Algorithmic influence and consumer decision-making: empirical evidence on the limitations of predictive AI in marketing communication management
DOI:
http://doi.org/10.5902/1983465994997Keywords:
Artificial Intelligence, Decision making, Visual Attention, Marketing, ManagementAbstract
Purpose: This study investigates the convergence and divergence between visual attention predictions generated by predictive Artificial Intelligence (AI) models and empirical patterns of visual attention of Brazilian consumers, and discusses the limitations of AI-generated models in supporting managerial decision-making in marketing and communication.
Design/methodology/approach: We adopt a comparative empirical design that integrates three studies based on eye tracking with Brazilian consumers—two from the literature and one original experiment with menu-type stimuli. The empirical data were compared with results generated by a predictive AI system predominantly trained with Euro-American databases.
Results: The results show consistent divergences between human attentional patterns and algorithmic predictions. While AI tended to overestimate visually salient elements, Brazilian consumers showed greater sensitivity to contextual, textual, and semantically relevant information for decision-making.
Limitations/implications of the study: The study focuses on a single cultural context and a specific predictive AI system, which limits the generalizability of the results to other markets and algorithmic models.
Practical implications: The findings alert managers to the risks of uncritical use of predictive AI in marketing communication management, indicating the need for local empirical validation and complementary use of algorithmic tools and consumer research.
Social implications: The study contributes to the debate on decision-making autonomy and consumer well-being by showing that inaccurate algorithmic predictions can increase cognitive overload and compromise consumer experiences.
Originality/value: The study offers unprecedented empirical evidence in an emerging market, expanding the literature on algorithmic influence by integrating cognitive, cultural, and managerial dimensions in the evaluation of AI use in marketing.
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References
Almourad, M., Bataineh, E., Hussein, M., & Wattar, Z. (2025). Strategic placement of branding elements in digital marketing: Insights from eye tracking data. Proceedings of the 27th International Conference on Enterprise Information Systems, 417–423. DOI: http://doi.org/10.5220/0013281500003929
Alshaketheep, K., Al-Ahmed, H., & Mansour, A. (2025). Beyond purchase patterns: harnessing predictive analytics to anticipate unarticulated consumer needs. Acta Psychologica, 257(105089), 105089. http://doi.org/10.1016/j.actpsy.2025.105089 DOI: http://doi.org/10.1016/j.actpsy.2025.105089
Alsharif, A. H., & Isa, S. M. (2025). Electroencephalography studies on marketing stimuli: A literature review and future research agenda. International Journal of Consumer Studies, 49(1). http://doi.org/10.1111/ijcs.70015 DOI: http://doi.org/10.1111/ijcs.70015
Anupama, T., & Rosita, S. (2024). Neuromarketing insights enhanced by artificial intelligence. ComFin Research, 12(2), 24–28. http://doi.org/10.34293/commerce.v12i2.7300 DOI: http://doi.org/10.34293/commerce.v12i2.7300
Atlı, D. (2024). Analyzing turkey’s premier e-commerce marketplaces by predictive eye tracking method. Journal of Business in The Digital Age. http://doi.org/10.46238/jobda.1490101 DOI: http://doi.org/10.46238/jobda.1490101
Azman, H., Amin, M. K. M., & Wibirama, S. (2019). Exploring the subconscious decision making in neuromarketing research using eye tracking technique. Journal of Advanced Manufacturing Technology (JAMT), 13(2(2)). http://jamt.utem.edu.my/jamt/article/view/5720
Barbierato, E., Berti, D., Ranfagni, S., Hernández-Álvarez, L., & Bernetti, I. (2023). Wine label design proposals: an eye tracking study to analyze consumers’ visual attention and preferences. International Journal of Wine Business Research, 35(3), 365–389. http://doi.org/10.1108/ijwbr-06-2022-0021 DOI: http://doi.org/10.1108/IJWBR-06-2022-0021
Bigne, E., Boksem, M., Casado-Aranda, L. A., García-Madariaga, J., Gier-Reinartz, N. R., Guerreiro, J., Loureiro, S., Kakaria, S., Smidts, A., & Wedel, M. (2025). How to conduct valuable marketing research with neurophysiological tools. Psychology & Marketing, 42(10), 2616–2649. http://doi.org/10.1002/mar.70002 DOI: http://doi.org/10.1002/mar.70002
Boltaeva, Z. M. (2023). Artificial intelligence and neuromarketing: The future of personalized advertising. Modern Problems in Education and Their Scientific Solutions, 93–96.
