The metaverse marks a transformative frontier in educational technology; however, factors influencing students’ adoption of metaverse-based learning environments remain unclear. This study extends the Technology Acceptance Model by integrating hedonic quality stimulation, cognitive engagement, immersion, subjective norms, and personal innovativeness as antecedents, while examining perceived privacy risk as a moderating factor. Using a hybrid partial least squares (PLS) and artificial neural networks (ANN), we analyzed survey data from 353 computer science students across three Malaysian public universities. The PLS-SEM results indicate that hedonic quality stimulation, subjective norms, immersion, cognitive engagement, and personal innovativeness significantly influence perceived usefulness and perceived ease of use, which collectively explain 66% of the variance in students’ intention to use metaverse technologies. Perceived privacy risk negatively moderates the relationship between perceived usefulness and usage intention, showing that privacy concerns can offset the benefits of perceived utility. The complementary ANN analysis captures nonlinear relationships and identifies subjective norms, hedonic quality stimulation, and perceived usefulness as the strongest predictors of perceived ease of use, perceived usefulness, and usage intention, respectively. Overall, the findings offer actionable insights for institutions designing immersive yet privacy-conscious metaverse learning ecosystems and demonstrate the methodological advantages of a hybrid PLS-ANN approach.
Asadi et al. (Thu,) studied this question.
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