Theoretical Convergence or Complexity? Integrating Task-Technology Fit into Unified Theory of Acceptance and Use of Technology 2 for Educational Technology Adoption
DOI:
https://doi.org/10.46328/ijtes.5181Keywords:
ttf, utaut2, technology acceptance, MASEM, OSMASEMAbstract
This study examined the integration of Task-Technology Fit (TTF) into the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) in the context of educational technology adoption. While UTAUT2 provided a behavioural perspective on technology acceptance, TTF offered a task-specific approach, highlighting the alignment between technology and user needs. The convergence of these models introduced theoretical and methodological challenges, particularly concerning construct overlap and model complexity. The analysis of existing literature on UTAUT2-TTF integration identified gaps in structural validity and construct differentiation. The findings indicated that while TTF enhanced the explanatory power for technology use behaviour (UB), its integration into UTAUT2 introduced redundancy, particularly between performance expectancy and task-technology fit. The study suggested that future research should refine model constructs to improve clarity and parsimony, ensuring theoretical coherence and empirical rigour. A meta-analytic structural equation modelling approach (MASEM) was recommended to enhance the evaluation of integrated models across multiple contexts. This study contributed to the ongoing discourse on technology adoption models, advocating for a balanced approach that maintained explanatory power while minimising complexity.
References
Alam, A., & Mohanty, A. (2023). Educational technology: Exploring the convergence of technology and pedagogy through mobility, interactivity, AI, and learning tools. Cogent Engineering, 10(2), 2283282. https://doi.org/10.1080/23311916.2023.2283282
Almaiah, M. A., & Alismaiel, O. A. (2019). Examination of factors influencing the use of mobile learning systems in universities: An empirical study. Education and Information Technologies, 24, 885–909. https://doi.org/10.1007/s10639-018-9810-7
Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88(3), 588–606. https://doi.org/10.1037/0033-2909.88.3.588
Bhimasta, R. A., & Suprapto, B. (2016, November). An empirical investigation of student adoption model toward mobile e-textbook: UTAUT2 and TTF model. In proceedings of the 2nd international conference on communication and information processing (pp. 167-173). https://doi.org/10.1145/3018009.3018046
Browne, M. W., & Cudeck, R. (1992). Alternative ways of assessing model fit. Sociological methods & research, 21(2), 230-258. https://doi.org/10.1177/0049124192021002005
Bu, F. (2022). Investigating the acceptance of meta-universe technology for football course training among university physical education teachers: A UTAUT2 and TTF model approach. Electrical & Electronic Engineering Research, 2. https://doi.org/10.37420/j.eeer.2022.001
Byrne, B. M. (2016). Structural equation modeling with AMOS: Basic concepts, applications, and programming (3rd ed.). Routledge.
Castanha, J., Pillai, S. K. B., & Indrawati. (2020). What influences consumer behavior toward information and communication technology applications: A systematic literature review of UTAUT2 model. ICT Systems and Sustainability: Proceedings of ICT4SD 2020, Volume 1, 317-327. https://doi.org/10.1007/978-981-15-8289-9_30
Cheung, M. W.-L. (2015). metaSEM: An R package for meta-analysis using structural equation modeling. Frontiers in Psychology, 5, 1521. https://doi.org/10.3389/fpsyg.2014.01521
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
Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers, 21(3), 719–734. https://doi.org/10.1007/s10796-017-9774-y
Faqih, K. M., & Jaradat, M. I. R. M. (2021). Integrating TTF and UTAUT2 theories to investigate the adoption of augmented reality technology in education: Perspective from a developing country. Technology in Society, 67, 101787. https://doi.org/10.1016/j.techsoc.2021.101787
Furneaux, B. (2012). Task-technology fit theory: A survey and synopsis of the literature. Information Systems Theory: Explaining and Predicting Our Digital Society, Vol. 1, 87-106. https//doi.org/10.1007/978-1-4419-6108-2_5
Gebauer, J., Shaw, M. J., & Gribbins, M. L. (2010). Task-technology fit for mobile information systems. Journal of Information Technology, 25(3), 259–272. https://doi.org/10.1057/jit.2010.10
Gengfu, C., Chotiyaputta, R., & Zhu, Y. (2019). Exploring e-learning adoption in Chinese universities: Combining TTF and UTAUT2. Journal of Educational Technology Development and Exchange, 12(2), 67–84. https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3654998
Gerhart, N., Peak, D. A., & Prybutok, V. R. (2015). Searching for New answers: the application of task‐technology fit to E‐Textbook usage. Decision Sciences Journal of Innovative Education, 13(1), 91-111. https://doi.org/10.1111/dsji.12056
George, B., & Wooden, O. (2023). Managing the strategic transformation of higher education through artificial intelligence. Administrative Sciences, 13(9), 196. https://doi.org/10.3390/admsci13090196
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
Grassini, S. (2023). Shaping the future of education: exploring the potential and consequences of AI and ChatGPT in educational settings. Education Sciences, 13(7), 692. https:/doi.org/10.3390/educsci13070692
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
Jak, S., & Cheung, M. W. L. (2020). Meta-analytic structural equation modeling with moderating effects on SEM parameters. Psychological methods, 25(4), 430. https://doi.org/10.1037/met0000245
Jak, S., Li, H., Kolbe, L., de Jonge, H., & Cheung, M. W. L. (2021). Meta‐analytic structural equation modeling made easy: A tutorial and web application for one‐stage MASEM. Research synthesis methods, 12(5), 590-606. https://doi.org/10.1002/jrsm.1498
Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). The Guilford Press.