Calderón-Fajardo, V., Anaya-Sánchez, R., Rejón-Guardia, F., Molinillo, S. (2024). Neurotourism insights: Eye Tracking and galvanic analysis of tourism destination brand logos and AI visuals. Tourism & Management Studies, 20(3), 53–78. http://doi.org/10.18089/tms.20240305 DOI: http://doi.org/10.18089/tms.20240305
Cao, X., Horváth-Mezőfi, Z., Sasvár, Z., Szabó, G., Gere, A., Hitka, G., & Radványi, D. (2025). Influence of visual quality and cultural background on consumer apple preferences: An eye tracking study with Chinese and Hungarian consumers. Applied Sciences (Basel, Switzerland), 15(2), 773. http://doi.org/10.3390/app15020773 DOI: http://doi.org/10.3390/app15020773
Colombo, L., & Bruno, A. (2024). Artificial intelligence for perception and artificial consciousness. http://ceur-ws.org/Vol-3923/
Corbetta, M., & Shulman, G. L. (2002). Control of goal-directed and stimulus-driven attention in the brain. Nature Reviews. Neuroscience, 3(3), 201–215. http://doi.org/10.1038/nrn755 DOI: http://doi.org/10.1038/nrn755
Daneshvar, A., Olfat, M., Pourghader Chobar, A., & Yadegari, E. (2025). Improving the performance of direct advertising campaigns by applying artificial intelligence techniques. Corporate Communications An International Journal. http://doi.org/10.1108/ccij-02-2025-0044 DOI: http://doi.org/10.1108/CCIJ-02-2025-0044
Dhillon, H. S., & Singh, J. (2012). Human eye tracking and related issues: A review. International Journal of Scientific and Research Publications.
Duchowski, A. T. (2002). A breadth-first survey of eye tracking applications. Behavior Research Methods, Instruments, & Computers: A Journal of the Psychonomic Society, Inc, 34(4), 455–470. http://doi.org/10.3758/bf03195475 DOI: http://doi.org/10.3758/BF03195475
Erden, A., Bilgili, A., Durmuş, B., & Çinko, M. (2025). Focusing area on advertising: An eye tracking application. Bilişim Teknolojileri Dergisi, 18(1), 77–83. http://doi.org/10.17671/gazibtd.1506664 DOI: http://doi.org/10.17671/gazibtd.1506664
Herold, E., Singh, A., Feodoroff, B., & Breuer, C. (2024). Data-driven message optimization in dynamic sports media: an artificial intelligence approach to predict consumer response. Sport Management Review, 27(5), 793–816. http://doi.org/10.1080/14413523.2024.2372122 DOI: http://doi.org/10.1080/14413523.2024.2372122
Hubert, M., & Kenning, P. (2008). A current overview of consumer neuroscience. Journal of Consumer Behaviour, 7(4–5), 272–292. http://doi.org/10.1002/cb.251 DOI: http://doi.org/10.1002/cb.251
Itti, L., & Koch, C. (2001). Computational modelling of visual attention. Nature Reviews. Neuroscience, 2(3), 194–203. http://doi.org/10.1038/35058500 DOI: http://doi.org/10.1038/35058500
Juárez-Varón, D., Mengual-Recuerda, A., Zuluaga, J. C. S., & Corvello, V. (2024). Application of artificial intelligence in neuromarketing to predict consumer behaviour towards brand stimuli: Case study - neurotechnologies vs. AI predictive model. International journal of software science and computational intelligence, 16(1), 1–18. http://doi.org/10.4018/ijssci.347214 DOI: http://doi.org/10.4018/IJSSCI.347214
Karmarkar, U. R., & Yoon, C. (2016). Consumer neuroscience: advances in understanding consumer psychology. Current opinion in psychology, 10, 160–165. http://doi.org/10.1016/j.copsyc.2016.01.010 DOI: http://doi.org/10.1016/j.copsyc.2016.01.010
Kawano, D. R. (2019). Resposta não declarada: contribuições do eye tracker e da resposta de condutância de pele para a pesquisa em publicidade. Tese de Doutorado, Escola de Comunicações e Artes, Universidade de São Paulo, São Paulo. doi:10.11606/T.27.2019.tde-14082019-113333. Recuperado em 2026-03-09, de www.teses.usp.br DOI: http://doi.org/10.11606/T.27.2019.tde-14082019-113333
