Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/10879
Title: Differences in intention to use educational RSS feeds between Lebanese and British students: A multi‑group analysis based on the technology acceptance model
Authors: Tarhini, A
Scott, M
Sharma, S
Abbasi, MS
Keywords: Cross-cultural;Technology adoption model;Developing countries;RSS;Virtual learning environments;Engagement
Issue Date: 2015
Publisher: Academic Conferences and Publishing International
Citation: Electronic Journal of e-Learning, 13(1): 14 - 29, ( January 2015)
Abstract: Really Simple Syndication (RSS) offers a means for university students to receive timely updates from virtual learning environments. However, despite its utility, only 21% of home students surveyed at a university in Lebanon claim to have ever used the technology. To investigate whether national culture could be an influence on intention to use RSS, the survey was extended to British students in the UK. Using the Technology Adoption Model (TAM) as a research framework, 437 students responded to a questionnaire containing four constructs: behavioral intention to use; attitude towards benefit; perceived usefulness; and perceived ease of use. Principle components analysis and structural equation modelling were used to explore the psychometric qualities and utility of TAM in both contexts. The results show that adoption was significantly higher, but also modest, in the British context at 36%. Configural and metric invariance were fully supported, while scalar and factorial invariance were partially supported. Further analysis shows significant differences between perceived usefulness and perceived ease of use across the two contexts studied. Therefore, it is recommended that faculty demonstrate to students how educational RSS feeds can be used effectively to increase awareness and emphasize usefulness in both contexts.
URI: http://www.ejel.org/volume13/issue1/p14
http://bura.brunel.ac.uk/handle/2438/10879
ISSN: 1479-4403
Appears in Collections:Dept of Computer Science Research Papers

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