Trust, ethics, and consumer acceptance of AI-driven emotional personalization: A systematic literature review and research agenda

Abstract

Artificial intelligence (AI) is increasingly embedded in emotionally personalized customer experiences, enabling firms to analyze behavioral and affective data in real time and deliver highly adaptive interactions at scale. However, the effectiveness, adoption, and societal legitimacy of these systems depend on consumer trust, ethical transparency, and perceived reliability. Although scholarly interest in AI-driven emotional personalization has grown substantially, existing research remains conceptually fragmented and methodologically heterogeneous, with limited integration of trust and ethical governance perspectives across marketing and consumer research contexts. To address this gap, this study conducts a PRISMA-based systematic literature review of peer-reviewed publications published between 2016 and 2026. A total of 153 articles were initially identified across major academic databases, of which 26 met the predefined inclusion criteria and were retained for in-depth analysis. The findings reveal that trust consistently emerges as a central determinant of consumer acceptance, particularly when AI systems are perceived as transparent, explainable, reliable, and ethically aligned with user expectations. Ethical concerns are primarily framed around privacy and data protection, while broader issues such as algorithmic fairness, emotional manipulation, and governance accountability remain comparatively underexplored in current scholarship. Furthermore, the predominance of cross-sectional survey designs limits current understanding of how trust evolves through repeated human-AI interactions over time and across contexts. This review synthesizes current knowledge, develops an integrative conceptual perspective linking trust, ethics, and consumer acceptance, and proposes a structured research agenda for future inquiry. The study highlights the need for multidimensional trust frameworks, longitudinal empirical designs, interdisciplinary theoretical integration, and stronger ethical governance mechanisms to ensure that AI-driven emotional personalization remains both commercially effective and socially responsible.