How to cite this paper
AL-Oudat, M & Altamimi, A. (2022). Factors influencing behavior intentions to use virtual reality in education.International Journal of Data and Network Science, 6(3), 733-742.
Refrences
Adhikari, A., & Integration, X. L.-I. 0 A. for F. L. (n.d.). Industry4.0 Applications for Full Lifecycle Integration of Build-ings Proceedings of the 21st International Conference on Construction Applications of Virtual Reality.
Alcaraz, C., De, N., García, C., Serrano, M. A., Rosado, D. G., Gull, H., Saeed, S., Zafar Iqbal, S., Bamarouf, Y. A., Alqahtani, M. A., Alabbad, D. A., Saqib, M., Hussein, S., Qahtani, A., Alamer, A., & Sa, A. A. (2022). An Empirical Study of Mobile Commerce and Customers Security Perception in Saudi Arabia. Mdpi.Com. https://doi.org/10.3390/electronics11030293
Alsharhan, A., Salloum, S., & 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.
Atiker, B. (2021). Augmented Reality Games. In igi-global.com (pp. 221–243). https://doi.org/10.4018/978-1-7998-8089-9.ch012
Behmadi, S., Asadi, F., Okhovati, M., & Ershad Sarabi, R. (2022). Virtual reality-based medical education versus lecture-based method in teaching start triage lessons in emergency medical students: Virtual reality in medical education. Journal of Advances in Medical Education & Professionalism, 10(1), 48–53. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8720154/
Bergamo, P. A. de S., Streng, E. S., de Carvalho, M. A., Rosenkranz, J., & Ghorbani, Y. (2022). Simulation-based training and learning: A review on technology-enhanced education for the minerals industry. In Minerals Engineering (Vol. 175). https://doi.org/10.1016/j.mineng.2021.107272
Bodendorf, F., & Franke, J. (2022). Application of the Technology Acceptance Model to an Intelligent Cost Estimation System: An Empirical Study in the Automotive Industry. Proceedings of the 55th Hawaii International Conference on System Sciences. https://doi.org/10.24251/hicss.2022.144
Bolkas, D., Chiampi, J. D., Fioti, J., & Gaffney, D. (2022). First Assessment Results of Surveying Engineering Labs in Immersive and Interactive Virtual Reality. Journal of Surveying Engineering, 148(1). https://doi.org/10.1061/(asce)su.1943-5428.0000388
Brandon-Jones, A., & Kauppi, K. (2018). Examining the antecedents of the technology acceptance model within e-procurement. International Journal of Operations and Production Management, 38(1), 22–42. https://doi.org/10.1108/IJOPM-06-2015-0346
Cattaneo, A. A. P., Antonietti, C., & Rauseo, M. (2022). How digitalised are vocational teachers? Assessing digital com-petence in vocational education and looking at its underlying factors. Computers and Education, 176. https://doi.org/10.1016/j.compedu.2021.104358
Cerny, B. A., & Kaiser, H. F. (1977). A study of a measure of sampling adequacy for factor-analytic correlation matrices. Multivariate Behavioral Research, 12(1), 43–47. https://doi.org/10.1207/S15327906MBR1201_3
Chen, C. (2022). Immersive virtual reality to train preservice teachers in managing students’ challenging behaviours: A pilot study. British Journal of Educational Technology. https://doi.org/10.1111/BJET.13181
da Silva, J. N., Kons, R. L., de Lucas, R. D., & Detanico, D. (2022). Jiu-Jitsu-Specific Performance Test: Reliability Anal-ysis and Construct Validity in Competitive Athletes. Journal of Strength and Conditioning Research, 36(1), 174–179. https://doi.org/10.1519/JSC.0000000000003429
Davis, F. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results. https://dspace.mit.edu/bitstream/handle/1721.1/15192/14927137-MIT.pdf
Doerner, R., Broll, W., Jung, B., Grimm, P., Göbel, M., & Kruse, R. (2022). Introduction to Virtual and Augmented Reali-ty. Virtual and Augmented Reality (VR/AR), 1–37. https://doi.org/10.1007/978-3-030-79062-2_1
Fernández, D., Liu, I., Preti, A., Haro, J. M., & Siddi, S. (2022). Launay–Slade Hallucination Scale-Extended: simplifying its interpretation. Psychosis, 1–10. https://doi.org/10.1080/17522439.2021.1983011
Ghozali, M. T., Dewi, P. E. N., & Trisnawati. (2022a). Implementing the technology acceptance model to examine user acceptance of the asthma control test app. International Journal of Systems Assurance Engineering and Management. https://doi.org/10.1007/s13198-021-01606-w
Ghozali, M. T., Dewi, P. E. N., & Trisnawati. (2022b). Implementing the technology acceptance model to examine user acceptance of the asthma control test app. International Journal of System Assurance Engineering and Management. https://doi.org/10.1007/S13198-021-01606-W
Graesser, A. C., Sabatini, J. P., & Li, H. (2022). Educational Psychology Is Evolving to Accommodate Technology, Multi-ple Disciplines, and Twenty-First-Century Skills. Annual Review of Psychology, 73(1), 547–574. https://doi.org/10.1146/ANNUREV-PSYCH-020821-113042
Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context: A systematic literature re-view. British Journal of Educational Technology, 50(5), 2572–2593. https://doi.org/10.1111/BJET.12864
Grobler, H., & van Wyk, E. (2022). Towards Incorporating Extended Reality Technology in the Education of Mining Pro-fessionals. 245–257. https://doi.org/10.1007/978-3-030-84315-1_14
Hussein, D. E.-S., El-Sheikh, M. A., Mohamed, R., Hady, A.-E., El-Wahab, O. A., & Araby, A. (n.d.). Usability of Virtual Reality for Alleviating Pain and Anxiety for Primiparity Women during 1st Stage of Labor and its Reflection on Labor Outcomes. Jnsbu.Journals.Ekb.Eg, 3, 2022. Retrieved January 22, 2022, from https://jnsbu.journals.ekb.eg/article_213959.html
Ibodullayev, S., Sardor, I., Bahromov, N., Ahror O’g’li, A., Al-Khwarizmi, M., Magistr, S. N. I., & Bahromov, A. A. (2022). A variety of virtual reality implementations for creative learning an 5 ways to implement virtual reality in learning process.
