Digital paradigm shift: Unraveling students’ intentions to embrace Tablet-based Learning through an extended UTAUT2 model

References

• Addae, D., Amponsah, S., & Gborti, B. J. (2022). COVID-19 pandemic and the shift to digital learning: Experiences of students in a community college in Ghana. Community College Journal of Research and Practice, 46(1–2), 101–33.
   Web of Science ®Google Scholar
• Aheto-Domi, B., Issah, S., & Dorleku, J. E. A. (2020). Virtual learning and readiness of tutors of colleges of education in Ghana. American Journal of Educational Research, 8(9), 653–658. https://doi.org/10.12691/education-8-9-6
   Google Scholar
• Ahmad, J. (2016). Technology assisted language learning is a silver bullet for enhancing language competence and performance: A case study. International Journal of Applied Linguistics and English Literature, 5(7), 118–131.
   Google Scholar
• Akter, S., Fosso Wamba, S., & Dewan, S. (2017). Why PLS-SEM is suitable for complex modelling? An empirical illustration in big data analytics quality. Production Planning & Control, 28(11–12), 1011–1021. https://doi.org/10.1080/09537287.2016.1267411
   Web of Science ®Google Scholar
• Al-Adwan, A. S., Albelbisi, N. A., Hujran, O., Al-Rahmi, W. M., & Alkhalifah, A. (2021). Developing a holistic success model for sustainable e-learning: A structural equation modeling approach. Sustainability, 13(16), 9453. https://doi.org/10.3390/su13169453
   Web of Science ®Google Scholar
• Al-Adwan, A. S., Yaseen, H., Alsoud, A., Abousweilem, F., & Al-Rahmi, W. M. (2022). Novel extension of the UTAUT model to understand continued usage intention of learning management systems: The role of learning tradition. Education and Information Technologies, 27(3), 1–27. https://doi.org/10.1007/s10639-021-10758-y
   PubMed Web of Science ®Google Scholar
• Alalwan, A. A., Dwivedi, Y. K., & Rana, N. P. (2017). Factors influencing adoption of mobile banking by jordanian bank customers: Extending UTAUT2 with trust. International Journal of Information Management, 37(3), 99–110. https://doi.org/10.1016/j.ijinfomgt.2017.01.002
   Web of Science ®Google Scholar
• Albahri, A. S., Alnoor, A., Zaidan, A. A., Albahri, O. S., Hameed, H., Zaidan, B. B., Peh, S. S., Zain, A. B., Siraj, S. B., Masnan, A. H. B., & Yass, A. A. (2022). Hybrid artificial neural network and structural equation modelling techniques: A survey. Complex & Intelligent Systems, 8(2), 1781–1801. https://doi.org/10.1007/s40747-021-00503-w
   PubMed Web of Science ®Google Scholar
• Alshammari, S. (2021). Determining the factors that affect the use of virtual classrooms: A modification of the UTAUT model. Journal of Information Technology Education Research, 20, 117. https://doi.org/10.28945/4709
   Web of Science ®Google Scholar
• Ameen, N., & Willis, R. (2019). Towards closing the gender gap in Iraq: Understanding gender differences in smartphone adoption and use. Information Technology for Development, 25(4), 660–685. https://doi.org/10.1080/02681102.2018.1454877
   Web of Science ®Google Scholar
• Ameri, A., Khajouei, R., Ameri, A., & Jahani, Y. (2020). Acceptance of a mobile-based educational application (LabSafety) by pharmacy students: An application of the UTAUT2 model. Education and Information Technologies, 25(1), 419–435. https://doi.org/10.1007/s10639-019-09965-5
   Web of Science ®Google Scholar
• Amid, A., & Din, R. (2021). Acceptance and use of massive open online courses: Extending UTAUT2 with personal innovativeness. Journal of Personalized Learning, 4(1), 57–66.
