Dharmendra Singh

This study aims to examine the demand-driven factors that attract foreign tourists to India. This study used an advanced gravity model to examine the factors that drew demand from 15 major foreign tourist destination countries from 1995 to 2018. Descriptive statistics, correlation, OLS, and panel GMM are the statistical tools used in data analysis. The results indicate that the per capita income of India and the countries of origin, the size of the Indian population, and a favorable visa policy all contribute to attracting foreign tourists to India. Meanwhile, the high cost of living in India compared to foreign tourist countries, terrorist incidents, and long distances between India and these countries are some of the factors that discourage foreign tourists from visiting India. The results of the study are robust and compelling and have significant implications for policymakers and the industry. Based on the findings, the study suggests that the Indian government should reduce the cost of living, especially for foreign tourists, by exempting them from paying goods and services tax and other taxes at the departure airport by showing the bill. In addition, security is an issue where the government should ensure the safety of foreign tourists.

This paper comprehensively evaluates the literature on retail investor overconfidence using a framework-based systematic approach to understand the various dimensions of overconfidence bias, its effect on investing choices, and market dynamics. A systematic review of 137 publications from the Scopus database have been done to detect the research trend concerning investor overconfidence bias from its inception. An integrated ADO-TCM framework has been employed to present a systematic analysis of the theory, context, and methodologies (TCM) employed in the reviewed studies. The ADO (Antecedents, Decisions, and Outcomes) framework thoroughly examines the antecedents, decisions, and results of investor overconfidence.
The study identified four broad sets of factors contributing to investor overconfidence, as found in the existing literature. These factors include demographic characteristics, personality traits of investors, their knowledge and experience, and the features of investments and investor types. The Prospect theory is the most popular theory in the literature, with much research using secondary data and experiment-based analysis. The prospective study directions, based on the gaps in the existing literature, are as follows: further investigation into the decision-making processes of overconfident retail and professional investors is a worthwhile subject. Future research may shift their focus from financial outcome variables to non-financial outcome variables such as the impact of investor overconfidence on individuals’ stress levels, subjective financial well-being, and overall life happiness.

Type of the article: Research Article

Abstract
This study aims to examine the impact of technological innovation on the carbon emissions of the G20 economies while accounting for the moderating role of economic policy uncertainty. To analyze the relationship between emissions and technological innovation in the presence of economic vulnerabilities, the study used the annual data from 2000 to 2023. Panel and quantile regression are used to test the heterogeneous relationship. The results reveal that research and development expenditure (R&D) has a positive and statistically significant impact on emissions at certain quantiles, suggesting that innovation alone may not uniformly contribute to emission reductions. Economic policy uncertainty is documented to have a heterogeneous relationship with emissions, wherein it reduces emissions at lower quantiles, with a positive relationship exhibited at higher quantiles. Similar results are obtained when economic policy uncertainty and R&D interact and are able to reduce emissions at all levels, as shown by quantile regression. The results provide valuable implications for policymakers, emphasizing the importance of fostering innovation while managing policy uncertainty to achieve carbon mitigation goals across varying emission levels within the G20.

Type of the article: Research Article

Abstract
This study examines the configurational causality among the shadow economy, corruption perception index, trade levels, population dynamics, urbanization, economic size and renewable energy use, and their impact on overall ecological footprints across the Digital Nations member countries and the seven emerging economies. To analyze the relationship, the study used annual data from 2012 to 2020, and fuzzy set qualitative comparative analysis (fsQCA) was used. While both groups show the absence of a necessary condition for high ecological footprints, there are instances of sufficient pathways across the Digital Nations groups. Based on the intermediate solution, digital grouping reveals a single pathway at a consistency threshold of 80% (consistency = 0.858, coverage = 0.2995), combining low shadow economy, high CPI, high GDP, low trade, high population, high urbanization, and low renewables. However, at the 70% threshold, there are two pathways, and the solution coverage increases to 0.423. Emerging economies yield no valid configurations via the intermediate solution, confirming heterogeneity. The results show how multiple factors, such as governance, demographics, and renewable energy adoption, can shape ecological footprints across economies with different digital, structural, and development statuses. These findings advance beyond traditional variance-based approaches by identifying precise causal recipes rather than net effects. The results offer policymakers targeted interventions prioritizing renewable energy deployment alongside strategic governance reforms to disrupt ecological footprint pathways across developmental contexts.

Acknowledgment
The authors would like to thank Modern College of Business and Science, Oman, for providing the financial support required to cover the article processing charges.