Abstract
Mobile-assisted language learning (MALL) applications (apps) can provide users with personalized learning content to meet their learning needs. Besides, from the learner perspective, the apps can be regarded as ‘social’ individuals, like anthropomorphic instructors who offer social support to help them with language learning. However, the current literature lacks an investigation of the dominant technological feature of MALL, that is, personalization function influencing users’ assessments of social support and trust towards MALL and subsequently determining their continuous usage intention toward MALL. To address this gap, using stimulus-organism-response theory and social support theory, this study develops a research model by investigating how personalization (stimulus) affects social support in terms of information, emotional and appraisal support and trust (organism), which eventually influence users’ continuance intentions toward MALL apps (response). A total of 455 valid questionnaires were collected, and the data were analysed by the partial least squares (PLS) method. The results showed that personalization increases users’ information, emotional and appraisal support. Information, emotional and appraisal support enhance user’ trust when using MALL. Users’ trust fosters their continuous usage of MALL. Moreover, the mediation analysis revealed that information, emotional support and appraisal support fully mediate the relationship between personalization and trust. Trust acts as a full mediator between information, emotional and appraisal support and continuance usage intention. This study provides theoretical contributions to the existing literature and practical suggestions for practitioners to develop MALL apps. Finally, research limitations and future research directions are also discussed.
Similar content being viewed by others
Data availability
The data analysed during the current study are available from the corresponding author upon reasonable request.
References
Armstrong, J. S., & Overton, T. S. (1977). Estimating nonresponse bias in mail surveys. Journal of Marketing Research, 14(3), 396–402.
Ashraf, A. R., Thongpapanl Tek, N., Anwar, A., Lapa, L., & Venkatesh, V. (2021). Perceived values and motivations influencing m-commerce use: A nine-country comparative study. International Journal of Information Management, 59, 102318.
Brislin, R. W. (1970). Back-translation for cross-cultural research. Journal of Cross-Cultural Psychology, 1(3), 185–216.
Brosseau, L., et al. (2015). Internet-based implementation of non-pharmacological interventions of the “people getting a grip on arthritis” educational program: An international online knowledge translation randomized trial design protocol. JMIR Research Protocols, 4(1), e19.
Cakmak, F. (2019). Mobile learning and mobile assisted language learning in focus. Language and Technology, 1(1), 30–48.
Carroll, M., Lindsey, S., Chaparro, M., & Winslow, B. (2021). An applied model of learner engagement and strategies for increasing learner engagement in the modern educational environment. Interactive Learning Environments 29(5), 757–771.
Chan, N. N., Walker, C., & Gleaves, A. (2015). An exploration of students' lived experiences of using smartphones in diverse learning contexts using a hermeneutic phenomenological approach. Computers & Education, 82(1), 96–106.
Chang, W.-L., & Lee, C.-Y. (2013). Trust as a learning facilitator that affects students’ learning performance in the Facebook community: An investigation in a business planning writing course. Computers & Education, 62, 320–327.
Chen, X., Zhang, X., & Xiao, Q. (2019). A study on users' willingness to continue knowledge sharing in online health communities: An integrated social support persistence and commitment - a model of trust theory. Modern Intelligence, 39(11), 55–68.
Cheng, X., Fu, S., Han, Y., & Zarifis, A. (2017). Investigating the individual trust and school performance in semi-virtual collaboration groups. Information Technology and People, 30(3), 691–707.
Chiou, E. K., Schroeder, N. L., & Craig, S. D. (2020). How we trust, perceive, and learn from virtual humans: The influence of voice quality. Computers & Education, 146, 103756.
Cho, W.-C., Lee, K. Y., & Yang, S.-B. (2019). What makes you feel attached to smartwatches? The stimulus–organism–response (S–O–R) perspectives. Information Technology & People, 32(2), 319–343.
