The Impact of AI Chatbot Personalization on Consumer Purchase Intent

Artificial intelligence (AI) chatbots now function as decision-support infrastructure in U.S. digital commerce, directly shaping how consumers evaluate risk, trust firms, and convert intent into transactions. As personalization capabilities deepen, the cause–effect–benefit pathway linking AI design to consumer welfare and market efficiency has become increasingly consequential (Davenport et al., 2020; Huang & Rust, 2021). This study develops a theory-forward framework that explains how AI chatbot personalization (cause) influences consumer purchase intent (effect) through three interrelated psychological mechanisms: perceived relevance, trust calibration, and engagement intensity. Synthesizing evidence across marketing, information systems, and human–computer interaction research, the paper demonstrates that personalization operates as a governance-sensitive mechanism rather than a purely technical feature (Aguirre et al., 2015; Komiak & Benbasat, 2006). The resulting framework yields testable hypotheses and actionable design principles that support scalable, trustworthy AI deployment—producing measurable benefits for consumers, firms, and the broader digital economy. By clarifying this causal chain, the study contributes to the national interest by strengthening theoretical foundations for responsible AI commercialization, enhancing consumer confidence, and supporting innovation-driven economic growth.