Jimenez, I. A. C., García, L. C. C., Violante, M. G., Marcolin, F., & Vezzetti, E. (2021). Commonly used external tam var-iables in e-learning, agriculture and virtual reality applications. Future Internet, 13(1), 1–21. https://doi.org/10.3390/FI13010007
Kabir, K. H., Hassan, F., Mukta, Most. Z. N., Roy, D., Darr, D., Leggette, H., & Ullah, S. M. A. (2022). Application of the technology acceptance model to assess the use and preferences of ICTs among field-level extension officers in Bang-ladesh. Digital Geography and Society, 3, 100027. https://doi.org/10.1016/j.diggeo.2022.100027
Katebi, A., Homami, P., & Najmeddin, M. (2022). Acceptance model of precast concrete components in building con-struction based on Technology Acceptance Model (TAM) and Technology, Organization, and Environment (TOE) framework. Journal of Building Engineering, 45. https://doi.org/10.1016/j.jobe.2021.103518
Kind, P., Jones, K., & Barmby, P. (2007). Developing attitudes towards science measures. International Journal of Sci-ence Education, 29(7), 871–893. https://doi.org/10.1080/09500690600909091
Kwok, P. K., Yan, M., Qu, T., & Lau, H. Y. K. (2020). User acceptance of virtual reality technology for practicing digital twin-based crisis management, 34(7–8), 874–887. https://doi.org/10.1080/0951192X.2020.1803502
Kwok, S. Y. C. L., Gu, M., & Tam, N. W. Y. (2022). A Multiple Component Positive Psychology Intervention to Reduce Anxiety and Increase Happiness in Adolescents: The Mediating Roles of Gratitude and Emotional Intelligence. Journal of Happiness Studies. https://doi.org/10.1007/S10902-021-00487-X
Lai, P. C. (n.d.). THE LITERATURE REVIEW OF TECHNOLOGY ADOPTION MODELS AND THEORIES FOR THE NOVELTY TECHNOLOGY. JISTEM-Journal of Information Systems and Technology Management, 14(1), 21–38. https://doi.org/10.4301/S1807-17752017000100002
Le, V. P., Do, S. H., & Nguyen, H. N. L. (2022). A Study on the Factors Affecting Intention of Using Online Banking Ser-vices in Vietnam. Global Changes and Sustainable Development in Asian Emerging Market Economies Vol. 1, 179–198. https://doi.org/10.1007/978-3-030-81435-9_14
Lin, H. C., Ho, C. F., & Yang, H. (2022). Understanding adoption of artificial intelligence-enabled language e-learning system: an empirical study of UTAUT model. International Journal of Mobile Learning and Organisation, 16(1), 74. https://doi.org/10.1504/IJMLO.2022.119966
Ma, N., Li, Y.-M., Guo, J.-H., Laurillard, D., & Yang, M. (2022). A learning model for improving in-service teachers’ course completion in MOOCs. Interactive Learning Environments, 1–16. https://doi.org/10.1080/10494820.2021.2025405
Machorro, F., Aguilar, D. A., Romero, M. V., & Colorado, E. M. (2022). Internet Access and Acceptance of a Scholar In-formation System in Mexican University Students. 739–748. https://doi.org/10.1007/978-981-16-5063-5_60
Marangunić, N., & Granić, A. (2014). Technology acceptance model: a literature review from 1986 to 2013. Universal Ac-cess in the Information Society 2014 14:1, 14(1), 81–95. https://doi.org/10.1007/S10209-014-0348-1
Mbunge, E., Jiyane, S., & Muchemwa, B. (2022). Towards emotive sensory Web in virtual health care: Trends, technolo-gies, challenges and ethical issues. Sensors International, 3, 100134. https://doi.org/10.1016/j.sintl.2021.100134
Mellinger, C. D., & Hanson, T. A. (2021). Latent Variables in Translation and Interpreting Studies. Contesting Epistemol-ogies in Cognitive Translation and Interpreting Studies, 104–128. https://doi.org/10.4324/9781003125792-7/LATENT-VARIABLES-TRANSLATION-INTERPRETING-STUDIES-CHRISTOPHER-MELLINGER-THOMAS-HANSON
Metallo, C., Agrifoglio, R., Lepore, L., & Landriani, L. (2022). Explaing users’ technology acceptance through national cultural values in the hospital context. BMC Health Services Research, 22(1). https://doi.org/10.1186/s12913-022-07488-3
Milutinović, V. (2022). Examining the influence of pre-service teachers’ digital native traits on their technology ac-ceptance: A Serbian perspective. Education and Information Technologies. https://doi.org/10.1007/S10639-022-10887-Y
Molnár, G. (2022). How to Make Different Thinking Profiles Visible Through Technology: The Potential for Log File Analysis and Learning Analytics. 125–145. https://doi.org/10.1007/978-3-030-80571-5_9
