What shapes mobile fintech consumers' post-adoption experience? A multi-analytical PLS-ANN-fsQCA perspective

Yun-Peng Yuan, Garry Wei-Han Tan and Keng-Boon Ooi

Technological Forecasting and Social Change, 2025, vol. 217, issue C

Abstract: Mobile Fintech, known as “Mobile financial technology”, is a category of mobile apps that provide various financial services to its customers. In China, the mobile fintech industry has grown into a mature stage, and most consumers have adopted fintech apps to fulfill their daily financial tasks. However, there is still a lack of understanding of the mechanism that shapes the post-adoption experience. Hence, this study aims to explore factors that shape mobile fintech consumers' post-adoption experience in China's market. We particularly investigate how personal attributes, mobile fintech applications' utilitarian value, privacy calculus, and network externality foster users' experience response (ER). A triple-stage approach combining Partial Least Squares-Structural Equation Modeling (PLS-SEM), Artificial Neural Network (ANN), and Fuzzy-Set Comparative Analysis (fsQCA) is utilized to detect linear, non-linear, and configurational influences of exogenous variables on the users' outcome pattern, with 1022 effective responses obtained from self-administrated questionnaires distributed by China's leading survey platforms. The PLS-SEM and ANN results indicate that mobile self-efficacy (MSE), personal innovativeness (PIIT), task-technology-fit (TTF), structural assurance (SA), and perceived critical mass (PCM) are important predictors of mobile fintech users' post-adoption experience (ER), while perceived privacy security risk (PPSR) is insignificant to ER. The findings inspire both scholars and industrial practitioners to comprehensively pay attention to the personal, technological, social, and privacy-related faces that influence consumers' mobile fintech post-adoption experience. In addition, the fsQCA findings revealed five different combinations of the above factors that all lead to ER. The five solutions were further categorized into three types of user decision patterns (i.e., user personas) that assist industry players in establishing a more accurate customer segmentation for user retention.

Keywords: Mobile fintech; Post-adoption experience; Consumer behavior; Mobile commerce; PLS-SEM; ANN; fsQCA (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:217:y:2025:i:c:s0040162525001933

DOI: 10.1016/j.techfore.2025.124162

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