Abstract
Recently, the education sector has received increased interest in terms of the adoption of Internet of things (IoT) services for learning activities. However, despite this interest, empirical studies on the adoption of IoT services in the smart classroom are limited. Thus, the present study examines students’ intention to use IoT services in the smart classroom. Towards this end, an integrated model based on the technology acceptance model (TAM), technology readiness index (TRI), and external factors (enjoyment, compatibility, and self-efficacy) is proposed. A quantitative research design was therefore used to determine the factors that affect students’ intention to use IoT services in the smart classroom, using a sample of 230 participants. The findings showed that compatibility, discomfort, enjoyment, and self-efficacy had a significant influence on both perceived ease of use (PEoU) and perceived usefulness (PU). Furthermore, innovativeness had a significant effect on PEoU, and insecurity had a significant impact on PU. The results also revealed that PU had a significant influence on the students’ behavioural intention to use. These findings extend the understanding of students’ intention to use IoT services in the smart classroom. This study could be beneficial to researchers, educators, and IoT developers. However, it also presents a number of limitations, such as a lack of qualitative methods and the small number of theories applied.
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The datasets generated during and/or analysed during the current study are available from the corresponding author upon reasonable request.
References
Abd Majid, F., & Mohd Shamsudin, N. (2019). Identifying factors affecting acceptance of virtual reality in classrooms based on Technology Acceptance Model (TAM). Asian Journal of University Education, 15(2), 1–10. https://doi.org/10.24191/ajue.v15i2.7556
Agustina, R., Suprianto, D., & Ariyanto, R. (2021, November). Technology Acceptance Model Analysis of User Behavioral Intentions on IoT Smart Board Devices. In 2021 1st Conference on Online Teaching for Mobile Education (OT4ME) (pp. 89–92). IEEE. https://doi.org/10.1109/OT4ME53559.2021.9638937
Ahmed, W., Hizam, S. M., Sentosa, I., Akter, H., Yafi, E., & Ali, J. (2020). Predicting IoT service adoption towards smart mobility in Malaysia: SEM-neural hybrid pilot study. arXiv preprint arXiv:2002.00152, 11(1), https://doi.org/10.48550/arXiv.2002.00152
Al Kurdi, B., Alshurideh, M., & Salloum, S. A. (2020). Investigating a theoretical framework for e-learning technology acceptance. International Journal of Electrical and Computer Engineering (IJECE), 10(6), 6484–6496. https://doi.org/10.11591/ijece.v10i6.pp6484-6496
Alhasan, A., Audah, L., Alhadithi, O. S., & Alwan, M. H. (2019). Quality of service mechanisms in internet of things: A comprehensive survey. Journal of Advanced Research in Dynamical and Control Systems, 11(2), 858–875. https://www.jardcs.org/abstract.php?id=378.
Alhasan, A., Audah, L., Alwan, M. H., & Alobaidi, O. R. (2021). An energy aware qos trust model for energy consumption enhancement based on clusters for IoT networks. Journal of Engineering Science and Technology, 16(2), 968–987.
Alhasan, A., Audah, L., Ibrahim, I., Al-Sharaa, A., Al-Ogaili, A. S., & Mohammed, J. M. (2020). A case-study to examine doctors’ intentions to use IoT healthcare devices in Iraq during COVID-19 pandemic. International Journal of Pervasive Computing and Communications, 18(5), 527–547. https://doi.org/10.1108/IJPCC-10-2020-0175
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. https://doi.org/10.1007/978-3-030-64987-6_9
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411. https://doi.org/10.1037/0033-2909.103.3.411
Ashrafi, A., Zareravasan, A., Rabiee Savoji, S., & Amani, M. (2020). Exploring factors influencing students’ continuance intention to use the learning management system (LMS): A multi-perspective framework. Interactive Learning Environments, 1–23,. https://doi.org/10.1080/10494820.2020.1734028
Ashton, K. (2009). That ‘internet of things’ thing. RFID Journal, 22(7), 97–114.
Babakus, E., & Mangold, W. G. (1992). Adapting the SERVQUAL scale to hospital services: An empirical investigation. Health Services Research, 26(6), 767.
