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The study aims to assess the awareness, perception and adoption of artificial intelligence (AI) in Bangladesh’s healthcare sector.

This study utilizes a quantitative methodology. A survey with structured questionnaire was conducted with a sample of 399 healthcare professionals and public members through stratified random sampling. Descriptive statistics summarized participant demographics, while inferential statistical techniques, including regression analysis, examined relationships between AI readiness and adoption.

Using a conceptual framework, the study explored factors influencing AI adoption in Bangladesh’s healthcare sector. The measurement model confirmed reliability and validity, with strong factor loadings and discriminant validity. Structural model analysis revealed that social media influence (SMI) and technological awareness (TA) significantly enhanced readiness for AI (RAI) (path coefficients: 0.354 and 0.162, respectively). Perceived risk (PR) had a weaker positive effect (0.123), while perceived susceptibility (PS) and personal innovativeness (PI) were insignificant. Mediation analysis showed that RAI significantly mediated the effects of TA and PR on the adaptation of AI (AAI).

The study suggests policymakers develop robust regulatory frameworks to address privacy concerns, enhance trust in AI and implement educational initiatives to improve AI literacy among healthcare stakeholders in Bangladesh.

This study offers insights into AI adoption in Bangladesh’s healthcare sector. It addresses gaps in awareness and perceptions among professionals and the public, contributing to the limited research in this context.

This study aims to investigate the factors influencing Indian banks’ choice of green loan disclosure practices. The study analyzes the effect of financial and governance variables to understand the sustainable reporting (through green lending) behavior of Indian banks.

The data on green loan disclosure has been hand-collected from the annual reports using a content analysis approach. Using the data of 26 banks for 12 years (2012–2023), the study uses the panel regression method to control for cross-sectional heterogeneity and generalized methods of the moment to address potential endogeneity issues.

The empirical results depict that larger banks with sufficient risk capital and a strong corporate governance framework demonstrate greater disclosure of green loans. However, growth opportunities and higher market value impedes the reporting of green lending.

The findings of the study will enhance the extant literature on sustainability disclosure by integrating the financial sector companies in the context of an emerging economy. However, future research may include nonbanking finance companies as well.

Banks use societal deposits to invest in productive avenues, and therefore, it is paramount to understand their social and environmental consciousness while evaluating a financing proposal. This research provides a thorough understanding of the sustainable reporting of banks through the lens of green lending.

This research provides unique evidence on the bank-specific determinants of green loan disclosure in an emerging economy context as against the extant literature which primarily focused on sustainable reporting of nonfinancial companies.

This paper aims to analyze the scientific trend of research on Islamic banking sustainability (IBS) through a bibliometric study. In particular, the paper extensively investigates all the articles issued through the Scopus database regarding the IBS.

The authors discovered 76 papers that met the function, subject and set requirements by using the phrase IBS. The authors used VOSviewer as an analytical tool and the Scopus website.

IBS publications were found in the period 2005–2022, and the publication trend of IBS research demonstrates that it is growing exponentially after 2018. Malaysia is the leading country in terms of productive authors, universities, number of documents, citations and collaboration research on IBS. The current research trends are summarized into five cluster maps for future research directions: sustainability measurement, sustainability practices, risk and governance, corporate social responsibility (CSR) and IBS theory. The Maqashid al Shariah approach conceptually influences the framework for constructing the dimensions and indicators used to measure the IBS.

The authors retrieved data for their research from the Scopus database; using other databases might result in totally different research patterns with this IBS bibliometric research.

The research encompasses valuable implications for Islamic banking as it offers valuable insights on how to assess the performance of IBS. Particularly, it contributes to identifying the dimensions and indicators needed to measure IBS performance. Furthermore, this research provides strategic initiatives to promote sustainable practices in Islamic banking in terms of green financing taxonomy, services, operations, risk management and governance.

This research is valuable for other scholars as it offers a foundation for the future growth of IBS research, focusing on important sustainability clusters obtained from selected reputable journals. This research is beneficial for regulators in enhancing the roadmap for establishing and enhancing long-term IBS with impacts on socio-economic, environmental and governance.

