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This study aims to conduct a comprehensive bibliometric analysis to determine the intellectual structure of cross-listing studies and suggests a road map for future research in this field.

A step-by-step procedure was carried out. With the help of a defined search string, 580 articles from reputed journals have been retrieved from the Scopus database. Bibliographic coupling and keyword analysis were executed to understand the current research scenario and future research directions in this research field. In addition, R Studio combined with VOSviewer was employed to analyse and visualise the data.

The results provide a deeper insight into publication trends, most prolific countries, institutions and journals in the area of cross-listing. The highest collaboration was observed between the authors in the USA and Canada. Moreover, the results contradict Bradford's and Lotka's laws. A thorough review of the literature identifies five clusters in this domain. Finally, keyword analysis offers a future road map in cross-listing research.

Researchers have shown greater interest in cross-listing topics over the past decades. Even though the research volume on this subject is increasing, the current retrospective is still insufficient. To the best of the authors' knowledge, this study is the first to provide valuable insights to practitioners, academicians, and prospective researchers about the intellectual structure of cross-listing and also offers future avenues in this research field through bibliometric analysis.

Bitcoin and Ethereum, although the most prominent cryptocurrencies, carry a high ticker price. Many investors carry an inherent bias against high price ticker securities and prefer only low prices securities. This paper aims to help market players generate adequate risk-adjusted returns by investing in only lower-priced cryptocurrencies.

The pairwise bivariate BEKK-GARCH (1,1) model is deployed to capture the short- and long-term volatility linkages between Litecoin, Stellar and Ripple from August 2015 to June 2020.

Litecoin is the most influential volatility sender in the basket of these three cryptocurrencies. The portfolio weights indicate that investors can create an optimized two asset portfolio with the lowest exposure to Stellar with Litecoin and Ripple. Market players with a long position in Ripple can have the cheapest hedge by shorting Stellar.

This study adds to the scant literature on the association between emerging cryptocurrencies and finding optimum portfolio weight and hedge ratios.

The increasing globalization and technological advancements have increased the information spillover on stock markets from various variables. However, there is a dearth of a comprehensive review of how stock market volatility is influenced by macro and firm-level factors. Therefore, this study aims to fill this gap by systematically reviewing the major factors impacting stock market volatility.

This study uses a combination of bibliometric and systematic literature review techniques. A data set of 54 articles published in quality journals from the Australian Business Deans Council (ABDC) list is gathered from the Scopus database. This data set is used to determine the leading contributors and contributions. The content analysis of these articles sheds light on the factors influencing market volatility and the potential research directions in this subject area.

The findings show that researchers in this sector are becoming more interested in studying the association of stock markets with “cryptocurrencies” and “bitcoin” during “COVID-19.” The outcomes of this study indicate that most studies found oil prices, policy uncertainty and investor sentiments have a significant impact on market volatility. However, there were mixed results on the impact of institutional flows and algorithmic trading on stock volatility, and a consensus cannot be established. This study also identifies the gaps and paves the way for future research in this subject area.

This paper fills the gap in the existing literature by comprehensively reviewing the articles on major factors impacting stock market volatility highlighting the theoretical relationship and empirical results.

The COVID-19 pandemic resulted in a dramatic downturn in the global stock markets. Investors look for safe stocks that can provide better risk-adjusted returns. Stocks with higher Environmental, Social, and Governance (ESG) scores can be good choices for investors. This study focuses on this argument by examining the relationship between ESG indicators and stock returns while considering financial and macroeconomic variables.

In this study, 39 non-financial firms listed in Nifty-50, for which data is available, have been included. Panel data from 2018 to 2021 is collected to examine this relationship in the presence of COVID-19. Additionally, the panel regression method is used.

The empirical findings indicate a positive relationship between ESG scores and stock returns. This relationship holds even when the control variables like Return on Assets (ROA), Gross Domestic Product (GDP), Return on Equity (ROE), age, size, leverage of the firm, inflation, and crisis period are used in the model.

