A Study on the Factors Affecting Artificial Intelligence Adoption Among Faculty Members in Higher Education Institutions in the Mumbai Region
DOI:
https://doi.org/10.29070/a3vxpa57Keywords:
Artificial Intelligence, AI Adoption, Higher Education, Faculty Members, Technology Acceptance Model, Perceived Usefulness, Perceived Ease of Use, Institutional SupportAbstract
By assisting with teaching, research, administrative tasks, and student involvement, artificial intelligence (AI) is progressively changing higher education. However, faculty members' acceptance of AI is conditional upon their perceptions of its use, usability, and institutional backing. This study looks at the variables influencing faculty members' adoption of AI in Mumbai-based higher education institutions. The study examines the impact of perceived usefulness, perceived ease of use, and institutional support on AI adoption using the Technology Acceptance Model (TAM). 103 faculty members from higher education institutions in the Mumbai area were given a standardized questionnaire with 18 Likert-scale items. Multiple linear regression, Cronbach's alpha, and descriptive statistics were used to analyse the data. The results show that AI adoption is most strongly positively impacted by perceived usefulness (β = 0.546, p < 0.001), followed by perceived ease of use (β = 0.222, p = 0.032). However, there is no statistically significant effect of institutional support for AI adoption (β = 0.124, p = 0.168). The regression model accounts for 43.1% of the variance in AI adoption (R2 = 0.431) and is statistically significant (F = 25.040, p < 0.001). The results emphasize how crucial it is to show the value and usability of AI tools in order to promote faculty adoption in higher education.
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References
1. Alenezi, M. (2024). Factors influencing artificial intelligence adoption in higher education institutions: The role of organizational support and technological readiness. Education and Information Technologies, 29(8), 10125–10147.
2. Amir-Rudin, A., et al. (2025). Validating the Acceptance of Artificial Intelligence (AI) in Higher Education Institutions. EBP Proceedings.
3. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
4. Ghimire, A., & Edwards, J. (2024). Generative AI Adoption in Classroom in Context of Technology Acceptance Model (TAM) and Innovation Diffusion Theory (IDT).
5. Ibrahim, F. (2025). The Technology Acceptance Model and Adopter Type: Understanding Attitudes Toward AI Usage. Frontiers in Artificial Intelligence.
6. Miranda, F. J., Rubio, S., Chamorro-Mera, A., & Pérez-Mayo, J. (2025). Exploring the adoption of generative artificial intelligence among university teachers: An extended technology acceptance model approach. Higher Education Research & Development. Advance online publication.
7. Najjar, N. (2025). Faculty perceptions of AI use in higher education: A case study. Education Sciences, 16(1), 55.
8. Popović Šević, N., Šević, A., Slijepčević, M., & Krstić, J. (2025). AI adoption in higher education: Exploring attitudes and perceived benefits between users and non-users. Online Journal of Communication and Media Technologies, 15(4), e202528.
9. Shata, A. (2025). Faculty perceptions and the adoption of generative AI. International Journal of Educational Technology in Higher Education, 22(1).
10. Song, Y. (2025). An Empirical Study Based on the Technology Acceptance Model and AI Technology Acceptance. Systems.