Montero-Martín, J., Bravo-Pérez, M., Albaladejo-Martínez, A., Antonio Hernández-Martín, L., María Rosel-Gallardo, E., & Montero Martín, J. (2009). Validity and reliability of three commonly used quality of life measures in a large Euro-pean population of coronary heart disease patients. Elsevier, 14(1), 44–50.
Montero-Martin, J., Bravo-Pérez, M., Albaladejo-Martínez, A., Hernández-Martin, L. A., & Rosel-Gallardo, E. M. (2009). Validation the Oral Health Impact Profile (OHIP-14sp) for adults in Spain. Medicina Oral, Patologia Oral y Cirugia Bucal, 14(1), 44–50. https://roderic.uv.es/handle/10550/60540
Moore, G. C., & Benbasat, I. (1991). Development of an Instrument to Measure the Perceptions of Adopting an Infor-mation Technology Innovation, 2(3), 192–222. https://doi.org/10.1287/ISRE.2.3.192
Moore, J., Whalen, S., Rowe, N., Lee, J., Ordon, M., & Lantz-Powers, A. (2021). A high-fidelity, virtual reality, tran-surethral resection of bladder tumor simulator: Validation as a tool for training. Canadian Urological Association Journal, 16(4). https://doi.org/10.5489/cuaj.7285
Nandal, S., Legal, D. K.-J. of, Regulatory, E. and, & 2022, U. (n.d.). ROLE OF GST KNOWLEDGE IN GST COMPLI-ANCE: EVIDENCE FROM SMALL ENTERPRISES OF HARYANA STATE IN INDIA.
Nascimento, A. G. M., Toledo, B. S., Guimarães, J. T., Ramos, G. L. P. A., da Cunha, D. T., Pimentel, T. C., Cruz, A. G., Freitas, M. Q., Esmerino, E. A., & Mársico, E. T. (2022). The impact of packaging design on the perceived quality of honey by Brazilian consumers. Food Research International, 151. https://doi.org/10.1016/j.foodres.2021.110887
Obamiro, K. O., Chalmers, L., & Bereznicki, L. R. E. (2016). Development and validation of an oral anticoagulation knowledge tool (AKT). PLoS ONE, 11(6). https://doi.org/10.1371/JOURNAL.PONE.0158071
Ojo, A. (2022). Mapping Africa’s Possible Future Political Geography Based on Governance Aspirations of Millennials. 73–95. https://doi.org/10.1007/978-3-030-88546-5_5
Özgen, D. S., Afacan, Y., & Sürer, E. (2019). Usability of virtual reality for basic design education: a comparative study with paper-based design. International Journal of Technology and Design Education 2019 31:2, 31(2), 357–377. https://doi.org/10.1007/S10798-019-09554-0
Picot, B., Dury, J., Néron, G., Samozino, P., Terrier, R., Rémy-Neris, O., & Forestier, N. (2022). Validity and reliability of video analysis to evaluate ankle proprioceptive reintegration during postural control. Gait and Posture, 91, 155–160. https://doi.org/10.1016/j.gaitpost.2021.10.022
Ran, W., Hu, Y., & Zhi, Y. (2022). Application of Digital Twins to Flexible Production Management: Taking a Shandong Factory as an Example. https://doi.org/10.21203/rs.3.rs-1232905/v1
Razak, F. Z. B. A., Bakar, A. A., & Abdullah, W. S. W. (2022). The linkage between information quality and e-government adoption: is gender a moderating factor. Electronic Government, an International Journal, 18(1), 94. https://doi.org/10.1504/EG.2022.119618
Robershaw, K. L., Bradley, K. D., & Waddington, R. J. (2022). Parents’ Awareness and Perspectives of School Choice Scale: Psychometric Evidence Using Rasch Modelling. Journal of School Choice, 1–31. https://doi.org/10.1080/15582159.2021.2004493
Robie, C., Meade, A., … S. R.-E. and, & 2022, undefined. (2022). Effects of Response Option Order on Likert-Type Psy-chometric Properties and Reactions. Journals.Sagepub.Com, 001316442110694. https://doi.org/10.1177/00131644211069406
Shafi, M.K, M., & Reddy, M. R. (2022). Financial inclusion through Kiosk-based banking services: a study with reference to business correspondent models in the state of Kerala. Benchmarking. https://doi.org/10.1108/BIJ-11-2020-0591
Shapiro, S. S., Wilk, M. B., & Chen, H. J. (1968). A Comparative Study of Various Tests for Normality. Journal of the American Statistical Association, 63(324), 1343–1372. https://doi.org/10.1080/01621459.1968.10480932
Singh, S., Sao, A., Studies, A. K.-A. of M., & 2022, U. (n.d.). THE IMPACT OF COVID-19 ON THE PURCHASE BE-HAVIOUR OF CONSUMERS IN AFFORDABLE HOUSING IN INDIAN REAL ESTATE-AN EMPIRICAL STUDY.
