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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.

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

  • 会议论文

  • 发表日期：

    2025年

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  • Victoria Oluwakemi Okesipe,
  • Théophile Demazure,
  • Jasmine Labelle,
  • Chenyi Huang,
  • Sylvain Sénécal,
  • Marc Fredette,
  • Romain Pourchon,
  • Constantinos K. Coursaris,
  • Alexander J. Karran,
  • Shang Lin Chen,
  • Pierre-Majorique Léger

摘要

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.