Mobile Learning Adoption: A perspective from a Form Six Students in Sabah, Malaysia
Despite the availability of studies on mobile learning adoption, its theoretical foundations have not yet matured. However, studies on mobile learning adoption in the context of form six student in Malaysia is still very limited. Against this concern, a study was conducted with the aim of investigating factors that could influence the adoption of mobile learning. Based on The Unified Theory of Acceptance and Use of Technology (UTAUT) and two other variables which are Perceived Playfulness and Self-Directed Learning, an empirical structured has been developed to identify predictors of mobile learning. A self-administered questionnaire was adopted and a total of 314 responses were employed for the analysis, using Structural Equation Modelling (SEM). The findings of the analysis revealed that all key constructs (except social influence) affect mobile learning adoption among form six students. Besides that, Self-Directed Learning become the strongest predictor and followed by Effort Expectancy. These findings provide crucial implications for educators and practitioners to take individual characteristic (Self-Directed Learning) into consideration while promoting mobile learning. This study represents one of the few attempts to reveal the extended UTAUT model could be increased explanation power of technology acceptance by the users. Directions for future study are suggested at the end of the paper.
Abdullah, M. M., Sharif, M., Azman, H., & Arshad, M. (2019). Mobile Learning Adoption among Tertiary Students. The Journal of Technology Management and Technopreneurship (JTMT), 7(1), 1-6.
Abu-Al-Aish, A., & Love, S. (2013). Factors influencing students’ acceptance of mobile learning: An investigation in higher education. The International Review of Research in Open and Distributed Learning, 14(5), 82-107.
Ahmed, M. S. (2016). Technology acceptance of smartphones as mobile learning tools: A contextual comparative study of engineering and education colleges.
Ajzen, I., Fishbein, M., (1975), Belief, attitude, intention and behaviour: An introduction to theory and research, Addison-Wesley, Reading
Ajzen, I., & Fishbein, M. (2000). Attitudes and the attitude-behaviour relation: Reasoned and automatic processes. European Review of Social Psychology, 11, 1–33.
Alam, T., & Aljohani, M. (2020). M-Learning: Positioning the Academics to the Smart devices in the Connected Future. JOIV: International Journal on Informatics Visualization, 4(2), 76-79.
Alrawashdeh, Thamer A, Muhairat, Mohammad I, & Alqatawnah, Sokyna M. (2012). Factors affecting acceptance of web-based training system: Using extended UTAUT and structural equation modeling. arXiv preprint arXiv:1205.1904
Amzaourou, O., & Oubaha, D. (2018). Investigating the cross-cultural dimensions of educational Web 2.0 acceptance: The case of Moroccan and American university students. Research in Comparative and International Education, 13(2), 319-341.
Ariffin, S. K., & Lim, K. T. (2020, May). Investigating Factors Affecting Intention to Use Mobile Payment Among Young Professionals in Malaysia. In First ASEAN Business, Environment, and Technology Symposium (ABEATS 2019) (pp. 6-11). Atlantis Press.
Bandura, A., (1986), Social foundations of thought and action: A social cognitive theory, Prentice Hall, New Jersey.
Baptista, G., & Oliveira, T., (2015). Understanding mobile banking: The unified theory of acceptance and use of technology combined with cultural moderators. Computers in Human Behaviour, 50, 418-430.
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.
Brata, A. H., & Amalia, F. (2018). Impact Analysis of Social Influence Factor on Using Free Blogs as Learning Media for Driving Teaching Motivational Factor. In Proceedings of the 4th International Conference on Frontiers of Educational Technologies (pp. 29-33).
Casey, T., & Wilson, E., (2012). Predicting uptake of technology innovations in online family dispute resolution services: An application and extension of the UTAUT. Computer in Human Behaviour, 28(6), 2034-2045.
Chou, J. P. C. (2006). Understanding user's perceived playfulness toward mobile information and entertainment services in New Zealand (Doctoral dissertation, Auckland University of Technology).
Chong, A. Y. L., (2013). Mobile commerce usage activities: The roles of demographic and motivation variables. Technological Forecasting and Social Change, 80(7), 1350-1359.
Chung, J., & Tan, F. B. (2004). Antecedents of perceived playfulness: An exploratory study on user acceptance of general information-searching websites. Information & Management, 41(7), 869-881.
Curum, B., & Khedo, K. (2019). AMBLE: A Context-Aware Mobile Learning Framework. EAI Endorsed Transactions on Context-Aware Systems and Applications, 6(19). 1-12.
Dakduk, S., Santalla-Banderali, Z., & van der Woude, D. (2018). Acceptance of blended learning in executive education. SAGE Open, 8(3), 2158244018800647.
Davis, F. D., (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-339.