Choi, S., Jang, Y., & Kim, H. (2022). Influence of pedagogical beliefs and perceived trust on teachers’ acceptance of educational artificial intelligence tools. International Journal of Human–Computer Interaction, 1-13.
Elwalda, A., Erkan, İ., Rahman, M., & Zeren, D. (2021). Understanding mobile users' information adoption behaviour: An extension of the information adoption model. Journal of Enterprise Information Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JEIM-04-2020-0129
Fatima, I., Halder, S., & Saleem, M. A. (2015). Smart CDSS: Integration of social media and interaction engine (SMIE) in healthcare for chronic disease patients. Multimedia Tools & Applications, 74(14), 5109–5129.
García Botero, G., Questier, F., Cincinnato, S., He, T., & Zhu, C. (2018). Acceptance and usage of mobile assisted language learning by higher education students. Journal of Computing in Higher Education, 30(3), 426–451.
Grosberg, D., et al. (2016). Frequent surfing on social health networks is associated with increased knowledge and patient health activation. Journal of Medical Internet Research, 18(8), e212.
Hair, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2013). A primer on partial least squares structural equation modeling (PLS-SEM). SAGE Publications.
Hao, S., Dennen, V. P., & Mei, L. (2017). Influential factors for mobile learning acceptance among Chinese users. Educational Technology Research and Development, 65, 101–123.
Harman, H. (1967). Modern factor analysis. University of Chicago Press.
He, S., Jiang, S., Zhu, R., et al. (2023). The influence of educational and emotional support on e-learning acceptance: An integration of social support theory and TAM. Education and Information Technologies. https://doi.org/10.1007/s10639-023-11648-1
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2016). Testing measurement invariance of composites using partial least squares. International Marketing Review, 33(3), 405–431.
Hoi, V. N. (2020). Understanding higher education learners' acceptance and use of mobile devices for language learning: A Rasch-based path modeling approach. Computers & Education, 146, 103761.
Hoi, V. N., & Mu, G. M. (2021). Perceived teacher support and students’ acceptance of mobile-assisted language learning: Evidence from Vietnamese higher education context. British Journal of Educational Technology, 52(2), 879–898.
Hsu, L. (2016). Examining EFL teachers’ technological pedagogical content knowledge and the adoption of mobile-assisted language learning: A partial least square approach. Computer Assisted Language Learning, 29(8), 1287–1297.
Hsu, H. T., & Lin, C. C. (2022). Extending the technology acceptance model of college learners' mobile-assisted language learning by incorporating psychological constructs. British Journal of Educational Technology, 53(2), 286–306.
Hsu, J. Y., Chen, C. C., & Ting, P. F. (2018). Understanding MOOC continuance: An empirical examination of social support theory. Interactive Learning Environments, 26(8), 1100–1118.
Hu, Y., Zhao, L., Luo, X. (R)., Gupta, S., & He, X. (2021). Trialing or combining? Understanding consumer partial switching in mobile application usage from the variety seeking perspective. Internet Research, 31(5), 1769–1802.
Ishaq, K., Zin, N. A. M., Rosdi, F., Jehanghir, M., Ishaq, S., & Abid, A. (2021). Mobile-assisted and gamification-based language learning: A systematic literature review. PeerJ Computer Science, 7, e496.
Jeon, J. (2022). Exploring a self-directed interactive app for informal EFL learning: A self-determination theory perspective. Education and Information Technologies. https://doi.org/10.1007/s10639-021-10839-y
Kamasak, R., Özbilgin, M., Atay, D., & Kar, A. (2021). The effectiveness of mobile-assisted language learning (MALL): A review of the extant literature. Handbook of research on determining the reliability of online assessment and distance learning, 194-212.
Karakaya, K., & Bozkurt, A. (2022). Mobile-assisted language learning (MALL) research trends and patterns through bibliometric analysis: Empowering language learners through ubiquitous educational technologies. System, 102925.
Kearney, M., Schuck, S., Burden, K., & Aubusson, P. (2012). Viewing mobile learning from a pedagogical perspective. Research in Educational Technology, 20(1), 1–17.
