The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Investigating the determinants and age and gender differences in the acceptance of mobile learning
Abstract
With the proliferation of mobile computing technology, mobile learning (m‐learning) will play a vital role in the rapidly growing electronic learning market. M‐learning is the delivery of learning to students anytime and anywhere through the use of wireless Internet and mobile devices. However, acceptance of m‐learning by individuals is critical to the successful implementation of m‐learning systems. Thus, there is a need to research the factors that affect user intention to use m‐learning. Based on the unified theory of acceptance and use of technology (UTAUT), which integrates elements across eight models of information technology use, this study was to investigate the determinants of m‐learning acceptance and to discover if there exist either age or gender differences in the acceptance of m‐learning, or both. Data collected from 330 respondents in Taiwan were tested against the research model using the structural equation modelling approach. The results indicate that performance expectancy, effort expectancy, social influence, perceived playfulness, and self‐management of learning were all significant determinants of behavioural intention to use m‐learning. We also found that age differences moderate the effects of effort expectancy and social influence on m‐learning use intention, and that gender differences moderate the effects of social influence and self‐management of learning on m‐learning use intention. These findings provide several important implications for m‐learning acceptance, in terms of both research and practice.
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
Evaluating Structural Equation Models with Unobservable Variables and Measurement Error
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.2307/3151312 · Cited by 109,314
Evaluating structural equation models with unobservable variables and measurement error
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.1177/002224378101800104 · Cited by 109,190
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
Moore, G. C., & Benbasat, I. (1991) · Information Systems Research · doi:10.1287/isre.2.3.192 · Cited by 9,150
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
Venkatesh, V. (2000) · Information Systems Research · doi:10.1287/isre.11.4.342.11872 · Cited by 6,664
Extrinsic and Intrinsic Motivation to Use Computers in the Workplace 1
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992) · Journal of Applied Social Psychology · doi:10.1111/j.1559-1816.1992.tb00945.x · Cited by 6,231
Agarwal, R., & Karahanna, E. (2000) · MIS Quarterly · doi:10.2307/3250951 · Cited by 4,726
Cited by
The unified theory of acceptance and use of technology (UTAUT): a literature review
Williams, M. D., Rana, N. P., & Dwivedi, Y. K. (2015) · Journal of Enterprise Information Management · doi:10.1108/jeim-09-2014-0088 · Cited by 1,339
Cheon, J., Lee, S., Crooks, S. M., & Song, J. (2012) · Computers & Education · doi:10.1016/j.compedu.2012.04.015 · Cited by 1,057
Review of trends from mobile learning studies: A meta-analysis
Wu, W.-H., Jim Wu, Y.-C., Chen, C.-Y., Kao, H.-Y., Lin, C.-H., & Huang, S.-H. (2012) · Computers & Education · doi:10.1016/j.compedu.2012.03.016 · Cited by 946
Demographic differences in perceived benefits from gamification
Koivisto, J., & Hamari, J. (2014) · Computers in Human Behavior · doi:10.1016/j.chb.2014.03.007 · Cited by 841
Extending the understanding of mobile banking adoption: When UTAUT meets TTF and ITM
Oliveira, T., Faria, M., Thomas, M. A., & Popovič, A. (2014) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2014.06.004 · Cited by 808
Escobar-Rodríguez, T., & Carvajal-Trujillo, E. (2014) · Tourism Management · doi:10.1016/j.tourman.2014.01.017 · Cited by 590
Students’ perception of Kahoot!’s influence on teaching and learning
Licorish, S. A., Owen, H. E., Daniel, B., & George, J. L. (2018) · Research and Practice in Technology Enhanced Learning · doi:10.1186/s41039-018-0078-8 · Cited by 521
Mobile-based assessment: Investigating the factors that influence behavioral intention to use
Nikou, S. A., & Economides, A. A. (2017) · Computers & Education · doi:10.1016/j.compedu.2017.02.005 · Cited by 388
Hamidi, H., & Chavoshi, A. (2018) · Telematics and Informatics · doi:10.1016/j.tele.2017.09.016 · Cited by 374
Fan, W., Liu, J., Zhu, S., & Pardalos, P. M. (2020) · Annals of Operations Research · doi:10.1007/s10479-018-2818-y · Cited by 346
Gender divide and acceptance of collaborative Web 2.0 applications for learning in higher education
Huang, W.-H. D., Hood, D. W., & Yoo, S. J. (2013) · The Internet and Higher Education · doi:10.1016/j.iheduc.2012.02.001 · Cited by 287
Ayaz, A., & Yanartaş, M. (2020) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2020.100032 · Cited by 246
Consumer acceptance of sports wearable technology: the role of technology readiness
Kim, T., & Chiu, W. (2019) · International Journal of Sports Marketing and Sponsorship · doi:10.1108/ijsms-06-2017-0050 · Cited by 242
Exploring consumers’ intention to accept smartwatch
Wu, L.-H., Wu, L.-C., & Chang, S.-C. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.07.005 · Cited by 226
Li, M., Ma, S., & Shi, Y. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1253549 · Cited by 439
“…a higher education level were found to achieve better learning outcomes (Lister, 2015; Kim and Castelli, 2021) as they were found of higher engagement towards the gamified intervention (Wang et al., 2009; Jahnke, 2010) and stronger sensitivity to the social modeling (Morris and Venkatesh, 2000).”
