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Predicting User Satisfaction and Recommendation Intentions: A Machine Learning Approach Using Psychophysiological and Self-Reported Data

In: Information Systems and Neuroscience

Author

Listed:
  • Victoria Oluwakemi Okesipe

    (HEC Montréal)

  • Théophile Demazure

    (HEC Montréal)

  • Jasmine Labelle

    (HEC Montréal)

  • Chenyi Huang

    (HEC Montréal)

  • Sylvain Sénécal

    (HEC Montréal)

  • Marc Fredette

    (HEC Montréal)

  • Romain Pourchon

    (Deloitte Digital)

  • Constantinos K. Coursaris

    (HEC Montréal)

  • Alexander J. Karran

    (HEC Montréal)

  • Shang Lin Chen

    (HEC Montréal)

  • Pierre-Majorique Léger

    (HEC Montréal)

Abstract

The finance sector, just like e-commerce, utilizes online platforms (websites or mobile apps) to deliver its services or products, making usability and user experience one of the key concerns of digital banking. Having identified a research gap in using psychophysiological data to understand the determinants of customer satisfaction on digital platforms, this study focuses on predicting factors influencing users’ satisfaction and intention to recommend a banking website using both self-reported and psychophysiological data. With a within-subject study design, we collected data on 100 participants. Our research-in-progress aims to develop a machine learning model capable of predicting real-time user satisfaction and the likelihood of a user recommending a digital banking experience to friends or colleagues. Results showed that psychophysiological metrics improved the prediction of users’ intention to recommend. Similar features such as Phasic EDA, pupil size, time-to-first-mouse-click, k-coefficient, emotional valence, and subjective success were found to be good predictors of both intention to recommend and customer satisfaction.

Suggested Citation

  • Victoria Oluwakemi Okesipe & Théophile Demazure & Jasmine Labelle & Chenyi Huang & Sylvain Sénécal & Marc Fredette & Romain Pourchon & Constantinos K. Coursaris & Alexander J. Karran & Shang Lin Chen , 2025. "Predicting User Satisfaction and Recommendation Intentions: A Machine Learning Approach Using Psychophysiological and Self-Reported Data," Lecture Notes in Information Systems and Organization, in: Fred D. Davis & René Riedl & Jan vom Brocke & Pierre-Majorique Léger & Adriane B. Randolph & Gernot (ed.), Information Systems and Neuroscience, pages 385-395, Springer.
    • Handle: RePEc:spr:lnichp:978-3-031-71385-9_34
    DOI: 10.1007/978-3-031-71385-9_34
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