Investigating Key Factors Influencing Students’ Acceptance and Satisfaction with a Block Model E-Learning System: An Integrated SEM-Neural Network Approach

Abstract

Abstract Intensive or Block models, along with e-learning, have become a vital element in the transformation of higher education, leveraging technological advancement and the necessity for flexible, student-centred learning. Although research investigating students’ acceptance and adoption of traditional semester-based e-learning is well-established, systematic empirical investigation of key factors for students’ perception of, engagement with and satisfaction with E-learning Block Models (EBMs) remains limited in higher education. The study developed a theoretical model based on information systems success and the technology acceptance model called Evaluating E-learning Systems Success (EESS), with additional factors for the block model aspect: social norms and self-regulatory learning. Data was collected from 974 undergraduate and postgraduate students across diverse disciplines of study in a regional university and analysed using two steps: structural equation modelling and artificial neural networks, to examine the relationships between variables and the relative importance of each factor for the benefits of EBMs. The findings reveal that system quality, information quality, and service quality significantly influence students’ perceived usefulness and actual use of the EBM system. Perceived usefulness, in turn, has a significant positive effect on user satisfaction with the EBM system. These perceptions drive system use and benefits, reinforcing EBM’s success. Additionally, the study identifies social norms and self-regulatory learning as essential influences on student behaviour, further contributing to the effectiveness of the EBMs environment. This research offers valuable insights into the multifaceted dimensions of EBM success and provides practical implications for universities aiming to enhance student engagement, satisfaction, and learning outcomes by introducing block model teaching and learning in digital education environments.

The authors' abstract, as published at the source. Technology Knowledge and Learning, 2026 · DOI ↗