   Google Scholar
• Ansong-Gyimah, K. (2020). Students’ perceptions and continuous intention to use E-Learning systems: The case of Google classroom. International Journal of Emerging Technologies in Learning (iJet), 15(11), 236–244. https://doi.org/10.3991/ijet.v15i11.12683
   Web of Science ®Google Scholar
• Appiah, K. M. (2022). A simplified model to enhance SMEs’ investment in renewable energy sources in Ghana. International Journal of Sustainable Energy Planning and Management, 35, 83–96. https://doi.org/10.54337/ijsepm.7223
   Google Scholar
• Arenas-Gaitán, J., Peral Peral, B., & Ramón Jerónimo, M. (2015). Elderly and internet banking: An application of UTAUT2. Journal of Internet Banking and Commerce, 20(1), 1–23.
   Google Scholar
• Arpaci, I. (2016). Understanding and predicting students’ intention to use mobile cloud storage services. Computers in Human Behaviour, 58, 150–157. https://doi.org/10.1016/j.chb.2015.12.067
   Web of Science ®Google Scholar
• Arthur, F., & Kaku, D. W. (2020). The use of technology in the teaching of economics: Perspectives of economics students. GSJ, 8(8), 2906–2919. https://www.researchgate.net/profile/Francis-Arthur-8/publication/344101313_THE_USE_OF_TECHNOLOGY_IN_THE_TEACHING_OF_ECONOMICS_PERSPECTIVES_OF_ECONOMICS_STUDENTS/links/5fc9d573a6fdcc697bdb8f96/THE-USE-OF-TECHNOLOGY-IN-THE-TEACHING-OF-ECONOMICS-PERSPECTIVES-OF-ECONOMICS-STUDENTS.pdf
   Google Scholar
• Awang, H., Rahman, A. A., Sukeri, S., Hashim, N., & Rashid, N. R. N. A. (2019). Adolescentfriendly health services in primary healthcare facilities in Malaysia and its correlation with adolescent satisfaction level. International Journal of Adolescence and Youth, 25(1), 551–561. https://doi.org/10.1080/02673843.2019.1685556
   Web of Science ®Google Scholar
• Ayasrah, F. T. M. (2020). Exploring E-Learning readiness as mediating between trust, hedonic motivation, students’ expectation, and intention to use technology in Taibah University. Journal of Education & Social Policy, 7(1), 101–109. https://doi.org/10.30845/jesp.v7n1p13
   Google Scholar
• Baker, T. L. (1994). Doing social research (2nd ed.). McGraw-Hill Inc.
   Google Scholar
• Bandura, A. (1989). Human agency in social cognitive theory. The American Psychologist, 44(9), 1175.
   PubMed Web of Science ®Google Scholar
• Batsila, M. A., Tsihouridis, C. A., & Tsihouridis, A. H. (2017). “All for one and one for all”-creating a mobile learning net for ESP students’ needs. International Journal of Emerging Technologies in Learning, 12(4), 17. https://doi.org/10.3991/ijet.v12i04.6428
   Google Scholar
• Bervell, B. B., Kumar, J. A., Arkorful, V., Agyapong, E. M., & Osman, S. (2022). Remodelling the role of facilitating conditions for Google classroom acceptance: A revision of UTAUT2. Australasian Journal of Educational Technology, 38(1), 115–135. https://doi.org/10.14742/ajet.7178
   Web of Science ®Google Scholar
• Bhimasta, R. A., & Suprapto, B. (2016). An empirical investigation of student adoption model toward mobile e-textbook: UTAUT2 and TTF model. In proceedings of the 2nd international conference on communication and information processing (pp. 167–173).
   Google Scholar
• Boomaars, C., Yorks, L., & Shetty, R. (2018). Employee learning motives, perceived learning opportunities and employability activities. Journal of Workplace Learning, 30(5), 335–350. https://doi.org/10.1108/JWL-01-2018-0020
   Web of Science ®Google Scholar
• Brossard, M., Carnelli, M., Chaudron, S., DiGioia, R., Dreesen, T., Kardefelt-Winther, D. … Yameogo, J. L. (2021). Digital learning for every child: Closing the gaps for an inclusive and prosperous future. G20 Insights Policy Brief, September.
