Abu-Al-Aish, A., & Love, S. (2013). Factors influencing students’ acceptance of m-learning: An investigation in higher education. International Review of Research in Open and Distributed Learning, 14, 82–107.
Article
Google Scholar
Alharbi, O., Alotebi, H., Masmali, A., & Alreshidi, N. (2017). Instructor acceptance of mobile learning in Saudi Arabia: A case study of Hail University. International Journal of Business and Management, 12, 27–35.
Article
Google Scholar
Ali, R. A., & Arshad, M. R. M. (2016). Perspectives of students’ behavior towards mobile learning (M-learning) in Egypt: An extension of the UTAUT model. Engineering, Technology & Applied Science Research, 6, 1109–1114.
Article
Google Scholar
Almaiah, M. A., Jalil, M. A., & Man, M. (2016). Extending the TAM to examine the effects of quality features on mobile learning acceptance. Journal of Computers in Education, 3, 453–485.
Article
Google Scholar
Althunibat, A. (2015). Determining the factors influencing students’ intention to use M-learning in Jordan higher education. Computers in Human Behavior, 52, 65–71.
Article
Google Scholar
Annoni, P., & Kozovska, K. (2010). EU regional competitiveness index 2010. European Commission, Joint Research Centre.
Google Scholar
Bourdieu, P. (1984). Distinction: A social critique of the judgement of taste. Harvard University Press.
Google Scholar
Briz-Ponce, L., Pereira, A., Carvalho, L., Juanes-Méndez, J. A., & García-Peñalvo, F. J. (2017). Learning with mobile technologies—Students’ behavior. Computers in Human Behavior, 72, 612–620.
Article
Google Scholar
Bruce, B. C., & Levin, J. A. (1997). Educational technology: Media for inquiry, communication, construction, and expression. Journal of Educational Computing Research, 17, 79–102.
Article
Google Scholar
Chai, C. S., Lin, P.-Y., Jong, M.S.-Y., Dai, Y., Chiu, T. K., & Qin, J. (2021). Perceptions of and behavioral intentions towards learning artificial intelligence in primary school students. Educational Technology & Society, 24, 89–101.
Google Scholar
Chang, T.-Y., Hsu, M.-L., Kwon, J.-S., Kusdhany, M. L. S., & Hong, G. (2021). Effect of online learning for dental education in Asia during the pandemic of COVID-19. Journal of Dental Sciences, 16, 1095–1101.
Article
Google Scholar
Chau, P. Y., & Hu, P. J. H. (2001). Information technology acceptance by individual professionals: A model comparison approach. Decision Sciences, 32, 699–719.
Article
Google Scholar
Cheng, Y. M. (2012). Effects of quality antecedents on e-learning acceptance. Internet Research: Electronic Networking Applications and Policy, 22(3), 361–390.
Article
Google Scholar
Cheung, R. (2014). Predicting user intentions for mobile learning in a project-based environment. International Journal of Electronic Commerce Studies, 4, 263–280.
Article
Google Scholar
Cheung, R., & Vogel, D. (2013). Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Computers & Education, 63, 160–175.
Article
Google Scholar
Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295, 295–336.
Google Scholar
Cho, V., Cheng, T. E., & Lai, W. J. (2009). The role of perceived user-interface design in continued usage intention of self-paced e-learning tools. Computers & Education, 53, 216–227.
Article
Google Scholar
Churchill, D., Fox, B., & King, M. (2016). Framework for designing mobile learning environments. Springer.
Book
Google Scholar
Cook, D. J. (2010). Learning setting-generalized activity models for smart spaces. IEEE Intelligent Systems, 2010, 1.
Google Scholar
Daniel, J. (2012). Making sense of MOOCs: Musings in a maze of myth, paradox and possibility. Journal of Interactive Media in Education, 2012(3), 1–20.
Article
Google Scholar
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 319–340.
Article
Google Scholar
Davis, F. D. (1993). User acceptance of information technology: System characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies, 38, 475–487.
