The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
University students' behavioral intention to use mobile learning: Evaluating the technology acceptance model
Abstract
As many Korean universities have recommended the implementation of mobile learning (m‐learning) for various reasons, the number of such tertiary learning opportunities has steadily grown. However, little research has investigated the factors affecting university students' adoption and use of m‐learning. A sample of 288 Konkuk university students participated in the research. The process by which students adopt m‐learning was explained using structural equation modeling technique and the Linear Structural Relationship (LISREL) program. The general structural model based on the technology acceptance model included m‐learning self‐efficacy, relevance for students' major (MR), system accessibility, subjective norm (SN), perceived usefulness, perceived ease of use, attitude (AT), and behavioral intention to use m‐learning. The study results confirmed the acceptability of the model to explain students' acceptance of m‐learning. M‐learning AT was the most important construct in explaining the causal process in the model, followed by students' MR and SN.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
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
(2003) · International Journal of Electronic Commerce · doi:10.1080/10864415.2003.11044275 · Cited by 5,715
Acceptance of Internet-based learning medium: the role of extrinsic and intrinsic motivation
Lee, M. K. O., Cheung, C. M. K., & Chen, Z. (2005) · Information & Management · doi:10.1016/j.im.2003.10.007 · Cited by 1,184
George Saadé, R., Nebebe, F., & Tan, W. (2007) · Interdisciplinary Journal of e-Skills and Lifelong Learning · doi:10.28945/392 · Cited by 223
Cited by
Technology acceptance model in educational context: A systematic literature review
Granić, A., & Marangunić, N. (2019) · British Journal of Educational Technology · doi:10.1111/bjet.12864 · Cited by 1,204
Abdullah, F., & Ward, R. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2015.11.036 · Cited by 986
Investigating users’ perspectives on e-learning: An integration of TAM and IS success model
Mohammadi, H. (2015) · Computers in Human Behavior · doi:10.1016/j.chb.2014.07.044 · Cited by 779
Abdullah, F., Ward, R., & Ahmed, E. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.05.014 · Cited by 693
Salloum, S. A., Qasim Mohammad Alhamad, A., Al-Emran, M., Abdel Monem, A., & Shaalan, K. (2019) · IEEE Access · doi:10.1109/access.2019.2939467 · Cited by 651
Metadata
- Authors
- Park, S. Y., Nam, M., & Cha, S.
- Venue
- British Journal of Educational Technology
- Publisher
- Wiley
- Published
- 2011-10-04
- Volume
- 43
- Issue
- 4
- Pages
- 592-605
- ISSN
- 0007-1013 (ISSN-L)
- 1467-8535
- OpenAlex
- W1496533776
- Cited by
- 755
- References
- 5
- FWCI
- 48.09
- Type
- journal article
- Language
- en
- Open access
- closed
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
Data sources
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.