The impact of factors on MALL usage behavior for English learning: Extension of TPB model

Thanh Thi Ngoc Phan; Tuan Thanh Nguyen

doi:10.46223/HCMCOUJS.soci.en.16.4.4265.2026

16(4)2026 (IN PRESS)

The impact of factors on MALL usage behavior for English learning: Extension of TPB model

Author - Affiliation:

Thanh Thi Ngoc Phan - Ho Chi Minh City Open University, Ho Chi Minh City , Vietnam

Tuan Thanh Nguyen - Ho Chi Minh City Open University, Ho Chi Minh City , Vietnam

Corresponding author: Tuan Thanh Nguyen - tuannt.24at@ou.edu.vn

DOI:10.46223/HCMCOUJS.soci.en.16.4.4265.2026

Submitted: 28-03-2025
Accepted: 02-05-2025
Published: 07-05-2025

Abstract

This study aims to explore the factors influencing the mobile-assisted language learning (MALL) behavior of English as a foreign language (EFL) learners in Ho Chi Minh City. Grounded in the Theory of Planned Behavior (TPB) and extended with social influence and facilitating conditions from the Unified Theory of Acceptance and Use of Technology (UTAUT), the research examines how these factors shape learners’ intentions and behaviors. A quantitative method was employed, using a structured questionnaire distributed via Google Forms to 383 participants. Data were analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM) using SmartPLS 3. The findings highlight the importance of both social influence and facilitating conditions in promoting effective MALL adoption. However, facilitating conditions may not significantly influence the intention to use. This perception contributes significantly to the theoretical understanding of the importance of MALL in EFL education. Practical implications suggest that educators should foster positive learning communities and ensure adequate technological infrastructure to sustain students' engagement with mobile learning platforms.

Keywords

behavioral intention; facilitating conditions; mobile-assisted language learning; usage behavior

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References

Abd Rahman, S. F., Md Yunus, M., & Hashim, H. (2021). Applying UTAUT in predicting ESL lecturers intention to use flipped learning. Sustainability, 13(15), 8571. https://doi.org/10.3390/su13158571

Adamopoulos, P., Ho, H., Sykes, G., Szekely, P., & Dommett, E. J. (2020). Learning approaches and attitudes toward cognitive enhancers in UK University students. Journal of Psychoactive Drugs, 52(3), 248-254. https://doi.org/10.1080/02791072.2020.1742949

Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T

Ajzen, I. (2020). The theory of planned behavior: Frequently asked questions. Human Behavior and Emerging Technologies, 2(4), 314-324. https://doi.org/10.1002/hbe2.195

Ajzen, I., & Fishbein, M. (1975). A Bayesian analysis of attribution processes. Psychological bulletin, 82(2), 261-277. https://doi.org/10.1037/h0076477

Alghazi, S. S., Kamsin, A., Almaiah, M. A., Wong, S. Y., & Shuib, L. (2021). For sustainable application of mobile learning: An extended UTAUT model to examine the effect of technical factors on the usage of mobile devices as a learning tool. Sustainability, 13(4), 1856. https://doi.org/10.3390/su13041856

Al-Nuaimi, M. N., & Al-Emran, M. (2021). Learning management systems and technology acceptance models: A systematic review. Education and Information Technologies, 26(5), 5499-5533. https://doi.org/10.1007/s10639-021-10513-3

AlQarni, A., Bown, A., Pullen, D., & Masters, J. (2020). Mobile Assisted Language Learning in Learning Arabic as a Second Language in Saudi Arabia. Saudi Journal of Humanities and Social Sciences, 5(2), 108-115. https://doi.org/10.36348/sjhss.2020.v05i02.009

An, X., Chai, C. S., Li, Y., Zhou, Y., & Yang, B. (2023). Modeling students’ perceptions of artificial intelligence assisted language learning. Computer Assisted Language Learning, 1-22. https://doi.org/10.1080/09588221.2023.2246519

Anthony Jr, B., Kamaludin, A., Romli, A., Mat Raffei, A. F., Phon, A. E., Nincarean, D., ... & Baba, S. (2020). Predictors of Blended Learning Deployment in Institutions of Higher Learning: Theory of Planned Behavior Perspective. International Journal of Information and Learning Technology, 37(4), 179-196. https://doi.org/10.1108/IJILT-02-2020-0013

