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Factors predicting online university students’ use of a mobile learning management system (m-LMS)

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Abstract

This study analyzed the relationships among factors predicting online university students’ actual usage of a mobile learning management system (m-LMS) through a structural model. Data from 222 students in a Korean online university were collected to investigate integrated relationships among their perceived ease of use, perceived usefulness, expectation-confirmation, satisfaction, continuance intention and actual usage of m-LMS. Results showed that perceived ease of use predicted perceived usefulness, but expectation-confirmation was not related to perceived usefulness. Perceived usefulness and expectation-confirmation predicted satisfaction. Perceived usefulness and satisfaction predicted continuance intention, but perceived ease of use was not related to continuance intention. Continuance intention predicted actual usage of m-LMS.

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References

  • Adams, D. A., Nelson, R. R., & Todd, P. A. (1992). Perceived usefulness, ease of use, and usage of information technology: A replication. MIS Quarterly, 16(2), 227–247.

    Article  Google Scholar 

  • Ajzen, I. (1985). From intention to actions: A theory of planned behavior. In J. Kuhl & J. Beckmann (Eds.), Action control: From cognition to behavior (pp. 11–39). Heidelberg: Springer.

    Chapter  Google Scholar 

  • Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.

    Article  Google Scholar 

  • Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs: Prentice Hall.

    Google Scholar 

  • Akour, H. (2010). Determinants of mobile learning acceptance: An empirical investigation in higher education (Doctoral dissertation). Stillwater: Oklahoma State University.

    Google Scholar 

  • Astin, A. (1993). What matters in college?: Four critical years revisited. San Francisco: Jossey-Bass Pulishers.

    Google Scholar 

  • Berge, Z. L., & Muilenburg, L. Y. (2013). Handbook of mobile learning. New York: Routledge.

    Google Scholar 

  • Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation- confirmation model. MIS Quarterly, 25(3), 351–370.

    Article  Google Scholar 

  • Chang, S. H. H., & Smith, R. A. (2008). Effectiveness of personal interaction in a learner-centered paradigm distance education class based on student satisfaction. Journal of Research on Technology in Education, 40(4), 407–426.

    Article  Google Scholar 

  • Chen, B., Sivo, S., Seilhamer, R., Sugar, A., & Mao, J. (2013). User acceptance of mobile technology: A campus-wide implementation of mobile learn application. Journal of Educational Computing Research, 49(3), 327–343.

    Article  Google Scholar 

  • Cheon, J., Lee, S., Crooks, S. M., & Song, J. (2012). An investigation of mobile learning readiness in higher education based on the theory of planned behavior. Computers & Education, 59(3), 1054–1064.

    Article  Google Scholar 

  • Chung, N., & Kwon, S. J. (2009). The effects of customers’ mobile experience and technical support on the intention to use mobile banking. Cyber Psychology & Behavior, 12(5), 539–543.

    Article  Google Scholar 

  • Commission, Korea Communications Standards. (2011). Smartphone arena: 20 million users in Korea. Seoul: Korea Communications Standards Commission.

    Google Scholar 

  • Corbeil, J. R., & Valdes-Corbeil, M. E. (2007). Are you ready for mobile learning? EDUCAUSE Quarterly, 30(2), 51–58.

    Google Scholar 

  • Corlett, D., Sharples, M., Bull, S., & Chan, T. (2005). Evaluation of a mobile learning organizer for university students. Journal of Computer Assisted learning, 21(3), 162–170.

    Article  Google Scholar 

  • Dahlstrom, E., & Bichsel, J. (2014). ECAR study of undergraduate students and information technology, 2014. Louisville: EDUCAUSE Center for Analysis and Research.

    Google Scholar 

  • Dahlstrom, E., & Brooks, D. C. (2014). Study of faculty and information technology, 2014. Louisville: EDUCAUSE Center for Analysis and Research.

    Google Scholar 

  • Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results (Doctoral dissertation). Cambridge: MIT Sloan School of Management.

    Google Scholar 

  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.

    Article  Google Scholar 

  • Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003.