Siyam, N., Hussain, M., & Alqaryouti, O. (2022). Factors impacting teachers’ acceptance and use of Bring Your Own De-vice (BYOD) in the classroom. SN Social Sciences, 2(1), 8. https://doi.org/10.1007/S43545-021-00307-2
Socio, B. A.-A. issues of modern development of. (n.d.). DIGITIZATION OF SOCIO-ECONOMIC PROCESSES IN TERM OF THE PANDEMIC.
Su, D. N., Nguyen, N. A. N., Nguyen, L. N. T., Luu, T. T., & Nguyen-Phuoc, D. Q. (2022). Modeling consumers’ trust in mobile food delivery apps: perspectives of technology acceptance model, mobile service quality and personalization-privacy theory. Journal of Hospitality Marketing & Management, 1–35. https://doi.org/10.1080/19368623.2022.2020199
Šumak, B., Heričko, M., behavior, M. P.-C. in human, & 2011, undefined. (2010). A meta-analysis of e-learning technolo-gy acceptance: The role of user types and e-learning technology types. Elsevier, 9. https://www.sciencedirect.com/science/article/pii/S0747563211001609
Temel Aslan, K., Ay, P., Kaş, D., Tosun, F., Yürükcü, İ., Kekeç, E., Furkan Şahin, M., & Apaydın Kaya, Ç. (2022). Adapta-tion and validation of the Turkish version of the vaccine hesitancy 5 point Likert Scale. Taylor & Francis, 1–7. https://doi.org/10.1080/21645515.2021.1953347
Teo, T., Ursavaş, Ö. F., & Bahçekapili, E. (2011). Efficiency of the technology acceptance model to explain pre-service teachers’ intention to use technology: A Turkish study. Campus-Wide Information Systems, 28(2), 93–101. https://doi.org/10.1108/10650741111117798/FULL/HTML
Utami, I. Q., Fahmiyah, I., Ningrum, R. A., Fakhruzzaman, M. N., Pratama, A. I., & Triangga, Y. M. (2022). Teacher’s ac-ceptance toward cloud-based learning technology in Covid-19 pandemic era. Journal of Computers in Education. https://doi.org/10.1007/S40692-021-00214-8
Vallade, J. I., Kaufmann, R., Frisby, B. N., & Martin, J. C. (2020). Technology acceptance model: investigating students’ intentions toward adoption of immersive 360° videos for public speaking rehearsals. Communication Education, 1–19. https://doi.org/10.1080/03634523.2020.1791351
Venkatesh, V. (2013). Absract - A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. https://www.jstor.org/stable/2634758
Villena-Taranilla, R., Tirado-Olivares, S., Cózar-Gutiérrez, R., & González-Calero, J. A. (2022). Effects of virtual reality on learning outcomes in K-6 education: A meta-analysis. Educational Research Review, 35. https://doi.org/10.1016/j.edurev.2022.100434
Wolf, M., Teizer, J., Wolf, B., Bükrü, S., & Solberg, A. (2022). Investigating hazard recognition in augmented virtuality for personalized feedback in construction safety education and training. Advanced Engineering Informatics, 51. https://doi.org/10.1016/j.aei.2021.101469
Yan, X., Li, T., & Zhou, Y. (2022). Virtual Reality’s Influence on Construction Workers’ Willingness to Participate in Safety Education and Training in China. Journal of Management in Engineering, 38(2). https://doi.org/10.1061/(asce)me.1943-5479.0001002
Yang, F., & Miang Goh, Y. (2022). VR and MR technology for safety management education: An authentic learning ap-proach. Safety Science, 148. https://doi.org/10.1016/j.ssci.2021.105645
Yap, B. W., & Sim, C. H. (2011). Comparisons of various types of normality tests. Journal of Statistical Computation and Simulation, 81(12), 2141–2155. https://doi.org/10.1080/00949655.2010.520163
Yazici, B., & Yolacan, S. (2007). A comparison of various tests of normality. Journal of Statistical Computation and Sim-ulation, 77(2), 175–183. https://doi.org/10.1080/10629360600678310
Alcaraz, C., De, N., García, C., Serrano, M. A., Rosado, D. G., Gull, H., Saeed, S., Zafar Iqbal, S., Bamarouf, Y. A., Alqahtani, M. A., Alabbad, D. A., Saqib, M., Hussein, S., Qahtani, A., Alamer, A., & Sa, A. A. (2022). An Empirical Study of Mobile Commerce and Customers Security Perception in Saudi Arabia. Mdpi.Com. https://doi.org/10.3390/electronics11030293
Alsharhan, A., Salloum, S., & 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.