Kessler, M. (2021). Supplementing mobile-assisted language learning with reflective journal writing: A case study of Duolingo users’ metacognitive awareness. Computer Assisted Language Learning, 1–24.
Kim, G.-M., & Lee, S.-J. (2016). Korean students’ intentions to use mobile-assisted language learning: Applying the technology acceptance model. International Journal of Contents, 12(3), 47–53.
Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1–10.
Krause, N., & Markides, K. (1990). Measuring social support among older adults. The International Journal of Aging and Human Development, 30(1), 37–53.
Lai, Y., Saab, N., & Admiraal, W. (2022). University students’ use of mobile technology in self-directed language learning: Using the integrative model of behavior prediction. Computers & Education, 179, 104413.
Lavorgna, L., et al. (2017). Health-related coping and social interaction in people with multiple sclerosis supported by a social network: Pilot study with a new methodological approach. Interactive Journal of Medical Research, 6(2), e10.
Lee, J. C., & Chen, C. Y. (2022a). Motivating members’ involvement to effectually conduct collaborative software process tailoring. Empir Software Eng, 27, 183. https://doi.org/10.1007/s10664-022-10225-3
Lee, J. C., & Chen, X. (2022b). Exploring users' adoption intentions in the evolution of artificial intelligence mobile banking applications: The intelligent and anthropomorphic perspectives. International Journal of Bank Marketing, 40(4), 631–658.
Lee, J. C., & Wang, J. (2023). From offline to online: Understanding users' switching intentions from traditional wealth management services to mobile wealth management applications. The International Journal of Bank Marketing, 41(2), 369–394.
Lee, J. C., & Xiong, L. N. (2022). Investigation of the relationships among educational application (APP) quality, computer anxiety and student engagement. Online Information Review, 46(1), 182–203.
Lee, J.C., Hsu, W.C. and Chen, C.Y. (2018). Impact of absorptive capability on software process improvement and firm performance. Information Technology and Management, 19, 21–35.
Lee, J.-C., Shiue, Y.-C., & Chen, C.-Y. (2020). An integrated model of the knowledge antecedents for exploring software process improvement success. Journal of Enterprise Information Management, 33(6), 1537–1556.
Lee, C.T., Pan, L.-Y., & Hsieh, S.H. (2021a). Artificial intelligent chatbots as brand promoters: A two-stage structural equation modeling-artificial neural network approach. Internet Research, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/INTR-01-2021-0030
Lee, J. C., Chou, I. C., & Chen, C. Y. (2021b). The effect of process tailoring on software project performance: The role of team absorptive capacity and its knowledge-based enablers. Information Systems Journal, 32(1), 120–147.
Li, C. Y. (2019). How social commerce constructs influence customers' social shopping intention? An empirical study of a social commerce website. Technological Forecasting and Social Change, 144(7), 282–294.
Li, F., Fan, S., & Wang, Y. (2022). Mobile-assisted language learning in Chinese higher education context: A systematic review from the perspective of the situated learning theory. Education and Information Technologies, 27, 9665–9688.
Liang, T. P., & Turban, E. (2011). Introduction to the special issue social commerce: A research framework for social commerce. International Journal of Electronic Commerce, 16(2), 5–14.
Lin, R.-R., & Lee, J.C. (2023). The supports provided by artificial intelligence to continuous usage intention of mobile banking: Evidence from China. Aslib Journal of Information Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/AJIM-07-2022-0337
Lin, R. R., Zheng, Y., & Lee, J. C. (2023). Artificial intelligence-based preimplementation interventions in users’ continuance intention to use mobile banking. International Journal of Mobile Communications, 21(4), 518–540.
Liu, C. L. (2020). Research on the influencing factors of continuous use intention of English learning APP users based on self-determination (pp. 21–65). Harbin Institute of Technology.
