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Studies in corporate social responsibility (CSR) disclosure were initially focused more on disclosure “Quantity” than “Quality” and while they have started to explore “Disclosure Quality”, their assessment mechanisms are found to be immature. Thus, while a number of papers have sought to assess the quality of CSR disclosure, this paper aims to suggest an approach tied closely to both expectations in assessing “quality” derived from the Conceptual Framework for Financial Reporting (revised 2018) and the global reporting initiative. The outcome is to offer a best practice approach to assessing CSR disclosure quality.

In this paper, prior literature is reviewed, qualitative characteristics from the Conceptual Framework for Financial Reporting (revised 2018) and globally recognised guidelines such as the GRI are reviewed. The framework for a “CSR disclosure quality index” as an assessment tool to assess CSR disclosure quality is developed from qualitative characteristics and criteria identified.

The proposed CSR disclosure quality index is developed in stages from the qualitative characteristics identified in the Conceptual Framework for Financial Reporting (revised 2018) and criteria identified from the guidelines discussed. A table was then developed linking the qualitative characteristics to criteria providing a Likert scale approach to assessing the disclosures made by companies to make an assessment of the quality of the companies’ reports. It is argued this provides a robust assessment, being a direct and comprehensive measure of disclosure quality.

As with most qualitative work, there are alternative approaches to establishing an index, but the authors believe this is an approach offering links (and, therefore, credibility) to globally recognised guidelines in the assessment of CSR disclosure quality. Future work could enhance the alignment of this index with the sustainable development goals (SDGs), building on the preliminary connections established in this study.

At a practical level this index offers an approach to reviewing the quality of CSR disclosures which could prove useful to policymakers and in the future development and expansion of this framework offering greater objectivity to assessments and justification for proposed improvement in reporting practice. Also, this index serves as a benchmarking tool for companies to meet the disclosure expectations of stakeholders.

This approach has the potential to substantially fulfil stakeholder expectations by addressing the growing demand for transparency in this area, while avoiding practices that could be perceived as superficial or misleading (greenwashing). Focusing on social issues enables stronger connections between companies and their stakeholders. Furthermore, the index helps companies link their CSR efforts with SDGs and show their commitment to long-term social value building in discussion of governance factors to show accountability expectations are being met.

This paper contributes to CSR disclosure quality literature and provides a reliable method of assessing the quality of CSR disclosures. Opportunities for further and broader developments can be envisaged while offering a credible and reliable approach.

The purpose of this paper is to provide a systematic literature review on Shariah governance and Islamic banks.

The literature was searched from Scopus and Web of Science using various queries related to Shariah governance and Islamic banks. Through a screening process, 93 articles were considered fit for the systematic literature review.

The paper provides a systematic review based on different themes, including measurement of Shariah governance in Islamic banks, disclosure of Shariah governance and its determinants, the impact of Shariah governance on performance, risk management and other outcomes of Islamic banks. Finally, issues and challenges of Shariah governance in Islamic banks are discussed, followed by conclusions and recommendations related to future research.

This study is the first of its kind, to the authors’ knowledge, to provide a comprehensive systematic literature on Shariah governance and Islamic banks by exploring different themes and highlighting multiple future avenues of research.

Executive decisions regarding capital financing are an important management aspect, especially during financing constraints and growth opportunities. The current study examines the impact of managerial skills of a company on capital financing decisions. Furthermore, it analyzed this nexus in financing constraints and growth opportunity situations.

The authors use the GMM (generalized method of moments) estimation approach on a dataset of 20,651 firm-year observations of Chinese A-share companies from 2010 to 2019.

The authors’ findings are compatible with management signaling and reputation enhancement theories, since they show that managerial skill is connected with more substantial debt financing. Managers with high management skills are likely to have more debt financing as they can foresee the economic future of their companies and tactfully convey private information, lowering information inequality and enhancing their reputation. Furthermore, the authors also show that firms with restricted financial resources and growth opportunities make this relationship stronger. Capital structure and managerial skill findings are unaffected by alternative specifications, omitted factors, industry group bias and endogeneity.

This study sheds fresh light on the essential manager personality trait of managing ability and how it influences complicated corporate decision-making, particularly in the tough environment due to financing constraints and competitive growth. The authors argue that high-ability managers are compelled to use debt financing not only to lessen information asymmetry but also to guarantee that the market finds their superior ability. This work contributes significantly to the managerial ability literature and the capital structure literature supporting signaling theory.

