Unlocking green innovation and environmental performance: the mediated moderation of green absorptive capacity and green innovation climate.

This study extends the foundations of the natural resource-based view (NRBV) by introducing a mediation-moderation framework by analyzing the influence of green intellectual capital (GIC) on both green innovation performance (GIP) and environmental performance (EP) while simultaneously considering the mediating role of green absorptive capacity (GAC) and the moderating influence of the green innovation climate (GICL). The data for this study was gathered from a sample of 575 participants employed within small and medium enterprises' (SMEs') manufacturing firms. This dataset was utilized to evaluate the proposed model; this study uses the PLS-SEM approach to comprehensively examine the complex interactions among these variables. This model adds to the theoretical understanding of NRBV and enhances its practical applicability. The findings of this study reveal a positive relationship between GIC, GAC, GIP, and EP within organizations. Furthermore, our investigation reveals a positive correlation between a GICL and the relationships involving GIC, GAC, GIP, and EP. Importantly, this research introduces a novel perspective by clarifying the complex relations among these variables and highlighting the positive correlation between a GICL and the relationships involving GIC, GAC, GIP, and EP. This novel approach enhances the theoretical understanding of NRBV and its practical applicability in fostering GIP and EP within manufacturing SMEs operating in Pakistan.

Artificial neural network (ANN)-based estimation of the influence of COVID-19 pandemic on dynamic and emerging financial markets.

The COVID-19 pandemic is a serious global issue destroying financial markets awfully. The proper estimation effect of COVID-19 pandemic on dynamic emerging financial markets is a big challenge due to a complex multidimensional data. However, the present study proposes a Deep Neural Network (DNN)-based multivariate regression approach with backpropagation algorithm and structural learning-based Bayesian network with constraint-based algorithm to investigate the influence of COVID-19 pandemic on the currency and derivatives markets of an emerging economy. The output shows that the COVID-19 pandemic has negatively influenced the financial markets as indicated by sharply depreciating currency value around 10 % to 12 % and reducing short-position of futures derivatives around 3 % to 5 % for currency risk hedging. The robustness estimation shows that there have probabilistic distributed between Traded Futures Derivatives Contracts (TFDC), Currency Exchange Rate (CER), and Daily Covid Cases (DCC) and Daily Covid Deaths (DCD). Moreover, the output represents that the futures derivatives market conditionally depends on the currency market volatility given percentage of COVID-19 pandemic. This study may help to policymakers of financial markets in decision-making to control CER volatility that may promote currency market stability to enhance currency market activities and boost confidence of foreign investors in extreme financial crisis circumstances.