Little, R. J. A., & Rubin, D. B. (2020). Statistical analysis with missing data (3rd ed.). Wiley.
McGill, T. J., & Klobas, J. E. (2009). A task–technology fit view of learning management system impact. Computers & Education, 52(2), 496–508. https://doi.org/10.1016/j.compedu.2008.10.002
McGill, T. J., Klobas, J. E., & Renzi, S. (2014). Critical success factors for the continuation of e-learning initiatives. Internet and Higher Education, 22, 24–36. https://doi.org/10.1016/j.iheduc.2014.04.001
Gengfu, M., & Chotiyaputta, V. (2019). Acceptance and use predictors of E-books: a case at universities in Sichuan, China. Asian Administration & Management Review, 2(2). https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3654998
Or, C. (2023). The role of attitude in the Unified Theory of Acceptance and Use of Technology: A meta-analytic structural equation modelling study. International Journal of Technology in Education and Science, 7(4), 552–570. https://doi.org/10.46328/ijtes.504
Or, C. (2024). Watch that attitude! Examining the role of attitude in the Technology Acceptance Model through meta-analytic structural equation modelling. International Journal of Technology in Education and Science, 8(4), 558–582. https://doi.org/10.46328/ijtes.575
Or, C. C. P. Examining Unified Theory of Acceptance and Use of Technology 2 through Meta-analytic Structural Equation Modelling. Journal of Applied Learning and Teaching, 6(2). https://doi.org/10.37074/jalt.2023.6.2.7
Sharif, A., Afshan, S., & Qureshi, M. A. (2019). Acceptance of learning management system in university students: an integrating framework of modified UTAUT2 and TTF theories. International Journal of Technology Enhanced Learning, 11(2), 201-229. https://doi.org/10.1504/IJTEL.2019.098810
Spies, R., Grobbelaar, S., & Botha, A. (2020, April). A scoping review of the application of the task-technology fit theory. In Conference on e-Business, e-Services and e-Society (pp. 397-408). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-44999-5_33
Tamilmani, K., Rana, N. P., Wamba, S. F., & Dwivedi, R. (2021). The extended Unified Theory of Acceptance and Use of Technology (UTAUT2): A systematic literature review and theory evaluation. International Journal of Information Management, 57, 102269. https://doi.org/10.1016/j.ijinfomgt.2020.102269
Teo, T., & Zhou, M. (2014). Explaining the intention to use technology among university students: A structural equation modeling approach. Journal of Computing in Higher Education, 26(2), 124–142. https://doi.org/10.1007/s12528-014-9080-3
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
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, 36(1), 157–178. https://doi.org/10.2307/41410412
Wang, F., Wijaya, T. T., Habibi, A., & Liu, Y. (2022). Predictors influencing urban and rural area students to use tablet computers as learning tools: combination of UTAUT and TTF models. Sustainability, 14(21), 13965. https://doi.org/10.3390/su142113965
Wu, B., & Chen, X. (2017). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task-technology fit (TTF). Computers in Human Behavior, 67, 221–232. https://doi.org/10.1016/j.chb.2016.10.028
Zigurs, I., & Buckland, B. K. (1998). A theory of task-technology fit and group support systems effectiveness. MIS Quarterly, 22(3), 313–334. https://doi.org/10.2307/249668
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