BackgroundNikolopoulou, K., Gialamas, V., & Lavidas, K. (2020) · Education and Information Technologies · doi:10.1007/s10639-020-10157-9 · Cited by 205
Information technology adoption: a review of the literature and classification
Salahshour Rad, M., Nilashi, M., & Mohamed Dahlan, H. (2018) · Universal Access in the Information Society · doi:10.1007/s10209-017-0534-z · Cited by 181
Chauhan, S., & Jaiswal, M. (2016) · The International Journal of Management Education · doi:10.1016/j.ijme.2016.05.005 · Cited by 180
Sabah, N. M. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.09.009 · Cited by 157
Salloum, S. A., & Shaalan, K. (2019) · Advances in Intelligent Systems and Computing · doi:10.1007/978-3-319-99010-1_43 · Cited by 128
Fowler, L. A., Holt, S. L., & Joshi, D. (2016) · Addictive Behaviors · doi:10.1016/j.addbeh.2016.06.008 · Cited by 123
Terblanche, N., & Kidd, M. (2022) · Sage Open · doi:10.1177/21582440221096136 · Cited by 120
(2013) · Journal of electronic commerce research · Cited by 102
Tran, A. Q., Nguyen, L. H., Nguyen, H. S. A., Nguyen, C. T., Vu, L. G., Zhang, M., Vu, T. M. T., Nguyen, S. H., Tran, B. X., Latkin, C. A., et al. (2021) · Frontiers in Public Health · doi:10.3389/fpubh.2021.755644 · Cited by 87
Hanafi, Y., Murtadho, N., Ikhsan, M. A., & Diyana, T. N. (2020) · International Journal of Interactive Mobile Technologies (iJIM) · doi:10.3991/ijim.v14i02.11380 · Cited by 73
Sidik, D., & Syafar, F. (2020) · Education and Information Technologies · doi:10.1007/s10639-019-10018-0 · Cited by 68
Teck Soon, H., & Syed A. Kadir, S. L. (2017) · Internet Research · doi:10.1108/intr-08-2016-0256 · Cited by 53
Behavioral intention to continuously use learning apps: A comparative study from Taiwan universities
Huang, D.-H., & Chueh, H.-E. (2022) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2022.121531 · Cited by 42
Let's Talk It Out: A Chatbot for Effective Study Habit Behavioral Change
Tian, X., Risha, Z., Ahmed, I., Lekshmi Narayanan, A. B., & Biehl, J. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3449171 · Cited by 37
Sarosa, S. (2019) · Procedia Computer Science · doi:10.1016/j.procs.2019.11.106 · Cited by 30
Welch, R., Alade, T., & Nichol, L. (2020) · IADIS INTERNATIONAL JOURNAL ON COMPUTER SCIENCE AND INFORMATION SYSTEMS · doi:10.33965/ijcsis_2020150107 · Cited by 19
Maita, I., Saide, Indrajit, R. E., & Irmayani, A. (2018) · Proceedings of the 2018 1st International Conference on Internet and e-Business · doi:10.1145/3230348.3230351 · Cited by 11
Metadata
- Authors
- Wang, Y., Wu, M., & Wang, H.
- Venue
- British Journal of Educational Technology
- Publisher
- Wiley
- Published
- 2008-12-23
- Volume
- 40
- Issue
- 1
- Pages
- 92-118
- ISSN
- 0007-1013 (ISSN-L)
- 1467-8535
- OpenAlex
- W2157446202
- Cited by
- 1,218
- References
- 20
- FWCI
- 48.70
- 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.
