EN
TR
BEYOND TRADITIONAL MARKETING: A SYSTEMATIC LITERATURE REVIEW OF AI AND NEUROMARKETING IN CONSUMER BEHAVIOR
Öz
This study explores the integration of artificial intelligence (AI) and neuromarketing in consumer behavior research. By combining AI-driven analytics with neuromarketing techniques, it aims to improve marketing precision and deepen understanding of consumer decision-making. A systematic literature review was conducted, in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, analyzing 43 peer-reviewed articles published between 2015 and 2024, following a defined elimination process. Articles were retrieved from Scopus using predefined keywords. The content analysis focused on four dimensions: thematic areas and research topics, theoretical foundations, research design, and methodological patterns. The findings reveal a marked increase in publications after 2018, with a notable peak in 2024 at 32.56%. Three dominant themes were identified: neuromarketing techniques in consumer behavior (44.2%), AI applications in marketing and engagement (27.9%), and ethical concerns in AI-driven research (27.9%). While psychological and cognitive theories are frequently used, most studies rely on primary data. The growing use of EEG, machine learning, and sentiment analysis signals a methodological shift. This review addresses a gap in the literature by mapping the contribution of AI to neuromarketing. It provides a structured agenda for future research, illustrating how AI can enhance traditional methods and generate new insights into consumer behavior.
Anahtar Kelimeler
Kaynakça
- Alipour, P., Gallegos, E. E., & Sridhar, S. (2024). AI-driven marketing personalization: Deploying convolutional neural networks to decode consumer behavior. International Journal of Human–Computer Interaction. https://doi.org/10.1080/10447318.2024.2432455
- Alonso-Dos-Santos, M., Quilodrán Ulloa, R., Salgado Quintana, Á., Vigueras Quijada, D., & Farías Nazel, P. (2019). Nutrition Labeling Schemes and the Time and Effort of Consumer Processing. Sustainability, 11(4), 1079. https://doi.org/10.3390/su11041079
- Álvarez-Pato, V. M., Sánchez, C. N., Domínguez-Soberanes, J., Méndoza-Pérez, D. E., & Velázquez, R. (2020). A Multisensor Data Fusion Approach for Predicting Consumer Acceptance of Food Products. Foods, 9(6), 774. https://doi.org/10.3390/foods9060774
- Archana, C., & Mahajan, A. (2023). Neuro marketing: An astonishing addition to the marketing world. In A. Mahajan (Ed.), Neuromarketing and consumer behavior (pp. 33–41). Springer. https://doi.org/10.1007/978-981-99-7058-2_3
- Atkinson, R. C., & Shiffrin, R. M. (1968). Human memory: A proposed system and its control processes. In K. W. Spence & J. T. Spence (Eds.), The psychology of learning and motivation: Advances in research and theory (Vol. 2, pp. 89–195). Academic Press.
