Explore the evidence.
Understand the reach.

This paper explores the relationship between environmental, social, and governance performance (ESGP) and firm value in both emerging and developed economies, with the moderating impact of environmental uncertainty and competitive advantage. Using secondary unbalanced data spanning from 2011 to 2022, we examine both the direct and non-linear effects of ESG performance on firm value, employing advanced econometric estimation techniques such as two-step system GMM (2sGMM) and two-stage least square (2SLS). The analysis finds that: (1) ESG performance positively influences firm value in both Chinese and US banks, and the results remain valid after robustness test; (2) The results also confirm that ESG performance non-linearly influence firm value across the samples, with an inverted U-shape relationship; (3) Additionally, we further investigate the moderating effects of environmental uncertainty and competitive advantage, uncovering that environmental uncertainty dampens the ESGP-firm value relationship, while competitive advantage further amplifies it. This study contributes to the understanding of how ESG performance can act as strategic assets that align managerial and stakeholder interests, and improve firm performance, by underpinning agency, stakeholder, and the resource-based view theory. Practical insights are also provided for policymakers, regulators, and financial institutions regarding the use of ESGP as a competitive tool in uncertain and dynamic market environments.

The growing prevalence of mental health issues, particularly depression and suicidal thoughts, points to the need to develop automated tools capable of detecting such sentiments from online communication. This study addresses some critical challenges by introducing a novel sentiment analysis framework for Bangla text, aimed at classifying content into non-depressive, depressive, and suicidal categories. We propose a hybrid deep learning model leveraging the strengths of transformer-based architectures, designed to manage long textual sequences effectively, a critical aspect in the context of natural language processing. Our model integrates RoBERTa (Robustly Optimised BERT Pre-Training Approach) with a Self-Attention Network (SAN), creating a synergistic framework for nuanced sentiment detection in Bangla social media posts, comments, and articles. This method addresses the scarcity of Bangla specific datasets by utilising a dataset curated for the study. The results demonstrate the superiority of our model, achieving an accuracy of 82.58%, alongside precision, recall, and F1-scores of 82%. Subsequently, it emphasises the potential for the proposed model to support early identification of mental health concerns, thereby enabling timely interventions and contributing to mental health awareness and prevention efforts. In the future, deploying the model as a real-time chatbot or browser extension could scan Bangla social media for depressive, non-depressive, and suicidal content and alert professionals to the risk factors.

Recent evidence indicates that citizens exhibit low involvement in financial activities within formal financial institutions. Therefore, this study explores how Digital Financial Literacy (DFL) influences savings in formal financial institutions. Using data from the Global Findex Database 2021 and the Standard & Poor’s Global FinLit Survey 2014, covering 135 countries, the study applies multiple regression analysis to assess how DFL predicts savings in formal financial institutions, while controlling for various country-level factors. The results show that among the three dimensions of digital financial literacy, financial skills and behaviors positively affect savings. In contrast, financial attitudes like trust in financial institutions and confidence in managing financial matters negatively affect savings behavior. Overall, financial literacy explains 78.8% of the variation in savings at the formal financial institutions across countries, with cross-validation supporting the robustness of the findings. This study contributes to existing literature by integrating several digital and segmented dimensions of financial literacy and enhancing external validity by including a broad range of countries for cross-country generalization in financial inclusion research.  

This study investigates the relationship between banks' corporate governance practice and its impact on financial sustainability. In this study, twenty-four private commercial banks' data have been analyzed from 2014 to 2023. Our investigations included a variety of econometric models, including cross-sectional GLS, fixed effect, random effect, and pooled OLS. In our study, we found that including women on the Board of directors plays a positive role in ensuring the financial sustainability of banking sectors in Bangladesh. Apart from that, increasing the participation of independent directors on the board of directors, expenditures in research and development in the best banking practices, and inflation positively increase banks' profitability, which translates into financial sustainability. This finding is consistent with the findings of the random effect model. On the other hand, whenever we used ROE as an explained variable, we found that both in pooled OLS  and random effect methods, board diversity, CEO tenure, firms audited by the big four firms, and inflation have a positive association with ROE. In the fixed effect method, we observed that audit committee size and ownership concentration play a vital role in explaining the financial sustainability of private commercial banks in Bangladesh. In the FGLS method, We found that CEO tenure, age of the firm, firms audited by the big four firms, presence of the independent director on the board, and inflations significantly impact the financial sustainability of commercial banks of Bangladesh.

Financial inclusion aims to include the unbanked population in the official financial system. To quickly serve the unbanked population, this study aims to investigate the concept of agent banking as a means of financial inclusion and to assess the elements that influence the acceptance of agent banking in Bangladesh. The study also covered the main advantages of agent banking and pertinent barriers to its adoption. Using a basic judgmental sampling technique, 195 respondents from the Chittagong region made up the study's sample. Face-to-face interviews and a structured questionnaire were used to collect the data. The impact was investigated using statistical measures such as mean, standard deviation, correlation, and regression. Kendall's W test, also known as Kendall's coefficient of concordance, was utilized to evaluate the benefits and drawbacks of accepting agent banking in this study. According to the result, there is a significant positive correlation between acceptance of agent banking and independent variables - perceived trust, convenience, perceived risk, awareness, and service quality. The study also found that easy banking is the key reason for agent banking, marked by the respondents, followed by the nearest location and adding new customers. Lack of training is the major challenge, followed by a lack of logistic support and cyber security risk in implementing agent banking in Bangladesh.

