The extended technology acceptance model for Web 2.0 technologies in teaching




Technology acceptance, pre-service teachers, Web 2.0, intention, tool literacy, metacognitive self-regulation


Web 2.0-based learning enables collaborative learning and knowledge sharing and makes an important contribution to student learning. This study extended the original Technology Acceptance Model (TAM) by considering the effects of tool literacy, metacognitive self-regulation, subjective norm, facilitating conditions, and institutional support to understand pre-service teachers' intentions to use Web 2.0 technology in their courses. The sample data of 318 responses were from pre-service teachers. Structural equation modeling results showed a good fit for the extended model, indicating that metacognitive self-regulation and subjective norm had a significant influence on perceived ease of use and perceived usefulness, while institutional support and enabling conditions were not significantly associated with them. In addition, perceived ease of use and perceived usefulness influenced attitude, which in turn had a significant effect on intention. Furthermore, perceived ease of use, perceived usefulness, and attitude acted as significant mediators of behavioral intention. The indirect effect of perceived ease of use on perceived usefulness and attitude, and the indirect effect of perceived usefulness on attitude were also significant. Overall, the current study helps researchers and practitioners better understand future teachers' intentions to use Web 2.0 technologies in their courses.


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Abdullah, F., & Ward, R. (2016). Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by analysing commonly used external factors. Computers in human behavior, 56, 238-256.

Adov, L., & Mäeots, M. (2021). What can we learn about science teachers’ technology use during the COVID-19 pandemic? Education sciences, 11(6), 255.

Ajjan, H., & Hartshorne, R. (2008). Investigating faculty decisions to adopt Web 2.0 technologies: Theory and empirical tests. The internet and higher education, 11(2), 71-80.

Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.

Ajzen, I. (2006). Constructing a theory of planned behavior questionnaire.

Al-Dokhny, A., Drwish, A., Alyoussef, I., & Al-Abdullatif, A. (2021). Students’ intentions to use distance education platforms: An investigation into expanding the technology acceptance model through social cognitive theory. Electronics, 10(23), 1-23.

Alenezi, A. R. Karim, A. M. A., & Veloo, A. (2011). Institutional support and e-learning acceptance: an extension of the technology acceptance model. International Journal of Instructional Technology and Distance Learning, 8(2), 3-16

Armenteros, M., Liaw, S. S., Fernández, M., Díaz, R. F., & Sánchez, R. A. (2013). Surveying FIFA instructors' behavioral intention toward the Multimedia Teaching Materials. Computers & Education, 61, 91-104.

Ate?, H., & Garzón, J. (2022a). Drivers of teachers’ intentions to use mobile applications to teach science. Education and Information Technologies, 27(2), 2521-2542.

Ate?, H., & Garzón, J. (2022b). An integrated model for examining teachers’ intentions to use augmented reality in science courses. Education and Information Technologies.

Butler, J. W. (2012). Grappling with change: Web 2.0 and teacher educators. In D. Polly, C. Mims, & K. A. Persichitte (Eds.), Developing technology-rich teacher education programs: Key issues (pp. 135-150). IGI Global.

Camilleri, M. A., & Camilleri, A. C. (2022). Learning from anywhere, anytime: Utilitarian motivations and facilitating conditions for mobile learning. Technology, Knowledge and Learning.

Chen, Y. C., Hwang, R. H., & Wang, C. Y. (2012). Development and evaluation of a Web 2.0 annotation system as a learning tool in an e-learning environment. Computers & Education, 58(4), 1094-1105.

China Internet Network Information Centre (CNNIC). (2016). The 37th statistical report on China Internet development.

Dangi, M. R. M., & Saat, M. M. (2021). 21st Century Educational Technology Adoption in Accounting Education: Does Institutional Support Moderates Accounting Educators Acceptance Behaviour and Conscientiousness Trait towards Behavioural Intention? International Journal Academic Research in Business and Social Sciences, 11(1), 304–333.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.

Davis, F. D. (1993). User acceptance of information technology: system characteristics, user perceptions and behavioral impacts. International journal of man-machine studies, 38(3), 475-487.

Delen, E., & Liew, J. (2016). The use of interactive environments to promote self-regulation in online learning: A literature review. European Journal of Contemporary Education, 15(1), 24–33.

Faizi, R. (2018). Teachers’ perceptions towards using Web 2.0 in language learning and teaching. Education and Information Technologies, 23(3), 1219-1230.

Fussell, S. G., & Truong, D. (2021). Accepting virtual reality for dynamic learning: an extension of the technology acceptance model. Interactive Learning Environments.

Hair, J.F., Black, W.C., Babin, B.J., & Anderson, R.E. (2019). Multivariate data analysis. Cengage Learning.

