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Conflict and violence are the main drivers of globally escalating humanitarian needs. Local grassroots initiatives are pivotal in distributing humanitarian supplies in the acute response phase until more established humanitarian aid organizations can enter. Nevertheless, scant research exists regarding the role of grassroots associations in providing humanitarian assistance during a military conflict. The purpose of this paper is to understand the role of grassroots associations and identify important themes for effective operations.

This paper adopts a case-study approach of three Ukrainian grassroots associations that began operating in the immediate days of the full-scale invasion of Ukraine. The findings are based on analyzing primary sources, including interviews with Ukrainian volunteers, and are supported by secondary sources.

Grassroots associations have local contacts and a contextual understanding of population needs and can respond more rapidly and effectively than large intergovernmental agencies. Four critical themes regarding the operations of grassroots associations emerged: information management, inventory management, coordination and performance measurement. Grassroots humanitarian response operations during conflict are challenged by personal security risks, the unpredictability of unsolicited supplies, emerging volunteer roles, dynamic transportation routes and shifting demands.

Grassroots responses are central to humanitarian responses during the acute phase of a military conflict. By examining the operations of grassroots associations in the early months of the 2022 war in Ukraine, the authors provide a unique perspective on humanitarian logistics. Nonetheless, more inclusive models of humanitarian responses are needed to harness the capacities and resilience of grassroots operations in practice.

This paper aims to examine the impact of environmental, social and governance (ESG) disclosure and firm value (FV), as well as, pinpoints the role of the audit quality (AQ) as a moderating variable on such impact; where the authors hypothesize that AQ modulates the relationship between ESG disclosure and the FV.

Data of a sample of firms listed on the Egyptian Stock Exchange Market (EGX) were collected over the period of 2017–2021 and analyzed using the regression and 2SLS models.

The results suggested that: (1) the ESG has a significant positive impact on the FV in the EGX, and (2) AQ has a significant impact, as a moderating variable, on the relationship between ESG disclosure and FV.

The findings would help the Egyptian market authorities in realizing the importance of integrating ESG information within the financial reports of the listed firms. The findings could also help in developing effective disclosure procedures to provide shareholders with useful information.

This paper contributes to the literature regarding the ESG disclosure components and the FV value by considering AQ in testing such relationship.

The purpose of this paper is to construct a digital collection and database of traditional clothing that is convenient for the digital dissemination and application of traditional clothing and provide resources for research on clothing fashion, traditional clothing techniques, clothing culture, history and clothing teaching.

A real object analysis method was used in this paper, based on 15 core elements of the internationally common DC metadata standard, and with consideration to the characteristics of clothing products and clothing industry application specifications, the core elements of DC are expanded to facilitate the detailed record of the characteristic information of clothing, especially the implicit clothing culture. A code symbol compilation method was developed to give each piece of clothing a unique number, facilitating identification, classification and recording. At last, a metadata construction scheme for traditional clothing was developed. A traditional embroidered children's hat and Mamianqunt serve as examples to demonstrate the metadata elements.

The clothing meta-database provides a main body of traditional clothing while also paying attention to the collection of cultural elements. It is composed of five layers of classified data, source data, characteristic data, connotation data and management data, as well as 28 data elements, providing ease of sharing and interoperation.

This paper expands the subset of fashion metadata by describing traditional clothing metadata, especially the excavation of clothing cultural elements, and developing code compilation methods so that each clothing product can obtain a unique identification number, thereby building a traditional clothing metadata construction scheme consisting of five data layers and containing 28 data elements. This scheme records the information about each layer of traditional clothing in detail and provides shared data for discipline research and industry applications.

Over the last decade, investments in green energy companies have witnessed noticeable growth rates. However, the glacial pace of the world economic restoration due to COVID-19 pandemic placed a high degree of uncertainty over this market. Therefore, this study investigates the short- and long-term relationships between COVID-19 new cases and WilderHill New Energy Global Innovation Index (NEX) using daily data over the period from January 23, 2020 to February 1, 2023.

