Browsing by Author "MAGHAYDAH, SAFWAN SULEIMAN MGHIED"

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    An Integrated Model for Metaverse Adoption in Higher Education Institutions: A Structural Equation Modelling and Artificial Neural Network Approach
    (The British University in Dubai (BUiD), 2024-06) MAGHAYDAH, SAFWAN SULEIMAN MGHIED; Dr Piyush Maheshwari
    Information and Communication Technology (ICT) is experiencing a significant shift with the advent of the Metaverse, merging physical reality with a virtual world via virtual and augmented reality headsets. This development promises to revolutionize traditional online interactions, offering new educational opportunities through enhanced collaboration, communication, and immersive learning environments. The interest of Higher Education Institutions (HEIs) in the United Arab Emirates (UAE) in the Metaverse is notably high, attributed to its potential to transform education. However, research on the factors influencing Metaverse adoption in the UAE remains scarce, which limits the educational sector's progress and the effective utilization of Metaverse technology for educational purposes. This study aims to identify the determinants affecting the adoption of Metaverse technology within UAE's HEIs by proposing an integrative model that merges the Unified Theory of Acceptance and Use of Technology (UTAUT2) with Task-Technology Fit (TTF) and Protection Motivation Theory (PMT) elements. The study focuses on the adoption behavior of students towards Metaverse technology in UAE's higher education setting. A conceptual model was developed to test 14 hypotheses and address the research questions, fulfilling the study's main objective. Three research objectives were formulated based on this model, with subsequent sections providing results and explanations. Data were gathered from 760 undergraduate and postgraduate students’ responses from different universities in the UAE. Hypothesis testing revealed significant influences of performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivations, price value, task characteristics, technology characteristics, task technology fit, response efficacy, self-efficacy, and perceived vulnerability on Metaverse adoption, with only response costs and perceived severity not supported. The ANN analysis, conducted to avoid overfitting and to analyze nonlinear correlations, showed the superiority of ANN predictions over traditional regression methods, indicating high levels of precision and reliability of the ANN models. Sensitivity analysis highlighted technology characteristics as the most significant predictor of task-technology fit. In addition, performance expectancy is the most significant predictor of students' Metaverse adoption behavior, followed by self-efficacy, while perceived vulnerability had the least impact. This comprehensive analysis contributes significantly to understanding the factors influencing Metaverse adoption in HEIs, offering insights for policymakers and practitioners in integrating Metaverse technology into educational infrastructure.