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Advancing retail and service strategies: AI-driven consumer behavior prediction, gamification, and ethical marketing

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  • Tran, Minh Tung

Abstract

This study addresses the research gap in understanding the combined effects of AI-driven predictive analytics on consumer personalization accuracy, engagement via gamification, and ethical governance in retailing and consumer services. The objective is to examine how predictive analytics enhances personalization and ROI, how gamification drives engagement, and how ethical challenges (e.g., privacy, bias) can be governed responsibly. Employing a convergent mixed-methods design, the study combines quantitative analysis of consumer data (n = 3900) with multi-case studies of Spotify, Netflix, and Amazon. Findings reveal that AI significantly improves personalization (β = 0.42, p < 0.001) and campaign ROI (R2 = 0.18), while gamification increases engagement by satisfying psychological needs. Ethical risk mitigation through frameworks such as the EU AI Act is demonstrated. Practical implications highlight actionable strategies for ethical AI adoption. Limitations include reliance on secondary qualitative sources and non-probability sampling; future research should explore probability samples and cross-cultural validation. This research contributes an integrated technical, psychological, and ethical framework, advancing theory on consumer trust and responsible innovation in retailing and consumer services.

Suggested Citation

  • Tran, Minh Tung, 2026. "Advancing retail and service strategies: AI-driven consumer behavior prediction, gamification, and ethical marketing," Journal of Retailing and Consumer Services, Elsevier, vol. 88(C).
    • Handle: RePEc:eee:joreco:v:88:y:2026:i:c:s0969698925003376
    DOI: 10.1016/j.jretconser.2025.104558
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    References listed on IDEAS

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    1. Liu, Xiaodi & Zhou, Zengze & Yuen, Kum Fai & Wang, Xueqin, 2024. "Green and gamified! An investigation of consumer participation in green last-mile from a gamification affordance perspective," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
    2. Hermann, Erik & Puntoni, Stefano, 2024. "Artificial intelligence and consumer behavior: From predictive to generative AI," Journal of Business Research, Elsevier, vol. 180(C).
    3. Levy, Shalom & Gvili, Yaniv, 2024. "Self as source: The interplay of sharing eWOM with consumer engagement and incentive acceptance," Journal of Retailing and Consumer Services, Elsevier, vol. 80(C).
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