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Investment decisions hold immense significance for investors and eventually affect their portfolio performance. Investors are advised to weigh the costs and benefits associated with every decision in order to make rational investment decisions. However, behavioral finance research reveals that investors' choices often stem from a blend of economic, psychological and sociological factors, leading to irrationality. Moreover, environmental, social and corporate governance (ESG) factors, aligned with behavioral finance hypotheses, also sway opinions and stock prices. Hence, this study aims to identify how individual equity investors prioritize key determinants of investment decisions in the Indian stock market.

The current research gathered data from 391 individual equity investors through a structured questionnaire. Thereafter, a fuzzy analytic hierarchy process (F-AHP) was used to meet the purpose of the research.

Information availability, representative heuristics belonging to psychological factors and macroeconomic indicators falling under economic factors were discovered to be the three most prioritized criteria, whereas environmental issues within the realm of ESG factors, recommendations of brokers or investment consultants of sociological factors, and social issues belonging to ESG factors were found to be the least prioritized criteria, respectively.

Only active and experienced individual equity investors were surveyed in this study. Furthermore, with a sample size of 391 participants, the study was confined to individual equity investors in one nation, India.

This research has implications for individual investors, institutional investors, market regulators, corporations, financial advisors, portfolio managers, policymakers and society as a whole.

To the best of the authors' knowledge, no real attempt has been made to comprehend how active and experienced individual investors prioritize critical determinants of investment decisions by taking economic, psychological, sociological and ESG factors collectively under consideration.

The objective of this research is to identify and prioritize the critical factors that influence the adoption of blockchain technology within the banking sector.

A well-known theoretical framework, the “Technology Organization Environment (TOE),” was chosen to analyze what criteria and sub-criteria affect blockchain adoption in the banking sector after a thorough assessment of the prior literature. Following that, 3 evaluation criteria and 14 sub-criteria were selected and verified using expert opinion. A survey design was created, and data for the study has been collected from various information technology (IT) managers/officers in the banking sector. A fuzzy analytic hierarchy process (Fuzzy-AHP) was then used to meet the purpose of the research.

The study identified that the organizational dimension is the most significant criteria for blockchain adoption in the banking sector, followed by the environmental dimension. In contrast, the technological dimension is the least influential criterion. Clientele pressure, IT resources, financial resources, pressure from competitors and relative advantage are the most influential sub-criteria for blockchain adoption.

This study provides valuable insights to bank managers, blockchain and IT developers, third-party service providers and policymakers. For instance, adopting the same blockchain platform is easier for both large and small banks for banking operations by using third-party service provider. At the same time, banks should have the banks' own core team to implement the blockchain-based systems or to have control over the third-party service providers during the adoption stage.

To the best of the authors' knowledge, no empirical studies have used a holistic organizational context to understand the factors influencing the adoption of blockchain technology from traditional to blockchain-based banking systems.

Research in the domain of behavioral finance has proven that investors demonstrate irrational behavior while making investment decisions. In a similar domain, the primary objective of this research is to prioritize the behavioral biases that influence cryptocurrency investors' investment decisions in the Indian context.

A fuzzy analytic hierarchy process (F-AHP) was used to prioritize the behavioral factors impacting cryptocurrency investors' investment decisions. Overconfidence and optimism, anchoring, representativeness, information availability, herding, regret aversion, and loss aversion are among the primary biases evaluated in the present study.

The findings suggested that the two most important influential criteria were herding and regret aversion, with loss aversion and information availability being the least influential criteria. Opinions of family, friends, and colleagues about investment in cryptocurrency, the sale of cryptocurrencies that have increased in value, the avoidance of selling currencies that have decreased in value, the agony of holding losing cryptocurrencies for too long rather than selling winning cryptocurrencies too soon, and the purchase of cryptocurrencies that have fallen significantly from their all-time high are the most important sub-criteria.

This survey only covered active cryptocurrency participants. Additionally, the study was limited to individual crypto investors in one country, India, with a sample size of 467 participants. Although the sample size is appropriate, a larger sample size might reflect the more realistic scenario of the Indian crypto market.

The study is relevant to individual and institutional cryptocurrency investors, crypto portfolio managers, policymakers, researchers, market regulators, and society at large.

To the best of the authors' knowledge, no prior research has attempted to explain how the overall importance of various criteria and sub-criteria related to behavioral factors that influence the decision-making process of crypto retail investors can be assessed and how the priority of focus can be established, particularly in the Indian context.

This paper aims to empirically examine the performance of the high-ESG (environment, social and governance) portfolio vis-à-vis the low-ESG portfolio at the Indian stock market before and during the Covid19 pandemic.