Duarte, P., & Pinho, J. C., (2019). A mixed methods UTAUT2-based approach to assess mobile health adoption. Journal of Business Research, 102, 140-150.
Durak, H. Y. (2019). Examining the acceptance and use of online social networks by preservice teachers within the context of unified theory of acceptance and use of technology model. Journal of Computing in Higher Education, 31(1), 173-209.
Erazo-Garzón, L., Patiño, A., Cedillo, P., & Bermeo, A. (2019). CALMS: A Context-Aware Learning Mobile System Based on Ontologies. In 2019 Sixth International Conference on eDemocracy & eGovernment (ICEDEG) (pp. 84-91).
Frost, J. (2013). Regression analysis: How do I interpret r-squared and assess the goodness-of-fit? [Web log post]. The Minitab Blog, 30.
Grant, M. M. (2019). Difficulties in defining mobile learning: Analysis, design characteristics, and implications. Educational Technology Research and Development, 67(2), 361-388.
Gu, J. (2016). Understanding self-directed learning in the context of mobile Web 2.0–case study with workplace learners. Interactive Learning Environments, 24(2), 306-316.
Gupta, A., Dogra, N., & George, B., (2018). What determines tourist adoption of smartphone apps? An analysis based on the UTAUT-2 framework. Journal of Hospitality and Tourism Technology, 9(1), 50-64.
Hadi F. Z. and Kishik, A. A. (2014). Acceptance of Mobile Learning Among University Students in Malaysia. Journal of Computing & Organizational Dynamics 1(1).
Hair Jr, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2017). Advanced issues in partial least squares structural equation modeling. saGe publications.
Huang, W. D., & Wu, C. G. (2017). Understanding motivational system in open learning: Learners’ engagement with a Traditional Chinese-based open educational resource system. Educational Technology Research and Development, 65(6), 1495-1521.
Iqbal, S. and Qureshi, I. A. (2012). M-learning Adoption: A Perspective from a Developing Country. The International Review of Research in Open and Distance Learning, 13(3), 147-164.
Jalil, A.; Beer, M.; Crowther, P. (2015). Pedagogical requirements for mobile learning: A review on Mobile Learning task model. Jurnal Interact. Media Educ, 12, 1–17. [CrossRef].
Jambulingam, M. (2013). Behavioural Intention to Adopt Mobile Technology among Tertiary Students. World Applied Sciences Journal, 22(9), 1262-1271.
Jasperson, J., Carter, P. E., & Zmud, R. W. (2005). A comprehensive conceptualization of the post-adoptive behaviours associated with IT-enabled work systems. MIS Quarterly, 29(3), 525-557.
Jeng, D. J. F., & Tzeng, G. H., (2012). Social influence on the use of clinical decision support systems: Revisiting the unified theory of acceptance and use of technology by the fuzzy DEMATEL technique. Computer and Industrial Engineering, 62(3), 819-828.
Jones, Kelvyn. (2016). Re: What is the acceptable r-squared value?
Retrieved from: https://www.researchgate.net/post/what_is_the_acceptable_r-squared_value/57d2be49615e27f1605e6ff3/citation/download.
J.-W. Moon and Y.-G. Kim., (2001). Extending the TAM for the World-Wide-Web context. Information and Management, 38, 217-230.
Kaliisa, R.; Picard, M. (2017). A systematic review on mobile learning in higher education: The African perspective. Turkish Online J. Educ. Technol, 16, 1–18.
Karimi, S. (2016). Do learners’ characteristics matter? An exploration of mobile-learning adoption in self-directed learning. Computers in Human Behavior, 63, 769-776.
Kim, S. S., Malhotra, N. K., & Narasimhan, S., (2005). Two competing perspectives on automatic use: A theoretical and empirical comparison. Information Systems Research, 16(4), 418-432.
Kissi, P. S., Nat, M., & Armah, R. B. (2018). The effects of learning–family conflict, perceived control over time and task-fit technology factors on urban–rural high school students’ acceptance of video-based instruction in flipped learning approach. Educational Technology Research and Development, 66(6), 1547-1569.
Knowles, M. S. (1975). Self-directed learning: A guide for learners and teachers. New York: Association Press
Kukulska-Hulme, A., & Traxler, J. (2007), Learning Design with Mobile and Wireless Technologies. In H. Beetham, & R. Sharpe (Eds.), Rethinking Pedagogy for the Digital Age: Designing and Delivering E-learning, Routledge, London.
Kumar, B. A., & Chand, S. S. (2019). Mobile learning adoption: A systematic review. Education and Information Technologies, 24(1), 471-487.
Kumar, V. R., Lall, A., & Mane, T. (2017). Extending the TAM model: Intention of management students to use mobile banking: Evidence from India. Global Business Review, 18(1), 238-249.
