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This study aims to examine the impact of renewable energy consumption on agricultural productivity while accounting for the effect of financial inclusion and foreign direct investment in Brazil, Russia, India, China and South Africa (BRICS) countries during 2000–2020.

The study has used the latest data from World Bank and International Monetary Fund databases. The dependent variable in the study is agricultural productivity. Renewable energy consumption, carbon emissions, financial inclusion and foreign direct investment are independent variables. Autoregressive distributed lag (ARDL) approach was used to examine the short-run and long-run impact of renewable energy consumption, carbon emissions, foreign direct investment and financial inclusion on agricultural productivity.

The findings imply that consumption of renewable energy, carbon emissions and foreign direct investment have a positive impact on agricultural productivity while financial inclusion in terms of access does not seem to have any significant impact on agricultural productivity. Providing farmers, access to financial services can be beneficial, but its usage holds more importance in impacting rural outcomes. The problem lies in the fact that there is still a gap between access and usage of financial services.

Policymakers should encourage the increase in the usage of renewable energy and become less reliant on non-renewable energy sources which will eventually help in tackling the problems associated with climate change as well as enhance agricultural productivity.

Most of the earlier studies were based on tabular analysis without any empirical base to establish the causal relationship between determinants of agricultural productivity and renewable energy consumption. These studies were also limited to a few regions. The study is one of its kind in exploring the severity of various factors that determine agricultural productivity in the context of emerging economies like BRICS while accounting for the effect of financial inclusion and foreign direct investment.

The study endeavors to explore whether the financialization of agricultural commodities, traditionally viewed as a catalyst for price volatility, has any repercussions on food security in BRICS economies.

The empirical analysis employs the examination of three agricultural commodities, namely wheat, maize and soybean. Utilizing data from the Chicago Board of Trade on futures trading for these commodities, we focus on parameters such as annual trading volume, annual open interest contracts and the ratio of annual trading volume to annual open interest contracts. The study spans the period 2000–2021, encompassing pre- and post-financial crisis analyses and specifically explores the BRICS countries namely the Brazil, Russia, India, China and South Africa. To scrutinize the connections between financialization indicators and food security measures, the analysis employs econometric techniques such as panel data regression analysis and a moderating effects model.

The results indicate that the financialization of agricultural products contributes to the heightened food price volatility and has adverse effects on food security in emerging economies. Furthermore, the study reveals that the impact of the financialization of agricultural commodities on food security was more pronounced in emerging nations after the global financial crisis of 2008 compared to the pre-crisis period.

This paper seeks to draw increased attention to the financialization of agricultural commodities by presenting empirical evidence of its potential impact on food security in BRICS economies. The findings serve as a valuable guide for policymakers, offering insights to help them safeguard the security and availability of the world’s food supply.

Very few studies have explored the effect of financialization of agricultural commodities on food security covering a sample of developing economies, with sample period from 2000 to 2021, especially at the individual agriculture commodity level. Understanding the evolving effects of financialization is further improved by comparing pre and post-financial crisis times.

Deep learning (DL) is a new and relatively unexplored field that finds immense applications in many industries, especially ones that must make detailed observations, inferences and predictions based on extensive and scattered datasets. The purpose of this paper is to answer the following questions: (1) To what extent has DL penetrated the research being done in finance? (2) What areas of financial research have applications of DL, and what quality of work has been done in the niches? (3) What areas still need to be explored and have scope for future research?

This paper employs bibliometric analysis, a potent yet simple methodology with numerous applications in literature reviews. This paper focuses on citation analysis, author impacts, relevant and vital journals, co-citation analysis, bibliometric coupling and co-occurrence analysis. The authors collected 693 articles published in 2000–2022 from journals indexed in the Scopus database. Multiple software (VOSviewer, RStudio (biblioshiny) and Excel) were employed to analyze the data.

The findings reveal significant and renowned authors' impact in the field. The analysis indicated that the application of DL in finance has been on an upward track since 2017. The authors find four broad research areas (neural networks and stock market simulations; portfolio optimization and risk management; time series analysis and forecasting; high-frequency trading) with different degrees of intertwining and emerging research topics with the application of DL in finance. This article contributes to the literature by providing a systematic overview of the DL developments, trajectories, objectives and potential future research topics in finance.

The findings of this paper act as a guide for literature review for anyone interested in doing research in the intersection of finance and DL. The article also explores multiple areas of research that have yet to be studied to a great extent and have abundant scope.

