[38] AI-ENABLED PERSONALIZATION AND DIGITAL TRUST IN ONLINE CONSUMER DECISION-MAKING: A STRUCTURED INTEGRATIVE REVIEW AND TRUST-CALIBRATED MODEL: How to Cite the Article: Nimish Mishra & Praveen Kumar Rai (2026). AI-Enabled Personalization and Digital Trust in Online Consumer Decision-Making: A Structured Integrative Review and Trust-Calibrated Model. International Journal of Multidisciplinary Research & Reviews, 5(4),457- 465.  https://doi.org/10.56815/ijmrr.v5i4.2026.457-465

Dr. Nimish Mishra & Dr. Praveen Kumar Rai

Authors

Dr. Nimish Mishra & Dr. Praveen Kumar Rai Assistant Professors, Dr. Rammanohar Lohia Avadh University, Ayodhya, India.

Abstract

Artificial intelligence (AI) increasingly supports online search, recommendation, service, and purchase decisions, but its benefits coexist
with privacy concerns, opacity, perceived manipulation, and algorithm aversion. This structured integrative review explains when AI-enabled personalization improves consumer decision-making and when it weakens trust. Evidence is organized around capability quality, perceived usefulness, personalization relevance, privacy calculus, transparency, consumer control, trust, and satisfaction. The synthesis shows that personalization is valuable when recommendations are accurate, context-appropriate, explainable, fair, and correctable. Trust and satisfaction connect these evaluations with purchase intention and loyalty, whereas privacy intrusion, task complexity, product type, and digital literacy condition the effects. A trust-calibrated model and seven propositions are developed. The paper also links its technology-governance route to a companion paper on influencer credibility, electronic word-of-mouth, and social commerce. The review contributes an empirically testable framework and practical guidance for explainable recommendations, responsible data use, consumer control, human escalation, and balanced performance measurement.