Enhancing Artificial Intelligence-Enabled Transformation Acceptance among Employees of Higher Education Institutions

Zahir Osman; Malik  Yatam

This study aims to the direct relationship between perceived usefulness, perceived ease of use, technological innovativeness, and acceptance with self-efficacy as a mediator among employees in higher education institutions. To collect primary data, a thorough survey was designed and conducted based on previous research. Structural equation modeling, known for its ability to analyze complex interactions between variables, was used to analyze a comprehensive data set of 422 responses, and convergent and discriminant validity was confirmed. Evaluation of the structural model crucially confirmed the hypotheses and revealed nine direct relationships and four mediated relationships. The findings reveal that perceived usefulness, perceived ease of use, and technological innovativeness significantly influence artificial intelligence-enabled transformation acceptance, with attitude serving as a crucial mediator. Notably, perceived ease of use emerges as the most impactful factor, emphasizing the need for user-friendly interfaces and streamlined processes. The study demonstrates the relative importance of each latent variable in shaping acceptance, providing practical implications for HEIs. The results underscore the need for targeted interventions to enhance technological innovativeness and the cultivation of a positive attitude among employees. Moreover, the research contributes to theoretical frameworks by integrating attitude as a mediator, enriching our understanding of artificial intelligence-enabled transformation acceptance dynamics. Contextually, the study highlights the importance of organizational climate and culture in fostering a conducive environment for technological innovation. Future studies are suggested to adopt longitudinal designs, cross-cultural analyses, and intervention strategies, ensuring a comprehensive exploration of AI-ET acceptance and facilitating successful integration in the ever-evolving landscape of higher education.

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(Osman & Yatam, 2024)
Osman, Z., & Yatam, M. (2024). Enhancing Artificial Intelligence-Enabled Transformation Acceptance among Employees of Higher Education Institutions. International Journal of Academic Research in Accounting Finance and Management Sciences, 14(2), 289–304.

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Enhancing Artificial Intelligence-Enabled Transformation Acceptance among Employees of Higher Education Institutions

Zahir Osman, Malik Yatam

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