Bannigan, K., & Watson, R. (2009). Reliability and validity in a nutshell. Journal of Clinical Nursing, 18(23), 3237–3243. https://doi.org/10.1111/j.1365-2702.2009.02939.x
Benden, M., Mehta, R., Pickens, A., Harp, B., Smith, M. L., Towne, S. D., & Peres, S. C. (2021). Health-related consequences of the type and utilization rates of electronic devices by college students. BMC Public Health, 21(1), 1–12. https://doi.org/10.1186/s12889-021-11975-3
Brar, P. S., Shah, B., Singh, J., Ali, F., & Kwak, D. (2022). Using Modified Technology Acceptance Model to Evaluate the Adoption of a Proposed IoT-Based Indoor Disaster Management Software Tool by Rescue Workers. Sensors, 22(5), 1866. https://doi.org/10.3390/s22051866
Caison, A., Bulman, D., Pai, S., & Neville, D. (2008). Exploring the technology readiness of nursing and medical students at a Canadian University. Journal of Interprofessional Care, 22(3), 283–294. https://doi.org/10.1080/13561820802061809
Chen, C. C., & Tsai, J. L. (2019). Determinants of behavioral intention to use the Personalized Location-based Mobile Tourism Application: An empirical study by integrating TAM with ISSM. Future Generation Computer Systems, 96, 628–638. https://doi.org/10.1016/j.future.2017.02.028
da Chen, L., Gillenson, M. L., & Sherrell, D. L. (2002). Enticing online consumers: An extended technology acceptance perspective. Information and Management, 39(8), 705–719. https://doi.org/10.1016/S0378-7206(01)00127-6
Cheng, Y. M. (2011). Antecedents and consequences of e-learning acceptance. Information Systems Journal, 21(3), 269–299. https://doi.org/10.1111/j.1365-2575.2010.00356.x
Choi, J., & Kim, S. (2016). Is the smartwatch an IT product or a fashion product? A study on factors affecting the intention to use smartwatches. Computers in Human Behavior, 63, 777–786. https://doi.org/10.1016/j.chb.2016.06.007
Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189–211. https://doi.org/10.2307/249688
Comrey, A. L., & Lee, H. B. (2013). A first course in factor analysis. Psychology Press. https://doi.org/10.4324/9781315827506
Crespo, Á.H., de los Salmones Sánchez, M.M.G., del Bosque, I.R. (2013). Influence of Users’ Perceived Compatibility and Their Prior Experience on B2C e-Commerce Acceptance. In: Matsuo, T., Colomo-Palacios, R. (Eds.), Electronic Business and Marketing. Studies in Computational Intelligence (vol 484). Springer. https://doi.org/10.1007/978-3-642-37932-1_8
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace 1. Journal of Applied Social Psychology, 22(14), 1111–1132. https://doi.org/10.1111/j.1559-1816.1992.tb00945.x
El Alfy, S., Gómez, J. M., & Ivanov, D. (2017). Exploring instructors’ technology readiness, attitudes and behavioral intentions towards e-learning technologies in Egypt and United Arab Emirates. Education and Information Technologies, 22(5), 2605–2627. https://doi.org/10.1007/s10639-016-9562-1
Eraslan Yalcin, M., & Kutlu, B. (2019). Examination of students’ acceptance of and intention to use learning management systems using extended TAM. British Journal of Educational Technology, 50(5), 2414–2432. https://doi.org/10.1111/bjet.12798
Ferguson, C. J. (2016). An effect size primer: a guide for clinicians and researchers. In A. E. Kazdin (Ed.), Methodological issues and strategies in clinical research (pp. 301–310). American Psychological Association. https://doi.org/10.1037/14805-020
Fernández-Caramés, T. M., & Fraga-Lamas, P. (2019). Towards next generation teaching, learning, and context-aware applications for higher education: A review on blockchain, IoT, fog and edge computing enabled smart campuses and universities. Applied Sciences, 9(21), 4479. https://doi.org/10.3390/app9214479
Fishbein, M., & Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley series in social psychology, United States,10(2).
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
Gao, L., & Bai, X. (2014). A unified perspective on the factors influencing consumer acceptance of internet of things technology. Asia Pacific Journal of Marketing and Logistics, 26(2), 211–231. https://doi.org/10.1108/APJML-06-2013-0061
Godoe, P., & Johansen, T. (2012). Understanding adoption of new technologies: Technology readiness and technology acceptance as an integrated concept. Journal of European psychology students, 3(1), 38–53. https://doi.org/10.5334/jeps.aq
Gómez, J., Huete, J. F., Hoyos, O., Perez, L., & Grigori, D. (2013). Interaction system based on Internet of things as support for education. Procedia Computer Science, 21, 132–139. https://doi.org/10.1016/j.procs.2013.09.019
Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572–2593. https://doi.org/10.1111/bjet.12864
Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2010) Multivariate data analysis: A global perspective, 7th edn. Pearson Education.
