The study’s aim is to investigate how FinTech users’ perceived risk influences their continuance intention to use FinTech services. The new model, which was based on the Expectation Confirmation Model, was created to achieve the study’s aim. The Partial Least Square Structural Equation Model was used to investigate the proposed model and the relationship between the adopted constructs. The sample consists of 802 individual survey responses from northern India from April to June 2022. The proposed model explains 45.4% of the variance in the continuance intention of FinTech users, which is significantly influenced by perceived usefulness and satisfaction. Furthermore, perceived risk, as a moderator, significantly moderates continuance intention through satisfaction and satisfaction through confirmation. However, perceived risk was found to have an insignificant moderating effect on the relationship between perceived usefulness and satisfaction as well as perceived usefulness and continuance intention. The findings provide insights to FinTech service providers about the factors that influence users’ intent to continue using FinTech services.

In this chapter, using a combination of the expectation-confirmation theory (ECT) and the latent variable model, the authors have analyzed what factors contribute to one's intention to continue usage with conformation, performance expectancy, and satisfaction. Based on a sample of 678 smart wearable users, collected from Northern India, the authors have used structural equation modelling (Smart PLS4) and identified the impact of satisfaction post-adoption technology, willingness to pay a premium price, and health information accuracy on the continuing intention of smart wearable users. According to the findings, consumer satisfaction, health information accuracy, and willingness to pay the premium price are key variables that have a substantial impact on the long-term purpose to repurchase and use smart wearables. This study has many implications for smartwatch manufacturers and designers looking to increase users' intent to continue using their products.

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In this study, we examined the influence of users’ experiences with the unified payments interface (UPI) system on the usage behavior of central bank digital currency (CBDC) in India. Our research developed a novel conceptual framework that investigated the relationships between technology, cognitive factors, and behavioral intentions towards CBDC use. The framework integrated UPI usage experience as a moderator within existing models of behavioral intentions and use behaviors. We collected data through a survey conducted in major Indian cities during the pilot launch of CBDC. By utilizing a partial least squares structural equation model (PLS-SEM), we analyzed the proposed model and the relationships between the constructs. Our findings revealed the significant impact of hedonic motivation and performance expectancy on users’ behavioral intentions towards CBDC. Social influence also played a significant role in CBDC usage. Furthermore, we identified that prior UPI usage negatively moderated the relationship between performance expectancy and behavioral intention, as well as the relationship between social influence and use behavior. However, prior UPI usage did not significantly moderate the relationships between perceived risk, hedonic motivation, behavioral intention, and use behavior. These findings contribute to our understanding of the factors influencing CBDC adoption and usage behavior in India.