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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
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    References listed on IDEAS

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    1. Jörg Uffen & Michael H. Breitner, 2013. "Management of Technical Security Measures: An Empirical Examination of Personality Traits and Behavioral Intentions," International Journal of Social and Organizational Dynamics in IT (IJSODIT), IGI Global, vol. 3(1), pages 14-31, January.
    2. Christian Janiesch & Patrick Zschech & Kai Heinrich, 2021. "Machine learning and deep learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 685-695, September.
    3. Parra, Carlos M. & Gupta, Manjul & Cadden, Trevor, 2022. "Towards an understanding of remote work exhaustion: A study on the effects of individuals’ big five personality traits," Journal of Business Research, Elsevier, vol. 150(C), pages 653-662.
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    5. Liam Drew, 2023. "The rise of brain-reading technology: what you need to know," Nature, Nature, vol. 623(7986), pages 241-243, November.
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    7. Ricardo Buettner, 2017. "Predicting user behavior in electronic markets based on personality-mining in large online social networks," Electronic Markets, Springer;IIM University of St. Gallen, vol. 27(3), pages 247-265, August.
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    Full references (including those not matched with items on IDEAS)

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    Keywords

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    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

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