Hall, A. C., Lineweaver, T. T., Hogan, E. E., & O’Brien, S. W. (2020). On or off task: The negative influence of laptops on neighboring students’ learning depends on how they are used. Computers & Education, 153, 103901. https://doi.org/10.1016/j.compedu.2020.103901
Holden, H., & Rada, R. (2011). Understanding the influence of perceived usability and technology self-efficacy on teachers’ technology acceptance. Journal of Research on Technology in Education, 43(4), 343–367. https://doi.org/10.1080/15391523.2011.10782576
Huang, F., & Teo, T. (2021). Examining the role of technology-related policy and constructivist teaching belief on English teachers’ technology acceptance: A study in Chinese universities. British Journal of Educational Technology, 52(1), 441–460. https://doi.org/10.1111/bjet.13027
Hung, S. W., & Cheng, M. J. (2013). Are you ready for knowledge sharing? An empirical study of virtual communities. Computers and Education, 62, 8–17. https://doi.org/10.1016/j.compedu.2012.09.017
Hussein, M. H., Ow, S. H., Ibrahim, I., & Mahmoud, M. A. (2020). Measuring instructors continued intention to reuse Google Classroom in Iraq: A mixed-method study during COVID-19. Interactive Technology and Smart Education, 18(3), 380–402. https://doi.org/10.1108/ITSE-06-2020-0095
Johnson, L., Becker, S. A., Cummins, M., Estrada, V., Freeman, A., & Hall, C. (2016). NMC Horizon Report; 2016 Higher Education Edition. https://www.learntechlib.org/p/171478/
Karahoca, A., Karahoca, D., & Aksöz, M. (2018). Examining intention to adopt to internet of things in healthcare technology products. Kybernetes, 47(4), 742–770. https://doi.org/10.1108/K-02-2017-0045
Kim, H. Y., Lee, J. Y., Mun, J. M., & Johnson, K. K. (2017). Consumer adoption of smart in-store technology: Assessing the predictive value of attitude versus beliefs in the technology acceptance model. International Journal of Fashion Design, Technology and Education, 10(1), 26–36. https://doi.org/10.1080/17543266.2016.1177737
Kowalczuk, P. (2018). Consumer acceptance of smart speakers: A mixed methods approach. Journal of Research in Interactive Marketing, 12(4), 418–433. https://doi.org/10.1108/JRIM-01-2018-0022
Kuo, K. M., Liu, C. F., & Ma, C. C. (2013). An investigation of the effect of nurses’ technology readiness on the acceptance of mobile electronic medical record systems. BMC Medical Informatics and Decision Making, 13(1), 1–14. https://doi.org/10.1186/1472-6947-13-88
Kwet, M., & Prinsloo, P. (2020). The ‘smart’ classroom: A new frontier in the age of the smart university. Teaching in Higher Education, 25(4), 510–526. https://doi.org/10.1080/13562517.2020.1734922
Lee, V. H., Hew, J. J., Leong, L. Y., Tan, G. W. H., & Ooi, K. B. (2020). Wearable payment: A deep learning-based dual-stage SEM-ANN analysis. Expert Systems with Applications, 157, 113477. https://doi.org/10.1016/j.eswa.2020.113477
Li, Y., Yang, H. H., & MacLeod, J. (2019). Preferences toward the constructivist smart classroom learning environment: Examining pre-service teachers’ connectedness. Interactive Learning Environments, 27(3), 349–362. https://doi.org/10.1080/10494820.2018.1474232
Lin, J. S. C., & Chang, H. C. (2011). The role of technology readiness in self-service technology acceptance. Managing Service Quality, 21(4), 424–444. https://doi.org/10.1108/09604521111146289
Marquez, J., Villanueva, J., Solarte, Z., Garcia, A. (2016). IoT in Education: Integration of Objects with Virtual Academic Communities. In: Rocha, Á., Correia, A., Adeli, H., Reis, L., Mendonça Teixeira, M. (eds), New Advances in Information Systems and Technologies. Advances in Intelligent Systems and Computing (vol. 444). Springer. https://doi.org/10.1007/978-3-319-31232-3_19
