Impact of mobile banking quality, perceived trust, and perceived risk on post-adoption behavior: The mediating role of customer satisfaction

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Type of the article: Research Article

Abstract
Mobile banking has emerged as a critical delivery channel for banks, particularly in emerging economies such as India, where sustained usage is essential for realizing long-term value. Despite extensive research on adoption, relatively less attention has been given to post-adoption behavior. This study aims to examine the impact of mobile banking quality, perceived trust, and perceived risk on post-adoption behavior, specifically customer satisfaction and continuance intention, and to analyze the mediating role of customer satisfaction. Data were collected from 345 active mobile banking users in India through a structured questionnaire. The focus on active users ensures that the findings reflect post-adoption evaluations based on actual usage experience. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to analyze the data, with mobile banking quality modeled as a second-order construct comprising service quality, system quality, and information quality. The results indicate that mobile banking quality has a significant positive effect on customer satisfaction (β = 0.567, p < 0.001) and continuance intention (β = 0.245, p < 0.001). Perceived trust positively influences customer satisfaction (β = 0.118, p < 0.05) and continuance intention (β = 0.322, p < 0.001), while perceived risk negatively affects customer satisfaction (β = −0.217, p < 0.001) and continuance intention (β = −0.129, p < 0.001). Customer satisfaction also significantly mediates the relationships between mobile banking quality, perceived trust, perceived risk, and continuance intention. The findings highlight the importance of improving overall mobile banking quality, strengthening user trust, and reducing perceived risk to enhance customer satisfaction and promote sustained usage.

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    • Figure 1. Conceptual model of mobile banking continuance intention showing hypothesized relationships
    • Figure 2. Empirical validation of the structural model
    • Table 1. Demographic characteristics of respondents
    • Table 2. Descriptive statistics of study constructs
    • Table 3. Measurement model results: reliability and convergent validity
    • Table 4. Discriminant validity assessment using Fornell-Larcker criterion and HTMT ratio
    • Table 5. Structural model results and hypothesis testing
    • Table 6. Total effects on customer satisfaction and continuance intention
    • Table 7. Effects of control variables on customer satisfaction and continuance intention
    • Conceptualization
      S. Saibaba
    • Data curation
      S. Saibaba
    • Formal Analysis
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    • Investigation
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    • Methodology
      S. Saibaba
    • Project administration
      S. Saibaba
    • Validation
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    • Visualization
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    • Writing – original draft
      S. Saibaba
    • Writing – review & editing
      S. Saibaba