Huang, F., Teo, T., & Guo, J. (2021). Understanding English teachers’ non-volitional use of online teaching: A Chinese study. System, 101, 102574.

Huang, F., Teo, T., & Zhou, M. (2020). Chinese students’ intentions to use the Internet-based technology for learning. Educational Technology Research and Development, 68(1), 575-591.

Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural equation modeling: a multidisciplinary journal, 6(1), 1-55.

Hursen, C. (2021). The effect of problem-based learning method supported by web 2.0 tools on academic achievement and critical thinking skills in teacher education. Technology, Knowledge and Learning, 26(3), 515-533.

Jimoyiannis, A. (2010). Integrating Web 2.0 in education: Towards a framework for Pedagogy 2.0. In R. Hackney & C. Evans (Eds.), Web 2.0 Conference Abstracts (p. 5). Brunel University.

Khlaisang, J., Teo, T., & Huang, F. (2021). Acceptance of a flipped smart application for learning: a study among Thai university students. Interactive Learning Environments, 29(5), 772-789.

Lai, C. (2015). Modeling teachers' influence on learners' self-directed use of technology for language learning outside the classroom. Computers & Education, 82, 74-83.

Lai, H. M., & Chen, C. P. (2011). Factors influencing secondary school teachers’ adoption of teaching blogs. Computers & Education, 56(4), 948-960.

Li, Y., Wang, Q., & Lei, J. (2019). Modeling Chinese Teachers' Attitudes Toward Using Technology for Teaching with a SEM Approach. Computers in the Schools, 36, 122-141.

Liaw, S., & Huang, H. (2013). Perceived satisfaction, perceived usefulness and interactive learning environments as predictors to self-regulation in e-learning environments. Computers & Education, 60(1), 14-24.

Lim, J., & Newby, T. J. (2020). Preservice teachers’ Web 2.0 experiences and perceptions on Web 2.0 as a personal learning environment. Journal of Computing in Higher Education, 32(2), 234–260.

Lim, J., & Newby, T. J. (2021). Preservice teachers’ attitudes toward Web 2.0 personal learning environments (PLEs): Considering the impact of self-regulation and digital literacy. Education and Information Technologies, 26(4), 3699-3720.

Ng, W. (2012). Can we teach digital natives digital literacy? Computers & Education, 59(3), 1065-1078.

Olaniran, B. A. (2009). Culture, learning styles, and Web 2.0. Interactive Learning Environments, 17(4), 261-271.

Olpak, Y. Z., & Ates, H. (2018). Pre-Service science teachers’ perceptions toward additional instructional strategies in biology laboratory applications: Blended learning. Science Education International, 29(2), 88-95.

Padilla-Hernández, A. L., Gámiz-Sánchez, V. M., & Romero-López, M. A. (2019). Proficiency levels of teachers’ digital competence: a review of recent international frameworks. Innoeduca. International Journal of Technology and Educational Innovation, 5(2), 140-150.

Papakostas, C., Troussas, C., Krouska, A., & Sgouropoulou, C. (2022). Exploring Users’ Behavioral Intention to Adopt Mobile Augmented Reality in Education through an Extended Technology Acceptance Model. International Journal of Human–Computer Interaction.

Park, N., Rhoads, M., Hou, J., & Lee, K. M. (2014). Understanding the acceptance of teleconferencing systems among employees: An extension of the technology acceptance model. Computers in Human Behavior, 39, 118-127.

Pintrich, P., Smith, D., García, T., & McKeachie, W. (1991). A manual for the use of the motivated strategies for learning questionnaire (MSLQ). Ann Arbor.

Pituch, K.A., & Lee, Y. (2006). The influence of system characteristics on e-learning use. Computers & Education, 47, 222-244.

Rahimi, E., van den Berg, J., & Veen, W. (2015). Facilitating student-driven constructing of learning environments using Web 2.0 personal learning environments. Computers & Education, 81, 235–246.

Sadaf, A., Newby, T. J., & Ertmer, P. A. (2012a). Exploring pre-service teachers' beliefs about using Web 2.0 technologies in K-12 classroom. Computers & Education, 59(3), 937-945.

Sadaf, A., Newby, T. J., & Ertmer, P. A. (2012b). Exploring factors that predict preservice teachers’ intentions to use Web 2.0 technologies using decomposed theory of planned behavior. Journal of Research on Technology in Education, 45(2), 171-196.

Sadaf, A., Newby, T. J., & Ertmer, P. A. (2016). An investigation of the factors that influence preservice teachers’ intentions and integration of Web 2.0 tools. Educational Technology Research and Development, 64(1), 37-64.

