Factors affecting students’ intention to use m-learning: Extending the technology acceptance model (TAM)

References

• Abdullah, F., & Ward, R. (2016). Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by analysing commonly used external factors. Computers in Human Behavior, 56, 238–256. https://doi.org/10.1016/j.chb.2015.11.036
   Web of Science ®Google Scholar
• Ahorsu, D. K., Lin, C.-Y., Imani, V., Saffari, M., Griffiths, M. D., & Pakpour, A. H. (2022). The fear of COVID-19 scale: Development and initial validation. International Journal of Mental Health and Addiction, 20(3), 1537–1545. https://doi.org/10.1007/s11469-020-00270-8
   PubMed Web of Science ®Google Scholar
• Akour, I., Alshurideh, M., Al Kurdi, B., Al Ali, A., & Salloum, S. (2021). Using machine learning algorithms to predict people’s intention to use mobile learning platforms during the COVID-19 pandemic: Machine learning approach. JMIR Medical Education, 7(1), e24032. https://doi.org/10.2196/24032
   PubMedGoogle Scholar
• Al-Emran, M., Arpaci, I., & Salloum, S. A. (2020). An empirical examination of continuous intention to use m-learning: An integrated model. Education and Information Technologies, 25(4), 2899–2918. https://doi.org/10.1007/s10639-019-10094-2
   Web of Science ®Google Scholar
• Al-Hamad, M. Q., Mbaidin, H. O., AlHamad, A. Q. M., Alshurideh, M. T., Kurdi, B. H. A., & Al-Hamad, N. Q. (2021). Investigating students’ behavioral intention to use mobile learning in higher education in UAE during Coronavirus-19 pandemic. International Journal of Data & Network Science, 5(3), 321–330. https://doi.org/10.5267/j.ijdns.2021.6.001
   Google Scholar
• Alhumaid, K. (2021). Developing an educational framework for using mobile learning during the era of COVID-19. International Journal of Data & Network Science, 5(3), 215–230. https://doi.org/10.5267/j.ijdns.2021.6.012
   Google Scholar
• Alhumaid, K., Habes, M., & Salloum, S. A. (2021). Examining the factors influencing the mobile learning usage during COVID-19 pandemic: An integrated SEM-ANN method. IEEE Access, 9, 102567–102578. https://doi.org/10.1109/ACCESS.2021.3097753
   Web of Science ®Google Scholar
• Almaiah, M. A., & Alismaiel, O. A. (2019). Examination of factors influencing the use of mobile learning system: An empirical study. Education and Information Technologies, 24(1), 885–909. https://doi.org/10.1007/s10639-018-9810-7
   Web of Science ®Google Scholar
• Al-Maroof, R. S., Alshurideh, M. T., Salloum, S. A., AlHamad, A. Q. M., & Gaber, T. Acceptance of Google Meet during the spread of coronavirus by Arab University students. (2021). Informatics, 8(2), 24. Article 2. https://doi.org/10.3390/informatics8020024
   Google Scholar
• Al-Maroof, R. S., Salloum, S. A., Hassanien, A. E., & Shaalan, K. (2020). Fear from COVID-19 and technology adoption: The impact of Google Meet during Coronavirus pandemic. Interactive Learning Environments, 31(3), 1–16. https://doi.org/10.1080/10494820.2020.1830121
   Web of Science ®Google Scholar
• AL-Qadri, A. H., & Zhao, W. (2021). Emotional intelligence and students’ academic achievement. Problems of Education in the 21st Century, 79(3), 360. https://doi.org/10.33225/pec/21.79.360
   Web of Science ®Google Scholar
• Al-Qadri, A. H., Zhao, W., Li, M., Al-Khresheh, M. H., & Boudouaia, A. (2021). The prevalence of the academic learning difficulties: An observation tool. Heliyon, 7(10), e08164. https://doi.org/10.1016/j.heliyon.2021.e08164
   PubMed Web of Science ®Google Scholar
• Alrajawy, I., Daud, N. M., Isaac, O., & Mutahar, A. M. (2017). Examine factors influencing the intention to use mobile learning in Yemen public universities. Asian Journal of Information Technology, 16(2), 287–297.
