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This study undertakes a comparative analysis of the regulatory framework for sharia-compliant financial technology (fintech) in Gulf Cooperation Council (GCC) nations. The purpose of this study is to identify the strengths and weaknesses of this regulatory framework as well as enhance opportunities and best practices. This study also investigates the potential impact of Islamic fintech on financial inclusion in the GCC nations.

This study uses a qualitative research methodology, including semi-structured interviews with key stakeholders in the Islamic fintech industry, such as entrepreneurs, investors, regulators and policymakers. This study recruited interview participants from the Islamic fintech industry in GCC countries, including Saudi Arabia, United Arab Emirates, Bahrain, Oman, Qatar and Kuwait.

This study’s main finding is that Islamic fintech has the potential to promote financial inclusion in GCC countries. According to this study’s findings, Islamic fintech provides a more ethical and accessible alternative to traditional banking services, particularly for individuals and businesses that are underserved or excluded from mainstream financial services.

This study has practical implications for policymakers and regulators in GCC countries, providing valuable insights for promoting the growth and development of the Islamic fintech industry while ensuring that the regulatory framework is conducive to its growth. This study contributes to the broader literature on regulatory frameworks for fintech by highlighting the need for regulatory frameworks to adapt to technological advances in the rapidly evolving fintech field.

This study derives originality and value from a comparative analysis of the regulatory framework for Islamic fintech in GCC nations and its prospective impact on financial inclusion.

This study aims to understand the dynamics of Australian boards by focusing on the influence of board gender diversity on firms' cash holdings, within the distinctive Australian “if not, why not” regulatory framework.

The study uses ordinary least squares (OLS), fixed effects, generalized method of moments (GMM) and quasi-experimental methods such as difference-in-differences and propensity score matching to analyze the data.

There is a significantly negative relationship between board gender diversity and corporate cash holdings. This relationship is more pronounced when two or more female directors are on the board, supporting the critical mass theory. The results also reveal that the observed pattern can be attributed to the heightened monitoring intensity of female independent directors. Our quasi-experimental methods and pre-post analysis reveal that the observed effects are genuinely attributable to the increase in board gender diversity following regulatory reforms in Australia.

The findings provide practical insights for companies and policymakers, emphasizing the tangible effects of gender diversity on a company's financial strategy and corporate cash holdings. This information is crucial for organizations aiming to make informed decisions regarding board compositions and governance structures.

This research offers fresh insights into an important relationship between gender diversity on boards and corporate financial strategies in the Australian context, enriching the global conversation on the significance of gender diversity in corporate leadership.

This study draws on financial slack, agency, and critical mass theories to investigate risky firms’ ESG engagement, board gender diversity’s moderating role between firm risk and ESG engagement, market reaction to risky firms’ ESG engagement, and board gender diversity’s role in moderating market reaction to risky firms’ ESG engagement.

The study uses a sample of 44,129 firm-year observations between 2005 and 2019 across nine industries and 61 countries. We adopt Refinitiv’s (LSEG Workspace database) scheme in assessing firm ESG performance.

We find that firm risk is significantly and negatively associated with ESG performance. Board gender diversity (1) negatively moderates between firm risk and the environmental pillar (2) negatively moderates between firm risk and the social pillar, (3) negatively moderates between firm risk and CSR strategy metric of governance pillar but positively moderates between firm risk and management metric of the governance pillar. We show that as the number of female director increases, their moderating effect between firms’ risk and ESG performance becomes stronger. The existence of a critical mass of female directors on the board alleviates the market’s negative reaction to ESG engagements.

Although plenty of prior studies focused on board gender diversity’s role in driving firm outcomes, its role in risky firms’ ESG engagement is yet to be explored. It is imperative to investigate risky firms’ engagement in ESG because these firms face more financial distress and are more concerned about their short-term survival whilst investing in ESG is specifically sensitive to the accessibility of slack resources. Consequently, risky firms may have less flexibility to initiate ESG activities or cease them.