   Google Scholar
• Cai, L., Yuen, K. F., Xie, D., Fang, M., & Wang, X. (2021). Consumer’s usage of logistics technologies: Integration of habit into the unified theory of acceptance and use of technology. Technology in Society, 67, 101789. https://doi.org/10.1016/j.techsoc.2021.101789
   Web of Science ®Google Scholar
• Chao, C. M. (2019). Factors determining the behavioural intention to use mobile learning: An application and extension of the UTAUT model. Frontiers in Psychology, 10, 1652. https://doi.org/10.3389/fpsyg.2019.01652
   PubMed Web of Science ®Google Scholar
• Cheah, J.-H., Ting, H., Cham, T. H., & Memon, M. A. (2019). The effect of selfie promotion and celebrity endorsed advertisement on decision-making processes: A model comparison. Internet Research, 29(3), 552–577. https://doi.org/10.1108/IntR-12-2017-0530
   Web of Science ®Google Scholar
• Chiu, J. L., Bool, N. C., & Chiu, C. L. (2017). Challenges and factors influencing initial trust and behavioural intention to use mobile banking services in the Philippines. Asia Pacific Journal of Innovation and Entrepreneurship, 11(2), 246–278. https://doi.org/10.1108/APJIE-08-2017-029
   Web of Science ®Google Scholar
• Cohen, J. (2013). Statistical power analysis for the behavioural sciences. Academic.
   Google Scholar
• Davis, F. D. (1989). Technology acceptance model: TAM. Information Seeking Behavior and Technology Adoption.
   Google Scholar
• Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111–1132.
   Web of Science ®Google Scholar
• Dillman, D. A., Smyth, J. D., & Christian, L. M. (2014). Internet, phone, mail, and mixed-mode surveys: The tailored design method. John Wiley & Sons.
   Google Scholar
• Dumford, A. D., & Miller, A. L. (2018). Online learning in higher education: Exploring advantages and disadvantages for engagement. Journal of Computing in Higher Education, 30, 452–465.
   Web of Science ®Google Scholar
• Dutton, K. (2018). Professional development: Investigating the relationships between employee motives, learning opportunities, and activities. Development & Learning in Organizations: An International Journal, 32(5), 32–35. https://doi.org/10.1108/DLO-09-2018-127
   Google Scholar
• Dzandza, P. E. (2020). Digitizing the intellectual output of Ghanaian universities. Collection and Curation, 39(3), 69–75. https://doi.org/10.1108/CC-05-2019-0012
   Web of Science ®Google Scholar
• Edem Adzovie, D., & Jibril, A. B. (2022). Assessment of the effects of COVID-19 pandemic on the prospects of e-learning in higher learning institutions: The mediating role of academic innovativeness and technological growth. Cogent Education, 9(1), 2041222. https://doi.org/10.1080/2331186X.2022.2041222
   Web of Science ®Google Scholar
• Feng, G. C., Su, X., Lin, Z., He, Y., Luo, N., & Zhang, Y. (2021). Determinants of technology acceptance: Two model-based meta-analytic reviews. Journalism & Mass Communication Quarterly, 98(1), 83–104. https://doi.org/10.1177/1077699020952400
   Web of Science ®Google Scholar
• Fernandez, S. C., & Mammen, K. J. (2020). Perceptions of selected stakeholders on the use of tablets for University teaching. TEM Journal, 9(4), 1809. https://doi.org/10.18421/TEM94-64
   Google Scholar
• Fianu, E., Blewett, C., Ampong, G. O. A., & Ofori, K. S. (2018). Factors affecting MOOC usage by students in selected Ghanaian universities. Education Sciences, 8(2), 70. https://doi.org/10.3390/educsci8020070
   Web of Science ®Google Scholar
• Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Don Mills.
   Google Scholar
• 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
   Web of Science ®Google Scholar
• Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2018). How to design and evaluate research in education. (10th ed.). McGraw Hill.
   Google Scholar
• Furió, D., Juan, M. C., Seguí, I., & Vivó, R. (2015). Mobile learning vs. traditional classroom lessons: A comparative study. Journal of Computer Assisted Learning, 31(3), 189–201. https://doi.org/10.1111/jcal.12071
   Web of Science ®Google Scholar
• Ghauri, P., Grønhaug, K., & Strange, R. (2020). Research methods in business studies. Cambridge University Press.