The study presents a concise review of the bibliometric study in IBS and provides recommendations for future research directions in cluster mapping of themes and subthemes. There is still insufficient research that examines the IBS, in particular, complete insights into the IBS literature review.

This study aims to examine the mediating role of green finance in the relationship between green banking practices and the sustainability performance of banking institutions in developing economies.

The authors performed an empirical investigation by applying the “partial least squares structural equation modeling (PLS-SEM)” based on a representative sample of 414 bank employees working in the National Capital Region, India.

The study’s outcome confirms that employee, top-management, operation and policy related practices substantially influence green finance and banks’ sustainability performance. On the contrary, customer related practices insignificantly influence banks’ sustainability performance. Further, green finance substantially influences the sustainability performance of banking institutions.

This study shed light on green banking practices that can assist in achieving the vision of the “Clean India Mission” of the Indian government. In addition, it encourages policymakers and bank managers to fulfill their social responsibility by engaging employees and customers in cleaner operations to promote banks’ sustainability performance.

This is ground-breaking research that enriches the understanding of green banking practices and green finance by providing a novel theoretical framework concerning the sustainability performance of banking institutions. Theoretically, this paper also broadens the scope of corporate social responsibility literature by applying the resource-based view theory in finance and banking.

This study examines the impact of chief executive officer (CEO) power, institutional investors and their interaction on green financing provided by Bangladeshi financial institutions and the moderating effect of government policy and CEO political connections on these relations.

We employ ordinary least squares (OLS) regressions and interaction terms among variables of interest for the empirical analysis.

Green financing decreases with CEO power, implying that CEOs of this country’s financial institutions are averse to green loans, whereas institutional investors increase green financing extended by these institutions. The government policy, which includes financial incentives for complying financial institutions, strengthens institutional investors' positive impact on green financing, but it does not change CEOs' aversion to green loans. Institutional investors have a positive moderating effect on the relationship between green finance (GF) and CEO power, but this positive moderating effect is negated in banks where the government owns a stake, possibly because CEOs of state-owned financial institutions are politically connected, which reduces institutional investors’ influence over them.

This study is unique in that it is the first to examine how the interaction among different stakeholders affects green financing in a unique setting. As the literature is almost silent on this topic, the findings of this paper are expected to raise policymakers’ awareness of the obstacles that hamper the efforts of developing countries to go green.

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.

This study aims to investigate the effects of environmental, social and governance (ESG) performance on financial risk disclosure of European companies. It analyzed the relationships between ESG factors and financial risk disclosure between 2010 and 2020.

To test their hypotheses in this study, the authors used the multivariate regression analysis on panel data using the Thomson Reuters ASSET4 database and the annual reports of 154 European companies listed in the ESG index between 2010 and 2020.

Empirical evidence shows a positive association between European companies' environmental and governance performance with financial risk disclosure, whereas social performance does not influence financial risk disclosure. Concerning the control variables, the findings demonstrate that firm size and profitability are significant factors in changing the financial risk disclosure. Nevertheless, firms’ leverage is insignificantly correlated with financial risk disclosure.

This study extends the stream of accounting literature by focusing on the financial risk disclosure, a topic that has received little attention in previous research. Furthermore, to the best of the authors’ knowledge, this study is one of the first that provides ESG companies with evidence of the effect of ESG factors on financial risk disclosure in a developed market like Europe.

In today’s competitive business landscape, organizations are increasingly recognizing the strategic advantage of implementing sustainable practices to gain a competitive edge. This study aims to investigate the effect of green artificial intelligence (AI) on achieving a green competitive advantage, examining the mediating roles of green organizational learning, green product innovation and green process innovation. Additionally, the research explores the moderating role of perceived green climate in the relationship between green AI and these mediating factors.

This research examined companies in Isfahan, Iran, that have varying levels of artificial intelligence adoption within their business processes. The target population consisted of 148 senior managers from these companies. This study uses structural equation modeling to examine the proposed model.

