Learning management system usage with postgraduate school: an application of UTAUT model

The study examined the relationship between selected factors (performance expectancy, effort expectancy, social influence and facilitating condition) and students’ intention to use the learning management system (LMS). In addition, this study investigated the factors predicting students’ intention t...

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Bibliographic Details
Main Authors: Samaila, Kamaludeen, Abdulfattah, Khashyaullah, Amir, Ahmad Fahimi
Format: Article
Language:English
Published: International Institute of Academic Research and Development 2017
Online Access:http://psasir.upm.edu.my/id/eprint/58579/1/Learning%20management%20system%20usage%20with%20postgraduate%20school.pdf
http://psasir.upm.edu.my/id/eprint/58579/
https://www.iiardpub.org/journal/?j=IJEE
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Summary:The study examined the relationship between selected factors (performance expectancy, effort expectancy, social influence and facilitating condition) and students’ intention to use the learning management system (LMS). In addition, this study investigated the factors predicting students’ intention to use the LMS at postgraduate school, University Putra Malaysia. The study was exclusively conducted at the Faculty of Educational Study of University Putra Malaysia. It was a quantitative study in which questionnaires were distributed to 277 respondents using a stratified random sampling technique. SPSS Version 22 was employed to analyze the data; the result of the study showed that performance expectancy (r = .69, p< .01), effort expectancy (r = .60, p< .01), social influence (r = .61, p< .01), and facilitating condition(r = .42, p< .01), were significantly related to students’ intention to use the LMS. However, the result also discovered that performance expectancy (β = .436, p <.05), social influence (β = .232, p <.05), and effort expectancy (β = .193, p <.05) were strong predictors of students’ intention to use the LMS. The analysis further revealed that (R2) is 0.054 which means that 54% of variation in the dependent variable is explained by the entire predictor variables entered into the regression model. Understanding the factors that affect students’ intention to use the LMS could help the LMS managers and lectures to develop the policies that may attract students to use the LMS.