Atiker, B. (2021). Augmented Reality Games. In igi-global.com (pp. 221–243). https://doi.org/10.4018/978-1-7998-8089-9.ch012
Behmadi, S., Asadi, F., Okhovati, M., & Ershad Sarabi, R. (2022). Virtual reality-based medical education versus lecture-based method in teaching start triage lessons in emergency medical students: Virtual reality in medical education. Journal of Advances in Medical Education & Professionalism, 10(1), 48–53. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8720154/
Bergamo, P. A. de S., Streng, E. S., de Carvalho, M. A., Rosenkranz, J., & Ghorbani, Y. (2022). Simulation-based training and learning: A review on technology-enhanced education for the minerals industry. In Minerals Engineering (Vol. 175). https://doi.org/10.1016/j.mineng.2021.107272
Bodendorf, F., & Franke, J. (2022). Application of the Technology Acceptance Model to an Intelligent Cost Estimation System: An Empirical Study in the Automotive Industry. Proceedings of the 55th Hawaii International Conference on System Sciences. https://doi.org/10.24251/hicss.2022.144
Bolkas, D., Chiampi, J. D., Fioti, J., & Gaffney, D. (2022). First Assessment Results of Surveying Engineering Labs in Immersive and Interactive Virtual Reality. Journal of Surveying Engineering, 148(1). https://doi.org/10.1061/(asce)su.1943-5428.0000388
Brandon-Jones, A., & Kauppi, K. (2018). Examining the antecedents of the technology acceptance model within e-procurement. International Journal of Operations and Production Management, 38(1), 22–42. https://doi.org/10.1108/IJOPM-06-2015-0346
Cattaneo, A. A. P., Antonietti, C., & Rauseo, M. (2022). How digitalised are vocational teachers? Assessing digital com-petence in vocational education and looking at its underlying factors. Computers and Education, 176. https://doi.org/10.1016/j.compedu.2021.104358
Cerny, B. A., & Kaiser, H. F. (1977). A study of a measure of sampling adequacy for factor-analytic correlation matrices. Multivariate Behavioral Research, 12(1), 43–47. https://doi.org/10.1207/S15327906MBR1201_3
Chen, C. (2022). Immersive virtual reality to train preservice teachers in managing students’ challenging behaviours: A pilot study. British Journal of Educational Technology. https://doi.org/10.1111/BJET.13181
da Silva, J. N., Kons, R. L., de Lucas, R. D., & Detanico, D. (2022). Jiu-Jitsu-Specific Performance Test: Reliability Anal-ysis and Construct Validity in Competitive Athletes. Journal of Strength and Conditioning Research, 36(1), 174–179. https://doi.org/10.1519/JSC.0000000000003429
Davis, F. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results. https://dspace.mit.edu/bitstream/handle/1721.1/15192/14927137-MIT.pdf
Doerner, R., Broll, W., Jung, B., Grimm, P., Göbel, M., & Kruse, R. (2022). Introduction to Virtual and Augmented Reali-ty. Virtual and Augmented Reality (VR/AR), 1–37. https://doi.org/10.1007/978-3-030-79062-2_1
Fernández, D., Liu, I., Preti, A., Haro, J. M., & Siddi, S. (2022). Launay–Slade Hallucination Scale-Extended: simplifying its interpretation. Psychosis, 1–10. https://doi.org/10.1080/17522439.2021.1983011
Ghozali, M. T., Dewi, P. E. N., & Trisnawati. (2022a). Implementing the technology acceptance model to examine user acceptance of the asthma control test app. International Journal of Systems Assurance Engineering and Management. https://doi.org/10.1007/s13198-021-01606-w
Ghozali, M. T., Dewi, P. E. N., & Trisnawati. (2022b). Implementing the technology acceptance model to examine user acceptance of the asthma control test app. International Journal of System Assurance Engineering and Management. https://doi.org/10.1007/S13198-021-01606-W
Graesser, A. C., Sabatini, J. P., & Li, H. (2022). Educational Psychology Is Evolving to Accommodate Technology, Multi-ple Disciplines, and Twenty-First-Century Skills. Annual Review of Psychology, 73(1), 547–574. https://doi.org/10.1146/ANNUREV-PSYCH-020821-113042
Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context: A systematic literature re-view. British Journal of Educational Technology, 50(5), 2572–2593. https://doi.org/10.1111/BJET.12864
Grobler, H., & van Wyk, E. (2022). Towards Incorporating Extended Reality Technology in the Education of Mining Pro-fessionals. 245–257. https://doi.org/10.1007/978-3-030-84315-1_14
Hussein, D. E.-S., El-Sheikh, M. A., Mohamed, R., Hady, A.-E., El-Wahab, O. A., & Araby, A. (n.d.). Usability of Virtual Reality for Alleviating Pain and Anxiety for Primiparity Women during 1st Stage of Labor and its Reflection on Labor Outcomes. Jnsbu.Journals.Ekb.Eg, 3, 2022. Retrieved January 22, 2022, from https://jnsbu.journals.ekb.eg/article_213959.html
Ibodullayev, S., Sardor, I., Bahromov, N., Ahror O’g’li, A., Al-Khwarizmi, M., Magistr, S. N. I., & Bahromov, A. A. (2022). A variety of virtual reality implementations for creative learning an 5 ways to implement virtual reality in learning process.
