AI adoption readiness in universities: A multivariate regression and machine learning analysis of Malaysia and Indonesia
This study investigates the determinants of AI adoption in higher education institutions in Malaysia and Indonesia using an integrated analytical framework that combines behavioral, institutional, and training-related factors. A quantitative cross-sectional survey was conducted in 2025, yielding 748...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Research and Scientific Innovation Society
2026
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| Online Access: | http://eprints.utem.edu.my/id/eprint/29497/2/00645210120261117342940.pdf http://eprints.utem.edu.my/id/eprint/29497/ https://rsisinternational.org/journals/ijriss/article.php?id=4625 https://doi.org/10.47772/IJRISS.2025.91200317 |
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| Summary: | This study investigates the determinants of AI adoption in higher education institutions in Malaysia and Indonesia using an integrated analytical framework that combines behavioral, institutional, and training-related factors. A quantitative cross-sectional survey was conducted in 2025, yielding 748 valid responses from academic staff and students (response rate: 34%). The analysis employed logistic regression, ordinal regression, structural path modeling, heatmap segmentation, and machine learning clustering. Results demonstrate that
perceived ease of use and perceived usefulness are the strongest predictors of AI usage and user satisfaction,
with standardized effects exceeding those of demographic variables. AI training significantly increases adoption likelihood, raising sustained AI usage probability by over 40% among trained participants. Malaysian institutions exhibit higher adoption maturity, with AI training participation of 68.3% compared to 54.1% in Indonesian institutions. However, satisfaction levels in both countries remain largely neutral to moderate, indicating that AI integration is still at a transitional stage. Compared with prior research, this study advances understanding of AI adoption by integrating advanced statistical modeling with machine learning methods, offering stronger empirical evidence for policy design and leadership decision-making in higher education. |
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