Trusting GenAI Shopping Assistants: A Trust-Mediated Benefit–Risk Model with AI Literacy: Evidence from Saudi E-Commerce

Generative artificial intelligence (GenAI) shopping assistants can provide personalized shopping guidance while raising concerns about conversational data use. Drawing on privacy calculus, trust theory, and information systems success logic, this study examines a Trust-mediated benefit–risk model using cross-sectional survey data from 295 consumers in Riyadh, Saudi Arabia, with prior GenAI shopping-assistant experience. Partial least squares structural equation modeling indicates that perceived conversational quality is positively associated with Personalization Value (β = 0.563) and Privacy Concern (β = 0.235). Personalization Value is positively associated with Trust (β = 0.514), while Privacy Concern is negatively associated with Trust (β = −0.122). Trust is strongly associated with Purchase Intention (β = 0.731). The relationship between Privacy Concern and Trust is less negative at higher levels of self-assessed overall AI Literacy (interaction β = 0.144). Additional analyses indicate complementary partial mediation for Personalization Value and indirect-only mediation for Privacy Concern. The findings suggest that valued personalization and Privacy Concern can coexist during AI-assisted shopping, with Trust linking these appraisals to Purchase Intention. AI Literacy is interpreted as a global self-assessment rather than empirically separable competencies. Given the cross-sectional, non-experimental design, all findings reflect associations rather than causal effects.