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This research looks at the link between corporate social responsibility (CSR) and environmental performance, considering the immediate mutual interaction and the potential mediation of specific variables like green innovation and green human resource management (GHRM).

Partial least squares path modeling was used to investigate a sample of 460 respondents in multinational textile manufacturing companies in Ethiopia.

The findings of this study reveal a direct and positive relationship between CSR and environmental performance. In addition, the researchers observed an indirect effect on the relationship by using GHRM and green innovation as mediators.

The study applied a cross-sectional methodology, and experts are not sure that CSR, GHRM, and green innovation in Textile manufacturing companies provide the same results over time. Consequently, future researchers can utilize the same method of investigation to see if outcomes change or stay the same over time. Second the study was conducted in Ethiopia. As a resut, it is possible that our study results will not be generalizable to other emerging nations. We propose expanding research to include more nations with developing markets.

Executives of textile manufacturing companies can adopt the present study framework of performance in developing economies to reduce waste, pollution and air emissions, and conserve water, energy and nonrenewable resources that enhance environmental performance.

The discovery of the present research makes significant contributions to the literature on the impact of CSR on environmental performance as a pioneering study by incorporating CSR, GHRM, green innovation and environmental performance under one research model in an emerging economy context.

The purpose of this study is to provide evidence that government financing behavior has an impact on the outward foreign direct investment (OFDI) of enterprises.

This paper uses debt data from local government financing vehicles to measure the local government debt in China. Based on the data of listed manufacturing firms in China from 2010 to 2018, this paper uses the Tobit model to verify the impact of local government debt and firms' OFDI.

The results indicate that local government debt impedes firms' OFDI, with a more pronounced impact on state-owned enterprises (SOEs) and those with higher political connections. Furthermore, our study suggests that the dampening effect of local governments on firms' OFDI is mitigated in regions following the implementation of the Local Government Debt Management Act.

This study verifies the negative impact of local government debt activity on firms' overseas investments. This is not due to debt crowding out, but rather to the fact that local governments prefer to keep resources locally to stimulate the economy. This paper offers novel insights into the theoretical mechanisms by which local government behavior influences firms' investment activities in emerging markets.

This study aims to investigate the role of interfirm communication on the complementary effect between corporate political action (CPA) and research and development (R&D) investments under transaction costs.

A quasi-experiment study design was used, combining business game simulation and a public goods experiment with communication as a controlled variable. A fixed-effect regression analysis was performed on panel data collected from 72 students.

The findings indicate that collective CPA is positively and significantly associated with R&D investments, particularly when interfirm communication is present. Conversely, for non-communicating firms, the effect is limited to the relationship between individual CPA and R&D investment.

Despite advancing the research on political ties and innovation, the study acknowledges limitations related to framing effects and institutional variability.

The complementary effects indicate that institutional arrangements (i.e. business associations) may foster interfirm communication and cooperation in CPA efforts, mitigating opportunistic behaviours and legitimising CPA strategy towards innovation.

This study contributes to understanding how firms’ political and R&D decisions can positively impact innovation, despite the challenges of ex post transaction costs.

The positive effect of communication on political action and cooperation on firms’ innovation, even in weak institutional environments, is highlighted. An innovative methodological approach combining business games and economic experiments was used to examine participants’ decisions under transaction costs.

How does entrepreneurship flourish amidst persistent resource scarcity, market imperfections, underdeveloped infrastructure and institutional voids? In recent years, bricolage has emerged in the entrepreneurship literature as an effective form of resource mobilization in resource-constrained environments and crisis situations. The purpose of this study is to investigate the role of bricolage in new venture creation by examining the use of bricolage at each stage of the entrepreneurial process of opportunity discovery, development and exploitation.

The author conducted a qualitative analysis of 10 new business ventures established in the Philippines during the COVID-19 pandemic.

The author found a prevalence of bricolage at every stage of the entrepreneurial process in all cases, showing that bricolage was embedded in the behavior and decision-making of entrepreneurs throughout the process.

The finding have implications for policymakers aiming to support entrepreneurship in emerging economies.

This paper contributes to the literature by providing empirical evidence of bricolage behavior identified at every step of the entrepreneurial process in a specific emerging economy context.

Financial literacy, capability and behavior are crucial factors in personal financial management, which in turn plays a significant role in individual and societal financial well-being. The objective of this investigation is to explain critical factors and dimensions of personal financial management systems by employing a hybrid approach that encompasses a bibliometric analysis and a systematic review of the literature.

The research team carefully evaluated a selection of 606 scholarly articles from the Scopus database and studied the evolution of personal financial management behavior over 38 years (1986–2023). This research adopted several graphical representations and network structures to comprehend publishing tendencies, high-impact papers, theoretical frameworks, intellectual constructs as well as the current state of research collaboration.

Four major clusters were identified in the field of personal financial management behavior: the relationship between financial literacy and financial capability, factors influencing financial behavior, the impact of financial behavior on financial well-being and the financial behavior of different demographic groups. In addition, by performing content analysis on papers published within the last five years, new themes in personal financial management behavior were identified.

