How to cite this paper
Aburayya, A., Salloum, S., Alderbashi, K., Shwedeh, F., Shaalan, Y., Alfaisal, R., Malaka, S & Shaalan, K. (2023). SEM-machine learning-based model for perusing the adoption of metaverse in higher education in UAE.International Journal of Data and Network Science, 7(2), 667-676.
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Ashraf, A. R., Thongpapanl, N., Menguc, B., & Northey, G. (2017). The Role of M-Commerce Readiness in Emerging and Developed Markets. Https://Doi.Org/10.1509/Jim.16.0033, 25(2), 25–51. https://doi.org/10.1509/JIM.16.0033
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Al-Maroof, R. S., & Salloum, S. A. (2021). An Integrated Model of Continuous Intention to Use of Google Classroom. Studies in Systems, Decision and Control, 295, 311–335. https://doi.org/10.1007/978-3-030-47411-9_18/COVER
Al-Maroof, R. S., AlAhbabi, N. M. N., Akour, I., Alhumaid, K., Ayoubi, K., Alnnaimi, M., Thabit, S., Alfaisal, R., Aburayya, A., & Salloum, S. A. (2022). Students’ perception towards behavioral intention of audio and video teaching styles: An acceptance study. International Journal of Data and Network Science, 6(2), 603–618. https://doi.org/10.5267/j.ijdns.2021.11.004
Al-Maroof, R. S., Alnazzawi, N., Akour, I. A., Ayoubi, K., Alhumaid, K., AlAhbabi, N. M., Alnnaimi, M., Thabit, S., Alfaisal, R., Aburayya, A., & Salloum, S. (2021). The Effectiveness of Online Platforms after the Pandemic: Will Face-to-Face Classes Affect Students’ Perception of Their Behavioural Intention (BIU) to Use Online Plat-forms? Informatics, 8(4), 83. https://doi.org/10.3390/informatics8040083
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Almarzouqi, A., Aburayya, A., & Salloum, S. A. (2022b). Prediction of User’s Intention to Use Metaverse System in Med-ical Education: A Hybrid SEM-ML Learning Approach. IEEE Access, 10, 43421–43434. https://doi.org/10.1109/ACCESS.2022.3169285
Almarzouqi, A., Aburayya, A., & Salloum, S. A. (2022c). Determinants predicting the electronic medical record adoption in healthcare: A SEM-Artificial Neural Network approach. PLOS ONE, 17(8), e0272735. https://doi.org/10.1371/journal.pone.0272735
Alomari, K. M., Alhamad, A. Q., Mbaidin, H. O., & Salloum, S. (2019). Prediction of the Digital Game Rating Systems based on the ESRB. Opción, 35, 1368–1393. https://produccioncientificaluz.org/index.php/opcion/article/view/24085
Al-Rahmi, W. M., Yahaya, N., Alamri, M. M., Alyoussef, I. Y., Al-Rahmi, A. M., & Kamin, Y. bin. (2019). Integrating in-novation diffusion theory with technology acceptance model: supporting students’ attitude towards using a massive open online courses (MOOCs) systems. Https://Doi.Org/10.1080/10494820.2019.1629599, 29(8), 1380–1392. https://doi.org/10.1080/10494820.2019.1629599
Alsharhan, A. M., Salloum, S. A., & Aburayya, A. (2022). Technology acceptance drivers for AR smart glasses in the middle east: A quantitative study. International Journal of Data and Network Science, 6(1), 193–208. https://doi.org/10.5267/j.ijdns.2021.9.008
Anwar, A., Thongpapanl, N., & Ashraf, A. R. (2021). Strategic imperatives of mobile commerce in developing countries: the influence of consumer innovativeness, ubiquity, perceived value, risk, and cost on usage. Journal of Strategic Mar-keting, 29(8), 722–742.
Arpaci, I. (2019). A hybrid modeling approach for predicting the educational use of mobile cloud computing services in higher education. Computers in Human Behavior, 90, 181–187. https://doi.org/10.1016/J.CHB.2018.09.005
Ashraf, A. R., Thongpapanl, N., Menguc, B., & Northey, G. (2017). The Role of M-Commerce Readiness in Emerging and Developed Markets. Https://Doi.Org/10.1509/Jim.16.0033, 25(2), 25–51. https://doi.org/10.1509/JIM.16.0033
Barclay, D., Higgins, C., & Thompson, R. (1995). The Partial Least Squares (pls) Approach to Casual Modeling: Personal Computer Adoption Ans Use as an Illustration.
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