The global economic recession has caused pessimism in terms of prospects of sales recovering in the future. The present study is an attempt to investigate the cost stickiness behavior by focusing on specific characteristics of companies. The research was done through documentary analysis and access to quantitative data, with the use of statistical methods for analysis as panel data. The statistical population of the actual study included all companies listed on the India stock exchange from 2017 to 2021. They were selected after screening 128 listed companies. The regression method was used to examine the relationship between variables and to present a forecast model. The results of testing the first hypothesis showed that companies’ costs are sticky and according to the results of this hypothesis, an increase in costs when the level of activity increases is greater than the level of reduction in costs when the volumes of the activities are decreased. The results of the second hypothesis showed a remarkable relationship between the cost stickiness and specific characteristics of companies (size, number of employees, long-term assets, financial leverage, and accuracy of profits forecast). Based on the third hypothesis, there is a notable difference between cost stickiness at different levels of specific characteristics of companies. Therefore, the results show that environmental uncertainty such as COVID-19, increases cost stickiness.

The paper analyzes the corporate carbon emissions and GDP contributions of the top ten companies by turnover for 2020–2023 in Germany, South Korea, China and the United Kingdom. Focusing on Scope 1, 2, and 3, the study explores the contribution of these companies to carbon intensity across different sectors and economies. The analysis shows that there are significant gaps in carbon efficiency, with the UK’s and Germany’s firms emitting the lowest emissions per unit of GDP contribution, followed by China and South Korea. Additionally, the study further examines the impact of Economic Policy Uncertainty on both firm carbon intensity and economic productivity. While EPU is positively associated with GDP contributions, its impact on emissions is nuanced. Firms apparently respond to policy uncertainty by increasing energy efficiency in direct (Scope 1) and energy-related (Scope 2) emissions but find it more difficult to manage supply chain emissions (Scope 3) in that case. The results point out the critical role of comprehensive ESG reporting frameworks in enhancing transparency and addressing Scope 3 emissions, which remain the largest and most volatile component of corporate carbon footprints. The paper then emphasizes the importance of standardized ESG reporting and bespoke policy intervention for promoting sustainability, especially in carbon-intensive industries. This research contributes to the understanding of how industrial and policy frameworks affect carbon efficiency and economic growth in different national contexts.

This study analyzes the interaction between legitimacy, innovation, uncertainty, and electric vehicle (EV) purchase intention in Spain, Portugal, Italy, and Greece. Using partial least squares structural equation modeling (PLS-SEM) and data from 2016 to 2023, the relationships between these key variables are assessed. The results show that legitimacy has a positive impact on purchase intention, while innovation influences legitimacy but does not directly affect purchase intention. Uncertainty moderates these relationships in complex ways. The findings suggest that enhancing the perception of legitimacy is crucial to increase EV purchase intention, and strategies promoting innovation and managing uncertainty can improve market acceptance.

The effective allocation of resources within police patrol departments is crucial for maintaining public safety and operational efficiency. Traditional methods often fail to account for uncertainties and variabilities in police operations, such as fluctuating crime rates and dynamic response requirements. This study introduces a fuzzy multi-state network (FMSN) model to evaluate the reliability of resource allocation in police patrol departments. The model captures the complexities and uncertainties of patrol operations using fuzzy logic, providing a nuanced assessment of system reliability. Virtual data were generated to simulate various patrol scenarios. The model’s performance was analyzed under different configurations and parameter settings. Results show that resource sharing and redundancy significantly enhance system reliability. Sensitivity analysis highlights critical factors affecting reliability, offering valuable insights for optimizing resource management strategies in police organizations. This research provides a robust framework for improving the effectiveness and efficiency of police patrol operations under conditions of uncertainty.