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This study aims to examine how environmental, social and governance (ESG) initiatives and ISO 14001, which is an internationally agreed standard to set out the requirements for an environmental management system, affect firm performance in the context of the Industry 4.0 supply chain.

The authors develop a new chance-constrained network data envelopment analysis (DEA) in the presence of non-positive data to estimate innovation, operational and profitability performances for three main relation groups (suppliers, partners and customers) in Microsoft's supply chain.

Results of this study show the following: (1) the application of ISO 14001 will reduce profitability but increase overall performance (OP); (2) ESG implementation has a convex U-shaped influence on profitability and OP, which means that firms will benefit when ESG investment goes beyond a particular level; (3) the nonlinear U-shape is presented in the E and G components, but not in the S of the individual ESG initiatives, and (4) only specific subcomponents of S and G in the subcomponent of individual ESG initiatives are nonlinearly connected to OP. Research's results reveal that the customer group has a higher performance value than the other two groups, which suggests that this group will create competitive advantages for Microsoft.

Overall, the authors provide an insightful viewpoint into supply chain management by examining the ESG initiatives, ISO 14001 and performances of Microsoft's supply chain.

This study aims to investigate organizational learning, represented by the absorptive capacity, as a condition for the firm to learn about marketing data and make more informed decisions. The authors also aimed to understand how the behavior of micro, small and medium enterprises (MSME) businesses differ in this scenario through a multilevel perspective.

Placing absorptive capacity as a mediator of the relationship between business analytics and rational marketing decisions, the authors analyzed data from 224 Brazilian retail companies using structural equation modeling estimated with partial least squares. To test the cross-level moderation effect, the authors also performed a multilevel analysis in RStudio.

The authors found a partial mediation of the absorptive capacity in the relation between business analytics and rational marketing decisions. The authors also discovered that, in the MSMEs firms’ group, even if smaller companies find it more difficult to use data, those that do may reap more benefits than larger ones. This is due to the influence of size in how firms handle information.

The sample size, despite having shown to be consistent and valid, is considered small for a multilevel study. This suggests that our multilevel results should be viewed as suggestive, rather than conclusive, and subjected to further validation.

Rather than solely positioning business analytics as a tool for decision support, the authors’ analysis highlights the importance for firms to develop the absorptive capacity to enable ongoing acquisition, exploration and management of knowledge.

MSMEs are of economic and social importance to most countries, especially developing ones. This research aimed to improve understanding of how this group of firms could transform knowledge into better decisions. The authors also highlight micro and small firms’ difficulties with the use of marketing data so that they can have more effective practices.

The research contributes to the understanding of organizational mechanisms to absorb and learn from the vast amount of current marketing information. Recognizing the relevance of MSMEs, a preliminary multilevel analysis was also conducted to comprehend differences within this group.

The coronavirus (COVID-19) is the worst pandemic in recent memory in terms of its economic and social impacts. Deadly second wave of COVID-19 in India shook the country and reshaped the ways organizations functions and societies behave. Medical infrastructure was unaffordable and unsupportive which created high distress in the Indian society, especially for poor. At this juncture, some pharmaceutical firms made a unique social investment when they reduced price of drugs used to treat COVID-19 patients. This study aims to examine how the market and the society respond to the price reduction announcement during the psychological distress of COVID-19.

Market reactions have been analyzed by conducting an event study on stock market data and visual analytics-based sentiment analysis on Twitter data.

Overall, this study finds positive abnormal returns on the day and around the day of event. Interestingly, this study finds that returns during the time of high distress are significantly higher. Sentiment analysis conveys that net sentiment is favorable to the pharmaceutical firms around the day of event and it sustains more during the time of high distress.

This study is unique in contributing to the business and industrial management literature by highlighting market reactions to social responsibility of business during the time of psychological distress in emerging economies.

Implementing the 17 Sustainable Development Goals (SDGs) unarguably demands huge financial investments. However, the United Nations has acknowledged the huge financial gap militating against the implementation of the SDGs worldwide, leading experts to question the possibility of complete implementation of the goals by their terminal dateline of 2030. While the bulk of the finance currently outlaid on the SDGs comes from traditional sources including foreign direct investments (FDIs), there is the need to focus more attention on developing and exploiting impact investments that are more suitable for financing development programmes and projects. In this chapter, the SDG implementation profiles of the 12 Arab West Asia countries concerning the five most targeted SDGs were evaluated and sustainable finance issues were discussed. Secondary data were retrieved from World Bank's DataBank. The data were descriptively analyzed. Based on the profiles generated, debt relief is put forward as a possible impact investment mechanism suitable for funding the SDGs. Specifically, this chapter recommends that outright cancellation of debts based on the debt-for-SGD swap could serve as some of the impact investments needed to boost the global drive for a developed, peaceful, and just world.

