IDEAS home Printed from https://ideas.repec.org/h/spr/lnichp/978-3-031-80125-9_13.html
   My bibliography  Save this book chapter

Executive AI Literacy: A Text-Mining Approach to Understand Existing and Demanded AI Skills of Leaders in Unicorn Firms

Author

Listed:
  • Marc Pinski

    (Technical University of Darmstadt, Information Systems and Electronic Services)

  • Thomas Hofmann

    (Technical University of Darmstadt, Information Systems and Electronic Services)

  • Alexander Benlian

    (Technical University of Darmstadt, Information Systems and Electronic Services)

Abstract

Despite the growing relevance of artificial intelligence (AI) for businesses, there is a lack of research on how top-level executives must be skilled in AI. Drawing on upper echelons theory, this paper explores executive AI literacy, defined as the combined AI skills of top-level executives, and its relevance for different executive roles. We conducted a text-mining analysis of 1625 executives’ online profiles and 1033 executive job postings from unicorn firms retrieved via web-scraping from an online professional social network. We find that AI skills are mostly required in product-related executive roles (vs. administrative roles). Thus, we provide an AI-specific perspective complementing prior information systems research on executives, which asserts that (non-AI) IT is driven by administrative executive roles. Our paper contributes to AI literacy literature by shedding light on the substance of executive AI literacy within firms. Lastly, we provide implications for AI-related information systems strategy.

Suggested Citation

  • Marc Pinski & Thomas Hofmann & Alexander Benlian, 2025. "Executive AI Literacy: A Text-Mining Approach to Understand Existing and Demanded AI Skills of Leaders in Unicorn Firms," Lecture Notes in Information Systems and Organization,, Springer.
    • Handle: RePEc:spr:lnichp:978-3-031-80125-9_13
    DOI: 10.1007/978-3-031-80125-9_13
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:spr:lnichp:978-3-031-80125-9_13. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.