingyu Wu, 2022. "Environmental information disclosure and Labour investment efficiency," Applied Economics Letters, Taylor & Francis Journals, vol. 29(3), pages 238-244, February.
    3. Viktor Koval & Yuliia Prymush & Viktoriia Popova, 2017. "The Influence Of The Enterprise Life Cycle On The Efficiency Of Investment," Baltic Journal of Economic Studies, Publishing house "Baltija Publishing", vol. 3(5).
    4. Morgan R. Frank & David Autor & James E. Bessen & Erik Brynjolfsson & Manuel Cebrian & David J. Deming & Maryann Feldman & Matthew Groh & José Lobo & Esteban Moro & Dashun Wang & Hyejin Youn & Iyad Ra, 2019. "Toward understanding the impact of artificial intelligence on labor," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 116(14), pages 6531-6539, April.
    5. Prasanna Tambe, 2014. "Big Data Investment, Skills, and Firm Value," Management Science, INFORMS, vol. 60(6), pages 1452-1469, June.
    6. Jingbo Luo & Xiaorong Li & Kam C. Chan, 2020. "Political uncertainty and labour investment efficiency," Applied Economics, Taylor & Francis Journals, vol. 52(43), pages 4677-4697, September.
    7. Martin Obschonka & David B. Audretsch, 2020. "Artificial intelligence and big data in entrepreneurship: a new era has begun," Small Business Economics, Springer, vol. 55(3), pages 529-539, October.
    8. Violeta Sima & Ileana Georgiana Gheorghe & Jonel Subić & Dumitru Nancu, 2020. "Influences of the Industry 4.0 Revolution on the Human Capital Development and Consumer Behavior: A Systematic Review," Sustainability, MDPI, vol. 12(10), pages 1-28, May.
    9. Gaowen Kong & T. Dongmin Kong & Ni Qin & Li Yu, 2023. "Ethnic Diversity, Trust and Corporate Social Responsibility: The Moderating Effects of Marketization and Language," Journal of Business Ethics, Springer, vol. 187(3), pages 449-471, October.
    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. Li, Bin & Guo, Fei & Xu, Lei & Meng, Siqi, 2024. "Fintech business and corporate social responsibility practices," Emerging Markets Review, Elsevier, vol. 59(C).
    2. Claudio Vitari & Elisabetta Raguseo, 2016. "Big data value and financial performance: an empirical investigation [Digital data, dynamic capability and financial performance: an empirical investigation in the era of Big Data]," Post-Print halshs-01923271, HAL.
    3. Bertschek, Irene & Kesler, Reinhold, 2022. "Let the user speak: Is feedback on Facebook a source of firms’ innovation?," Information Economics and Policy, Elsevier, vol. 60(C).
    4. Feng, Wei & Sun, Shujun & Yuan, Hang, 2023. "Research on the efficiency of factor allocation in the pilot free trade zones," Economic Analysis and Policy, Elsevier, vol. 79(C), pages 727-745.
    5. Yingjie Zhang & Beibei Li & Ramayya Krishnan, 2020. "Learning Individual Behavior Using Sensor Data: The Case of Global Positioning System Traces and Taxi Drivers," Information Systems Research, INFORMS, vol. 31(4), pages 1301-1321, December.
    6. Rita Strohmaier & Marlies Schuetz & Simone Vannuccini, 2019. "A systemic perspective on socioeconomic transformation in the digital age," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 46(3), pages 361-378, September.
    7. Montobbio, Fabio & Staccioli, Jacopo & Virgillito, Maria Enrica & Vivarelli, Marco, 2022. "Robots and the origin of their labour-saving impact," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    8. Pan Liu & Shu-ping Yi, 2018. "Investment decision-making and coordination of a three-stage supply chain considering Data Company in the Big Data era," Annals of Operations Research, Springer, vol. 270(1), pages 255-271, November.
    9. Zhu, Jun & Zhang, Jingting & Feng, Yiqing, 2022. "Hard budget constraints and artificial intelligence technology," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    10. Jiyoung Kimjeon & Per Davidsson, 2022. "External Enablers of Entrepreneurship: A Review and Agenda for Accumulation of Strategically Actionable Knowledge," Entrepreneurship Theory and Practice, , vol. 46(3), pages 643-687, May.
    11. Belém Barbosa & José Ramón Saura & Dag Bennett, 2024. "How do entrepreneurs perform digital marketing across the customer journey? A review and discussion of the main uses," The Journal of Technology Transfer, Springer, vol. 49(1), pages 69-103, February.
    12. Tzu-Chieh Lin & Kung Jeng Wang, 2021. "Project-based maturity assessment model for smart transformation in Taiwanese enterprises," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-19, July.
    13. Mykola Odrekhivskyi & Orysya Pshyk-Kovalska & Volodymyr Zhezhukha & Iryna Ivanochko, 2022. "Intelligent Management of Enterprise Business Processes," Mathematics, MDPI, vol. 11(1), pages 1-15, December.
    14. Viktor Zamlynskyi & Anastasiia Zerkal & Andrii Antonov, 2019. "A Conceptual Framework To Apply Financial Engineering At The Enterprise," Baltic Journal of Economic Studies, Publishing house "Baltija Publishing", vol. 5(1).
    15. Bhagwan, N. & Evans, M., 2023. "A review of industry 4.0 technologies used in the production of energy in China, Germany, and South Africa," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    16. Özköse, Hakan & Güney, Gül, 2023. "The effects of industry 4.0 on productivity: A scientific mapping study," Technology in Society, Elsevier, vol. 75(C).
    17. Ljunge, Martin & Stenkula, Mikael, 2021. "Fertile soil for intrapreneurship: impartial institutions and human capital," Journal of Institutional Economics, Cambridge University Press, vol. 17(3), pages 489-508, June.
    18. Rehana Anwar & Jaleel A. Malik, 2020. "When Does Corporate Social Responsibility Disclosure Affect Investment Efficiency? A New Answer to an Old Question," SAGE Open, , vol. 10(2), pages 21582440209, June.
    19. Restuning Widiasih & Maria Komariah & Iqbal Pramukti & Raini Diah Susanti & Habsyah Saparidah Agustina & Hidayat Arifin & Yulia Kurniawati & Katherine Nelson, 2022. "VNursLab 3D Simulator: A Web-Based Nursing Skills Simulation of Knowledge of Nursing Skill, Satisfaction, and Self-Confidence among Nursing Students," Sustainability, MDPI, vol. 14(9), pages 1-11, April.
    20. Hyo Geun Song & Hyeon Jo, 2023. "Understanding the Continuance Intention of Omnichannel: Combining TAM and TPB," Sustainability, MDPI, vol. 15(4), pages 1-20, February.

    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:58:y:2023:i:pc:s1544612323009091. 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.