Abstract
The current status of the 4th industrial revolution has offered some sophisticated technological tools for higher education institutions. One of these technologies is distance learning based on Internet of Things tools and cloud computing, that make the student the main pillar of learning, through engaging in Flipped Learning Model (FLM). For successful IT integration in higher education, designing a distance-learning training platform is vital. Although research has identified the factors that can directly determine the behavioral intention to use technology, little is known about the effects of these factors on the successful design of a distance-training platform. Therefore, this study examines the effects of these factors in determining the successful design and use of IoT-applications through distance learning platform based on FLM for higher education within the context of Oman technological colleges. This would be implemented by developing a conceptual framework in scope of the Unified Theory of Acceptance and Use of Technology (UTAUT), Technology Acceptance Model (TAM), and literature review. Data will be collected from employees at these colleges, and analyzed using partial least squares—structural equation modeling (PLS-SEM), in addition to the Importance-Performance Map Analysis (IPMA).
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References
Ali, M., et al.: IoTFLiP: IoT-based flipped learning platform for medical education. Digit. Commun. Netw. 3(3), 188–194 (2017)
Gilboy, M.B., Heinerichs, S., Pazzaglia, G.: Enhancing student engagement using the flipped classroom. J. Nutr. Educ. Behav. 47(1), 109–114 (2015)
Abowd, G.D., Tech, G.: Beyond Weiser: from ubiquitous to collective computing. Computer 49(1), 17–23 (2016)
Patel, P., Cassou, D.: Enabling high-level application development for the Internet of Things. J. Syst. Softw. 103, 62–84 (2015)
Leal, J.R.D.S.: Explaining organizational adoption of technology: the case of cloud platforms for Internet of Things. Master’s thesis, Dept. Information Systems Science, University of Jyväskylä (2015)
Karen Rose, L.C., Eldridge, S.: The Internet of Things: an overview. The Internet Society, vol. 80, pp. 1–50 (2015)
Cavalcante, E., et al.: On the interplay of Internet of Things and Cloud Computing: a systematic mapping study. Comput. Commun. 89–90, 17–33 (2016)
Davalos, A.G., Marquez, J., Villanueva, J., Solarte, Z.: IoT in education: integration of objects with virtual academic communities. Adv. Intell. Syst. Comput. 445(115), V–VI (2016)
Selinger, M., Sepulveda, A., Buchan, J.: Education and the internet of everything. Int. Bus. Manag. 10(18), 4301–4303 (2016)
Ervin, R.: How Digital Textbooks, Tech-Friendly Furniture, and Better Data Are Boosting Engagement at Community Colleges—EdSurge News. EdSurge (2017) [online]. Available at: https://www.edsurge.com/news/2017-01-30-how-digital-textbooks-tech-friendly-furniture-and-better-data-are-boosting-engagement-at-community-colleges
The Statistics Portal: Internet of Things (IoT) connected devices installed base worldwide from 2015 to 2025 (in billions). © Statista 2018 (2016) [online]. Available at: https://www.statista.com/statistics/471264/iot-number-of-connected-devices-worldwide/
The Cisco Portal: The Internet of Everything in Higher Education (2013) [online]. Available at: http://www.cisco.com/c/dam/en_us/solutions/industries/education/us_education/c ampusconnection_100814_ioe.pdf
Demirer, V., Aydin, B., Çelik, S.B.: Exploring the educational potential of Internet of Things (IoT) in seamless learning. The Internet of Things: Breakthroughs in Research and Practice, pp. 1–15 (2017)
Movahed, H., Bagheri, M.: The effect of the Internet of Things (IoT) on education business model the effect of the Internet of Things (IoT) on education business model. In: 2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), Naples, 2016, pp. 435–441