    Article  Google Scholar 

  • Davis, F. D., & Venkatesh, V. (1996). A critical assessment of potential measurement biases in the technology acceptance model: Three experiments. International Journal of Human Computer Studies, 45(1), 19–45.

    Article  Google Scholar 

  • El-Hussein, M. O. M., & Cronje, J. C. (2010). Defining mobile learning in the higher education landscape. Educational Technology & Society, 13(3), 12–21.

    Google Scholar 

  • Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading: Addison-Wesley.

    Google Scholar 

  • Gikas, J., & Grant, M. M. (2013). Mobile computing devices in higher education: Student perspectives on learning with cellphones, smartphones & social media. The Internet and Higher Education, 19, 18–26.

    Article  Google Scholar 

  • Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1995). Multivariate data analysis with readings. Englewood Cliffs: Prentice Hall.

    Google Scholar 

  • Hayashi, A., Chen, C., Ryan, T., & Wu, J. (2004). The role of social presence and moderating role of computer self-efficacy in predicting the continuance usage of e-learning systems. Journal of Information Systems Education, 15(2), 139–154.

    Google Scholar 

  • Hong, S. J., Thong, J. Y. L., & Tam, K. Y. (2006). Understanding continued information technology usage behavior: A comparison of three models in the context of mobile internet. Decision Support Systems, 42(3), 1819–1834.

    Article  Google Scholar 

  • Hoppe, H. U., Joiner, R., Milrad, M., & Sharples, M. (2003). Guest editorial: Wireless and mobile technologies in education. Journal of Computer Assisted Learning, 19, 255–259.

    Article  Google Scholar 

  • Huang, J. H., Lin, Y. R., & Chuang, S. T. (2007). Elucidating user behavior of mobile learning: A perspective of the extended technology acceptance model. The Electronic Library, 25(5), 586–599.

    Article  Google Scholar 

  • Igbaria, M., & Livari, J. (1995). The effects of self-efficacy on computer usage. Omega, 23(6), 587–605.

    Article  Google Scholar 

  • Industry, National I. T., & Agency, Promotion. (2013). Survey of Korean e-Learning Industry. Seoul: Information Technology Research & Development Business.

    Google Scholar 

  • Johnson, L., Adams, B. S., Estrada, V., & Freeman, A. (2015). NMC horizon report: 2015 higher education edition. Retrieved from http://cdn.nmc.org/media/2015-nmc-horizon-report-HE-EN.pdf.

  • Karahanna, E., Straub, D. W., & Chervany, N. L. (1999). Information technology adoption across time: A cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Quarterly, 23(2), 183–213.

    Article  Google Scholar 

  • Kim, S. H. (2008). Moderating effects of job relevance and experience on mobile wireless technology acceptance: Adoption of a smartphone by individuals. Information & Management, 45(6), 387–393.

    Article  Google Scholar 

  • Kinash, S., Brand, J., & Mathew, T. (2012). Challenging mobile learning discourse through research: Student perceptions of Blackboard mobile learn and iPads. Australasian Journal of Educational Technology, 28(4), 639–655.

    Article  Google Scholar 

  • King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43(6), 740–755.

    Article  Google Scholar 

  • Kishton, J. M., & Widaman, K. F. (1994). Unidimensional versus domain representative parceling of questionnaire items: An empirical example. Educational and Psychological Measurement, 54(3), 757–765.

    Article  Google Scholar 

  • Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). New York≪: Guilford Press.

    Google Scholar 

  • Korea Education and Research Information Service. (2012). White paper on ICT in education. Seoul: Korea Education and Research Information Service.

    Google Scholar 

  • Kukulska-Hulme, A. (2012). How should the higher education workforce adapt to advancements in technology for teaching and learning? The Internet and Higher Education, 15(4), 247–254.

    Article  Google Scholar 

  • Kukulska-hulme, A., & Traxler, J. (2005). Mobile learning: A handbook for educators and trainers. New York: Routledge.

