Digital technology acceptance and self-reported mastery–use differences during teaching practicum among preservice vocational teachers

Abstract

Drawing on an adapted Unified Theory of Acceptance and Use of Technology 2 framework, this study examined factors associated with behavioral intention and reported digital-technology use and compared self-reported mastery with frequent teaching use during school-based practicum. A non-probability sample of 119 preservice vocational teachers from eight programs was analyzed using partial least squares structural equation modeling, a Wilcoxon signed-rank test, and exact McNemar tests with Holm adjustment. Internal consistency and convergent validity were acceptable overall, although one facilitating-conditions indicator loaded weakly. Habit was associated with intention and use; social influence was associated with intention; and facilitating conditions and intention were associated with use. Post-hoc power was high for the two habit paths and the intention-to-use path, but lower for several smaller paths. Self-reported mastery breadth exceeded frequent-use breadth, with adjusted category-level differences for social media, YouTube, and e-learning. The findings provide complementary, noncausal indicators of digital readiness during practicum.

Keywords
  • Preservice vocational teachers,
  • Digital learning technologies,
  • Teaching practicum,
  • Digital technology acceptance,
  • Vocational education
References
  1. Aminah, S., Aditya, A., & Kanthi, Y. A. (2024). The Role of UTAUT2 in Understanding Technology Adoption: A Study of the Merdeka Mengajar Platform Among Indonesian Teachers. Teknika, 13(3), 460–470. https://doi.org/10.34148/teknika.v13i3.1082
  2. Avcı, S. (2022). Examining the factors affecting teachers’ use of digital learning resources with UTAUT2. Malaysian Online Journal of Educational Technology, 10(3), 200–214. https://doi.org/10.52380/mojet.2022.10.3.399
  3. Bervell, B., Kumar, J. A., Arkorful, V., Agyapong, E. M., & Osman, S. (2021). Remodelling the role of facilitating conditions for Google Classroom acceptance: A revision of UTAUT2. Australasian Journal of Educational Technology, 115–135. https://doi.org/10.14742/ajet.7178
  4. Calderón-Garrido, D., & Gil-Fernández, R. (2023). Pre-service Teachers’ Use of General Social Networking Sites Linked to Current Scenarios: Nature and Characteristics. Technology, Knowledge and Learning, 28(3), 1325–1349. https://doi.org/10.1007/s10758-022-09609-7
  5. Cattaneo, A. A. P., Antonietti, C., & Rauseo, M. (2025). How do vocational teachers use technology? The role of perceived digital competence and perceived usefulness in technology use across different teaching profiles. Vocations and Learning, 18(1), 5. https://doi.org/10.1007/s12186-025-09359-4
  6. Çebi, A., Bahçekapılı Özdemir, T., Reisoğlu, İ., & Çolak, C. (2022). From digital competences to technology integration: Re-formation of pre-service teachers’ knowledge and understanding. International Journal of Educational Research, 113, 101965. https://doi.org/10.1016/j.ijer.2022.101965
  7. Diaz, P., Hrastinski, S., & Norström, P. (2025). How teacher students used digital response systems during student teaching. Education and Information Technologies, 30(7), 8953–8978. https://doi.org/10.1007/s10639-024-13165-1
  8. Du, W., & Liang, R. (2024). Teachers’ Continued VR Technology Usage Intention: An Application of the UTAUT2 Model. Sage Open, 14(1). https://doi.org/10.1177/21582440231220112
  9. Fagerland, M. W., Lydersen, S., & Laake, P. (2013). The McNemar test for binary matched-pairs data: mid-p and asymptotic are better than exact conditional. BMC Medical Research Methodology, 13(1), 91. https://doi.org/10.1186/1471-2288-13-91
  10. Funke, M., Bergmann-Gering, A., Müller, K., & Zabel, J. (2025). Pre-Service Teachers’ Beliefs on the Subject-Specific Use of Digital Media in Biology Classrooms. Technology, Knowledge and Learning, 30(2), 789–807. https://doi.org/10.1007/s10758-024-09800-y
  11. Habibi, A., Razak, R. A., Yusop, F. D., Muhaimin, M., Asrial, A., Mukminin, A., & Jamila, A. (2022). Exploring the factors affecting pre-service science teachers’ actual use of technology during teaching practice. South African Journal of Education, 42(1), 1–11. https://doi.org/10.15700/saje.v42n1a1955
  12. Habibi, A., Riady, Y., Samed Al-Adwan, A., & Awni Albelbisi, N. (2023). Beliefs and Knowledge for Pre-Service Teachers’ Technology Integration during Teaching Practice: An Extended Theory of Planned Behavior. Computers in the Schools, 40(2), 107–132. https://doi.org/10.1080/07380569.2022.2124752
  13. Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
  14. Hakimi, T. I., Jaafar, J. A., Mohamad, M. A., & Omar, M. (2024). Unified theory of acceptance and use of technology (UTAUT) applied in higher education research: A systematic literature review and bibliometric analysis. Multidisciplinary Reviews, 7(12), 2024303. https://doi.org/10.31893/multirev.2024303
  15. 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. https://doi.org/10.1007/s11747-014-0403-8
  16. Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6(2), 65–70.
