A MODEL OF STUDENT SATISFACTION FOR THE CONTINUANCE INTENTION TO USE ELECTRONIC LEARNING IN SAUDI UNIVERSITIES

Abstract

E-learning has been widely adopted by universities worldwide, transforming teaching and learning in higher education. While extensive research has examined e- learning adoption and acceptance, studies focussing on students' satisfaction in relation to continuance intention (CI) to use e-learning systems remain limited. A literature review indicates that student satisfaction and continuance intention to use e-learning in Saudi universities is generally low. Despite existing studies on e-learning CI, there is still no consensus on the factors influencing student satisfaction for CI, highlighting a gap in understanding the individual and system characteristics that contribute to e- learning satisfaction and CI. This study aims to develop a model that identifies key factors influencing student satisfaction for continuance intention in e-learning systems using a quantitative research approach. The research was conducted in three phases. The first phase employed a Systematic Literature Review (SLR) to identify key factors influencing student satisfaction with CI, revealing ten key factors: self-efficacy, perceived ease of use, motivation, system quality, information quality, service quality, confirmation, perceived usefulness, satisfaction, and continuance intention. The second phase used the Delphi technique to refine these factors and develop a satisfaction model for CI. The third phase involved collecting data from 404 students across four public universities in northern Saudi Arabia. Data were analysed using Structural Equation Modeling. The results showed that nine factors significantly influence students' satisfaction with CI, whereas service quality did not have a significant effect, highlighting its practical implications for e-learning system design. The model demonstrated varying explanatory power, with student satisfaction accounting for 44% and Continuance Intention accounting for 22% of the variance. Overall, the model explained between 22% and 44.7% of the variance across key constructs. The findings provide valuable insights into the determinants of e-learning satisfaction for CI. By integrating individual and system characteristics, the study offers practical guidance for e- learning developers in designing more user-centred systems that address both user needs and system functionality. Overall, the research model offers a novel integration of individual and system factors, providing a more comprehensive understanding of e-learning continuance intention compared to existing models