Article
Google Scholar
Du, S., Bhattacharya, C., & Sen, S. (2015). Corporate social responsibility, multi-faceted job-products, and employee outcomes. Journal of Business Ethics, 131, 319–335.
Article
Google Scholar
Fayez, A. N., Ghabban, F. M., & Ameerbakhsh, O. (2021). Advantages and challenges of smart learning in higher education institutions in Saudi Arabia. Creative Education, 12, 974–982.
Article
Google Scholar
Field, A. (2009). Discovering statistics using SPSS: (And sex and drugs and rock’n’roll). Sage.
Google Scholar
Fornell, C., & Bookstein, F. L. (1982). Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Two Structural Equation Models: LISREL and PLS Applied to Consumer Exit-Voice Theory., 19, 440–452.
Google Scholar
Gefen, D., & Straub, D. (2005). A practical guide to factorial validity using PLS-Graph: Tutorial and annotated example. Communications of the Association for Information Systems, 16, 5.
Article
Google Scholar
Goodman, S. (2003). Teaching youth media: A critical guide to literacy, video production & social change. Teachers College Press.
Google Scholar
Gwak, D. (2010). The meaning and predict of smart learning. In Smart Learning Korea Proceeding, Korean e-Learning Industry Association.
Ha, C., & Lee, S.-Y. (2019). Elementary teachers’ beliefs and perspectives related to smart learning in South Korea. Smart Learning Environments, 6, 1–15.
Article
Google Scholar
Ha, S., & Stoel, L. J. J. O. B. R. (2009). Consumer e-shopping acceptance: Antecedents in a technology acceptance model. Journal of Business Research, 62, 565–571.
Article
Google Scholar
Hair, J. F., Jr., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.
Google Scholar
Hamidi, H., & Chavoshi, A. (2018). Analysis of the essential factors for the adoption of mobile learning in higher education: A case study of students of the University of Technology. Telematics and Informatics, 35, 1053–1070.
Article
Google Scholar
Hao, S., Dennen, V. P., & Mei, L. (2017). Influential factors for mobile learning acceptance among Chinese users. Educational Technology Research and Development, 65, 101–123.
Article
Google Scholar
Hasan, B. (2007). Examining the effects of computer self-efficacy and system complexity on technology acceptance. Information Resources Management Journal (IRMJ), 20, 76–88.
Article
Google Scholar
Hassanzadeh, A., Kanaani, F., & Elahi, S. (2012). A model for measuring e-learning systems success in universities. Expert Systems with Applications, 39, 10959–10966.
Article
Google Scholar
Higginson, S., Mckenna, E., Hargreaves, T., Chilvers, J., & Thomson, M. (2015). Diagramming social practice theory: An interdisciplinary experiment exploring practices as networks. Indoor and Built Environment, 24, 950–969.
Article
Google Scholar
Huang, L.-Y., & Hsieh, Y.-J. (2012). Consumer electronics acceptance based on innovation attributes and switching costs: The case of e-book readers. Electronic Commerce Research and Applications, 11, 218–228.
Article
Google Scholar
Hubert, M., Blut, M., Brock, C., Backhaus, C., & Eberhardt, T. (2017). Acceptance of smartphone-based mobile shopping: Mobile benefits, customer characteristics, perceived risks, and the impact of application context. Psychology & Marketing, 34, 175–194.
Article
Google Scholar
Hwang, G.-J. (2014). Definition, framework and research issues of smart learning environments—A context-aware ubiquitous learning perspective. Smart Learning Environments, 1, 4.
Article
Google Scholar
Hwang, G.-J., Tsai, C.-C., & Yang, S. J. (2008). Criteria, strategies and research issues of context-aware ubiquitous learning. Journal of Educational Technology & Society, 11, 81–91.
Google Scholar
Iqbal, S., & Qureshi, I. A. (2012). M-learning adoption: A perspective from a developing country. International Review of Research in Open and Distributed Learning, 13, 147–164.
Article
Google Scholar
Jandrić, P., Hayes, D., Truelove, I., & Levinson, P. (2020) Teaching in the age of Covid-19. Postdigital Science and Education, 2, 1069–1230.
