IDEAS home Printed from https://ideas.repec.org/a/eee/fambus/v15y2024i4s1877858524000391.html
   My bibliography  Save this article

Using machine learning to identify and measure the family influence on companies

Author

Listed:
  • Amore, Mario Daniele
  • D’Angelo, Valentino
  • Le Breton-Miller, Isabelle
  • Miller, Danny
  • Pelucco, Valerio
  • Van Essen, Marc

Abstract

Many studies have focused on family firms. Yet, grasping the nature of these organizations remains challenging because firms’ familiness can take many forms, which are hard to trace with traditional data. We use ChatGPT – an application of machine learning – to try to unravel the complex and intangible influence of families on firms in large datasets. Whereas it often classifies family firms consistently with equity criteria, ChatGPT appears able to gauge families’ legacy and values. Hence, it detects more family firms in countries where families have a strong influence on firms even without large equity stakes. Also, ChatGPT often treats lone-founder firms as non-family firms, whereas it assigns a higher family score to firms that are eponymous, heir-led, and with multiple family directors. Finally, classifying family firms using ChatGPT provides financially relevant information to investors.

Suggested Citation

  • Amore, Mario Daniele & D’Angelo, Valentino & Le Breton-Miller, Isabelle & Miller, Danny & Pelucco, Valerio & Van Essen, Marc, 2024. "Using machine learning to identify and measure the family influence on companies," Journal of Family Business Strategy, Elsevier, vol. 15(4).
    • Handle: RePEc:eee:fambus:v:15:y:2024:i:4:s1877858524000391
    DOI: 10.1016/j.jfbs.2024.100644
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1877858524000391
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jfbs.2024.100644?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:fambus:v:15:y:2024:i:4:s1877858524000391. 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/wps/find/journaldescription.cws_home/719791/description#description .

    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.