Jimenez, I. A. C., García, L. C. C., Violante, M. G., Marcolin, F., & Vezzetti, E. (2021). Commonly used external tam var-iables in e-learning, agriculture and virtual reality applications. Future Internet, 13(1), 1–21. https://doi.org/10.3390/FI13010007
Kabir, K. H., Hassan, F., Mukta, Most. Z. N., Roy, D., Darr, D., Leggette, H., & Ullah, S. M. A. (2022). Application of the technology acceptance model to assess the use and preferences of ICTs among field-level extension officers in Bang-ladesh. Digital Geography and Society, 3, 100027. https://doi.org/10.1016/j.diggeo.2022.100027
Katebi, A., Homami, P., & Najmeddin, M. (2022). Acceptance model of precast concrete components in building con-struction based on Technology Acceptance Model (TAM) and Technology, Organization, and Environment (TOE) framework. Journal of Building Engineering, 45. https://doi.org/10.1016/j.jobe.2021.103518
Kind, P., Jones, K., & Barmby, P. (2007). Developing attitudes towards science measures. International Journal of Sci-ence Education, 29(7), 871–893. https://doi.org/10.1080/09500690600909091
Kwok, P. K., Yan, M., Qu, T., & Lau, H. Y. K. (2020). User acceptance of virtual reality technology for practicing digital twin-based crisis management, 34(7–8), 874–887. https://doi.org/10.1080/0951192X.2020.1803502
Kwok, S. Y. C. L., Gu, M., & Tam, N. W. Y. (2022). A Multiple Component Positive Psychology Intervention to Reduce Anxiety and Increase Happiness in Adolescents: The Mediating Roles of Gratitude and Emotional Intelligence. Journal of Happiness Studies. https://doi.org/10.1007/S10902-021-00487-X
Lai, P. C. (n.d.). THE LITERATURE REVIEW OF TECHNOLOGY ADOPTION MODELS AND THEORIES FOR THE NOVELTY TECHNOLOGY. JISTEM-Journal of Information Systems and Technology Management, 14(1), 21–38. https://doi.org/10.4301/S1807-17752017000100002
Le, V. P., Do, S. H., & Nguyen, H. N. L. (2022). A Study on the Factors Affecting Intention of Using Online Banking Ser-vices in Vietnam. Global Changes and Sustainable Development in Asian Emerging Market Economies Vol. 1, 179–198. https://doi.org/10.1007/978-3-030-81435-9_14
Lin, H. C., Ho, C. F., & Yang, H. (2022). Understanding adoption of artificial intelligence-enabled language e-learning system: an empirical study of UTAUT model. International Journal of Mobile Learning and Organisation, 16(1), 74. https://doi.org/10.1504/IJMLO.2022.119966
Ma, N., Li, Y.-M., Guo, J.-H., Laurillard, D., & Yang, M. (2022). A learning model for improving in-service teachers’ course completion in MOOCs. Interactive Learning Environments, 1–16. https://doi.org/10.1080/10494820.2021.2025405
Machorro, F., Aguilar, D. A., Romero, M. V., & Colorado, E. M. (2022). Internet Access and Acceptance of a Scholar In-formation System in Mexican University Students. 739–748. https://doi.org/10.1007/978-981-16-5063-5_60
Marangunić, N., & Granić, A. (2014). Technology acceptance model: a literature review from 1986 to 2013. Universal Ac-cess in the Information Society 2014 14:1, 14(1), 81–95. https://doi.org/10.1007/S10209-014-0348-1
Mbunge, E., Jiyane, S., & Muchemwa, B. (2022). Towards emotive sensory Web in virtual health care: Trends, technolo-gies, challenges and ethical issues. Sensors International, 3, 100134. https://doi.org/10.1016/j.sintl.2021.100134
Mellinger, C. D., & Hanson, T. A. (2021). Latent Variables in Translation and Interpreting Studies. Contesting Epistemol-ogies in Cognitive Translation and Interpreting Studies, 104–128. https://doi.org/10.4324/9781003125792-7/LATENT-VARIABLES-TRANSLATION-INTERPRETING-STUDIES-CHRISTOPHER-MELLINGER-THOMAS-HANSON
Metallo, C., Agrifoglio, R., Lepore, L., & Landriani, L. (2022). Explaing users’ technology acceptance through national cultural values in the hospital context. BMC Health Services Research, 22(1). https://doi.org/10.1186/s12913-022-07488-3
Milutinović, V. (2022). Examining the influence of pre-service teachers’ digital native traits on their technology ac-ceptance: A Serbian perspective. Education and Information Technologies. https://doi.org/10.1007/S10639-022-10887-Y
Molnár, G. (2022). How to Make Different Thinking Profiles Visible Through Technology: The Potential for Log File Analysis and Learning Analytics. 125–145. https://doi.org/10.1007/978-3-030-80571-5_9
Montero-Martín, J., Bravo-Pérez, M., Albaladejo-Martínez, A., Antonio Hernández-Martín, L., María Rosel-Gallardo, E., & Montero Martín, J. (2009). Validity and reliability of three commonly used quality of life measures in a large Euro-pean population of coronary heart disease patients. Elsevier, 14(1), 44–50.
