Factors Affecting The Adoption Of Mobile Learning In Vocational High Schools And High Schools Using Extended UTAUT
DOI:
https://doi.org/10.59188/eduvest.v4i8.1718Keywords:
M-Learning, Mobile Learning, UTAUT, Structural Equation Modelling, AMOSAbstract
M-Learning is a learning process that uses technology or mobile devices such as smartphones, tablets or wearable devices to support the learning process. This is still being done because there are many different theoretical models proposed. However, there is no model that can be generally accepted as an established theoretical model in the application of M-learning in vocational and high school education environments in Sidoarjo. This research is expected to make a significant contribution to the development of a better theoretical understanding of the determining factors that influence M-learning adoption using the Unified Theory of Acceptance and Use of The Technology (UTAUT). To collect data, researchers distributed questionnaires to respondents using Google Form. The data used were 444 M-learning users. Theoretical model research was carried out using Structural Equation Modeling (SEM) analysis, then SPSS and Amos as analysis support. There are seven factors that determine the results of acceptance of M-Learning adoption in this research, namely Facilitating Condition, Performance Expectancy, Effort Expectancy, Perceived Convenience, Social Influence, School Management Support. The six factors that show a positive and significant relationship are Facilitating Condition, Performance Expectancy, Effort Expectancy, Perceived Convenience, Social Influence, School Management Support. Perceived Convenience has the first strongest positive and significant value, and Performance Expectancy has the second strongest value. Each factor has a moderate influence on Intention to Use. This factor is the most influential in implementing M-Learning in vocational and high schools in the Sidoarjo area.
References
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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
Voicu, M. C., & Muntean, M. (2023). Factors That Influence Mobile Learning among University Students in Romania. Electronics (Switzerland), 12(4). https://doi.org/10.3390/electronics12040938
Al-Bashayreh, M., Almajali, D., Altamimi, A., Masa’deh, R., & Al-Okaily, M. (2022). An Empirical Investigation of Reasons Influencing Student Acceptance and Rejection of Mobile Learning Apps Usage. Sustainability (Switzerland), 14(7). https://doi.org/10.3390/su14074325
Alfalah, A. A. (2023). Factors influencing students’ adoption and use of mobile learning management systems (m-LMSs): A quantitative study of Saudi Arabia. International Journal of Information Management Data Insights, 3(1), 100143. https://doi.org/10.1016/j.jjimei.2022.100143
Alghazi, S. S., Wong, S. Y., Kamsin, A., Yadegaridehkordi, E., & Shuib, L. (2020). Towards sustainable mobile learning: A brief review of the factors influencing acceptance of the use of mobile phones as learning tools. Sustainability (Switzerland), 12(24), 1–19. https://doi.org/10.3390/su122410527
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
Chand, S. S., Kumar, B. aklesh, Goundar, M. S., & Narayan, A. (2022). Extended UTAUT Model for Mobile Learning Adoption Studies. International Journal of Mobile and Blended Learning, 14(1), 1–20. https://doi.org/10.4018/ijmbl.312570
Chao, C. M. (2019). Factors determining the behavioral intention to use mobile learning: An application and extension of the UTAUT model. Frontiers in Psychology, 10(JULY), 1–14. https://doi.org/10.3389/fpsyg.2019.01652
Hafidz, M. Al. (2022). Acceptance of e-Learning Applications at Indonesian Universities Using the Extended Technology Acceptance Model. Sistemasi, 11(2), 526. https://doi.org/10.32520/stmsi.v11i2.1993
Hunde, M. K., Demsash, A. W., & Walle, A. D. (2023). Behavioral intention to use e-learning and its associated factors among health science students in Mettu university, southwest Ethiopia: Using modified UTAUT model. Informatics in Medicine Unlocked, 36(December 2022), 101154. https://doi.org/10.1016/j.imu.2022.101154
Izkair, A. S., & Lakulu, M. M. (2021). Experience moderator effect on the variables that influence intention to use mobile learning. Bulletin of Electrical Engineering and Informatics, 10(5), 2875–2883. https://doi.org/10.11591/eei.v10i5.3109
Lisana, L., & Suciadi, M. F. (2021). The Acceptance of Mobile Learning: A Case Study of 3D Simulation Android App for Learning Physics. International Journal of Interactive Mobile Technologies, 15(17), 205–214. https://doi.org/10.3991/IJIM.V15I17.23731
Ozuorcun, N. C., & Tabak, F. (2012). Is M-learning Versus E-learning or are They Supporting Each Other? Procedia - Social and Behavioral Sciences, 46, 299–305. https://doi.org/10.1016/j.sbspro.2012.05.110
Pramana, E. (2018). Determinants of the adoption of mobile learning systems among university students in Indonesia. Journal of Information Technology Education: Research, 17, 365–398. https://doi.org/10.28945/4119
Pramana, E. (2021). The Mobile Payment Adoption: A Systematic Literature Review. 3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021, 265–269. https://doi.org/10.1109/EIConCIT50028.2021.9431846
Salem, M. A., & Elshaer, I. A. (2023). Educators’ Utilizing One-Stop Mobile Learning Approach amid Global Health Emergencies: Do Technology Acceptance Determinants Matter? Electronics (Switzerland), 12(2). https://doi.org/10.3390/electronics12020441
Sánchez-Prieto, J. C., Olmos-Migueláñez, S., & García-Peñalvo, F. J. (2016). Informal tools in formal contexts: Development of a model to assess the acceptance of mobile technologies among teachers. Computers in Human Behavior, 55, 519–528. https://doi.org/10.1016/j.chb.2015.07.002
Tarhini, A., AlHinai, M., Al-Busaidi, A. S., Govindaluri, S. M., & Shaqsi, J. Al. (2024). What drives the adoption of mobile learning services among college students: An application of SEM-neural network modeling. International Journal of Information Management Data Insights, 4(1), 100235. https://doi.org/10.1016/j.jjimei.2024.100235
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
Voicu, M. C., & Muntean, M. (2023). Factors That Influence Mobile Learning among University Students in Romania. Electronics (Switzerland), 12(4). https://doi.org/10.3390/electronics12040938
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