Green AI positively impacted green organizational learning and green process innovation but not green product innovation. In addition, the results showed that green organizational learning, green product innovation and green process innovation had positive effects on green competitive advantage. Finally, the results showed that the perceived green climate did not play a moderating role in the relationship between green AI and these mediating factors.

Organizations should prioritize green AI initiatives, foster a culture of green learning and invest in green innovation to achieve sustainable growth and outpace competitors in the environmentally conscious marketplace.

This study positions itself at the forefront of research on green AI and green competitive advantage. It offers a unique framework by examining the combined effects of green AI, green learning and both product and process innovation on achieving a sustainable competitive advantage.

Islamic banking windows have attracted a considerable deal of scholarly attention lately, as shown by the sharp rise in publications across the globe. The growing number of Islamic banking windows being published demonstrates how much scholarly interest there has been in this topic. To evaluate the body of knowledge on Islamic banking windows, this paper aims to conduct a bibliometric analysis.

A bibliometric examination of literature samples pulled in July 2023 from the Scopus database served as the study’s methodology. In total, 79 publications that met the criteria for selection and were published between 2008 and 2023 made up the study’s sample size. Software like RStudio (Rshiny), VOSviewer and Microsoft Excel have been used to evaluate the data. Three major kinds of analyses were carried out: overall performance indicators, citation analysis and cross-dimensional keyword analysis. A quick content analysis to pinpoint research flows then followed.

The number of publications increased noticeably between 2019 and 2021, with Abdul Majid M. contributing the most with four articles published during that time. The findings also include the countries, groups, publications, works, sources and topics that have contributed the most. This examination found five research streams that have already been examined by other writers out of 79 papers. Comparative efficiency, Islamic banking’s customer, performance, risk and liquidity management are some of the research areas covered. With precise research questions, this study further outlined the future research agenda.

The scope of this study is restricted to English-language journal articles appearing in sources that are Scopus-indexed. A variety of databases, including Dimensions and the Web of Science, as well as expanding the investigated units, could be used in future research. Future studies might center on the systematic review of publications published on certain topics, whereas this study only focused on bibliometric analysis and research streams.

Despite the fact that Islamic banking is a fascinating topic for research, as far as the authors’ knowledge, this study is the first bibliometric analysis of Islamic banking windows. The five research streams identified in this study also offer numerous potential research topics in a unique way.

This paper aims to explore the integration of technology platforms of Fintech service providers in Bangladesh and its outcome on the users’ acceptance and intention to use it. It has considered Bangla QR, a Bangladesh-specific unified payment platform, for the said purpose.

The study investigates the usage and acceptance of Bangla QR’s financial service application by collecting data on user demographics and usage patterns. The data have been collected from Bangla QR users who have firsthand experience using a unified payment system in Bangladesh. Structural equation modeling (SEM) has been used to analyze the data and investigate the relationships between latent constructs, such as Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Hedonic Motivation (HM) and Behavioral Intention (BI).

Results reveal that the experience of users from earlier platforms and their expectation of improved technologies have a positive and significant relationship with the behavior intention of users regarding the acceptance of a fresh fintech platform. Specifically, PE, EE and FC have a significant effect on HM. Moreover, HM mediates the relationship between PE and BI, EE and BI and FC and BI. Startlingly, SI does not affect HM and BI.

The study posits that users of Bangla QR are embracing the unified payment scheme willingly. Experience with new technology in the financial aspects has given them the confidence to continue this in the future. However, social awareness about the intrinsic worth of unified payment should be raised by financial institutions and regulators in Bangladesh. The lack of Bangla QR’s accessibility limits the number of respondents, and its newness hinders the researchers from focusing on price value and habit constituting BI.

First, this is the first paper to explore the fintech users’ intention to continue a unified payment platform in Bangladesh. Second, it uses the UTAUT2 model with modifications as per the requirement of the study. Finally, this study theorizes specific reasons to develop the overall scenario of a unified payment system in the future in Bangladesh.