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Face reading technology: improving preference prediction from self-reports using micro-expressions

Pantelis Karapanagiotis et al.

Journal of Business Research2026https://doi.org/10.1016/j.jbusres.2026.116050article
AJG 3ABDC A
Weight
0.50

Abstract

Traditional market research primarily relies on self-reports (SR) to assess consumer preferences, yet these methods are prone to biases and limited in capturing subconscious emotional responses. Psychophysiological and neurophysiological methods offer objective alternatives, but their high cost and intrusiveness limit practical use. This study examines automated Facial Expression Analysis (FEA), focusing on micro-expression (ME) emotion data, as a scalable, non-intrusive approach to improve the accuracy of predicting consumer choices. In a controlled experiment exposing participants to both video and poster ads, we compare the predictive power of ME and SR emotion data using machine learning and artificial neural network models. Results demonstrate that ME data significantly enhance both multinomial and binomial choice prediction accuracy compared to SR data, particularly for dynamic video ads where ME patterns capture real-time emotional fluctuations more effectively. Beyond its methodological contributions, our study underscores practical implications and discusses the ethical considerations related to consumer privacy.

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https://doi.org/https://doi.org/10.1016/j.jbusres.2026.116050

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@article{pantelis2026,
  title        = {{Face reading technology: improving preference prediction from self-reports using micro-expressions}},
  author       = {Pantelis Karapanagiotis et al.},
  journal      = {Journal of Business Research},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.jbusres.2026.116050},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.