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The set of 2,509 documents related to the human-centric aspect of manufacturing were retrieved from Scopus database and systmatically analyzed. Using an unsupervised machine learning approach based on Latent Dirichlet Allocation we were able to identify latent topics related to human-centric aspect of Industry 5.0.

This study aims to create a scientific map of the human-centric aspect of manufacturing and thus provide a systematic framework for further research development of Industry 5.0.

In this study a 140 unique research topics were identified, 19 of which had sufficient research impact and research interest so that we could mark them as the most significant. In addition to the most significant topics, this study contains a detailed analysis of their development and points out their connections.

Industry 5.0 has three pillars – human-centric, sustainable, and resilient. The sustainable and resilient aspect of manufacturing has been the subject of many studies in the past. The human-centric aspect of such a systematic description and deep analysis of latent topics is currently just passing through.

This study aims to investigate the determinants of sustainability reporting in the Kingdom of Saudi Arabia (KSA).

Twenty unstructured interviews were conducted to understand thoroughly the determinants and motivations of sustainability reporting among Saudi petrochemical shareholding companies.

This study finds that cultural aspects, compliance with international best practice, competitiveness, reputation and legitimacy are common motivations for sustainability reporting in KSA.

This study has significant implications for industry, especially petrochemical and other highly polluting industries, and for policymakers. There are economic benefits to industry in adopting sustainability reporting, including transparency; and it is suggested that policymakers encourage industries to give more attention to sustainability reporting.

This study provides an original contribution to the extant literature on sustainability reporting, and incrementally adds to knowledge on sustainability reporting in KSA, Gulf cooperation council and Middle East North Africa region countries.

This study aims to assess the impact of personality traits, risk perception and perceived coronavirus disease 2019 (COVID-19) disruption on the investment behavior of individual investors in the Indian stock market.

This study adopts a survey approach. The sample comprises 315 active retail investors investing in the Indian stock exchange. Two-stage analysis technique regression and Artificial Neural Network (ANN) were used for data analysis. Study hypotheses were tested through regression and ANN was adopted to validate the regression results.

Two regression models were modeled to test the research hypotheses. Findings showed that risk perception and COVID-19 disruption have a significant positive and neuroticism has a significant negative impact on short-term investment decisions, while the role of conscientiousness in determining short-term investment decisions was not found significant. Results also showed a positive impact of neuroticism and conscientiousness and a negative impact of risk perception on long-term investment decisions. The role of COVID-19 disruption was found negative but insignificant in predicting long-term investment decisions.

This study has practical implications for many parties like retail investors, financial advisors and policymakers. This study will assist the investors to realize that they do not always take rational financial decisions. This study will suggest the financial advisors to use the knowledge of behavioral finance in making the advisors' advisory and wealth management decisions. This study will also assist the policymakers to outline behaviorally well-informed policy decisions to protect the interests of investors.

India is one of the fast-growing economies in the world. India has a vast population of active investors and determining investors' investment behavior adds novelty to this study as developed economies have remained the main focus of previous studies. The other novel feature of this study is that this study tries to assess the impact of COVID-19 disruption along with personality traits and risk perception on investment behavior. The other valuable factor of this study is the use of ANN to predict the relative importance of the exogenous variables.

This study seeks to investigate the impact of board attributes on environmental, social and governance (ESG) performance, along with exploring the mediating role of carbon emissions in this relationship.

To address this objective, the panel data approach was used to analyze the data were collected from 1,621 European companies from 2017 to 2021.

This study shows that board gender diversity, audit committee independence, expertise and board meeting attendance help enhance ESG performance. On the contrary, board size and composition do not affect ESG performance. The findings also showed that board gender diversity, audit committee independence, expertise and board meeting attendance are negatively related to carbon emissions performance. However, board size is related positively to carbon emissions performance. This indicates that the larger boards of directors may have diverse experiences that enhance the environmental performance of companies. Furthermore, the finding showed companies that contribute to lowering carbon emissions are more willing to improve their ESG performance. Also, carbon emissions mediate the relationship between the board's attributes and ESG performance.

