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

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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
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    References listed on IDEAS

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    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)

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