Beyond Star Ratings: Understanding Consumer Trust and Adoption of Intelligent Review-Support Features in E-Commerce

Online reviews are essential for e-commerce decision-making, yet their growing volume creates significant information overload and credibility challenges. This study examines consumer trust and the adoption of intelligent review support features across three major platforms: Amazon, Flipkart, and Meesho. Utilizing a quantitative, cross-sectional design, weanalyzed a secondary dataset of 453 survey responses covering 29 variables related to review credibility, fake review experiences, user interface (UI) perceptions, and feature preferences. The findings reveal a nuanced preference landscape: while review summaries were the most selected feature in forced-choice questions (48.4%), thematic analysis of open-ended responses (n = 31) indicated that consumers spontaneously prioritized foundational utilities, such as rental options (29.0%) and brand verification (25.8%), over AI-driven summaries (6.5%). Furthermore, a weak but statistically significant positive correlation was observed between overwhelming UI experiences and summary preference (r = 0.126, p = 0.007). Conversely, logistic regression indicated that review-related problems (e.g. fake reviews) do not significantly predict the demand for intelligent summaries (Pseudo R2 = 0.0107). Due to low internal consistency in the multi-item trust construct (α = 0.174), analyses were conducted at the item level to preserve measurement validity. Ultimately, this study bridges the gap between review credibility and interface design, suggesting that e-commerce platforms must prioritize foundational trust and utility features before deploying advanced AI summarization tools.