IDEAS home Printed from https://ideas.repec.org/a/eee/chieco/v92y2025ics1043951x2500080x.html
   My bibliography  Save this article

Board structure and corporate strategic aggressiveness - an examination based on the double machine learning method

Author

Listed:
  • Yaowu, Yang
  • Qidong, Cheng
  • Yang, Liu

Abstract

China's newly revised “Company Law” makes clear provisions regarding the composition and powers of the board of directors, which impact the formulation and implementation of corporate strategies. Taking the data of A-share listed companies in Shanghai and Shenzhen from 2008 to 2022 as the research sample, we employ double machine learning (DML) to verify the impact of board structure on corporate strategy aggressiveness, its mechanism of action and heterogeneous effects from the perspective of corporate boards. The study reveals that board structure has a significant effect on corporate strategic aggressiveness. Specifically, CEO duality enhances corporate strategic aggressiveness, a large size of the board of directors reduces corporate strategic aggressiveness, and the percentage of independent directors has a significant positive effect on corporate strategic aggressiveness. These effects can be realized through both internal control and compensation incentives. Further research shows that the effect of board structure on corporate strategic aggressiveness is more pronounced in firms whose executives have overseas backgrounds and in firms in highly competitive environments. Moreover, there is significant heterogeneity in the effect of board structure on corporate strategic aggressiveness in the presence of major exogenous shocks.

Suggested Citation

  • Yaowu, Yang & Qidong, Cheng & Yang, Liu, 2025. "Board structure and corporate strategic aggressiveness - an examination based on the double machine learning method," China Economic Review, Elsevier, vol. 92(C).
    • Handle: RePEc:eee:chieco:v:92:y:2025:i:c:s1043951x2500080x
    DOI: 10.1016/j.chieco.2025.102422
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1043951X2500080X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chieco.2025.102422?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.

    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:chieco:v:92:y:2025:i:c:s1043951x2500080x. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/chieco .

    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.