- Bansal, S., & Gupta, M. (2022). Towards using artificial intelligence in neuromarketing. In P. Mishra (Ed.), Promoting consumer engagement through emotional branding and sensory marketing (pp. 16–23). IGI Global. https://doi.org/10.4018/978-1-6684-5897-6.ch002
- Bolón-Canedo, V., Morán-Fernández, L., Cancela, B., & Alonso-Betanzos, A. (2024). A review of green artificial intelligence: Towards a more sustainable future. Neurocomputing, 599, 128096. https://doi.org/10.1016/j.neucom.2024.128096
- Brandtzaeg, P. B., Skjuve, M., & Følstad, A. (2022). My AI friend: How users of a social chatbot understand their human-AI friendship. Human Communication Research, 48(3), 404–429. https://doi.org/10.1093/hcr/hqac008
Ayrıntılar
Birincil Dil
İngilizce
Konular
Pazarlama (Diğer)
Bölüm
Derleme
Yayımlanma Tarihi
29 Haziran 2026
Gönderilme Tarihi
23 Ekim 2025
Kabul Tarihi
2 Şubat 2026
Yayımlandığı Sayı
Yıl 2026 Cilt: 27 Sayı: 1
APA
Delen, E., & Hızıroğlu, A. (2026). BEYOND TRADITIONAL MARKETING: A SYSTEMATIC LITERATURE REVIEW OF AI AND NEUROMARKETING IN CONSUMER BEHAVIOR. Dokuz Eylül Üniversitesi İşletme Fakültesi Dergisi, 27(1), 102-125. https://doi.org/10.24889/ifede.1809547
AMA
1.Delen E, Hızıroğlu A. BEYOND TRADITIONAL MARKETING: A SYSTEMATIC LITERATURE REVIEW OF AI AND NEUROMARKETING IN CONSUMER BEHAVIOR. Dokuz Eylül Üniversitesi İşletme Fakültesi Dergisi. 2026;27(1):102-125. doi:10.24889/ifede.1809547
Chicago
Delen, Ezgi, ve Abdulkadir Hızıroğlu. 2026. “BEYOND TRADITIONAL MARKETING: A SYSTEMATIC LITERATURE REVIEW OF AI AND NEUROMARKETING IN CONSUMER BEHAVIOR”. Dokuz Eylül Üniversitesi İşletme Fakültesi Dergisi 27 (1): 102-25. https://doi.org/10.24889/ifede.1809547.
EndNote
Delen E, Hızıroğlu A (01 Haziran 2026) BEYOND TRADITIONAL MARKETING: A SYSTEMATIC LITERATURE REVIEW OF AI AND NEUROMARKETING IN CONSUMER BEHAVIOR. Dokuz Eylül Üniversitesi İşletme Fakültesi Dergisi 27 1 102–125.
IEEE
[1]E. Delen ve A. Hızıroğlu, “BEYOND TRADITIONAL MARKETING: A SYSTEMATIC LITERATURE REVIEW OF AI AND NEUROMARKETING IN CONSUMER BEHAVIOR”, Dokuz Eylül Üniversitesi İşletme Fakültesi Dergisi, c. 27, sy 1, ss. 102–125, Haz. 2026, doi: 10.24889/ifede.1809547.
ISNAD
Delen, Ezgi - Hızıroğlu, Abdulkadir. “BEYOND TRADITIONAL MARKETING: A SYSTEMATIC LITERATURE REVIEW OF AI AND NEUROMARKETING IN CONSUMER BEHAVIOR”. Dokuz Eylül Üniversitesi İşletme Fakültesi Dergisi 27/1 (01 Haziran 2026): 102-125. https://doi.org/10.24889/ifede.1809547.
JAMA
1.Delen E, Hızıroğlu A. BEYOND TRADITIONAL MARKETING: A SYSTEMATIC LITERATURE REVIEW OF AI AND NEUROMARKETING IN CONSUMER BEHAVIOR. Dokuz Eylül Üniversitesi İşletme Fakültesi Dergisi. 2026;27:102–125.
MLA
Delen, Ezgi, ve Abdulkadir Hızıroğlu. “BEYOND TRADITIONAL MARKETING: A SYSTEMATIC LITERATURE REVIEW OF AI AND NEUROMARKETING IN CONSUMER BEHAVIOR”. Dokuz Eylül Üniversitesi İşletme Fakültesi Dergisi, c. 27, sy 1, Haziran 2026, ss. 102-25, doi:10.24889/ifede.1809547.
Vancouver
1.Ezgi Delen, Abdulkadir Hızıroğlu. BEYOND TRADITIONAL MARKETING: A SYSTEMATIC LITERATURE REVIEW OF AI AND NEUROMARKETING IN CONSUMER BEHAVIOR. Dokuz Eylül Üniversitesi İşletme Fakültesi Dergisi. 01 Haziran 2026;27(1):102-25. doi:10.24889/ifede.1809547