IDEAS home Printed from https://ideas.repec.org/a/eme/arjpps/arj-05-2016-0056.html
   My bibliography  Save this article

CEO power and labor productivity

Author

Listed:
  • Emily Breit
  • Xuehu (Jason) Song
  • Li Sun
  • Joseph Zhang

Abstract

Purpose - This paper aims to examine how Chief Executive Officer (CEO) power affects firm-level labor productivity. Design/methodology/approach - The authors rely on regression analysis to examine the relation between CEO power and labor productivity. Findings - Following prior research (i.e. the sequential rank order tournament theory), the authors predict that powerful CEOs lead to high labor productivity. They find a significant and positive relationship between CEO power and labor productivity. They further decompose labor productivity into labor efficiency and labor cost components and find a positive (negative) relationship between CEO power and labor efficiency (cost) component, suggesting that more powerful CEOs better manage labor efficiency and control labor cost. The results are also robust to various additional tests. Originality/value - This study contributes to two streams of research: the CEO power literature in finance and the labor productivity and cost literature in accounting. To the best of the authors’ knowledge, it is the first study that performs a direct empirical test on the relation between CEO power and labor productivity.

Suggested Citation

  • Emily Breit & Xuehu (Jason) Song & Li Sun & Joseph Zhang, 2019. "CEO power and labor productivity," Accounting Research Journal, Emerald Group Publishing Limited, vol. 32(2), pages 148-165, July.
  • Handle: RePEc:eme:arjpps:arj-05-2016-0056
    DOI: 10.1108/ARJ-05-2016-0056
    as

    Download full text from publisher

    File URL: https://www.emerald.com/insight/content/doi/10.1108/ARJ-05-2016-0056/full/html?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: Access to full text is restricted to subscribers

    File URL: https://www.emerald.com/insight/content/doi/10.1108/ARJ-05-2016-0056/full/pdf?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: Access to full text is restricted to subscribers

    File URL: https://libkey.io/10.1108/ARJ-05-2016-0056?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Meng Chao & Chen Chen & Xu Heng & Li Ting, 2024. "Asset Pricing and Portfolio Investment Management Using Machine Learning: Research Trend Analysis Using Scientometrics," Economics - The Open-Access, Open-Assessment Journal, De Gruyter, vol. 18(1), pages 1-20.
    2. Jongmoo Jay Choi & Ming Ju & Jose M. Plehn-Dujowich & Xiaotian Tina Zhang, 2022. "Outsourcing as a cooperative game between the CEO and labor: theory and evidence," Review of Quantitative Finance and Accounting, Springer, vol. 59(3), pages 1095-1131, October.

    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:eme:arjpps:arj-05-2016-0056. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Emerald Support (email available below). General contact details of provider: .

    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.