Integrating SEM and Artificial Neural Networks in Bridging Adoption Intention for Central Bank Digital Currency Payments

Abstract

China has emerged as a global leader in mobile payment systems and pioneered the establishment of central bank digital currency payments by employing more secure technologies to supplant third-party mobile payment methods. At this juncture, it is imperative to evaluate the factors prompting the acceptance of central bank digital currency payment among Chinese users. This study aims to determine the positive   roles of perceived security, user interface attractiveness, monetary value, alternative attractiveness and national identity in affecting user adoption of central bank digital currency payments. The data was gathered from 302 Chinese mobile payment users through a self-administered online questionnaire. The data was analysed utilising a combination of the partial least squares structural equation modelling and artificial neural networks. The findings indicate that alternative attractiveness and monetary value have a significant effect on switching intention. The data enables central bank digital currency managers to recognize key parameters that affect the utilization of central bank digital currency payments.