Montero-Martin, J., Bravo-Pérez, M., Albaladejo-Martínez, A., Hernández-Martin, L. A., & Rosel-Gallardo, E. M. (2009). Validation the Oral Health Impact Profile (OHIP-14sp) for adults in Spain. Medicina Oral, Patologia Oral y Cirugia Bucal, 14(1), 44–50. https://roderic.uv.es/handle/10550/60540
Moore, G. C., & Benbasat, I. (1991). Development of an Instrument to Measure the Perceptions of Adopting an Infor-mation Technology Innovation, 2(3), 192–222. https://doi.org/10.1287/ISRE.2.3.192
Moore, J., Whalen, S., Rowe, N., Lee, J., Ordon, M., & Lantz-Powers, A. (2021). A high-fidelity, virtual reality, tran-surethral resection of bladder tumor simulator: Validation as a tool for training. Canadian Urological Association Journal, 16(4). https://doi.org/10.5489/cuaj.7285
Nandal, S., Legal, D. K.-J. of, Regulatory, E. and, & 2022, U. (n.d.). ROLE OF GST KNOWLEDGE IN GST COMPLI-ANCE: EVIDENCE FROM SMALL ENTERPRISES OF HARYANA STATE IN INDIA.
Nascimento, A. G. M., Toledo, B. S., Guimarães, J. T., Ramos, G. L. P. A., da Cunha, D. T., Pimentel, T. C., Cruz, A. G., Freitas, M. Q., Esmerino, E. A., & Mársico, E. T. (2022). The impact of packaging design on the perceived quality of honey by Brazilian consumers. Food Research International, 151. https://doi.org/10.1016/j.foodres.2021.110887
Obamiro, K. O., Chalmers, L., & Bereznicki, L. R. E. (2016). Development and validation of an oral anticoagulation knowledge tool (AKT). PLoS ONE, 11(6). https://doi.org/10.1371/JOURNAL.PONE.0158071
Ojo, A. (2022). Mapping Africa’s Possible Future Political Geography Based on Governance Aspirations of Millennials. 73–95. https://doi.org/10.1007/978-3-030-88546-5_5
Özgen, D. S., Afacan, Y., & Sürer, E. (2019). Usability of virtual reality for basic design education: a comparative study with paper-based design. International Journal of Technology and Design Education 2019 31:2, 31(2), 357–377. https://doi.org/10.1007/S10798-019-09554-0
Picot, B., Dury, J., Néron, G., Samozino, P., Terrier, R., Rémy-Neris, O., & Forestier, N. (2022). Validity and reliability of video analysis to evaluate ankle proprioceptive reintegration during postural control. Gait and Posture, 91, 155–160. https://doi.org/10.1016/j.gaitpost.2021.10.022
Ran, W., Hu, Y., & Zhi, Y. (2022). Application of Digital Twins to Flexible Production Management: Taking a Shandong Factory as an Example. https://doi.org/10.21203/rs.3.rs-1232905/v1
Razak, F. Z. B. A., Bakar, A. A., & Abdullah, W. S. W. (2022). The linkage between information quality and e-government adoption: is gender a moderating factor. Electronic Government, an International Journal, 18(1), 94. https://doi.org/10.1504/EG.2022.119618
Robershaw, K. L., Bradley, K. D., & Waddington, R. J. (2022). Parents’ Awareness and Perspectives of School Choice Scale: Psychometric Evidence Using Rasch Modelling. Journal of School Choice, 1–31. https://doi.org/10.1080/15582159.2021.2004493
Robie, C., Meade, A., … S. R.-E. and, & 2022, undefined. (2022). Effects of Response Option Order on Likert-Type Psy-chometric Properties and Reactions. Journals.Sagepub.Com, 001316442110694. https://doi.org/10.1177/00131644211069406
Shafi, M.K, M., & Reddy, M. R. (2022). Financial inclusion through Kiosk-based banking services: a study with reference to business correspondent models in the state of Kerala. Benchmarking. https://doi.org/10.1108/BIJ-11-2020-0591
Shapiro, S. S., Wilk, M. B., & Chen, H. J. (1968). A Comparative Study of Various Tests for Normality. Journal of the American Statistical Association, 63(324), 1343–1372. https://doi.org/10.1080/01621459.1968.10480932
Singh, S., Sao, A., Studies, A. K.-A. of M., & 2022, U. (n.d.). THE IMPACT OF COVID-19 ON THE PURCHASE BE-HAVIOUR OF CONSUMERS IN AFFORDABLE HOUSING IN INDIAN REAL ESTATE-AN EMPIRICAL STUDY.