   Google Scholar
• Giebelhausen, M., Lawrence, B., & Chun, H. H. (2020). Doing good while behaving badly: Checkout charity process mechanisms. Journal of Business Ethics, 172(1), 1–17. https://doi.org/10.1007/s10551-019-04413-3
   Web of Science ®Google Scholar
• Goto, J., & Munyai, A. (2022). The acceptance and use of online learning by law students in a South African University: An application of the UTAUT2 model. The African Journal of Information Systems, 14(1), 3.
   Web of Science ®Google Scholar
• Gunawan, A., Muchardie, B. G., & Liawinardi, K. (2019). Millennial behavioural intention to adopt e-book using UTAUT2 model. In 2019 International Conference on Information Management and Technology (ICIMTech) (Vol. 1, pp. 98–102). IEEE.
   Google Scholar
• Hair, J. F., Jr., Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101–110. https://doi.org/10.1016/j.jbusres.2019.11.069
   Web of Science ®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
• Hair, J. F., Hult, T., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Sage.
   Google Scholar
• Hair, J., Hult, T., Ringle, C., & Sarstedt, M. (2014). A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications, Inc.
   Google Scholar
• Hair, J. F., Jr., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial least squares structural equation modeling (PLS-SEM) using R: A workbook. Springers.
   Google Scholar
• Hair, J. F., Jr., Matthews, L. M., Matthews, R. L., & Sarstedt, M. (2017). PLS-SEM or CB-SEM: Updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2), 107–123. https://doi.org/10.1504/IJMDA.2017.087624
   Google Scholar
• Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Planning, 46(1–2), 1–12. https://doi.org/10.1016/j.lrp.2013.01.001
   Web of Science ®Google Scholar
• Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
   Web of Science ®Google Scholar
• Hair, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2018). Advanced issues in partial least squares structural equations modeling (PLS-SEM). Sage.
   Google Scholar
• Haleem, A., Javaid, M., Qadri, M. A., & Suman, R. (2022). Understanding the role of digital technologies in education: A review. Sustainable Operations and Computers, 3, 275–285. https://doi.org/10.1016/j.susoc.2022.05.004
   Google Scholar
• Hameed, F., Qayyum, A., & Khan, F. A. (2022). A new trend of learning and teaching: Behavioural intention towards mobile learning. Journal of Computers in Education, 1–32. https://doi.org/10.1007/s40692-022-00252-w
   Web of Science ®Google Scholar
• Hamzah, W. W., Yusoff, M., Ismail, I., & Yacob, A. (2020). The behavioural intentions of secondary school students to use tablet as a mobile learning device. International Journal of Interactive Mobile Technologies (iJim), 14(13), 161. https://doi.org/10.3991/ijim.v14i13.13027
   Google Scholar
• Hartelina, H., Batu, R., & Hidayanti, A. (2021). What can hedonic motivation do on decisions to use online learning services? International Journal of Data & Network Science, 5(2), 121–126. https://doi.org/10.5267/j.ijdns.2021.2.002
   Google Scholar
• Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43, 115–135.
   Web of Science ®Google Scholar
• Hooda, A., Gupta, P., Jeyaraj, A., Giannakis, M., & Dwivedi, Y. K. (2022). The effects of trust on behavioural intention and use behaviour within e-government contexts. International Journal of Information Management, 67, 102553. https://doi.org/10.1016/j.ijinfomgt.2022.102553
   Web of Science ®Google Scholar
• Huang, X. (2018). Social media use by college students and teachers: An application of UTAUT2 (Doctoral dissertation, Walden University).
   Google Scholar
• Hussein, Z. (2017). Leading to intention: The role of attitude in relation to technology acceptance model in e-learning. Procedia Computer Science, 105, 159–164. https://doi.org/10.1016/j.procs.2017.01.196
   Google Scholar
• Kabra, G., Ramesh, A., Akhtar, P., & Dash, M. K. (2017). Understanding behavioural intention to use information technology: Insights from humanitarian practitioners. Telematics and Informatics, 34(7), 1250–1261.
   Web of Science ®Google Scholar
• Khalilzadeh, J., Ozturk, A. B., & Bilgihan, A. (2017). Security-related factors in extended UTAUT model for NFC based mobile payment in the restaurant industry. Computers in Human Behavior, 70, 460–474.