Kheddache, F., & Ferroudj, M. A. (2025). The use of Eye Tracking and Artificial Intelligence (Ai) in assessing the hotels website design aesthetic element: The case of Algerian hotels websites. International Journal of Professional Business Review, 10(2), e05256. http://doi.org/10.26668/businessreview/2025.v10i2.5256 DOI: http://doi.org/10.26668/businessreview/2025.v10i2.5256
Kondak, A. (2023). The application of eye tracking and artificial intelligence in contemporary marketing communication management. Scientific Papers of Silesian University of Technology Organization and Management Series, 2023(186), 239–253. http://doi.org/10.29119/1641-3466.2023.186.18 DOI: http://doi.org/10.29119/1641-3466.2023.186.18
Kumar, V., Rajan, B., Venkatesan, R., & Lecinski, J. (2019). Understanding the role of artificial intelligence in personalized engagement marketing. California Management Review, 61(4), 135–155. http://doi.org/10.1177/0008125619859317 DOI: http://doi.org/10.1177/0008125619859317
Laeng, B., Suegami, T., & Aminihajibashi, S. (2016). Wine labels: an eye tracking and pupillometry study. International Journal of Wine Business Research, 28(4), 327–348. http://doi.org/10.1108/ijwbr-03-2016-0009 DOI: http://doi.org/10.1108/IJWBR-03-2016-0009
Leon, F. A. D., Spers, E. E., & de Lima, L. M. (2020). Self-esteem and visual attention in relation to congruent and non-congruent images: A study of the choice of organic and transgenic products using eye tracking. Food Quality and Preference, 84(103938), 103938. http://doi.org/10.1016/j.foodqual.2020.103938 DOI: http://doi.org/10.1016/j.foodqual.2020.103938
Lopes, E., Mesquita, E., Herrero, E., Santini, F. de O. S., & Pandey, S. (2025). When stock disappears, psychology appears:The moderating effect of the regulatory focus on consumer reactions to out-of-stock. Zenodo. http://doi.org/10.5281/ZENODO.16763763 DOI: http://doi.org/10.1590/1807-7692bar2025240180
Malheiros, B. A., Spers, E. E., Contreras Castillo, C. J., Aroeira, C. N., & de Lima, L. M. (2025). The role of visual attention and quality cues in consumer purchase decisions for fresh and cooked beef: An eye tracking study. Applied Sciences (Basel, Switzerland), 15(13), 7360. http://doi.org/10.3390/app15137360 DOI: http://doi.org/10.3390/app15137360
Marques, J. A. L., Neto, A. C., Silva, S. C., & Bigne, E. (2025). Predicting consumer ad preferences: Leveraging a machine learning approach for EDA and FEA neurophysiological metrics. Psychology & Marketing, 42(1), 175–192. http://doi.org/10.1002/mar.22118 DOI: http://doi.org/10.1002/mar.22118
Mendoza, A. D. B., & Cardenas, L. A. V. (2024). The influence of artificial intelligence in digital marketing. 2024 Tenth International Conference on eDemocracy & eGovernment (ICEDEG), 1–8. DOI: http://doi.org/10.1109/ICEDEG61611.2024.10702078
Mohd Isa, S., & Anuar, N. N. A. (2024). Neuromarketing cues: an eye tracking study on mother’s visual attention to organic vegetable advertisement. Neuroscience research notes, 7(4). http://doi.org/10.31117/neuroscirn.v7i4.363 DOI: http://doi.org/10.31117/neuroscirn.v7i4.363
Moriuchi, E., & Moriyoshi, N. (2024). A cross‐cultural study on online reviews and decision making: An eye‐tracking approach. Journal of Consumer Behaviour. http://doi.org/10.1002/cb.2165 DOI: http://doi.org/10.1002/cb.2165
Pentus, K., Ploom, K., Mehine, T., Koiv, M., Tempel, A., & Kuusik, A. (2020). Mobile and stationary eye tracking comparison – package design and in-store results. The Journal of Consumer Marketing, 37(3), 259–269. http://doi.org/10.1108/jcm-04-2019-3190 DOI: http://doi.org/10.1108/JCM-04-2019-3190
