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This paper investigates the factors that form bank risks in Bangladeshi state-owned banks. As a representation of bank risk, non-performing loans have been considered the dependent variable for this study. The relevant data have been extracted from six state-owned banks in Bangladesh from 2013 to 2022. The final dataset consists of 60 firm-year observations. The ordinary least square (OLS) regression with robust standard errors model is utilized to find the relationship of bank risks with determinants like asset size, efficiency, liquidity, profitability, leverage ratio, capital adequacy ratio, and loan to asset ratio. The findings say that bank size and loan-to-asset ratio have a highly significant positive relationship with bank risk, whereas profitability, capital adequacy ratio, and bank efficiency have a significant negative association with bank risk. On the other hand, leverage and liquidity are negatively and positively correlated with no statistical significance. To check the study's robustness, another proxy of bank risk, loan loss provision has been used and the results are almost similar. State-owned banks operate in every economy with a different mindset than the general commercial banks. Hence, policymakers should take necessary initiatives based on this study to bring down the high bank risks in state-owned banks in Bangladesh.

This study examines the Job facilities and the factors affecting employee satisfaction levels and how significantly they are correlated to each other. In the world of business research, one of the vital challenges is employee commitment toward the company. According to research, employee dedication and performance are strongly correlated with employee contentment. That is why employers and researchers are constantly looking for explanations for employee happiness. Several aspects affect employee satisfaction, some of which are obvious while others are not. This study explores how certain financial and non-financial variables relate to employee contentment. The survey has been done by a Likert scale questionnaire on a total of 200 employees from 10 Insurance companies in Bangladesh. According to this empirical study, a few non-financial variables, except salary, have a substantial relationship with employee satisfaction.

The fraudulent claims for Unemployment Insurance (UI) have also risen massively in the United States especially during the onset of COVID-19 pandemic with billions of dollars that were lost. These approaches applied formerly in fraud detection and prevention have been challenged by new and advanced fraud systems. For this reason, AI, Big Data and Predictive Analytics are now crucial for improving fraud mitigation in UI programs. The aim of this research is to understand how far AI, Big Data and Predictive Analytics have been utilized, for how effective they are and the barriers they pose in tackling unemployment insurance fraud in the United States. An online quantitative self-administered questionnaire was administered where 200 participants from the unemployment insurance agencies of the U.S, covering fraud investigators, IT personnel, data analysts and policymakers, were included in the study. Data were analyzed to establish the correlation between technology adoption to application of anti-fraud measures using descriptive statistics, chi-square test and regression analysis. The survey shows that about 42.5% of agencies actively use AI and 34% of them use Big Data very often. However, difficulties like high costs, integration troubles and limited availability of specialized staff are still issues that make adoption a problem. The results of using AI and Big Data were moderate and optimistic but rather elevated, as seen through regression analysis; on the other hand, predictive analytics demonstrated large potential when applied successfully. The study’s results indicate that AI, Big Data and Predictive Analytics present enormous potential for mitigating UI fraud, although broad integration will not be possible without overcoming essential infrastructural and resource limitations.

This study aims to examine online shopping behaviour on social media platforms. This study formulates a research model integrating trust with the flow theory and some basic constructs of the UTAUT. To analyze the data from an online survey involving 305 participants actively making online purchases through social media platforms. This study applied the Structural Equation Modeling-Artificial Neural Networks (SEM-ANN) technique. Incorporating statistically significant SEM findings, the ANN model was used to analyze linear and nonlinear interactions among proposed variables. The research findings demonstrate that flow emerges as the most significant determinant, succeeded by effort expectancy, social influence, and performance expectancy, in defining the concept of trust. However, the sensitivity analysis using ANN indicates that effort expectancy is the most important factor in establishing trust, followed by flow, social influence, and performance expectancy. The predictive power of intention to use is noteworthy in determining actual use behaviour, with trust and flow emerging as influential factors favourably impacting this intention.

This study examines the correlation between industry-specific loan disbursement and profitability in the banking sector of Bangladesh, with a particular focus on the influence of targeted lending on profitability. The study examines the impact of different variables such as total assets, total revenue, non-performing loans, and sector-specific loans (agricultural, transportation, construction, textile, pharmaceuticals, and food procurement), on profit after tax (PAT) by analyzing data from 41 commercial banks between 2009 and 2022. By employing various techniques such as Pooled Ordinary Least Squares (POLS), Two-Stage Least Squares (2SLS), and the Generalized Method of Moments (GMM), we have determined that total assets have a noteworthy adverse effect on PAT in both POLS and 2SLS models. Conversely, construction loans have a positive influence on PAT in these models. The total revenue consistently has a favorable effect on the profit after tax (PAT) in all models. In the GMM model, transportation loans and textile loans have a notable adverse effect on PAT, but pharmaceutical loans have a slight favorable effect. The presence of non-performing loans, total loans, agricultural loans, and food procurement loans does not have a substantial impact on the profitability after tax (PAT) in any of the models. So overall total revenue, construction loan and pharmaceutical loan have a positive impact on PAT and total assets, transportation loan and textile loan have a negative impact on PAT. This study emphasizes the significance of strategic lending in underdeveloped industries and provides practical implications for banking professionals and regulators to identify high-risk lending sectors and devise measures to improve long-term profitability. 

