Neuromarketing in education: how emotional content affects the perceived effectiveness of university videos
https://doi.org/10.52821/2789-4401-2025-3-106-121
Abstract
Purpose of the Research. This study investigates respondents’ emotional reactions to Almaty Management University’s promotional videos and examines how those reactions influence perceived attractiveness of the videos and recall of their content. Understanding these effects may inform the development of more effective educational-marketing strategies by showing how emotional engagement drives prospective students’ interest.
Methodology. The research is based on the observations of the facial expressions of subjects showing two promotional videos developed by the marketing department of Almaty Management University. Twenty prospective students (ages 16–20), who were considering applying to the university, took part in the study. Facial expressions were recorded using FaceReader during video viewing, after which each participant completed surveys to assess ad recall and perceived attractiveness, allowing to examine their relationship with the recorded emotional responses. To predict the memory output and the perceived attractiveness based on FaceReader results machine learning models such as Random Forest and Gradient Boost were employed.
Originality/Value of the Research. By having combined facial expression analysis with predictive analytics, this study intends to serve as a contribution in the growing area of neuromarketing within educational marketing. Contrary to traditional marketing methods which mainly rely on self-report, our research provides an objective evaluation for emotional involvement and its cognitive consequences.
Findings. Positive emotions like joy and sentimentality significantly enhance memory retention and perceived attractiveness of university promo videos. Fear and anger, however, had lower predictive power. According to machine learning models, positive emotion is important for student engagement and recall. Positive emotion-engaging video content will prove a strong tool in educational marketing to increase brand recall and prospective students’ decision-making. The results are of practical importance in providing necessary information to universities to better their marketing strategies.
About the Authors
G. BekenovaKazakhstan
Bekenova Gulsanat – Master’s Student
Almaty
E. B. Orazgaliyeva
Kazakhstan
Orazgaliyeva Elmaira Bolatbekovna – PhD
Almaty
References
1. Tyng, C. M., Amin, H. U., Saad, M. N. M., & Malik, A. S. (2017). The influences of emotion on learning and memory. Frontiers in Psychology, 8, 1454. https://doi.org/10.3389/fpsyg.2017.01454
2. Lackmann, S., Léger, P.-M., Charland, P., Aubé, C., & Talbot, J. (2021). The influence of video format on engagement and performance in online learning. Brain Sciences, 11(2), 128. https://doi.org/10.3390/brainsci11020128
3. Weinberg, P., & Gottwald, W. (1982). Impulsive consumer buying as a result of emotions. Journal of Business Research, 10(1), 43–57. https://doi.org/10.1016/0148-2963(82)90016-9
4. Breiter, H. C., Block, M., Blood, A. J., Calder, B., Chamberlain, L., Lee, N., Livengood, S., Mulhern, F. J., Raman, K., Schultz, D., Stern, D. B., Viswanathan, V., & Zhang, F. (2015). Redefining neuromarketing as an integrated science of influence. Frontiers in Human Neuroscience, 8, 1073. https://doi.org/10.3389/fnhum.2014.01073
5. Assielou, K. A., Haba, C. T., Kadjo, T. L., Yao, K. D., & Gooré, B. T. (2020). Emotional impact for predicting student performance in intelligent tutoring systems (ITS). International Journal of Advanced Computer Science and Applications, 11(7), 219–225. https://doi.org/10.14569/IJACSA.2020.0110728
6. Royo-Vela, M., & Varga, Á. (2022). Unveiling neuromarketing and its research methodology. Encyclopedia, 2(2), 729–751. https://doi.org/10.3390/encyclopedia2020051
7. Smykova, M. R., Orazgaliyeva, E. B., Kazybaeva, A. M., & Abuzhalitova, A. A. (2021). Consumer attitudes towards university advertising: A neuromarketing approach. Bulletin of Karaganda University, 4(104), 148–158. https://doi.org/10.31489/2021Ec4/148-158
8. Rodas, J. A., & Montoya-Restrepo, L. A. (2019). Measurement and analysis of television commercials based on the computer tools EyeTracking and FaceReader. Informacion Tecnologica, 30(2), 3–10.
