Work

Examining Metaverse Intention to Use Among Computer Science and Engineering Students Via UTAUT2, DOI Through PLS-SEM, ML & Network Analysis

Year: 2025

Type: article

Abstract: This study explores Engineering and Computer Science students’ inclination to adopt Metaverse systems by integrating the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and the Diffusion of Innovations (DOI) models. To gain complementary insights authors employed a multi-analytical approach combining Partial Least Squares Structural Equation Modeling (PLS-SEM), Machine Learning (ML), and Network Analysis. The PLS-SEM analysis found “Facilitating Conditions” as significant predictor of Behavioral Intention (BI), while found “Price Value” have a minimal impact. Additionally, Compatibility, Complexity, and Observability unfold as key factors in Metaverse adoption. ML analysis found that Linear Discriminant Analysis outperforms other classifiers, showing high predictive accuracy (0. 882) for Behavioral Intention. Network analysis highlights the centrality of Behavioral Intention within the adoption network, confirming its crucial role and showing strong alignment with PLS-SEM findings. All these results together offer a comprehensive understanding of factors influencing Metaverse adoption, supporting aimed interventions and future research to enhance student engagement with Metaverse technologies.

Source: SN Computer Science

DOI: 10.1007/s42979-025-04553-6

Authors: Khaled Mohammad Alomari, Safwan Maghaydah, Muhammad Jahanzaib Khan

Institution: Abu Dhabi University

Language: English

Topic: Technology Adoption and User Behaviour

Subfield: Information Systems and Management

Field: Decision Sciences

Domain: Social Sciences

SDG: Quality education

Open Access status: hybrid

Estimated APC paid: $3,190