Our way of life has evolved due to the advent of AI. Artificial intelligence (AI) is already having an impact on many parts of the industry and will likely find additional uses in the future. This research study investigates the students' readiness to adopt AI. An exploratory research design undergirds this investigation. A questionnaire (based on a prior study) survey was used to gather data. The SPSS and AMOS Graphics are used to conduct EFA and CFA. 387 respondents were taken into consideration for the study. In this investigation, the KMO value is 0.839. Factor loading is larger than 0.45, with 22 items. This study extracts five components: educational content (EC), institutional facilities (IF), artificial intelligence awareness (AW), acceptance barriers (AB), and usage/application (AP). The Eigen values of those components are 5.056, 2.420, 1.640, 1.411, and 1.000, explaining 52.4% of the total variation. The CFA model fit findings show that CMIN/DF=2.360, GFI= 0.0.913, AGFI= 0.884, CFI= 0.880, RMSEA=0.05, SRMR=0.05, Hoelter's N returns value at 5% significant level = 195 and at 1% significant level = 210, Chi square= 372.870, df=158, p=0.000. Today, AI may boost tertiary students' productivity. Engaging pupils with technology in our country helps determine their AI interests. The study shows how educational content, institutional facilities, artificial intelligence knowledge, acceptance hurdles, and usage/application affect students' preparedness to adopt AI. This research will help higher education authorities to introduce IT infrastructure, teach students to understand AI technology, and inspire future academics to contribute to this era of intelligent robots.

The growing prevalence of mental health issues, particularly depression and suicidal thoughts, points to the need to develop automated tools capable of detecting such sentiments from online communication. This study addresses some critical challenges by introducing a novel sentiment analysis framework for Bangla text, aimed at classifying content into non-depressive, depressive, and suicidal categories. We propose a hybrid deep learning model leveraging the strengths of transformer-based architectures, designed to manage long textual sequences effectively, a critical aspect in the context of natural language processing. Our model integrates RoBERTa (Robustly Optimised BERT Pre-Training Approach) with a Self-Attention Network (SAN), creating a synergistic framework for nuanced sentiment detection in Bangla social media posts, comments, and articles. This method addresses the scarcity of Bangla specific datasets by utilising a dataset curated for the study. The results demonstrate the superiority of our model, achieving an accuracy of 82.58%, alongside precision, recall, and F1-scores of 82%. Subsequently, it emphasises the potential for the proposed model to support early identification of mental health concerns, thereby enabling timely interventions and contributing to mental health awareness and prevention efforts. In the future, deploying the model as a real-time chatbot or browser extension could scan Bangla social media for depressive, non-depressive, and suicidal content and alert professionals to the risk factors.

Recent evidence indicates that citizens exhibit low involvement in financial activities within formal financial institutions. Therefore, this study explores how Digital Financial Literacy (DFL) influences savings in formal financial institutions. Using data from the Global Findex Database 2021 and the Standard & Poor’s Global FinLit Survey 2014, covering 135 countries, the study applies multiple regression analysis to assess how DFL predicts savings in formal financial institutions, while controlling for various country-level factors. The results show that among the three dimensions of digital financial literacy, financial skills and behaviors positively affect savings. In contrast, financial attitudes like trust in financial institutions and confidence in managing financial matters negatively affect savings behavior. Overall, financial literacy explains 78.8% of the variation in savings at the formal financial institutions across countries, with cross-validation supporting the robustness of the findings. This study contributes to existing literature by integrating several digital and segmented dimensions of financial literacy and enhancing external validity by including a broad range of countries for cross-country generalization in financial inclusion research.  

  This study aims to explore the effect of foreign direct investment on the educational attainment.  This paper employed the POLS and 2SLS models. In Pooled Ordinary Least Square Model, educational attainment in primary, lower secondary, upper secondary and masters’ level have the significant positive relationship with foreign direct investment. In Two stage Least Square Model, educational attainment in primary, lower secondary, upper secondary, tertiary and masters’ level have the significant positive relationship with foreign direct investment. This study contributes to look at the impact foreign direct investment on educational attainment. Panel data from 38 Asian countries was gathered for this study, which spanned the years 2011 to 2022. The study results have important policy implications as they are directly linked to the United Nations’ Sustainable Development Goals (SDGs) Target 4.1, which aims to guarantee that all girls and boys get free, equitable, and high-quality primary and secondary education by 2030, resulting in relevant and effective learning outcomes.

This study investigates the relationship between corporate governance and firm performance in the Bangladeshi insurance sector, focusing on variables like board independence, female directors, audit committee size, leverage, and firm age. Using a sample of 42 insurance firms from 2017 to 2021, regression analysis is employed to assess these relationships. The findings show that board independence positively impacts performance, while audit committee size, leverage, and firm age have negative effects. Female directors exhibit a marginal but weak positive influence. This research contributes to the literature by providing empirical evidence from a developing economy, where limited studies exist. It also offers insights into governance practices in emerging markets. The implications suggest that policymakers and corporate leaders in Bangladesh should prioritize enhancing board independence, managing leverage, and promoting gender diversity to improve performance. Future research could explore causal relationships and extend the analysis to other sectors or emerging markets, considering cultural and regulatory factors.