IDEAS home Printed from https://ideas.repec.org/a/wly/isacfm/v31y2024i1ne1542.html
   My bibliography  Save this article

An application of artificial neural networks in corporate social responsibility decision making

Author

Listed:
  • Nguyen Thi Thanh Binh

Abstract

Neural networks in deep learning are changing the way we interact with the world. This paper focuses on building a logit artificial neural network (ANN) and through it finds out the factors affecting the decision to join corporate social responsibility (CSR) of firms. This study contributes to suggesting new directions for research in the artificial intelligence (AI) era on the relationship between corporate governance and CSR. The dataset of 817 Taiwanese electronic firms is analyzed for the period 2014–2020. The empirical results show that when the power of the board of directors, supervisors, and CEOs are higher, firms do not choose to participate in CSR. The independent board has not yet promoted its corporate oversight of CSR participation. The decision not to participate in CSR of the firms is made when they are more equipped with the background of accounting, finance, and law. Only firms with higher debt, asset value, and profitability are willing to join CSR. These research results suggest some important points for future policy reforms towards sustainability.

Suggested Citation

  • Nguyen Thi Thanh Binh, 2024. "An application of artificial neural networks in corporate social responsibility decision making," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 31(1), March.
  • Handle: RePEc:wly:isacfm:v:31:y:2024:i:1:n:e1542
    DOI: 10.1002/isaf.1542
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/isaf.1542
    Download Restriction: no

    File URL: https://libkey.io/10.1002/isaf.1542?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
    ---><---

    More about this item

    Statistics

    Access and download statistics

    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:wly:isacfm:v:31:y:2024:i:1:n:e1542. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/1099-1174/ .

    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.