The study's results have significant implications for firm managers in enhancing the efficiency of board decisions in determining environmental practices that matter to various groups of stakeholders. In addition, this study provides valuable input to regulators and policymakers regarding strengthening the regulations and controlling tools that enhance environmental performance.

Given the distinct and unique climates in these countries, research conducted in other parts of the world may not be directly applicable. Therefore, it is crucial to conduct research tailored to the specific climatic conditions of Australia and New Zealand to ensure accuracy and relevance.

Given population growth, urban expansions and predicted climate change, researchers should provide a deeper understanding of microclimatic conditions and outdoor thermal comfort in Australia and New Zealand. The study’s objectives can be classified into three categories: (1) to analyze previous research works on urban microclimate and outdoor thermal comfort in Australia and New Zealand; (2) to highlight the gaps in urban microclimate studies and (3) to provide a summary of recommendations for the neglected but critical aspects of urban microclimate.

The findings of this study indicate that, despite the various climate challenges in these countries, there has been limited investigation. According to the selected papers, Melbourne has the highest number of microclimatic studies among various cities. It is a significant area for past researchers to examine people’s thermal perceptions in residential areas during the summer through field measurements and surveys. An obvious gap in previous research is investigating the impacts of various urban contexts on microclimatic conditions through software simulations over the course of a year and considering the predicted future climate changes in these countries.

This paper aims to review existing studies in these countries, provide a foundation for future research, identify research gaps and highlight areas requiring further investigation.

This study aims to explore the role of eXtensible Business Reporting Language (XBRL) adoption in improving financial statements transparency in the Jordanian context.

The partial least squares structural equation modeling approach was used to analyze the obtained data.

The empirical outcomes indicated that the adoption of XBRL contributes to improving financial statements transparency in listed Jordanian firms in the Amman Stock Exchange, whereas information technology (IT) infrastructure was found to moderate the relationship between XBRL adoption and improving financial statements transparency and hence the related hypotheses were accepted.

This study encouraged the importance of shifting to the adoption of the XBRL which will contribute to improving transparency of financial data and information in listed Jordanian firms and then support the process of decision-making.

The purpose of this study is to investigate the impact of Shariah Advisory Board (SAB), Audit committee (AC) and board of directors (BD) characteristics on the performance of Islamic banks (IBs) in the MENA region.

The paper employs a quantitative approach, utilizing both ordinary least squares (OLS) regression and panel data analysis (random effects models) to examine the relationship between corporate governance variables and the performance of IBs. The sample consists of 50 IBs from 10 countries, spanning a seven-year period (2010–2016), with the exclusion of the Covid-19 pandemic period. To ensure the robustness of the results, various sensitivity tests were conducted, including pooled regression OLS and subsample analysis based on adhering to the Accounting and Auditing Organization for Islamic Financial Institutions (AAOIFI) standards.

The study's findings suggest that the size of the SAB and the membership of at least one member of the SAB on the AAOIFI have a notable adverse effect on the performance of IBs. On the other hand, the AC independence has a positive influence on bank performance. However, there was no significant impact observed for AC size, meeting frequency and BD characteristics on bank performance. The research also revealed nuanced relationships between governance variables and bank performance when analyzing the sample based on AAOIFI adoption. Among banks not adhering to AAOIFI standards, SAB size and CEO duality negatively affected return on assets, while AC independence positively impacted it. For AAOIFI-compliant banks, AC independence significantly improved bank performance, whereas AC meetings exhibited a negative effect. Furthermore, there were no significant relationships observed for return on equity among banks not adhering to AAOIFI standards, whereas AAOIFI-compliant banks experienced positive impacts from AC independence. These results offer valuable insights into the intricate connection between governance attributes and bank performance, particularly in the context of AAOIFI standards adoption.