   Web of Science ®Google Scholar
• Khrais, L. T., & Alghamdi, A. M. (2021). The role of mobile application acceptance in shaping e-customer service. Future Internet, 13(3), 77. https://doi.org/10.3390/fi13030077
   Web of Science ®Google Scholar
• Kock. (2021). WarpPLS user manual: Version 7.0. ScriptWarp Systems.
   Google Scholar
• Kosiba, J. P. B., Odoom, R., Boateng, H., Twum, K. K., & Abdul-Hamid, I. K. (2022). Examining students’ satisfaction with online learning during the COVID-19 pandemic-an extended UTAUT2 approach. Journal of Further and Higher Education, 46(7), 988–1005. https://doi.org/10.1080/0309877X.2022.2030687
   Web of Science ®Google Scholar
• Kumar, J. A., & Bervell, B. (2019). Google classroom for mobile learning in higher education: Modelling the initial perceptions of students. Education and Information Technologies, 24(2), 1793–1817. https://doi.org/10.1007/s10639-018-09858-z
   Web of Science ®Google Scholar
• Kurt, E. Ö., & Tingoy, Ö. (2017). The acceptance and use of a virtual learning environment in higher education: An empirical study in Turkey, and the UK. International Journal of Educational Technology in Higher Education, 14(1), 1–15.
   Google Scholar
• Langthaler, M., & Bazafkan, H. (2020). Digitalization, education and skills development in the global South: An assessment of the debate with a focus on sub-Saharan Africa (No. 28). ÖFSE Briefing Paper.
   Google Scholar
• Laukkanen, T. (2016). Consumer adoption versus rejection decisions in seemingly similar service innovations: The case of the Internet and mobile banking. Journal of Business Research, 69(7), 2432–2439.
   Web of Science ®Google Scholar
• Leow, L. P., Phua, L. K., & Teh, S. Y. (2021). Extending the social influence factor: Behavioural intention to increase the usage of information and communication technology-enhanced student-centered teaching methods. Educational Technology Research & Development, 69(3), 1853–1879. https://doi.org/10.1007/s11423-021-10017-4
   Web of Science ®Google Scholar
• Liu, D., & Guo, X. (2017). Exploring gender differences in acceptance of mobile computing devices among college students. Information Systems and E-Business Management, 15(1), 197–223. https://doi.org/10.1007/s10257-016-0315-x
   Web of Science ®Google Scholar
• Matthews, L. M. (2017). Applying multi-group analysis in PLS-SEM: A step-by-step process. In H. Latan & R. Noonan (Eds.), Partial least squares structural equation modeling – basic concepts, methodological issues and applications in press. Springer International Publishing AG.
   Google Scholar
• Meet, R. K., Kala, D., & Al-Adwan, A. S. (2022). Exploring factors affecting the adoption of MOOC in generation Z using extended UTAUT2 model. Education and Information Technologies, 27(7), 10261–10283. https://doi.org/10.1007/s10639-022-11052-1
   PubMed Web of Science ®Google Scholar
• Memon, M. A., Salleh, R., Mirza, M. Z., Cheah, J. H., Ting, H., Ahmad, M. S., & Tariq, A. (2020). Satisfaction matters: The relationships between HRM practices, work engagement and turnover intention. International Journal of Manpower, 42(1), 21–50. https://doi.org/10.1108/IJM-04-2018-0127
   Web of Science ®Google Scholar
• Memon, M. A., Ting, H., Cheah, J. H., Thurasamy, R., Chuah, F., & Cham, T. H. (2020). Sample size for survey research: Review and recommendations. Journal of Applied Structural Equation Modeling, 4(2), 1–20. https://doi.org/10.47263/JASEM.4(2)01
   Google Scholar
• Ministry of Education. (2018a). Education strategic plan.
