ISSN: 2222-6990
Open access
This study delves into the intricate factors influencing the intention to use mobile learning among students in online flexible distance learning higher education institutions, with self-efficacy serving as a pivotal mediator. The primary objective is to comprehensively assess the direct and indirect impacts of performance expectancy, facilitating conditions, effort expectancy, and self-management learning on the intention to engage with mobile learning platforms. Through a meticulous survey methodology, questionnaires were disseminated via email using purposive sampling, yielding a commendable response rate. Out of the 544 surveys distributed, 431 were successfully collected, and 403 clean datasets were meticulously analyzed. Employing advanced Structural Equation Modeling (SEM) techniques with the aid of Smartpls4 software, the study rigorously examined the relationships between key variables. The empirical findings unveiled compelling insights, showcasing the significant positive relationships between effort expectancy, self-management learning, and self-efficacy to utilize mobile learning. Moreover, facilitating conditions and self-efficacy emerged as influential factors, with self-efficacy playing a crucial mediating role in shaping students' intentions. The study's theoretical implications extend beyond conventional technology acceptance models, shedding light on the intricate interplay of cognitive, motivational, and contextual factors in mobile learning usage. These findings offer a robust foundation for advancing existing technology acceptance frameworks and provide a nuanced understanding of the multifaceted dynamics influencing students' intentions toward mobile learning adoption. Furthermore, the practical implications underscore the importance of enhancing mobile learning initiatives by prioritizing usability, accessibility, technical support, and fostering learners' self-efficacy. This holistic approach aims to optimize the educational experience and promote effective utilization of mobile learning technologies in online flexible distance learning environments.
Adan?r, A. G., & Muhametjanova, G. (2021). University students’ acceptance of mobile
learning: A comparative study in Turkey and Kyrgyzstan. Education and Information Technologies, 26(5), 6163-6181.
Afari, E., Eksail, F. A. A., Khine, M. S., & Alaam, S. A. (2023). Computer self-efficacy and ICT
integration in education: Structural relationship and mediating effects. Education and Information Technologies, 28(9), 12021-12037.
Afzal, J., & Anwar, G. (2023). An empirical study on academic sustainability of mobile learning
at university level. Pakistan Journal of Educational Research, 6(2).
Ahadzadeh, A. S., Wu, S. L., Ong, F. S., & Deng, R. (2021). The mediating influence of the
unified theory of acceptance and use of technology on the relationship between internal health locus of control and mobile health adoption: cross-sectional study. Journal of medical Internet research, 23(12), e28086.
Al-Adwan, A. S., Al-Adwan, A., & Berger, H. (2018). Solving the mystery of mobile learning
adoption in higher education. Int. J. Mobile Communications, 16(1), 24-49
Al-Bashayreh, M., Almajali, D., Altamimi, A., Masa’deh, R. E., & Al-Okaily, M. (2022). An
empirical investigation of reasons influencing student acceptance and rejection of mobile learning apps usage. Sustainability, 14(7), 4325.
Alowayr, A. (2022). Determinants of mobile learning adoption: extending the unified theory
of acceptance and use of technology (UTAUT). The International Journal of Information and Learning Technology, 39(1), 1-12.
Al-Rahmi, A. M., Al-Rahmi, W. M., Alturki, U., Aldraiweesh, A., Almutairy, S., & Al-Adwan, A.
S. (2022). Acceptance of mobile technologies and M-learning by university students: An empirical investigation in higher education. Education and Information Technologies, 27(6), 7805-7826.
Chao, C. M. (2019). Factors determining the behavioral intention to use mobile learning: An
application and extension of the UTAUT model. Frontiers in psychology, 10, 446627.
Chao, C. M., & Chen, W. R. (2023). Factors determining behavioural intention of nursing
students to use mobile learning: an application and extension of the UTAUT model. International Journal of Mobile Communications, 21(4), 472-493.
Chau, K. F. (2024). Understanding mature students' intention to use mobile learning in higher
education: integrating personality traits and UTAUT. International Journal of Educational Management.
Chen, J. (2022). Adoption of M-learning apps: A sequential mediation analysis and the
moderating role of personal innovativeness in information technology. Computers in Human Behavior Reports, 8, 100237.
Chen, Y., Yang, S., Zhang, S., & Yao, J. (2023). Does self-management of learning matter?
Exploring mobile learning continuance from a valence framework perspective. International Journal of Mobile Communications, 22(4), 429-448.