The authors utilize an autoregressive distributed lag bounds testing estimation technique.

The results show a significant positive impact of COVID-19 new cases on the returns of NEX index in the short run, whereas it has a significant negative impact in the long run. It is also found that the S&P Global Clean Energy Index has a significant positive impact on the returns of NEX index. Although oil has an influential effect on stock returns, the results show insignificant impact.

Governments have the chance to flip this trend by including investment in green energy in their economic growth stimulation policies. Governments should highlight the fundamental advantages of investing in this type of energy such as creating job vacancies while reducing emissions and promoting innovation.

First, as far as the authors are aware, the authors are the first to examine the effect of oil prices on clean energy stocks during COVID-19. Second, the authors contribute to studies on the relationship between oil prices and renewable energy. Third, the authors add to the emerging strand of literature on the impact of COVID-19 on various sectors of the economy. Fourth, the findings of the paper can add to the growing literature on sustainable development goals, in specific the papers related to energy sustainability.

The study aims is to explore the cointegration level among major Asian stock indices from pre- COVID-19 to post COVID-19 times.

Johansen cointegration test is employed to know the long run relationship among the stock market indices of Hong Kong, Indonesia, Malaysia, Korea, India, Japan, China, Taiwan, Israel and South Korea. The empirical testing was done to analyze whether any significant change has been induced by the COVID-19 pandemic on the cointegrating relationship of the selected markets or not. Through statistics of trace test and maximum eigen value, total number of cointegrating equations present among all the indices during different study periods were analyzed.

The presence of cointegration was found during all the sample periods and the findings suggests that the selected stock markets are associated with each other in general. During COVID-19 crisis period the cointegration level was reduced and again it regained its original level in the next year and again reduced in the subsequent next year. So, the cointegrating relationship among selected stock market indices remains dynamic and no evidence of impact of COVID-19 on this dynamism was found.

The study has explored the level of cointegration among the major stock indices of Asian nations in the pre, during, post-crisis and the most recent periods. The interconnectedness of the stock markets during the COVID-19 times has been compared with similar periods in different years immediately preceding and succeeding the COVID-19 times which has not been done in any of the existing study.

The paper aims to provide empirical evidence of the impact of IFRS adoption on the value relevance of accounting information in the emerging market of Saudi Arabia.

The sample consists of 98 non-financial listed firms operating in Saudi Arabia from 2014 to 2019, representing the years before and after IFRS adoption. The authors apply basic and extended price models to examine the value relevance of select accounting figures.

The authors findings provide evidence that accounting information is, generally, value relevant to the Saudi Arabian capital market. However, mixed results exist for particular accounting variables. Both earnings and cash flows are value-relevant in the period before and after IFRS adoption; equity is only relevant in the post-adoption period. Furthermore, IFRS adoption also increases the explanatory power of earnings. An increase in the value relevance of earnings and equity hurts the value relevance of cash flows. The effects are moderated by leverage and dividend policy.

The authors contribute to the ongoing discussion of the economic effects of IFRS adoption in emerging markets. The empirical findings show that initial concerns about IFRS adoption, as reflected by the negative coefficient within the regression analysis, are mitigated once the usefulness of the individual accounting variables published in financial statements is investigated.

This study identified how investor sentiment on Twitter is associated with Brazilian stock market return and trading volume.

The study analyzes 314,864 tweets between January 1, 2017, to December 31, 2018, collected with the Tweepy library. The companies’ financial data were obtained from Refinitiv Eikon. Using the netnographic method, a Twitter Investor Sentiment Index (ISI) was constructed based on terms associated with the stocks. This Twitter sentiment was attributed through machine learning using the Google Cloud Natural Language API. The associations between Twitter sentiment and market performance were performed using quantile regressions and vector auto-regression (VAR) models, because the variables of interest are heterogeneous and non-normal, even as relationships can be dynamic.

In the contemporary period, the ISI is positively correlated with stock market returns, but negatively correlated with trading volume. The autoregressive analysis did not confirm the expectation of a dynamic relationship between sentiment and market variables. The quantile analysis showed that the ISI explains the stock market return, however, only at times of lower returns. It is possible to state that this effect is due to the informational content of the tweets (sentiment), and not to the volume of tweets.

