Title: A hybrid structural equation modelling and neural network approach to examine student's acceptance of e-LMS

Authors: Shard; Devesh Kumar; Sapna Koul

Addresses: Yogananda School of AI, Computer and Data Sciences, Faculty of Engineering and Technology, Shoolini University, Solan-Oachghat-Kumarhatti Highway, Bajhol, Distt Solan, Himachal Pradesh-173229, India ' School of Commerce and Management Studies, Central University of Himachal Pradesh, Dhauladhar Parisar-II, Dharamshala, Kangra, Himachal Pradesh-176215, India ' Faculty of Applied Sciences and Biotechnology, Shoolini University, Solan-Oachghat-Kumarhatti Highway, Bajhol, Distt Solan, Himachal Pradesh-173229, India

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.

Keywords: e-learning; information system models; learning management system; students; web-based learning.

DOI: 10.1504/IJLT.2024.139031

International Journal of Learning Technology, 2024 Vol.19 No.2, pp.243 - 270

Received: 23 Apr 2023
Accepted: 08 Aug 2023
Published online: 10 Jun 2024 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article