This study aims to examine whether board gender diversity is significantly and positively associated with corporate environmental, social and governance (ESG) performance, as well as the link between board gender diversity and each of the three ESG pillars.

Aligning with stakeholder and critical mass theories, this study uses ordinary least squares regression and fixed-effects models on the sample comprising New Zealand listed firms from 2016 to 2022.

The findings reveal that board gender diversity positively and significantly is associated with firms’ overall ESG performance. In addition, female board representation positively and significantly associates with the governance pillar but, not with the other two. The findings highlight that a higher proportion of female board representation enhances a firm’s overall ESG performance by improving its corporate governance initiative.

Besides adding to the literature on board gender diversity’s association with New Zealand listed firms’ ESG performance, the findings provide insights for companies, policymakers and stakeholders.

This study aims to focuse on the flow behavior of a specific nanofluid composed of blood-based iron oxide nanoparticles, combined with motile gyrotactic microorganisms, in a ciliated channel with electroosmosis.

This study applies a powerful mathematical model to examine the combined impacts of bio convection and electrokinetic forces on nanofluid flow. The presence of cilia, which are described as wave-like motions on the channel walls, promotes fluid propulsion, which improves mixing and mass transport. The velocity and dispersion of nanoparticles and microbes are modified by the inclusion of electroosmosis, which is stimulated by an applied electric field. This adds a significant level of complexity.

To ascertain their impact on flow characteristics, important factors such as bio convection Rayleigh number, Grashoff number, Peclet number and Lewis number are varied. The results demonstrate that while the gyrotactic activity of microorganisms contributes to the stability and homogeneity of the nanofluid distribution, electroosmotic forces significantly enhance fluid mixing and nanoparticle dispersion. This thorough study clarifies how to take advantage of electroosmosis and bio convection in ciliated micro channels to optimize nanofluid-based biomedical applications, such as targeted drug administration and improved diagnostic processes.

First paper discussed “Numerical Computation of Cilia Transport of Prandtl Nanofluid (Blood-Fe₃O₄) Enhancing Convective Heat Transfer along Micro Organisms under Electroosmotic effects in Wavy Capillaries”.

Engaged employees assure organizational competitiveness and sustainability. The purpose of this study is to explore the relationship between job resources and employee turnover intentions, with employee engagement as a mediating variable.

Data were collected from 934 employees of eight wholly-owned pharmaceutical industries. The proposed model and hypotheses were evaluated using structural equation modeling. Construct reliability and validity was established through confirmatory factor analysis.

Data supported the hypothesized relationship. The results show that job autonomy and employee engagement were significantly associated. Supervisory support and employee engagement were significantly associated. However, performance feedback and employee engagement were nonsignificantly associated. Employee engagement had a significant influence on employee turnover intentions. The results further show that employee engagement mediates the association between job resources and employee turnover intentions.

The generalizability of the findings will be constrained due to the research’s pharmaceutical industry focus and cross-sectional data.

The study’s findings will serve as valuable pointers for stakeholders and decision-makers in the pharmacuetical industry to develop a proactive and well-articulated employee engagement intervention to ensure organizational effectiveness, innovativeness and competitiveness.

By empirically demonstrating that employee engagement mediates the nexus of job resources and employee turnover intentions, the study adds to the corpus of literature.

Implementing big data analytics (BDA) for supply chain ambidexterity (agility and adaptability) and green supply chain (GRSC) presents various organizational challenges. These include leveraging BDA capabilities to balance agility and adaptability, integrating this combined approach with GRSC and aligning these efforts to enhance firm performance. This study explores the associations between BDA, supply chain agility and adaptability, GRSC and their impact on firm performance.

Incorporating a resource-based view and contingency theory, we developed a research framework and validated it with data from 355 Chinese firms. Partial least squares structural equation modeling was used to analyze the data.