The study's findings have important practical implications for various stakeholders in the Islamic banking industry. For bank practitioners and management, the study highlights the significance of enhancing the independence of AC to improve decision-making and risk management, leading to better bank performance. Moreover, careful selection of SAB members can mitigate potential negative effects on performance. Policymakers may consider promoting AAOIFI standards to shape the relationship between governance and bank performance. Investors can use the insights to make informed decisions, and banks with stronger governance may attract more investments.

Through quantitative analysis and AAOIFI-based sample division, this study adds to the growing literature on corporate governance and the performance of IBs by examining the impact of multiple corporate governance variables on the performance of IBs in the MENA region. To provide a theoretical basis for this relationship, three theories, namely agency, stewardship and stakeholder theories, are employed and discussed.

Automatically extracting stance information from natural language texts is a significant research problem with various applications, particularly after the recent explosion of data on the internet via platforms like social media sites. Stance detection system helps determine whether the author agree, against or has a neutral opinion with the given target. Most of the research in stance detection focuses on the English language, while few research was conducted on the Arabic language.

This paper aimed to address stance detection on Arabic tweets by building and comparing different stance detection models using four transformers, namely: Araelectra, MARBERT, AraBERT and Qarib. Using different weights for these transformers, the authors performed extensive experiments fine-tuning the task of stance detection Arabic tweets with the four different transformers.

The results showed that the AraBERT model learned better than the other three models with a 70% F1 score followed by the Qarib model with a 68% F1 score.

A limitation of this study is the imbalanced dataset and the limited availability of annotated datasets of SD in Arabic.

Provide comprehensive overview of the current resources for stance detection in the literature, including datasets and machine learning methods used. Therefore, the authors examined the models to analyze and comprehend the obtained findings in order to make recommendations for the best performance models for the stance detection task.

Additive manufacturing of polymer composites is a transformative technology that leverages the benefits of both composite material and 3D printing to produce highly customizable, lightweight and efficient composites for a wide range of applications.

In this research work, glass fiber-reinforced polylactic acid (PLA) filament is used to print the specimen via fusion deposition modeling process. The process parameters such as infill densities (40%, 50% and 60%) and raster angle/orientations (0°, 45° and 90°) are varied, and the specimens for tensile, flexural, impact, hardness and wear testing are prepared as per their respective ASTM standards.

The results revealed that with an increase in infill density, the mechanical properties of glass fiber-PLA specimens increase progressively. Optimal tensile properties and flexural properties are obtained at 0° and 90° raster angle orientations and 60% infill density. Minimum wear rate is achieved at 0° raster angle orientation and it increases at 45° and 90° raster angle orientations.

Using SEM, the microscopic analysis of the fractured specimen was analyzed to study the interface between the fibers and matrix and it indicates the presence of good adhesion between the layers at 60% infill density and 0° print orientation.

This study aims to address the noted gap in comprehensive overviews detailing the developmental trajectory of Islamic finance (IF) as an interdisciplinary academic field.

The study introduces a unique approach using the combined methodologies of co-word analysis and main path analysis (MPA) by examining a broad collection of IF research articles.

The investigation identifies dominant themes and foundational works that have influenced the IF discipline. The data reveals prominent areas such as Shariah governance, financial resilience, ethical dimensions and customer-centric frameworks. The MPA offers detailed insights, narrating a journey from the foundational principles of IF to its current challenges and opportunities. This journey covers harmonizing religious beliefs with contemporary financial models, changes in regulatory landscapes and the continuous effort to align with broader socioeconomic aspirations. Emerging areas of interest include using new technologies in IF, standardizing global Islamic banking and assessing its socioeconomic effects on broader populations.

This study represents a pioneering effort to map out and deepen the understanding of the IF field, highlighting its dynamic evolution and suggesting potential avenues for future academic exploration.