Lamptey, H.K.; Boateng, R. (2017). Mobile learning in developing countries: A synthesis of the past to define the future. Int. J. Soc. Behav. Educ. Econ. Bus. Ind. Eng, 11, 420–427.
Lin, C., Wu, S., & Tsai, R. (2005). Integrating Perceived Playfulness into expectation-confirmation model for web portal context. Information & Management, 42(5), 683-693.
Lowenthal, J. N. (2010). Using mobile learning: Determinates impacting behavioural intention. The American Journal of Distance Education, 24(4), 195-206.
Mabruri, H., Ahmadi, F., & Suminar, T. (2019). The Development of Science Mobile Learning Media to Improve Primary Students Learning Achievements. Journal of Primary Education, 8(1), 108-116.
Maita, I., Indrajit, R. E., & Irmayani, A. (2018, April). User behaviour analysis in academic information system using unified theory of acceptance and use of technology (UTAUT). In Proceedings of the 2018 International Conference on Internet and e-Business (pp. 223-228).
Makoe, M. (2010). Linking mobile learning to the student-centred approach. Retrieved May 12, 2011, from http://www.checkpointelearning.com/article/8044.html
Masrek, M. N., & Shahibi, M. S. (2019). Mobile Learning Adoption: The Case of Malaysian University Students. International Journal for e-Learning Security (IJeLS), 8(1), 574-564.
MCMC. (2020) Internet user survey –(Infografic). Pdf
Mohd Azli Yeop, Mohd Faiz Mohd Yaakob, Kung Teck Wong, Yahya Don, Farah Mohamad Zain. (2019). Implementation of ICT Policy (Blended Learning Approach): Investigating factors of Behavioural Intention and Use Behaviour. International Journal of Instruction, 12(1), 767-782.
Moon, J. W., & Kim, Y. G. (2001). Extending the TAM for a World-Wide-Web context. Information & Management, 38(4), 217-230.
Mosunmola, A., Mayowa, A., Okuboyejo, S., & Adeniji, C. (2018). Adoption and use of mobile learning in higher education: the UTAUT model. In Proceedings of the 9th International Conference on E-Education, E-Business, E-Management and E-Learning (pp. 20-25).
Mustafa, N., Nordin, N. M., Embi, M. A., & Norman, M. H. (2018). Testing the Usability of a Mobile Learning Module. International Journal of Engineering & Technology, 7(4.21), 113-117.
Nassuora, A. B. (2013). Students Acceptance of Mobile Learning for Higher Education in Saudi Arabia. International Journal of Learning Management Systems, 1(1), 1-9.
Nawi, N. C., Mamun, A. A., Nasir, N. A. M., & Muniady, R. (2019). Factors Affecting the Adoption of Social Media as a Business Platform: A Study among Student Entrepreneurs in Malaysia. Vision, 23(1), 1-11.
Nistor, N., Göğüş, A., & Lerche, T. (2013). Educational technology acceptance across national and professional cultures: a European study. Educational Technology Research and Development, 61(4), 733-749.
Okai-Ugbaje, S., Ardzejewska, K., & Imran, A. (2020). Readiness, roles, and responsibilities of stakeholders for sustainable mobile learning adoption in higher education. Education Sciences, 10(3), 49.
Okai-Ugbaje, S.; Ardzejewska, K.; Imran, A. (2017). A systematic review of mobile learning adoption in higher education: The African perspective. I-Mang. J. Mob. Appl. Technol, 4, 1–13.
Oliver, R. L., (1980). A cognitive model of the antecedents and consequence of satisfaction decisions. Journal of Marketing Research, 17(4), 460-469.
Paetzold, Ramona. (2016). Re: Low R-squared values in multiple regression analysis?
Retrieved from: https://www.researchgate.net/post/Low_R-squared_values_in_multiple_regression_analysis/56f40be793553b1f9a11fcb3/citation/download.
Park, J., Parsons, D., & Ryu, H. (2010). To flow and not to freeze: Applying flow experience to mobile learning. Learning Technologies, IEEE Transactions on, 3(1), 56-67.
Pedersen, P. E., & Ling, R. (2003). Modifying adoption research for mobile Internet service adoption: Cross-disciplinary interactions. In Paper presented at the system Sciences, 2003. Proceedings of the 36th annual Hawaii international conference.
Pedro Isaias, Francisco Reis, Clara Coutinho, Jose Alberto Lencastre, (2017). Empathic technologies for distance/mobile learning: An empirical research based on the unified theory of acceptance and use of technology (UTAUT). Interactive Technology and Smart Education, 14(2), 159-180,
Ramakrishnan, K., Yasin, N. M., Selvaraj, K. R., & Periyasamy, J. (2019). Building the Bridge between Higher Learning Institution and Social Media Technologies through Mobile Learning in Malaysia. International Journal of Engineering and Advanced Technology, 8(5C), 937-944.