Anthony, B., Kamaludin, A., Romli, A., Raffei, A. F. M., Nincarean A/L Eh Phon, D., Abdullah, A., ... & Baba, S. (2019). Exploring the role of blended learning for teaching and learning effectiveness in institutions of higher learning: An empirical investigation. Education and Information Technologies, 24(6), 3433-3466. https://doi.org/10.1007/s10639-019-09941-z

Bai, B., Wang, J., & Chai, C. S. (2021). Understanding Hong Kong primary school English teachers’ continuance intention to teach with ICT. Computer Assisted Language Learning, 34(4), 528-551. https://doi.org/10.1080/09588221.2019.1627459

Bauwens, R., Muylaert, J., Clarysse, E., Audenaert, M., & Decramer, A. (2020). Teachers’ acceptance and use of digital learning environments after hours: Implications for work-life balance and the role of integration preference. Computers in Human Behavior, 112, 106479. https://doi.org/10.1016/j.chb.2020.106479

Buhmann, A., & Brønn, P. S. (2018). Applying Ajzen’s theory of planned behavior to predict practitioners’ intentions to measure and evaluate communication outcomes. Corporate Communications: An International Journal, 23(3), 377-391. https://doi.org/10.1108/CCIJ-11-2017-0107

Chai, C. S., Lin, P. Y., Jong, M. S. Y., Dai, Y., Chiu, T. K., & Qin, J. (2021). Perceptions of and behavioral intentions towards learning artificial intelligence in primary school students. Educational Technology & Society, 24(3), 89-101. https://doi.org/10.30191/ETS.202107_24(3).0007

Chang, C. C., Liang, C., & Chiu, Y. C. (2020). Direct or indirect effects from “perceived characteristic of innovation” to “intention to pay”: mediation of continuance intention to use e-learning. Journal of Computers in Education, 7(4), 511-530. https://doi.org/10.1007/s40692-020-00165-6

Chen, S. Y., Su, Y. S., Ku, Y. Y., Lai, C. F., & Hsiao, K. L. (2022). Exploring the factors of students' intention to participate in AI software development. Library Hi Tech. https://doi.org/10.1108/LHT-12-2021-0480

Chin, W., Cheah, J. H., Liu, Y., Ting, H., Lim, X. J., & Cham, T. H. (2020). Demystifying the role of causal-predictive modeling using partial least squares structural equation modeling in information systems research. Industrial Management & Data Systems, 120(12), 2161-2209. https://doi.org/10.1108/IMDS-10-2019-0529

Dağdeler, K. O., Konca, M. Y., & Demiröz, H. (2020). The effect of mobile-assisted language learning (MALL) on EFL learners’ collocation learning. Journal of Language and Linguistic Studies, 16(1), 489-509. https://doi.org/10.17263/jlls.712891

Dakduk, S., Santalla-Banderali, Z., & Van Der Woude, D. (2018). Acceptance of blended learning in executive education. Sage Open, 8(3), 2158244018800647. https://doi.org/10.1177/2158244018800647

Darsih, E., & Asikin, N. A. (2020). Mobile assisted language learning: EFL learners’perceptions toward the use of mobile applications in learning english. English Review: Journal of English Education, 8(2), 183-194. https://doi.org/10.25134/erjee.v8i2.2999

Devisakti, A., & Ramayah, T. (2023). Sense of belonging and grit in e-learning portal usage in higher education. Interactive Learning Environments, 31(8), 4850-4864. https://doi.org/10.1080/10494820.2021.1983611

Dijkstra, T. K., & Henseler, J. (2015). Consistent and asymptotically normal PLS estimators for linear structural equations. Computational Statistics & Data Analysis, 81, 10-23. https://doi.org/10.1016/j.csda.2014.07.008

Dwivedi, Y. K., Hughes, D. L., Coombs, C., Constantiou, I., Duan, Y., Edwards, J. S., ... & Upadhyay, N. (2020). Impact of COVID-19 pandemic on information management research and practice: Transforming education, work and life. International journal of information management, 55, 102211. https://doi.org/10.1016/j.ijinfomgt.2020.102211

Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers, 21, 719-734. https://doi.org/10.1007/s10796-017-9774-y

Elshahawy, K. E. M. (2020). Practicing English through digital devices: Practices and perceptions of the EFL undergraduate students majoring in English language. International Journal of Language and Literary Studies, 2(1), 21-37. https://doi.org/10.36892/ijlls.v2i1.109

Fernandez Sesma, M. G. (2020). Blended learning: An examination of EFL teachers and students’ use, continuance intention to use, and attitudes towards Information and Communication Technologies (Doctoral dissertation, University of Southampton). http://eprints.soton.ac.uk/id/eprint/445272

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. https://doi.org/10.1007/s12528-018-9177-1

Gyamfi, S. A. (2021). Influencing factors of students’ smartphones use for academic purposes: A developing country’s perspective. International Journal of Emerging Technologies in Learning (IJET), 16(23), 233-246. https://doi.org/10.3991/ijet.v16i23.26675

Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial least squares structural equation modeling (PLS-SEM) using R: A workbook (p. 197). Springer Nature. https://doi.org/10.1007/978-3-030-80519-7

Hair Jr, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24. https://doi.org/10.1108/EBR-11-2018-0203

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. https://doi.org/10.1016/j.compedu.2019.103761

Hollett, R. C., Gignac, G. E., Milligan, S., & Chang, P. (2020). Explaining lecture attendance behavior via structural equation modeling: Self-Determination Theory and the Theory of Planned Behavior. Learning and Individual Differences, 81, 101907. https://doi.org/10.1016/j.lindif.2020.101907

Huang, H., & Cheng, E. W. (2022). The Role of Commitment in an Extended Theory of Planned Behavior: Test of Its Mediating Effect with Partial Least Squares Structural Equation Modeling. Mathematics, 10(7), 1049. https://doi.org/10.3390/math10071049

Huang, R. T., Jabor, M. K., Tang, T. W., & Chang, S. C. (2022). Examine the moderating role of mobile technology anxiety in mobile learning: A modified model of goal-directed behavior. Asia Pacific Education Review, 23(1), 101-113. https://doi.org/10.1007/s12564-021-09703-y

Jiang, L. (2022). Factors influencing EFL teachers’ implementation of SPOC-based blended learning in higher vocational colleges in China: A study based on grounded theory. Interactive Learning Environments, 1-20. https://doi.org/10.1080/10494820.2022.2100428

Keezhatta, M. S., & Omar, A. (2019). Enhancing reading skills for Saudi secondary school students through mobile assisted language learning (MALL): An experimental study. International Journal of English Linguistics, 9(1), 437-447. https://doi.org/10.5539/ijel.v9n1p437

Kim, E. J., Kim, J. J., & Han, S. H. (2021). Understanding student acceptance of online learning systems in higher education: Application of social psychology theories with consideration of user innovativeness. Sustainability, 13(2), 896. https://doi.org/10.3390/su13020896

Kim, M. J., & Hall, C. M. (2020). What drives visitor economy crowdfunding? The effect of digital storytelling on unified theory of acceptance and use of technology. Tourism Management Perspectives, 34, 100638. https://doi.org/10.1016/j.tmp.2020.100638

Kumar, J. A., Bervell, B., Annamalai, N., & Osman, S. (2020). Behavioral intention to use mobile learning: Evaluating the role of self-efficacy, subjective norm, and WhatsApp use habit. IEEE Access, 8, 208058-208074. https://doi.org/10.1109/ACCESS.2020.3037925

Li, X., Jiang, M. Y. C., Jong, M. S. Y., Zhang, X., & Chai, C. S. (2022). Understanding Medical Students’ Perceptions of and Behavioral Intentions toward Learning Artificial Intelligence: A Survey Study. International Journal of Environmental Research and Public Health, 19(14), 8733. https://doi.org/10.3390/ijerph19148733

Lin, J. W., & Lin, H. C. K. (2019). User acceptance in a computer-supported collaborative learning (CSCL) environment with social network awareness (SNA) support. Australasian Journal of Educational Technology, 35(1), 100-115. https://doi.org/10.14742/ajet.3395