Cohen, J. (1992). A power primer. Psychological Bulletin, 112, 155–159. doi:10.1037/0033-
2909.112.1.155
Dahri, N. A., Al-Rahmi, W. M., Almogren, A. S., Yahaya, N., Vighio, M. S., Al-maatuok, Q., &
Al-Adwan, A. S. (2023). Acceptance of mobile learning technology by teachers: influencing mobile self-efficacy and 21st-century skills-based training. Sustainability, 15(11), 8514.
Davis, F.D. (1989), Perceived usefulness, perceived ease of use, and user acceptance of
information technology, MIS Quarterly, 13(3), 319-340.
Deng, P., Chen, B., & Wang, L. (2023). Predicting students’ continued intention to use E-
learning platform for college English study: the mediating effect of E-satisfaction and habit. Frontiers in Psychology, 14, 1182980.
Do Huynh, C. B., Nguyen, A. H., Nguyen, T. H. T., & Le, A. H. (2023). Factors affecting students’
intention to use mobile learning at universities: an empirical study. Journal of System and Management Sciences, 13(1), 281-304.
Donaldson, J. L. (2011). Mobile learning in university contexts based on the unified theory of
acceptance and use of technology (UTAUT). NAER Journal, 8(1), 1-15.
Ghaleb, M. M. S., & Alshiha, A. A. (2023). Empowering Self-Management: Unveiling the Impact
Of Artificial Intelligence in Learning on Student Self-Efficacy and Self-Monitoring. Eurasian Journal of Educational Research, 107(107), 68-94.
Gharaibeh, M. K., & Gharaibeh, N. K. (2020). An empirical study on factors influencing the
intention to use mobile learning. Adv. Sci. Technol. Eng. Syst. J, 5(5), 1261-1265.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Thousand
Oaks, CA: SAGE.
Hair, J. F., Sarstedt, M., Ringle, C. M., and Gudergan, S. P. (2018). Advanced issues in partial
least squares structural equation modeling. Thousand Oakes, CA: Sage Publications
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (3 ed.). Thousand Oaks, CA: Sage.
Henseler, J., Ringle, C. M., and Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling, Journal of the Academy of Marketing Science, 43(1): 115-135.
Irwanto, I., Saputro, A. D., Widiyanti, W., & Laksana, S. D. (2023). Global trends on mobile
learning in higher education: A bibliometric analysis (2002–2022). IJIET: International Journal of Information and Education Technology, 13(2), 223-231.
Kebah, M., Raju, V., & Osman, Z. (2019). Growth of online purchase in Saudi Arabia retail industry. International Journal of Recent Technology and Engineering, 8(3), 869-872.. ISSN: 2277-3878
Kebah, M., Raju, V., & Osman, Z. (2019). Online purchasing trend in the retail industry in Saudi. International Journal of Recent Technology and Engineering (IJRTE), 8(3), 865-868. ISSN: 2277-3878
Kock, N., & Lynn, G. S. (2012). Lateral collinearity and misleading results in variance-based SEM: An illustration and recommendations. Journal of the Association for
Information Systems, 13(7), 546-580.
Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach.
International Journal of e-Collaboration, 11(4), 1-10.
Latip, M. S. A., Noh, I., Tamrin, M., & Latip, S. N. N. A. (2020). Students’ acceptance for e-
learning and the effects of self-efficacy in Malaysia. International Journal of Academic Research in Business and Social Sciences, 10(5), 658-674.
Li, X. T., Rahman, A., Connie, G., & Osman, Z. (2020). Examining customers' perception of electronic shopping mall's e-service quality. International Journal of Services, Economics and Management, 11(4), 329-346.
Liengaard, B. D., Sharma, P. N., Hult, G. T. M., Jensen, M. B., Sarstedt, M., Hair, J. F., &
Ringle, C. M. (2021). Prediction: Coveted, Yet Forsaken? Introducing a Cross-
validated Predictive Ability Test in Partial Least Squares Path Modeling. Decision Sciences, 52(2), 362-392.
Magsayo, R. T. (2023). Mobile learning adoption continuance: Role of locus of control on its
determinants. Interactive Technology and Smart Education, 20(2), 177-208.
Mshali, H., & Al-Azawei, A. (2022). Predicting online learning adoption: The role of
compatibility, self-efficacy, knowledge sharing, and knowledge acquisition. Journal of Information Science Theory and Practice, 10(3), 24-39.
