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This study aims to investigate the interrelationships and elasticities between the production of renewable energy (RE) and three key variables: oil prices, gross domestic product (GDP) and carbon dioxide (CO₂) emissions.

The research uses panel data and time-series analyses for 10 developed and 16 emerging countries for the period 1976–2018, to identify panel and country-specific elasticity of RE production and dynamic causal relationships between these variables. The study uses an autoregressive distributed lag model to determine the long- and short-run dynamics between RE production and the three variables in each country.

Results show a long-run elasticity between RE and GDP, and short-run dynamics between RE and oil prices and CO₂ emissions in the developed countries. Whereas in the emerging countries category, there were long-run relationships between RE and GDP, CO₂ emissions and oil prices.

Results of this study are in fact crucial and can be applied in the drafting of resilience policies to tackle energy vulnerability as well as sustainable growth. The study results will inform and guide governments on the right policies to stimulate RE production in their own countries in the interests of both their national security and sustainable development globally.

This paper attempts to contribute to the literature in at least two ways. First, research on identifying common determining factors, including socioeconomic factors, in both emerging and advanced economies is considerably scarce. Most of the previous research in this field has focused only on the absolute value of RE production in a particular geographical area. Second, many studies have focused on RE consumption. This research differs from them by focusing on the production of RE. Thus, the main contribution of this study is to fill these gaps. The study also presents novel empirical evidence to determine RE production elasticity from 26 countries.

Over the past two decades, the study of team resilience has evolved from focusing primarily on team performance to recognizing its importance in various aspects of team functioning, including psychological health, teamwork, and overall Well-Being. This evolution underscores the need for a broader, more inclusive understanding of team resilience, advocating for a shift from a narrow performance-centric view to a holistic perspective that encompasses the multifaceted impact of resilience on teams.

In advocating for this holistic perspective, this chapter reviews the extant literature, highlighting that resilience is not merely about sustaining performance but also about fostering a supportive, adaptive, and psychologically safe environment for team members. Significant areas for further exploration, including the nuanced nature of adversities teams face, the processes underpinning resilient behaviors, and the broad spectrum of outcomes resilience can influence beyond task performance are also discussed.

The chapter serves as a call to action for a more inclusive examination of how resilience manifests and benefits teams in organizational settings. The proposed shift in perspective aims to deepen understanding of team resilience, promoting strategies for building resilient teams that thrive not only in performance but in all aspects of their functioning.

This study aims to propose a moderated mediation model to investigate the moderating effects of environmental, social and governance (ESG) performance on the relationship between inefficient investment and firm performance and the mediating effect of firms that participate in institutional research on the relationship between investment efficiency and performance. This study also analyses the heterogeneity of the corporate nature, intensity of industrial research and development (R&D), industrial competition and regional marketization.

This study uses a panel data fixed-effects model to conduct a regression analysis of 1,918 Chinese listed firms from 2016 to 2020. A Fisher’s permutation test is used to examine the differences between state-owned and nonstate-owned firms.

Inefficient investment negatively impacts corporate performance and higher ESG performance exacerbates this effect by attracting more institutional research which reveals more problems. State-owned enterprises perform significantly better than nonstate-owned enterprises in terms of ESG transformation. Industrial R&D intensity, competition and regional marketization also mitigate the negative effects of inefficient investment on corporate performance.

This study suggests that companies should consider inefficient investments that arise from agency issues in corporate ESG transformation. In addition, state-owned enterprises in ESG transformation should take the lead to achieve sustainable development more efficiently. China should balance regional marketization, encourage enterprises to increase R&D intensity, reduce industry concentration, encourage healthy competition and prevent market monopolies.

This study combines the agency and stakeholder theories to reveal how inefficient investments that arise from agency issues inhibit value creation in ESG initiatives.