   Google Scholar
• Arain, A. A., Hussain, Z., Rizvi, W. H., & Vighio, M. S. (2019). Extending UTAUT2 toward acceptance of mobile learning in the context of higher education. Universal Access in the Information Society, 18(3), 659–673. https://doi.org/10.1007/s10209-019-00685-8
   Web of Science ®Google Scholar
• Arbaugh, J. B., & Hwang, A. (2013). Uses of multivariate analytical techniques in online and blended business education: An assessment of current practice and recommendations for future research. Journal of Management Education, 37(2), 229–260. https://doi.org/10.1177/1052562912453919
   Google Scholar
• Baru, M., Tenggara, W. N., & Mataram, M. U. (2020). Promoting students‟ autonomy through online learning media in EFL class. International Journal of Higher Education, 9(4), 320–331. https://doi.org/10.5430/ijhe.v9n4p320
   Google Scholar
• Becker, S. A., Cummins, M., Davis, A., Freeman, A., Hall, C. G., & Ananthanarayanan, V. (2017). NMC horizon report: 2017 higher education edition.
   Google Scholar
• Broche-Pérez, Y., Fernández-Fleites, Z., Jiménez-Puig, E., Fernández-Castillo, E., & Rodríguez-Martin, B. C. (2022). Gender and fear of COVID-19 in a Cuban population sample. International Journal of Mental Health and Addiction, 20(1), 83–91. https://doi.org/10.1007/s11469-020-00343-8
   PubMed Web of Science ®Google Scholar
• Carleton, R. N., & Asmundson, G. J. G. (2009). The multidimensionality of fear of pain: Construct independence for the fear of pain questionnaire-short form and the pain anxiety symptoms Scale-20. The Journal of Pain, 10(1), 29–37. https://doi.org/10.1016/j.jpain.2008.06.007
   PubMed Web of Science ®Google Scholar
• Chin, W. (1998). Issues and opinion on structural equation modeling management. Information Systems Quarterly, 22(1), 19–24.
   Google Scholar
• Collier, J. E. (2020). Applied structural equation modeling using AMOS: Basic to advanced techniques. Routledge.
   Google Scholar
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
   Web of Science ®Google Scholar
• El-Masri, M., & Tarhini, A. (2017). Factors affecting the adoption of e-learning systems in Qatar and USA: Extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Educational Technology Research & Development, 65(3), 743–763. https://doi.org/10.1007/s11423-016-9508-8
   Web of Science ®Google Scholar
• Ezzelden, I. M. I. (2019). Mobile learning for English language learning: Benefits and challenges. European Journal of Open Education and E-Learning Studies, 4(1). https://doi.org/10.46827/ejoe.v0i0.2290
   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
• Garone, A., Pynoo, B., Tondeur, J., Cocquyt, C., Vanslambrouck, S., Bruggeman, B., & Struyven, K. (2019). Clustering university teaching staff through UTAUT: Implications for the acceptance of a new learning management system. British Journal of Educational Technology, 50(5), 2466–2483. https://doi.org/10.1111/bjet.12867
   Web of Science ®Google Scholar
• Gerhold, L. (2020). COVID-19: Risk perception and coping strategies. Freie Universität.
   Google Scholar
• Hair, J. F., Jr., Hult, G. T. M., Ringle, C. M., & Sarstedt, M., Danks, N. P., Ray, S. (2021). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications. https://doi.org/10.1007/978-3-030-80519-7
   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
• Han, J.-H., & Sa, H. J. (2021). Acceptance of and satisfaction with online educational classes through the technology acceptance model (TAM): The COVID-19 situation in Korea. Asia Pacific Education Review, 23(3), 403–415. https://doi.org/10.1007/s12564-021-09716-7
   Web of Science ®Google Scholar
• Holland, K. (2018). Anxiety: Causes, Symptoms, Treatment, and More. https://www.healthline.com/health/anxiety
   Google Scholar
• Hsu, L. (2016). Examining EFL teachers’ technological pedagogical content knowledge and the adoption of mobile-assisted language learning: A partial least square approach. Computer Assisted Language Learning, 29(8), 1287–1297. https://doi.org/10.1080/09588221.2016.1278024
   Web of Science ®Google Scholar
