Technology Acceptance in Architecture: Systematic Literature Review of TAM and Related Models (2005-2025)

As digital transformation in architecture accelerates, moving from CAD to BIM, and from parametric design to VR/AR and generative AI, the practical value of these technologies depends on adoption decisions made by users and organisations. Yet technology-acceptance research in the architecture, engineering and construction context remains fragmented across different technologies, user groups and theoretical models. This study aims to systematically map how the Technology Acceptance Model (TAM) and related frameworks (TAM2/TAM3, UTAUT) have been applied in architecture, identify dominant themes, patterns of variables and architecture-specific determinants, and propose an agenda for future research. Following PRISMA principles, a two-stage systematic review was conducted covering 2015–2025. The search spanned Scopus, Web of Science, ScienceDirect, SpringerLink, Taylor Francis Online, Emerald and MDPI. For national coverage, TR Dizin and the Council of Higher Education (YÖK) National Thesis Centre were also screened. In the first stage, 350 records were thematically classified at title and abstract level. In the second stage, 176 studies were coded by technology type, target user group, acceptance model employed, and added external variables, and were synthesised through mixed qualitative and quantitative content analysis. The findings show that the literature concentrates on professional practice and production technologies. At the same time, a rapidly expanding stream foregrounds representation, experience and stakeholder communication through VR/AR and virtual environments. Although perceived usefulness (PU) and perceived ease of use (PEOU) retain their central role, trust, social influence and facilitating conditions become more salient for technologies that require collaboration and organisational transformation. In educational settings, self-efficacy and studio-culture-specific social learning dynamics stand out. The study renders core and extended acceptance variables comparatively visible and argues that “usefulness” should be expanded beyond productivity to include design quality and creativity. It therefore outlines a research agenda towards architecture-specific acceptance structures and, in particular, context-sensitive models for generative AI.