Adoption model for a hybrid SEM-neural network approach to education as a service

Learning systems are widely adopted as educational tools. The success of a learning system depends on the level of acceptance by instructors and learners. Research has identified Education as a Service (EaaS) as a resource that enables instructors and learners to access a new kind of service for learning system, containing: (1) support tools and services, (2) curriculum, and (3) cloud service. This paper aims to develop an adoption model to understand the causal relation and predict the effect of personal characteristic (Perceived usefulness and Perceived ease of use) and quality characteristics (Service quality, System quality, and Information quality) on the continuous intention to use EaaS from instructors and learners, which is critical to success. A total of 1570 participants were involved in the survey, including instructors and learners in Thailand's universities. The Structural Equation Model (SEM) was employed to test the causal relation, while the Neural Network (NN) model was employed in the prediction of EaaS adoption with 72.9% accuracy.