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This study aims to investigate the association between corporate governance and financial transparency, using the moderating role of an Egyptian currency devaluation decision as a policy shock.

Data was collected for a sample of companies listed on the Egyptian stock exchange from 2014 to 2019. To control for time-invariant unobserved heterogeneity, the authors analyse panel data using an estimated generalised least squares regression model.

The findings underline the pitfalls of assuming that corporate governance mechanisms are effective regardless of circumstances and support the complementary roles of a number of theories in interpreting the empirical findings.

This study is limited to non-financial companies and includes only corporate board and audit committee governance mechanisms. The study results have important implications for policymakers, international lending institutions, investors and accounting standards setters. It is of particular importance to policymakers in other less-developed countries with similar economic conditions.

To the best of the authors’ knowledge, this study is the first empirical attempt to provide evidence of the impact of a currency devaluation shock on the relationship between corporate governance and financial transparency within the Egyptian context as an example of a transitional economy. Hence, it provides a significant theoretical and empirical contribution to the literature.

This paper aims to find the best tools to influence the improvement of sustainability in food supply chains (FSCs) by conducting a systematic review of articles. The reader will learn how the different industry 4.0 tools (I4.0T) benefit the FSC and the limitations of each tool.

A review of 436 articles published during the period 2019 to 2022 referenced in the Scopus and Web of Science databases was performed. The review was limited to articles published in English and directly related to Industry 4.0, circular economy and sustainability in the food supply chain.

The results show different contributions of I4.0, with some being more influential than others in improving sustainability in FSCs; for example, Internet of Things and Blockchain have been shown to contribute more toward transparency, traceability, process optimization and waste reduction.

The paper's contribution consisted of ranking according to their importance and the I4.0T that affect sustainability in FSCs by classifying the aspects of each tool and the sustainability factors through a categorization by the Analysis Hierarchy Process.

This paper aims to investigate how organization capital influences different forms of corporate risk. It also explores how the relationship between organization capital and risks varies in the cross-section of firms.

To test the hypothesis, this study employs the ordinary least squares (OLS) regression model using a large sample of the United States (US) data over the 1981–2019 period. It also uses an instrumental variable approach and an errors-in-variables panel regression approach to mitigate endogeneity problems.

The empirical results show that organization capital is positively related to both idiosyncratic risk and total risk but negatively related to systematic risk. The cross-sectional analysis shows that the positive relationship between organization capital and idiosyncratic risk is significantly more pronounced for the subsample of firms with high information asymmetry and human capital. Moreover, the negative relationship between organization capital and systematic risk is significantly more pronounced for firms with greater efficiency and firms facing higher industry- and economy-wide risks.

The findings have important implications for investors and policymakers. For example, since organization capital increases idiosyncratic risk and total risk but reduces systematic risk, investors should take organization capital into account in portfolio formation and risk management. Moreover, the findings lend support to the argument on the recognition of intangible assets in financial statements. In particular, the study suggests that standard-setting bodies should consider corporate reporting frameworks to incorporate the disclosure of intangible assets into financial statements, particularly given the recent surge of corporate intangible assets and their critical impact on corporate risks.

To the best of the authors' knowledge, this is the first study to adopt a large sample to provide systematic evidence on the relationship between organization capital and a wide range of risks at the firm level. The authors show that the effect of organization capital on firm risks differs remarkably depending on the kind of firm risk a particular risk measure captures. This study thus makes an original contribution to resolving competing views on the effect of organization capital on firm risks.

This paper aims to examine the comovement among green bonds, energy commodities and stock market to determine the advantages of adding green bonds to a diversified portfolio.

Generic 1 Natural Gas and Energy Select SPDR Fund are used as proxies to measure energy commodities, bonds index of S&P Dow Jones and Bloomberg Barclays MSCI are used to represent green bonds and the New York Stock Exchange is considered to measure the stock market. Granger causality test, wavelet analysis and network analysis are applied to daily price for the select markets from August 26, 2014, to March 30, 2021.

Results from the Granger causality test indicate no causality between any pair of variables, while cross wavelet transform and wavelet coherence analysis confirm strong coherence at a high scale during the pandemic, validating comovement among the three asset classes. In addition, network analysis further corroborates this connectedness, implying a strong association of the stock market with the energy commodity market.