The absolute rate of return and several risk-adjusted performance measures, for instance, Sharpe ratio, Modigliani–Modigliani measure, Treynor ratio, Jensen’s alpha, information ratio, Fama’s decomposition measure and Fama and French’s three-factor model, have been used in this study along with the t-test.

All three indices (CARBONEX, GREENEX and BSE 500) had better returns during Covid19 period as compared to the pre-Covid19 period. However, these returns were not statistically significant. During Covid19, the risk of the indices also rose, but they provided better returns for the additional risk taken. Finally, it is concluded that the performance of high-ESG and low-ESG stock portfolios did not differ significantly in both periods.

The study is relevant to individual and institutional investors, financial advisors, portfolio managers, corporations, policymakers, market regulators and society at large.

This study emphasized the need to expand the role of ESG investment in India for the benefit of people, communities and society as a whole.

This research is the first of its kind, to the best of the authors’ knowledge, that compares the performance of a high-ESG portfolio with a low-ESG portfolio both before and during the Covid19, particularly in the Indian context.

The primary objective of the study is to discover the most prominent criteria and sub-criteria among environmental issues, social dimensions and corporate governance factors that may impact individual equity investors' investment decisions.

The present study collected data from 438 individual equity investors from the North Indian region. To achieve the objectives of the study, a fuzzy analytic hierarchy process (Fuzzy AHP) was applied. The key considerations of the study were environmental, social and governance (ESG) factors.

The governance criterion was discovered to be the most significant factor influencing individual equity investors' investment decisions among the three ESG factors, followed by environmental criteria, while social criteria were shown to be the least influential.

The present study solely looked at ESG issues as drivers of stock investors' investment decisions. In the current world, however, many other factors, including behavioral biases, accounting information, ownership structure and fundamental analysis, can have a substantial influence on investors' investment decisions.

The study's findings widen the theoretical contribution in the field of responsible investment by asserting how ESG factors influence investors' investment decisions in the equity market. From a practical standpoint, this study applies to retail and institutional investors, portfolio managers, financial advisors, market regulators, corporations and society at large.

To the best of authors knowledge, no attempt has been made to prioritize the ESG issues that impact the investment decisions of individual equity investors. Ergo, this study contributes to the existing literature on socially responsible investment.

COVID-19 has been the subject of a number of inquiries recently. All country's capital market practices have been affected by the COVID-19 outbreak. Economic woes, along with the stock market crash, have hit emerging markets and developing economies in a variety of directions.

This study is an attempt to focus on the Indian economy to provide the gist of the situation and recovery mode of an economy with the help of growth indicators of the economy.

This study is based on secondary data. The researchers applied some econometric tools, viz, unit root test Augmented Dickey-Fuller (ADF), Panel Granger Causality, and Panel ARDL Bound Test were applied to examine the relationship of economic indicators and stock market benchmark in two periods: March 2020–June 2021 (during period) and July 2021 to March 2022 (post period).

The findings of this study explored the different causal relationships for the selected variables in both periods. The study discussed the reasons for ARDL (Auto Regressive Distributed Lag) bound for all selected factors. The study revealed the story of crude oil prices and Gold as trusted investment avenues during the crises.

As we know, the capital market's backlash is reflected in movements in stock prices and stock exchange volume, which are concerned with the economic effects of the pandemic and urged the segment to react. Investors can use the information in the event to make investment decisions.

Introduction: Governance is the management of various actions to improve human capacities and boost the efficiency with which services are delivered to the general public. The study analyses the relationship between two variables: geographic location and desire to switch to an E-governance system. The study also aims to explore the relevance of artificial intelligence (AI) in supporting E-Governance.

Objectives of study: (1) To investigate the notion of e-governance and the many approaches available. (2) To research various government E-Governance initiatives and raise awareness about the difficulties and opportunities facing India’s e-government system. (3) To study the acceptance of E-governance by the public from rural and urban districts.

Methodology: This study will use a descriptive research approach as its research strategy. Primary data was collected to check for the preference for an acceptance rate of E-Governance based on geographic location (URBAN and RURAL). The current investigation is conducted on 200 respondents from Northern India’s selected urban and rural districts.

Finding and implications: The report summarises the significance of e-governing system adoption in India and offers ways to improve the operation of these systems in the future. The results of the test show that both factors are highly significant. The study recommends that future research integrate our search for scientific studies with a search for non-scientific publications, as journal and conference publications may lag behind the most recent breakthroughs in the implications of AI use in public administration.