This paper aims to investigate the potential influence of managers on sustainability assurance. When the quality of sustainability reporting is questionable because of subsequent restatements, the authors explore whether assurance is used to enhance its credibility as a legitimization tool or as an impression management strategy. Additionally, the authors analyze how capital markets react to this potential managerial capture and, particularly, whether investors penalize this practice through the cost of capital.

Using an international sample from 2012 to 2016 and panel data regressions, this study relies on DICTION’s master variables of optimism and certainty to examine the impact of managers on assurance and the market’s reaction to these practices.

The study shows that some managers might use assurance as a legitimization tool rather than as a means of reinforcing the credibility of sustainability reporting. In such cases, the results reveal that investors penalize (reward) managerial influence (no influence) on assurance.

The new findings help companies understand that they will not improve their financing terms if investors perceive that managers have influenced assurance. Moreover, these findings emphasize the need for standardization to clarify assurance criteria and prevent managerial influence.

Managerial influence on assurance raises doubts about its value in terms of reducing information asymmetry and especially improving investors’ decision-making.

The present study represents the first evidence of the potential use of assurance for non-informative purposes. The authors provide clear evidence of how investors penalize managerial influence on assurance, in contrast to the mainstream literature, which shows that this practice always improves investors’ decision-making and is rewarded.

This paper aims to investigate the reciprocal nonlinear relationship between corporate social responsibility (CSR) and firm performance (FP).

The authors used a sample of 814 European firms over the period 2008–2017. The Panel Smooth Transition Regression (PSTR) model was performed as an econometric approach.

Firstly, results show a threshold effect in the CSR–FP relationships within the two directions. More specifically, the authors found that firms are more likely to engage in CSR by surpassing a threshold of 1.231% for return on assets (ROA) and 0.821% for Tobin’s Q ratio. Secondly, the authors also found that the impact of CSR on FP is positive and significant only if the environment, social and governance score surpasses the threshold of 56.780% when the dependent variable is ROA and 41.02% when Tobin’s Q ratio measures performance.

A significant part of the literature supports the linear relationship between CSR and FP from the unique direction (CSR → FP). This study comes to fill this gap by assessing the possible nonlinear relationship. In addition, this nonlinear relationship is tested under the two directions. Therefore, defining the threshold of FP that allows companies to engage in CSR, on the one hand, and the threshold of engagement in CSR that improves FP, on the other hand, could be an exciting topic.

To get the full benefit from CSR effects, firms should be with better financial performance to be socially responsible.

To the best of our knowledge, few studies have explored the nonlinear relationship between CSR and FP. In addition, this study raises the question of whether this relation is causal. The authors assess the two nonlinear relationships between CSR ? FP and FP ? CSR by determining the optimal thresholds.

本文旨在探究企業社會責任 (以下簡稱企社責) 與公司業績之間的相互非線 性關係。

研究所採用的樣本為814間歐洲公司, 涵蓋期為2008年至2017年。研究人 員使用縱橫平滑轉換模型、作為經濟計量方法和工具去進行研究。

研究結果顯示、在有關的兩個方向內, 企社責與公司業績之間的關聯上是 存在著閾值效應的。更具體地說, 研究人員發現, 若企業的資產報酬率超過1.231%的 水平, 以及托賓的Q比率 (Tobin’s Q Ratio) 0.821%的水平的話, 它們會更願意承擔企 社責。其次, 研究結果亦顯示, 企社責對企業的業績會產生積極的影響; 另外, 只有 當資產報酬率是因變數、而環境、社會和公司治理的分數 (ESGS) 超過56.780%, 以 及當托賓的Q比率用來測量績效、而數值為41.02%時, 企社責對企業的業績所產生的 影響會較為顯著。

過去的學術文獻、大部份都是以唯一的方向 (企社責 ->公司業績) 去確認 企社責與企業業績之間的線性關係。本研究評估了兩者之間可能存在的非線性關係; 而且, 這非線性關係是在有關的兩個方向下而進行測試的; 因此, 本研究一方面給可 讓公司以企社責的精神和理念去營運的企業業績的閾值下了定義; 另一方面, 又給參 與企社責為公司帶來業績的改善的閾值下了定義。這均為令人興奮的課題。

企業若想取得因參與企社責而帶來的完全好處, 它們必須擁有更佳 的財務績效、以能盡其社會責任。

盡我們所知, 探究企社責與企業業績之間的非線性關係的研究實在不 多; 而且, 本研究對這兩者的關係是否是因果關係提出了質疑; 就此, 我們藉著釐定 最佳的相對閾值、來評估企社責 ->企業業績與企業業績 ->企社責之間的兩個非線性的 關係。

This study investigated the micro–macro link in the hospitality and tourism (H&T) sector, specifically considering whether the gender diversity, independence and board attendance rates of H&T firms' boards, alongside the moderation effect of board policies, played a significant role in tourism sector performance.

The 2011–2018 data were retrieved from the World Bank and the Thomson Reuters Eikon databases, and fixed effects panel regression was conducted.

