What will cause adopters to continually adopt the DCEP? an expanded analysis by PLS-SEM, NCA, and FsQCA
摘要
Digital Currency Electronic Payment (DCEP) offers a more secure and inclusive approach to electronic transactions. However, the factors influencing its continuance adoption after initial use have not been comprehensively investigated, particularly within the framework of triadic interaction. Grounded in social cognitive theory (SCT), this study examines the underlying mechanisms affecting users’ continued adoption of DCEP. Utilizing survey data from 370 Chinese DCEP users, this study employs Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine the primary influencing factors, Necessary Condition Analysis (NCA) to identify essential determinants, and fuzzy-set Qualitative Comparative Analysis (fsQCA) to investigate the interaction between two dimensions. The PLS-SEM results indicate that individual factors, namely trust, financial literacy, personal innovativeness, habit, and self-efficacy, as well as the environmental factor of social influence, positively impact the intention to continue adopting DCEP. However, financial incentives do not play a significant role in shaping the continuance adoption of DCEP. Notably, the NCA analysis reveals that financial incentives are necessary for achieving a high level of continuance intention. Furthermore, the fsQCA analysis identifies five configurations that lead to a high continuance intention to adopt DCEP. These five solutions demonstrate that the integration of both individual and environmental dimension factors is essential to attaining a high continuance intention to adopt DCEP. This study develops strategies for DCEP service providers (i.e., central banks), DCEP designers (i.e., technical staff), marketers (i.e., bank employees and promoters), and the government to enhance users’ continued intention to adopt DCEP. This study indicates that examining of factors affecting the continuance adoption of DCEP should extend beyond single-method analyses and consider the impacts of multiple-factor combinations.