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User Acceptance Towards Web-based Learning Systems: Investigating the Role of Social, Organizational and Individual Factors in European Higher Education
Author(s) -
Ali Tarhini,
Kate Hone,
Xiaohui Liu
Publication year - 2013
Publication title -
procedia computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.334
H-Index - 76
ISSN - 1877-0509
DOI - 10.1016/j.procs.2013.05.026
Subject(s) - computer science , knowledge management , social web , web 2.0 , web application , world wide web , social media , web service
Due to the rapid growth of internet technology, British universities and higher educational institutions around the world are investing heavily in web-based learning systems to support their traditional teaching and to improve their students’ learning experience and performance. However, the success of an e-learning system depends on the factors that influence the students’ acceptance and usage of such learning systems. So far little research has been done on the important role that social, institutional and individual factors may play in the use and adoption of the e-learning system. In this paper, the technology acceptance model (TAM) is extended to include social, institutional and individual factors in the general structural model to empirically investigate and study whether students are willing to adopt and use e-learning systems. Data were collected using a cross-sectional survey completed by a total of 604 British university students who are using web-based learning systems at Brunel University in England. After performing the satisfactory reliability and validity checks, the hypothesized research model was estimated using structural equation modeling. The results have revealed that perceived ease of use (PEOU), perceived usefulness (PU), social norms (SN), quality of work Life (QWL), computer self-efficacy (SE) and facilitating conditions (FC) are all having a significant positive influence on the adoption and usage of Blackboard system. With QWL; the newly added variable; was found to be the strongest and the most important factor. Overall, the proposed model achieves acceptable fit and explains for 69% of its variance of which is higher than that of the original TAM. Our findings have demonstrated policy makers should take into account that e- learning implementation is not simply a technological solution, but they should also address individual differences by considering a set of critical success factors such as social, institutional and individual factors

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