This study contributes by examining the linkage between ESG indicators and stock return while controlling the impact of the financial and macroeconomic variables in Indian markets, which has not been undertaken so far. Moreover, this is the first study to use the ESG score data of S&P Global, which gives more weight to the material factors of a firm.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-10-2023-0819.

The present study aims to examine the households’ attitudes and intentions to adopt an indoor air purifier against the smog crisis in India by using a comprehensive theoretical framework based on the combination of the Protective Action Decision Model (PADM) and the Theory of Planned Behavior (TPB). The United Nations Sustainable Development Goals (SDGs) 2030 also emphasized ensuring a healthy and safe life, especially by achieving SDG-3, SDG-11 and SDG-13.

Using purposive sampling, the data were collected through a survey questionnaire distributed to 382 households, and study hypotheses were assessed by using partial least squares structural equation modeling employing SmartPLS.

The results revealed that mental health risk perception (MHRP) was the most influential determinant of households’ attitudes toward adopting air purifiers, followed by smog knowledge, physical health risk perception (PHRP), information seeking and product knowledge. Notably, results revealed that households’ attitude is a leading determinant of their adoption intention toward the air purifier compared to subjective norms (SN) and perceived behavioral control (PBC).

To the best of the authors’ knowledge, the present study is the first to provide new insights into an individual’s protective behavior response toward ecological hazards by examining the households’ adoption intention toward the air purifier against the smog crisis using PADM and TPB model inclusively. In addition, the present study analyzes the impact of both PHRP and MHRP on individuals’ protective behavior separately. Also, this study provides theoretical contributions and important practical implications for the government, manufacturers and air purifier sellers.

This study aims to investigate into the dynamic connection between ESG scores and the volatility term structure for Indian companies listed BSE. The study divides the BSE-100 listed companies into two panels based on their median ESG scores in 2022, creating high and low ESG scoring groups to capture volatility structure.

The study employs time-varying symmetric and asymmetric GARCH models and followed by continuous Wavelet to capture volatility structure and explore comparative resilience behavior.

The study found similar volatility patterns regardless of ESG scores, nudging doubt on the direct impact of ESG on volatility. Additionally, both high- and low-ESG-scored companies displayed high vulnerabilities during the pandemic, raising questions about the effectiveness of ESG frameworks in capturing risks. Finally, by examining the resilience behavior of ESG-scored companies during the pandemic, our study contributes to the evolving understanding of the intersection between ESG performance and crisis response.

The study carries vital implications for investors and policymakers. It highlights the urgent need to strengthen the ESG framework and scores to shield investors from short- and long-term volatilities and economic vulnerabilities.

To the best of the authors’ knowledge, this is the first study investigating the Indian market by examining the volatility structure and resilience behavior of high- and low-ESG-scored companies during the pandemic.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-02-2024-0113

The growing trend in environmental, social and governance (ESG) research, along with its relevance to the financial performance of firms, has gained a lot of attraction in academia and industry. This study aims to fill the existing gap in the literature by conducting a thorough systematic review with the latest research articles in this area.

This study adopted a blend of systematic literature review and bibliometric techniques. A proper search string was used to retrieve the data from the Scopus database. The final dataset comprises 296 documents used for science mapping, and the review was done of 60 articles finalised after further refining the documents.

The results of this study indicate that stakeholder, legitimacy and signalling theories are the foundation for ESG and financial performance. Social firms have a lower capital cost because of their low-risk potential. Moreover, this study provides the knowledge structure by framing four clusters, “CSR/ESG determinants and firm performance”, “Moderators and Mediators”, “Investors’ perception” and “CSR in the tourism sector”.

This study has reviewed the literature with both tools, that is, qualitative (systematic review) and quantitative (bibliometric). Moreover, this study presents the latest synthesis of the literature.

This study aims to examine the determinants of adoption intention toward the rooftop solar photovoltaic (RSPV) systems among residents of peri-urban villages of Gurugram, Haryana, India. This study also analyzes the impact of the adoption of RSPV systems on carbon neutrality from a behavioral perspective.