Lin, S. H., Lee, H. C., Chang, C. T., & Fu, C. J. (2020). Behavioral intention towards mobile learning in Taiwan, China, Indonesia, and Vietnam. Technology in Society, 63, 101387. https://doi.org/10.1016/j.techsoc.2020.101387

Liu, N., & Pu, Q. (2023). Factors influencing learners’ continuance intention toward one-to-one online learning. Interactive Learning Environments, 31(3), 1742-1763. https://doi.org/10.1080/10494820.2020.1857785

Ma, M., Chen, J., Zheng, P., & Wu, Y. (2022). Factors affecting EFL teachers’ affordance transfer of ICT resources in China. Interactive Learning Environments, 30(6), 1044-1059. https://doi.org/10.1080/10494820.2019.1709210

Nikolopoulou, K., Gialamas, V., & Lavidas, K. (2021). Habit, hedonic motivation, performance expectancy and technological pedagogical knowledge affect teachers’ intention to use mobile internet. Computers and Education Open, 2, 100041. https://doi.org/10.1016/j.caeo.2021.100041

Nguyen, H. T., & Chu, Q. P. (2021). Estimating university students’ acceptance of technological tools for studying English through the UTAUT model. International Journal of TESOL & Education, 1(3), 209-234. https://i-jte.org/index.php/journal/article/view/96

Pham, L., Limbu, Y. B., Bui, T. K., Nguyen, H. T., & Pham, H. T. (2019). Does e-learning service quality influence e-learning student satisfaction and loyalty? Evidence from Vietnam. International Journal of Educational Technology in Higher Education, 16(1), 1-26. https://doi.org/10.1186/s41239-019-0136-3

Rahaman, E. U., Mahmud, I., Himel, R., Begum, A., & Jahan, N. (2022). Mathematical Modelling of Teachers’ Intention to Participate in Online Training During Covid-19 Lockdown: Evidence from Emerging Economy. International Journal of Emerging Technologies in Learning, 17(12), 170-183. https://doi.org/10.3991/ijet.v17i12.30465

Rehman, S. U., Bhatti, A., Mohamed, R., & Ayoup, H. (2019). The moderating role of trust and commitment between consumer purchase intention and online shopping behavior in the context of Pakistan. Journal of Global Entrepreneurship Research, 9(1), 1-25. https://doi.org/10.1186/s40497-019-0166-2

Reychav, I., Elyakim, N., & McHaney, R. (2023). Lifelong learning processes in professional development for online teachers during the Covid era. In Frontiers in Education. 8, 1041800. https://doi.org/10.3389/feduc.2023.1041800

Richter, N. F., Hauff, S., Ringle, C. M., Sarstedt, M., Kolev, A. E., & Schubring, S. (2023). How to apply necessary condition analysis in PLS-SEM. In Partial Least Squares Path Modeling: Basic Concepts, Methodological Issues and Applications. (pp. 267-297). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-37772-3_10

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. https://doi.org/10.1111/bjet.12839

Sussman, R., & Gifford, R. (2019). Causality in the theory of planned behavior. Personality and Social Psychology Bulletin, 45(6), 920-933. https://doi.org/10.1177/0146167218801363

Tamilmani, K., Rana, N.P., Prakasam, N., Dwivedi, Y.K., (2019). The battle of Brain vs. Heart: a literature review and meta-analysis of “hedonic motivation” use in UTAUT2. Int. J. Inf. Manag. 46, 222–235. https://doi.org/10.1016/j.ijinfomgt.2019.01.008

Thi, H. P., Tran, Q. N., La, L. G., Doan, H. M., & Vu, T. D. (2023). Factors motivating students' intention to accept online learning in emerging countries: the case study of Vietnam. Journal of Applied Research in Higher Education, 15(2), 324-341. https://doi.org/10.1108/JARHE-05-2021-0191

Thủy, T. T. (2025). Tìm hiểu thái độ và kỳ vọng của sinh viên Đại học ngành Ngôn Ngữ Anh đối với môn ngữ âm. TẠP CHÍ KHOA HỌC ĐẠI HỌC MỞ THÀNH PHỐ HỒ CHÍ MINH-KHOA HỌC XÃ HỘI, 20(1), 5-18. https://doi.org/10.46223/HCMCOUJS.soci.vi.20.1.3498.2025

