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International Journal of Academic Research in Progressive Education and Development

Open Access Journal

ISSN: 2226-6348

Extended Unified Theory of Acceptance and Use of Technology in Mobile Learning: A Systematic Review

Fei Zhang, Ramiza Haji Darmi, Ngee Thai Yap, Vahid Nimehchisalem

http://dx.doi.org/10.6007/IJARPED/v11-i3/14650

Open access

Technology acceptance, as a prerequisite for the successful implementation of mobile learning, has received much academic effort based on different theories. As a comprehensive theory in exploring individual technology acceptance, the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) has gained increasing attention in information systems and beyond. Nevertheless, there is a gap in existing knowledge regarding literature that systematically synthesizes research on UTAUT2 in an educational context. Given this, the present study was conducted to comprehensively review existing studies on the acceptance of mobile learning (m-learning) so as to get a clear and in-depth understanding of learners’ needs and preferences. We searched studies that empirically examined m-learning acceptance based on UTAUT2 from four databases in October 2020. Following the guidelines in the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement, 20 studies were identified and included. The results highlighted the current research trend of previous studies in terms of year of publication, distribution of country and journal, type of technology, and research method. Besides, the determinants of the acceptance of m-learning were identified. The main findings include that hedonic motivation was the most validated predictor of users’ behavioural intention, followed by performance expectancy, habit and social influence, while effort expectancy, facilitating conditions and price value were reported to be nonsignificant in more than half of the studies reviewed. Most studies applied a part of UTAUT2 in a particular research context, but a few studies extended the model with external variables such as trust, technological innovativeness, and personal innovativeness. The findings also reveal that the investigation of moderating effects was lacking in the existing literature. Most studies were undertaken in developing countries in Asia in the context of higher education and self-reported questionnaire surveys were the single method of data collection used in all studies with the partial least squares structural equation modeling (PLS-SEM) being the most frequently adopted data analysis method. Further efforts can be dedicated to extending UTAUT2 with external variables to tailor to the m-learning context and to further examine the effect of moderating variables. A great diversity of respondents is also encouraged in future studies so that deeper insights can be gained from students and teachers at different educational stages. Furthermore, longitudinal studies are needed to explore technology acceptance at various phases such as adoption, initial use, or post-adoptive use.

