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Globally, the landscape of corporate carbon disclosures (CCD) is continually evolving as societal, environmental and regulatory expectations change over time. The goal of this study is to examine the challenges faced by Indian firms’ corporate carbon reporting (CCR). The literature recognized the hurdles to reaching net zero emissions and decarbonization, which are equally applicable to carbon disclosure (CD).

The scope 3 emission disclosure barriers (S3EDBs) identified from the literature were ranked, and their relationships were discovered using the “Grey-based decision-making trial and evaluation laboratory” (Grey- DEMATEL) technique.

The key findings are the S3EDBs, the most prominent barriers, their interrelationships and important insights for managers of organizations in prioritizing the action area for scope 3 CD. Eight S3EDBs were categorized in terms of cause and effect, threshold value is calculated as 0.78. “Quality, and reliability of data,” “Government policies and statutory requirement on emission disclosure” and “Traceability and managing supply chain partners” are the most prominent S3EDBs.

The results will help industry people in countries with emerging economies that have significant scope 3 carbon footprints. The managers can plan to deal with top S3EDBs as a step towards decarbonization and ultimately fighting climate change (CC).

This study is one of the first to rank these barriers to CD so that industry practitioners can prioritize their actions. The core contribution of this research is to detect the most significant S3EDBs and their interdependencies.

The new technology aspects of Industry 4.0 (I4.0), such as digital technologies including artificial intelligence (AI), block chain, big data analysis and the internet of things (IoT) as a digital cosmos, have the potential to fundamentally transform the future of business and supply chain management. By augmenting the functional components of the food supply chain (FSC), these technologies can transform it into an intelligent food supply chain (iFSC). The purpose of this study is to identify the I4.0 utilization for FSC to become an iFSC. Additionally, it suggests future research agendas to bridge the academic knowledge gaps.

This study utilizes the bibliometric analysis methodology to investigate the techno-functional components of iFSC in the context of I4.0. The study followed steps of bibliometric analysis to assess existing components’ knowledge in the area of intelligent food supply chain management. It further reviews the selected articles to explore the need for I4.0 technologies’ adoption as well as its barriers and challenges for iFSC.

This study examines the integration of emerging technologies in FSC and concludes that the main emphasis is on the adoption of blockchain and internet of things technology. To convert it into iFSC, it should be integrated with I4.0 and AI-driven FSC systems. In addition to traditional responsibilities, emerging technologies are acknowledged that are relatively uncommon but possess significant potential for implementation in FSC. This study further outlines the challenges and barriers to the adoption of new technologies and presents a comprehensive research plan or collection of topics for future investigations on the transition from FSC to iFSC. Utilizing artificial intelligence techniques to enhance performance, decision-making, risk evaluation, real-time safety, and quality analysis, and prioritizing the elimination of barriers for new technologies.

The uniqueness of this study lies in the provision of an up-to-date review of the food supply chain. In doing so, the authors have expanded the current knowledge base on the utilization of all I4.0 technologies in FSC. The review of designated publications yield a distinctive contribution by highlighting hurdles and challenges for iFSC. This information is valuable for operations managers and policymakers to consider.

The purpose of this paper is to identify and rank critical factors influencing investment decisions of venture capitalists.

To identify and prioritize factors affecting investment decisions of venture capitalists, a two-phase methodology was adopted: in the first phase, critical factors influencing venture capitalists’ investment decisions were identified using exploratory factor analysis; the second phase entailed the use of a multi-criteria decision-making technique – analytical hierarchal process (AHP) which involved assigning weights to, and prioritizing the identified criteria and sub-criteria.

Seven factors were found to significantly influence investment decisions of venture capitalists: entrepreneur’s characteristics, product or services, market characteristics, management skills, financial consideration, economic environment and institutional and regulatory environment. Findings revealed that entrepreneur’s characteristics, financial consideration and product or services were prime influencers of venture capitalists’ investment decisions.

As for limitations, first, the study considers limited number of factors influencing investment decisions of venture capitalists; there may be other influencers not considered in this study. Second, the AHP methodology assumes that the various decision-making criteria and sub-criteria are independent of each other; in real life, there may be inter-dependency among criteria. Third, the hierarchal model has been tested in the Indian venture capital industry only, and generalizability of results with respect to other industries is questionable.

The present study identifies and ranks seven factors found to significantly influence investment decisions of venture capitalists. Venture capitalists could use this list of factors as a guideline before making investment decisions, and if considering all factors is not possible, take into account the factors given top rank so that they arrive at informed and intelligent decisions.

