AI and Neuromarketing – Understanding Consumer Decision Making with Artificial Intelligence – Systematic Review

Dinesh Deckker; Subhashini Sumanasekara

doi:10.55927/ijba.v5i2.13990

Authors

Dinesh Deckker Wrexham University
Subhashini Sumanasekara University of Gloucesteshire

DOI:

https://doi.org/10.55927/ijba.v5i2.13990

Keywords:

AI in Neuromarketing, Consumer Behavior, Facial Recognition, Emotion Detection, Biometrics, Ethical AI, Machine Learning, Consumer Decision-Making, Marketing AI

Abstract

Neuromarketing studies human behavior through artificial intelligence (AI) technologies, which allow scientists to use advanced analytical methods with data-based techniques. The paper systematically analyzes how AI functions in neuromarketing, including research about subconscious consumer assessment, predictive behavioral patterns, and in-the-moment biometric measurements. The study examines four leading AI technologies, machine learning, deep learning, natural language processing, and computer vision, to show their value in enhancing traditional marketing theory development and decision-making models. Future research must conduct time-based studies about AI's impact on consumer actions and create dependable ethical methods to manage responsible AI deployment. Every stakeholder in neuromarketing research, including marketers and policymakers, will find helpful information about AI innovation in this field within this review.

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References

Ailyn, D. (2024). Explainable AI (XAI) for transparency and trust in AI-driven customer interactions. ResearchGate. https://www.researchgate.net/publication/381879120_Explainable_AI_XAI

Besse, P., Castets-Renard, C., Garivier, A., & Loubes, J. M. (2018). Can everyday AI be ethical: Fairness of machine learning algorithms. arXiv preprint, arXiv:1810.01729.

Camerer, C. F. (2017). Artificial intelligence and behavioral economics. National Bureau of Economic Research. https://conference.nber.org/confer/2017/AIf17/Camerer.pdf

Dwivedi, Y. K., Hughes, D. L., Baabdullah, A. M., Ribeiro-Navarrete, S., Giannakis, M., Al-Debei, M. M., Dennehy, D., Metri, B., Buhalis, D., Cheung, C. M. K., Conboy, K., Duan, Y., Dubey, R., Dutot, V., Edwards, J., Eirug, A., Elbanna, A., Ferraris, A., Gupta, B., ... Wade, M. (2023). How generative AI is (will) change consumer behaviour: Postulating a future research agenda. Psychology & Marketing. https://doi.org/10.1002/mar.21725

Fernández, R. G., Maimó, L. F., Celdrán, A. H., & Pérez, M. Q. (2024). Explainable artificial intelligence (xAI) in neuromarketing/consumer neuroscience. Frontiers in Human Neuroscience. https://doi.org/10.3389/fnhum.2024.1305164

Hsu, C. T., Lin, Y. T., & Hsu, W. Y. (2023). DeePay: Deep learning decodes EEG to predict consumer's willingness to pay. Frontiers in Neuroscience, 17, 1027755. https://doi.org/10.3389/fnins.2023.1027755

Kable, J. W., & Glimcher, P. W. (2007). The neural correlates of subjective value during intertemporal choice. Nature Neuroscience, 10(12), 1625–1633. https://doi.org/10.1038/nn2007

Mouammine, A., & Azouzi, M. (2024). AI in neuromarketing: Assessing consumer emotions through facial expression analysis. International Journal of Research in Marketing, 41(1), 45-60.

Pandey, P., Swarnkar, R., Kakaria, S., & Miyapuram, K. P. (2020). Understanding consumer preferences for movie trailers from EEG using machine learning. arXiv preprint, arXiv:2007.10756. https://doi.org/10.48550/arXiv.2007.10756

Patwardhan, A. S., & Knapp, G. M. (2017). Multimodal affect analysis for product feedback assessment. arXiv preprint, arXiv:1705.02694.

Phutela, N., Abhilash, P., Sreevathsan, K., & Krupa, B. N. (2022). Intelligent analysis of EEG signals to assess consumer decisions: A study on neuromarketing. arXiv preprint, arXiv:2206.07484. https://doi.org/10.48550/arXiv.2206.07484

Quiles Pérez, M., Martínez Beltrán, E. T., López Bernal, S., Horna Prat, E., Montesano Del Campo, L., Fernández Maimó, L., & Huertas Celdrán, A. (2022). Data fusion in neuromarketing: Multimodal analysis of biosignals, lifecycle stages, current advances, datasets, trends, and challenges. arXiv preprint, arXiv:2209.00993. https://doi.org/10.48550/arXiv.2209.00993

Rajendran, A. (2023). Neuromarketing: How AI is powering predictive consumer behavior models. Medium. https://arthirajendran.medium.com/ai-and-neuromarketing-how-python-is-powering-predictive-consumer-behavior-models-fd526c82ef2c

Shen, F. X., & Wolf, S. M. (2024). Ethical, legal, and policy challenges in field-based neuroimaging research using emerging portable MRI technologies: Guidance for investigators and for oversight. Frontiers in Human Neuroscience.

Shukla, A., Gullapuram, S. S., Katti, H., Kankanhalli, M., Winkler, S., & Subramanian, R. (2019). Recognition of advertisement emotions with application to computational advertising. arXiv preprint, arXiv:1904.01778. https://doi.org/10.48550/arXiv.1904.01778

Smidts, A. (2002). Kijken in het brein: Over de mogelijkheden van neuromarketing [Looking into the brain: On the potential of neuromarketing]. ERIM Inaugural Address Series Research in Management. https://doi.org/10.2139/ssrn.3488617

Snyder, J. (2024, December 12). Neuromarketing: AI-enabled brain-computer interfaces shaping our future. Forbes. https://www.forbes.com/sites/jasonsnyder/2024/12/12/neuromarketing-ai-enabled-brain-computer-interfaces-shaping-our-future/

Veale, M., & Edwards, L. (2018). Clarity, surprises, and further questions in the Article 29 Working Party draft guidance on automated decision-making and profiling. Computer Law & Security Review, 34(2), 398-404.

Wachter, S., Mittelstadt, B., & Floridi, L. (2017). Why a right to explanation of automated decision-making does not exist in the General Data Protection Regulation. International Data Privacy Law, 7(2), 76-99.

Zarsky, T. Z. (2016). Incompatible: The GDPR in the age of big data. Seton Hall Law Review, 47, 995-1020.