The National Payments Corporation of India (NPCI) has invested a sizable amount of money in the country's massive payment infrastructure in an effort to enhance the user experience. However, in order for investments to be profitable, NPCI must guarantee the ongoing use of technological solutions and post-adoptive behaviors like continuance and recommendation intention. The impact of cognitive factors (i.e. Performance expectancy, effort expectancy, social influences, facilitating conditions; personal innovativeness) and affective factors (such as satisfaction) on conative factors (such as continuation and recommendation intention) in the perspective of UPI applications (apps) was investigated using the UTAUT model. Partial Least Square Structural Equation Modeling when applied on 651 users (PLS-SEM) showed that satisfaction had a direct impact on continuation intentions, which in turn had an impact on recommendations intentions. It was discovered that all cognitive factors, including performance expectations, effort expectations, and facilitating conditions, have an impact on satisfaction. According to the study, adding a significant individual difference variable—personal innovativeness with regard to information technology—would aid in our understanding of the role that these factors play in the development of continuous intention. It further examines the influence of trust and security, and the pace of innovation on continued intentions. Through the mediating function of user satisfaction, it also looked at the impact of performance expectancy, effort expectancy, social influence, facilitating variable, and personal innovativeness on the continuance intentions of the UPI system. All factors have been shown to be significant. Future researchers will find it extremely helpful that the study used a validated instrument to better understand user adherence and referral intentions. Therefore, this study adds to the limited body of knowledge in the payment industry literature by examining how users perceive UPI apps and post-adoption behaviors.

Global disruptions, such as health crises and geopolitical tensions, significantly impact both climate and commodity dynamics. This study, well-grounded on environmental finance theory, input–output modelling, socio-transition philosophy and behavioural finance perspectives, explores the evolving interactions between carbon emissions (CE) and metal markets during COVID-19 pandemic and Russia–Ukraine war (RUW). The study uses time-varying parameter vector autoregressive model (TVP-VAR) technique to evaluate time and frequency varying connectedness between CE and metal markets from 3 January 2020 to 28 June 2024. During the COVID-19 pandemic, initial connectedness among selected markets peaked at 85%, averaging 46%, highlighting a significant CE–metal nexus that necessitates strategic responses. In the RUW period, connectedness averaged 47.82%. CE influence metal markets primarily in the short term. Wavelet coherence analysis reveals that palladium and platinum are highly sensitive to CE over the long term, while gold and silver may serve as effective diversifiers and hedges against carbon-related risks in metal investments. The study is relevant for investors in the metal sector with environmental considerations. JEL Classifications: G110, Q430, L720

This research provides a bibliometric account of the research on Entrepreneurial Intention (EI) from the “Scopus” database. Based on 1,345 articles published during a period of 28 years (i.e., 1993–2021), the study analyzes the conceptual structure of the work on EI. The study finds prospective synergies and interactions, thereby classifying the scientific literature into various clusters that address similar problems and share common themes. The development of imminent research streams built on quantitative and qualitative content analyses of the clustered publications highlighting previously overlooked idiosyncrasies of EI and their role across multiple levels of analysis and varied contexts can be considered as an accomplishment of this review. The study furthermore proposes a framework for the conceptualization of novel models for future exploration of EI. However, the research suggests some pathways to develop and empirically test statistical models emerging from the flow of scientific inquiry. The study urges researchers to use the case study method to evaluate EI. This study will help both pioneer and veteran EI researchers and other stakeholders benefit, who also intend to explore new directions for future investigations offering integration and synchronization with other fields of research.

Appreciation of the impact of spot and futures markets on each other and their respective function in price discovery is the central idea of this study’s microstructure design. The volatility spillover and co-movement between the NIFTY spot and futures indices during the period of 2011–2021 are investigated. Maximal overlap discrete wavelet transformation (MODWT)-based DCC GARCH and Diebold and Yilmaz (2012) models have been applied to understand the dynamic associations and spillovers between returns of NIFTY spot and futures indices. To uncover the time-varying vibrant communications among the markets at diverse scales, a wavelet coherence technique has been used. The study found that index prices have a strong and significant dynamic conditional correlation at all scales, and there is diffusion of news in the short as well as long terms. The study uses novel granular wavelet-based research models to decode time–frequency varying behavioural patterns of spot and futures stock indices of India. It highlights the relationships between variables of interest over a range of timescales and allows market participants to rapidly evaluate their investment horizons at varied frequency band scales when building portfolio choices. Therefore, the study offers added understanding to investors and risk managers. JEL Classifications: G11, G12, G13, G14