  17. Kotler, P., & Kelle, K. L. (2022). Marketing Management (16th editi). Pearson.
  18. Kusuma, I. P. I. (2022). “Why this and not that social media?” Reasons for using technology during online practice teaching. The JALT CALL Journal, 18(2), 264–285. https://doi.org/10.29140/jaltcall.v18n2.593
  19. Liengaard, B. D., Sharma, P. N., Hult, G. T. M., Jensen, M. B., Sarstedt, M., Hair, J. F., & Ringle, C. M. (2021). Prediction: Coveted, Yet Forsaken? Introducing a Cross‐Validated Predictive Ability Test in Partial Least Squares Path Modeling. Decision Sciences, 52(2), 362–392. https://doi.org/10.1111/deci.12445
  20. Limayem, M., Hirt, S. G., & Cheung, C. M. K. (2007). How habit limits the predictive power of intention: The case of information systems continuance. MIS Quarterly, 31(4), 705–737. https://doi.org/10.2307/25148817
  21. Lu, C., Lee, J. C.-K., & Gu, M. M. (2025). Integrating digital technologies into teaching: A study on pre-service language teachers’ perceptions and practice. Education and Information Technologies, 30(14), 19537–19557. https://doi.org/10.1007/s10639-025-13511-x
  22. Martin, D. A., Carey, M. D., McMaster, N., & Clarkin, M. (2024). Assessing primary school preservice teachers’ confidence to apply their TPACK in specific categories of technologies using a self-audit survey. The Australian Educational Researcher, 51(5), 1877–1905. https://doi.org/10.1007/s13384-023-00669-x
  23. Max, A., Lukas, S., & Weitzel, H. (2022). The relationship between self‐assessment and performance in learning TPACK : Are self‐assessments a good way to support preservice teachers’ learning? Journal of Computer Assisted Learning, 38(4), 1160–1172. https://doi.org/10.1111/jcal.12674
  24. McNemar, Q. (1947). Note on the Sampling Error of the Difference Between Correlated Proportions or Percentages. Psychometrika, 12(2), 153–157. https://doi.org/10.1007/BF02295996
  25. Momdjian, L., Manegre, M., & Gutiérrez-Colón, M. (2025). A study of preservice teachers’ digital competence development: Exploring the role of direct instruction, integrated practice, and modeling. Evaluation and Program Planning, 109, 102538. https://doi.org/10.1016/j.evalprogplan.2025.102538
  26. Mourlam, D., Chesnut, S., & Bleecker, H. (2021). Exploring preservice teacher self-reported and enacted TPACK after participating in a learning activity types short course. Australasian Journal of Educational Technology, 152–169. https://doi.org/10.14742/ajet.6310
  27. Nelson, M. J., & Voithofer, R. (2022). Coursework, field experiences, and the technology beliefs and practices of preservice teachers. Computers & Education, 186, 104547. https://doi.org/10.1016/j.compedu.2022.104547
  28. Ogegbo, A. A., Penn, M., Ramnarain, U., Pila, O., Van Der Westhuizen, C., Mdlalose, N., Moser, I., Hlosta, M., & Bergamin, P. (2024). Exploring pre-service teachers’ intentions of adopting and using virtual reality classrooms in science education. Education and Information Technologies, 29(15), 20299–20316. https://doi.org/10.1007/s10639-024-12664-5
  29. Özbek, T., Wekerle, C., & Kollar, I. (2024). Fostering pre-service teachers’ technology acceptance – does the type of engagement with tool-related information matter? Education and Information Technologies, 29(5), 6139–6161. https://doi.org/10.1007/s10639-023-12047-2
  30. Ratumanan, E. A., & Krismiyati, K. (2025). The Use of AI for Learning Planning Viewed from the UTAUT Perspective. International Journal of Active Learning, 10(2), 74–84. https://doi.org/10.15294/ijal.v10i2.32387
  31. Riady, Y., Alqahtany, T. M., Habibi, A., Sofyan, S., & Albelbisi, N. A. (2022). Factors affecting teachers’ social media use during covid-19. Cogent Social Sciences, 8(1). https://doi.org/10.1080/23311886.2022.2115658
  32. Ringle, C. M., Wende, S., & Becker, J.-M. (2024). SmartPLS 4 (4.1.1.8). SmartPLS. https://www.smartpls.com
  33. Roll, M. J. J., & Ifenthaler, D. (2021). Multidisciplinary digital competencies of pre-service vocational teachers. Empirical Research in Vocational Education and Training, 13(1), 7. https://doi.org/10.1186/s40461-021-00112-4
