TY - JOUR T1 - THE INTENTION OF GENERATION Z TO USE MOBILE LEARNING: THE ROLE OF SELF-EFFICACY AND ENJOYMENT AU - Anggoro, Subuh AU - Fitriati, Azmi AU - Talib, Corrienna Abdul AU - Toh, Tin Lam PY - 2025 DA - January Y2 - 2024 DO - 10.17718/tojde.1445234 JF - Turkish Online Journal of Distance Education JO - TOJDE PB - Anadolu University WT - DergiPark SN - 1302-6488 SP - 85 EP - 100 VL - 26 IS - 1 LA - en AB - The Technology Acceptance Model (TAM) is a concise and efficient predictive model used to explain the acceptance of m-learning technology. However, several studies have shown that TAM cannot fully explain the acceptance of m-learning among Generation Z. This study aims to formulate TAM as a model of m-learning acceptance for Generation Z. TAM developed based on self-efficacy and enjoyment is expected to explain the behavior of Generation Z in accepting m-learning. This study uses a survey approach, utilizing PLS-SEM as an analysis tool and primary data collected through questionnaires. Participants in this study were 563 students who used m-learning (on class application) at the Muhammadiyah University of Purwokerto, Indonesia. The results contribute to the formulation of a successful m-learning implementation model for Generation Z. These results provide empirical support indicating that selfefficacy and perceived enjoyment cause them to use m-learning now and in the future. Generation Z, who grew up in the digital era, has a high level of proficiency in using technology. Self-efficacy increases user optimism. They are confident in their ability to complete tasks and solve problems when using m-learning. Enjoyment can increase the belief that m-learning is user-friendly and useful. The results of this study support the theory of self-efficacy which states that user beliefs serve as the best predictors of their behavior in using technology in mobile learning. KW - Technology acceptance model KW - mobile learning KW - Generation Z KW - self-efficacy KW - enjoyment KW - intention to use. CR - Abduljawad, M., & Ahmad, A. (2023). An Analysis of Mobile Learning (M-Learning) in Education. Multicultural Education, 9(2), 145–152. https://doi.org/https://doi.org/10.5281/zenodo.7665894 CR - Al-Araibi, A. A. M., Mahrin, M. N. bin, & Yusoff, R. C. M. (2019). Technological aspect factors of E-learning readiness in higher education institutions: Delphi technique. Education and Information Technologies, 24(1), 567–590. https://doi.org/10.1007/s10639-018-9780-9 CR - Al-Emran, M., Mezhuyev, V., & Kamaludin, A. (2020). Journal Pre-proof. Contemporary Educational Psychology, 60. https://doi.org/10.1016/j.cedpsych.2019.101829 CR - Al-Fraihat, D., Joy, M., Masa’deh, R., & Sinclair, J. (2020). Evaluating E-learning systems success: An empirical study. Computers in Human Behavior, 102, 67–86. https://doi.org/10.1016/j.chb.2019.08.004 CR - Al-Gahtani, S. S. (2016). Empirical investigation of e-learning acceptance and assimilation: A structural equation model. Applied Computing and Informatics, 12(1), 27–50. https://doi.org/10.1016/j.aci.2014.09.001 UR - https://doi.org/10.17718/tojde.1445234 L1 - https://dergipark.org.tr/en/download/article-file/3764248 ER -