Pereira, M. H. N., Melo, F. L. N. B. de, Soares, A. M. J., Ferreira, P. B. S., Silva, M. P. da, & Morya, E. (2024). Eye tracking como correlato fisiológico do comportamento do consumidor: uma revisão sistemática da literatura: Uma revisão sistemática da literatura. Revista Brasileira de Marketing, 23(1), 300–365. http://doi.org/10.5585/remark.v23i1.23271 DOI: http://doi.org/10.5585/remark.v23i1.23271
Pieters, R., Warlop, L., & Wedel, M. (2002). Breaking through the clutter: Benefits of advertisement originality and familiarity for brand attention and memory. Management Science, 48(6), 765–781. http://doi.org/10.1287/mnsc.48.6.765.192 DOI: http://doi.org/10.1287/mnsc.48.6.765.192
Posner, M. I., & Petersen, S. E. (1990). The attention system of the human brain. Annual Review of Neuroscience, 13(1), 25–42. http://doi.org/10.1146/annurev.ne.13.030190.000325 DOI: http://doi.org/10.1146/annurev.ne.13.030190.000325
Serbia, Milić Keresteš, N., Golubović, G., Dedijer, S., Pavlović, Ž., & Janjić, T. (2024). Ai models for predicting visual attention in digital applications: A comparative pilot analysis with eye tracking results. Proceedings - The Twelfth International Symposium GRID 2024. http://doi.org/10.24867/grid-2024-p47 DOI: http://doi.org/10.24867/GRID-2024-p47
Shukla, R. P., Juneja, D., & Monga, S. (2024). Predictive analytics in marketing using artificial intelligence. Em Lecture Notes in Networks and Systems (p. 213–224). Springer Nature Singapore. DOI: http://doi.org/10.1007/978-981-99-9531-8_17
Simonetti, A., & Bigne, E. (2024). Does banner advertising still capture attention? An eye tracking study. Spanish Journal of Marketing-ESIC, 28(1), 3–20. http://doi.org/10.1108/sjme-11-2022-0236 DOI: http://doi.org/10.1108/SJME-11-2022-0236
Šola, H. M., Qureshi, F. H., & Khawaja, S. (2024). AI-powered eye tracking for bias detection in online course reviews: A Udemy case study. Big Data and Cognitive Computing, 8(11), 144. http://doi.org/10.3390/bdcc8110144 DOI: http://doi.org/10.3390/bdcc8110144
Treue, S. (2003). Visual attention: the where, what, how and why of saliency. Current Opinion in Neurobiology, 13(4), 428–432. http://doi.org/10.1016/s0959-4388(03)00105-3 DOI: http://doi.org/10.1016/S0959-4388(03)00105-3
Usman, S. M., Khalid, S., Tanveer, A., Imran, A. S., & Zubair, M. (2025). Multimodal consumer choice prediction using EEG signals and eye tracking. Frontiers in Computational Neuroscience, 18, 1516440. http://doi.org/10.3389/fncom.2024.1516440 DOI: http://doi.org/10.3389/fncom.2024.1516440
Wedel, M., & Pieters, R. (2006). Eye tracking for visual marketing. Foundations and Trends® in Marketing, 1(4), 231–320. http://doi.org/10.1561/1700000011 DOI: http://doi.org/10.1561/1700000011
Wickham, H. (2016). Ggplot2: Elegant graphics for data analysis (2o ed.). Springer International Publishing. DOI: http://doi.org/10.1007/978-3-319-24277-4_9
Wolfe, J. M., & Horowitz, T. S. (2004). What attributes guide the deployment of visual attention and how do they do it? Nature Reviews. Neuroscience, 5(6), 495–501. http://doi.org/10.1038/nrn1411 DOI: http://doi.org/10.1038/nrn1411
Zhang, J., & Lee, E.-J. (2022). “Two Rivers” brain map for social media marketing: Reward and information value drivers of SNS consumer engagement. Journal of Business Research, 149, 494–505. http://doi.org/10.1016/j.jbusres.2022.04.022 DOI: http://doi.org/10.1016/j.jbusres.2022.04.022
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