  34. Sawiji, H. (2024). Determining factors influencing teachers’ intention to use web-based e-learning platforms in vocational secondary schools in Indonesia. Cogent Education, 11(1). https://doi.org/10.1080/2331186X.2024.2355830
  35. Shandilya, A. K., & Kumar, D. (2025). Students’ usage behavior toward digital exam pads in Indian university settings. International Journal of Evaluation and Research in Education (IJERE), 14(4), 2762. https://doi.org/10.11591/ijere.v14i4.32245
  36. Sofwan, M. (2023). UTAUT Model in Teaching Biology: Structural Equation Modeling. BIODIK, 9(1), 87–96. https://doi.org/10.22437/bio.v9i1.24788
  37. Sukmawati, F., Santosa, E. B., Juwita, R., Cahyono, B. T., Trisnaningsih, S., & Ridhani, J. (2024). The exploration of science teacher’s acceptance towards virtual laboratory (VL). Multidisciplinary Science Journal, 6(12), 2024274. https://doi.org/10.31893/multiscience.2024274
  38. Talan, T., Doğan, Y., & Kalinkara, Y. (2024). Digital Natives’ Mobile Learning Adoption in terms of UTAUT-2 Model: a Structural Equation Model. Innoeduca. International Journal of Technology and Educational Innovation, 10(1), 100–123. https://doi.org/10.24310/ijtei.101.2024.17440
  39. Tomczyk, Ł., Fedeli, L., Włoch, A., Limone, P., Frania, M., Guarini, P., Szyszka, M., Mascia, M. L., & Falkowska, J. (2023). Digital Competences of Pre-service Teachers in Italy and Poland. Technology, Knowledge and Learning, 28(2), 651–681. https://doi.org/10.1007/s10758-022-09626-6
  40. Tondeur, J., Trevisan, O., Howard, S. K., & van Braak, J. (2025). Preparing preservice teachers to teach with digital technologies: An update of effective SQD-strategies. Computers & Education, 232, 105262. https://doi.org/10.1016/j.compedu.2025.105262
  41. Vejvoda, J., Stadler, M., Schultz-Pernice, F., Fischer, F., & Sailer, M. (2023). Getting ready for teaching with digital technologies: Scenario-based self-assessment in teacher education and professional development. Unterrichtswissenschaft, 51(4), 511–532. https://doi.org/10.1007/s42010-023-00186-x
  42. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward A Unified View1. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
  43. Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology1. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412
  44. von Kotzebue, L. (2022). Beliefs, Self-reported or Performance-Assessed TPACK: What Can Predict the Quality of Technology-Enhanced Biology Lesson Plans? Journal of Science Education and Technology, 31(5), 570–582. https://doi.org/10.1007/s10956-022-09974-z
  45. Wang, P., & Ko, J. (2024). ICT competency and practicum of preservice teachers as digital natives: a mixed-method study. Asia Pacific Journal of Education, 44(4), 901–916. https://doi.org/10.1080/02188791.2022.2083581
  46. Widarto, W., Wijanarka, B. S., & Wibowo, A. E. (2024). Implementation of e-learning by vocational-school teachers of mechanical engineering. Jurnal Cakrawala Pendidikan, 43(2), 439–451. https://doi.org/10.21831/cp.v43i2.58033
  47. Wijaya, T. T., Zhou, Y., Houghton, T., Weinhandl, R., Lavicza, Z., & Yusop, F. D. (2022). Factors Affecting the Use of Digital Mathematics Textbooks in Indonesia. Mathematics, 10(11), 1808. https://doi.org/10.3390/math10111808
  48. Wilcoxon, F. (1945). Individual Comparisons by Ranking Methods. Biometrics Bulletin, 1(6), 80. https://doi.org/10.2307/3001968
  49. Xie, Y., Wan, C., & Kong, K. (2024). Factors influencing Chinese pre-service teachers’ behavioral intention and use behavior to adopt VR training system: based on the UTAUT2 model. Humanities and Social Sciences Communications, 11(1), 1300. https://doi.org/10.1057/s41599-024-03832-6
  50. Xue, L., Rashid, A. M., & Ouyang, S. (2024). The Unified Theory of Acceptance and Use of Technology (UTAUT) in Higher Education: A Systematic Review. Sage Open, 14(1). https://doi.org/10.1177/21582440241229570