While female directors were a significant driver of tourism sector performance in terms of tourist arrivals and tourism receipts, independent directors were effective in improving tourist arrivals only. Furthermore, moderation analyses demonstrated the inefficacy of board policies in enhancing these directors' contributions to the sector's development. Moreover, the findings revealed the inefficiency of board meetings.

Concerning the efficacy of board policies, the results suggest that firms' boards should review and revise their policies. Surprisingly, while board-diversity policies made no difference to female directors' role in the sector's development (although females were influential), board-independence policies produced unexpected results. In the absence of a board-independence policy, independent directors are influential, but if a policy exists, they are not.

Although prior firm-level studies tested whether board characteristics enhanced firms' performance in the H&T sector, they did not investigate whether board characteristics promoted tourism sector performance. Moreover, the moderating effect of board policies on boards' structures and tourism sector performance has not yet been examined.

This study aims to investigate the influence of disclosing environmental, social and governance (ESG) factors on financial performance, taking into account the moderating effect of the COVID-19 pandemic.

A sample of the top 100 non-financial firms listed on the Bombay Stock Exchange, for the years 2019–2022, has been considered. Suitable panel regression models have been used to assess the impact of non-financial disclosure on accounting and market measures of firm performance. In addition, a panel data moderating effect model is used to assess the moderating impact.

The outcomes of the study partially favour the value-creation role of ESG disclosure. Specifically, the disclosure of already established ESG metrics, particularly social and governance aspects, positively impacts the market performance while environmental transparency negatively impacts the accounting performance. Of the three ESG components, only extended governance disclosure adds to market value. Results of the moderation effect reveal a significant impact of the pandemic on the ESG disclosure–financial performance relation. However, a more pronounced effect before the pandemic is observed. The results are robust to endogeneity.

This study sheds light on the financial consequences of ESG disclosure within the context of an emerging nation. This is done by using a novel holistic ESG reporting framework to obtain more accurate results. Furthermore, the study distinguishes itself by examining the long-term moderating influence of the unexpected COVID-19 crisis on the ESG disclosure–financial performance relation.

This paper aims to develop an indigo-dyed denim fabric treated with a nanosilver colloid in the presence of a natural crosslinker of citric acid for possible surgical gown fabrication applications.

A bleached denim fabric was dyed with the sustainable indigo dye followed by silver nanofinishing through citric acid crosslinking under the pad-dry-cure method. The prepared denim samples were analyzed for chemo-physical, textile, dyeing, antibacterial and finish release properties.

The results demonstrated that the comfort and textile characteristics of nanosilver-treated/indigo-dyed cellulosic fabric were affected due to the crosslinking, surface amphiphilicity and air permeability. These properties were, still, in the acceptable range for the fabrication of naturally dyed and antibacterial nanofinished denim gowns.

The dyeing of denim with synthetic dyes may cause harmful effects on the skin and health of the wearer, and the authors present an eco-friendly sustainable approach.

The authors used the fabric substrate, natural indigo dye and reducing/crosslinker agent of citric acid, all being bio-based, in the fabrication of antibacterial dyed fabric for health care garments.

A community demonstrates the unique qualities and relationships between its members that distinguish it from other communities within a network. Network analysis relies heavily on community detection. Despite the traditional spectral clustering and statistical inference methods, deep learning techniques for community detection have grown in popularity due to their ease of processing high-dimensional network data. Graph convolutional neural networks (GCNNs) have received much attention recently and have developed into a potential and ubiquitous method for directly detecting communities on graphs. Inspired by the promising results of graph convolutional networks (GCNs) in analyzing graph structure data, a novel community graph convolutional network (CommunityGCN) as a semi-supervised node classification model has been proposed and compared with recent baseline methods graph attention network (GAT), GCN-based technique for unsupervised community detection and Markov random fields combined with graph convolutional network (MRFasGCN).

This work presents the method for identifying communities that combines the notion of node classification via message passing with the architecture of a semi-supervised graph neural network. Six benchmark datasets, namely, Cora, CiteSeer, ACM, Karate, IMDB and Facebook, have been used in the experimentation.

In the first set of experiments, the scaled normalized average matrix of all neighbor's features including the node itself was obtained, followed by obtaining the weighted average matrix of low-dimensional nodes. In the second set of experiments, the average weighted matrix was forwarded to the GCN with two layers and the activation function for predicting the node class was applied. The results demonstrate that node classification with GCN can improve the performance of identifying communities on graph datasets.

The experiment reveals that the CommunityGCN approach has given better results with accuracy, normalized mutual information, F1 and modularity scores of 91.26, 79.9, 92.58 and 70.5 per cent, respectively, for detecting communities in the graph network, which is much greater than the range of 55.7–87.07 per cent reported in previous literature. Thus, it has been concluded that the GCN with node classification models has improved the accuracy.