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The purposes of this research were to 1) develop the structural equation model of the actual use of cloud learning for higher education students in the 21st century (SEMAC), 2) investigate the validity of the SEMAC, and 3) study the effects of the SEMAC. This study was a correlation research. The research sample consisted of 1,170 undergraduate students, randomly selected using multi-staging, from 18 universities in Thailand. The research instruments were questionnaires about system quality, convenience, social interaction, perceived ease of use, perceived usefulness, and actual use. Data analyses were descriptive statistics and the analysis for model validation used LISREL 9.2. The study found that the validation of the structural equation model indicating actual use of cloud learning showed that the model fit to the empirical data (χ2 = 34.659 df = 23 p = .056 GFI = .989 AGFI = .974 RMR = .006). The variables in the structural equation model could explain 62.4 of the variance in actual use. The research results can be used as data to improve the actual use of cloud learning.
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- Abbas, T., Mahmonir, B. (2013). Ranking Information System Success Factors in Mobile Banking Systems with VIKOR. Middle-East Journal of Scientific Research, 13(11), 1515–25.
- Alshibly, H. (2014). An empirical investigation into factors influencing the intention to use e-learning system: An extended technology acceptance model. British Journal of Applied Science & Technology, 4(17), 2440.
- Alzu’Bi, S. K., Hassan, S. (2016). Factor Affecting the Success of Mobile Learning Implementation: A Study ofjordanian Universities. Asian Journal of Information Technology, 15(1), 113–121.
- Bagci, K., Celik, H. E. (2018). Examination of Factors Affecting Continuance Intention to use Web-Based Distance Learning System via Structural Equation Modelling. Eurasian Journal of Educational Research, 18(78), 43-66.
- Bertrand, M., Bouchard, S. (2008). Applying the technology acceptance model to VR with people who are favorable to its us. Journal of Cyber Therapy & Rehabilitation, 1(2), 200-207.
- Buyya, R., Pandey, S., & Vecchiola, C. (2012). Market-oriented cloud computing and the cloudbus toolkit. arXiv preprint arXiv:1203.5196.
- Calisir, F., Altin Gumussoy, C., Bayraktaroglu, A. E., & Karaali, D. (2014). Predicting the intention to use a web‐based learning system: Perceived content quality, anxiety, perceived system quality, image, and the technology acceptance model. Human Factors and Ergonomics in Manufacturing & Service Industries, 24(5), 515-531.
- Chang, L. C., Chiang, H. K. (2012). Designing a mixed digital signage and multi-touch interaction for social learning. In Transactions on Edutainment VIII: Springer Berlin Heidelberg, 77-87.
- Chang, C. C., Tseng, K. H., Liang, C., & Yan, C. F. (2013). The influence of perceived convenience and curiosity on continuance intention in mobile English learning for high school students using PDAs. Technology, Pedagogy and Education, 22(3), 373-386.
- Chanin Thitipetchakul, Narong Sompong and Nattaphon Rampai. (2020). Development of Learning Management Model on Cloud Computing System Based on Connectivism to Enhance Information and Communication Technology Literacy for Undergraduate Students. Ratchaphruek Journal, 18(1), 38-48.
- Chtourou S., Souiden, N. (2010). Rethinking the TAM model: time to consider fun. Journal of Consumer Marketing, 27(4), 336-344.
- Chinyamurindi, W. T., Mahembe, B., Chimucheka, T., & Rungani, E. (2017). Factors influencing student usage of an online learning community: the case of a rural South African university. International Journal of Education Economics and Development, 8(2/3), 116-132.
- Davis, F.D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results. Doctoral Dissertation, Massachusetts Institute of Technology.
- Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 13(3), 319-340.
- Davis, F. D., Bogozzi, R., P., & Warshaw, P., R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 135, 982-1003.
- DeLone, W. H., McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Information systems research, 3(1), 60-95.