Shapiro, J. J., & Hughes, S. K. (1996). Information literacy as a liberal art? Educom Review, 31, 31-35.

Shihab, M. (2008). Web 2.0 tools improve teaching and collaboration in English language classes. In Presented at the National Educational Computing Conference 2008, San Antonio, TX.

Su, A. Y., Yang, S. J., Hwang, W. Y., & Zhang, J. (2010). A Web 2.0-based collaborative annotation system for enhancing knowledge sharing in collaborative learning environments. Computers & Education, 55(2), 752-766.

Sungur-Gül, K., & Ate?, H. (2021). Understanding pre-service teachers’ mobile learning readiness using theory of planned behavior. Educational Technology & Society, 24(2), 44-57.

Taufique, K. M. R., & Vaithianathan, S. (2018). A fresh look at understanding green consumer behavior among young urban Indian consumers through the lens of theory of planned behavior. Journal of cleaner production, 183, 46-55.

Teo, T. (2009). The impact of subjective norm and facilitating conditions on pre-service teachers' attitude toward computer use: A structural equation modeling of an extended technology acceptance model. Journal of Educational Computing Research, 40(1), 89-109.

Teo, T., (2010). A path analysis of pre-service teachers' attitudes to computer use: applying and extending the technology acceptance model in an educational context. Interactive Learning Environments, 18(1), 65-79.

Teo, T. (2012). Modelling the Influences of Beliefs on Preservice Teachers' Attitudes towards Computer Use. European journal of educational research, 1, 13-22.

Teo, T., & Dai, H. M. (2022). The role of time in the acceptance of MOOCs among Chinese university students. Interactive Learning Environments, 30(4), 651-664.

Teo, T., Sang, G., Mei, B., & Hoi, C. K. W. (2019). Investigating pre-service teachers’ acceptance of Web 2.0 technologies in their future teaching: a Chinese perspective. Interactive Learning Environments, 27(4), 530-546.

Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization. MIS quarterly, 15(1), 125-143.

Torras Virgili, M. E. (2021). Emergency Remote Teaching: ICT applied to education during confinement by COVID-19. Innoeduca. International Journal of Technology and Educational Innovation, 7(1), 122-136.

Tu, C. H., Yen, C. J., & Sujo-Montes, L. E. (2015). Personal learning environments and self-regulated learning. In R. Papa (Ed.), Media rich instruction: Connecting curriculum to all learners (pp. 35–48). Springer.

Vargo, D., Zhu, L., Benwell, B., & Yan, Z. (2021). Digital technology use during COVID?19 pandemic: A rapid review. Human Behavior and Emerging Technologies, 3(1), 13-24.

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.

Wang, Y., Yu, L., & Yu, Z. (2022b). An extended CCtalk technology acceptance model in EFL education. Education and Information Technologies. Advance online publication.

Wang, Y., Zhang, X., & Wang, L. (2022a). Assessing the intention to use sports bracelets among Chinese university students: an extension of technology acceptance model with sports motivation. Frontiers in Psychology, 13, 1-11.

Winne, P. H. (2011). A cognitive and metacognitive analysis of self-regulated learning. In B. J. Zimmerman & D. H. Schunk (Eds.), Handbook of self-regulation of learning and performance (pp. 15–32). Routledge.

Winne, P. H., & Hadwin, A. F. (1998). Studying as self-regulated learning. In D. J. Hacker & J. Dunlosky (Eds.), Metacognition in educational theory and practice (pp. 277–304). The Educational Psychology Series. Erlbaum.

Yen, C. J., Tu, C. H., Sujo-Montes, L. E., Armfield, S. W., & Chan, J. Y. (2013). Learner self regulation and web 2.0 tools management in personal learning environment. International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), 8(1), 46–65.

Yu, T. K., Lin, M. L., & Liao, Y. K. (2017). Understanding factors influencing information communication technology adoption behavior: The moderators of information literacy and digital skills. Computers in Human Behavior, 71, 196-208.

Zhou, L., Xue, S., & Li, R. (2022). Extending the Technology Acceptance Model to explore students’ intention to use an online education platform at a University in China. SAGE Open, 12(1), 1-13.

Zimmerman, B. J. (1998). Developing self-fulfilling cycles of academic regulation: An analysis of exemplary instructional models. In D. H. Schunk & B. J. Zimmerman (Eds.), Self-regulated learning: From teaching to self-reflective practice (pp. 1–19). Guilford Press.

Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13-39). Academic press.




How to Cite

Şimşek, A. S., & Ateş, H. (2022). The extended technology acceptance model for Web 2.0 technologies in teaching. Innoeduca. International Journal of Technology and Educational Innovation, 8(2), 165–183.