A conceptual framework for determining metaverse adoption in higher institutions of gulf area : an empirical study using hybrid SEM-ANN approach

Akour, IA, Al-Maroof, RS, Alfaisal, R and Salloum, S ORCID: https://orcid.org/0000-0002-6073-3981 2022, 'A conceptual framework for determining metaverse adoption in higher institutions of gulf area : an empirical study using hybrid SEM-ANN approach' , Computers and Education: Artificial Intelligence, 3 , p. 100052.

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The metaverse is a kind of imagined world with immersive digital spaces that increase, allowing a more interactive environment in educational settings. The metaverse is an expansion of the synchronous communication that embraces an effective number of users to share different experiences. The study aims to investigate the students' perceptions towards metaverse system for educational purposes in the Gulf area. The conceptual model comprises the adoption properties, namely trialability, observability, compatibility, and complexity, users' satisfaction, personal innovativeness, and Technology Acceptance Model (TAM) constructs. The novelty of the paper lies in its conceptual model that correlates both personal-based characteristics and technology-based features. In addition, the novel approach of hybrid analysis will be used in the current study to perform deep-learning-based analysis of structural equation modelling (SEM) and artificial neural network (ANN). Moreover, the importance-performance map analysis (IPMA) is used in the current study to evaluate the involved factors for their importance and performance. The study identified Perceived Usefulness (PU) to be an essential predictor of the factor of Users’ Intention to Use the Metaverse System (MS). The fact was discovered during ANN and IPMA analysis. Furthermore, this study is practically significant, as it helped the concerned authorities in educational sector in understanding the significance of each factor and allowed them to make efforts and plans according to the order of significance of factors. Another important implication of the study is methodological in nature. It validates that deep ANN architecture can offer deep insight into non-linear relationships shared by various factors of a theoretical model.

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: Computers and Education: Artificial Intelligence
Publisher: Elsevier
ISSN: 2666-920X
Related URLs:
Depositing User: USIR Admin
Date Deposited: 14 Mar 2022 11:04
Last Modified: 17 Aug 2022 10:17
URI: https://usir.salford.ac.uk/id/eprint/63377

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