Home » Extending the Technology Acceptance Model using perceived user resources in higher education web-based online learning courses. by Cheng-Hsin Ku
Extending the Technology Acceptance Model using perceived user resources in higher education web-based online learning courses. Cheng-Hsin Ku

Extending the Technology Acceptance Model using perceived user resources in higher education web-based online learning courses.

Cheng-Hsin Ku

Published
ISBN : 9781109161694
NOOKstudy eTextbook
187 pages
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 About the Book 

The purpose of this research was to examine students acceptance of the World Wide Web Course Tools (WebCT) online learning system. The Perceived Resources and Technology Acceptance Model (PRATAM) was created based on previous research to address theMoreThe purpose of this research was to examine students acceptance of the World Wide Web Course Tools (WebCT) online learning system. The Perceived Resources and Technology Acceptance Model (PRATAM) was created based on previous research to address the factors of perceived resources, perceived usefulness, perceived ease of use, attitude toward using, behavioral intention to use and actual system use. The aim for this research was to investigate the critical determinants and provide the causal relationships regarding students acceptance behaviors when using WebCT.-While institutions are expecting to adopt online learning to reach more students, there are still many challenges for institutions to retain students in their online courses. The literature review conducted in this research indicated that the Technology Acceptance Model (TAM) has successfully explained students behaviors when they use educational information systems. In addition, the additional perceived resources variable in the PRATAM also showed a significant influence on the other belief and intention variables.-The study analyzed a total of 115 students responses in two surveys administered during two WebCT based courses taught at a large southeastern public university. The beliefs, attitudes, intentions, and behavioral constructs of PRATAM showed significant goodness-of-fit indices and coefficient of determination after analyzing the data in both surveys. However, the results indicated several exceptions on PRATAMs constructs and causal relationships. First, the path coefficient between perceived resources to behavioral intention to use in both pre-test and post-test were insignificant.-Second, the path coefficient between behavioral intention to use and actual system use in pre-test was insignificant. Third, the path coefficient between perceived resources and perceived usefulness in post-test were insignificant. In addition, the research also suggested an additional link between perceived ease of use and behavioral intention to use at the pre-test data. Overall, this research validated the influences of PRATAMs constructs factors to students acceptance behaviors toward WebCT. The findings of this research could provide a guideline for future implementations of online learning systems in higher education.