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A Decade of UTAUT in Education: A PRISMA 2020 Systematic Review of Technology Acceptance Studies (2015–2025) Agus Widayoko; Syunu Trihantoyo; Nunuk Hariyati
AL-ISHLAH: Jurnal Pendidikan Vol 18, No 1 (2026): MARCH 2026
Publisher : STAI Hubbulwathan Duri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35445/alishlah.v18i1.9160

Abstract

The increasing integration of digital technologies in education, particularly during and after the COVID-19 pandemic, has intensified interest in understanding technology acceptance. The Unified Theory of Acceptance and Use of Technology (UTAUT) has been widely applied in educational research; however, existing reviews remain fragmented and lack a longitudinal, model-focused synthesis.This study employed a PRISMA 2020–guided systematic literature review of UTAUT-based research in educational contexts published between 2015 and 2025. A structured search of the Scopus database identified 13,131 records. Following duplicate removal and multi-stage screening based on predefined inclusion criteria, 56 empirical journal articles were retained. Study quality was assessed using a combined framework integrating the Mixed Methods Appraisal Tool (MMAT) and Kitchenham’s checklist. Data were analysed using descriptive and thematic synthesis.Findings indicate a substantial increase in UTAUT-based studies during the COVID-19 period, with e-learning, mobile learning, and learning management systems as dominant technologies. Quantitative approaches, particularly Structural Equation Modelling, predominated. Performance Expectancy emerged as the most consistent predictor of behavioural intention, followed by Effort Expectancy, Social Influence, and Facilitating Conditions. Research remains geographically concentrated in Asia and largely focused on behavioural intention rather than actual use behaviour.This review highlights the sustained relevance of UTAUT in educational technology research while identifying methodological and conceptual gaps, including limited attention to learning outcomes, overreliance on cross-sectional designs, and emerging extensions related to AI-enabled learning. Future research should integrate longitudinal and mixed-method approaches and align technology acceptance with educational effectiveness.