   Google Scholar
• Mohan, M. M., Upadhyaya, P., & Pillai, K. R. (2020). Intention and barriers to use MOOCs: An investigation among the post graduate students in India. Education and Information Technologies, 25(6), 5017–5031. https://doi.org/10.1007/s10639-020-10215-2
   Web of Science ®Google Scholar
• Moon, S. J. (2021). Investigating beliefs, attitudes, and intentions regarding green restaurant patronage: An application of the extended theory of planned behaviour with moderating effects of gender and age. International Journal of Hospitality Management, 92, 102727. https://doi.org/10.1016/j.ijhm.2020.102727
   Web of Science ®Google Scholar
• Moorthy, K., Yee, T. T., T’ing, L. C., & Kumaran, V. V. (2019). Habit and hedonic motivation are the strongest influences in mobile learning behaviours among higher education students in Malaysia. Australasian Journal of Educational Technology, 35(4). https://doi.org/10.14742/ajet.4432
   Web of Science ®Google Scholar
• Mubuke, F., Kutosi Masaba, A., Ogenmungu, C., & Mayoka Kituyi, G. Examining the effect of facilitating conditions as an imperative input in enhancing the intention to use mobile learning systems in universities. 2017.
   Google Scholar
• Nassar, A. A., Othman, K., & Nizah, M. A. B. M. (2019). The impact of the social influence on ict adoption: Behavioural intention as mediator and age as moderator. The International Journal of Academic Research in Business & Social Sciences, 9(11), 963–978. https://doi.org/10.6007/IJARBSS/v9-i11/6620
   Google Scholar
• Nisa, U. K., & Solekah, N. A. (2022). The influence of TAM, social influence, security relationship toward intention to use E wallet through attitude and trust. Iqtishoduna: Jurnal Ekonomi dan Bisnis Islam, 18(1), 35–50.
   Google Scholar
• Nouri, J., & Cerratto Pargman, T. (2016). When teaching practices Meet tablets’ affordances. Insights on the materiality of learning. In K. Verbert, M. Sharples, & T. Klobučar (Eds.), Adaptive and adaptable learning. EC-TEL 2016. Lecture notes in computer science (Vol. 9891, pp. 179–192). Springer.
   Google Scholar
• Osei, H. V., Kwateng, K. O., & Boateng, K. A. (2022). Integration of personality trait, motivation and UTAUT 2 to understand e-learning adoption in the era of COVID-19 pandemic. Education and Information Technologies, 27(8), 10705–10730. https://doi.org/10.1007/s10639-022-11047-y
   PubMed Web of Science ®Google Scholar
• Owolabi, H. O., Ayandele, J. K., & Olaoye, D. D. (2020). A Systematic Review of Structural Equation Model (SEM). Open Journal of Educational Development (ISSN: 2734-2050), 1(2), 27–39. https://doi.org/10.52417/ojed.v1i2.163
   Google Scholar
• Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioural research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
   PubMed Web of Science ®Google Scholar
• Polit, D. F., & Beck, C. T. (2017). Nursing research (10th ed.). Wolters Kluwer.
   Google Scholar
• Quaicoe, J. S., & Pata, K. (2018). Basic school teachers’ perspective to digital teaching and learning in Ghana. Education and Information Technologies, 23(3), 1159–1173. https://doi.org/10.1007/s10639-017-9660-8
   Web of Science ®Google Scholar
• Rahi, S., Ghani, M., Alnaser, F., & Ngah, A. (2018). Investigating the role of unified theory of acceptance and use of technology (UTAUT) in internet banking adoption context. Management Science Letters, 8(3), 173–186.
   Google Scholar
• Ringle, C. M., Sarstedt, M., Sinkovics, N., & Sinkovics, R. R. (2023). A perspective on using partial least squares structural equation modelling in data articles. Data in Brief, 48, 109074.
   PubMed Web of Science ®Google Scholar
• Ringle, C. M., Wende, S., & Becker, J.-M. (2015). SmartPLS 3. SmartPLS GmbH.
   Google Scholar
• Roberts, R., Flin, R., Millar, D., & Corradi, L. (2021). Psychological factors influencing technology adoption: A case study from the oil and gas industry. Technovation, 102, 102219. https://doi.org/10.1016/j.technovation.2020.102219
   Web of Science ®Google Scholar
• Rogers, E. M. (1962). Diffusion of innovations. Free Press.
   Google Scholar
• Sarstedt, M., & Mooi, E. (2019). A concise guide to market research. The process, data, and methods using IBM SPSS statistics (2016 Saunders, M. N., & Townsend, K. eds, 3rd ed.) Springer.
   Google Scholar
• Saunders, M., Lewis, P., & Thornhill, A. (2009). Research methods for business students (5th ed.). Pearson Education.
