The Role of Hedonic Quality Stimulation, Immersion, and Privacy Concerns in Metaverse Adoption

Evidence From Higher Education

Bibliographic Data

ID22416067
AuthorsShahla Asadi (0000-0002-8199-2122, Kent State University, corresponding author), Mostafa K Ardakani (Kent State University), Mohammad Iranmanesh (0000-0001-6964-6238, Taylor's University), Janet Kropff (University of Akron), Morteza Ghobakhloo (0000-0002-9341-2690, Uppsala University), Behzad Foroughi (0000-0002-0621-4814, I-Shou University), Erfan Babaee Tirkolaee (0000-0003-1664-9210, Duy Tan University), Erfan Babaee Tirkolaeee (Duy Tan University)
Year2026
Publication date2026-03-26
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueJournal of Educational Computing Research (JOURNAL)
Journal identifiersISSN: 0735-6331 • E-ISSN: 1541-4140
PublisherSAGE Publications (PUBLISHER • US)
DOI10.1177/07356331261437560
OpenAlexW7140750953
LanguageEN
References cited66

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.

Cognition · Metaverse · Need for cognition · Structural equation modeling · Survey data collection · Usability · AI in Service Interactions · Technology Adoption and User Behaviour · Virtual Reality Applications and Impacts

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