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This study aims to examine the impact of corporate social responsibility (CSR) awards on firms’ market value considering these awards as a signal and proxy for the effectiveness of CSR practice.

There are 342 announcements of CSR awards in China from 2006 to 2017 screened and analyzed using the event study methodology.

The stock market reacts significantly negatively to CSR award announcements in the short term. Firms that are state-owned, belong to the manufacturing industry, outside east China, repeatedly win awards and are listed in the Chinese H-share market, experience a stronger stock market reaction. Interestingly, the long-term stock returns of award winners are significantly positive for multiyear holding periods.

The findings offer stakeholders clear guidelines on how to manage communications in the market to extract enhanced financial performance from CSR award announcements.

This study chooses CSR awards as a proxy for the effectiveness of excellent CSR practice. This study also contributes to the CSR literature by analyzing how investors use the award information to make investment decisions.

Despite its devastating nature, the COVID-19 pandemic has also catalyzed a substantial surge in the adoption and integration of technological tools within economies, exerting a profound influence on the dissemination of information among participants in stock markets. Consequently, this present study delves into the ramifications of post-pandemic dynamics on stock market behavior. It also examines the relationship between investors' sentiments, underlying behavioral drivers and their collective impact on global stock markets.

Drawing upon data spanning from 2012 to 2023 and encompassing major world indices classified by Morgan Stanley Capital International’s (MSCI) market and regional taxonomy, this study employs a threshold regression model. This model effectively distinguishes the thresholds within these influential factors. To evaluate the statistical significance of variances across these thresholds, a Wald coefficient analysis was applied.

The empirical results highlighted the substantive role that investors' sentiments and behavioral determinants play in shaping the predictability of returns on a global scale. However, their influence on developed economies and the continents of America appears comparatively lower compared with the Asia–Pacific markets. Similarly, the regions characterized by a more pronounced influence of behavioral factors seem to reduce their reliance on these factors in the post-pandemic landscape and vice versa. Interestingly, the post COVID-19 technological advancements also appear to exert a lesser impact on developed nations.

This study pioneers the investigation of these contextual dissimilarities, thereby charting new avenues for subsequent research studies. These insights shed valuable light on the contextualized nexus between technology, societal dynamics, behavioral biases and their collective impact on stock markets. Furthermore, the study's revelations offer a unique vantage point for addressing market inefficiencies by pinpointing the pivotal factors driving such behavioral patterns.

Extant research has shown that workplace ostracism (WO) elicits counterproductive work behaviors, such as employee silence (ES), culminating in reduced job performance. However, lesser is known about the factors that buffer against this underlying linkage. With an emphasis on conservation of resource (COR) theory and social identity theory, this study investigates the hitherto unexplored moderating roles of moral identification (MI) and organizational identification (OI) in the relationship between WO and ES.

The study employed a time-lagged design to collect multi-source data from 250 employees working in the service sector organizations in Pakistan. Data are analyzed in SMARTPLS (v 3.3.3) to assess the measurement model and the structural model.

Results reveal that WO is positively correlated with ES and negatively correlated with job performance. At the same time, ES mediates the negative relationship between WO and job performance. In addition, MI and OI buffer against the positive connection between WO and ES. The positive association between WO and ES is less pronounced at high levels of MI and OI and vice versa.

The findings indicate that there is potential value in developing MI and OI, for which several interventions are discussed.

This study is one of the few efforts to outstretch the boundary conditions of ES. Moreover, this is the first study to investigate the role of identity-based perspective in the relationship between WO and ES.

The sustainable development of the energy–economy–environment (3E) needs to ensure the balanced interplay between the energy–economy and the environment. Likewise, creating such balance has become a critical policy issue among countries worldwide. However, in the past, studies have ignored to create a balanced interplay of the energy–economy and environment. Therefore, to address said research gap, this paper aims to develop a graph model for conflict resolution (GMCR)-based strategy for the sustainable development of the 3E to ensure better environmental quality.