   Google Scholar
• Shaikh, A., & Nikooherafmaher, S. “The effects of digitalization on students’ learning experience after the rise of the COVID-19 pandemic”: A qualitative study on institutional and student behaviours because of disruptive digitalization after the rise of the COVID-19 pandemic. 2022.
   Google Scholar
• Sharma, S. K., Gaur, A., Saddikuti, V., & Rastogi, A. (2017). Structural equation model (SEM)-neural network (NN) model for predicting quality determinants of e-learning management systems. Behaviour & Information Technology, 36(10), 1053–1066. https://doi.org/10.1080/0144929X.2017.1340973
   Web of Science ®Google Scholar
• Shuhaiber, A. (2016). How facilitating conditions impact students’ intention to use virtual lectures? An empirical evidence. AICT, 2016, 79.
   Google Scholar
• Songkram, N., Chootongchai, S., Osuwan, H., Chuppunnarat, Y., & Songkram, N. (2023). Students’ adoption towards behavioural intention of digital learning platform. Education and Information Technologies, 1–23.
   Google Scholar
• Steil, A. V., de Cuffa, D., Iwaya, G. H., & Pacheco, R. C. D. S. (2020). Perceived learning opportunities, behavioural intentions and employee retention in technology organizations. Journal of Workplace Learning, 32(2), 147–159. https://doi.org/10.1108/JWL-04-2019-0045
   Web of Science ®Google Scholar
• Streletskaya, N. A., Bell, S. D., Kecinski, M., Li, T., Banerjee, S., Palm‐Forster, L. H., & Pannell, D. (2020). Agricultural adoption and behavioral economics: Bridging the gap. Applied Economic Perspectives and Policy, 42(1), 54–66.
   Web of Science ®Google Scholar
• Svenningsson, J., Höst, G., Hultén, M., & Hallström, J. (2021). Students’ attitudes toward technology: Exploring the relationship among affective, cognitive and behavioral components of the attitude construct. International Journal of Technology and Design Education, 32(3), 1–21. https://doi.org/10.1007/s10798-021-09657-7
   Web of Science ®Google Scholar
• Tamilmani, K., Rana, N. P., & Dwivedi, Y. K. (2018). Mobile application adoption predictors: Systematic review of UTAUT2 studies using weight analysis. In Challenges and Opportunities in the Digital Era: 17th IFIP WG 6.11 Conference on e-Business, e-Services, and e-Society, I3E 2018, Kuwait City, Kuwait, October 30–November 1, 2018, Proceedings 17 (pp. 1–12). Springer International Publishing.
   Google Scholar
• Tamilmani, K., Rana, N. P., & Dwivedi, Y. K. (2019). Use of ‘habit’ is not a habit in understanding individual technology adoption: A review of UTAUT2 based empirical studies. In Smart Working, Living and Organising: IFIP WG 8.6 International Conference on Transfer and Diffusion of IT, TDIT 2018, Portsmouth, UK, June 25, 2018, Proceedings, Portsmouth, UK (pp. 277–294). Springer International Publishing.
   Google Scholar
• Tamilmani, K., Rana, N. P., & Dwivedi, Y. K. (2021). Consumer acceptance and use of information technology: A meta-analytic evaluation of UTAUT2. Information Systems Frontiers, 23(4), 987–1005. https://doi.org/10.1007/s10796-020-10007-6
   Web of Science ®Google Scholar
• Tamilmani, K., Rana, N. P., Wamba, S. F., & Dwivedi, R. (2021). The extended unified theory of acceptance and use of technology (UTAUT2): A systematic literature review and theory evaluation. International Journal of Information Management, 57, 102269.