• Huang, R.-T., Hsiao, C.-H., Tang, T.-W., & Lien, T.-C. (2014). Exploring the moderating role of perceived flexibility advantages in Mobile Learning Continuance Intention (MLCI). International Review of Research in Open & Distributed Learning, 15(3), 140–157. https://doi.org/10.19173/irrodl.v15i3.1722
   Web of Science ®Google Scholar
• Huang, F., Teo, T., & Zhou, M. (2020). Chinese students’ intentions to use the Internet-based technology for learning. Educational Technology Research & Development, 68(1), 575–591. https://doi.org/10.1007/s11423-019-09695-y
   Web of Science ®Google Scholar
• Hu, L., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3(4), 424–453. https://doi.org/10.1037/1082-989X.3.4.424
   Web of Science ®Google Scholar
• Igbaria, M., & Iivari, J. (1995). The effects of self-efficacy on computer usage. Omega, 23(6), 587–605. https://doi.org/10.1016/0305-0483(95)00035-6
   Web of Science ®Google Scholar
• Jackson, C. (2013). Fear in and about education. In R. Brooks, M. McCormack, & K. Bhopal (Eds.), Contemporary debates in the sociology of education (pp. 185–201). Palgrave Macmillan UK. https://doi.org/10.1057/9781137269881_11
   Google Scholar
• Jan, A. U., & Contreras, V. (2011). Technology acceptance model for the use of information technology in universities. Computers in Human Behavior, 27(2), 845–851. https://doi.org/10.1016/j.chb.2010.11.009
   Web of Science ®Google Scholar
• Lee, C., Yeung, A. S., & Cheung, K. W. (2019). Learner perceptions versus technology usage: A study of adolescent English learners in Hong Kong secondary schools. Computers & Education, 133, 13–26. https://doi.org/10.1016/j.compedu.2019.01.005
   Web of Science ®Google Scholar
• Lim, H., Denise Murdoch, Y., & Cho, J. (2022). Online EMI learner engagement and perceptions of teaching and learning during the COVID-19 pandemic. Innovations in Education and Teaching International, 59(5), 597–608. https://doi.org/10.1080/14703297.2021.1905030
   Web of Science ®Google Scholar
• Liu, G.-Z., Chen, J.-Y., & Hwang, G.-J. (2018). Mobile-based collaborative learning in the fitness center: A case study on the development of English listening comprehension with a context-aware application. British Journal of Educational Technology, 49(2), 305–320. https://doi.org/10.1111/bjet.12581
   Web of Science ®Google Scholar
• Liu, J., Zhu, X., Huang, C., Wang, Y., & Chang, L. (2022). Study on factors influencing college students’ motivation to engage with online learning during the COVID-19 pandemic. Innovations in Education and Teaching International, 1–12. https://doi.org/10.1080/14703297.2022.2130394
   Google Scholar
• Louw, I., & Thukane, M. (2019). Teaching and learning sciences in times of disruption: Blended learning in action. Journal for New Generation Sciences, 17(2), 31–43. https://doi.org/10.10520/ejc-newgen_v17_n2_a3
   Web of Science ®Google Scholar
• MacCallum, 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. https://doi.org/10.1016/j.chb.2014.05.024
   Web of Science ®Google Scholar
• Macheka, M. T., Taru, J., & Sibanda, F. (2022). Emergency Remote Instruction (ERI) in times of COVID-19 pandemic: Experiences of educators at Zimbabwean higher education institutions. In E. Mogaji, V. Jain, F. Maringe, & R. E. Hinson (Eds.), Re-imagining educational futures in developing countries: Lessons from global health crises (pp. 221–240). Springer International Publishing. https://doi.org/10.1007/978-3-030-88234-1_12
   Google Scholar
• Maheshwari, G. (2021). Factors affecting students’ intentions to undertake online learning: An empirical study in Vietnam. Education and Information Technologies, 26(6), 6629–6649. https://doi.org/10.1007/s10639-021-10465-8
   PubMed Web of Science ®Google Scholar
• Mergany, N. N., Dafalla, A.-E., & Awooda, E. (2021). Effect of mobile learning on academic achievement and attitude of Sudanese dental students: A preliminary study. BMC Medical Education, 21(1), 121. https://doi.org/10.1186/s12909-021-02509-x
   PubMed Web of Science ®Google Scholar
• Mittal, N., & Alavi, S. (2020). Construction and psychometric analysis of teachers mobile learning acceptance questionnaire. Interactive Technology & Smart Education, 17(2), 171–196. https://doi.org/10.1108/ITSE-07-2019-0039