To develop a strategic framework for the balanced interplay between the energy–economy and the environment, the study used the GMCR approach and designed a mechanism for the sustainable development of the 3E to ensure better environmental quality in the context of Pakistan.

The results from the GMCR indicate that sustainable development of the 3E to ensure better environmental quality is possible when government focuses on the sustainable growth of the economy through environmental policies and the use of renewable energy.

By solving the 3E conflict, this study provides policy insights for the government of Pakistan for the sustainable development of the energy economy and the environment to ensure better environmental quality in the country.

For the first time, this paper tried to put forward a way through which conflicting objectives can be achieved together for the sustainable development of the 3E to ensure better environmental quality.

The paper aims to clarify the relationship between the output value of unit sown area (OVUSA) and its major influential factors by taking Jiangsu Province, People's Republic of China as an instance, followed by some suggestions for adjusting the agricultural inputs.

The paper is characterized by an exploratory research by constructing a grey incidence model (GIM) based on the grey system theory (GST) initiated by Chinese professor Julong Deng. The original data processed in the model are quoted from Jiangsu Statistics web site and the Statistical Yearbook of Jiangsu Province.

The analysis indicates several noteworthy points in the following areas. To start with, the proportion of irrigated area to sown area has turned out to be a stabilizing factor influencing OVUSA; next, the contributions of nitrogen and phosphorus fertilizers' output to the growth of the OVUSA were reduced, while the marginal outputs of nitrogen and phosphorus fertilizers seemed to be lower; then the outputs of potash and compound fertilizers have made significant contributions to the growth of the OVUSA, although their application rates were drastically increased; besides, the total power of agricultural machinery was a major factor boosting the growth of the OVUSA; furthermore, the decrease of labor force was obviously remarkably related to the reduction of OVUSA; finally, the areas affected by natural disasters have had a foremost impact on OVUSA reduction.

Suggestions are also proposed to the related policy-makers as follows: to reasonably control the application rates of nitrogen and phosphorus fertilizers, to encourage fertilizer manufacturing enterprises to develop potassium mineral resources with the purpose of guaranteeing the potash fertilizer's supply, to scientifically design the composition of compound fertilizers in order to satisfy the needs of cultivated lands in different areas, to improve the quality and structure of agricultural labor forces with the aim of meeting emerging trends in the agricultural industry, to make every effort to further modernize the current agricultural industry, and to enhance the disaster-prevention capability of the agricultural industry by gradually converting it into a controllable one.

This paper aims to design a vulnerability assessment model considering the multidimensional and systematic approach to disaster risk and vulnerability. This model serves to both risk mitigation and disaster preparedness phases of humanitarian logistics.

A survey of 27,218 households in Pueblo Rico and Dosquebradas was conducted to obtain information about disaster risk for landslides, floods and collapses. We adopted a cross entropy-based approach for the measure of disaster vulnerability (Kullback–Leibler divergence), and a maximum-entropy estimation for the reconstruction of risk a priori categorization (logistic regression). The capabilities approach of Sen supported theoretically our multidimensional assessment of disaster vulnerability.

Disaster vulnerability is shaped by economic, such as physical attributes of households, and health indicators, which are in specific morbidity indicators that seem to affect vulnerability outputs. Vulnerability is heterogeneous between communities/districts according to formal comparisons of Kullback–Leibler divergence. Nor social dimension, neither chronic illness indicators seem to shape vulnerability, at least for Pueblo Rico and Dosquebradas.

The results need a qualitative or case study validation at the community/district level.

We discuss how risk mitigation policies and disaster preparedness strategies can be driven by empirical results. For example, the type of stock to preposition can vary according to the disaster or the kind of alternative policies that can be formulated on the basis of the strong relationship between morbidity and disaster risk.

Entropy-based metrics are not widely used in humanitarian logistics literature, as well as empirical data-driven techniques.