9. Cordeiro, R., Reis, A., Ferreira, B. M., & Mendes Bacalhau, L. (2024). Neuromarketing: Decoding the role of emotions and senses and consumer behavior. IGI Global. https://doi.org/10.4018/979-8-3693-1858-4.ch005
10. Osugi, A., & Ohira, H. (2018). Emotional arousal at memory encoding enhanced P300 in the concealed information test. Frontiers in Psychology, 8, 2334. https://doi.org/10.3389/fpsyg.2017.02334
11. System1 Group. (n.d.). Emotion's key role in digital ad effectiveness. Retrieved from https://system-1group.com/blog/emotions-key-role-in-digital-ad-effectiveness
12. Vences, N. A., Díaz-Campo, J., & Rosales, D. F. G. (2020). Neuromarketing as an emotional connection tool between organizations and audiences in social networks: A theoretical review. Frontiers in Psychology, 11, 1787. https://doi.org/10.3389/fpsyg.2020.01787
13. RAM. (2016). Emotions & the impact on advertising effectiveness. Research & Analysis of Media. Retrieved from https://www.rampanel.com/
14. Murti, A., & Ghosh, R. (2023). The impact of emotional appeals in neuromarketing: Analyzing the brain responses of consumers to emotional advertising campaigns. International Journal of Enhanced Research in Management & Computer Applications, 12(9), 23–32. https://doi.org/10.55948/IJERMCA.2023.0905
15. Otamendi, F. J., & Sutil Martín, D. L. (2020). The emotional effectiveness of advertisement. Frontiers in Psychology, 11, 2088. https://doi.org/10.3389/fpsyg.2020.02088
16. Azad, M. S., Khan, S. S., Hossain, R., Rahman, R., & Momen, S. (2023). Predictive modeling of consumer purchase behavior on social media: Integrating theory of planned behavior and machine learning for actionable insights. PLoS ONE, 18(12), e0296336. https://doi.org/10.1371/journal.pone.0296336
17. Deniz, E., & Bülbül, S. Ç. (2024). Predicting customer purchase behavior using machine learning models. Information Technology in Economics and Business, 1(1), 1–6. https://doi.org/10.69882/adba.iteb.2024071
18. Whitehill, J., Serpell, Z., Lin, Y.-C., Foster, A., & Movellan, J. R. (2014). The faces of engagement: Automatic recognition of student engagement from facial expressions. IEEE Transactions on Affective Computing, 5(1), 86–98. https://doi.org/10.1109/TAFFC.2014.2316163
19. Soloviev, V. (2018). Machine learning approach for student engagement automatic recognition from facial expressions. Scientific Publications of the State University of Novi Pazar, Series A: Applied Mathematics, Informatics and Mechanics, 10(2), 79–86. https://doi.org/10.5937/SPSUNP1802079S
20. Šola, H. M., Qureshi, F. H., & Khawaja, S. (2024). Exploring the untapped potential of neuromarketing in online learning: Implications and challenges for the higher education sector in Europe. Behavioral Sciences, 14(2), 80. https://doi.org/10.3390/bs14020080
21. Rejer, I., Jankowski, J., Dreger, J., & Lorenz, K. (2024). Viewer engagement in response to mixed and uniform emotional content in marketing videos—An electroencephalographic study. Sensors, 24(2), 517. https://doi.org/10.3390/s24020517
22. Graham, A. (2021, June 7). The importance of emotional connection in higher-ed marketing. Big Sea. https://bigsea.co/ideas/the-importance-of-emotional-connection-in-higher-ed-marketing/
23. ESOMAR. (n.d.). Guideline for researchers and clients involved in primary data collection. https://esomar.org/code-and-guidelines/guideline-for-researchers-and-clients-involved-in-primary-data-collection
