Calibrated Explanation-Detail Transparency in AI Recommendation Explanations: Trust, Privacy Concern, and Purchase Intention in E-Commerce

Abstract

Artificial intelligence (AI)-based recommender systems increasingly provideexplanations for product recommendations in e-commerce. Although suchexplanations can reduce algorithmic opacity and strengthen consumer trust,they can also make data collection, profiling, and personalization practicesmore salient. This study develops and tests a calibrated explanation-detailtransparency model in which recommendation explanations serve as bothcompetence signals and privacy-risk signals. We conducted a one-factor,three-level, between-subjects scenario experiment with 472 valid responsesretained from 623 submissions. Participants were randomly assigned to low-,medium-, or high-detail explanation conditions for the same product scenario.The data were analyzed using regression, PROCESS serial mediation analysis,covariance-based structural equation modeling, alternative-modelcomparisons, robustness checks, and statistical power sensitivity analysis.Higher-detail explanation conditions were associated with greater perceivedalgorithm understandability and platform trust, providing evidence consistentwith a positive trust-benefit mechanism. They were also associated withgreater perceived data intrusiveness and information privacy concern,providing evidence consistent with a negative privacy-cost mechanism.Purchase intention exhibited an asymmetric concave pattern across the threediscrete conditions and was highest in the medium-detail condition. These findings support calibrated, layered, and privacy-sensitive explanation designin AI recommendation interfaces.

Keywords: AI recommendation, explanation-detail transparency,recommender systems, platform trust, information privacy concern,purchase intention, electronic commerce