Role of AI-Based Personalization in Enhancing Consumer Confidence and Purchase Decisions
Author
Mr. Sachin Vasant Chaugule, Dr. Bhalchandra Bite
Abstract
AI-based personalization has transitioned from a competitive differentiator to a baseline operational expectation in digital commerce, yet the precise cognitive and affective mechanisms through which personalization shapes consumer confidence and the quality of purchase decisions remain theoretically fragmented and empirically underexplored. This study advances a novel integrated framework—rooted in the Elaboration Likelihood Model (ELM), the Stimulus-Organism-Response (S-O-R) paradigm, and Trust Transfer Theory—to explain how five AI personalization stimuli (predictive recommendation fit, dynamic pricing personalisation, personalised search ranking, adaptive push notification timing, and AI-driven post-purchase follow-up) influence two cognitive-affective organism states (consumer confidence and perceived personalisation quality) that drive three purchase decision outcomes (purchase decision quality, decision regret minimisation, and repeat purchase commitment). Trust Transfer—the process by which trust in AI technology transfers to trust in the platform vendor—is hypothesised as a pivotal mediator, while AI literacy and involvement level are tested as moderators within the ELM framework. A structured questionnaire was administered to 468 consumers of five major Indian digital service platforms (Swiggy, Zomato, BookMyShow, MakeMyTrip, and Practo). Structural Equation Modeling (SEM) using AMOS 27.0 with Maximum Likelihood Estimation confirmed excellent model fit. Predictive Recommendation Fit (β = 0.462, p < 0.001) and Adaptive Push Notification Timing (β = 0.318, p < 0.001) are the strongest confidence drivers. Trust Transfer significantly mediates the consumer confidence–purchase decision quality relationship (indirect β = 0.287, 95% BCa CI: [0.231, 0.348]). AI Literacy moderates the personalization–confidence relationship such that high-literacy consumers form confidence through central route processing while low-literacy consumers rely on peripheral cues. The model explains 71.8% variance in Purchase Decision Quality and 68.4% in Repeat Purchase Commitment. Findings advance ELM application to AI commerce and provide nuanced design and communication prescriptions for AI-powered service platforms.
Keywords
AI Personalization; Consumer Confidence; Purchase Decision Quality; Elaboration Likelihood Model; Trust Transfer; S-O-R Framework; Perceived Personalisation Fit; Decision Regret; SEM; Digital Service Platforms; India
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