The findings demonstrate that BDA capabilities had direct impact on supply chain agility and adaptability, GRSC and firm performance. Moreover, the combination of supply chain agility and adaptability affected GRSC; which in turn significantly influenced firm performance. Supply chain agility and adaptability mediated the relationship between BDA capabilities and GRSC. Additionally, GRSC mediated the relationship between BDA capabilities, supply chain agility and adaptability and firm performance.

This study offers both a theoretical and empirical examination of the relationships between BDA capabilities, supply chain agility and adaptability, GRSC and firm performance. By assessing the direct and mediating effects of these factors on China’s industrial sector, it presents new theoretical and practical insights into BDA and GRSC, thereby enhancing the value of the existing literature.

The purpose of this study is to find out the relationship between the corporate social responsibility (CSR) and green performance (GP) of Pakistani manufacturing SMEs. This study further explores the mediating roles of green human resource management (GHRM) and green innovation (GI) in the relationship between CSR and GP.

A survey method was used to collect data from manufacturing SMEs. Data were collected from 366 respondents working in higher positions and playing a decisive role in the organization. The collected data were analysed by applying structural equation modelling with the help of smart PLS.

The study shows that CSR (customers, society, employees) helps significantly improve a firm's GP. Furthermore, this study explores how GI (process, product) and GHRM (skills development, motivation and involvement) mediate the relationship between CSR and GP.

This study is limited to manufacturing SMEs and a single developing country, Pakistan. However, this study will significantly contribute to the existing literature on GP and help manufacturing firms’ top management take steps to minimize carbon emissions and improve GP. Furthermore, this study will also provide valuable insights to government agencies in the Asian context to adjust their policies regarding the manufacturing sector to reduce pollution in the country.

As a pioneering study encompassing CSR, GHRM, GI and GP under one research paradigm in an emerging economy environment, the current research provides substantial additions to the literature on the impact of CSR on GP.

The purpose of this study is to evaluate the impact of green human resource management (GHRM) practices on Indian companies of different sectors and to identify the most critical GHRM practices that can lead to a more sustainable and environmentally friendly workplace.

This study uses an integrated Analytic Hierarchy Process-Evaluation based on Distance from Average Solution approach to determine the importance of 32 GHRM practices classified into eight categories, as identified through literature review and expert consultation. This study also identifies the best sector for GHRM practices in India.

This study reveals that employee engagement is the most critical practice among all the GHRM practices identified. India’s Information Technology-Enabled Services sector benefited the most from GHRM practices, followed by the Insurance sector.

This study contributes to the literature on GHRM practices and their impact on organisations and sectors. The integrated Analytic Hierarchy Process-Evaluation based on Distance from Average Solution approach used in this study is innovative and can be helpful for Indian companies to prioritise and implement effective GHRM practices.

This study investigates clean energy, commodities, green bonds and environmental, social and governance (ESG) index prices forecasting and assesses the predictive performance of various factors on these asset prices, used for the development of a robust forecasting support decision model using machine learning (ML) techniques. More specifically, we explore the impact of the financial stress on forecasting price.

We utilize feature selection techniques to evaluate the predictive efficacy of various factors on asset prices. Moreover, we have developed a forecasting model for these asset prices by assessing the accuracy of two ML models: specifically, the deep learning long short-term memory (LSTM) neural networks and the extreme gradient boosting (XGBoost) model. To check the robustness of the study results, the authors referred to bootstrap linear regression as an alternative traditional method for forecasting green asset prices.

The results highlight the significance of financial stress in enhancing price forecast accuracy, with the financial stress index (FSI) and panic index (PI) emerging as primary determinants. In terms of the forecasting model's accuracy, our analysis reveals that the LSTM outperformed the XGBoost model, establishing itself as the most efficient algorithm among the two tested.

This research enhances comprehension, which is valuable for both investors and policymakers seeking improved price forecasting through the utilization of a predictive model.

To the authors' best knowledge, this marks the inaugural attempt to construct a multivariate forecasting model. Indeed, the development of a robust forecasting model utilizing ML techniques provides practical value as a decision support tool for shaping investment strategies.