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Using the HOT-fit model to predict the determinants of E-learning readiness in higher education: a developing Country’s perspective
E-learning readiness has been initiated in higher education institutions (HEI) as an attempt to improve institutions’ service delivery. Meeting and managing the expectations of students using e-learning systems to facilitate teaching and learning activities is a prominent way to make HEI competitive...
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Published in: | Education and information technologies 2019-11, Vol.24 (6), p.3555-3576 |
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creator | Mirabolghasemi, Marva Choshaly, Sahar Hosseinikhah Iahad, Noorminshah A. |
description | E-learning readiness has been initiated in higher education institutions (HEI) as an attempt to improve institutions’ service delivery. Meeting and managing the expectations of students using e-learning systems to facilitate teaching and learning activities is a prominent way to make HEI competitive. The purpose of this study is to investigate the impact of human, organizational, and technological factors on students’ e-learning readiness. This study was conducted by using a survey method in a private university in the north region of Iran with a total number of 153 respondents. Survey data were analyzed using the partial least squares (PLS) method while Smart PLS was used to test the hypotheses and to validate the proposed model. The results indicated that computer self-efficacy, management support, relative advantage, compatibility, and complexity are significant factors that influence students’ e-learning readiness. The findings provide a basis for assessing the determinants of e-learning readiness in developing countries. |
doi_str_mv | 10.1007/s10639-019-09945-9 |
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Meeting and managing the expectations of students using e-learning systems to facilitate teaching and learning activities is a prominent way to make HEI competitive. The purpose of this study is to investigate the impact of human, organizational, and technological factors on students’ e-learning readiness. This study was conducted by using a survey method in a private university in the north region of Iran with a total number of 153 respondents. Survey data were analyzed using the partial least squares (PLS) method while Smart PLS was used to test the hypotheses and to validate the proposed model. The results indicated that computer self-efficacy, management support, relative advantage, compatibility, and complexity are significant factors that influence students’ e-learning readiness. 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subjects | Analysis College Students Computer Appl. in Social and Behavioral Sciences Computer Science Computers and Education Developing countries Education Education parks Education, Higher Educational Technology Electronic Learning Foreign Countries Higher education Influences Information Systems Applications (incl.Internet) LDCs Learning strategies Methods Online education Online instruction Private Colleges Program Effectiveness Readiness School facilities Self Efficacy Student Attitudes Teaching Methods User Interfaces and Human Computer Interaction |
title | Using the HOT-fit model to predict the determinants of E-learning readiness in higher education: a developing Country’s perspective |
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