This study offers new evidence of the temporal association among the US stock market, energy commodities and green bonds during the COVID-19 crisis. It presents a novel approach that measures and evaluates comovement among the constituent series, simultaneously using both wavelet and network analysis.

The coronavirus disease 2019 (COVID-19) pandemic has become a global humanitarian challenge. This scourge has impacted people from all walks of life as well as every economic sector and activity, from travel to automotives, hotels to banking, and supply chain to retail. The pandemic has affected not only physical and mental health but also financial health. Studies have examined the pandemic's economic impact, but very few have examined its impact on personal finances. Efforts to contain the pandemic's spread, such as lockdowns, have resulted in suspended business operations throughout the world that have intensified joblessness. To prepare and protect people from such unforeseen situations, financial education and planning are necessary. We attempt to expand the evidence on this issue by applying a structural equation modelling approach to identify the mediating role of financial literacy programs in preparing and protecting household wealth against sudden worldwide setbacks. The research design is descriptive and exploratory using snowball sampling technique. The data was collected through an internet survey. In total, 400 survey responses were obtained. After testing the measurement model for key validity dimensions, the hypothesised causal relationships are examined in several path models. The results indicated that coronavirus awareness exerts a direct or indirect influence on the financial health of individuals through financial literacy. We conclude that financial literacy has a full mediating effect on the personal finance of individuals during the COVID-19 pandemic. The findings not only contributed to the need and understanding of financial literacy but also have managerial implications. Financial literacy programs provide investment advice and suggestions which are actionable and also work to help individuals to come out stronger in terms of knowledge and skill set when the COVID-19 crisis passes.

The purpose of this study is to understand the profit implications of analytics-driven centralized discriminatory pricing at the headquarter level compared with sales force price delegation in the purchase of an aftermarket good through an indirect retail channel with symmetric information.

Using individual-level loan application and approval data from a North American financial institution and segment-level customer risk as the price discrimination criterion for the firm, the authors develop a three-stage model that accounts for the salesperson’s price decision within the limits of the latitude provided by the firm; the firm’s decision to approve or not approve a sales application; and the customer’s decision to accept or reject a sales offer conditional on the firm’s approval. Next, the authors compare the profitability of this sales force price delegation model to that of a segment-level centralized pricing model where agent incentives and consumer prices are simultaneously optimized using a quasi-Newton nonlinear optimization algorithm (i.e. Broyden–Fletcher–Goldfarb–Shanno algorithm).

The results suggest that implementation of analytics-driven centralized discriminatory pricing and optimal sales force incentives leads to double-digit lifts in firm profits. Moreover, the authors find that the high-risk customer segment is less price-sensitive and firms, upon leveraging this segment’s willingness to pay, not only improve their bottom-line but also allow these marginalized customers with traditionally low approval rates access to loans. This points out the important customer welfare implications of the findings.

Substantively, to the best of the authors’ knowledge, this paper is the first to empirically investigate the profitability of analytics-driven segment-level (i.e. discriminatory) centralized pricing compared with sales force price delegation in indirect retail channels (i.e. where agents are external to the firm and have access to competitor products), taking into account the decisions of the three key stakeholders of the process, namely, the consumer, the salesperson and the firm and simultaneously optimizing sales commission and centralized consumer price.

The study aims to investigate the influence of corporate governance practices on enhancing firm value in manufacturing industries in Bangladesh.

The study sample consists of 131 companies from 10 manufacturing industries listed in Dhaka stock exchange (DSE). Using the multiple regression method, the study analyzed 1,193 firm-year observations from 2012 to 2021.

The outcome reveals that managerial ownership, foreign ownership, ownership concentration, board size, board independence, board diligence and auditor quality have a significant positive influence on firm value. In contrast, audit committee size has no significant influence on firm value.

The practical implications of the current study demonstrated that good corporate governance creates value and must be invigorated for the interest of all stakeholders. Policymakers should formulate specific guidelines regarding firms' ownership structure and audit quality issues.

This article aims to investigate the financial constraints and nonlinearity of farm size growth.

Farm size growth is measured with land, labor and output using data from the Farm Accountancy Data Network (FADN) for Hungary and Slovenia. A dynamic panel model is applied to assess financial constraints and nonlinearity of farm size growth.

