A hybrid structural equation modelling and neural network approach to examine student's acceptance of e-LMS
by Shard; Devesh Kumar; Sapna Koul
International Journal of Learning Technology (IJLT), Vol. 19, No. 2, 2024

Abstract: This study aims to examine the effects of e-learning management system - quality characteristics and student characteristics on continuous intention to use e-LMS. This empirical and quantitative study focuses on emerging nations, specifically India, and explores the acceptance of e-LMS among students. While previous research has examined student acceptance of e-LMS, a limited number of studies investigate the impact of e-learning management system quality and student characteristics on continuing e-LMS usage. This study aims to fill this gap by identifying the key variables that influence the adoption of e-LMS. The survey was disseminated online over four weeks in several departments of an HEI located in a rural, remote, and isolated area of the Himalayan foothills. 469 valid replies were obtained using the purposive sampling technique. The data analysis using the structural equation model showed that learner control, technology experience, motivation for learning related to technology, information quality, system quality, and service quality all had a statistically significant impact on students' continued use of e-LMS. According to the neural network (NN) model results, the most significant factors influencing continuous intention to use e-learning are information quality, technological experience, service quality, system quality, motivation for learning, learner control, and personnel innovativeness.

Online publication date: Mon, 10-Jun-2024

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Learning Technology (IJLT):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com