Agudo-Peregrina, Á. F., Hernández-García, Á., & Pascual-Miguel, F. J. (2014). Behavioral intention, use behavior and the acceptance of electronic learning systems: Differences between higher education and lifelong learning. Computers in Human Behavior, 34, 301–314. https://doi.org/10.1016/j.chb.2013.10.035.
Al-Azawei, A., & Alowayr, A. (2020). Predicting the intention to use and hedonic motivation for mobile learning: A comparative study in two Middle Eastern countries. Technology in Society, 62, 101325. https://doi.org/10.1016/j.techsoc.2020.101325.
Al-Emran, M., Mezhuyev, V., & Kamaludin, A. (2018). Technology Acceptance Model in M-learning context: A systematic review. Computers and Education, 125, 389–412. https://doi.org/10.1016/j.compedu.2018.06.008.
Almaiah, M. A., Alamri, M. M., & Al-Rahmi, W. (2019). Applying the UTAUT Model to Explain the Students’ Acceptance of Mobile Learning System in Higher Education. IEEE Access, 7, 174673–174686. https://doi.org/10.1109/ACCESS.2019.2957206.
Almaiah, M. A., Jalil, M. @. M. A., & Man, M. (2016). Empirical investigation to explore factors that achieve high quality of mobile learning system based on students’ perspectives. Engineering Science and Technology, an International Journal, 19(3), 1314–1320. https://doi.org/10.1016/j.jestch.2016.03.004.
Almaiah, M. A., & Al Mulhem, A. (2019). Analysis of the essential factors affecting of intention to use of mobile learning applications. In Education and Information Technologies (Vol. 24, Issue 2). Education and Information Technologies.
Almarwani, M. (2016). E3-Electronic Education for English: Developing mobile learning and teaching in Saudi Arabia.?. 8th Annual International Conference on Education and New Learning Technologies (EDULEARN16), July. http://eprints.lincoln.ac.uk/24208/1/24208 AlMarwaniManal-Education-July 2016.pdf.
Althunibat, A. (2015). Determining the factors influencing students’ intention to use m-learning in Jordan higher education. Computers in Human Behavior, 52(November 2015), 65–71. https://doi.org/10.1016/j.chb.2015.05.046.
Ameri, A., Khajouei, R., Ameri, A., & Jahani, Y. (2020). Acceptance of a mobile-based educational application (LabSafety) by pharmacy students: An application of the UTAUT2 model. Education and Information Technologies, 25(1), 419–435.
https://doi.org/10.1007/s10639-019-09965-5.
Arain, A. A., Hussain, Z., Rizvi, W. H., & Vighio, M. S. (2019). Extending UTAUT2 toward acceptance of mobile learning in the context of higher education. Universal Access in the Information Society, 18(3), 659–673. https://doi.org/10.1007/s10209-019-00685-8.
Baharin, A. T., Lateh, H., Nathan, S. S., & Nawawi, H. mohd. (2015). Evaluating Effectiveness of IDEWL Using Technology Acceptance Model. Procedia - Social and Behavioral Sciences, 171, 897–904. https://doi.org/10.1016/j.sbspro.2015.01.207.
Bennett, S., Maton, K., & Kervin, L. (2008). The “digital natives” debate: A critical review of the evidence. British Journal of Educational Technology, 39(5), 775–786. https://doi.org/10.1111/j.1467-8535.2007.00793.x.
Bharati, V. J., & Srikanth, R. (2018). Modified UTAUT2 model for m-learning among students in India. International Journal of Learning and Change, 10(1), 5–20.
https://doi.org/10.1504/IJLC.2018.089532.
Botero, G. 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.
Chen, F. H., Looi, C. K., & Chen, W. (2009). Integrating technology in the classroom: A visual conceptualization of teachers’ knowledge, goals and beliefs. Journal of Computer Assisted Learning, 25(5), 470–488. https://doi.org/10.1111/j.1365-2729.2009.00323.x
Chu, H. C., Hwang, G. J., Tsai, C. C., & Tseng, J. C. R. (2010). A two-tier test approach to developing location-aware mobile learning systems for natural science courses. Computers and Education, 55(4), 1618–1627.
https://doi.org/10.1016/j.compedu.2010.07.004
Chung, H.-H., Chen, S.-C., & Kuo, M.-H. (2015). A Study of EFL College Students’ Acceptance of Mobile Learning. Procedia - Social and Behavioral Sciences, 176, 333–339. https://doi.org/10.1016/j.sbspro.2015.01.479.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems, 13(3), 319–339. https://doi.org/10.2307/249008.
Edwards, J. R. (2017). Undergraduate Student Acceptance and Use of Mobile Learning in Speech Preparation Using Skype. May.
Farooq, M. S., Salam, M., Jaafar, N., Fayolle, A., Ayupp, K., Radovic-Markovic, M., & Sajid, A. (2017). Acceptance and use of lecture capture system (LCS) in executive business studies: Extending UTAUT2. Interactive Technology and Smart Education, 14(4), 329–348. https://doi.org/10.1108/ITSE-06-2016-0015.
Godwin-Jones, R. (2017). Smartphones and language learning. Language Learning and Technology, 21(2), 3–17.
Hair, 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
Hair, J. J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). 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(1), 101–123. https://doi.org/10.1007/s11423-016-9465-2.