The study presents unprecedented evidence for the Brazilian market that investor sentiment can be identified on Twitter, and that this sentiment can be useful for the formation of an investment strategy, especially in times of lower returns. These findings are original and relevant to market agents, such as investors, managers and regulators, as they can be used to obtain abnormal returns.

Chinese-listed firms with Belt and Road Initiatives (BRI) play a crucial role in advancing the outward investment policy of China. Board diversity can be vital, and intellectual capital disclosure (ICD) showing future earnings can build investor confidence in these firms. This study examines these two relationships in Chinese-listed firms with BRI projects during a predictable business outlook period (2019, pre-Covid period) and unpredictable business outlook period (2020, Covid period).

The study used least squares regression that analysed the target population comprising 79 listed Chinese firms with BRI projects in 2019 and 2020. The China Stock Market and Accounting Research (CSMAR) database provided board diversity data. Analysing annual reports using content analysis provided the ICD data, collected by following an established intellectual capital (IC) coding framework in the literature. After collecting board-related data, the study calculated the diversity between boards in firms (diversity of boards – DOB) using cluster analysis. The study estimated the diversity within each board (diversity in boards – DIB) using Blau's Index.

The findings indicate that in the predictable business outlook environment, DOB positively associates with ICD, and DIB negatively associates with ICD. In the unpredictable business outlook environment, the DIB and DOB interaction negatively associates with ICD, and DOB positively associates with ICD.

The findings apply to Chinese-listed firms with BRI projects and further research is required to generalise findings beyond them. This study used annual reports to collect ICD, but a future study could examine BRI firms' social media and website disclosures. The attributes selected for board diversity dimensions can contribute to bounded findings, and future studies could expand the board diversity attributes included.

The findings provide insights into firms' board composition and structure associated with ICD.

This is one of the first studies providing empirical evidence about board diversity and ICD of Chinese-listed firms with BRI projects.

This paper aims to examine the association between green innovation and the cost of equity in China. This study relies on the investors’ base perspective and shareholders’ perceived risk perspective to investigate the relation between green innovation and the cost of equity in China.

The paper uses firm-fixed effect regression for a sample of Chinese public companies for the period 2008–2018.

The authors find a negative relationship between green innovation and the cost of equity capital. This negative association is found to be more pronounced for less financially constrained firms, during periods of high economic policy uncertainty, and for firms with a strong internal control environment. Finally, the paper shows that the negative association became more pronounced after the passage of the Environmental Protection Law of China in 2012. The results remain robust to possible endogeneity concerns.

This study contributes to the green innovation literature by documenting that shareholders favorably view firms implementing green innovation policies. The study also has policy implications for Chinese regulators in improving the green credit policy.

The purpose of this study is to better understand technology adoption and working from home (WFH) behaviour of micro and small enterprises (MSE) with 4 to 49 employees during the first (2020) and second (2021) COVID-19 lockdowns in Switzerland.

This study uses two data sets gathered using computer-assisted telephone interviewing surveys conducted with 503 managing directors of Swiss MSEs after the first and 506 MDs after the second COVID-19 lockdown period.

The study revealed that during the COVID-19 pandemic, WFH arrangements are related to the adoption of technology by Swiss industry groups. Furthermore, industry characteristics and technology adoption strategies are also associated with the long-term prospect of WFH. The overall result confirms the predominant role of technology pioneers.

The study focuses on MSEs in Switzerland during a specific period. The data set includes mainly quantitative data. Future studies could investigate larger enterprises in international contexts, integrating employees’ viewpoints founded on long-term gathered qualitative data. The implications of this study include predictions about future WFH behaviour in Swiss MSEs.

To the best of the authors’ knowledge, this is the first study collecting data in Swiss MSEs after the two COVID-19 lockdowns in 2020 and 2021. As a result, this study offers a unique perspective on a specific business segment, which accounts for around 70% of global employment.