What Moderates Generative AI Acceptance and Use? Disentangling Subscription Tier from Prior Usage Experience

Research on generative AI acceptance increasingly identifies new moderators, notably subscription tier, alongside the classic moderators of gender, age, and prior usage experience. Prior studies rarely ask whether these factors condition acceptance once their mutual confounding is addressed, and comparisons of paid and free users routinely contrast groups that also differ in prior usage experience. This study examines which factors moderate users’ acceptance and use of generative AI. Drawing on an extended UTAUT2 model with personal innovativeness and trust, and a use construct capturing reported duration, frequency, and intensity of use, survey data from 765 Croatian users were analyzed using PLS-SEM. Subscription tier, gender, age, and prior usage experience were examined as candidate moderators, with subscription tier and prior usage experience entered jointly as interaction terms on the full sample. Habit showed the strongest association with both behavioral intention and use behavior. The subscription tier initially appeared to moderate the effect of behavioral intention on use behavior, but that difference did not persist once prior usage experience was modeled alongside it. Gender and age produced no robust differences. Prior usage experience was the only candidate whose moderation survived joint estimation, control variables, and adjustment for multiple testing: trust was strongly associated with behavioral intention among newer users and much less so among experienced ones. Trustworthiness signals therefore serve newer users, while sustained use among experienced users rests on habit.