This study examines the relationships among intrinsic motivation, critical variables related to technology adoption, and students’ behavioral intention in mobile-assisted language learning (MALL). To test the hypothesized model through a path analysis, 169 survey responses were collected from undergraduate students who were foreign language learners of English in a Chinese research university. The results indicated that although intrinsic motivation did not have a direct influence on students’ behavioral intention in MALL, it had a positive influence on students’ behavioral intention through the two intervening variables, perceived usefulness and task technology fit. Perceived ease of use, however, was not associated with students’ behavioral intention directly, nor was it predicted by intrinsic motivation. The findings suggested proper instructional design that is aligned with and supports the language learning task was important to increase students’ behavioral intention to adopt mobile devices for language learning.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
Abdous, M., Camarena, M., & Facer, B. (2009). MALL technology: Use of academic podcasting in the foreign language classroom. ReCALL,21(1), 76–95.
Abdous, M., Facer, B., & Yen, C.-J. (2012). Academic effectiveness of podcasting: A comparative study of integrated versus supplemental use of podcasting in second language classes. Computers & Education,58(1), 43–52.
Abdullah, F., & Ward, R. (2016). Developing a general extended technology acceptance model for E-learning (GETAMEL) by analyzing commonly used external factors. Computers in Human Behavior,56, 238–256.
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.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes,50(2), 179–211.
Ajzen, I. (2002). Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior. Journal of Applied Social Psychology,32(4), 665–683.
Al-Aulamie, A., Mansour, A., Daly, H., & Adjei, O. (2012). The effect of intrinsic motivation on learners’ behavioral intention to use e-learning systems. In International Conference on Information Technology Based Higher Education and Training (ITHET) IEEE (pp. 1–4). Retrieved from https://library3.hud.ac.uk/summon/.
Baker, S. R. (2004). Intrinsic, extrinsic, and amotivational orientations: Their role in university adjustment, stress, well-being, and subsequent academic performance. Current Psychology,23(3), 189–202.
Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice Hall.
Bandura, A. (1997). Self-efficacy. Harvard Mental Health Letter,13(9), 4–7.
Biggs, J. (2014). Enhancing learning: A matter of style or approach? In R. Sternberg & L. Zhang (Eds.), Perspectives on thinking, learning, and cognitive styles (pp. 73–102). Abingdon: Routledge.
Chang, H. H. (2008). Intelligent agent’s technology characteristics applied to online auctions’ task: A combined model of TTF and TAM. Technovation,28(9), 564–577.
Chen, X. B., & Kessler, G. (2013). Tablets for informal language learning: Student usage and attitudes. Language Learning & Technology,17(1), 20–36.
Chen, Y., Lin, Y., Yeh, R., & Lou, S. (2013). Examining factors affecting college students’ intention to use web-based instruction systems: Towards an integrated model. Turkish Online Journal of Educational Technology-TOJET, 12(2), 111–121. Retrieved from https://library3.hud.ac.uk/summon/.
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.
Comas-Quinn, A., Mardomingo, R., & Valentine, C. (2009). Mobile blogs in language learning: Making the most of informal and situated learning opportunities. ReCALL,21(1), 96–112.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly,13, 319–340.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology,22(14), 1111–1132.
Dawes, J. (2008). Do data characteristics change according to the number of scale points used? An experiment using 5-point, 7-point and 10-point scales. International Journal of Market Research,50(1), 61–104.
Dishaw, M. T., & Strong, D. M. (1999). Extending the technology acceptance model with task-technology fit constructs. Information & Management,36(1), 9–21.
Dishaw, M. T., Strong, D., & Bandy, D. B. (2002). Extending the task-technology fit model with self-efficacy constructs. AMCIS 2002 Proceedings,143, 194.
Dörnyei, Z. (1998). Motivation in second and foreign language learning. Language Teaching,31(3), 117–135.
Finstad, K. (2010). Response interpolation and scale sensitivity: Evidence against 5-point scales. Journal of Usability Studies,5(3), 104–110.
Goodhue, D. L., Klein, B. D., & March, S. T. (2000). User evaluations of IS as surrogates for objective performance. Information & Management,38(2), 87–101.
Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly,19(2), 213–236.
Hu, L., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to under parameterized model misspecification. Psychological Methods,3(4), 424–453.
Jolliet, Y. (2007). M-Learning: A pedagogical and technological model for language learning on mobile phones. In J. Fong & F.-L. Wang (Eds.), Blended learning: Proceeding of workshop on blended learning 2007 (pp. 327–339). Hong Kong: City University of Hongkong.