Patriotism is often described as the “love of country” that individuals display in the acclamation of their national community. Despite the prominence of this sentiment in various societies around the world, organizational research on patriotism is largely absent. This omission is surprising because entrepreneurs, human resource (HR) divisions, and firms frequently embrace both patriotism and patriotic organizational practices. These procedures include (among other interventions) national symbol embracing, HR practices targeted toward military members and first responders, the adulation of patriots and celebration of patriotic events, and patriotic-oriented corporate social responsibility (CSR). Here, the authors argue that research on HR management and organization studies will likely be further enhanced with a deeper understanding of the national obligation that can spur employee productivity and loyalty. In an attempt to jumpstart the collective understanding of this phenomenon, the authors explore the antecedents of patriotic organizational practices, namely, the effects of founder orientation, employee dispersion, and firm strategy. It is suggested that HR practices such as these lead to a patriotic organizational image, which in turn impacts investor, customer, and employee responses. Notably, the effect of a patriotic organizational image on firm-related outcomes is largely contingent on how it fits with the patriotic views of other stakeholders, such as investors, customers, and employees. After outlining this model, the authors then present a thought experiment of how this model may appear in action. The authors then discuss ways the field can move forward in studying patriotism in HR management and organizational contexts by outlining several future directions that span multiple levels (i.e., micro and macro). Taken together, in this chapter, the authors introduce a conversation of something quite prevalent and largely unheeded – the patriotic organization.

This chapter discusses how the real options theory can be useful for understanding the adoption of human resources management (HRM) practices. The authors review how the real options theory has provided insights into the processes through which firms manage uncertainties involved in the adoption of HRM practices. The authors offer propositions for future HRM research from the real options perspective. The authors contend that analyzing HRM practice adoptions through the lens of real options theory can enhance our understanding of the mechanisms through which firms choose which HRM practices to adopt and how they adjust the timing, scale, and methods of investment in these practices. Specifically, the authors suggest that differences in information relevant to valuation of HRM options are the source of distinct choices of HRM options across firms. Finally, the authors propose advancing knowledge on HRM practice adoptions by using a portfolio of options approach, as well as considering factors like competitors, path dependence, and switching options.

This study aims to measure the impact of information and communication technology (ICT) infrastructure on divorce for a sample of developed countries for the period (2012-2021).

This paper uses fixed effect and generalized method of moments (GMM) estimation to delve into the impact of eight ICT infrastructure factors on divorce. The analyses are based on data collected from the World Bank Development Indicators.

The fixed effect analysis revealed that an increase in both fixed-telephone subscriptions and population covered by at least a 4G led to an increase in divorce. However, an increase in mobile cellular subscriptions and active mobile broadband subscriptions lead to a decrease in divorce. The results of the GMM estimator showed that fixed broadband internet prices, mobile broadband internet prices, the percentage of the population covered by the 4 G mobile network and fixed-telephone subscriptions had a substantial effect on divorce. While fixed broadband subscriptions, active mobile broadband subscriptions and mobile cellular basket were found to hinder divorce.

The findings of this study contribute to the existing literature by highlighting the impact of ICT on divorce in a sample of developed countries. Furthermore, it helps decision-makers and families to understand the causes of divorce related to ICT infrastructure in these societies, so they can understand this phenomenon and provide proactive policies.

This study used multiple factors to measure ICT infrastructure as well as the GMM estimator to delve into the impact of ICT on divorce.

Stress concentration factors (SCFs) are commonly used to assess the fatigue life of tubular T-joints in offshore structures. SCFs are usually estimated from parametric equations derived from experimental data and finite element analysis (FEA). However, these equations provide the SCF at the crown and saddle points of tubular T-joints only, while peak SCF might occur anywhere along the brace. Using the SCF at the crown and saddle can lead to inaccurate hotspot stress and fatigue life estimates. There are no equations available for calculating the SCF along the T-joint's brace axis under in-plane and out-of-plane bending moments.

In this work, parametric equations for estimating SCFs are developed based on the training weights and biases of an artificial neural network (ANN), as ANNs are capable of representing complex correlations. 1,250 finite element simulations for tubular T-joints with varying dimensions subjected to in-plane bending moments and out-of-plane bending moments were conducted to obtain the corresponding SCFs for training the ANN.