Results show that, except for land in Slovenia and output in Hungary, liquidity constraints are less important for farm size growth than endogenous factors based on farm size growth expectations and steady farm size restructuring. Smaller farms are growing faster than larger ones. The hypothesis that a higher level of subsidies would increase farm size is not supported for Hungary. When farms reach a certain size, the land area of the largest farms increases. Farm debts in Hungary are linked with land growth and in Slovenia with output growth.

Further research on the impact of liquidity constraints and subsidies can be conducted at a disaggregate farm-type level to examine whether there is variability in the underlying interlinkages at the farm-type specialization level.

The implication that farm size growth is dependent on initial size and that smaller farms are growing faster than bigger ones indicates that it is not necessary to favor the fastest growing smaller farms thus supports the application of a non-discriminatory farm size policy for observing farm size structural changes.

The dynamic panel econometric model that incorporates cash flow as a measure of financial constraints provides insight into farm size growth in cross-country comparison in relation to potential farm liquidity constraints, farm debt and the nonlinearity of farm size, which information is of relevance to policy makers and practitioners.

In this study, the authors examine artificial knowledge as a fundamental stream of knowledge management for sustainable and resilient business models in supply chain management (SCM). The study aims to provide a comprehensive overview of artificial knowledge and digitalization as key enablers of the improvement of SCM accountability and sustainable performance towards the UN 2030 Agenda.

Using the SCOPUS database and Google Scholar, the authors analyzed 135 English-language publications from 1990 to 2022 to chart the pattern of knowledge production and dissemination in the literature. The data were collected, reviewed and peer-reviewed before conducting bibliometric analysis and a systematic literature review to support future research agenda.

The results highlight that artificial knowledge and digitalization are linked to the UN 2030 Agenda. The analysis further identifies the main issues in achieving sustainable and resilient SCM business models. Based on the results, the authors develop a conceptual framework for artificial knowledge and digitalization in SCM to increase accountability and sustainable performance, especially in times of sudden crises when business resilience is imperative.

The study results add to the extant literature by examining artificial knowledge and digitalization from the resilience theory perspective. The authors suggest that different strategic perspectives significantly promote resilience for SCM digitization and sustainable development. Notably, fostering diverse peer exchange relationships can help stimulate peer knowledge and act as a palliative mechanism that builds digital knowledge to strengthen and drive future possibilities.

This research offers valuable guidance to supply chain practitioners, managers and policymakers in re-thinking, re-formulating and re-shaping organizational processes to meet the UN 2030 Agenda, mainly by introducing artificial knowledge in digital transformation training and education programs. In doing so, firms should focus not simply on digital transformation but also on cultural transformation to enhance SCM accountability and sustainable performance in resilient business models.

This study is, to the authors' best knowledge, among the first to conceptualize artificial knowledge and digitalization issues in SCM. It further integrates resilience theory with institutional theory, legitimacy theory and stakeholder theory as the theoretical foundations of artificial knowledge in SCM, based on firms' responsibility to fulfill the sustainable development goals under the UN's 2030 Agenda.

This study analyzes the static and dynamic risk spillover between US/Chinese stock markets, cryptocurrencies and gold using daily data from August 24, 2018, to January 29, 2021. This study provides practical policy implications for investors and portfolio managers.

The authors use the Diebold and Yilmaz (2012) spillover indices based on the forecast error variance decomposition from vector autoregression framework. This approach allows the authors to examine both return and volatility spillover before and after the COVID-19 pandemic crisis. First, the authors used a static analysis to calculate the return and volatility spillover indices. Second, the authors make a dynamic analysis based on the 30-day moving window spillover index estimation.

Generally, results show evidence of significant spillovers between markets, particularly during the COVID-19 pandemic. In addition, cryptocurrencies and gold markets are net receivers of risk. This study provides also practical policy implications for investors and portfolio managers. The reached findings suggest that the mix of Bitcoin (or Ethereum), gold and equities could offer diversification opportunities for US and Chinese investors. Gold, Bitcoin and Ethereum can be considered as safe havens or as hedging instruments during the COVID-19 crisis. In contrast, Stablecoins (Tether and TrueUSD) do not offer hedging opportunities for US and Chinese investors.

The paper's empirical contribution lies in examining both return and volatility spillover between the US and Chinese stock market indices, gold and cryptocurrencies before and after the COVID-19 pandemic crisis. This contribution goes a long way in helping investors to identify optimal diversification and hedging strategies during a crisis.