Liu, K., & Tao, D. (2022). The roles of trust, personalization, loss of privacy, and anthropomorphism in public acceptance of smart healthcare services. Computers in Human Behavior, 127, 107026.
Lu, B., Wang, Z., & Zhang, S. (2021). Platform-based mechanisms, institutional trust, and continuous use intention: The moderating role of perceived effectiveness of sharing economy institutional mechanisms. Information & Management, 58(7), 103504.
Ma, Q. (2017). A multi-case study of university students’ language-learning experience mediated by mobile technologies: A socio-cultural perspective. Computer Assisted Language Learning, 30(3-4), 183–203.
Malecki, C. K., & Demaray, M. K. (2003). What type of support do they need? Investigating student adjustment as related to emotional, informational, appraisal, and instrumental support. School Psychology Quarterly, 18(3), 231–252.
Mehrabian, A., & Russell, J. A. (1974). An approach environmental psychology. Massachusetts Institute of Technology.
Milani, R. V., & Lavie, C. J. (2015). Health care 2020: Reengineering health care delivery to combat chronic disease. The American Journal of Medicine, 128, 337–343.
Min, H., Park, J., & Kim, H. J. (2016). Common method bias in hospitality research: A critical review of literature and an empirical study. International Journal of Hospitality Management, 56, 126–135.
Nederhof, A.J. (1985). Methods of coping with social desirability bias: A review. European Journal of Social Psychology, 15(3), 263–280.
Paris, T. N. S. T., Manap, N. A., Abas, H., & Ling, L. M. (2021). Mobile-assisted language learning (MALL) in language learning. Journal of Asian Behavioural Studies, 6(19), 61–73.
Putra, I., Saukah, A., Basthomi, Y., et al. (2020). The acceptance of the English language learning Mobile application hello English across gender and experience differences. International Journal of Emerging Technologies in Learning, 15(15), 219–228.
Qin, F., Li, K., & Yan, J. (2020). Understanding user trust in artificial intelligence-based educational systems: Evidence from China. British Journal of Educational Technology, 51(5), 1693–1710.
Reychav, I., Dunaway, M., & Kobayashi, M. (2015). Understanding mobile technology-fit behaviors outside the classroom. Computers & Education, 87(1), 142–150.
Ringle, C.M., Wende, S., & Becker, J.M. (2015). SmartPLS 3. Boenningstedt: SmartPLS GmbH. http://www.smartpls.com
Rousseau, D. M., Sitkin, S. B., Burt, R. S., & Camerer, C. (1998). Not so different after all: A cross-discipline view of trust. Academy of Management Review, 23(3), 393–404.
Russo, D., & Stol, K. J. (2021). PLS-SEM for software engineering research: An introduction and survey. ACM Computing Surveys, 54(4), 1–38.
Schroeder, N. L., Chiou, E. K., & Craig, S. D. (2021). Trust influences perceptions of virtual humans, but not necessarily learning. Computers & Education, 160, 104039.
Sendra, A., Farré, J., & Vaagan, R. W. (2020). Seeking, sharing and co-creating: A systematic review of the relation between social support theory, social media use and chronic diseases. Social Theory & Health, 18(3), 317–339.
Shadiev, R., Liu, T., & Hwang, W. Y. (2020). Review of research on mobile-assisted language learning in familiar, authentic environments. British Journal of Educational Technology, 51(3), 709–720.
Shen, X.-L., Li, Y.-J., & Sun, Y. (2018). Wearable health information systems intermittent discontinuance: A revised expectation-disconfirmation model. Industrial Management & Data Systems, 118(3), 506–523.
Shortt, M., Tilak, S., Kuznetcova, I., Martens, B., & Akinkuolie, B. (2021). Gamification in mobile-assisted language learning: A systematic review of Duolingo literature from public release of 2012 to early 2020. Computer Assisted Language Learning, 1-38.
Song, S. J., Tan, K. H., & Awang, M. M. (2021). Generic digital equity model in education: Mobile-assisted personalized learning (MAPL) through e-modules. Sustainability, 13(19), 11115.