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Nikolopoulou, K., Gialamas, V., & Lavidas, K. (2020). Acceptance of mobile phone by university students for their studies: An investigation applying UTAUT2 model. Education and Information Technologies, 25(5), 4139–4155. https://doi.org/10.1007/s10639-020-10157-9
   Web of Science ®Google Scholar
• O’Doherty, D., Dromey, M., Lougheed, J., Hannigan, A., Last, J., & McGrath, D. (2018). Barriers and solutions to online learning in medical education – an integrative review. BMC Medical Education, 18(1), 130. https://doi.org/10.1186/s12909-018-1240-0
   PubMed Web of Science ®Google Scholar
• Pakpour, A. H., & Griffiths, M. D. (2020). The fear of COVID-19 and its role in preventive behaviors. Journal of Concurrent Disorders, 2(1), 58–63. https://doi.org/10.54127/wcic8036
   Google Scholar
• Papakostas, C., Troussas, C., Krouska, A., & Sgouropoulou, C. (2022). Exploring users’ behavioral intention to adopt mobile augmented reality in education through an extended technology acceptance model. International Journal of Human–Computer Interaction, 39(6), 1–9. https://doi.org/10.1080/10447318.2022.2062551
   Web of Science ®Google Scholar
• Qashou, A. (2021). Influencing factors in M-learning adoption in higher education. Education and Information Technologies, 26(2), 1755–1785. https://doi.org/10.1007/s10639-020-10323-z
   Web of Science ®Google Scholar
• Ramaiah, C. K. (2014). Emerging trends in electronic learning for library & information science professionals. Knowledge, Library and Information Networking, 328–350. https://doi.org/10.1007/978-3-642-24148-2_10
   Google Scholar
• Ramayah, T., Hwa, C., Chuah, F., Ting, H., & Memon, M. (2017). PLS-SEM Using SmartPls 3.0: Chapter 13: Assessment of Moderation Analysis. https://www.researchgate.net/publication/341357609
   Google Scholar
• Raza, S. A., Qazi, W., Khan, K. A., & Salam, J. (2021). Social isolation and acceptance of the Learning Management System (LMS) in the time of COVID-19 pandemic: An expansion of the UTAUT model. Journal of Educational Computing Research, 59(2), 183–208. https://doi.org/10.1177/0735633120960421
   Web of Science ®Google Scholar
• Rodríguez-Hidalgo, A. J., Pantaleón, Y., Dios, I., & Falla, D. (2020). Fear of COVID-19, stress, and anxiety in university undergraduate students: A predictive model for depression. Frontiers in Psychology, 11, 11. https://www.frontiersin.org/articles/10.3389/fpsyg.2020.591797
   PubMed Web of Science ®Google Scholar
• Romero-Hall, E., & Jaramillo Cherrez, N. (2023). Teaching in times of disruption: Faculty digital literacy in higher education during the COVID-19 pandemic. Innovations in Education and Teaching International, 60(2), 152–162. https://doi.org/10.1080/14703297.2022.2030782
   Web of Science ®Google Scholar
• Saadé, R., & Bahli, B. (2005). The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: An extension of the technology acceptance model. Information & Management, 42(2), 317–327. https://doi.org/10.1016/j.im.2003.12.013
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1016/j.chb.2016.09.009
   Web of Science ®Google Scholar
• Salehi, L., Rahimzadeh, M., Molaei, E., Zaheri, H., & Esmaelzadeh-Saeieh, S. (2020). The relationship among fear and anxiety of COVID-19, pregnancy experience, and mental health disorder in pregnant women: A structural equation model. Brain and Behavior, 10(11), e01835. https://doi.org/10.1002/brb3.1835
   PubMed Web of Science ®Google Scholar
• Saroia, A. I., & Gao, S. (2019). Investigating university students’ intention to use mobile learning management systems in Sweden. Innovations in Education and Teaching International, 56(5), 569–580. https://doi.org/10.1080/14703297.2018.1557068
   Web of Science ®Google Scholar
• Sharma, S. K., & Kitchens, F. L. (2004). Web services architecture for m-learning. Electronic Journal of E-Learning, 2(1), 203–216.
   Google Scholar
• Shenoy, V., Mahendra, S., & Vijay, N. (2020). COVID 19 lockdown technology adaption, teaching, learning, students engagement and faculty experience. Mukt Shabd Journal, 9(4), 698–702.