Hoi, V. N. (2020). Understanding higher education learners’ acceptance and use of mobile devices for language learning: A Rasch-based path modeling approach. Computers and Education, 146(August 2019), 103761. https://doi.org/10.1016/j.compedu.2019.103761
Higgins, J., & Green, S. (2011). Handbook for systematic reviews of interventions version 5.1. 0 [updated March 2011]. The Cochrane Collaboration, 5, 252-258.
Hu, P. J. H., Clark, T. H. K., & Ma, W. W. (2003). Examining technology acceptance by school teachers: A longitudinal study. Information and Management, 41(2), 227–241. https://doi.org/10.1016/S0378-7206(03)00050-8
Huang, X. (2018). Social media use by college students and teachers: an application of UTAUT2 (Doctoral dissertation, Walden University).
Hwang, G. J., & Tsai, C. C. (2011). Research trends in mobile and ubiquitous learning: A review of publications in selected journals from 2001 to 2010. British Journal of Educational Technology, 42(4), 65–70. https://doi.org/10.1111/j.1467-8535.2011.01183.x.
Issaramanoros, E., Khlaisang, J., & Pugsee, P. (2018). Auto mechanic students’ perceptions and readiness toward mobile learning in Thailand. International Journal of Interactive Mobile Technologies, 12(5), 28–42. https://doi.org/10.3991/ijim.v12i5.8906.
Kaliisa, R., Palmer, E., & Miller, J. (2019). Mobile learning in higher education: A comparative analysis of developed and developing country contexts. British Journal of Educational Technology, 50(2), 546–561. https://doi.org/10.1111/bjet.12583.
Kang, M., Liew, B. T., Lim, H., Jang, J., & Lee, S. (2015). Investigating the Determinants of Mobile Learning Acceptance in Korea Using UTAUT2. Emerging Issues in Smart Learning, 209–216. https://doi.org/10.1007/978-3-662-44188-6.
Karimi, S. (2016). Do learners’ characteristics matter? An exploration of mobile-learning adoption in self-directed learning. Computers in Human Behavior, 63, 769–776. https://doi.org/10.1016/j.chb.2016.06.014.
Kumar, J. A., & Bervell, B. (2019). Google Classroom for mobile learning in higher education: Modelling the initial perceptions of students. Education and Information Technologies, 24(2), 1793–1817. https://doi.org/10.1007/s10639-018-09858-z.
Kumar, B. A., & Chand, S. S. (2019). Mobile learning adoption: A systematic review. Education and Information Technologies, 24(1), 471–487. https://doi.org/10.1007/s10639-018-9783-6.
Liu, Y., Li, H., & Carlsson, C. (2010). Factors driving the adoption of m-learning: An empirical study. Computers & Education, 55, 1211–1219.
https://doi.org/10.1016/j.compedu.2010.05.018.
Malhotra, M. K., & Grover, V. (1998). An assessment of survey research in POM: From constructs to theory. Journal of Operations Management, 16(4), 407–425. https://doi.org/10.1016/S0272-6963(98)00021-7.
Marangunic, N., & Granic, A. (2015). Technology acceptance model: a literature review from 1986 to 2013. Universal Access in the Information Society, 14(1), 81–95. https://doi.org/10.1007/s10209-014-0348-1.
Masrek, M. N. (2015). PREDICTORS OF MOBILE LEARNING ADOPTION AMONG UNDERGRADUATE NURSING FACULTY IN A SOUTHEASTERN STATE. 317–324. https://doi.org/10.15849/icit.2015.0063
Memon, M. A., Ting, H., Cheah, J. H., Ramayah, T., Chuah, F., & Cham, T. H. (2020). Sample size for survey research: review and recommendations. Journal of Applied Structural Equation Modelling, 4(2), i–xx. https://doi.org/10.47263/jasem.4(2)01.
Motiwalla, L. F. (2007). Mobile learning: A framework and evaluation. Computers and Education, 49(3), 581–596. https://doi.org/10.1016/j.compedu.2005.10.011
Mohammadi, H. (2015). Investigating users’ perspectives on e-learning: An integration of TAM and IS success model. Computers in Human Behavior, 45, 359–374. https://doi.org/10.1016/j.chb.2014.07.044.
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., Altman, D., Antes, G., Atkins, D., Barbour, V., Barrowman, N., Berlin, J. A., Clark, J., Clarke, M., Cook, D., D’Amico, R., Deeks, J. J., Devereaux, P. J., Dickersin, K., Egger, M., Ernst, E., … Tugwell, P. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Medicine, 6(7). https://doi.org/10.1371/journal.pmed.1000097.
Moorthy, K., Yee, T. T., T’ing, L. C., & Kumaran, V. V. (2019). Habit and hedonic motivation are the strongest influences in mobile learning behaviours among higher education students in Malaysia. Australasian Journal of Educational Technology, 35(4), 174–191. https://doi.org/10.14742/ajet.4432
Motiwalla, L. F. (2007). Mobile learning: A framework and evaluation. Computers and Education, 49(3), 581–596. https://doi.org/10.1016/j.compedu.2005.10.011.
Mtebe, J. S., Mbwilo, B., & Kissaka, M. M. (2016). Factors influencing teachers’ use of multimedia enhanced content in secondary schools in Tanzania. International Review of Research in Open and Distributed Learning, 17(2), 65-84.
https://doi.org/10.19173/irrodl.v17i2.2280
Nair, P. K., Ali, F., & Lim, C. L. (2015). Acceptance and Use of Lecture Capture System (LCS) in Executive Business Studies: Extending UTAUT2. Interactive Technology and Smart Education, 12(3), 183–201. https://doi.org/10.1021/j100460a022.