Stockwell, G. (2007). Vocabulary on the move: Investigating an intelligent mobile phone-based vocabulary tutor. Computer Assisted Language Learning, 20(4), 365–383.
Sun, Y., & Gao, F. (2020). An investigation of the influence of intrinsic motivation on students’ intention to use mobile devices in language learning. Educational Technology Research & Development, 68(3), 1181–1198.
Sun, H., & Zhang, P. (2006). The role of moderating factors in user technology acceptance. International Journal of Human-Computer Studies, 64(2), 53–78.
Sun, T., Xia, L. X., Li, X., et al. (2021). A meta-analysis of the influencing factors of social reading users' continuation intention. Information Science, 39(7), 153–161.
Talke, K., & Heidenreich, S. (2014). How to overcome pro-change Bias: Incorporating passive and active innovation resistance in innovation decision models. Journal of Product Innovation Management, 31(5), 894–907.
Ünal, E., & Güngör, F. (2021). The continuance intention of users toward mobile assisted language learning: The case of DuoLingo. Asian Journal of Distance Education, 16(2). Retrieved from http://www.asianjde.com/ojs/index.php/AsianJDE/article/view/589. Accessed 1 Feb 2023.
Viberg, O., & Grönlund, Å. (2013). Cross-cultural analysis of users' attitudes toward the use of mobile devices in second and foreign language learning in higher education: A case from Sweden and China. Computers & Education, 69, 169–180.
Viberg, O., Andersson, A., & Wiklund, M. (2018). Designing for sustainable mobile learning re-evaluating the concepts ‘formal’ and ‘informal’. Interactive Learning Environments, 29(1), 130–141.
Wang, E. S. T., & Lin, R. L. (2017). Perceived quality factors of location-based apps on trust, perceived privacy risk, and continuous usage intention. Behaviour & Information Technology, 36(1), 2–10.
Wang, X., Lu, A., Lin, T., et al. (2022). Perceived usefulness predicts second language learners’ continuance intention toward language learning applications: A serial multiple mediation model of integrative motivation and flow. Education and Information Technologies, 27, 5033–5049.
Wu, S., Wong, I. A., & Lin, Z. C. (2021). Understanding the role of atmospheric cues of travel apps: A synthesis between media richness and stimulus–organism–response theory. Journal of Hospitality and Tourism Management, 49, 226–234.
Yang, S. Q., Zhou, S. S., & Cheng, X. Y. (2019). Why do college students continue to use mobile learning? Learning involvement and self- determination theory. British Journal of Educational Technology, 50(2), 626–637.
Yuan, Y., Lai, F., & Chu, Z. (2019). Continuous usage intention of internet banking: A commitment-trust model. Information Systems and e-Business Management, 17(1), 1–25.
Zhang, X., Wu, Y., Xia, H. S., et al. (2019). A study on influencing factors of Users' continued knowledge contribution willingness in online health community——From the perspective of social exchange theory. Journal of Medical Informatics, 40(3), 2–9.
Zhao, X., Lynch, J. G., Jr., & Chen, Q. (2010). Reconsidering baron and Kenny: Myths and truths about mediation analysis. Journal of Consumer Research, 37(2), 197–206.
Acknowledgements
The authors thank the editor and reviewers for their constructive comments and suggestions, which helped us enhance the quality of this manuscript. We also thank Miss Leiyu Chen for her assistance in preparing this manuscript.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The authors have no competing interests to declare that are relevant to the content of this article.
Conflict of interest
There are no conflicts of interest to declare.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix
Appendix
Table 9
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Lee, JC., Xiong, L. Exploring learners’ continuous usage decisions regarding mobile-assisted language learning applications: A social support theory perspective. Educ Inf Technol 28, 16743–16769 (2023). https://doi.org/10.1007/s10639-023-11884-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10639-023-11884-5