IDEAS home Printed from https://ideas.repec.org/a/spr/rvmgts/v18y2024i4d10.1007_s11846-023-00696-z.html
   My bibliography  Save this article

The GenAI is out of the bottle: generative artificial intelligence from a business model innovation perspective

Author

Listed:
  • Dominik K. Kanbach

    (HHL Leipzig Graduate School of Management
    Woxsen University)

  • Louisa Heiduk

    (HHL Leipzig Graduate School of Management)

  • Georg Blueher

    (HHL Leipzig Graduate School of Management)

  • Maximilian Schreiter

    (HHL Leipzig Graduate School of Management)

  • Alexander Lahmann

    (HHL Leipzig Graduate School of Management)

Abstract

The introduction of ChatGPT in November 2022 by OpenAI has stimulated substantial discourse on the implementation of artificial intelligence (AI) in various domains such as academia, business, and society at large. Although AI has been utilized in numerous areas for several years, the emergence of generative AI (GAI) applications such as ChatGPT, Jasper, or DALL-E are considered a breakthrough for the acceleration of AI technology due to their ease of use, intuitive interface, and performance. With GAI, it is possible to create a variety of content such as texts, images, audio, code, and even videos. This creates a variety of implications for businesses requiring a deeper examination, including an influence on business model innovation (BMI). Therefore, this study provides a BMI perspective on GAI with two primary contributions: (1) The development of six comprehensive propositions outlining the impact of GAI on businesses, and (2) the discussion of three industry examples, specifically software engineering, healthcare, and financial services. This study employs a qualitative content analysis using a scoping review methodology, drawing from a wide-ranging sample of 513 data points. These include academic publications, company reports, and public information such as press releases, news articles, interviews, and podcasts. The study thus contributes to the growing academic discourse in management research concerning AI's potential impact and offers practical insights into how to utilize this technology to develop new or improve existing business models.

Suggested Citation

  • Dominik K. Kanbach & Louisa Heiduk & Georg Blueher & Maximilian Schreiter & Alexander Lahmann, 2024. "The GenAI is out of the bottle: generative artificial intelligence from a business model innovation perspective," Review of Managerial Science, Springer, vol. 18(4), pages 1189-1220, April.
  • Handle: RePEc:spr:rvmgts:v:18:y:2024:i:4:d:10.1007_s11846-023-00696-z
    DOI: 10.1007/s11846-023-00696-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11846-023-00696-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11846-023-00696-z?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

    Keywords

    Artificial intelligence; Generative AI; ChatGPT; Business model innovation; Large language models;
    All these keywords.

    JEL classification:

    • M10 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - General
    • M13 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - New Firms; Startups
    • M15 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - IT Management
    • M19 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Other

    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:spr:rvmgts:v:18:y:2024:i:4:d:10.1007_s11846-023-00696-z. 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.