Application-based mobile payment systems: continuance intention and intention to recommend

Abstract

This research paper valuates the factors of continuance intention and intention to recommend application-based mobile payment systems. The research model has been developed based on several strands of theories in information systems. The model has been tested using 360 respondents' data collected from Wuhan, PR China. This study used a hybrid method by relating structural equation modelling (SEM) and artificial neural network (ANN) for analysing data. The SEM confirms that satisfaction, perceived usefulness, perceived enjoyment, habit, and context are the key predictors of the continuance intention. The continuance intention positively affects the intention to recommend the application-based mobile payment. This study also confirms the moderating effect of context on the relationship between satisfaction and continuance intention. The ANN concludes that the most significant predictor of continuance intention is satisfaction, while the least important factor is habit. This study's implications provide worthy insights to the researchers, practitioners, and managers, which assist them in devising effective strategies for implementing application-based mobile payment.