Joo, Y. J., Lee, H. W., & Ham, Y. (2014). Integrating user interface and personal innovativeness into the TAM for mobile learning in Cyber University. Journal of Computing in Higher Education, 26, 143–158.
Article
Google Scholar
Khan, M. S. H., Abdou, B. O., Kettunen, J., & Gregory, S. (2019). A phenomenographic research study of students’ conceptions of mobile learning: An example from higher education. SAGE Open, 9, 2158244019861457.
Google Scholar
Kim, T., Cho, J. Y., & Lee, B. G. (2012). Evolution to smart learning in public education: a case study of Korean public education. In IFIP WG 3.4 international conference on open and social technologies for networked learning (pp. 170–178). Springer.
Kim, D.-G., Lee, H.-C., Rhee, Y.-W., & Shin, S.-Y. (2016). Instructor’s smart learning acceptance: Focusing on TAM model. Journal of the Korea Institute of Information and Communication Engineering, 20, 1081–1086.
Article
Google Scholar
Kim, H.-J., Lee, J.-M., & Rha, J.-Y. (2017). Understanding the role of user resistance on mobile learning usage among university students. Computers & Education, 113, 108–118.
Article
Google Scholar
Kurz, T., Gardner, B., Verplanken, B., & Abraham, C. (2015). Habitual behaviors or patterns of practice? Explaining and changing repetitive climate-relevant actions. Wiley Interdisciplinary Reviews: Climate Change, 6, 113–128.
Google Scholar
Lin, S. H., Lee, H.-C., Chang, C.-T., & Fu, C. J. (2020). Behavioral intention towards mobile learning in Taiwan, China, Indonesia, and Vietnam. Technology in Society, 63, 101387.
Article
Google Scholar
Lorenzo, N., & Gallon, R. (2019). Smart pedagogy for smart learning. Springer.
Book
Google Scholar
Marinova, D., de Ruyter, K., Huang, M.-H., Meuter, M. L., & Challagalla, G. (2017). Getting smart: Learning from technology-empowered frontline interactions. Journal of Service Research, 20, 29–42.
Article
Google Scholar
Marsden, G., Mullen, C., Bache, I., Bartle, I., & Flinders, M. (2014). Carbon reduction and travel behaviour: Discourses, disputes and contradictions in governance. Transport Policy, 35, 71–78.
Article
Google Scholar
Meyer, B., & Latham, N. J. (2008). Implementing electronic portfolios: Benefits, challenges, and suggestions. Educause Quarterly, 31, 34.
Google Scholar
Middleton, A. (2015). Smart learning: Teaching and learning with smartphones and tablets in post-compulsory education. Media-enhanced learning special interest group and Sheffield Hallam.
Google Scholar
Milošević, I., Živković, D., Manasijević, D., & Nikolić, D. (2015). The effects of the intended behavior of students in the use of M-learning. Computers in Human Behavior, 51, 207–215.
Article
Google Scholar
Mohammadi, H. (2015). Social and individual antecedents of M-learning adoption in Iran. Computers in Human Behavior, 49, 191–207.
Article
Google Scholar
Moore, G. (1991). Crossing the Chasm: Marketing and selling high-tech goods to mainstream customers. Harper Business.
Google Scholar
Presti, A. L., de Rosa, A., & Viceconte, E. (2021). I want to learn more! Integrating technology acceptance and task-technology fit models for predicting behavioural and future learning intentions. Journal of Workplace Learning. https://doi.org/10.1108/JWL-11-2020-0179
Article
Google Scholar
Putnik, Z. (2016). Mobile learning, student concerns and attitudes. Springer.
Book
Google Scholar
Reckwitz, A. (2002). Toward a theory of social practices: A development in culturalist theorizing. European Journal of Social Theory, 5, 243–263.
Article
Google Scholar
Rettie, R., Burchell, K., & Riley, D. (2012). Normalising green behaviours: A new approach to sustainability marketing. Journal of Marketing Management, 28, 420–444.