- Essam, S., Al-Ammary, J. (2013). The impact of motivation and social interaction on the e-learning at Arab Open University, Kingdom of Bahrain. Creative Education, 4(10), 21.
- Elkaseh, A. M., Wong, K. W., & Fung, C. C. (2016). Perceived ease of use and perceived usefulness of social media for e-learning in Libyan higher education: A structural equation modeling analysis. International Journal of Information and Education Technology, 6(3), 192.
- Grand-Clement, S. (2017). Digital Learning: Education and Skills in the Digital Age. RAND Europe.
- Hsu, H. H., & Chang, Y. Y. (2013). Extended TAM model: Impacts of convenience on acceptance and use of Moodle. Online Submission, 3(4), 211-218.
- Heng, H., Xia, W., Jin, Y., Zhang, B., & Tian, P. (2016). Innovation of teaching methods in university based on mobile cloud computing. World Transactions on Engineering and Technology Education, 14(1), 208-213.
- Jenny, W.A.N.G. (2017). Cloud Computing Technologies in Writing Class: Factors Influencing Students’ Learning Experience. Turkish Online Journal of Distance Education, 18(3), 197-213.
- Mac Callum, K., Jeffrey, L., & Kinshuk, K., (2014). Comparing the role of ICT literacy and anxiety in the adoption of mobile learning. Computers in Human Behavior, 39, 8-19.
- Mooc-Maker (2016). The Application of Cloud-Based Tools in: Experiences and Findings. http://www.mooc-maker.org/wp-content/files/WDP10_Final.pdf
- Mohammad, O. K. J. (2018). Recent Trends of Cloud Computing Applications and Services in Medical, Educational, Financial, Library and Agricultural Disciplines. https://dl.acm.org/doi/pdf/10.1145/3233347.3233388
- Ministry of Information and Communication Technology (2016). Thailand Digital Economy and Society Development Plan2016 – 2037. https://www.dga.or.th/upload/download/file_9fa5ae40143e13a6503388d226efd8.pdf
- Office of the Higher Education Commission. (2016). 12th Higher Education Development Plan: 2017 - 2021https://goo.gl/vi7sRC
- Park, S. Y. (2009). An Analysis of the Technology Acceptance Model in Understanding University Students' Behavioral Intention to Use e-Learning. Educational Technology & Society, 12(3), 150–162.
- Rummel, R. J. (1976). Understanding conflict and war: vol. 2: the conflict helix. Beverly Hills: Sage.
- Sathaporn Yoosomboon (2014). Enterprise Resource Planning on Cloud Computing Journal of Vocational and Technical Education. 4(8).
- The Economist Intelligence Unit Limited. (2016). Ascending cloud The adoption of cloud computing in five industries. https://eiuperspectives.economist.com/sites/default/files/EIU_AscendingcloudMBP_PDF_1.pdf
- Tripathi, S. (2018). Moderating effects of age and experience on the factors influencing the actual usage of cloud computing. Journal of International Technology and Information Management, 27(2), 121-158.
- Urbach, N. and Müller, B. (2012). The updated DeLone and McLean model of information systems success. In Information systems theory. Springer New York.
- Yamin, M. (2019). Information technologies of 21st century and their impact on the society. International Journal of Information Technology, 11(4), 759-766.
- Yung Ming Cheng. (2015). Towards an understanding of the factors affecting m-learning acceptance: Roles of technological characteristics and compatibility. Asia Pacific Management Review, 20(3), 109-119.
- Wikipedia. (2019). Convenience. https://en.wikipedia.org/wiki/Convenience
- Wikipedia. (2019). Social interaction.
- WP. (2020). COVID-19’ reforms education around the world! Using new learning technology 'Thai universities' teach online. https://www.marketingoops.com/exclusive/business -case/covid-19-reinvent-global-education-system-with-educational-technology/?fbclid=technology/?f bclid=IwAR2N-NX4DGCEM-4Fli1fQyxC2kZVuP ykfeRZydcSaSGqOWvs8XFSrhxLRDI