This study is the first to identify economic factors (economic environment and institutional & regulatory environment) as influencers of venture capitalists’ investment decisions. Further, no study in the past has attempted to rank or prioritize factors influencing venture capitalists’ investment decisions; this is the first attempt of the kind.

The purpose of this paper is to identify key determinants of foreign direct investment (FDI) inflows in developing countries by using unbalanced panel data set pertaining to the years 1990-2012. This study considers 20 developing countries from the whole of South, East and South-East Asia.

Using seven explanatory variables (market size, trade openness, infrastructure, inflation, interest rate, research and development and human capital), the authors have tried to find the best fit model from the two models considered (fixed effect model and random effect model) with the help of Hausman test.

Fixed effect estimation indicates that market size, trade openness, interest rate and human capital yield significant coefficients in relation to FDI inflow for the panel of developing countries under study. The findings reveal that market size is the most significant determinant of FDI inflow.

Like any other study, this work also has some limitations. Lack of data on key determinants such as labor cost, exchange rate, corruption, natural resources, effectiveness of rule of law and political risk may be considered one such limitation. Further, controlling for variables such as exchange rate, corruption, labor cost and political risk could make significant improvements to this study.

This study has significant implications for policy makers, mangers and investors. Policy makers would be able to understand the importance of the major determinants of FDI mentioned in the paper, and take steps to formulate policies that encourage FDI. Such measures could include developing market size, making regulations more international trade friendly and investing in the nation’s human capital. Further, steps could be taken to keep interest rates and inflation rates under control as these factors have been found to influence FDI.

The sample of 20 developing nations chosen for this study has not been considered by any study earlier. This is a unique contribution to existing body of research, and highlights the originality value of this paper.

The consumption of supplementary cementitious materials (SCMs) has increased enormously in the construction industry. These SCMs are often waste materials or industrial by-products. This study aims to investigate the bond strength using reinforcing bars in Normal Strength Concrete (M20 grade) and High Strength Concrete (M40 grade), developed using SCMs and data was compared with concrete prepared with ordinary portland cement (OPC). The findings of the study will help in reducing the dependency on OPC and promote the utilization of waste materials in Construction.

In the present study, the bond behavior between the steel bars and the concrete was investigated in controlled, binary and quaternary concretes of M20 and M40 grades. Following the conventional procedures, samples were prepared and mechanical tests conducted (as per IS:2770–1 code for M20 and M40 grade concrete structures), which showed an improvement in the bond strength depending on the extent of overall calcium and silica content in these composite mixtures, and thus reflected the importance of vigilant utilization of used industrial waste in the OPC as a replacement without exceeding silica content beyond certain percentages for enhanced structural properties.

Experimental evaluation of bond behavior results showed a brittle nature for the controlled (OPC) concrete mixtures. While binary and quaternary concrete was able to resist the load-carrying capacity under large deformations and prevented the split cracking and disintegration of the concretes. Among different variations in the chemistry, for both M20 and M40 grades, the maximum bond strengths were observed for 10% Metakaolin + 10% Silica Fume + 30% Fly Ash + 50% OPC composition and this could be attributed to the fineness of the additives, better packing and enhanced calcium silicate hydrate (C-S-H).

Quaternary concrete may be a future option in place of OPC concrete. Very limited data is available related to the bond strength of quaternary concrete. Experimental analysis on quaternary concrete shows that its use in construction can reduce both construction cost and a burden on natural raw materials used to make OPC.

This study aims to explore the intricate relationship between uncertainty indicators and volatility of commodity futures, with a specific focus on agriculture and energy sectors.

The authors analyse the volatility of Indian agriculture and energy futures using the GARCH-MIDAS model, taking into account different types of uncertainty factors. The evaluation of out-sample predictive capability involves the application of out-sample R-squared test and computation of various loss functions.

The research outcomes underscore the significant impact of diverse uncertainty factors such as domestic economic policy uncertainty (EPU), global EPU (GEPU), US EPU and geopolitical risk (GPR) on long-run volatility of Indian energy and agriculture (agri) futures. Additionally, the study demonstrates that GPR exhibits superior predictive capability for crude oil futures volatility, while domestic EPU stands out as an effective predictor for agri futures, particularly castor seed and guar gum.

The study offers practical implications for market participants and policymakers to adopt a comprehensive perspective, incorporating diverse uncertainty factors, for informed decision-making and effective risk management in commodity markets.

The research makes an inaugural attempt to examine the impact of domestic and global uncertainty indicators on modelling and predicting volatility in energy and agri futures. The distinctive feature of considering an emerging market also adds a novel dimension to the research landscape.

