A Literature Study on GenAI Adoption Behavior from the UTAUT Perspective
Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and its extended frameworks, this study addresses the existing issues in current research on Generative Artificial Intelligence (GenAI) adoption behavior, including scattered research scenarios with insufficient cross-scenario c...
| Main Authors: | , |
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| Format: | Article |
| Language: | English English |
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INTI International University
2025
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| Subjects: | |
| Online Access: | http://eprints.intimal.edu.my/2193/ http://eprints.intimal.edu.my/2193/1/ij2025_37.pdf http://eprints.intimal.edu.my/2193/2/746 |
| Summary: | Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and its extended frameworks, this study addresses the existing issues in current research on Generative Artificial Intelligence (GenAI) adoption behavior, including scattered research scenarios with insufficient cross-scenario comparisons, inadequate focus on dynamic adoption processes, gaps in research on specific population groups, and incomplete systematic application context of the UTAUT framework. Employing the literature review method, the study systematically retrieves and organizes literature related to the developmental history of the UTAUT theory and GenAI adoption behavior, aiming to clarify the research context of GenAI adoption behavior and identify the conclusions and limitations of existing studies. The research findings indicate that existing studies have confirmed that technological characteristics, individual traits, and environmental support are key factors influencing GenAI adoption, with widespread attention paid to trust and ethical issues. However, the aforementioned limitations still persist. Ultimately, at the theoretical level, the study proposes future research directions—strengthening cross-disciplinary integration, exploring dynamic evolution mechanisms, and expanding sample coverage—to enrich the relevant theoretical system. At the practical level, it provides targeted recommendations for enterprises, organizations, and policymakers to enhance the efficiency and rationality of GenAI adoption |
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