Structural equation model (SEM)-neural network (NN) model for predicting quality determinants of e-learning management systems. Issue 10 (3rd October 2017)

• Record Type: Journal Article
• Authors: Sharma, Sujeet Kumar; Gaur, Avinash; Saddikuti, Venkataramanaiah; Rastogi, Ashish
• Is Part Of: Behaviour & information technology. Volume 36:Issue 10(2017)
• Page Start: 1053
• Page End: 1066
• Publication Date: 2017-10-03
• Subjects: E-learning -- LMS -- MOODLE -- technology adoption -- personal innovation -- service quality -- Oman; Electronic data processing -- Periodicals; Human engineering -- Periodicals; Information technology -- Periodicals; 303.4833
• DOI: 10.1080/0144929X.2017.1340973
• Languages: English
• ISSNs: 0144-929X

Abstract

ABSTRACT: The success of e-learning management systems (e-LMSs) such as MOODLE depends on the usage of students as well as instructor acceptance in a virtual learning environment. E-Learning enables instructors to access educational resources to support traditional classroom teaching. This paper attempts to develop a model to understand and predict the effect of individual characteristics (technology experience [TE] and personal innovativeness [PI]) and e-LMS quality determinants (system quality [SYS-Q], information quality, and service quality) on the continuous use of e-LMS by instructors, which is critical to its success. A total of 219 instructors using MOODLE responded to the survey. The structural equation model (SEM) was employed to test the proposed research model. The SEM results showed that SYS-Q, PI, service quality, and TE have a statistically significant influence on continuous usage of e-LMS by instructors. Furthermore, all determinants of the research model were given as input to an NN model to overcome the simplistic nature of the SEM model. The NN model results showed that service quality is the most important predictor of e-learning acceptance followed by SYS-Q, PI, information quality, and TE. This paper attempts to develop a causal and predictive statistical model for predicting instructor e-LMS acceptance.