IRCI Article ID: IRCI-AR-0000001610

A Conceptual Review of Consumer Behavior in AI-Personalized Digital Environments

Journal: International Journal of Economics, Management and Accounting

Publication: 2025-11-30 · Vol. 3 No. 6 · pp. 488–495

DOI: 10.47353/ijema.v3i6.366

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Abstract

The rapid integration of artificial intelligence (AI) into digital platforms has fundamentally transformed consumer behavior through personalized interactions and algorithmic decision support. This study provides a conceptual review of consumer behavior in AI-personalized digital environments, aiming to reconceptualize traditional theories that are increasingly inadequate in explaining contemporary market dynamics. Drawing on interdisciplinary literature from marketing, behavioral economics, and information systems, this study identifies critical shifts in how consumer decisions are formed, influenced, and constrained by algorithmic systems. The findings highlight three major theoretical developments. First, consumer decision-making is no longer solely bounded by cognitive limitations but is increasingly shaped by algorithmic bounded rationality, where technological architectures define available choices. Second, AI personalization contributes to preference closure, reinforcing existing behaviors while limiting exploratory consumption. Third, consumer autonomy is reframed as constructed autonomy, where perceived freedom of choice exists within algorithmically curated environments. Additionally, the study identifies emerging phenomena such as the transparency paradox, algorithmic trust, and privacy resignation. Based on these insights, this study proposes an integrated conceptual framework that emphasizes the dynamic interaction between consumer, algorithmic, and structural domains. The study contributes to the literature by offering a novel theoretical perspective on consumer behavior in the digital age and by challenging conventional assumptions of rationality and autonomy. Managerially, the findings underscore the importance of balancing personalization with transparency, trust, and ethical considerations. Future research is encouraged to empirically validate the proposed framework across different digital contexts.

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Authors

Usman Jayadi

Affiliation: , Universitas Dr. Soetomo, Indonesia Author Origin : Indonesia

ORCID: 0000-0002-4377-9259

References

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Brynjolfsson, E., & McAfee, A. (2017). Machine, platform, crowd: Harnessing our digital future. W. W. Norton.

Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165–1188.

Chen, H., Li, X., & Yin, Y. (2021). The impact of artificial intelligence on consumer behavior. Decision Support Systems, 148, 113550. https://doi.org/10.1016/j.dss.2021.113550

Davenport, T. H., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24–42.

De Keyser, A., Verleye, K., Lemon, K. N., Keiningham, T. L., & Klaus, P. (2019). Moving the customer experience field forward. Journal of Service Management, 30(3), 1–23.

Dwivedi, Y. K., et al. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges and opportunities. International Journal of Information Management, 57, 101994.

Grewal, D., Roggeveen, A. L., & Nordfält, J. (2017). The future of retailing. Journal of Retailing, 93(1), 1–6.

Haenlein, M., & Kaplan, A. M. (2019). A brief history of artificial intelligence. Business Horizons, 62(1), 5–14.

Hoyer, W. D., et al. (2020). Consumer behavior in the digital age. Journal of Marketing, 84(1), 1–25.

Huang, M. H., & Rust, R. T. (2021). Artificial intelligence in service. Journal of Service Research, 24(1), 3–13.

Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.

Kaplan, A. M., & Haenlein, M. (2020). Rulers of the world, unite! Business Horizons, 63(1), 37–50.

Lambrecht, A., & Tucker, C. (2019). Algorithmic bias? Management Science, 65(5), 2019–2035.

Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience. Journal of Marketing, 80(6), 69–96.

Martin, K., & Murphy, P. (2017). The role of data privacy in marketing. Journal of the Academy of Marketing Science, 45(2), 135–155.

Mikalef, P., Krogstie, J., Pappas, I. O., & Pavlou, P. (2020). Big data analytics capabilities. Information & Management, 57(2), 103169.

Pariser, E. (2011). The filter bubble. Penguin.

Rust, R. T. (2020). The future of marketing. International Journal of Research in Marketing, 37(1), 15–26.

Shankar, V. (2018). How artificial intelligence is reshaping retailing. Journal of Retailing, 94(4), 1–8.

Smith, B., & Linden, G. (2017). Two decades of recommender systems at Amazon. IEEE Internet Computing, 21(3), 12–18.

Sunstein, C. R. (2017). #Republic: Divided democracy in the age of social media. Princeton University Press.

Thaler, R. H. (2015). Misbehaving. W. W. Norton.

Tucker, C. (2018). Privacy and personalization. Journal of Marketing Research, 55(5), 687–702.

Verhoef, P. C., et al. (2021). Digital transformation. Journal of Business Research, 122, 889–901.

Wedel, M., & Kannan, P. K. (2016). Marketing analytics. Journal of Marketing, 80(6), 97–121.

Zuboff, S. (2019). The age of surveillance capitalism. PublicAffairs.

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