Osman, Z., Mohamad, W., Mohamad, R. K., Mohamad, L., & Sulaiman, T. F. T. (2018). Enhancing students’ academic performance in Malaysian online distance learning institutions. Asia Pacific Journal of Educators and Education, 33, 19-28.
Ozturk, A. B., Bilgihan, A., Nusair, K., and Okumus, F. (2016). What keeps the mobile hotel
booking users loyal? Investigating the roles of self-efficacy, compatibility, perceived ease of use, and perceived convenience. Int. J. Inf. Manag. 36, 1350–1359. doi: 10.1016/j.ijinfomgt.2016.04.005
Rahman, M. K., Bhuiyan, M. A., Mainul Hossain, M., & Sifa, R. (2023). Impact of technology
self-efficacy on online learning effectiveness during the COVID-19 pandemic. Kybernetes, 52(7), 2395-2415.
Rajam, V., Banerjee, S., & Alok, S. (2024). Can MOOC be a medium of lifelong learning?
Examining the role of Perceived Reputation and Self-efficacy on Continuous Use Intention of MOOC. Journal of e-Learning and Knowledge Society, 20(1), 1-14.
Ringle, C. M., and Sarstedt, M. (2016). Gain more insight from your PLS-SEM results: The
importance-performance map analysis. Industrial Management & Data Systems. 116: 1865–1886.
Ringle, Christian M., Wende, Sven, & Becker, Jan-Michael. (2022). SmartPLS 4. Oststeinbek:
SmartPLS. Retrieved from https://www.smartpls.com
Sang, G., Wang, K., Li, S., Xi, J., & Yang, D. (2023). Effort expectancy mediates the
relationship between instructors’ digital competence and their work engagement: evidence from universities in China. Educational technology research and development, 71(1), 99-115.
Sharma, S., & Saini, J. R. (2022). On the role of teachers’ acceptance, continuance intention
and self-efficacy in the use of digital technologies in teaching practices. Journal of Further and Higher Education, 46(6), 721-736.
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. Journal of Educators Online, 20(1).
Shmueli, G., Ray, S., Estrada, J. M., and Chatla, S. B. (2016). The elephant in the
room: predictive performance of PLS models. Journal of Business Research, 69: 4552–4564.
Sidik, D., & Syafar, F. (2020). Exploring the factors influencing student’s intention to use
mobile learning in Indonesia higher education. Education and Information Technologies, 25(6), 4781-4796.
Ghaleb, S. M. M., & Alshiha, A. A. (2023). Empowering Self-Management: Unveiling the
Impact of Artificial Intelligence in Learning on Student Self-Efficacy and Self-Monitoring. Eurasian Journal of Educational Research (EJER), (107).
Tarhini, A., AlHinai, M., Al-Busaidi, A. S., Govindaluri, S. M., & Al Shaqsi, J. (2024). What drives
the adoption of mobile learning services among college students: An application of SEM-neural network modeling. International Journal of Information Management Data Insights, 4(1), 100235.
Thomas, T. D., Singh, L., & Gaffar, K. (2013). The utility of the UTAUT model in explaining
mobile learning adoption in higher education in Guyana. International Journal of Education and Development using Information and Communication Technology, 9(3), 71-85.
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information
technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157-178.
Venkatesh, V., Morris, M. G., Davis, G. B., Davis, F. D. (2003) User acceptance of information
technology: Toward a unified view. MIS Q., 27, 425–478.
Voicu, M. C., & Muntean, M. (2023). Factors that influence mobile learning among university
students in Romania. Electronics, 12(4), 938.
Wu, Y., Wen, J., Wang, X., Wang, Q., Wang, W., Wang, X., & Cong, L. (2022). Associations
between e-health literacy and chronic disease self-management in older Chinese patients with chronic non-communicable diseases: a mediation analysis. BMC Public Health, 22(1), 2226.
Yu, C. W., Chao, C. M., Chang, C. F., Chen, R. J., Chen, P. C., & Liu, Y. X. (2021). Exploring
behavioral intention to use a mobile health education website: an extension of the UTAUT 2 model. Sage Open, 11(4), 21582440211055721.
(Osman et al., 2024)
Osman, Z., Kasbun, N., & Mohamad, R. K. (2024). Unearthing the Intention to Use Mobile Learning among Students in Online Flexible Distance Learning Higher Education Institutions. International Journal of Academic Research in Business and Social Sciences, 14(7), 1193–1210.
Copyright: © 2024 The Author(s)
Published by HRMARS (www.hrmars.com)
This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at: http://creativecommons.org/licences/by/4.0/legalcode