IDEAS home Printed from https://ideas.repec.org/a/taf/ujbmxx/v61y2023i3p1314-1343.html
   My bibliography  Save this article

“Hasta la vista, baby” – will machine learning terminate human literature reviews in entrepreneurship?

Author

Listed:
  • Sebastian Robledo
  • Andrés Mauricio Grisales Aguirre
  • Mathew Hughes
  • Fabian Eggers

Abstract

Can, and should, artificial intelligence (AI) and its machine learning (ML) variant be applied to study scholarly literature? With AI and ML rapidly disrupting industries, we investigate how scholars in entrepreneurship and small business management can capitalize on AI and ML to support their scholarship and comprehensively review, catalog, and analyze the literature. We examine various ML tools and deploy these tools against a published literature review to consider whether ML complements or substitutes scholars’ agency. We show that ML can reinforce human findings to support replicability and robustness, adding additional layers of transparency and validity to conclusions from human-derived systematic reviews. Our contributions provide scholars with valuable guidance and a blueprint for adopting ML into their scholarship and not replacing their scholarship.

Suggested Citation

  • Sebastian Robledo & Andrés Mauricio Grisales Aguirre & Mathew Hughes & Fabian Eggers, 2023. "“Hasta la vista, baby” – will machine learning terminate human literature reviews in entrepreneurship?," Journal of Small Business Management, Taylor & Francis Journals, vol. 61(3), pages 1314-1343, May.
    • Handle: RePEc:taf:ujbmxx:v:61:y:2023:i:3:p:1314-1343
    DOI: 10.1080/00472778.2021.1955125
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00472778.2021.1955125
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00472778.2021.1955125?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.

    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:taf:ujbmxx:v:61:y:2023:i:3:p:1314-1343. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/ujbm .

    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.