Kim, M. J., Chung, N., Lee, C. K., & Preis, M. W. (2015). Motivations and use context in mobile tourism shopping: Applying contingency and task–technology fit theories. International Journal of Tourism Research,17(1), 13–24.
Kim, T. T., Suh, Y. K., Lee, G., & Choi, B. (2010). Modelling roles of task technology fit and self-efficacy in hotel employees’ usage behaviors of hotel information systems. International Journal of Tourism Research,12(6), 709–725.
Kline, R. B. (2005). Principles and practice of structural equation modeling (2nd ed.). New York: Guilford Press.
Klopping, I. M., & McKinney, E. (2004). Extending the technology acceptance model and the task-technology fit model to consumer e-commerce. Information Technology, Learning, and Performance Journal,22(1), 35–48.
Kukulska-Hulme, A. (2009). Will mobile learning change language learning? ReCALL,21(2), 157–165.
Lai, C., Wang, Q., & Lei, J. (2012). What factors predict undergraduate students’ use of technology for learning? A case from Hong Kong. Computers & Education,59(2), 569–579.
Lan, Y.-J., Sung, Y.-T., & Chang, K.-E. (2007). A mobile-device-supported peer-assisted learning system for collaborative early EFL reading. Language Learning & Technology,11(3), 130–151.
Lee, D. Y., & Lehto, M. R. (2013). User acceptance of YouTube for procedural learning: An extension of the technology acceptance model. Computers & Education,61(1), 193–208.
Lin, N., Kajita, S., & Mase, K. (2007). Story-based CALL for Japanese kanji characters: A study on student learning motivation. The JALT CALL Journal, 3(1–2), 25–44. Retrieved from http://www.jaltcall.org.
Lin, T. C., & Huang, C. C. (2008). Understanding knowledge management system usage antecedents: An integration of social cognitive theory and task technology fit. Information & Management,45(6), 410–417.
Liu, T. Y., & Chu, Y. L. (2010). Using ubiquitous games in an English listening and speaking course: Impact on learning outcomes and motivation. Computers & Education,55(2), 630–643.
McAuley, E., Duncan, T., & Tammen, V. V. (1989). Psychometric properties of the intrinsic motivation inventory in a competitive sport setting: A confirmatory factor analysis. Research Quarterly for Exercise and Sport,60(1), 48–58.
Murphy, P., Bollen, D., & Langdon, C. (2012). Mobile technology, collaborative reading, and elaborative feedback. In J. Díaz-Vera (Ed.), Left to my own devices: Learner autonomy and mobile-assisted language learning innovation and leadership in English language teaching (pp. 131–159). Bingley: Emerald Group Publishing Limited.
Nunnaly, J. (1978). Psychometric theory. New York: McGraw-Hill.
Oroujlou, N., & Vahedi, M. (2011). Motivation, attitude, and language learning. Procedia-Social and Behavioral Sciences,29(1), 994–1000.
Park, S. Y., Nam, M. W., & Cha, S. B. (2012). University students’ behavioral intention to use mobile learning: Evaluating the technology acceptance model. British Journal of Educational Technology,43(4), 592–605.
Pintrich, P. R. (2004). A conceptual framework for assessing motivation and self-regulated learning in college students. Educational Psychology Review,16(4), 385–407.
Ryan, R. M. (1982). Control and information in the intrapersonal sphere: An extension of cognitive evaluation theory. Journal of Personality and Social Psychology,43(3), 450–461.
Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology,25(1), 54–67.
Schumacker, R. E., & Lomax, R. G. (2004). A beginner’s guide to structural equation modeling (2nd ed.). Mahwah: Lawrence Erlbaum Associates.
Shao, Y. (2011). Second language learning by exchanging cultural contexts through the mobile group blog. In S. Thouësny & L. Bradley (Eds.), Second language teaching and learning with technology: Views of emergent researchers (pp. 143–168). Dublin: Research-publishing.net.
Steiger, J. H. (1990). Structural model evaluation and modification: An interval estimation approach. Multivariate Behavioural Research,25(2), 173–180.
Stockwell, G. (2008). Investigating learner preparedness for and usage patterns of mobile learning. ReCALL,20(3), 253–270.
Stockwell, G., & Hubbard, P. (2013). Some emerging principles for mobile-assisted language learning. Monterey, CA: The International Research Foundation for English Language Education. Retrieved from http://www.tirfonline.org/english-in-the-workforce/mobile-assisted-language-learning.
Ushida, E. (2005). The role of students’ attitudes and motivation in second language learning in online language courses. CALICO Journal,23(1), 49–78.
Ushioda, E. (2013). Motivation matters in mobile language learning: A brief commentary. Language Learning & Technology,17(3), 1–5.
