Partial Least Squares Structural Equation Modeling (PLS-SEM) in Higher Education Research: An Evidence from Using Technology Acceptance Model (TAM) and Innovation Resistance Theory (IRT)

Abstract

In higher education, PLS-SEM supports the study of multidimensional relationships, such as technology acceptance behavior and educational innovation effectiveness. PLS-SEM is a powerful analytical tool used to test complex theoretical models, especially in cases where data are limited or non-normally distributed. TAM provides a theoretical foundation for usefulness and ease of use, while IRT emphasizes psychological and functional barriers to innovation. Data were collected from 420 lecturers at Vietnamese higher education institutions on the acceptance of using artificial intelligence (AI) in higher education to test the integrated model of TAM and IRT. The results show that perceived usefulness (PU) and perceived ease of use (PEU) positively affect attitudes and intentions to adopt AI. In contrast, IRT barriers such as complexity of use, value, risk, and tradition have adverse effects, limiting lecturers’ application of AI in teaching. The research results contribute to the expansion of PLS-SEM applications in higher education and provide recommendations for managers to promote the acceptance of AI technology and reduce barriers to innovation in this field.

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