The Impact of Generative AI Literacy on User Adoption Intention: A Structural Equation Modeling Approach

Abstract

This article investigates how generative AI literacy shapes users' adoption intention through the mediating roles of perceived ease of use, perceived usefulness, trust, social influence, and perceived risk. The study treats literacy not as a narrow technical skill but as a multidimensional capability that includes conceptual understanding, operational ability, evaluative judgment, and ethical awareness. Drawing on a structured dataset of 912 respondents and applying a structural equation modeling logic with validated measurement constructs, the paper evaluates the direct and indirect pathways from literacy to adoption intention. The empirical results indicate that generative AI literacy exerts both a direct positive effect and several indirect effects through ease of use, usefulness, and trust. Perceived risk weakens adoption intention, but its negative influence declines as literacy improves. The study shows that adoption is more stable when users understand what generative AI can do, where it fails, and how it should be used responsibly. The article advances the literature by positioning literacy as a strategic antecedent of sustainable AI adoption and by offering implications for education, platform design, workforce development, and public policy.

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