In today’s knowledge-based economy, companies are hugely driven by intangible resources such as intellectual capital. However, whether corporate governance of a company drives intellectual capital is less explored in emerging economies. We examine the impact of intellectual capital efficiency on firm performance for Indian firms, considering the moderating role of board gender diversity.

We have created a framework for panel data analysis and conducted estimation using the dynamic panel data model to control for endogeneity and heteroskedasticity issues. We use alternate performance and gender diversity measures for our sample of top 500 listed companies for a period of six years, that is 2015–2020.

The results demonstrate a significant positive association between intellectual capital and performance. However, moderating impact of gender diversity on the relationship between intellectual capital and performance is not significant.

The findings indicate that IC plays a crucial role in a company’s performance, which may boost economic growth. Further, the findings reveal that despite the mandatory quota for women on boards in Indian companies, their impact on IC is subliminal. It may be because the critical mass is yet to be achieved, which should be considered by policy-makers while framing policies in this area.

Our study is one of the foremost studies to consider the impact of mandatory gender quotas while examining the association between tangible and intangible firm performance. It makes an incremental contribution to literature to enrich our understanding on the influence of gender diversity on intellectual capital-performance linkages.

The primary objective of this research is to provide a precise interpretation of the constructed machine learning model and produce definitive summaries that can evaluate the influence of investor sentiment on the overall sales of non-fungible token (NFT) assets. To achieve this objective, the NFT hype index was constructed as well as several approaches of XAI were employed to interpret Black Box models and assess the magnitude and direction of the impact of the features used.

The research paper involved the construction of a sentiment index termed the NFT hype index, which aims to measure the influence of market actors within the NFT industry. This index was created by analyzing written content posted by 62 high-profile individuals and opinion leaders on the social media platform Twitter. The authors collected posts from the Twitter accounts that were afterward classified by tonality with a help of natural language processing model VADER. Then the machine learning methods and XAI approaches (feature importance, permutation importance and SHAP) were applied to explain the obtained results.

The built index was subjected to rigorous analysis using the gradient boosting regressor model and explainable AI techniques, which confirmed its significant explanatory power. Remarkably, the NFT hype index exhibited a higher degree of predictive accuracy compared to the well-known sentiment indices.

The NFT hype index, constructed from Twitter textual data, functions as an innovative, sentiment-based indicator for investment decision-making in the NFT market. It offers investors unique insights into the market sentiment that can be used alongside conventional financial analysis techniques to enhance risk management, portfolio optimization and overall investment outcomes within the rapidly evolving NFT ecosystem. Thus, the index plays a crucial role in facilitating well-informed, data-driven investment decisions and ensuring a competitive edge in the digital assets market.

The authors developed a novel index of investor interest for NFT assets (NFT hype index) based on text messages posted by market influencers and compared it to conventional sentiment indices in terms of their explanatory power. With the application of explainable AI, it was shown that sentiment indices may perform as significant predictors for NFT sales and that the NFT hype index works best among all sentiment indices considered.

Given the lack of focus on a standardized measurement framework (e.g. benchmarking tool) to assess and quantify complexity within the supply chain, this study has developed a unified supply chain complexity (SCC) index and validated its utility by examining the relationship with firm performance. More importantly, it examines the role of firm owners' business knowledge, sales strategy and board management on the relationship between SCC and firm performance.

In this study, the unit of analysis is Indian manufacturing companies listed on the Bombay Stock Exchange (BSE). This research has merged panel data from two secondary data sources: Bloomberg and Prowess and empirically operationalized five key SCC drivers, namely, number of suppliers, the number of supplier countries, the number of products, the number of plants and the number of customers. The study employs panel data regression analyses to examine the proposed conceptual model and associated hypotheses. Moreover, the present study employs models that incorporate robust standard errors to account for heteroscedasticity.

The results show that complexity has a negative and significant effect on firm performance. Further, the study reveals that an owner's business knowledge and the firm's effective sales strategy and board management can significantly lessen the negative effect of SCC.

This study develops an SCC index and validates its utility. Also, it presents a novel idea to operationalize the measure for SCC characteristics using secondary databases like Prowess and Bloomberg.

The purpose of this study is to make property price forecasts for the Chinese housing market that has grown rapidly in the last 10 years, which is an important concern for both government and investors.

