The adoption of metaverse systems: a hybrid SEM - ML method

AlHamad, A, Alomari, K, Alshurideh, M, Al Kurdi, B, Salloum, S ORCID: https://orcid.org/0000-0002-6073-3981 and Al-Hamad, A 2022, The adoption of metaverse systems: a hybrid SEM - ML method , in: 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), 16-18 November 2022, Maldives.

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Abstract

Seeing high-tech medical devices from other nations and witnessing surgery to learn has become nearly unattainable. The pandemic of coronavirus disease 2019 (COVID-19) has created cross-border medical education challenging. Nevertheless, to cater to the increase in non-face-to-face education, instructional techniques entailing the “metaverse” are being initiated in the medical field, since medical staff from all over the globe who frequented the UAE to acquire skills in medical technology and medical students who require to exercise have already had minimal prospects to collaborate closely with patients attributable to COVID-19. Employing video-conferencing technology like Zoom to provide effective medical education is similarly difficult. The research's goal is to learn the perception of students in the UAE towards the metaverse system (MV) used for medical training. The conceptual model includes The Technology Acceptance Model (TAM) elements and adoption aspects of perceived value. The research's conceptual model, which connects both personal-based traits and technological features, is what makes it novel. Additionally, the novel hybrid analysis approach will be applied in the present research to conduct machine learning (ML) driven structural equation modeling (SEM) evaluation.

Item Type:	Conference or Workshop Item (Paper)
Schools:	Schools > School of Computing, Science and Engineering
Journal or Publication Title:	2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)
Publisher:	IEEE
SWORD Depositor:	Publications Router
Depositing User:	Publications Router
Date Deposited:	01 Mar 2023 09:02
Last Modified:	01 Mar 2023 09:02
URI:	https://usir.salford.ac.uk/id/eprint/66148

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