Siyam, N., Hussain, M., & Alqaryouti, O. (2022). Factors impacting teachers’ acceptance and use of Bring Your Own De-vice (BYOD) in the classroom. SN Social Sciences, 2(1), 8. https://doi.org/10.1007/S43545-021-00307-2
Socio, B. A.-A. issues of modern development of. (n.d.). DIGITIZATION OF SOCIO-ECONOMIC PROCESSES IN TERM OF THE PANDEMIC.
Su, D. N., Nguyen, N. A. N., Nguyen, L. N. T., Luu, T. T., & Nguyen-Phuoc, D. Q. (2022). Modeling consumers’ trust in mobile food delivery apps: perspectives of technology acceptance model, mobile service quality and personalization-privacy theory. Journal of Hospitality Marketing & Management, 1–35. https://doi.org/10.1080/19368623.2022.2020199
Šumak, B., Heričko, M., behavior, M. P.-C. in human, & 2011, undefined. (2010). A meta-analysis of e-learning technolo-gy acceptance: The role of user types and e-learning technology types. Elsevier, 9. https://www.sciencedirect.com/science/article/pii/S0747563211001609
Temel Aslan, K., Ay, P., Kaş, D., Tosun, F., Yürükcü, İ., Kekeç, E., Furkan Şahin, M., & Apaydın Kaya, Ç. (2022). Adapta-tion and validation of the Turkish version of the vaccine hesitancy 5 point Likert Scale. Taylor & Francis, 1–7. https://doi.org/10.1080/21645515.2021.1953347
Teo, T., Ursavaş, Ö. F., & Bahçekapili, E. (2011). Efficiency of the technology acceptance model to explain pre-service teachers’ intention to use technology: A Turkish study. Campus-Wide Information Systems, 28(2), 93–101. https://doi.org/10.1108/10650741111117798/FULL/HTML
Utami, I. Q., Fahmiyah, I., Ningrum, R. A., Fakhruzzaman, M. N., Pratama, A. I., & Triangga, Y. M. (2022). Teacher’s ac-ceptance toward cloud-based learning technology in Covid-19 pandemic era. Journal of Computers in Education. https://doi.org/10.1007/S40692-021-00214-8
Vallade, J. I., Kaufmann, R., Frisby, B. N., & Martin, J. C. (2020). Technology acceptance model: investigating students’ intentions toward adoption of immersive 360° videos for public speaking rehearsals. Communication Education, 1–19. https://doi.org/10.1080/03634523.2020.1791351
Venkatesh, V. (2013). Absract - A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. https://www.jstor.org/stable/2634758
Villena-Taranilla, R., Tirado-Olivares, S., Cózar-Gutiérrez, R., & González-Calero, J. A. (2022). Effects of virtual reality on learning outcomes in K-6 education: A meta-analysis. Educational Research Review, 35. https://doi.org/10.1016/j.edurev.2022.100434
Wolf, M., Teizer, J., Wolf, B., Bükrü, S., & Solberg, A. (2022). Investigating hazard recognition in augmented virtuality for personalized feedback in construction safety education and training. Advanced Engineering Informatics, 51. https://doi.org/10.1016/j.aei.2021.101469
Yan, X., Li, T., & Zhou, Y. (2022). Virtual Reality’s Influence on Construction Workers’ Willingness to Participate in Safety Education and Training in China. Journal of Management in Engineering, 38(2). https://doi.org/10.1061/(asce)me.1943-5479.0001002
Yang, F., & Miang Goh, Y. (2022). VR and MR technology for safety management education: An authentic learning ap-proach. Safety Science, 148. https://doi.org/10.1016/j.ssci.2021.105645
Yap, B. W., & Sim, C. H. (2011). Comparisons of various types of normality tests. Journal of Statistical Computation and Simulation, 81(12), 2141–2155. https://doi.org/10.1080/00949655.2010.520163
Yazici, B., & Yolacan, S. (2007). A comparison of various tests of normality. Journal of Statistical Computation and Sim-ulation, 77(2), 175–183. https://doi.org/10.1080/10629360600678310