Examination of factors influencing the use of mobile learning system: An empirical study
Past studies have placed little emphasis on quality factors as the detebile learning application provides me a promptrminants of mobile learning adoption. Thus, this study’s purpose is to integrate the Technology Acceptance Model (TAM) with the updated DeLone and McLean’s model (DL&ML) to examine whether quality factors (including system quality, information quality, and service quality) and individual beliefs (including perceived usefulness and perceived ease of use) are the antecedents to students’ satisfaction and their intention to use, leading to enhancing their actual usage of mobile learning system. A total of 400 questionnaires were distributed. The results showed that quality factors (including system quality, information quality, and service quality) had significant effects on students’ satisfaction and their intention to use mobile learning; besides, perceived usefulness has significantly strong impacts on intention to use mobile learning, and satisfaction and intention to use both have significant effects on actual use of mobile learning. This study opens future work for using the identified quality factors as guidelines for researchers and designers to design and develop mobile learning applications.
KeywordsMobile learning Quality factors DeLone and McLean’s model TAM model Satisfaction Jordan
The authors acknowledges the Deanship of Scientific Research at King Faisal University for the financial support.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
- Abu-Al-Aish, A., & Love, S. (2013). Factors influencing students’ acceptance of m-learning: An investigation in higher education. The International Review of Research in Open and Distributed Learning, 14(5).Google Scholar
- Almaiah, M. A. (2018). Acceptance and usage of a mobile information system services in University of Jordan. Education and Information Technologies, 1–23.Google Scholar
- Almaiah, M. A., Jalil, M. A., & Man, M. (2016a). Preliminary study for exploring the major problems and activities of mobile learning system: A case study of Jordan. Journal of Theoretical & Applied Information Technology, 93(2).Google Scholar
- Almarashdeh, I. A., Sahari, N., Zin, N. A. M., & Alsmadi, M. (2010). The success of learning management system among distance learners in Malasian universities. Journal of Theoretical & Applied Information Technology, 21(2).Google Scholar
- Almasri, A. K. M. (2014). The influence on mobile learning based on technology acceptance model (tam), mobile readiness (Mr) and perceived interaction (pi) for higher education students. International Journal of Technical Research and Applications, 2(1), 05–11.Google Scholar
- Al-Shboul, M., Rababah, O., Al-Sayyed, R., Sweis, G., & Aldreabi, H. (2013). Roadmap to advance e-learning management system at the University of Jordan. Journal of American Science, 9(1), 531–545.Google Scholar
- Byrne, B. M. (2013). Structural equation modeling with LISREL, PRELIS, and SIMPLIS: Basic concepts, applications, and programming. Hove: Psychology PressGoogle Scholar
- Dahlstrom, E., Walker, J. D., & Dziuban, C. (2013). ECAR study of undergraduate students and information technology. 2013.Google Scholar
- Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319–340.Google Scholar
- Fathema, N., Shannon, D., & Ross, M. (2015). Expanding the technology acceptance model (TAM) to examine faculty use of learning management systems (LMSs) in higher education institutions. Journal of Online Learning & Teaching, 11(2).Google Scholar
- Field, A. (2009). Discovering statistics using SPSS. Sage publications.Google Scholar
- Gefen, D., Straub, D., & Boudreau, M. C. (2000). Structural equation modeling and regression: Guidelines for research practice. Communications of the Association for Information Systems, 4(1), 7.Google Scholar
- Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (Vol. 6). Upper Saddle River: Pearson Prentice Hall.Google Scholar
- Iqbal, S., & Qureshi, I. A. (2012). M-learning adoption: A perspective from a developing country. The International Review of Research in Open and Distributed Learning, 13(3), 147–164.Google Scholar
- Jaradat, M. I. R. M. (2014). Understanding individuals' perceptions, determinants and the moderating effects of age and gender on the adoption of mobile learning: Developing country perspective. International Journal of Mobile Learning and Organisation, 8(3–4), 253–275.MathSciNetCrossRefGoogle Scholar
- Lwoga, E. T. (2014). Critical success factors for adoption of web-based learning management systems in Tanzania. International Journal of Education and Development using Information and Communication Technology, 10(1), 4.Google Scholar
- Molenet.org (2009). The Mobile learning network (MoLeNET) retrieved APR. 28th, 2016, From http://www.molenet.org.uk/
- Prieto, J. C. S., Migueláñez, S. O., & García-Peñalvo, F. J. (2014). Mobile learning adoption from informal into formal: an extended TAM model to measure mobile acceptance among teachers. In Proceedings of the Second International Conference on Technological Ecosystems for Enhancing Multiculturality (pp. 595–602). ACM.Google Scholar
- Sekaran, U. (2009). Bougie. M,“research methods for business: A skill building approach”. UK: John Wiley & sons.Google Scholar
- The Jordan Times. (2014). 95% of Jordanians own mobiles; 47% use the Internet. Retrieved 21 April, 2016 from http://jordantimes.com/95-of-jordanians-own-mobiles-47-use-the-internet.
- Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425–478.Google Scholar
- Williams, P. W. (2009). Assessing mobile learning effectiveness and acceptance. Doctoral dissertation, The George Washington University.Google Scholar