Journal of Intelligent Marketing Management

Journal of Intelligent Marketing Management

The End of the Age of Choice: After Artificial Intelligence, Is the Customer Still an Autonomous Being or the Outcome of Algorithmic Predictions?

Editor-in-Chief Lecture

Authors
1 PhD in Business Administration, University of Tehran, Tehran, Iran; Editor-in-Chief of the Journal of Intelligent Marketing Management.
2 Department of Management, Abrar Institute of Higher Education, Tehran, Iran.
Abstract
The advent of artificial intelligence (AI) technologies in contemporary marketing has not merely transformed executional tools but has fundamentally altered the nature of the brand–consumer relationship. Machine learning algorithms, deep neural networks, and reinforcement learning systems have propelled marketers beyond the stage of responding to explicit needs into the realm of predicting, simulating, and even engineering unconscious preferences. In this inaugural editorial for the Journal of Smart Marketing Management, we adopt an analytical–theoretical approach grounded in the scientific literature of 2024–2026 to address the foundational question: In the age of hyper-personalization and the Digital Twin of the Customer (DToC), does any space remain for human volition and agency, or is the consumer gradually becoming an emergent property of probabilistic models and continuous algorithmic optimization? Recent scholarship demonstrates that AI-driven dark patterns, algorithmic choice architectures, and the feedback loops of recommender systems have so thoroughly permeated consumer decision-making processes that distinguishing between intrinsic and algorithmically induced preferences has become exceedingly difficult. Nevertheless, emerging research in consumer psychology and the philosophy of technology underscores the necessity of reclaiming autonomy through algorithmic transparency, regulatory oversight, and digital consumer literacy. This article argues that the future of smart marketing lies neither in the complete domination of consumer will nor in the illusion of unfettered choice, but in the design of ecosystems that simultaneously honor predictive efficiency and respect for consumer agency. This stance establishes the theoretical framework for the journal’s future inquiries: a transition from data-driven marketing to dignity-based marketing.
Keywords

Bacalhau, L. M., Pereira, M. C., & Neves, J. (2025). A bibliometric analysis of AI bias in marketing: Field evolution and future research agenda. Journal of Brand Management, 32(3), 1–15. https://doi.org/10.1057/s41270-025-00406-6
Borimnejad, H., & Borimnejad, V. (2025). Emerging trends challenges and research opportunities in artificial intelligence applications in marketing. Discover Artificial Intelligence, 6(1), 1–15. https://doi.org/10.1007/s44163-025-00705-y
Brüns, T., & Meißner, M. (2024). Artificial intelligence and consumer behavior: From predictive to generative AI. Journal of Business Research, 186, 114944. https://doi.org/10.1016/j.jbusres.2024.114944
Feng, H., et al. (2025). A reinforcement-learning-enhanced LLM framework for automated A/B testing in personalized marketing. In Proceedings of the 2025 2nd International Conference on Digital Society and Artificial Intelligence (DSAI '25). Association for Computing Machinery. https://doi.org/10.1145/3748825.3748903
Frank, B., & Otterbring, T. (2024). Consumer acceptance of high-autonomy AI assistants is driven by perceived benefits in online shopping settings characterized by scarcity. Psychology & Marketing, 42(1), 1–15. https://doi.org/10.1002/mar.70074
Huber, G., Szymoniuk, B., Maciaszczyk, M., Kocot, M., Sobon, J., Baldowski, D., & Kandefer, K. (2026). The impact of dark AI patterns on consumer purchase decisions and impulsive buying. European Research Studies Journal, 29(1), 490–499. https://ersj.eu/journal/4324
McColl-Kennedy, J. R., Zaki, M., Andreassen, T. W., Coote, L. V., Brea, E., Willer, F., & Andrade, J. (2025). Digital twins: A game changer in customer experience. Journal of Service Management, 36(1), 1–20. https://doi.org/10.1108/JOSM-12-2024-0540
Nokhiz, P., & Ruwanpathirana, A. K. (2025). Consumer autonomy or illusion? Rethinking consumer agency in the age of algorithms. Journal of Social Computing, 6(3), 184–208. https://doi.org/10.23919/JSC.2025.0015
Silva, S. C., Tortajada, E. G., & Sousa, N. (2026). Dark patterns: Reclaiming autonomy in online shopping in the age of AI. In Encyclopedia of Artificial Intelligence in Marketing (pp. 1–15). Springer. https://doi.org/10.1007/978-3-031-75316-9_108-1
Singh, P., & Kaunert, C. (2024). Harnessing artificial intelligence for hyper-personalization in digital marketing: A comparative analysis of predictive models and consumer behavior. Journal of Marketing Analytics, 9(1), 47–55.
Sun, B. (2025). Data-driven personalized marketing strategy optimization based on user behavior modeling and predictive analytics: Sustainable market segmentation and targeting. PLoS ONE, 20(7), e0328151. https://doi.org/10.1371/journal.pone.0328151
Vorster, L., Brown, D. M., & Thompson, P. (2025). Understanding customer responses to AI-driven personalized journeys: Impacts on the customer experience. Journal of Advertising, 54(2), 176–195. https://doi.org/10.1080/00913367.2025.2460985