Exploring the Moderating Role of Perceived Flexibility Advantages in Mobile Learning Continuance Intention (MLCI)
ARTICLE
Rui-Ting Huang, Chia-Hua Hsiao, Tzy-Wen Tang, Tsung-Cheng Lien
IRRODL Volume 15, Number 3, ISSN 1492-3831 Publisher: Athabasca University Press
Abstract
The primary purpose of this study was to explore the key factors that could affect mobile learning continuance intention (MLCI), and examine the moderating effect of perceived flexibility advantages (PFA) on the relationship between key mobile learning elements and continuance intention. Five hundred undergraduate students who had previously adopted mobile devices to learn English took part in this study. Partial least squares (PLS) analysis was utilized to test the hypotheses in this study. It has been found that the perceived usefulness of mobile technology, subjective norm, and self-management of learning could be closely linked to mobile learning continuance intention. With particular respect to the moderating role of perceived flexibility advantages, it has been demonstrated that PFA could moderate the relationship between perceived usefulness of mobile technology and mobile learning continuance intention, as well as the association between subjective norm and mobile learning continuance intention, whereas PFA did not moderate the link between self-management of learning and mobile learning continuance intention.This report has further added to the body of knowledge in the field of mobile learning through empirical examination.
Citation
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). The International Review of Research in Open and Distributed Learning, 15(3), 140-157. Athabasca University Press. Retrieved March 28, 2024 from https://www.learntechlib.org/p/156184/.
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Keywords
References
View References & Citations Map- Aggelidis, V.P., & Chatzoglou, P.D. (2009). Using a modified technology acceptance model in hospitals. International Journal of Medical Informatics, 78, 115-126.
- Arbaugh, J.B. (2000).Virtual classroom characteristics and student satisfaction with internet-based MBA courses. Journal of Management Education, 24, 32-54.
- Chen, G.D., Chang, C.K., & Wang, C.Y. (2008). Ubiquitous learning website: Scaffold learners by mobile devices with information-aware techniques. Computers& Education, 50, 77-90. Doi:10.1016/J.compedu.2006.03.004
- Chen, C-M., & Chung, C-J. (2008). Personalized mobile English vocabulary learning system based on item response theory and learning memory cycle. Computers& Education, 51, 624-645. Doi:10.1016/J.compedu.2007.06.011
- Chen, C.S. (2002). Self-regulated learning strategies and achievement in an introduction to information systems course. Information Technology, Learning, and Performance Journal, 20, 11-25.
- Chen, Y. (2010). Dictionary use and EFL Learning. A contrastive study of pocket electronic dictionaries and paper dictionaries. International Journal of Lexicography, 23, 275-306.
- Chen, S.-C., Yen, D.C., & Hwang, M.I. (2012). Factors influencing the continuance intention to the usage of Web 2.0: An empirical study. Computers in Human Behavior, 28, 933-941.
- Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 318-340.
- Evans, C. (2008). The effectiveness of m-learning in the form of podcast revision lectures in higher education. Computers& Education, 50, 491-498.
- Fornell, C., & Bookstein, F.L. (1982). Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing Research, 19, 440–452.
- Gardner, D., & Miller, L. (2011). Managing self-access language learning: Principles and practice. System, 39, 78-89.
- Hamzaee, R.G. (2005). A survey and a theoretical model of distance education programs. International Advances in Economic Research, 11, 215-229.
- Hsu, C-L., & Lin, J.C-C. (2008). Acceptance of blog usage: The roles of technology acceptance, social influence and knowledge sharing motivation. Information& Management, 45(1), 65-74.
- Huang, R-T., Lee, H-W., & Yang, F-Y. (2012, May). A case study to probe into key factors that affect learners’ mobile English learning continuance intention (MELCI). In 2012 XVth International CALL Research Conference (pp. 300303).
- Kavaliauskienę, G., & Kaminskienę, L. (2009). A complementary approach to lifelong learning strategies. Iberica, 18, 153-169. Retrieved from http://www.aelfe.org/documents/09_18_Kavaliauskiene.pdf
- Keil, M., Tan, B.C.Y., Wei, K.K., & Saarinen, T. (2000). Across-cultural study on escalation of commitment behavior in software projects. MIS Quarterly, 24, 299–325.
- Kukulska-Hulme, A. (2007). Mobile usability in educational contexts: What have we learnt? International Review of Research in Open and Distance Learning, 8(2). Retrieved from http://www.irrodl.org/index.php/irrodl/article/view/356/907
- Kung, S-C. (2002). Factors that affect students' decision to take distance learning courses: A survey study of technical college students in Taiwan. Educational Media International, 39, 299-305. Doi:10.1080/09523980210166044Vol 15 | No 3 July/14
- Huang, Hsiao, Tang, and LienLee, Y.C. (2006). An empirical investigation into the factors influencing the adoption of an e-learning system. Online Information Review, 30, 517-541.
- Liaw, S., Hatala, M., & Huang, H. (2009). Investigating acceptance toward mobile learning to assist individual knowledge management: Based on activity theory approach. Computers& Education, 54, 446-454.
- López-Nicolás, C., Molina-Castillo, F.J., & Bouwman, H. (2008). An assessment of advanced mobile services acceptance: Contributions from TAM and diffusion theory models. Information& Management, 45, 359-364.
- Marks, R.B., Sibley, S.D., & Arbaugh, J.B. (2005). A structural equation model of predictors for effective online learning. Journal of Management Education, 29, 531-563. Doi:10.1177/1052562904271199
- McGorry, S.Y. (2003). Measuring quality in online programs. The Internet and Higher Education, 6, 159-177. Doi:10.1016/S1096-7516(03)00022-8
- Ommundsen, Y., Haugen, R., & Lund, T. (2005). Academic self‐concept, implicit theories of ability, and self‐regulation strategies. Scandinavian Journal of Educational Research, 49, 461–474. 15 | No 3 July/14
- Huang, Hsiao, Tang, and LienRegan, J.A. (2003). Motivating students towards self-directed learning. Nurse Education Today, 23, 593-599. Doi:10.1016/S0260-6917(03)00099-6
- Roca, J.C., & Gagné, M. (2008). Understanding e-learning continuance intention in the workplace: A self-determination theory perspective. Computers in Human Behavior, 24, 1585-1604.
- Roca, J.C., Chiu, C.M., & Martínez, F.J. (2006). Understanding e-learning continuance intention: An extension of the technology acceptance model. International Journal of Human-Computer Studies, 64, 683-696.
- Van Raaij, E.M., & Schepers, J.J.L. (2008). The acceptance and use of a virtual learning environment in China. Computers& Education, 50, 838-852.
- Wang, Y., Wu, M., & Wang, H. (2009). Investigating the determinants and age and gender differences in the acceptance of mobile learning. British Journal of Educational Technology, 40, 92-118.
- Weschler, R., & Pitts, C. (2000). An experiment using electronic dictionaries with EFL Students. The Internet TESL Journal, VI. Retrieved from http://iteslj.org/Articles/Weschler-ElectroDict.html
- Yuen, H.K., & Ma, W.K. (2008). Exploring teacher acceptance of e-learning technology. Asia-Pacific Journal of Teacher Education, 36, 229-243. Doi:10.1080/13598660802232779Vol 15 | No 3 July/14
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