Article
Google Scholar
Rossi, A. (2014). How American universities turned into corporations.
Sabah, N. M. (2016). Exploring students’ awareness and perceptions: Influencing factors and individual differences driving M-learning adoption. Computers in Human Behavior, 65, 522–533.
Article
Google Scholar
Scott, K., & Benlamri, R. (2010). Context-aware services for smart learning spaces. IEEE Transactions on Learning Technologies, 3, 214–227.
Article
Google Scholar
Shah, S. K., & Zhongjun, T. (2021). Elaborating on the consumer’s intention–behavior gap regarding 5G technology: The moderating role of the product market-creation ability. Technology in Society, 66, 101657.
Article
Google Scholar
Shah, S. K., Zhongjun, T., Sattar, A., & Xinhao, Z. (2021). Consumer’s intention to purchase 5G: Do environmental awareness, environmental knowledge and health consciousness attitude matter? Technology in Society, 65, 101563.
Article
Google Scholar
Shilling, C. J. S. (1991). Educating the body: Physical capital and the production of social inequalities. Sociology, 25, 653–672.
Article
Google Scholar
Shove, E., & Pantzar, M. (2005). Consumers, producers and practices: Understanding the invention and reinvention of Nordic walking. Journal of Consumer Culture, 5, 43–64.
Article
Google Scholar
Shove, E., Pantzar, M., & Watson, M. (2012). The dynamics of social practice: Everyday life and how it changes. Sage Publications.
Book
Google Scholar
Sinclair, J., Kriskova, A., & Aho, A.-M. (2021). Innovation in ICT Course Provision: Meeting stakeholders’ needs. In International workshop on learning technology for education challenges (pp. 197–207). Springer.
Southerton, D. (2003). Squeezing time’ allocating practices, coordinating networks and scheduling society. Time & Society, 12, 5–25.
Google Scholar
Spotswood, F., Chatterton, T., Tapp, A., & Williams, D. (2015). Analysing cycling as a social practice: An empirical grounding for behaviour change. Transportation Research Part F: Traffic Psychology and Behaviour, 29, 22–33.
Article
Google Scholar
Sunarto, M. (2021). Change unplanned into planned online learning: An effort to follow health protocols at an information technology college during the Covid-19 pandemic period. Studies in Learning and Teaching, 2, 16–27.
Article
Google Scholar
Teräs, M., Suoranta, J., Teräs, H., & Curcher, M. (2020). Post-Covid-19 education and education technology ‘solutionism’: A seller’s market. Postdigital Science and Education, 2, 863–878.
Article
Google Scholar
Trikoilis, D. (2021). ICT implementation to improve rural students’ achievement in physics. European Journal of Physics Education, 12, 22–33.
Google Scholar
Ullah, N., Mugahed Al-Rahmi, W., Alzahrani, A. I., Alfarraj, O., & Alblehai, F. M. (2021). Blockchain technology adoption in smart learning environments. Sustainability, 13, 1801.
Article
Google Scholar
Urbach, N., & Ahlemann, F. (2010). Structural equation modeling in information systems research using partial least squares. Journal of Information Technology Theory and Application, 11(2), 5–40.
Google Scholar
Uwantege, A., Kituyi, A., Oyebimpe, A., & Mugiraneza, F. (2021). Students’ attitude and smart learning in public secondary schools of Bugesera District-Rwanda. Journal of Education, 4, 23–44.
Google Scholar
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27, 425–478.
Article
Google Scholar
Wang, S., Fan, J., Zhao, D., Yang, S., & Fu, Y. (2016). Predicting consumers’ intention to adopt hybrid electric vehicles: Using an extended version of the theory of planned behavior model. Transportation, 43, 123–143.
Article
Google Scholar
Warde, A. (2005). Consumption and theories of practice. Journal of Consumer Culture, 5, 131–153.
Article
Google Scholar
Winthrop, R., Mcgivney, E., Williams, T. P., & Shankar, P. (2016). Innovation and technology to accelerate progress in education: Report to the International Commission on Financing Global Education Opportunity. Background Paper, The Learning Generation.