Yakubu, M.N., Dasuki, S.I., Abubakar, A.M. et al. (1 more author) (2020) Determinants of learning management systems adoption in Nigeria : a hybrid SEM and artificial neural network approach. Education and Information Technologies, 25 (5). pp. 3515-3539. ISSN 1360-2357
Abstract
Research has shown that technology, when used prudently, has the potential to improve instruction and learning both in and out of the classroom. Only a handful of African tertiary institutions have fully deployed learning management systems (LMS) and the literature is devoid of research examining the factors that foster the adoption of LMS. To fill this void, the present research investigates the factors contributing to students’ acceptance of LMS. Survey data were obtained from registered students in four Nigerian universities (n = 1116); the responses were analyzed using artificial neural network (ANN) and structural equation modeling (SEM) techniques. The results show that social influence, facilitating conditions, system quality, perceived ease of use, and perceived usefulness are important predictors for students’ behavioral intention to use LMS. Students’ behavioral intention to use LMS also functions as a predictor for actual usage of LMS. Implications for practice and theory are discussed.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 Springer Nature. This is an author-produced version of a paper subsequently published in Education and Information Technologies. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Technology acceptance model; Nigerian students; learning management systems; higher education; structural equation modeling; artificial neural network |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 19 Feb 2020 14:46 |
Last Modified: | 10 Dec 2021 16:12 |
Status: | Published |
Publisher: | Springer |
Refereed: | Yes |
Identification Number: | 10.1007/s10639-020-10110-w |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:157403 |