Data was collected using a self-administrated structured questionnaire from 208 male villagers (195 usable) of 22 villages using the purposive sampling technique.

Results revealed that relative advantage, followed by simplicity, trialability, observability and compatibility, positively and significantly impact villagers’ attitude toward adopting RSPV systems in their homes. Perceived severity and perceived vulnerability significantly influence the perceived behavioral control of villagers toward adopting the RSPV systems. The results show villagers’ attitudes, subjective norms and perceived behavioral control are the essential predictors of their adoption intention of the RSPV systems. Most notably, carbon neutrality was significantly affected by villagers’ adoption intention of RSPV systems as the renewable energy source in their homes.

The findings of this study provide that innovation attributes are important factors in shaping the adoption intentions of customers toward RSPV systems. This study is also the extent of previous studies measuring customers’ perception of adopting renewable energy in developed and emerging countries worldwide.

The present study examines the foremost determinants of teachers' perception, i.e. teachers' satisfaction, attitude and continuance intention towards adopting e-learning in Higher Educational Institutions (HEIs) in India during the COVID-19 pandemic.

Data were collected through online Google forms from 1,111 (1,060 considered useable) teachers of different HEIs in India using the purposive sampling technique and was analyzed by PLS-SEM (performing partial least squares-structural equation modeling).

Results of this study show that perceived usefulness (PU) followed by institutional support, perceived ease of use (PEOU), and teacher-student interaction positively and significantly impact teachers' satisfaction. Results also revealed that perceived usefulness (PU), institutional support, and satisfaction significantly affect teachers' attitude. Finally and most notably, teachers' continuance intention towards using online teaching in HEIs is most significantly influenced by teachers' satisfaction than perceived usefulness (PU), perceived ease of use (PEOU), and attitude.

The authors anticipate that this study brings a significant and valuable input to the existing literature by providing inclusive research in a more harmonizing understanding of the teachers' satisfaction, attitude, and continuance intention with online teaching-learning practices in diverse educational institutions.

Several empirical studies have proven that emerging countries are attractive destinations for Foreign Institutional Investors (FIIs) because of high expected returns, weak market efficiency and high growth that make them attractive destination for diversification of funds. But higher expected returns come coupled with high risk arising from political and economic instability. This study aims to compare the linear (symmetric) and non-linear (asymmetric) Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models in forecasting the volatility of top five major emerging countries among E7, that is, China, India, Indonesia, Brazil and Mexico.

The volatility of financial markets of five major emerging countries has been empirically investigated for a period of two decades from January 2000 to December 2019 using univariate volatility models including GARCH 1, 1, Exponential Generalized Autoregressive Conditional Heteroscedasticity (E-GARCH 1, 1) and Threshold Generalized Autoregressive Conditional Heteroscedasticity (T-GARCH-1, 1) models. Further, to examine time-varying volatility, the distinctions of structural break have been captured in view of the global financial crisis of 2008. Thus, the period under the study has been segregated into pre- and post-crisis, that is, January 2001–December 2008 and January 2009–December 2019, respectively.

The findings indicate that GARCH (1, 1) model is superior to non-linear GARCH models for forecasting volatility because the effect of leverage is insignificant. China has been considered as most volatile, whereas India is volatile but positively skewed and Indonesia is the least volatile country. The results can help investors in better international diversification of their portfolio and identifying best suitable hedging opportunities.

This study can help investors to construct a more risk-adjusted returns international portfolio. Further, it adds to the scant literature available on the inconclusive debate on the choice of linear versus non-linear models to forecast market volatility.

Earlier studies related to univariate volatility models are mostly applications of the models. Only few studies have considered the robustness while applying the models. However, none of the studies to the best of the authors’ searches have considered these models for identifying the diversification opportunity among the emerging countries. Hence, this study is able to derive diversification and hedging opportunities by applying wide ranges of the statistical applications and models, that is, descriptive, correlations and univariate volatility models. It makes the study more rigorous and unique compared to the previous literature.