Thủy, T. T., Linh, K., & Tâm, P. T. T. (2024). Áp dụng Flipped Classroom trong dạy và học EFL đối với sinh viên Đại học: Thái độ và nhận thức. TẠP CHÍ KHOA HỌC ĐẠI HỌC MỞ THÀNH PHỐ HỒ CHÍ MINH-KHOA HỌC XÃ HỘI, 19(1), 85-96. https://doi.org/10.46223/HCMCOUJS.soci.vi.19.1.3018.2024

Ullah, M. S., Khandakar, M. S. A., Aziz, M. A., & Kee, D. M. H. (2022). Technology Enabling the New Normal: How Students Respond to Classes. The International Review of Research in Open and Distributed Learning, 23(4), 35-56. https://doi.org/10.19173/irrodl.v23i4.6295

Van der Spoel, I., Noroozi, O., Schuurink, E., & van Ginkel, S. (2020). Teachers’ online teaching expectations and experiences during the Covid19-pandemic in the Netherlands. European journal of teacher education, 43(4), 623-638. https://doi.org/10.1080/02619768.2020.1821185

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly: Management Information Systems, 27(3), 425-478. https://doi.org/10.2307/30036540

Venkatesh, V., Rabah, J., Fusaro, M., Couture, A., Varela, W., & Alexander, K. (2016). Factors impacting university instructors’ and students’ perceptions of course effectiveness and technology integration in the age of web 2.0. McGill Journal of Education, 51(1), 533-561. https://doi.org/10.7202/1037358ar

Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly: Management Information Systems, 36(1), 157-178. https://doi.org/10.2307/41410412

Wiziack, J. C., & Dos Santos, V. M. P. D. (2021). Evaluating an integrated cognitive competencies model to enhance teachers’ application of technology in large-scale educational contexts. Heliyon, 7(1). https://doi.org/10.1016/j.heliyon.2021.e05928

Wu, T. (2023). An Integrated Model to Assess EFL Learners' Online Learning Behaviour. International Journal of Technology-Enhanced Education (IJTEE), 2(1), 1-17. https://doi.org/10.4018/IJTEE.323453

Xu, X. L., Shen, W. Q., Islam, A. A., Shen, J. Y., & Gu, X. Q. (2023). Modeling Chinese teachers’ behavioral intention to use recording studios in primary schools. Interactive Learning Environments, 31(7), 4182-4199. https://doi.org/10.1080/10494820.2021.1955713

Yao, Y., Wang, P., Jiang, Y., Li, Q., & Li, Y. (2022). Innovative online learning strategies for the successful construction of student self-awareness during the COVID-19 pandemic: Merging TAM with TPB. Journal of Innovation & Knowledge, 7(4), 100252. https://doi.org/10.1016/j.jik.2022.100252

Yuan, Y. P., Tan, G. W. H., Ooi, K. B., & Lim, W. L. (2021). Can COVID-19 pandemic influence experience response in mobile learning?. Telematics and Informatics, 64, 101676. https://doi.org/10.1016/j.tele.2021.101676

Zhao, L., Ao, Y., Wang, Y., & Wang, T. (2022). Impact of home-based learning experience during COVID-19 on future intentions to study online: a Chinese university perspective. Frontiers in Psychology, 13, 862965. https://doi.org/10.3389/fpsyg.2022.862965

Zhao, Y., Wang, N., Li, Y., Zhou, R., & Li, S. (2021). Do cultural differences affect users’e‐learning adoption? A meta‐analysis. British Journal of Educational Technology, 52(1), 20-41. https://doi.org/10.1111/bjet.13002

Zou, B., Yan, X., & Li, H. (2020). Students' perspectives on using online sources and apps for EFL learning in the mobile-assisted language learning context. In Language learning and literacy: Breakthroughs in research and practice (pp. 515-531). IGI Global. https://doi.org/10.4018/978-1-5225-9618-9.ch027

DOI: https://doi.org/10.46223/HCMCOUJS.soci.en.16.4.4265.2026