This paper aims to investigate the effect of economic policy uncertainty (EPU) shocks on Indian equity market sectors. The effect of domestic (Indian) and foreign (USA) EPU shocks is examined on ten major Bombay Stock Exchange sectors.

The study uses data covering the period from September 2005 to July 2023 and uses the methodology of quantile regression to investigate the heterogenous response of stock market sectors under diverse market conditions explained through the analysis of conditional quantiles distribution.

The results demonstrate that domestic and foreign EPU shocks negatively affect most of the sectors in bearish market conditions. Industrials, commodities, utilities, consumer discretionary and financial services are the most affected sectors by domestic EPU. However, the information technology sector is found to be immune to domestic EPU shocks but negatively affected by foreign EPU shocks. On the other hand, energy, financial services and fast-moving consumer goods sectors are found to be immune to foreign EPU shocks but are negatively affected by domestic EPU shocks.

Understanding the heterogeneous response of different sectors to EPU shocks could help investors and portfolio managers identify portfolio diversification opportunities.

This study makes an inaugural attempt to examine the responses of Indian stock market sectors to domestic and foreign EPU shocks using the approach of quantile regression and unveils the previously unexamined diverse reactions of Indian stock market sectors to EPU shocks originating from both India and USA.

This study examined Indian institutional investors' holding data to understand their investment strategy (Portfolio Concentration/Diversification) and explored whether their skills were associated with their portfolio strategy and performance. The study introduced a new proxy to identify skilled investors by forecasting abnormal returns. Moreover, the study also highlighted where skilled Indian investors put their money for long-term investment.

This study measures portfolio concentration based on the number of holdings, the Hirschman–Herfindahl index (HHI) and benchmarks adjusted industry concentration. The study introduced a new proxy to identify skilled investors. We measured Investors' performance with the help of Carhart's four factors model and examined the relationship between variables through various regression models.

The study concluded a negative relationship between portfolio concentration and performance. However, skilled Indian investors get rewards from portfolio concentration decisions. It was found that skilled investors with few stocks and an industry concentration in their portfolio show a positive association between concentration and fund performance. Additionally, this study found Indian investors showing their faith in the financial sector for long-term investment.

This study examined Indian institutional investors' portfolio concentration strategy and introduced a new proxy to measure investors' skills.

This paper aims to examine the hedge, diversifier and safe haven properties of the global listed infrastructure sector and subsector indices against two traditional asset classes, stocks and bonds, and four alternative asset classes, including commodities, real estate, private equity and hedge funds during extreme negative stock market movements.

Using dynamic conditional correlation and quantile regression, the authors analyze a data set of 12 indices comprising listed infrastructure and traditional asset classes from 2010 to 2019.

Overall, the findings indicate that listed infrastructure acts as an effective diversifier but not as a strong safe haven or hedge when considered in a multiasset context. With minor exceptions, listed infrastructure cannot be concluded as a safe haven against other asset classes under investigation.

The present study has implications for institutional investors looking to incorporate infrastructure in their multiasset portfolios for increased portfolio diversification benefits.

Despite the increased influence of infrastructure as an asset class, to the best of the authors’ knowledge, this is the first study to investigate the hedge, safe haven and diversifying properties of infrastructure in a multi-asset context.

Creditor rights reduce or increase agency problems in corporations, affecting financial decisions. This study examines the impact of India’s Insolvency and Bankruptcy Code of 2016 (hereinafter referred to as the Code) on firms’ investment and investment sensitivity based on their health, considering this Code as more creditor-friendly than pre-existing bankruptcy laws.

This study uses a quasi-natural experiment that employs the difference-in-differences (DID) and propensity score matching difference-in-differences (PSM DiD) approach by considering the Code as treatment and categorizing the firms into distressed (treated group) and healthy (control group) firms. For the purpose of analysis, a fixed-effect regression model is used.

This study finds that distressed firms reduce their investment after the Code, but healthy firms do not observe any change. It shows that the reduction in investment of the distressed group is significantly greater than that of the healthy group due to agency conflict and the liquidation bias hypothesis. However, the reduction in investment is not followed by the change in investment sensitivity.

This study adds to the existing studies on the impact of the Insolvency and Bankruptcy Code, 2016 on investment. No study explores the relationship between this Code and investment based on the financial health of the firms. Also, none of the studies explores the impact of the Code on investment sensitivity. The results show that this Code has provided stronger protection to the creditors, which hurts the internal stakeholders’ interests. The study has implications for policymakers and academicians.