This investigation serves to equip financial advisors, policy architects and scholarly investigators with a deeper insight into the intricacies of personal financial management behavior and aids in pinpointing prospective domains for forthcoming research.

This study seeks to address a significant vacuum in the current body of research by providing a thorough bibliometric analysis that specifically examines financial literacy, ability and conduct. To the best of our knowledge, no previous research has conducted such a comprehensive investigation in this field. This research aims to identify important researchers and influential works in the subject by using a mixed-methods approach that combines qualitative and quantitative methodologies, including content analysis. The purpose of doing this is to provide exclusive insights and expertise that can be highly valuable to scholars, practitioners, policymakers and other stakeholders who are interested in furthering the comprehension and encouragement of financial literacy and responsible financial behavior.

We aim to clarify the effect of economic uncertainty on Chinese stock market fluctuations. We extend the understanding of the asymmetric connectedness between economic uncertainty and stock market risk and provide different characteristics of spillovers from economic uncertainty to both upside and downside risk. Furthermore, we aim to provide the different impact patterns of stock market volatility following several exogenous shocks.

We construct a Chinese economic uncertainty index using a Factor-Augmented Variable Auto-Regressive Stochastic Volatility (FAVAR-SV) model for high-dimensional data. We then examine the asymmetric impact of realized volatility and economic uncertainty on the long-term volatility components of the stock market through the asymmetric Generalized Autoregressive Conditional Heteroskedasticity-Mixed Data Sampling (GARCH-MIDAS) model.

Negative news, including negative return-related volatility and higher economic uncertainty, has a greater impact on the long-term volatility components than positive news. During the financial crisis of 2008, economic uncertainty and realized volatility had a significant impact on long-term volatility components but did not constitute long-term volatility components during the 2015 A-share stock market crash and the 2020 COVID-19 pandemic. The two-factor asymmetric GARCH-MIDAS model outperformed the other two models in terms of explanatory power, fitting ability and out-of-sample forecasting ability for the long-term volatility component.

Many GARCH series models can also combine the GARCH series model with the MIDAS method, including but not limited to Exponential GARCH (EGARCH) and Threshold GARCH (TGARCH). These diverse models may exhibit distinct reactions to economic uncertainty. Consequently, further research should be undertaken to juxtapose alternative models for assessing the stock market response.

Our conclusions have important implications for stakeholders, including policymakers, market regulators and investors, to promote market stability. Understanding the asymmetric shock arising from economic uncertainty on volatility enables market participants to assess the potential repercussions of negative news, engage in timely and effective volatility prediction, implement risk management strategies and offer a reference for financial regulators to preemptively address and mitigate systemic financial risks.

First, in the face of domestic and international uncertainties and challenges, policymakers must increase communication with the market and improve policy transparency to effectively guide market expectations. Second, stock market authorities should improve the basic regulatory system of the capital market and optimize investor structure. Third, investors should gradually shift to long-term value investment concepts and jointly promote market stability.

This study offers a novel perspective on incorporating a Chinese economic uncertainty index constructed by a high-dimensional FAVAR-SV model into the asymmetric GARCH-MIDAS model.

This work aims to empirically investigate the effects of financial development on crop productivity of India.

Time series data such as crop production index, International Monetary Fund’s (IMF) financial development index, gross domestic product (GDP) per capita, arable land, rural population, trade openness and physical capital from 1980 to 2020 was used. The autoregressive distributed lag (ARDL) bounds testing approach of cointegration was used to determine the long-run equilibrium relationship between the selected time series. Also, ARDL long- and short-run coefficients were estimated to examine the effects of selected variables on crop productivity. Furthermore, to establish the robustness of results, long-run estimators such as fully modified least squares and the dynamic least squares were also used. Finally, using the vector error-correction model, causality between the selected time series was examined.

The ARDL cointegration test confirmed the existence of long-run equilibrium relationship among agricultural productivity, financial development, capital formation, GDP per capita, arable land, rural population and trade openness. The estimated long-run elasticities from all the three techniques and the short-run elasticities of ARDL have consistently suggested that the elasticity of financial development is higher (1.55% and 1.40%, respectively) in explaining the crop productivity of India. The short-run causality estimates indicated the presence of positive bidirectional causality between crop productivity and financial development and seven positive unidirectional causal relationships between the selected variables.

Agricultural credit being an important non-land input and essential for overall growth and sustenance of agricultural sector, the policymakers should ensure the overall development of its financial sector which will reduce the intermediation, informational and other transactional costs associated with agricultural credit. This will possibly result in timely availability and access to adequate and low-cost credit from institutional sources.

Though extensive research is available on the effects of financial development on economic growth, limited research is available concerning the impact of financial development on crop productivity, especially for an emerging economy like India. For India, predominantly studies have investigated the impact of farm credit on crop productivity but have not exclusively examined the effects of financial development on agricultural productivity. Therefore, this study not only adds to the empirical literature but also provides new evidence on the nexus between financial development and crop productivity by examining the effects of financial development on crop productivity using the composite financial development index developed by the IMF using the ARDL bounds test for cointegration and other econometric estimators.

