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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.

There are growing concerns about ongoing digital innovation in the private sector, which poses the possibility of central banks losing their autonomy and even being backward in payment efficiency. Hence, CBDC has become the reality of central banks with several economies' ongoing digitalization and adaptation. This review explores the growing literature on CBDC design, its emergence, variations, potentials, and challenges for smooth future applications. It addressed research articles and gray literature to accumulate the current implementation concerns. Findings show that CBDC poses prospects for exploiting emerging technology, efficient payment systems, tax collections, and tracking illegal financial activities. However, there were mixed expectations regarding financial inclusion, financial stability, bank runs, disintermediation, monetary policy, privacy, security, technology, sustainability, and regulatory framework. Finally, as CBDCs will reduce the influence of private money creation and digital alternatives, they ensure that the central bank remains at the center of the financial system in the future. 

This paper examines the exchange rate pass-through (ERPT) and its impact on home inflation in Bangladesh, particularly in the post-pandemic era. Examining important economic factors including the nominal exchange rate (NER), money supply (M2), industrial production (IP), and global commodity index (GCI) using monthly data from January 2002 to August 2023 helps one to grasp their combined influence on consumer price inflation (CPI). By means of Johansen cointegration and Vector Error Correction Model (VECM) approaches, the study reveals important short-term and long-term dynamics. Contrary to patterns in many emerging nations, the results show that changes in exchange rates have apparently little impact on home prices. Rather, changes in the money supply and world commodities prices mostly influence inflation; the latter becomes a major factor. The study also exposes an unexpected positive link between industrial production and CPI, implying complexity in Bangladesh's production-driven economic structure. Moreover, this investigation underlines how complex monetary expansion, external commodities shocks, and devaluation of money interact to shape inflationary pressures. These results offer legislators important new perspectives and support strong monetary policies and focused involvement in world commodities markets to help to lower inflation. This study clarifies ERPT in developing countries and provides direction on inflation control techniques for Bangladesh.

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.  

The research investigates how goal contagion impacts the acceptance of digital technologies and also explores the moderating effect of COVID-19 and survival instinct within the interconnection between goal contagion and the acceptance of digital technologies. The research has undertaken partial least squares structural equation modeling to identify the direct as well as moderating effects based on a sample of 250 individuals across diverse firms in Bangladesh. The research also involved the Importance-Performance Map Analysis approach (IPMA) to explore key areas of importance and performance, thus providing a holistic understanding of key factors impacting the acceptance of digital technologies. Then, an ANN model analysis was used to determine how important the components determined by the PLS-SEM study were in terms of relative influence. The study revealed that others’ behavior and situational context have a significant impact on goal contagion. The outcome also showed that goal contagion has a significant impact on the acceptance of digital technologies; besides, survival instinct and COVID-19 have a statistically significant moderating effect between goal contagion and the acceptance of digital technologies. The IPMA findings showed that COVID-19 and goal contagion are the most important factors for the acceptance of digital technologies. The ANN findings revealed that situational context is the most contributing construct for goal contagion. The findings also demonstrated that COVID-19 and goal contagion are the most influential constructs for the acceptance of digital technologies. The findings of this research contribute significantly to the relevant knowledge by promoting a theoretical framework for comprehending the factors that influence the acceptance of digital technologies. Furthermore, this research illustrates valuable insights for companies that are planning to make strategic decisions regarding their digital technologies.

Employee promotion in State-Owned Commercial Bank (SOCB) and Private Commercial Bank (PCB) depends on Key Determining Factors (KDF). The study aims to identify these factors. The objective is to compare the impact of KDF on employee promotion between SOCB and PCB. The study finds that Education, Qualifications, Job performance and Skills do not impact promotion in SOCB, whereas Experiences, Tenures, Work ethics and Dependability determine employee promotion in PCB. Social relationships and interpersonal relationships both influence employee promotion at PCB. Bias and Favouritism exist from a certain level in SOCB, whereas the study unveils a strong presence of Bias and Favouritism in PCB. There is no presence of Age, Gender and Ethnicity in employee promotion in SOCB, while PCB experienced a mixed influence of Age, Gender and Ethnicity on employee promotion. While there is no influence of Union Status, Firm Size on employee promotion in PCB, an indirect influence on employee promotion has been observed in this study. Luck and External factors both influence employee promotion in SOCB and PCB. Seniority is maintained up to a certain level in SOCB, after that, both merit and seniority is considered whereas performance plays a pivotal role in employee promotion in PCB.

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.