Cover Image

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...

Full description

Saved in:
Bibliographic Details
Main Authors: Mirabolghasemi, Marva, Choshaly, Sahar Hosseinikhah, A. Iahad, Noorminshah
Format: Article
Published: Springer New York LLC 2019
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/88140/
http://dx.doi.org/10.1007/s10639-019-09945-9
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.utm.88140
record_format eprints
spelling my.utm.881402020-12-15T00:12:49Z http://eprints.utm.my/id/eprint/88140/ Using the HOT-fit model to predict the determinants of E-learning readiness in higher education: a developing Country’s perspective Mirabolghasemi, Marva Choshaly, Sahar Hosseinikhah A. Iahad, Noorminshah QA75 Electronic computers. Computer science 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. Springer New York LLC 2019-11 Article PeerReviewed Mirabolghasemi, Marva and Choshaly, Sahar Hosseinikhah and A. Iahad, Noorminshah (2019) Using the HOT-fit model to predict the determinants of E-learning readiness in higher education: a developing Country’s perspective. Education and Information Technologies, 24 (6). pp. 3555-3576. ISSN 1360-2357 http://dx.doi.org/10.1007/s10639-019-09945-9
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Mirabolghasemi, Marva
Choshaly, Sahar Hosseinikhah
A. Iahad, Noorminshah
Using the HOT-fit model to predict the determinants of E-learning readiness in higher education: a developing Country’s perspective
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.
format Article
author Mirabolghasemi, Marva
Choshaly, Sahar Hosseinikhah
A. Iahad, Noorminshah
author_facet Mirabolghasemi, Marva
Choshaly, Sahar Hosseinikhah
A. Iahad, Noorminshah
author_sort Mirabolghasemi, Marva
title Using the HOT-fit model to predict the determinants of E-learning readiness in higher education: a developing Country’s perspective
title_short Using the HOT-fit model to predict the determinants of E-learning readiness in higher education: a developing Country’s perspective
title_full Using the HOT-fit model to predict the determinants of E-learning readiness in higher education: a developing Country’s perspective
title_fullStr Using the HOT-fit model to predict the determinants of E-learning readiness in higher education: a developing Country’s perspective
title_full_unstemmed Using the HOT-fit model to predict the determinants of E-learning readiness in higher education: a developing Country’s perspective
title_sort using the hot-fit model to predict the determinants of e-learning readiness in higher education: a developing country’s perspective
publisher Springer New York LLC
publishDate 2019
url http://eprints.utm.my/id/eprint/88140/
http://dx.doi.org/10.1007/s10639-019-09945-9
_version_ 1687393531196342272
score 13.211869

Similar Items