Prediction of User’s Intention to Use Metaverse System in Medical Education: A Hybrid SEM-ML Learning Approach

Almarzouqi, A, Aburayya, A ORCID: https://orcid.org/0000-0002-1428-0547 and Salloum, S ORCID: https://orcid.org/0000-0002-6073-3981 2022, 'Prediction of User’s Intention to Use Metaverse System in Medical Education: A Hybrid SEM-ML Learning Approach' , IEEE Access, 10 , pp. 43421-43434.

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Abstract

Metaverse (MS) is a digital universe accessible through a virtual environment. It is established through the merging of virtually improved physical and digital reality. Metaverse (MS) offers enhanced immersive experiences and a more interactive learning experience for students in learning and educational settings. It is an expanded and synchronous communication setting that allows different users to share their experiences. The present study aims to evaluate students’ perception of the application of MS in the United Arab Emirates (UAE) for medical-educational purposes. In this study, 1858 university students were surveyed to examine this model. The study’s conceptual framework consisted of adoption constructs including Technology Acceptance Model (TAM), Personal innovativeness (PI), Perceived Compatibility (PCO), User Satisfaction (US), Perceived Triability (PTR), and Perceived Observability (POB). The study was unique because the model correlated technology-based features and individual-based features. The study also used hybrid analyses such as Machine Learning (ML) algorithms and Structural Equation Modelling (SEM). The present study also employs the Importance Performance Map Analysis (IPMA) to assess the importance and performance factors. The study finds US as an essential determinant of users’ intention to use the metaverse (UMS). The present study’s finding is useful for stakeholders in the educational sector in understanding the importance of each factor and in making plans based on the order of significance of each factor. The study also methodologically contributes to Information Systems (IS) literature because it is one of the few studies that have used a complementary multi-analytical approach such as ML algorithms to investigate the UMS metaverse systems.

Item Type: Article
Additional Information: ** Article version: VoR ** From Crossref journal articles via Jisc Publications Router ** Licence for VoR version of this article starting on 01-01-2022: https://creativecommons.org/licenses/by/4.0/legalcode **Journal IDs: eissn 2169-3536 **History: published 2022 **License for this article: starting on 01-01-2022, , https://creativecommons.org/licenses/by/4.0/legalcode
Schools: Schools > School of Health and Society
Journal or Publication Title: IEEE Access
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
ISSN: 2169-3536
SWORD Depositor: Publications Router
Depositing User: Publications Router
Date Deposited: 15 Jun 2022 11:34
Last Modified: 15 Jun 2022 11:34
URI: http://usir.salford.ac.uk/id/eprint/63912

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