Ramírez-Correa, P., Rondán-Cataluña, F. J., Arenas-Gaitán, J., & Martín-Velicia, F., (2019). Analysing the acceptation of online games in mobile devices: An application of UTAUT2. Journal of Retailing and Consumer Services, 50, 85-93.
Rogers, E., (1962), Diffusion of innovation, Free Press, New York.
Ryan, R. M., and Deci, E. L. (2000). Intrinsic and extrinsic motivations: classic definitions and new directions. Contemp. Educ. Psychol, 25, 54–67.
Savin-Baden, M., & Major, C. H. (2004). Foundations of problem-based learning. Berkshire: SRHE & Open University Press.
Schofield, C. P., West, T. and Taylor E. (2011) Going Mobile In Executive Education How Mobile Technologies Are Changing The Executive Learning Landscape, Berkhamsted Hertfordshire Ashridge.
Shankar, A., & Datta, B. (2018). Factors affecting mobile payment adoption intention: An Indian perspective. Global Business Review, 19(3_suppl), S72-S89.
Shorfuzzaman, M., Hossain, M. S., Nazir, A., Muhammad, G., & Alamri, A. (2019). Harnessing the power of big data analytics in the cloud to support learning analytics in mobile learning environment. Computers in Human Behavior, 92, 578-588.
Sivathanu, B., (2019). Adoption of digital payment systems in the era of demonetization in India: An empirical study. Journal of Science and Technology Policy Management, 10(1), 143-171.
Stone, B. K., Scibilia, B., Pammer, C., Steele, C., & Keller, D. (2013). Regression analysis: How do I interpret R-squared and assess the goodness-of-fit. Retrieved August, 2, 2018.
Suartama, I. K., Setyosari, P., Sulthoni, S., & Ulfa, S. (2019). Development of an instructional design model for mobile blended learning in higher education. International Journal of Emerging Technologies in Learning (iJET), 14(16), 4-22.
Suki, N. M., & Suki, N. M. (2019). Structural relationships in the embedding of role-play games in a class for Japanese language proficiency: Towards a Unified View. Technology, Knowledge and Learning, 24(1), 65-87.
Teo, T. S. H., Lim, V. K. G., & Lai, R. Y. C., (1999). Intrinsic and extrinsic motivation in Internet usage. Omega, 27(1), 25-37
Thomas, T., Singh, L., & Gaffar, K. (2013). The utility of the UTAUT model in explaining mobile learning adoption in higher education in Guyana. International Journal of Education and Development using ICT, 9(3).
Thompson, R. L., Higgins, C. A., & Howell, J. M., (1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly, 15(1), 125-143.
Van Schaik, P. (2009). Unified theory of acceptance and use for websites used by students in higher education. Journal of Educational Computing Research, 40(2), 229–257.
Venkatesh, V., Morris, M., Davis, G., & Davis, F., (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478
Venkatesh, V., Thong, J. Y-L., Xu, X., (2012). Consumer acceptance and use of information technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 36(1), 157–178.
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, l40(1), 92-118.
Wang, L., & Xiao, J. (2018). Research on influencing factors of learners' intention of online learning behaviour in open education based on UTAUT model. In Proceedings of the 10th International Conference on Education Technology and Computers (pp. 92-98).
Wang, H. Y., & Wang, S. H., (2010). Predicting mobile hotel reservation adoption: Insight from a perceived value standpoint. International Journal of Hospitality Management, 29(4), 598-608.
Williams, B., & Brown, T. (2013). A confirmatory factor analysis of the Self‐Directed Learning Readiness Scale. Nursing & health sciences, 15(4), 430-436.
Wong, S. M., Leong, C. M., & Puah, C. H. (2020). Mobile internet adoption in Malaysian suburbs: The moderating effect of gender. Asian Journal of Business Research, 9(3).
Yakubu, M. N., & Dasuki, S. I. (2019). Factors affecting the adoption of e-learning technologies among higher education students in Nigeria: A structural equation modelling approach. Information Development, 35(3), 492-502.
Yang, H. H., Feng, L., & MacLeod, J. (2019). Understanding college students’ acceptance of cloud classrooms in flipped instruction: integrating UTAUT and connected classroom climate. Journal of Educational Computing Research, 56(8), 1258-1276.
Yip, M. H., Kee, C. Y., Lee, J. W., Lee, Y. J., & Soh, Y. Y. (2018). Determinants of continuance intention of mobile learning among academicians in Malaysian private universities (Doctoral dissertation, UTAR).
Yu, Chian-Son. (2012). Factors affecting individuals to adopt mobile banking: Empirical evidence from the UTAUT model. Journal of Electronic Commerce Research, 13(2), 104-121.
Zhang, A., & Cristol, D. (Eds.). (2019). Handbook of mobile teaching and learning. Springer.