Min, S., So, K. K. F., & Jeong, M. (2019). Consumer adoption of the Uber mobile application: Insights from diffusion of innovation theory and technology acceptance model. Journal of Travel & Tourism Marketing, 36(7), 770–783. https://doi.org/10.1080/10548408.2018.1507866
Mircea, M., Stoica, M., & Ghilic-Micu, B. (2021). Investigating the impact of the internet of things in higher education environment. IEEE Access, 9, 33396–33409. https://doi.org/10.1109/ACCESS.2021.3060964
Mohamed, H., & Lamia, M. (2018). Implementing flipped classroom that used an intelligent tutoring system into learning process. Computers and Education, 124, 62–76. https://doi.org/10.1016/j.compedu.2018.05.011
Mohammadi, H. (2015). Investigating users’ perspectives on e-learning: An integration of TAM and IS success model. Computers in Human Behavior, 45, 359–374. https://doi.org/10.1016/j.chb.2014.07.044
Mummalaneni, V., Meng, J., & Elliott, K. M. (2016). Consumer technology readiness and e-service quality in e-tailing: What is the impact on predicting online purchasing? Journal of Internet Commerce, 15(4), 311–331. https://doi.org/10.1080/15332861.2016.1237232
Pal, D., Funilkul, S., Charoenkitkarn, N., & Kanthamanon, P. (2018). Internet-of-Things and Smart Homes for Elderly Healthcare: An End User Perspective. IEEE Access, 6, 10483–10496. https://doi.org/10.1109/ACCESS.2018.2808472
Parasuraman, A. (2000). Technology Readiness Index (TRI) a multiple-item scale to measure readiness to embrace new technologies. Journal of Service Research, 2(4), 307–320. https://doi.org/10.1177/109467050024001
Park, E., Cho, Y., Han, J., & Kwon, S. J. (2017). Comprehensive approaches to user acceptance of Internet of Things in a smart home environment. IEEE Internet of Things Journal, 4(6), 2342–2350. https://doi.org/10.1109/JIOT.2017.2750765
Park, N., Rhoads, M., Hou, J., & Lee, K. M. (2014). Understanding the acceptance of teleconferencing systems among employees: An extension of the technology acceptance model. Computers in Human Behavior, 39, 118–127. https://doi.org/10.1016/j.chb.2014.05.048
Park, S. Y., Nam, M. W., & Cha, S. B. (2012). University students’ behavioral intention to use mobile learning: Evaluating the technology acceptance model. British Journal of Educational Technology, 43(4), 592–605. https://doi.org/10.1111/j.1467-8535.2011.01229.x
Perry, A. (2016). Consumers’ acceptance of smart virtual closets. Journal of Retailing and Consumer Services, 33, 171–177. https://doi.org/10.1016/j.jretconser.2016.08.018
Rahi, S. (2017). Research design and methods: A systematic review of research paradigms, sampling issues and instruments development. International Journal of Economics & Management Sciences, 6(2), 1–5. https://doi.org/10.4172/2162-6359.1000403
Rahman, M. M., Lesch, M. F., Horrey, W. J., & Strawderman, L. (2017). Assessing the utility of TAM, TPB, and UTAUT for advanced driver assistance systems. Accident Analysis & Prevention, 108, 361–373. https://doi.org/10.1016/j.aap.2017.09.011
Rangsom, K., & Khan-Am, W. (2019). Readiness and reasoned for using internet of things. RMUTT Global Business Accounting and Finance Review, 2(3), 1–13.
Reisdorf, B. C., Triwibowo, W., & Yankelevich, A. (2020). Laptop or bust: How lack of technology affects student achievement. American Behavioral Scientist, 64(7), 927–949. https://doi.org/10.1177/0002764220919145
Roumeliotis, M., & Maria, T. (2014). Perception and adoption of technology based services by students of higher education. International Journal of Scientific and Research Publications, 4(3), 1–5.