   Web of Science ®Google Scholar
• Tarhini, A., Alalwan, A. A., Cao, D., & Al-Qirim, N. (2021). Integrating emotional attachment, resource sharing, communication and collaboration into UTAUT2 to examine students’ behavioural intention to adopt social media networks in education. International Journal of Technology Enhanced Learning, 13(1), 1–23. https://doi.org/10.1504/IJTEL.2021.111588
   Web of Science ®Google Scholar
• Tarhini, A., Hone, K., Liu, X., & Tarhini, T. (2017). Examining the moderating effect of individual-level cultural values on users’ acceptance of E-learning in developing countries: A structural equation modeling of an extended technology acceptance model. Interactive Learning Environments, 25(3), 306–328. https://doi.org/10.1080/10494820.2015.1122635
   Web of Science ®Google Scholar
• Taylor, S., & Todd, P. (1995). Decomposition and crossover effects in the theory of planned behavior: A study of consumer adoption intentions. International Journal of Research in Marketing, 12(2), 137–155.
   Google Scholar
• Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization, 125–143.
   Google Scholar
• Twum, K. K., Ofori, D., Keney, G., & Korang-Yeboah, B. (2022). Using the UTAUT, personal innovativeness and perceived financial cost to examine student’s intention to use E-learning. Journal of Science and Technology Policy Management, 13(3), 713–737. https://doi.org/10.1108/JSTPM-12-2020-0168
   Web of Science ®Google Scholar
• Utomo, P., Kurniasari, F., & Purnamaningsih, P. (2021). The effects of performance expectancy, effort expectancy, facilitating condition, and habit on behaviour intention in using mobile healthcare application. International Journal of Community Service & Engagement, 2(4), 183–197. https://doi.org/10.47747/ijcse.v2i4.529
   Google Scholar
• Van der Sluis, L. E., & Poell, R. F. (2003). The impact on career development of learning opportunities and learning behaviour at work. Human Resource Development Quarterly, 14(2), 159–179. https://doi.org/10.1002/hrdq.1058
   Google Scholar
• 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.
   Web of Science ®Google Scholar
• Wang, F., Wijaya, T. T., Habibi, A., & Liu, Y. (2022). Predictors influencing urban and rural Area students to use tablet computers as learning tools: Combination of UTAUT and TTF models. Sustainability, 14(21), 13965. https://doi.org/10.3390/su142113965
   Web of Science ®Google Scholar
• Yidana, M. B., & Arthur, F. (2023). Exploring economics teachers’ efficacy beliefs in the teaching of economics. Cogent Education, 10(1), 1–21. https://doi.org/10.1080/2331186X.2023.2222652
   Web of Science ®Google Scholar
• Yidana, M. B., Arthur, F., Ababio, B. T., & Yu, Z. (2022). Teachers’ application of multiple intelligences approach in teaching economics. Education Research International, 2022(2875555), 1–16. https://doi.org/10.1155/2022/2875555
   Google Scholar
• Yidana, M. B., Arthur, F., Kaku, D. W., & Anti Partey, P. (2023). Predictors of economics students’ behavioural intention to use tablets: A two-staged structural equation modelling-artificial neural network approach. Mobile Information Systems, 1–20. https://doi.org/10.1155/2023/9629405
   Google Scholar
• Youssef, A., & Issam, E. I. (2022). Students’ adoption of e-learning: Evidence from a Moroccan business school in the COVID-19 era. Arab Gulf Journal of Scientific Research, 40(1). https://doi.org/10.1108/AGJSR-05-2022-0052 54–78
   Google Scholar
• Zacharis, G., & Nikolopoulou, K. (2022). Factors predicting University students’ behavioral intention to use eLearning platforms in the post-pandemic normal: An UTAUT2 approach with ‘learning value’. Education and Information Technologies, 27(9), 1–18. https://doi.org/10.1007/s10639-022-11116-2
   PubMed Web of Science ®Google Scholar
• Zhang, L., & Nouri, J. (2018). A systematic review of learning and teaching with tablets. International Association for Development of the Information Society.
   Google Scholar
• Zhang, T., Tao, D., Qu, X., Zhang, X., Zeng, J., Zhu, H., & Zhu, H. (2020). Automated vehicle acceptance in China: Social influence and initial trust are key determinants. Transportation Research Part C: Emerging Technologies, 112, 220–233. https://doi.org/10.1016/j.trc.2020.01.027
   Web of Science ®Google Scholar
• Zheng, J., & Li, S. (2020). What drives students’ intention to use tablet computers: An extended technology acceptance model. International Journal of Educational Research, 102, 101612. https://doi.org/10.1016/j.ijer.2020.101612
   Web of Science ®Google Scholar