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The rapid spread of artificial intelligence (AI) tools in education, healthcare, and digital services demands a clear understanding of user acceptance mechanisms. Building on the Technology Acceptance Model (TAM), this study proposes the AI Technology Acceptance Model (AITAM), integrating habit, trust, and social influence with classical constructs of perceived usefulness (PU), perceived ease of use (PEOU), and behavioral intention (BI). Data from 1,022 respondents were analyzed using partial least squares structural equation modeling (PLS-SEM). Results highlight PU as the strongest predictor of BI, while trust and habit significantly influence both intention and actual usage. Social influence strongly drives both trust and habit. Sensitivity analysis shows that excluding trust or social influence notably reduces explanatory power, underscoring their central roles. The AITAM framework advances theory by extending TAM with socio-cognitive dimensions and offers practical guidance for designers, educators, and policymakers promoting AI adoption in digitally transformed environments.
Sebetci Özel (Tue,) studied this question.