   Google Scholar
• Shen, R., Wang, M., & Pan, X. (2008). Increasing interactivity in blended classrooms through a cutting-edge mobile learning system. British Journal of Educational Technology, 39(6), 1073–1086. https://doi.org/10.1111/j.1467-8535.2007.00778.x
   Web of Science ®Google Scholar
• Sleiman, K. A. A., Juanli, L., Lei, H., Liu, R., Ouyang, Y., & Rong, W. (2021). User trust levels and adoption of mobile payment systems in China: An empirical analysis. SAGE Open, 11(4), 21582440211056600. https://doi.org/10.1177/21582440211056599
   Web of Science ®Google Scholar
• Steimer, T. (2002). The biology of fear- and anxiety-related behaviors. Dialogues in Clinical Neuroscience, 4(3), 231–249. https://doi.org/10.31887/DCNS.2002.4.3/tsteimer
   PubMedGoogle Scholar
• Syvänen, A., Ahonen, M., Jäppinen, A., Pehkonen, M., Turunen, H., & Vainio, T. (2005). Accessibility and mobile learning. In P. Nicholson, M. Ruohonen, J. B. Thompson, & J. Multisilta (Eds.), E-training practices for professional organizations (pp. 281–288). Springer US. https://doi.org/10.1007/0-387-23572-8_34
   Google Scholar
• Tarhini, A., Arachchilage, N. A. G., Masa’deh, R., & Abbasi, M. S. (2015). A critical review of theories and models of technology adoption and acceptance in information system research. International Journal of Technology Diffusion (IJTD), 6(4), 58–77. https://doi.org/10.4018/ijtd.2015100104
   Google Scholar
• Thatcher, J. B., & Perrewé, P. L. (2002). An empirical examination of individual traits as antecedents to computer anxiety and computer self-efficacy. MIS Quarterly, 26(4), 381–396. https://doi.org/10.2307/4132314
   Web of Science ®Google Scholar
• Thongsri, N., Shen, L., & Bao, Y. (2020). Investigating academic major differences in perception of computer self-efficacy and intention toward e-learning adoption in China. Innovations in Education and Teaching International, 57(5), 577–589. https://doi.org/10.1080/14703297.2019.1585904
   Web of Science ®Google Scholar
• van Raaij, E. M., & Schepers, J. J. L. (2008). The acceptance and use of a virtual learning environment in China. Computers & Education, 50(3), 838–852. https://doi.org/10.1016/j.compedu.2006.09.001
   Web of Science ®Google Scholar
• Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342–365. https://doi.org/10.1287/isre.11.4.342.11872
   Web of Science ®Google Scholar
• Wai, I. S. H., Ng, S. S. Y., Chiu, D. K., Ho, K. K., & Lo, P. (2018). Exploring undergraduate students’ usage pattern of mobile apps for education. Journal of Librarianship and Information Science, 50(1), 34–47. https://doi.org/10.1177/0961000616662699
   Web of Science ®Google Scholar
• Yalçın, İ., Can, N., Mançe Çalışır, Ö., Yalçın, S., & Çolak, B. (2022). Latent profile analysis of COVID-19 fear, depression, anxiety, stress, mindfulness, and resilience. Current Psychology, 41(1), 459–469. https://doi.org/10.1007/s12144-021-01667-x
   PubMed Web of Science ®Google Scholar
• Yang, Y., & Wang, X. (2019). Modeling the intention to use machine translation for student translators: An extension of technology acceptance model. Computers & Education, 133, 116–126. https://doi.org/10.1016/j.compedu.2019.01.015
   Web of Science ®Google Scholar
• Younis, H., & Elbanna, S. (2022). Teaching in times of crisis: The impact of the COVID-19 pandemic on higher education. Innovations in Education and Teaching International, 60(4), 451–462. https://doi.org/10.1080/14703297.2022.2060850
   Web of Science ®Google Scholar
• Yuan, Y.-P., Wei-Han Tan, G., Ooi, K.-B., & Lim, W.-L. (2021). Can COVID-19 pandemic influence experience response in mobile learning? Telematics and Informatics, 64, 101676. https://doi.org/10.1016/j.tele.2021.101676
   PubMed Web of Science ®Google Scholar
• Zhang, J., & Yu, S. (2023). Reconceptualising digital pedagogy during the COVID-19 pandemic: A qualitative inquiry into distance teaching in China. Innovations in Education and Teaching International, 60(2), 174–184. https://doi.org/10.1080/14703297.2021.2000473
   Web of Science ®Google Scholar