Very few studies have explored the applications of machine learning (ML), namely, DL in finance, which is a much more specialized subset of ML. The authors look at the problem from the aspect of different techniques in DL that have been used in finance. This is the first qualitative (content analysis) and quantitative (bibliometric analysis) assessment of current research on DL in finance.

The authors examine network features such as connectivity, centrality, adjacency matrices, closeness and betweenness measures through a variety of indicators. The results of the study indicate that over time there is a tendency for markets to integrate and segment due to various factors such as pandemics, financial crises, global trade relations and international investments.

This paper employs a visualized network technique to study the dynamics of integration and comovements in global equity markets of emerging economies. Daily closing prices of stock market indices of 24 countries from January 2013 to July 2020 are used to construct a minimum spanning tree network (MSTN) and graph network (GN).

The authors identify India and China as global power hubs and clusters among the emerging economies. India and Bangladesh serve as bridging countries connecting to various other clusters. Bosnia serves as a center in the European region owing to Bosnia's trade relations with neighboring countries. Although Brazil has witnessed the worst recession in the early years of the decade, Brazil has risen to be a central cluster among the Latin American countries. Finally, the authors find that African countries tend to form links with the rest of the world rather than with economies within the Africa continent.

This is the pioneering study that uses network models such as MSTN and GN supplemented with measures of centrality and connectivity to study financial market integration in emerging countries. Against this backdrop, this paper aims to work on a network visualization strategy to examine global stock market integration. The authors also try to use graphs and the spanning trees instead of the correlation models to understand the association between the markets, avoiding the downsides of the existing models. The authors' approach tries to visualize the network integration to examine the interconnectedness in the global stock market.

The study examines herding in the Indian stock and commodity futures market including agricultural, metal and energy commodities. Herding is studied under various market conditions: rising and declining, high and low volatility. The study also examines spillover effects of herding.

The study adapts the cross-sectional absolute deviation model given by Chang et al. (2000) to examine herding in Indian stock and commodity futures markets.

The results of the study indicate absence of herding among commodity futures under all market conditions except for the declining market where herding is present among energy futures. The investors investing in agricultural and energy commodities have a higher tendency to herd during high volatility days as compared to low volatility days. Further, the study of herding spillover effects indicates that the price fluctuations in metal commodities affect herding in agricultural and energy commodities.

The results can help market participants to diversify the risk by investing in agricultural, metal and energy futures along with the stocks.

Majority of the previous studies explore herding among stocks and ignore commodities especially agricultural commodities. This study attempts to fill the gap by studying herding among various commodity futures. To the best of our knowledge this is the first study to explore herding spillover effects in the Indian stock and commodity futures market.

This study aims to examine the effect of social reputation on the relationship between boards and foreign ownership on the quality of sustainability disclosure.

The sample of this study consists of publicly-traded primary and secondary sector companies in Indonesia for 12 years, from 2009 to 2020. This study uses panel model regression to generate its results. The disclosure data are hand-collected data sourced from annual financial and company sustainability reports.

Higher foreign board component companies report lower quality of sustainability disclosure, whereas companies that possess foreign ownership components report a higher quality of sustainability disclosure. This result is strengthened by obtaining consistent results tested with economic, social and environmental disclosure components. In addition, if the company has a good social reputation, it will strengthen the relationship of foreign ownership to the quality of sustainability disclosure.

These findings are relevant for policymakers, professional organizations and practitioners in Indonesia and other developing countries.

The moderating effect of social reputation on the relation of the foreign board and foreign ownership-quality of sustainability disclosure as this study does remain rare in developing countries. This study complements various research conducted in developing countries, such as Indonesia, by offering a new dimension. The results indicate that social reputation has a moderating role in determining the impact of foreign ownership on the quality of sustainability disclosure.

The emerging technologies of the Fourth Industrial Revolution are transforming various industries, including agriculture. Unaware, young male and female farmers leave the agriculture profession as they perform unsustainable practices. Precision agriculture using the Internet of Things (IoT) is a solution to sustainable agriculture. Extension professionals are at the heart of disseminating agricultural advisory agricultural services in India. The discourse on the IoT is entering the space of extension advisory services (EASs) and social sciences. Thus, the present paper seeks to review the application of IoT in Indian agriculture, its challenges and its effect on EASs. The conceptual framework is drawn from disruptive and surveillance capitalist theories.