    Google Scholar 

  • Liaw, S. S., Hatala, M., & Huang, H. M. (2010). Investigating acceptance toward mobile learning to assist individual knowledge management: Based on activity theory approach. Computer & Education, 54(2), 446–454.

    Article  Google Scholar 

  • Limayem, M., & Cheung, C. M. K. (2008). Understanding information systems continuance: The case of Internet-based learning technologies. Information & Management, 45(4), 227–232.

    Article  Google Scholar 

  • Lin, C. S., Wu, S., & Tsai, R. J. (2005). Integrating perceived playfulness into expectation- confirmation model for web portal context. Information & Management, 42(5), 683–693.

    Article  Google Scholar 

  • Little, T. D., Cunningham, W. A., Shahar, G., & Widaman, K. F. (2002). To parcel or not to parcel: Exploring the question, weighting the merits. Structural Equation Modeling, 9(2), 151–173.

    Article  Google Scholar 

  • Liu, I., Chen, M., Sun, Y., Weble, D., & Kuo, C. (2010). Extending the TAM model to explore the factors that affect intention to use an online learning community. Computers & Education, 54(2), 600–610.

    Article  Google Scholar 

  • Liu, Y., & Han, S. (2010). Understanding the factors driving m-learning adoption: A literature review. Campus-Wide Information Systems, 27(4), 210–236.

    Article  Google Scholar 

  • Looi, C., Sun, D., Wu, L., Seow, P., Chia, G., Wong, L., & Norris, C. (2014). Implementing mobile learning curricula in a grade level: Empirical study of learning effectiveness at scale. Computers & Education, 77, 101–115.

    Article  Google Scholar 

  • Maniar, N., Bennett, E., Hand, S., & Allan, G. (2008). The effect of mobile phone screen size on video based learning. Journal of Software, 3(4), 51–61.

    Article  Google Scholar 

  • Mathieson, K. (1991). Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2(3), 173–191.

    Article  Google Scholar 

  • McConatha, D., & Praul, M. (2008). Mobile learning in higher education: An empirical assessment of a new educational tool. The Turkish Online Journal of Educational Technology, 7(3), 15–21.

    Google Scholar 

  • Ministry of Education Science and Technology. (2011). A project report selecting top online colleges to enhance the quality of competences. Seoul: Ministry of Education Science and Technology.

    Google Scholar 

  • Nguyen, L., Barton, S. M., & Nguyen, L. T. (2015). iPads in higher education-hype and hope. British Journal of Educational Technology, 46(1), 190–203.

    Article  Google Scholar 

  • Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17(4), 460–469.

    Article  Google Scholar 

  • Oliver, R. L., & DeSarbo, W. S. (1988). Response Determinants in Satisfaction Judgments. Journal of Consumer Research, 14(4), 495–507.

    Article  Google Scholar 

  • Ozkan, S., & Koseler, R. (2009). Multi-dimensional students’ evaluation of e-learning systems in the higher education context: An empirical investigation. Computers & Education, 53(4), 1285–1296.

    Article  Google Scholar 

  • Park, S. Y. (2009). An analysis of the technology acceptance model in understanding university students’ behavioral intention to use e-learning. Educational Technology & Society, 12(3), 150–162.

    Google Scholar 

  • Peng, H., Su, Y., Chou, C., & Tsai, C. (2009). Ubiquitous knowledge construction: Mobile learning re-defined and a conceptual framework. Innovations in Education & Teaching International, 46(2), 171–183.

    Article  Google Scholar 

  • Pinkwart, N., Hoppe, H. U., Milrad, M., & Perez, J. (2003). Educational scenarios for the cooperative use of personal digital assistants. Journal of Computer Assisted learning, 19(3), 383–391.

    Article  Google Scholar 

  • Pituch, K., & Lee, Y. (2006). The influence of system characteristics on e-learning use. Computers & Education, 47(2), 222–244.

    Article  Google Scholar 

  • Premkumar, G., & Bhattacherjee, A. (2008). Explaining information technology usage: A test of competing models. Omega, 36(1), 64–75.

