Abstract
This study examines the factors that influence mobile learning adoption among Chinese university students. China’s higher education market is large and mobile device ownership is considered a status symbol. Combined, these two factors suggest mobile learning could have a big impact in China. From the literature, we identified three major areas that may affect behavioral intention to adopt mobile learning in this context: pedagogical, personal, and social. A 27-item survey was administered online to 292 students at a northern Chinese university. Exploratory factor analysis was used to measure the reliability and validity of the survey items. Path analysis was then used to test the hypotheses in the proposed mobile learning acceptance model. Findings indicate that pedagogical factors have the greatest effect on students’ behavioral intention to adopt mobile learning. Social influences, especially social image and subjective norm, also play a role. Personal innovativeness was not found to be a main factor, although it has some indirect influences.
Similar content being viewed by others
References
Abachi, H. R., & Muhammad, G. (2014). The impact of m-learning technology on students and educators. Computers in Human Behavior, 30, 491–496.
Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204–215.
Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136–162). Newbury Park, CA: Sage.
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.
Cisco. (2014). Cisco visual networking index: Global mobile data traffic forecast update, 2013–2018. San Jose, CA: Cisco.
Cochrane, T., & Bateman, R. (2010). Smartphones give you wings: Pedagogical affordances of mobile Web 2.0. Australasian Journal of Educational Technology., 26(1), 1–14.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–339.
Deloitte. (2013). The state of the global mobile consumer, 2013: Divergence deepens. London: Deloitte.
Dennen, V. P., & Hao, S. (2014a). Paradigms of use, learning theory, and app design. In C. Miller & A. Doering (Eds.), The new landscape of mobile learning (pp. 20–41). New York: Routledge.
Dennen, V. P., & Hao, S. (2014b). Intentionally mobile pedagogy: The M-COPE framework for mobile learning in higher education. Technology, Pedagogy and Education, 23(3), 397–419.
Finney, S. J., & DiStefano, C. (2006). Non-normal and categorical data in SEM. In G. R. Hancock & R. O. Mueller (Eds.), Structural equation modeling: A second course. Greenwich, CO: Information Age Publishing.
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.
Fu, J., & Yang, X. (2009). Ten year review of domestic mobile learning theories and practices. China Educational Technology., 270(7), 36–41.
Gedik, N., Hanci-Karademirci, A., Kursun, E., & Cagiltay, K. (2012). Key instructional design issues in a cellular phone-based mobile learning project. Computers & Education, 58(4), 1149–1159. doi:10.1016/j.compedu.2011.12.002.
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. doi:10.1016/j.iheduc.2013.06.002.
Goodman, L. A. (1961). Snowball sampling. The Annals of Mathematical Statistics, 32, 148–170.
Green, N., Harper, R. H. R., Murtagh, G., & Cooper, G. (2001). Configuring the mobile user: Sociological and industry views. Personal and Ubiquitous Computing, 5(2), 146–156.
GSMA. (2011). Global education landscape report. London: GSM Association.
Hair, J., Black, W., Babin, B., Anderson, R., & Tatham, R. (2006). Multivariate data analysis (6th ed.). Englewood Cliffs, NJ: Pearson Prentice Hall.
Hu, L., & Bentler, P. M. (1999). Cutoff criterion for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55.
iResearch Consulting Group. (2012). 2011–2012 China mobile internet report (brief edition). Retrieved from http://www.iresearchchina.com/samplereports/4222.html
Johnson, L., Adams Becker, S., Cummins, M., Estrada, V., Freeman, A., & Ludgate, H. (2013). NMC Horizon report: 2013 Higher education. Austin, TX: The New Media Consortium.
Karahanna, E., & Straub, D. W. (1999). Information technology adoption across time: A cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Quarterly, 23(2), 183–214.
Kim, P., Hagashi, T., Carillo, L., Gonzales, I., Makany, T., Lee, B., et al. (2011). Socioeconomic strata, mobile technology, and education: A comparative analysis. Educational Technology Research and Development, 59(4), 465–486. doi:10.1007/s11423-010-9172-3.
