The influence of technology acceptance, academic self-efficacy, and gender on academic achievement through online tutoring
Open accessAbstract
The study focused on variables which were posited to capture undergraduate students' experiences with a large-scale online tutoring service, and relationships with the students’ perceptions of their academic capabilities and academic performance. A theoretical model incorporating variables from research on Technology Acceptance and Social Cognitive Theory was developed and tested. A total of 365 undergraduate students from a university located in Sydney, Australia, completed an online survey. Data were analysed using confirmatory factor analysis (CFA) and structural equation modeling (SEM), and multi-group analyses (MGA). The measurement model demonstrated configural, metric and scalar invariance. There were differences between males and females regarding latent means, with females scoring higher than males for facilitating conditions. The regression paths were consistent across males and females (i.e., invariant) in the full structural model. Facilitating conditions was positively associated with the perceived usefulness of technology, which in turn was positively associated with academic self-efficacy. Surprisingly, perceived ease of use did not have a statistically significant association with perceived usefulness. Academic self-efficacy was positively associated with academic achievement. Implications, particularly for online tutoring service providers, are discussed.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The gender moderation result is consistent with some former studies (Hanham et al., 2021; Tsai and Tsai, 2010).
“This result is consistent with some former studies (Hanham et al., 2021; Tsai and Tsai, 2010)”
From The causal relationship between digital literacy and students’ academic achievement: a meta-analysis (2025) · cites this paper as Hanham et al., 2021
Cites
1 retracted
Cao, J., Yang, T., Lai, I. K.-W., & Wu, J. (2023) · International Journal of Electrical Engineering & Education · doi:10.1177/00207209211003270 · Cited by 28 · see citing works
RetractedPerceived 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 · see citing works
Richardson, M., Abraham, C., & Bond, R. (2012) · Psychological Bulletin · doi:10.1037/a0026838 · Cited by 3,646 · see citing works
An effect size primer: A guide for clinicians and researchers.
Ferguson, C. J. (2009) · Professional Psychology: Research and Practice · doi:10.1037/a0015808 · Cited by 3,467 · see citing works
VanLEHN, K. (2011) · Educational Psychologist · doi:10.1080/00461520.2011.611369 · Cited by 2,323 · see citing works
Scaffolding in Teacher–Student Interaction: A Decade of Research
van de Pol, J., Volman, M., & Beishuizen, J. (2010) · Educational Psychology Review · doi:10.1007/s10648-010-9127-6 · Cited by 1,873 · see citing works
Sources of Self-Efficacy in School: Critical Review of the Literature and Future Directions
Usher, E. L., & Pajares, F. (2008) · Review of Educational Research · doi:10.3102/0034654308321456 · Cited by 1,514 · see citing works
The Power of Feedback Revisited: A Meta-Analysis of Educational Feedback Research
Wisniewski, B., Zierer, K., & Hattie, J. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.03087 · Cited by 1,199 · see citing works
Abdullah, F., & Ward, R. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2015.11.036 · Cited by 986 · see citing works
Effectiveness of Intelligent Tutoring Systems: A Meta-Analytic Review
Kulik, J. A., & Fletcher, J. D. (2016) · Review of Educational Research · doi:10.3102/0034654315581420 · Cited by 930 · see citing works
Steenbergen-Hu, S., & Cooper, H. (2014) · Journal of Educational Psychology · doi:10.1037/a0034752 · Cited by 355 · see citing works
Exploring feedback and student characteristics relevant for personalizing feedback strategies
Narciss, S., Sosnovsky, S., Schnaubert, L., Andrès, E., Eichelmann, A., Goguadze, G., & Melis, E. (2014) · Computers & Education · doi:10.1016/j.compedu.2013.09.011 · Cited by 260 · see citing works
Non-cognitive predictors of academic achievement: Evidence from TIMSS and PISA
Lee, J., & Stankov, L. (2018) · Learning and Individual Differences · doi:10.1016/j.lindif.2018.05.009 · Cited by 237 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
The causal relationship between digital literacy and students’ academic achievement: a meta-analysis
Li, F., Cheng, L., Wang, X., Shen, L., Ma, Y., & Islam, A. Y. M. A. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-04399-6 · Cited by 38 · see citing works
“This result is consistent with some former studies (Hanham et al., 2021; Tsai and Tsai, 2010), but it is inconsistent with the result of Jin et al. (2020).”
Academic Integrity and Students’ Ethical Use of ChatGPT in Higher Education
Shishakly, R., & Nachouki, M. (2026) · Journal of Information Technology Education: Research · doi:10.28945/5730 · Cited by 4 · see citing works
“( Hanham et al., 2021; Nair et al., 2025; Zhuo et al., 2023).”
Xu, W., Zhu, K., Zhou, D., Wang, C., & Wen, C. (2025) · Education Sciences · doi:10.3390/educsci15070828 · Cited by 4 · see citing works
- Authors
- Hanham, J., Lee, C. B., & Teo, T.
- Affiliations
- Western Sydney University (AU)
- Murdoch University (AU)
- Corresponding author
- Hanham, José
- Venue
- Computers & Education
- Publisher
- Elsevier BV
- Published
- 2021-10
- Volume
- 172
- Pages
- 104252
- ISSN
- 0360-1315 (ISSN-L)
- OpenAlex
- W3173084393
- Cited by
- 141
- References
- 13
- Citations by year
- 2026: 23 · 2025: 34 · 2024: 24 · 2023: 32 · 2022: 25 · 2021: 3
- FWCI
- 17.93
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Computers & Education (publishedVersion, cc-by-nc-nd)
- Murdoch University Research Portal (submittedVersion)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Social Sciences › Education › Online and Blended Learning
- Social Sciences › Gender Studies › Gender and Technology in Education
- Keywords
- online tutoring, technology acceptance, academic self-efficacy, gender differences, academic performance, perceived usefulness, perceived ease of use, facilitating conditions, social cognitive theory, undergraduate students
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