Samar, S., Ghani, M., & Alnaser, F. (2017). Predicting customer’s intentions to use internet banking: The role of technology acceptance model (TAM) in e-banking. Management Science Letters, 7(11), 513–524. https://doi.org/10.5267/j.msl.2017.8.004
Sayaf, A. M., Alamri, M. M., Alqahtani, M. A., & Al-Rahmi, W. M. (2021). Information and Communications Technology Used in Higher Education: An Empirical Study on Digital Learning as Sustainability. Sustainability, 13(13), 7074. https://doi.org/10.3390/su13137074
Stojanović, D., Bogdanović, Z., Petrović, L., Mitrović, S., & Labus, A. (2020). Empowering learning process in secondary education using pervasive technologies. Interactive Learning Environments, 1–15,. https://doi.org/10.1080/10494820.2020.1806886
Teo, T., & Zhou, M. (2014). Explaining the intention to use technology among university students: A structural equation modeling approach. Journal of Computing in Higher Education, 26(2), 124–142. https://doi.org/10.1007/s12528-014-9080-3
Tran-Dang, H., Krommenacker, N., Charpentier, P., & Kim, D. S. (2020). Toward the internet of things for physical internet: Perspectives and challenges. IEEE Internet of Things Journal, 7(6), 4711–4736. https://doi.org/10.1109/JIOT.2020.2971736
Tsai, H. T., Chien, J. L., & Tsai, M. T. (2014). The influences of system usability and user satisfaction on continued Internet banking services usage intention: Empirical evidence from Taiwan. Electronic Commerce Research, 14(2), 137–169. https://doi.org/10.1007/s10660-014-9136-5
Ullman, J. B., & Bentler, P. M. (2003). Structural equation modeling. In J. A. Schinka & W. F. Velicer (Eds.), Handbook of psychology: Research methods in psychology (vol. 2, pp. 607–634). John Wiley & Sons Inc. https://doi.org/10.1002/9781118133880.hop202023
Valencia-Arias, A., Chalela-Naffah, S., & Bermúdez-Hernández, J. (2019). A proposed model of e-learning tools acceptance among university students in developing countries. Education and Information Technologies, 24(2), 1057–1071. https://doi.org/10.1007/s10639-018-9815-2
Van der Heijden, H. (2004). User acceptance of hedonic information systems. MIS Quarterly, 695–704,. https://doi.org/10.2307/25148660
Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 157–178,. https://doi.org/10.2307/41410412
Walczuch, R., Lemmink, J., & Streukens, S. (2007). The effect of service employees’ technology readiness on technology acceptance. Information and Management, 44(2), 206–215. https://doi.org/10.1016/j.im.2006.12.005
Wang, W. T., & Wang, C. C. (2009). An empirical study of instructor adoption of web-based learning systems. Computers & Education, 53(3), 761–774. https://doi.org/10.1016/j.compedu.2009.02.021
Wang, Y. S., Wu, M. C., & Wang, H. Y. (2009). Investigating the determinants and age and gender differences in the acceptance of mobile learning. British Journal of Educational Technology, 40(1), 92–118. https://doi.org/10.1111/j.1467-8535.2007.00809.x
Wellek, S. (2017). A critical evaluation of the current “p-value controversy.” Biometrical Journal, 59(5), 854–872. https://doi.org/10.1002/bimj.201700001
Williams, C. (2007). Research methods. Journal of Business & Economics Research (JBER), 5(3), 65–872. https://doi.org/10.19030/jber.v5i3.2532
Williamson, B. (2017). Moulding student emotions through computational psychology: Affective learning technologies and algorithmic governance. Educational Media International, 54(4), 267–288. https://doi.org/10.1080/09523987.2017.1407080
Wook, M., Yusof, Z. M., Zakree, M., & Nazri, A. (2017). Educational data mining acceptance among undergraduate students. Education and Information Technologies, 22(3), 1195. https://doi.org/10.1007/s10639-016-9485-x
Wortmann, F., & Flüchter, K. (2015). Internet of things. Business & Information Systems Engineering, 57(3), 221–224. https://doi.org/10.1007/s12599-015-0383-3
Xia, Z., Zhang, S., Tian, X., & Liu, Y. (2021). Understanding waste sorting behavior and key influencing factors through internet of things: Evidence from college student community. Resources, Conservation and Recycling, 174, 105775. https://doi.org/10.1016/j.resconrec.2021.105775
Zaidi, M. F. A., & Belal, H. M. (2019). A preliminary study to understand the smes’readiness on iot in Malaysia. International Journal of Accounting, Finance and Business (IJAFB), 4(19), 1–12. http://www.ijafb.com/PDF/IJAFB-2019-19-06-01.pdf
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Alhasan, A., Hussein, M.H., Audah, L. et al. A case study to examine undergraduate students’ intention to use internet of things (IoT) services in the smart classroom. Educ Inf Technol 28, 10459–10482 (2023). https://doi.org/10.1007/s10639-022-11537-z
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DOI: https://doi.org/10.1007/s10639-022-11537-z