The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Using an extended Technology Acceptance Model to understand students’ use of e-learning during Covid-19: Indonesian sport science education context
Open accessAbstract
This study was to explore factors predicting the use of e-learning during Corona Virus Disease 2019 (Covid-19) among sport science education students In Indonesia Higher Education Institutions (HEIs). The study was conducted through survey with 974 participating students from five Indonesian HEIs. An extended Technology Acceptance Model (TAM) with facilitating condition as the external factor was implemented to be the theoretical framework of this study. An analysis method through Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to measure and assess the proposed model. The findings informed that: (1) the TAM-based proposed scale has been successfully explained factors predicting the use of e-learning among Indonesian sport science students during the pandemic; (2) the finding of significant relationships between facilitating condition and perceived ease of use and between facilitating condition and perceived usefulness was reported; and (3) the significant relationships among core components of TAM were found except for one, relationship between perceived usefulness and attitude.
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Cites
Perceived usefulness, perceived ease of use, and user acceptance of information technology
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Observational study of behavior: sampling methods
Altmann, J. (1974) · Behaviour · doi:10.1163/156853974x00534 · Cited by 13,891
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 · cited 2×
“Studies reported some external factors to accompany the original TAM constructs (Venkatesh and Bala, 2008; Venkatesh and Davis, 2000).”
Determination and Quantification Of Content Validity
LYNN, M. R. (1986) · Nursing Research · doi:10.1097/00006199-198611000-00017 · Cited by 6,775
Response to COVID-19 in Taiwan: Big Data Analytics, New Technology, and Proactive Testing
Wang, C. J., Ng, C. Y., & Brook, R. H. (2020) · JAMA · doi:10.1001/jama.2020.3151 · Cited by 1,391
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
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Emergency remote teaching in higher education: mapping the first global online semester
Bond, M., Bedenlier, S., Marín, V. I., & Händel, M. (2021) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-021-00282-x · Cited by 527
Rosli, M. S., Saleh, N. S., Md. Ali, A., Abu Bakar, S., & Mohd Tahir, L. (2022) · Sustainability · doi:10.3390/su141811389 · Cited by 126
What Drives Consumer Purchasing Intention in Live Streaming E-Commerce?
Qing, C., & Jin, S. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.938726 · Cited by 83
Behavioral intention to continuously use learning apps: A comparative study from Taiwan universities
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Metadata
- Authors
- Sukendro, S., Habibi, A., Khaeruddin, K., Indrayana, B., Syahruddin, S., Makadada, F. A., & Hakim, H.
- Venue
- Heliyon
- Publisher
- Elsevier BV
- Published
- 2020-11
- Volume
- 6
- Issue
- 11
- Pages
- e05410
- ISSN
- 2405-8440 (ISSN-L)
- OpenAlex
- W3096861852
- Cited by
- 403
- References
- 9
- FWCI
- 67.86
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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