Digital Adoption of Generative AI Tools: A Multi-Theory Model Linking Cognitive Load, User Perceptions, and System Attributes

Abstract

The rapid diffusion of “(GenAI)” Generative Artificial Intelligence systems has reshaped everyday activities, yet their adoption remains uneven and cognitively demanding for many users. Existing research has largely relied on conventional technology acceptance models, providing limited insight into cognitive burden and GenAI-specific system characteristics. To address this gap, this study develops an integrated framework combining the Technology Acceptance Model, Cognitive Load Theory, and the DeLone and McLean Information Systems Success Model to explain GenAI adoption among ordinary users. Survey data from 1001 active GenAI users were analyzed using partial least squares structural equation modeling (PLS-SEM). The results indicate that all core technology acceptance relationships are statistically significant (p < 0.001), while mental load negatively affects perceived usefulness and user attitudes. Moreover, GenAI system attributes—output quality, transparency, friction reduction, and system integration—significantly moderate key adoption pathways and strengthen the translation of behavioral intention into actual use. Predictive assessment indicates that the proposed model outperforms the baseline technology acceptance model, with stronger explanatory power and superior out-of-sample predictive performance (Q2predict > 0.35). The findings offer actionable insights for designing cognitively efficient, trustworthy, and sustainable GenAI systems.