Factors influencing students' behavioral intention to use mobile learning: a study of E-commerce majors in private higher vocational colleges
Journal: Region - Educational Research and Reviews DOI: 10.32629/rerr.v6i11.3156
Abstract
In today's world of rapid advances in information technology, mobile learning provides learners with a new way of acquiring knowledge, enabling them to access information anywhere according to their schedule. Mobile learning expands the scope of electronic and distance education, which makes modern technology and globalization possible. The purpose of this study is to explore the factors influencing the willingness of e-commerce students to use M-learning in private higher vocational colleges and universities based on the integration model of expectancy confirmation model (ECM) and unified theory of acceptance and use of technology (UTAUT). Data were collected from 219 students majoring in e-commerce in Guangzhou City Construction College through questionnaires. SPSS 26.0 and Smart-PLS 3.3.9 were used to analyze the data. The results showed that perceived usefulness, facilitating conditions, social influence, perceived enjoyment and satisfaction had a significant effect on students' willingness to use mobile learning. This study developed and validated a new mobile learning model. We encourage future researchers to investigate other predictors of M-learning intentions not found in this study.
Keywords
mobile learning; private higher vocational colleges; ECM; UTAUT
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[4] Abubakar U. 2024. Mobile pedagogy in higher education: A comprehensive analysis of mobile learning practices for classroom management in Usmanu Danfodiyo University, Sokoto State, Nigeria. Eduscape : Journal of Education
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[5] Al-Adwan AS, Albelbisi NA, Hujran O, et al. 2021. Developing a holistic success model for sustainable e-learning: a structural equation modeling approach. Sustainability , 13: 9453.
[6] Dahal N, Manandhar NK, Luitel L, et al. 2022. ICT tools for remote teaching and learning mathematics: A proposal for autonomy and engagements. Advances in Mobile Learning Educational Research, 2(1): 289-296.
[7] Alturki U, Aldraiweesh A. 2022. Students' perceptions of the actual use of mobile learning during COVID-19 pandemic in higher education. Sustainability, 14: 1125.
[8] Bhattacherjee A. 2021. Understanding information systems continuance: an expectation-confirmation model. MIS Q, 25(3): 351-370.
[9] Davis FD, Bagozzi RP, Warshaw PR. 1989. User acceptance of computer technology: a comparison of two theoretical models. Manag. Sci, 35: 982-1003.
[10] Voicu MC, Muntean M. 2023. Factors that influence mobile learning among university students in Romania. Electronics, 12: 938.
[11] Sayaf AM, Alamri MM, Alqahtani M, et al. 2022. Factors influencing university students' adoption of digital learning technology in teaching and learning. Sustainability, 14(1): 493.
[12] Sánchez-Prieto JC, Hernández-García Á, García-Peñalvo FJ, et al. 2019. Break the walls! Second-Order barriers and the acceptance of mLearning by first-year pre-service teachers. Comput. Hum. Behav, 95: 158-167.
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[14] Lutfi A, Saad M, Almaiah MA, et al. 2022. Actual use of mobile learning technologies during social distancing circumstances: case study of King Faisal University students. Sustainability, 14: 7323.
[15] Venkatesh V, Morris MG, Davis GB, et al. 2003. User acceptance of information technology: toward a unified view. MIS Q, 425-478.
[16] Su C-Y, Chao C-M. 2022. Investigating factors influencing nurses' behavioral intention to use mobile learning: Using a modified unified theory of acceptance and use of technology model. Front. Psychol, 13: 673350.
[17] Alraja MN. 2016. The effect of social influence and facilitating conditions on e-government acceptance from the individual employees' perspective. Pol. J. Manag. Stud, 14(2): 18-27. https://doi.org/10.17512/pjms.2016;14.2.02
[18] Lutfi A. 2022. Factors influencing the continuance intention to use accounting information system in Jordanian SMEs from the perspectives of UTAUT: Top management support and self-efficacy as predictor factors. Economies, 10: 75.
[19] Davis FD. 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q, 319-339.
[20] Aziz M, Pramana E, Junaedi H. 2022. Adoption of mobile learning at universities using the extended technology acceptance model. KINETIK, 7: 4.
[21] Hair JF, Risher JJ, Sarstedt M, Ringle CM. 2019. When to use and how to report the results of PLS-SEM. Eur. Bus. Rev, 31(1): 2-24.
[22] Fornell C, Larcker DF. 1981. Evaluating structural equation models with unobservable variables and measurement error. J. Mark.Res, 18: 39-50.
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[26] Afandi W. Saudi higher education student acceptance of mobile learning. International Journal of Information and Education Technology, 12(6).
[27] Shaya N, Baroudi S, Mohebi L. 2023. Examining factors determining the behavioral intention to use mobile learning systems in higher education: an integrative framework during the COVID-19 pandemic. All Works, 5659.
[28] Sabri S, Gani A, Yadegaridehkordi E, et al. 2022. A framework for mobile learning acceptance amongst formal part-time learners: from the andragogy perspective. IEEE, 10: 61213-61227.
[29] Shukla S. 2021. M-learning adoption of management students': A case of India. Educ Inf Technol, 26: 279-310.
[30] Al-Rahmi AM, Al-Rahmi WM, Alturki U, et al. 2021. Exploring the factors affecting mobile learning for sustainability in higher education. Sustainability, 13: 7893.
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