24. Kazybaeva, A. M. (2022). Neuromarketing. Individual Entrepreneur “Balausa.”
25. Vozzi, A., Ronca, V., Aricò, P., Borghini, G., Sciaraffa, N., Cherubino, P., Trettel, A., Babiloni, F., & Di Flumeri, G. (2021). The sample size matters: To what extent the participant reduction affects the outcomes of a neuroscientific research. A case-study in neuromarketing field. Sensors, 21(18), 6088. https://doi.org/10.3390/s21186088
26. iMotions. (n.d.). Facial expression analysis – Emotion detection software. Retrieved May 27, 2025, from https://imotions.com/products/imotions-lab/modules/fea-facial-expression-analysis/
27. Oakes, R. A., Peschel, L., & Barraclough, N. E. (2024). Inter-subject correlation of audience facial expressions predicts audience engagement during theatrical performances. iScience, 27(6), 109843. https://doi.org/10.1016/j.isci.2024.109843
28. Hendriks, A. (2024, June 26). How FaceReader is validated in research. Noldus Information Technology. https://noldus.com/blog/how-facereader-is-validated-in-research
29. International Chamber of Commerce. (n.d.). ICC/ESOMAR international code on market and social research. https://iccwbo.org/news-publications/policies-reports/iccesomar-international-code-on-market-andsocial-research/
30. Neuromarketing Science & Business Association. (n.d.). NMSBA code of ethics. https://www.nmsba.com/neuromarketing-companies/code-of-ethics
31. U.S. Department of Health & Human Services. (2023). Code of Federal Regulations, title 45, section 46.102—Definitions for purposes of this policy. Cornell Law School Legal Information Institute. Retrieved June 15, 2025, from https://www.law.cornell.edu/cfr/text/45/46.102
32. American Psychological Association. (2017). Ethical principles of psychologists and code of conduct. https://www.apa.org/ethics/code/ethics-code-2017.pdf
33. Morgan Lewis & Bockius LLP. (2023). Data protection in Kazakhstan: Overview [PDF]. https://www.morganlewis.com/-/media/files/publication/outside-publication/article/2023/data-protection-in-kazakhstanoverview.pdf
34. Tellis G. J., MacInnis D. J., Tirunillai S., Zhang Y. What Drives Virality (Sharing) of Online Digital Content? // Journal of Marketing. — 2019. — № 83(4). — С. 1–20. — DOI: 10.1177/0022242919841034.
35. Szucs, D., & Ioannidis, J. P. A. (2020). Sample size evolution in neuroimaging research: An evaluation of highly-cited studies (1990–2012) and of latest practices (2017–2018) in high-impact journals. NeuroImage, 221, 117164. https://doi.org/10.1016/j.neuroimage.2020.117164
36. Neuromarketing Science & Business Association. (n.d.-b). Sample size in neuromarketing. Retrieved May 28, 2025, from https://nmsba.com/news/656-sample-size-in-neuromarketing
37. Nightshade, L. (2023, June). Harnessing Gen Z’s attention span for successful social media marketing. Colormatics. https://www.colormatics.com/article/harnessing-gen-z-attention-span-for-successful-social/
38. Venkatraman, V., Dimoka, A., Pavlou, P. A., Vo, K., Hampton, W., Bollinger, B., Hershfield, H. E., Ishihara, M., & Winer, R. S. (2015). Predicting advertising success beyond traditional measures: New insights from neurophysiological methods and market response modeling. Journal of Marketing Research, 52(4), 436–452. https://doi.org/10.1509/jmr.13.0593
Review
For citations:
Bekenova G., Orazgaliyeva E.B. Neuromarketing in education: how emotional content affects the perceived effectiveness of university videos. Central Asian Economic Review. 2025;(3):106-121. https://doi.org/10.52821/2789-4401-2025-3-106-121