Satu, S., Roy, S., Akhter, F.: IoLT: an IoT based collaborative blended learning platform in higher education. In: 2018 International Conference on Innovation in Engineering and Technology (ICIET), pp. 1–6 (2018)
Förster, A., Dede, J., Könsgen, A., Udugama, A., Zaman, I.: Teaching the Internet of Things. GetMobile: Mob. Comput. Commun. 20(3), 24–28 (2017)
Lai, C., Hwang, G.: A self-regulated flipped classroom approach to improving students’ learning performance in a mathematics course. Comput. Educ. 100, 126–140 (2016)
Gros, B., LĂłpez, M.: Students as co-creators of technology-rich learning activities in higher education. Int. J. Educ. Technol. High. Educ. 13(28) (2016)
Strawser, M.G. (ed.): New Media and Digital Pedagogy: Enhancing the Twenty-First-Century Classroom (Studies in New Media). Lexington Books (2017)
Veeramanickam, M.R.M., Mohanapriya, M.: IOT enabled Futurus Smart Campus with effective E-learning: i-Campus. GSTF J. Eng. Technol. 3(4), 81–87 (2016)
Gao, L., Bai, X.: A unified perspective on the factors influencing consumer acceptance of internet of things technology. Asia Pac. J. Mark. Logist. 26(2), 211–231 (2014)
Accenture: The Internet of Things: The Future of Consumer Adoption (2014) [online]. Available at: https://www.accenture.com/t20150624T211456__w__/us-en/_acnmedia/Accenture/Conversion-Assets/DotCom/Documents/Global/PDF/Technology_9/Accenture-Internet-Things.pdf
Al-Momani, A.M., Mahmoud, M.A., Ahmad, S.M.: Modeling the adoption of internet of things services: a conceptual framework. Int. J. Appl. Res. 2(5), 361–367 (2016)
Davis, F.D., Bagozzi, R.P., Warshaw, P.R.: User acceptance of computer technology: a comparison of two theoretical models. Manag. Sci. 35(8), 982–1003 (1989)
Venkatesh, V., Morris, M., Davis, G., Davis, F.: User acceptance of information technology: toward a unified view. MIS Q. 27(3), 425–478 (2003)
El-Masri, M., Tarhini, A.: Erratum to: Factors affecting the adoption of e-learning systems in Qatar and USA: extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Educ. Technol. Res. Dev. 65(3), 765–767 (2017). https://doi.org/10.1007/s11423-016-9508-8
Lee, Y.K., Park, J.H., Chung, N., Blakeney, A.: A unified perspective on the factors influencing usage intention toward mobile financial services. J. Bus. Res. 65(11), 1590–1599 (2012)
Long, T.: Development and initial validation of a flipped classroom adoption inventory in higher education. Ph.D. dissertation, Dept. Graduate School, University of Tennessee (2016)
Svendsen, G.B., Johnsen, J.A.K., Almås-Sørensen, L., Vittersø, J.: Personality and technology acceptance: the influence of personality factors on the core constructs of the Technology Acceptance Model. Behav. Inf. Technol. 32(4), 323–334 (2013)
Al-Momani, A.M., Mahmoud, M.A., Ahmad, M.S.: Factors that influence the acceptance of internet of things services by customers of telecommunication companies in Jordan. J. Organ. End User Comput. 30(4), 51–63 (2018)
Alghatrifi, I., Khalid, H.: A systematic review of UTAUT and UTAUT2 as a baseline framework of information system research in adopting new technology: a case study of IPV6 adoption. In: 2019 6th International Conference on Research and Innovation in Information Systems (ICRIIS), Johor Bahru, Malaysia, 2019, pp. 1–6. https://doi.org/10.1109/icriis48246.2019.9073292
Marchewka, J., Liu, C., Kostiwa, K.: An application of the UTAUT model for understanding student perceptions using course management software. Commun. IIMA 7(2), 93 (2007)
Venkatesh, V., Thong, J., Xu, X.: Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Q. 36(1), 157–178 (2012)
Rogers, E.M.: Diffusion of Innovations, 5th edn. Simon and Schuster, Canada (2003)
Al-Qeisi, K., Dennis, C., Alamanos, E., Jayawardhena, C.: Website design quality and usage behavior: Unified theory of acceptance and use of technology. J. Bus. Res. 67(11), 2282–2290 (2014)
Zuiderwijk, A., Janssen, M., Dwivedi, Y.K.: Acceptance and use predictors of open data technologies: drawing upon the unified theory of acceptance and use of technology. Gov. Inf. Q. 32(4), 429–440 (2015)