This study examines Gaussian process regressions with different kernels and basis functions for monthly pre-owned housing price index estimates for ten major Chinese cities from March 2012 to May 2020. The authors do this by using Bayesian optimizations and cross-validation.

The ten price indices from June 2019 to May 2020 are accurately predicted out-of-sample by the established models, which have relative root mean square errors ranging from 0.0458% to 0.3035% and correlation coefficients ranging from 93.9160% to 99.9653%.

The results might be applied separately or in conjunction with other forecasts to develop hypotheses regarding the patterns in the pre-owned residential real estate price index and conduct further policy research.

Total risk score (TRS) is considered one of the main indicators for risk evaluation. Several studies attempted to employ different types of risk indices for the evaluation of cost overrun causes. Hence, this study aims at performing a comparative analysis to evaluate the efficiency of three different approaches for TRS calculation.

Thirty-eight unique causes of cost overrun in urban-related construction projects were identified and a survey was conducted among construction professionals in Iran. The TRS for each cost overrun cause is calculated using single-attribute (SA), double-attribute (DA), and multiple-attribute (MA) approaches, and eventually, causes were ranked. Furthermore, principal component analysis (PCA), logistic regression analysis (LRA), and K-means clustering are utilized to compare the differences in the generated TRS using different approaches.

The results revealed that the TRS generated through the MA approach demonstrated the highest efficiency in terms of generating correlation between causes and their identified latent constructs, prediction capability, and classification of the influential causes in the same group.

The originality of this study primarily stems from the adoption of statistical approaches in the evaluation of the recently introduced TRS calculation approach in comparison to traditional ones. Additionally, this study proposed a modified application of the relative importance index (RII) for risk prioritization. The results from this study are expected to fulfill the gap in previous literature toward exploring the most efficient TRS calculation approach for those researchers and practitioners who seek to utilize them as a measure to identify the influential cost overrun causes.

This study attempts to examine the time-varying volatility spillovers between environmentally sustainable assets and quantify the value-at-risk of the portfolios across various frequencies.

To accomplish these objectives, this paper utilizes a connectedness index-based TVP-VAR model and applies the wavelet-based VaR ratio to daily data spanning from January 2018 to September 2023.

The empirical findings reveal a notable increase in the connectedness index between green stocks and green bonds during the COVID-19 crisis, signifying evidence of a contagion effect. The portfolio’s risk ratio also exhibited a sharp rise amid the pandemic, particularly over medium and long-term horizons, driven by increased spillover among green assets. Notably, our analysis indicates that green bonds influence the connectedness system between green stocks and the value-at-risk ratio, reducing volatility spillover and portfolio risk ratios across various investment horizons. These results highlight the role of green bonds as an effective diversification asset against the risks associated with green equities.

This research investigates the dynamic connectedness and value-at-risk ratio between eight green sectoral renewable energy and non-energy equities and green bonds. We put forward some portfolio implications for green investors with an environmental consciousness who desire to decarbonize their portfolios and mitigate environmental issues.

The aim of this study is to develop a new multidimensional index to measure export potential of textile firms by using firm-level data.

After a conceptual model, a structural equation model is developed with five dimensions and 27 observed variables based on resource-based view theory. The measurement model is solved by Linear Structural Relations (LISREL) with maximum likelihood algorithm by using data collected from 454 textile firms in Türkiye.

In this study, a new multidimensional index that measures export potential of textile firms is developed. With the proposed model, the export potential of textile firms can be calculated numerically with the five dimensions: Resources, Dynamism, Knowledge, Innovation and Sustainability. The comparison of the output of the proposed model with the control variable, firm’s actual export values, shows a significantly high success ratio of 90.76%.

The model is applicable for textile firms at different export levels, regions and sub-sectors. The Export Potential Index for Textile Industry model is verified by using Turkish textile industry data. The robustness of the model may be increased by verifying the model by using some other countries data. This model can be implemented to other industrial sectors with some modification of the dimensions and variables.

The proposed model will contribute to the firms by calculating their export potential in five dimensions with their own variables numerically. The model will help firms to develop strategies to increase their export potential and to the governmental and industrial organizations to develop incentives policies.

This paper fills the gap in the literature by proposing a multidimensional index that determines a firm’s export potential numerically by using firm-level data.