The adoption of innovative educational tools has become imperative to meet the evolving needs of learners in this rapid technology era. This study explores the factors influencing the adoption of animated videos as an educational tool to enhance the learning. The research is grounded in the integration of two essential frameworks: Task-Technology Fit (TTF) and the Technology Acceptance Model (TAM). A sample size of 155 participants underwent rigorous analysis employing Structural Equation Modeling (SEM). This analytical approach allowed for a comprehensive examination of the relationships among variables and the identification of critical factors impacting the adoption of animated videos in education.
Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological bulletin, 107(2), 238.
Collier, J. E. (2020). Applied structural equation modeling using AMOS: Basic to advanced techniques. Routledge.
Chien, T. H., & Chang, C. C. (2012). Research and development of an animation teaching system for students' learning interest and cognitive loads. Computers & Education, 58(1), 243-252.
Clark, R. C., & Mayer, R. E. (2016). E-learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning (4th ed.). Wiley.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
Dalgarno, B., & Lee, M. J. (2010). What are the learning affordances of 3-D virtual environments? British Journal of Educational Technology, 41(1), 10-32.
Dajani, D., & Abu Hegleh, A. S. (2019). Behavior intention of animation usage among university students. Heliyon, 5(10), e02536. https://doi.org/10.1016/j.heliyon.2019.e02536
Dishaw, M. T., & Strong, D. M. (1999). Extending the technology acceptance model with task–technology fit constructs. Information & Management, 36(1), 9-21. https://doi.org/10.1016/S0378-7206(98)00101-3
Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics.
Srinivasalu, G. N. (2016). Using Cartoons as Effective Tools in Teaching Learning Process of Social Science. Scholarly Research Journal for Interdisciplinary Studies. 3(23), 1898-1905.
Goodhue, D. L., & Thompson, R. L. (1995). Task-Technology Fit and Individual Performance. MIS Quarterly, 19(2), 213–236. https://doi.org/10.2307/249689
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135.
Hu, L. T., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to under parameterized model misspecification. Psychological methods, 3(4), 424.
Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). The Guilford Press.
Keil, M., Beranek, P., & Konsynski, B. (1995). Usefulness and ease of use: Field study evidence regarding task considerations. Decision Support Systems, 13(1), 75-91. https://doi.org/10.1016/0167-9236(94)E0032-M.
Kalyuga, S., Chandler, P., & Sweller, J. (2004). When redundant on-screen text in multimedia technical instruction can interfere with learning. Human Factors: The Journal of the Human Factors and Ergonomics Society, 46(3), 567-581.
Klopping, I. M., & McKinney, E. M. (2004). Extending the technology acceptance model and the task-technology fit model to consumer e-commerce. Information Technology, Learning, and Performance Journal, 22(1), 35–48.
Khedekar, R. S., & Peters, G. B. (2013). Fit between task requirements and system features: A replication study. Journal of Computer Information Systems, 54(4), 70-78.
Lin, H. C., Hsieh, Y. H., & Pi, S. M. (2010). Effects of animated instructional materials on students' achievement and attitudes. Computers & Education, 55(2), 722-733.
Lim, J., Kim, Y., Chen, J. V., & Ryder, C. (2008). Extending the task-technology fit model with self-efficacy constructs. AMCIS 2008 Proceedings, 285.
Lederer, A. L., Maupin, D. J., Sena, M. P., & Zhuang, Y. (2000). The technology acceptance model and the World Wide Web. Decision Support Systems, 29(3), 269–282.
Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40(3), 191-204.
Mayer, R. E. (2017). Using multimedia for e-learning. Journal of Computer Assisted Learning, 33(5), 403-423.
Mayer, R. E. (2009). Multimedia Learning (2nd ed.). Cambridge University Press.
Moreno, R., & Mayer, R. E. (2007). Interactive multimodal learning environments. Educational Psychology Review, 19(3), 309-326.
Mathieson, K., & Keil, M. (1998). Beyond the interface: Ease of use and task/technology fit. Information & Management, 34(4), 221–230.
Nunnally, J. C., and Bernstein, I. H. (1994) The Assessment of Reliability. Psychometric Theory, 3, 248-292.
Park, C., & Raven, A. (2015). Information quality as a determinant of task-technology fit in using communication technology for simple task. Issues in Information Systems, 16(1), 189–199.
Park, C., Kim, D. G., Cho, S., & Han, H. J. (2019, March). Adoption of multimedia technology for learning and gender difference. Computers in Human Behavior, 92, 288–296. https://doi.org/10.1016/j.chb.2018.11.029
Ullman, J. B. (2001). Structural equation modeling. In B. G. Tabachnick & L. S. Fidell (2001). Using Multivariate Statistics (4th ed& pp 653- 771). Needham Heights, MA: Allyn & Bacon.
Spanjers, I. A. E., Wouters, P., van Gog, T., & van Merriënboer, J. J. G. (2011). An expertise reversal effect of segmentation in learning from animated worked-out examples. Computers in Human Behavior, 27(1), 46-52.
Sharma, S., Mukherjee, S., Kumar, A., and Dillon, W. R. (2005), "A simulation study to investigate the use of cutoff values for assessing model fit in covariance structure models," Journal of Business Research, 58 (1), 935-43.
Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory. Springer.
Suki, N. M., & Suki, N. M. (2017). Determining students’ behavioural intention to use animation and storytelling applying the UTAUT model: The moderating roles of gender and experience level. The International Journal of Management Education, 15(3), 528–538. https://doi.org/10.1016/j.ijme.2017.10.002
Taber, K. S. (2018) The use of cronbach’s alpha when developing and reporting research instruments in science education. Res Sci Educ 48:1273–1296.
Tindall-Ford, S., Chandler, P., & Sweller, J. (2010). When two sensory modes are better than one. Journal of Experimental Psychology: Applied, 16(2), 163-181.
Tversky, B., Morrison, J. B., & Betrancourt, M. (2002). Animation: Can it facilitate? International Journal of Human-Computer Studies, 57(4), 247-262.
Sekaran, U. (1992). Research Methods for Business: A Skill-Building Approach. New York: John Wiley & Sons, Inc.
Yen, D. C., Wu, C.-S., Cheng, F.-F., & Huang, Y.-W. (2010). Determinants of users' intention to adopt wireless technology: An empirical study by integrating TTF with TAM. Computers in Human Behavior, 26(5), 906–915.
Zhang, D., Zhou, L., Briggs, R. O., & Nunamaker Jr, J. F. (2006). Instructional video in e-learning: Assessing the impact of interactive video on learning effectiveness. Information & Management, 43(1), 15-27.