    Article  Google Scholar 

  • Quinn, C. (2001). Get ready for m-learning. Training and Development, 20(2), 20–21.

    Google Scholar 

  • Rai, A., Lang, S. S., & Welker, R. B. (2002). Assessing the validity of IS success models. Information Systems Research, 13(1), 50–59.

    Article  Google Scholar 

  • Roca, J. C., & Gagne, M. (2008). Understanding e-learning continuance intention in the workplace: A self-determination theory perspective. Computers in Human Behavior, 24(4), 1585–1604.

    Article  Google Scholar 

  • Rogers, E. M. (2003). Diffusion of innovation. New York: Free Press.

    Google Scholar 

  • Sass, D. A., & Smith, P. L. (2006). The effects of parceling unidimensional scales on structural parameter estimates in structural equation modeling. Structural Equation Modeling, 13(4), 566–586.

    Article  Google Scholar 

  • Sha, L., Looi, C.-K., Chen, W., & Zhang, B. H. (2009). Understanding mobile learning from the perspective of self-regulated learning. Journal of Computer Assisted learning, 28(4), 366–378.

    Article  Google Scholar 

  • Sharma, S. K., & Kitchens, F. L. (2004). Web services architecture for m-learning. Electronic Journal on e-Learning, 2(1), 203–216.

    Google Scholar 

  • Shin, N. (2003). Transactional presence as a critical predictor of success in distance learning. Distance Education, 24(1), 69–86.

    Article  Google Scholar 

  • Shin, D. H., & Kim, S. (2012). An expectation-confirmation approach to the users’ continued use of smart phones. Korean Journal of Journalism & Communication Studies, 56(2), 331–356.

    Google Scholar 

  • Shin, D. H., Shin, Y. J., Choo, H., & Beom, K. (2011). Smartphones as smart pedagogical tools: Implications for smartphones as u-learning devices. Computers in Human Behavior, 27(6), 2207–2214.

    Article  Google Scholar 

  • Spreng, R. A., MacKenzie, S. B., & Olshavsky, R. W. (1996). A reexamination of the determinants of consumer satisfaction. Journal of Marketing, 60(3), 15–32.

    Article  Google Scholar 

  • Sun, P. C., Tsai, R. J., Finger, G., Chen, Y. Y., & Yeh, D. (2008). What drives a successful e-Learning?: An empirical investigation of the critical factors influencing learner satisfaction. Computers & Education, 50(4), 1183–1202.

    Article  Google Scholar 

  • Taylor, S., & Todd, P. A. (1995). Understanding information on technology usage: A test of competing models. Information Systems Research, 6(2), 144–176.

    Article  Google Scholar 

  • Thong, J. Y. L., Hong, S., & Tam, K. Y. (2006). The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. International Journal of Human-Computer Studies, 64(9), 799–810.

    Article  Google Scholar 

  • Traxler, J. (2007). Current state of mobile learning. International Review on Research in Open and Distance Learning, 8(2), 9–24.

    Google Scholar 

  • Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.

    Article  Google Scholar 

  • Vogel, D., Kennedy, D., & Kwok, R. D. W. (2009). Does using mobile device applications lead to learning? Journal of Interactive Learning Research, 20(4), 469–485.

    Google Scholar 

  • Wixom, B. H., & Todd, P. A. (2005). A theoretical integration of user satisfaction and technology acceptance. Information Systems Research, 16(1), 85–102.

    Article  Google Scholar 

  • Zhang, N., Guo, X., & Chen, G. (2007). Extended information technology initial acceptance model and its empirical test. Systems Engineering-Theory & Practice, 27(9), 123–130.

    Article  Google Scholar 

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Acknowledgements

This study was supported by the National Research Foundation of Korea Grant, funded by the Korean Government (#2012-045331).

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Correspondence to Nari Kim.

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Joo, Y.J., Kim, N. & Kim, N.H. Factors predicting online university students’ use of a mobile learning management system (m-LMS). Education Tech Research Dev 64, 611–630 (2016). https://doi.org/10.1007/s11423-016-9436-7

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