Online literature review was conducted on electronic e-book Ebsco, Google scholar, PubMed, Jane, j gate, research4life, springer journal and Mendeley databases for full-text repositories, textbook, thesis, web articles, newspaper articles, reports, blogs for the year 1990 to May 2021 using keywords “IoT application in agriculture,” “emerging technologies in agriculture,” “challenges in IoT application,” “extension advisory services sources of information,” “big data and extension advisory, “IoT and extension advisory in India.” Only publications in the English language were included.

IoT aids progressive farmers and small farmers alike. Drones, robotics, precision irrigation, livestock tracking and crop disease surveillance are examples of IoT applications in agriculture. Only large corporations and governments access IoT, and for them, big data storage is an issue. Privacy and security concerns demand upgrades in IoT systems. Solutions to the convergence of IoT with the cloud will leverage agricultural EASs, resulting in fast computing, precise and proactive up-to-date problem solving. Hence, the need for communication between firms and clients has ceased. Thus, the jobs of extension agents are replaced.

The competence of future human extension agents lies in reskilling as a “knowledge broker” of relationships and expertise, as s/he cannot have all multidisciplinary knowledge.

Although IoT applications in agriculture are available from a technological standpoint, there remains an awareness gap regarding the impact of IoT applications in agricultural EASs. This study will aid in a better comprehension of IoT applications from current and prospective EASs.

This study aims to explore the mediating effect of the top-performing environmental, sustainability and governance (ESG) company’s corporate reputation and sustainability practices in translating corporate social responsibility (CSR) practices into competitive performance in an emerging economy like India.

The hypotheses proposed for this study were validated using partial least squares-based structural equation modeling on data obtained from the chief financial officers of the 122 top-performing ESG companies in India using a self-administered questionnaire.

According to the findings of this research, the relationship between CSR practices and the competitive performance of the top-performing ESG companies in India is partially mediated by corporate reputation and sustainability practices.

The findings of this study indicate that the corporate reputation of a company and sustainability measures accepted by an organization can improve an organization’s competitive position. Because there has been so little research done on India, this study has the potential to be regarded as a pioneering effort. It can provide a foundation for businesses operating in India to improve their competitive advantage by emphasizing their corporate reputation and sustainable business practices in addition to the CSR activities undertaken.

Small and medium-sized enterprises (SMEs) are a major source of employment and revenue growth in developing nations like India, but they also face challenges from resource shortages, shifting consumer demand and heightened competition. This research aims to discover the aspects that enhance SMEs' competitiveness and performance.

By analyzing literature and consulting experts, 10 factors that boost a firm's competitiveness were identified. The total interpretive structural modeling (TISM) method was then used to determine their interaction and structural hierarchy. Neutrosophic-MICMAC analysis was employed to assess the driving-dependence power of each factor.

The study discovered that the factor, namely “entrepreneurial orientation,” was found to be a significant one. “Manufacturing strategy” was found to be extremely dependent on the remaining competitive advantage factors.

This SME-focused framework can be adopted by large businesses to enhance organizational performance by focusing on critical factors. The study depends on experts' judgment, which might be biased. Findings will assist SMEs in identifying significant factors influencing competitive advantage and relationships, increasing awareness of factors contributing to competitive edge.

The results of the research may encourage SME sector managers and practitioners to prioritize the factors that contribute to a firm's competitive advantage.

The majority of research on SME competitive advantage focuses on individual aspects. To add to the body of knowledge on the subject, this study applies the TISM technique to Indian SMEs to identify the contextual interactions among factors that increase long-term competitiveness.

This study seeks to empirically examine the influence of corporate governance on corporate performance in Ghana.

The study employs data from 30 listed firms spanning from 2008 to 2018 and applies the generalized method of moments technique. The authors use economic value added, shareholder value added (SVA) and economic margin (EM) as measures of corporate performance.

The findings reveal that the presence of both inside directors and outside (nonexecutive) directors significantly improves corporate performance, lending credence to both the stewardship theory and the agency theory. The inclusion of women on the corporate boards and frequent meetings of the board reduce the economic profits of firms. The authors find that CEO duality impedes corporate performance, supporting the presumption of the agency theory. The study further reveals that audit committee size and ownership concentration positively drive the performance of quoted firms in Ghana.

Prior studies on corporate governance and firm performance nexus have chiefly adopted traditional accounting-based performance measures such as return on assets and return on equity to evaluate firm performance. However, these indicators are critiqued for being historic and fail to consider firms' cost of equity. In light of the shortcomings of the accounting-based proxies, this study takes a unique direction by using value-based metrics, which are considered superior measures of performance. Besides, to the best of the authors' knowledge, this study provides a first attempt to investigate the link between corporate governance and firm performance using SVA and EM as performance indicators.