King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43(6), 740–755.
Koole, M. L. (2009). A model for framing mobile learning. In M. Ally (Ed.), Mobile learning: Transforming the delivery of education and training (pp. 25–47). Athabasca: AU Press.
Koszalka, T. A., & Ntloedibe-Kuswani, G. S. (2010). Literature on the safe and disruptive learning potential of mobile technologies. Distance Education, 31(2), 139–157. doi:10.1080/01587919.2010.498082.
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. doi:10.1016/j.iheduc.2011.12.002.
Kukulska-Hulme, A., & Pettit, J. (2008). Semi-formal learning communities for professional development in mobile learning. Journal of Computing in Higher Education, 20(2), 35–47.
Law, N. W. Y., Yuen, A. H. K., Chan, C. K. K., Yuen, J. K. L., Pan, N. F. C., Lai, M., et al. (2009). New experiences, new epistemology, and the pressures of change: The Chinese learning in transition. In C. K. K. Chan & N. Rao (Eds.), Revisiting the Chinese learner (pp. 89–129). Hong Kong: Springer.
Lee, Y., Kozar, K. A., & Larsen, K. (2003). The technology acceptance model: Past, present, and future. Communications of the Association for Information Systems, 12(50), 752–780.
Levy, M., & Kennedy, C. (2005). Learning Italian via mobile SMS. In A. Kukulska-Hulme & J. Traxler (Eds.), Mobile learning: A handbook for educators and trainers (pp. 110–117). London: Routledge.
Lewis, W., Agarwal, R., & Sambamurthy, V. (2003). Sources of influence on beliefs about information technology use: an empirical study of knowledge workers. MIS Quarterly, 27(4), 657–679.
Liu, Y., Li, H., & Carlsson, C. (2010). Factors driving the adoption of m-learning: An empirical study. Computers & Education, 55(3), 1211–1219.
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(6), 359–364.
Lu, J., Liu, C., Yu, C. S., & Wang, K. (2008). Determinants of accepting wireless mobile data services in China. Information & Management, 45(1), 52–64.
Lu, J., Yao, J. E., & Yu, C.-S. (2005). Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology. The Journal of Strategic Information Systems, 14(3), 245.
Marangunić, N., & Granić, A. (2015). Technology acceptance model: A literature review from 1986 to 2013. Universal Access in the Information Society, 14(1), 81–95.
Martin, F., & Ertzberger, J. (2013). Here and now mobile learning: An experimental study on the use of mobile technology. Computers & Education, 68, 76–85. doi:10.1016/j.compedu.2011.04.003.
Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192–222.
Muthén L. K., & Muthén B. O. (1998–2016). Mplus User’s Guide (Version Sixth Edition). Los Angeles, CA: Muthén & Muthén.
Ng’ambi, D. (2013). Effective and ineffective uses of emerging technologies: Towards a transformative pedagogical model. British Journal of Educational Technology, 44(4), 652–661.
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.
Nield, K. (2004). Questioning the myth of the Chinese learner. International Journal of Contemporary Hospitality Management, 16(3), 190–197.
O’Sullivan, E. (1999). Transformative learning - Educational vision for the 21st century. London: Zed Books.
Park, S. Y., Nam, M.-W., & Cha, S.-B. (2011). University students’ behavioral intention to use mobile learning: Evaluating the technology acceptance model. British Journal of Educational Technology, 43(4), 592–605.
Park, J., Yang, S., & Lehto, X. (2007). Adoption of mobile technologies for Chinese consumers. Journal of Electronic Commerce Research, 8(3), 196–206.
Parsons, D., & Ryu, H. (2006). A framework for assessing the quality of mobile learning. Proceedings of the 11th International Conference for Process Improvement, Research and Education (INSPIRE), Southampton Solent University.
Pedersen, P. E., & R. Ling, (2002). Mobile end-user service adoption studies: A selective review. Working Paper, Agder University College and Telenor R&D, Grimstad/Oslo.
