The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.
Can teachers’ digital competence influence technology acceptance in vocational education?
Open accessAbstract
Acceptance of technology in educational contexts is a relevant factor in determining teachers' intention to use digital tools in their teaching practice. However, the mechanism through which teachers' digital competence can influence or enhance their technology acceptance and use intention remains relatively unexplored, especially in the context of vocational education and training. The aim of this study is twofold: to evaluate the fit of the technology acceptance model (TAM) in the context of vocational education, and to examine the relationship between self-assessed teachers' digital competence belief and their acceptance of and intention to use technology in their classroom. Data were collected via a self-administered questionnaire and the responses of 2011 vocational teachers were analysed. Applying structural equation modelling, the results show that the TAM adequately explains teachers' intention to use digital tools in vocational education; further, there are positive and significant relationships between teachers' beliefs about their digital competence and their beliefs about technology ease of use and the perceived usefulness of technology in teaching; this latter, positively correlates with technology use intention. Understanding the factors interplaying with teachers’ acceptance of technology and use intention is important for designing teacher training to enhance the successful integration of technology and to foster the connectivity between the different learning locations of vocational education.
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Cites
The theory of planned behavior
Ajzen, I. (1991) · Organizational Behavior and Human Decision Processes · doi:10.1016/0749-5978(91)90020-t · Cited by 90,587
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483
User Acceptance of Information Technology: Toward A Unified View1
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003) · MIS Quarterly · doi:10.2307/30036540 · Cited by 45,035
Self-efficacy: Toward a unifying theory of behavioral change
Bandura, A. (1977) · Psychological Review · doi:10.1037/0033-295x.84.2.191 · Cited by 43,925
Principles and Practice of Structural Equation Modeling
Tabri, N., & Elliott, C. M. (2012) · Canadian Graduate Journal of Sociology and Criminology · doi:10.15353/cgjsc-rcessc.v1i1.25 · Cited by 38,891
User Acceptance of Computer Technology: A Comparison of Two Theoretical Models
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989) · Management Science · doi:10.1287/mnsc.35.8.982 · Cited by 25,709
A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies
Venkatesh, V., & Davis, F. D. (2000) · Management Science · doi:10.1287/mnsc.46.2.186.11926 · Cited by 23,066
Technology Acceptance Model 3 and a Research Agenda on Interventions
Venkatesh, V., & Bala, H. (2008) · Decision Sciences · doi:10.1111/j.1540-5915.2008.00192.x · Cited by 8,159
Reporting Structural Equation Modeling and Confirmatory Factor Analysis Results: A Review
Schreiber, J. B., Nora, A., Stage, F. K., Barlow, E. A., & King, J. (2006) · The Journal of Educational Research · doi:10.3200/joer.99.6.323-338 · Cited by 6,937
Structural Equation Modeling With AMOS: Basic Concepts, Applications, and Programming, Third Edition
Byrne, B. M. (2016) · doi:10.4324/9781315757421 · Cited by 4,653
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Technology acceptance research: Meta-analysis
Marikyan, D., Papagiannidis, S., & Stewart, G. (2026) · Journal of Information Science · doi:10.1177/01655515231191177 · Cited by 140
Rosli, M. S., Saleh, N. S., Md. Ali, A., Abu Bakar, S., & Mohd Tahir, L. (2022) · Sustainability · doi:10.3390/su141811389 · Cited by 126
Roy, R., Babakerkhell, M. D., Mukherjee, S., Pal, D., & Funilkul, S. (2022) · IEEE Access · doi:10.1109/access.2022.3225555 · Cited by 109
ChatGPT adoption and its influence on faculty well-being: an empirical research in higher education
Cambra-Fierro, J. J., Blasco, M. F., López-Pérez, M.-E. E., & Trifu, A. (2025) · Education and Information Technologies · doi:10.1007/s10639-024-12871-0 · Cited by 97
Metadata
- Authors
- Antonietti, C., Cattaneo, A., & Amenduni, F.
- Venue
- Computers in Human Behavior
- Publisher
- Elsevier BV
- Published
- 2022-07
- Volume
- 132
- Pages
- 107266
- ISSN
- 0747-5632 (ISSN-L)
- OpenAlex
- W4220786408
- Cited by
- 312
- References
- 52
- FWCI
- 49.30
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.