Human or Artificial? A Meta-Analysis of the Effects of AI-Driven Marketing Interactions on Consumer Trust, Purchase Intention, and Brand Engagement
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Abstract
This systematic literature review employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol to synthesize empirical evidence on the effects of AI-driven marketing interactions including chatbots, virtual assistants, recommendation systems, AI customer service, personalized advertising, and conversational commerce on consumer trust, purchase intention, and brand engagement. Following a comprehensive search across eight reputable databases (Scopus, Web of Science, ScienceDirect, Emerald Insight, SpringerLink, Taylor & Francis, Wiley Online Library, and Sage Journals) for publications between 2019 and 2026, a total of 124 peer-reviewed empirical studies were included in the qualitative synthesis, with 48 studies providing sufficient quantitative data for meta-analytic procedures. The findings reveal that AI-driven marketing interactions predominantly exert positive effects on consumer trust (72% of studies), purchase intention (78%), and brand engagement (68%), contingent upon perceived anthropomorphism, transparency, personalization quality, and system responsiveness. Key mediating variables include perceived value, social presence, customer experience, and cognitive absorption, while privacy concerns, technology readiness, consumer innovativeness, and brand familiarity emerge as significant moderators. This review contributes theoretically by integrating Technology Acceptance Model (TAM), Social Presence Theory, Trust Theory, and Stimulus-Organism-Response (S-O-R) Framework into a comprehensive conceptual model explaining consumer responses to AI marketing. Practically, the findings provide actionable insights for marketers, brand managers, and AI developers to design human-centered, transparent, and ethically responsible AI marketing systems that foster meaningful consumer-brand relationships.
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