Mutlu, H.M., Der, A.: Unified theory of acceptance and use of technology: the adoption of mobile messaging application. Megatrend Rev. 14(1), 169–186 (2017)
Davis, F.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13(3), 319–340 (1989)
Sanny, L.: Analysis of online purchase behavior intention in SME in Indonesia. In: 2017 3rd International Conference on Information Management (ICIM), Chengdu, pp. 6–10 (2017)
Chong, A.Y.L., Chan, F.T.S., Ooi, K.B.: Predicting consumer decisions to adopt mobile commerce: cross country empirical examination between China and Malaysia. Decis. Support Syst. 53(1), 34–43 (2012)
Taherdoost, H.: A review of technology acceptance and adoption models and theories. Procedia Manuf. 22, 960–967 (2018)
Al Mansoori, K.A.: Use of a modified UTAUT model to investigate Emirati Citizens’ adoption of e-Government in Abu Dhabi. Ph.D. dissertation, Dept. Faculty of Business, University of Wollongong in Dubai (2017)
Gupta, A., Dogra, N.: Tourist adoption of mapping apps: a UTAUT2 perspective of smart travellers. Tour. Hosp. Manag. 23(2), 145–161 (2017)
Han, B., Wu, Y., Windsor, J.: User’s adoption of free third-party security apps. J. Comput. Inf. Syst. 54(3), 77–86 (2014)
Al-Emran, M., Salloum, S.A.: Students’ attitudes towards the use of mobile technologies in e-Evaluation. Int. J. Interact. Mob. Technol. 11(5), 195–202 (2017)
Al-Emran, M., Malik, S.I.: The impact of Google apps at work: higher educational perspective. Int. J. Interact. Mob. Technol. 10(4), 85–88 (2016)
Hair, J.F., Hult, G.T.M., Ringle, C.M., Sarstedt, M.: A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). SAGE, Inc., Thousand Oaks (2017)
Hair, J.F., Sarstedt, M., Hopkins, L., Kuppelwieser, V.G.: Partial least squares structural equation modeling (PLS-SEM): an emerging tool in business research. Eur. Bus. Rev. 26(2), 106–121 (2014)
Whitehead, A.L., Julious, S.A., Cooper, C.L., Campbell, M.J.: Estimating the sample size for a pilot randomised trial to minimise the overall trial sample size for the external pilot and main trial for a continuous outcome variable. Stat. Methods Med. Res. 25(3), 1057–1073 (2015)
Chin, W.W.: The partial least squares approach to structural equation modeling. Mod. Methods Bus. Res. 295(2), 295–336 (1998)
Morrison, L.C.L.M.K.: Research Methods in Education, 6th edn. Taylor & Francis Group, New York (2012)
Cooper, D.R., Wittenberg, P.S.S.: Business Research Methods, 12th edn. McGraw-Hill Education, New York (2013)
Garson, G.D.: Partial Least Squares: Regression & Structural Equation Models. Statistical Associates Publishers, Asheboro, NC (2016)
Saunders, M., Lewis, P., Thornhill, A.: Research Methods for Business Students, 5th edn. Pearson, London (2009)
Babbie, E.R.: The Basics of Social Research, 6th edn. Cengage Learning, Belmont, CA (2013)
Henseler, J., Ringle, C.M., Sarstedt, M.: A new criterion for assessing discriminant validity in variance-based structural equation modeling. J. Acad. Mark. Sci. 43(1), 115–135 (2015)
Fritz, M.S., Mackinnon, D.P.: Required sample size to detect the mediated effect. Psychol. Sci. 18(3), 233–239 (2010)
Preacher, K.J., Hayes, A.F.: Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behav. Res. Methods 40(3), 879–891 (2008)
Ringle, C.M., Sarstedt, M.: Gain more insight from your PLS-SEM results the importance-performance map analysis. Ind. Manag. Data Syst. 116(9), 1865–1886 (2016)
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Al-Musawi, A.S., Alghatrifi, I. (2021). Examining the Factors Affecting the Adoption of IoT Platform Services Based on Flipped Learning Model in Higher Education. In: Al-Emran, M., Shaalan, K. (eds) Recent Advances in Technology Acceptance Models and Theories. Studies in Systems, Decision and Control, vol 335. Springer, Cham. https://doi.org/10.1007/978-3-030-64987-6_9
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