Examination of relationships among students' self-determination, technology acceptance, satisfaction, and continuance intention to use K-MOOCs
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?
Academic environments with greater autonomy, competence, and connectivity are positively correlated with higher learning, higher retention and completion rates, and improved satisfaction of students' graduates.
“Many empirical studies have shown that academic environments with greater autonomy, competence, and connectivity are positively correlated with higher learning, higher retention and completion rates, and improved satisfaction of students' graduates [5].”
From Mapping the Research Landscape: A Comprehensive Bibliometric Analysis of Self-Determination Theory in Higher Education (2024) · cites this paper as [5]
Cites
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 · see citing works
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 · see citing works
Ryan, R. M., & Deci, E. L. (2000) · American Psychologist · doi:10.1037//0003-066x.55.1.68 · Cited by 27,815 · see citing works
Understanding information technology usage: a test of competing models
Taylor, S., & Todd, P. A. (1995) · Information Systems Research · doi:10.1287/isre.6.2.144 · Cited by 8,815 · see citing works
Understanding Information Systems Continuance: An Expectation-Confirmation Model1
Bhattacherjee, A. (2001) · MIS Quarterly · doi:10.2307/3250921 · Cited by 8,767 · see citing works
Perceived locus of causality and internalization: Examining reasons for acting in two domains.
Ryan, R. M., & Connell, J. P. (1989) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.57.5.749 · Cited by 3,311 · see citing works
Understanding e-learning continuance intention: An extension of the Technology Acceptance Model
Roca, J. C., Chiu, C.-M., & Martínez, F. J. (2006) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2006.01.003 · Cited by 1,399 · see citing works
Understanding Web-based learning continuance intention: The role of subjective task value
Chiu, C.-M., & Wang, E. T. G. (2008) · Information & Management · doi:10.1016/j.im.2008.02.003 · Cited by 869 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Pan, X. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.564294 · Cited by 329 · see citing works
“Previous studies have discussed learners’ acceptance of different types of technology, such as mobile technology (Pindeh et al., 2016; Nikou and Economides, 2017), computer-based communication technology (Park et al., 2014), social media (Hsieh et al., 2017), and MOOCS courses (Joo et al., 2018).”
BackgroundLi, K. (2023) · Sustainability · doi:10.3390/su15065221 · Cited by 157 · see citing works
Feitosa de Moura, V., Alexandre de Souza, C., & Noronha Viana, A. B. (2021) · Computers & Education · doi:10.1016/j.compedu.2020.104077 · Cited by 135 · see citing works
Ismail, M., Celebi, E., & Nadiri, H. (2019) · IEEE Access · doi:10.1109/access.2019.2934782 · Cited by 19 · see citing works
Xu, W., Zhu, K., Zhou, D., Wang, C., & Wen, C. (2025) · Education Sciences · doi:10.3390/educsci15070828 · Cited by 4 · see citing works
Lin, C., Zhang, Y., Wang, L., & Wu, J. (2024) · Proceedings of the 2024 2nd International Conference on Information Education and Artificial Intelligence · doi:10.1145/3724504.3724576
- Authors
- Joo, Y. J., So, H.-J., & Kim, N. H.
- Affiliations
- Ewha Womans University (KR)
- Corresponding author
- So, Hyo-Jeong
- Venue
- Computers & Education
- Publisher
- Elsevier BV
- Published
- 2018-07
- Volume
- 122
- Pages
- 260-272
- ISSN
- 0360-1315 (ISSN-L)
- OpenAlex
- W2783356839
- Cited by
- 420
- References
- 8
- Citations by year
- 2026: 19 · 2025: 36 · 2024: 58 · 2023: 79 · 2022: 66 · 2021: 72 · 2020: 62 · 2019: 24 · 2018: 4
- FWCI
- 51.13
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computers & Education (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Social Sciences › Education › Online and Blended Learning
- Keywords
- MOOCs, self-determination, technology acceptance, perceived usefulness, perceived ease of use, student satisfaction, continuance intention, university students, intrinsic motivation
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