To ensure the reliable and safe operation of elevated-temperature pipes and equipment in the long term, it is essential to thoroughly assess the creep rupture life. Nevertheless, there is currently no design code that specifies a creep rupture life evaluation method for non-nuclear elevated-temperature equipment. The paper aims to discuss this issue.

An analysis was conducted to compare the differences and conservativeness in calculating creep strain using three major codes (ASME-CC-2843, API-579 and BS-7910) based on the results of the 316H creep constitutive model and creep strain prediction. In addition, the creep resistances of 316H, 304H and 347H were compared. Subsequently, the ANSYS Usercreep subroutine was developed to compare the discrepancies between different codes under multiaxial stress conditions using numerical simulations.

BS-7910 employs the Norton creep model with calculation parameters for the average creep strain rate, which is not applicable for the engineering design stage. ASME-CC2843 code primarily focuses on the primary and secondary creep stages, making it more suitable for non-nuclear pipeline and equipment design. For 316H, the creep strain curves predicted by ASME-CC2843 and API-579 typically intersect at a specific point. By combining the creep strain predicted by ASME-CC2843 and API-579, 347H exhibits superior predicted creep resistance compared to 316H, whereas 316H exhibited better predicted creep resistance than 304H.

This study provides a guide for future evaluation methods and material choices for non-nuclear equipment and pipelines operating at elevated temperatures.

Aluminum foam-filled thin-walled unit structures have received much attention for their excellent energy absorption properties. To improve the energy absorption effect of car energy absorption box under axial compression, this paper optimizes the fiber lay-up sequence, fiber angle and aluminum foam density of aluminum foam filled carbon fiber reinforced plastic (CFRP) thin-walled square tubes.

Design of sample points required to construct the proxy model using design of experiments (DOE) method, and the data sample points of different models are obtained through Abaqus simulation and test. A double high-precision proxy model with the maximum specific energy absorption (SEA) and the minimum initial peak crash force (PCF) as the evaluation index is constructed based on the response surface function method. The NSGA-II multi-objective genetic algorithm was used to optimize the design parameters and obtain the optimal solution for the Pareto front, and the results were verified by using the multi-objective optimization toolbox in design-expert.

The results show that the optimal solution to the multi-objective optimization problem with the inclusion of the lay-up sequence is ρ = 0.5g/cm3 for a fiber lay-up angle varying in the range ±15–90° and an aluminum foam density varying in the range 0.2g/cm3-0.5g/cm3, with a lay-up method of [±87°/±16°/±15°/±89°]. The two optimization methods correspond to SEA and PCF errors of 2.109% and 4.1828%, respectively. The optimized SEA value is 18.2 J/g and the PCF value is 18,230 N. The optimized design reduces the peak impact force and increases the specific energy absorption, which improves the energy absorption effect of thin-walled energy-absorbing boxes for automobiles.

In this paper, the impact resistance of CFRP thin-walled square tubes filled with aluminum foam is optimized. Based on numerical simulations and experiments to obtain the sample point data for constructing the dual-agent model, we investigate the effect of filling with different densities of aluminum foam under the simultaneous change of fiber lay-up angle and order on its mechanical properties in this process, and carry out the multi-objective optimization design with NSGA-II algorithm.

This study aims to explore the pandemic's opportunities for enhancing the environmental practices of the food and beverages green supply chains and its effect on the supply chains' viability by exploring the relationship between fear and uncertainty of COVID-19, food and beverages green supply chain management (F&B-GSCM) and supply chains’ viability based on the two dimensions (robustness and resilience) and examine the moderating effect of innovative technology adoption like big data analysis (BDA) capabilities and blockchain technologies (BCT) on this relationship.

This study adopted partial least squares structural equation modeling (PLS-SEM) on a sample of 362 F&B small and medium enterprises (SMEs)’ managers in the Egyptian market for data analysis and hypothesis testing.

The empirical results show that the fear and uncertainty of the pandemic have a significant positive effect on green supply chain management (GSCM). Also, BDA moderates the relationship between fear and uncertainty of COVID-19 and GSCM. However, BCT do not moderate that relationship. Similarly, GSCM positively affects supply chain viability dimensions (robustness and resilience). In addition, F&B-GSCM significantly mediates the relationship between fear and uncertainty of COVID-19 and supply chain viability dimensions (robustness and resilience).

Food and beverages (F&B) managers could develop a consistent strategy for applying BCT and BDA to provide clear information and focus on their procedures to meet their stakeholders' needs during COVID-19. Governments and managers should develop a consistent strategy to apply food and beverages supply chains (F&B SCs)' green practices to achieve F&B SCs' resilience and robustness, especially during the pandemic.

The Egyptian F&B SCs have been linked directly with many European countries as a main source of many basic food and agriculture products, which have been affected lately by the pandemic. Based on the “social-cognitive,” “stakeholder” and “resource-based view” theories, this study sheds light on the optimistic side of the COVID-19 pandemic, as it also brings the concepts of F&B-GSCM, SC resilience, SC robustness and innovative technologies back into the light, which helps in solving F&B SC issues and helps to achieve their viability.