Vallerand, R. J. (1997). Toward a hierarchical model of intrinsic and extrinsic motivation. Advance. Experimental Social Psychology,29(1), 271–360.
Van Seters, J. R., Ossevoort, M. A., Tramper, J., & Goedhart, M. J. (2012). The influence of student characteristics on the use of adaptive e-learning material. Computers & Education,58(3), 942–952.
Vavoula, G., & Sharples, M. (2008). Challenges in evaluating mobile learning. In Proceedings of MLearn 2008, 8–10 Oct 2008, Wolverhampton, UK.
Venkatesh, V. (1999). Creation of favorable user perceptions: Exploring the role of intrinsic motivation. MIS Quarterly,23, 239–260.
Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research,11(4), 342–365.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science,46(2), 186–204.
Venkatesh, V., Morris, M. G., & Ackerman, P. L. (2000). A longitudinal field investigation of gender differences in individual technology adoption decision-making processes. Organizational Behavior and Human Decision Processes,83(1), 33–60.
Winne, P. H. (1995). Self-regulation is ubiquitous but its forms vary with knowledge. Educational Psychologist,30(4), 223–228.
Wolters, C. A. (1998). Self-regulated learning and college students’ regulation of motivation. Journal of Educational Psychology,90(2), 224.
Wu, W. C. V., Yen, L. L., & Marek, M. (2011). Using online EFL interaction to increase confidence, motivation, and ability. Journal of Educational Technology & Society,14(3), 118–129.
Yamada, M., Kitamura, S., Shimada, N., Utashiro, T., Shigeta, K., Yamaguchi, E., et al. (2011). Development and evaluation of English listening study materials for business people who use mobile devices: A case study. CALICO Journal,29(1), 44–66.
Zare, H., & Yazdanparast, S. (2013). The causal model of effective factors on intention to use of information technology among payamnoor and traditional universities students. Life Science Journal,10(2), 46–50.
This work was sponsored by Peak Discipline Construction Project of Education at East China Normal University, and the Project of Science & Technology Commission of Shanghai Municipality of China (Grant No. 17DZ2281800).
Conflict of interest
The authors declare that they have no conflict of interest.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix: Survey items used in the study
What kind of mobile devices do you owe?
Other Smart Phone (Please specify)
Other Tablet (Please specify)
Typically, how much time do you spend every day using your mobile devices for the following purposes?
(No use, rare use, about 5–10 min, about 11–30 min, about 31–60 min, about 1–2 h, about 2–3 h, more than 3 h)
Social networking (e.g. Wechat, QQ, Weibo)
Other (optional: please specify if you use mobile devices for other purposes
Rating scales Strongly disagree, disagree, somewhat disagree, neither agree nor disagree, somewhat agree, agree, strongly agree
Intrinsic motivation (11 items)
Please rate the following items regarding your motivation for English learning
I enjoy learning English very much
Learning English is fun.
I would describe English learning as very interesting.
I thought English learning is quite enjoyable.
I think I am pretty good at English.
I felt pretty competent in English.
I am satisfied with my English language proficiency.
I was pretty skilled at English language related learning tasks.
I put a lot of effort into English language learning.
I try very hard to learn English.
It is important for me to learn English well.
Perceived usefulness (5 items)
How useful do you think that mobile devices is for English learning?
Using mobile devices improves my ability to learn English.
Using mobile devices for English learning makes learning more accessible.
Using mobile devices for English learning makes learning more fun and engaging.
Using mobile devices for English learning helps improve my English.
Mobile devices are useful for my English learning.
Task technology fit (4 items)
In your opinion, would mobile devices work well for you to learn English?
I think that using mobile devices would be well suited for the way I like to learn English.
Mobile devices would be a good medium to provide the way I like to learn English.
Using mobile devices would fit well for the way I like to learn English.
I think that using mobile devices would be a good way to learn English.
Perceived ease of use (4 items)
How easy is it for you to use mobile devices for English learning?
I don’t have any problems learning about the features of the English learning applications/tools on my mobile device(s).
My interaction with these tools/applications is clear and understandable.
I believe that the English learning applications/tools on my mobile device(s) are easy to use.
I believe that the English learning applications/tools on my mobile device(s) are easy to operate.
I will continue using mobile devices for English language learning.
I will use mobile devices on a regular basis for English language learning in the future.
About this article
Cite this article
Sun, Y., Gao, F. An investigation of the influence of intrinsic motivation on students’ intention to use mobile devices in language learning. Education Tech Research Dev 68, 1181–1198 (2020). https://doi.org/10.1007/s11423-019-09733-9
- Mobile assisted language learning
- Path analysis
- Higher education