Theoretical and Methodological Foundations of Artificial Intelligence Adoption Research
The rapid diffusion of artificial intelligence (AI) across organizational and consumer contexts has intensified scholarly interest in understanding how individuals and institutions decide to adopt such technologies. Technology acceptance models offer a theoretically grounded framework for examining these decisions, yet their application to AI-specific tools remains fragmented across disciplines. This structured literature review synthesizes 15 peer-reviewed studies to examine how technology acceptance frameworks have been applied in AI adoption research, with particular attention to the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and the Technology–Organisation–Environment (TOE) framework. The analysis is organized around three thematic dimensions: individual-level determinants of AI acceptance, organizational and contextual drivers of adoption, and domain-specific adaptations of acceptance frameworks. The findings indicate that trust, perceived usefulness, and social influence consistently emerge as salient predictors, while sector-specific factors—including digital competence, digital leadership, educational context, and healthcare risk perception—shape adoption outcomes in important ways. The review also highlights key gaps related to longitudinal acceptance dynamics, ethical considerations, and cross-cultural generalizability, and outlines a corresponding research agenda. These insights carry direct implications for technology designers, organizational managers, and policymakers seeking to facilitate responsible AI integration.