The ANN was subsequently used to obtain equations to calculate the SCFs based on dimensionless parameters (α, β, γ and τ). The equations can predict the SCF around the T-joint's brace axis with an error of less than 8% and a root mean square error (RMSE) of less than 0.05.

Accurate SCF estimation for determining the fatigue life of offshore structures reduces the risks associated with fatigue failure while ensuring their durability and dependability. The current study provides a systematic approach for calculating the stress distribution at the weld toe and SCF in T-joints using FEA and ANN, as ANNs are better at approximating complex phenomena than typical data fitting techniques. Having a database of parametric equations enables fast estimation of SCFs, as opposed to costly testing and time-consuming FEA.

The stress concentration factor (SCF) is commonly utilized to assess the fatigue life of a tubular T-joint in offshore structures. Parametric equations derived from experimental testing and finite element analysis (FEA) are utilized to estimate the SCF efficiently. The mathematical equations provide the SCF at the crown and saddle of tubular T-joints for various load scenarios. Offshore structures are subjected to a wide range of stresses from all directions, and the hotspot stress might occur anywhere along the brace. It is critical to incorporate stress distribution since using the single-point SCF equation can lead to inaccurate hotspot stress and fatigue life estimates. As far as we know, there are no equations available to determine the SCF around the axis of the brace.

A mathematical model based on the training weights and biases of artificial neural networks (ANNs) is presented to predict SCF. 625 FEA simulations were conducted to obtain SCF data to train the ANN.

Using real data, this ANN was used to create mathematical formulas for determining the SCF. The equations can calculate the SCF with a percentage error of less than 6%.

Engineers in practice can use the equations to compute the hotspot stress precisely and rapidly, thereby minimizing risks linked to fatigue failure of offshore structures and assuring their longevity and reliability. Our research contributes to enhancing the safety and reliability of offshore structures by facilitating more precise assessments of stress distribution.

Precisely determining the SCF for the fatigue life of offshore structures reduces the potential hazards associated with fatigue failure, thereby guaranteeing their longevity and reliability. The present study offers a systematic approach for using FEA and ANN to calculate the stress distribution along the weld toe and the SCF in T-joints since ANNs are better at approximating complex phenomena than standard data fitting techniques. Once a database of parametric equations is available, it can be used to rapidly approximate the SCF, unlike experimentation, which is costly and FEA, which is time consuming.

This study aims to examine whether energy poverty impacts gender inequality and CO₂ emissions in African countries with the biggest economies by gross domestic product (GDP) per capita from 1996 to 2020. Additionally, this study examines the existence of the gender Kuznets curve (GKC) and the environmental Kuznets curve (EKC) theories. Furthermore, it evaluates the connection between economic development and carbon emissions, on the one hand, and economic development and gender inequality on the other.

This study uses the augmented Dickey–Fuller and Phillip–Perron unit root tests to determine the degree of integration between the variables. It also uses the Pedroni and Fisher–Combine Johansen cointegration tests to assess a long-run relationship between the variables. The authors adopted the pooled mean group (PMG)-autoregressive distributed lag model and used the E-Views 12 software to run the analysis.

The empirical analysis approves the long-run correlation among the variables used in this study. Increased energy poverty and GDP increase CO₂ emissions, whereas income square hurts CO₂ emissions. These results are consistent with the EKC hypothesis, which proposes a non-linear relationship between CO₂ emissions and economic growth in the studied areas (similar to an inverted U shape). Long-term foreign direct investment (FDI) has a negative correlation with CO₂ emissions. On the contrary, energy poverty, GDPsq and FDI find a positive relationship with gender inequality, whereas GDP finds a negative association with gender inequality. The negative relationship between GDPsq and gender inequality establishes a “U”-shaped connection between income and gender inequality. Thus, it supports the hypothesis of the GKC. Therefore, this study proposes that decreasing energy poverty is vital for promoting a clean environment and mitigating gender inequality.

This study supports the hypothesis of the GKC. Therefore, this study proposes that decreasing energy poverty is vital for promoting a clean environment and mitigating gender inequality.