Gravitating towards blockchain in sustainable higher education: a hybrid SEM-ANN technique

Abstract

The current study investigates the determinants affecting users’ intention to adopt blockchain technology in higher educational institutions (HEIs) through structural equation modeling (SEM), Artificial Neural Network, and the extended UTAUT2 model. The results from the SEM analysis serve as the inputs for the ANN models, and the root mean square error (RMSE) is utilised to evaluate the predicted accuracy of the ANN models. Data were gathered from university students in Bangladesh with a structured questionnaire. The findings demonstrate that performance expectancy, hedonic motivation, facilitating conditions, perceived trust, self-efficacy, and perceived risk significantly influence behavioural intention. Furthermore, perceived trust, self-efficacy, and perceived risk significantly influence actual usage. This study enhances existing knowledge by proposing a novel path association, providing valuable insights for blockchain developers and higher educational institutions to devise effective policies for the successful implementation of blockchain technology in sustainable education of developing nations.