AI Transparency, Privacy Control, and Consumer Trust as Drivers of Purchase Intention in Omnichannel Retail

Abstract

This study examines the effects of AI transparency and privacy control on purchase intention through consumer trust in omnichannel retail. Methods: An explanatory quantitative design was applied to survey data from 100 omnichannel consumers in Yogyakarta City selected through purposive sampling. The reflective measurement and structural models were evaluated using partial least squares structural equation modeling in SmartPLS 4. Findings: AI transparency and privacy control positively influence consumer trust and purchase intention. Consumer trust also positively affects purchase intention and partially mediates both antecedent relationships. The model explains 58.8% of the variance in consumer trust and 64.3% of the variance in purchase intention. Research Implications: Retailers should provide understandable explanations of AI-based recommendations and meaningful controls for consent, personalization preferences, and data withdrawal. These mechanisms can reduce uncertainty, strengthen trust, and support purchase intention across integrated channels. Originality: This study jointly positions AI transparency and privacy control as ethical antecedents of consumer trust and tests their direct and trust-mediated effects on purchase intention in an emerging-market omnichannel context.