IDEAS home Printed from https://ideas.repec.org/a/spr/elmark/v35y2025i1d10.1007_s12525-025-00778-8.html
   My bibliography  Save this article

A novel subject-independent deep learning approach for user behavior prediction in electronic markets based on electroencephalographic data

Author

Listed:
  • Pascal Penava

    (Helmut-Schmidt-University)

  • Ricardo Buettner

    (Helmut-Schmidt-University)

Abstract

Based on the work by Buettner (2017) showing a personality-based recommender system for electronic markets using social media data, we extend the work by proposing a novel deep learning-based engine to predict the user’s personality just based on electroencephalographic brain data. As brain-computer interfaces and hybrid intelligence devices enable access to human brains, using electroencephalographic brain data becomes more relevant in future. Contrary to the majority view of previous research, our results show that there is a link between personality traits and brain features of a user. With a four times higher probability of correctly predicting the personality of an independent user compared to naive prediction, we demonstrate the possibility of predicting a user’s personality based on their brain information and thus showing a new reliable approach for marketing purposes in electronic markets.

Suggested Citation

  • Pascal Penava & Ricardo Buettner, 2025. "A novel subject-independent deep learning approach for user behavior prediction in electronic markets based on electroencephalographic data," Electronic Markets, Springer;IIM University of St. Gallen, vol. 35(1), pages 1-20, December.
    • Handle: RePEc:spr:elmark:v:35:y:2025:i:1:d:10.1007_s12525-025-00778-8
    DOI: 10.1007/s12525-025-00778-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12525-025-00778-8
    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/s12525-025-00778-8?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

    Convolutional neural network; Predictive analysis; Five-factor model; Machine learning; Personality mining; Resting-state electroencephalogram;
    All these keywords.

    JEL classification:

    • C89 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other
    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General
    • D40 - Microeconomics - - Market Structure, Pricing, and Design - - - General
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing
    • M37 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Advertising

    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:elmark:v:35:y:2025:i:1:d:10.1007_s12525-025-00778-8. 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.