Pew Research Center. (2014). Emerging nations embrace Internet, mobile technology: Cell phones nearly ubiquitous in many countries. Retrieved from http://www.pewglobal.org/2014/02/13/emerging-nations-embrace-internet-mobile-technology/
Pimmer, C., Mateescu, M., & Gröhbiel, U. (2016). Mobile and ubiquitous learning in higher education settings. A systematic review of empirical studies. Computers in Human Behavior, 63, 490–501.
Rogers, E. (1995). Diffusion of innovations. New York: Free Press.
Saadé, R., & Bahli, B. (2005). The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: An extension of the technology acceptance model. Information & Management, 42(2), 317–327.
Samson, C., & Hornby, L. (1998). Spending for tomorrow. Far Eastern Economic Review, 161(50), 46–52.
Selim, H. M. (2007). Critical success factors for e-learning acceptance: Confirmatory factor models. Computers & Education, 49(2), 396–413.
Sevillano-Garcia, M. L., & Vazquez-Cano, E. (2015). The impact of digital mobile devices in higher education. Educational Technology & Society, 18(1), 106–119.
Stead, G. (2004). M-learning: small, engaging and at your leisure. The Learning Citizen, 8, 10–13.
Stockwell, G. (2008). Investigating learner preparedness for and usage patterns of mobile learning. Recall, 20(3), 253–270.
Teo, T. S., & Pok, S. H. (2003). Adoption of WAP-enabled mobile phones among Internet users. Omega, 31(6), 483–498.
Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly, 15(1), 124–143.
Tossell, C. C., Kortum, P., Shepard, C., Rahmati, A., & Zhong, L. (2015). You can lead a horse to water but you cannot make him learn: Smartphone use in higher education. British Journal of Educational Technology, 46(4), 713–724.
van der Heijden, H. (2004). User acceptance of hedonic information systems. MIS Quarterly, 28(4), 695–702.
Veletsianos, G. (2010). A definition of emerging technologies for education. In G. Veletsianos (Ed.), Emerging technologies in distance education (pp. 3–22). Athabasca: Athabasca University Press.
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315.
Venkatesh, V., & Davis, F. (2000). Theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.
Wang, Y. S., Wu, M. C., & Wang, H. Y. (2009). Investigating the determinants and age and gender differences in the acceptance of mobile learning. British Journal of Educational Technology, 40(1), 92–118.
Wei, R. (2006). Lifestyles and new media: Adoption and use of wireless communication technologies in China. New Media & Society, 8(6), 991–1008.
Wong, L.-H., & Looi, C.-K. (2011). What seams do we remove in mobile-assisted seamless learning? A critical review of the literature. Computers & Education, 57(4), 2364–2381. doi:10.1016/j.compedu.2011.06.007.
Wong, K., Wang, F. L., Ng, K. K., & Kwan, R. (2015). Investigating acceptance towards mobile learning in higher education students. In K. C. Li, T.-L. Wong, S. K. S. Cheung, J. Lam, & K. K. Ng (Eds.), Technology in education. Transforming educational practices with technology (pp. 9–19). Berlin, Heidelberg: Springer Berlin Heidelberg.
Wu, W. H., Jim, W. Y. C., Chen, C. Y., Kao, H. Y., Lin, C. H., & Huang, S. H. (2012). Review of trends from mobile learning studies: A meta-analysis. Computers & Education, 59(2), 817–827.
Yu, S., Wang, M., & Che, H. (2005). An exposition of the crucial issues in China’s educational informatization. Educational Technology Research and Development, 53(4), 88–101. doi:10.1007/BF02504688.
Zhang, X., & Prybutok, V. R. (2005). How the mobile communication markets differ in China, the US, and Europe. Communications of the ACM, 48(3), 111–114.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Hao, S., Dennen, V.P. & Mei, L. Influential factors for mobile learning acceptance among Chinese users. Education Tech Research Dev 65, 101–123 (2017). https://doi.org/10.1007/s11423-016-9465-2
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11423-016-9465-2