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.

Utilizing natural resources wisely, reducing pollution, and taking other environmental factors into account are now critical to the prospects for long-term economic growth and, by extension, sustainable development. We investigate the impact of total natural resource rents (NRR) on India’s GDP in this study. The data sample consists of NRR and GDP data from the World Bank’s official website collected between 1993 and 2020. In the study, the Granger causality test and an augmented autoregressive distributed lag (ARDL) bound test were used. The NNR have a significant impact on India’s GDP, according to the results of the ARDL model on the framed time series data set. Furthermore, the ARDL bound test reveals that the NRR have a significant short-term and long-term impact on the GDP of the Indian economy. This research contributes to understanding whether an exclusive policy is required for effective management of the complex interactions between various forces in the economic, political, and social environments. This is significant because there is no standard policy in India to improve the efficiency of utility extraction from natural resources.

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.

Fintech allows investors to explore previously unavailable investment opportunities; it provides new return opportunities while also introducing new risks. The aim of this study is to investigate the relationship between risk and return in the fintech industry in the Indian stock market. This article is based on market-based research that focuses on demonstrating the volatility in the fintech market’s prices and demystifying the opportunities. Secondary data were collected from the Bombay Stock Exchange’s official fintech industry website from January 2017 to July 2022 to determine whether there is any dynamic link between risk and return in the Indian fintech market. The variance-based Mean-GARCH (GARCH-M) model was used to determine whether there is a dynamic link between risk and return in the Indian fintech market. The findings emphasize the importance of taking the risk of investing in India’s fintech industry. The implications for stock investors’ and fund managers’ portfolio composition and holding periods of equities or market exposure are significant. Finally, depending on their investment horizons, the Indian fintech industry may yield significant profits for risk-taking individuals.

The cryptocurrency market has enormous growth potential. In this study, the aim is to investigate how the news (shocks) affects cryptocurrency market volatility. This is significant because, while cryptocurrencies are gaining popularity among investors, the market’s extreme volatility discourages some prospective buyers, while also causing large losses for inexperienced investors. From 8 March 2019 to 30 November 2022, data from Bitcoin, Binance Coin, Ethereum, Dogecoin, and XRP were collected for the current study. The E-GARCH model was applied to the framed dataset to achieve the research aim. We discovered that the value of the size factor for all currencies was statistically significant, indicating that the news (shocks) significantly impacts volatility. Furthermore, volatility persistence in all cryptocurrencies is found to be very high and statistically significant. These study findings can help investors understand the impact of the news (shocks) on volatility in cryptocurrency returns.

Employee engagement is one the most essential component of human resource management. For any organisation to survive and succeed, employee engagement is a must. The current study is based on bibliometric analysis of the existing literature. From the search criteria and article selection, only 207 papers from Scopus database were selected, and all these articles belonged to ABDC journals. The objective of the chapter was to look into the publication trends, top research articles, leading countries who are contributing into this field, as well as looking into the top keywords. In this chapter, thematic mapping and bibliometric coupling were used to provide a better idea about the study. Finally, at the end the research, limitations and endeavours for future research are discussed.