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Determinants of e-Learning Services: Indonesian Open University
by
Mailizar, Mailizar
, Habibi, Akhmad
, Yaqin, Lalu Nurul
, Tanu Wijaya, Tommy
, Jovanka, Della Raymena
, Kusmawan, Udan
, Yaakob, Mohd Faiz Mohd
in
distance learning
/ Educational Objectives
/ Educational research
/ Electronic Learning
/ higher education
/ Indonesian
/ Intention
/ Online instruction
/ Open Universities
/ Open university
/ PLS-SEM
/ Questionnaires
/ Sampling
/ Structural Equation Models
/ students
/ Technology Acceptance Model
2023
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Determinants of e-Learning Services: Indonesian Open University
by
Mailizar, Mailizar
, Habibi, Akhmad
, Yaqin, Lalu Nurul
, Tanu Wijaya, Tommy
, Jovanka, Della Raymena
, Kusmawan, Udan
, Yaakob, Mohd Faiz Mohd
in
distance learning
/ Educational Objectives
/ Educational research
/ Electronic Learning
/ higher education
/ Indonesian
/ Intention
/ Online instruction
/ Open Universities
/ Open university
/ PLS-SEM
/ Questionnaires
/ Sampling
/ Structural Equation Models
/ students
/ Technology Acceptance Model
2023
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Do you wish to request the book?
Determinants of e-Learning Services: Indonesian Open University
by
Mailizar, Mailizar
, Habibi, Akhmad
, Yaqin, Lalu Nurul
, Tanu Wijaya, Tommy
, Jovanka, Della Raymena
, Kusmawan, Udan
, Yaakob, Mohd Faiz Mohd
in
distance learning
/ Educational Objectives
/ Educational research
/ Electronic Learning
/ higher education
/ Indonesian
/ Intention
/ Online instruction
/ Open Universities
/ Open university
/ PLS-SEM
/ Questionnaires
/ Sampling
/ Structural Equation Models
/ students
/ Technology Acceptance Model
2023
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Determinants of e-Learning Services: Indonesian Open University
Journal Article
Determinants of e-Learning Services: Indonesian Open University
2023
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Overview
The Internet has been massively utilized for educational purposes during the last ten years with many platforms, including e-Learning Services (e-LS). This survey study aimed to report factors affecting the intention to use e-LS in the Indonesian Open University (OU) context. The original technology acceptance model (TAM) was extended, involving seven variables: facilitating conditions, perceived enjoyment, subjective norms, perceived ease of use, usefulness, attitudes, and intention to use. The survey questionnaire was piloted prior to the main data collection. Through random sampling, 2787 out of 341,956 Indonesian OU students responded to the questionnaire. The main data were analyzed through partial least square structural equation modeling (PLS-SEM) procedures. For predicting intention to use e-LS, the strongest affecting factor is attitudes. Subjective norms play a bigger than perceived usefulness in predicting intention to use. In predicting attitudes, perceived ease of use is the strongest predictor, followed by perceived usefulness. Subjective norms, facilitating conditions, and perceived enjoyment are significant predictors of perceived usefulness and perceived ease of use. Recommendations were elaborated for all stakeholders: researchers, tutors, policymakers, and OU students.
Publisher
Cogent,Taylor & Francis Ltd,Taylor & Francis Group
Subject
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