Nawaz, S. S., & Mohamed, R. (2020). Acceptance of mobile learning by higher educational institutions in Sri Lanka: An UTAUT2 approach. Journal of Critical Reviews, 7(12), 1036–1049. https://doi.org/10.31838/jcr.07.12.183.
Nikolopoulou, K., Gialamas, V., & Lavidas, K. (2020). Acceptance of mobile phone by university students for their studies: an investigation applying UTAUT2 model. Education and Information Technologies, 4139–4155. https://doi.org/10.1007/s10639-020-10157-9.
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. https://doi.org/10.1111/j.1467-8535.2011.01229.x.
Peng, H., Su, Y. J., Chou, C., & Tsai, C. C. (2009). Ubiquitous knowledge construction: Mobile learning re-defined and a conceptual framework. Innovations in Education and Teaching International, 46(2), 171–183. https://doi.org/10.1080/14703290902843828.
Pynoo, B., Devolder, P., Tondeur, J., Van Braak, J., Duyck, W., & Duyck, P. (2011). Predicting secondary school teachers’ acceptance and use of a digital learning environment: A cross-sectional study. Computers in Human Behavior, 27(1), 568–575. https://doi.org/10.1016/j.chb.2010.10.005.
Pynoo, B., Tondeur, J., Van Braak, J., Duyck, W., Sijnave, B., & Duyck, P. (2011). Assessing teachers’ acceptance of educational technologies: Beware for the congruency between user acceptance and actual use. Proceedings of the 19th International Conference on Computers in Education, ICCE 2011, 682–686.
Rowley, J. (2014). Designing and using research questionnaires. Management Research Review, 37(3), 308–330. https://doi.org/10.1108/MRR-02-2013-0027.
Saade, 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 and Management, 42(2), 317–327.
https://doi.org/10.1016/j.im.2003.12.013.
Sabah, N. M. (2016). Exploring students’ awareness and perceptions: Influencing factors and individual differences driving m-learning adoption. Computers in Human Behavior, 65, 522–533. https://doi.org/10.1016/j.chb.2016.09.009.
Schuitema, G., Anable, J., Skippon, S., & Kinnear, N. (2013). The role of instrumental, hedonic and symbolic attributes in the intention to adopt electric vehicles. Transportation Research Part A: Policy and Practice, 48, 39–49.
https://doi.org/10.1016/j.tra.2012.10.004
Sharma, S. K., Joshi, A., & Sharma, H. (2016). A multi-analytical approach to predict the Facebook usage in higher education. Computers in Human Behavior, 55, 340–353. https://doi.org/10.1016/j.chb.2015.09.020.
Tamilmani, K., Rana, N. P., & Dwivedi, Y. K. (2018). Mobile application adoption predictors: Systematic review of UTAUT2 studies using weight analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Vol. 11195 LNCS. Springer International Publishing. https://doi.org/10.1007/978-3-030-02131-3_1.
Tamilmani, K., Rana, N. P., & Dwivedi, Y. K. (2020). Consumer Acceptance and Use of Information Technology: A Meta-Analytic Evaluation of UTAUT2. Information Systems Frontiers. https://doi.org/10.1007/s10796-020-10007-6.
Teo, T., Doleck, T., Bazelais, P., & Lemay, D. J. (2019). Exploring the drivers of technology acceptance: a study of Nepali school students. Educational Technology Research and Development, 67(2), 495–517. https://doi.org/10.1007/s11423-019-09654-7.
Traxler, J. (2007). Defining, Discussing, and Evaluating Mobile Learning: The moving finger writes and having writ…. International Review of Research in Open and Distance Learning, 8(2), 1–12. https://doi.org/10.19173/irrodl.v8i2.346.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478.
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, 157-178.
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2016). Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the Association for Information Systems, 17(5), 328–376. https://doi.org/10.17705/1jais.00428.
Venkatesh, V., & Zhang, X. (2010). Unified theory of acceptance and use of technology: US vs. China. Journal of global information technology management, 13(1), 5-27.
Yang, T., Zhong, H., Mok, Q., Lai, I. K. W., & Ng, K. K. (2018). The acceptance of “flash class”–mobile mini-lessons through WeChat. In International Conference on Technology in Education (pp. 101-108). Springer, Singapore.
Yang, S. (2013). Understanding Undergraduate Students’ Adoption of Mobile Learning Model: A Perspective of the Extended UTAUT2. Journal of Convergence Information Technology, 8(10), 969–979. https://doi.org/10.4156/jcit.vol8.issue10.118.
Zwain, A. A. A. (2019). Technological innovativeness and information quality as neoteric predictors of users’ acceptance of learning management system: An expansion of UTAUT2. Interactive Technology and Smart Education, 16(3), 239–254. https://doi.org/10.1108/ITSE-09-2018-0065.

In-Text Citation: (Zhang et al., 2022)
To Cite this Article: Zhang, F., Darmi, R. H., Yap, N. T., & Nimehchisalem, V. (2022). Extended Unified Theory of Acceptance and Use of Technology in Mobile Learning: A Systematic Review. International Journal of Acdemic Research in Progressive Education and Development, 11(3), 846–865.