The novelty and advancement of technology have explored new avenues in the education sectors. Now e-learning has blended with face-to-face learning to increase its effectiveness. An integrated model is being proposed in this study to measure the continuance usage intention of students adopting e-learning services. With the constructs of ECM (confirmation, perceived usefulness), perceived ease of use and perceived self-efficacy proposed to measure their effect on satisfaction. Besides, to explore the impact of quality features on satisfaction and continuance intention, three major determinants of D&M’s ISS model such as system quality, service quality and information quality included in this study. 410 responses collected to do quantitative analysis. The PLS-SEM analysis showed that perceived ease of use has the strongest effect on perceived usefulness, followed by perceived self-efficacy on perceived ease of use and satisfaction with continuance intention.The study reveals that challenges in developing countries are unique and drives the learners differently than developed countries. The growing e-learning industry requires widespread research from national and institutional perspectives for holistic development in the Bangladesh education market.

This paper examines the consequences of banks’ performance on bank risk. The paper forms a theoretical model and delivers empirical evidence to identify that banks suffer in performance as the loans become bad. Using panel data from a sample of five (05) South-Asian emerging economies from 2011 to 2019, we have found that the banks are highly influenced by the development of non-performing loans (NPLs). We have primarily used Return on Asset (ROA) followed by Return on Equity (ROE) as a substitution to the performance of the banks and NPL as the proxy of bank risk. Simultaneous regression applying 3sls finds that Non-Performing Loan (NPL) hinders banks' growth, negatively affecting their profitability.

This study aims to explore key drivers of tourism sustainability practices and their impact on the sustainable competitive performance of the tourism accommodation industry. The study adopts an exploratory research design under qualitative research, using semi-structured interviewing of owners and managers of tourism accommodation firms using the TOE framework and RBV theory. The findings reveal that technological sustainability expertise, executive sustainability support, organizational resources and capability, service intermediary sustainability practices, collaborative innovation adoption capability, tourism sustainability regulatory policy, and sustainability practices perceived by residents are the key drivers of TSP. The outcomes also show that tourism sustainability practices have a positive impact on the SCP of accommodation firms. This study makes a theoretical contribution to the literature on tourism sustainability by exploring how TOE antecedents motivate firms to adopt sustainability practices as product innovation, which in turn enhance SCP. Also, this study provides strong backing for the premises of the TOE framework, and RBV theory offers valuable practical insights for managers and owners. This will aid in identifying the pivotal factors influencing TSP and SCP, enabling the implementation of innovative strategies to enhance firm competitiveness. Also, the research can help the government and policymakers formulate sustainable tourism policies to promote tourism innovations. The adoption of sustainable practices has been a focal point for both academic researchers and professionals in the tourism industry. However, there has been insufficient exploration of the TOE drivers that facilitate the integration of tourism sustainability within the context of TAFs in a developing country. Therefore, this study aims to fill this gap in knowledge.

Financial inclusion, especially the adoption of fintech, has emerged as a crucial avenue for removing the financial access divides in developing countries, including Bangladesh. Following the comprehensive analysis, this article intends to outline Bangladesh's recent experiences with fintech-based financial inclusion, identify the short-comings and hence make policy suggestions. Being a descriptive research, cross-sectional and observational research approaches are applied for the explanatory analysis of secondary data. Results suggest an urban-rural gap in the implementation of digital financial services, with urban areas consistently having higher rates of fully online branches and a greater number of financial machines. Despite this, the sharp rise in the issuance of active cards is a positive sign suggesting an expansion of consumer base and an adoption of electronic modes of payment. Moreover, inter-bank electronic fund transfers and online banking have experienced increasing uptake, revealing a progressing trend towards digital transactions within the banking industry. Additionally, the study identifies a speedy growth in the number of mobile financial services accounts in both urban and rural areas. This study adds to the exchange of ideas regarding financial inclusion by carrying out a detailed study of Bangladesh's digital financial transformation, exemplifying the accomplishments, obstacles, and future research directions. It highlights the need for targeted policies and ongoing research efforts to build inclusive financial systems to serve the entire society’s interest. Nevertheless, the observations will have both theoretical and practical significance, contributing towards building capability in harnessing technology for sustainability, community uplifting, and shaping a brighter future.  

The paper intended to evaluate the extent of corporate disclosure compliance and its effect on the financial performance of Bangladeshi pharmaceutical companies listed on the DSE during a twelve-year period, from 2007 to 2020. The study explored the affinity between the Corporate Governance Disclosure Index (CGDI) and firm financial performance employing econometric techniques, such as fixed effect and random effect models. The study calculated the disclosure scores as a percentage of a given firm's overall score to the probable score it could achieve. The CGDI of the sample companies showed a positive and substantial effect on company performance as an accounting measure ROA and an insignificant effect as a market measure Tobin’s Q. The study also revealed that sample firms followed the BSEC guidelines partially. These findings have implications for companies that fail to comply with the full disclosure principle, which holds that a company must disclose all material information in its financial statements in order to touch the reader's comprehension of those statements. In addition, these results imply that the code of CG requires a thorough examination to make essential modifications.

This paper aims to examine the financial performance of Bangladeshi listed commercial banks. The study analyzed the financial performances of banks categorized into three generations—first, second, and third—based on their year of commencement of operations in the country. The data of a total of 24 banks, 8 from each generation, listed in the Dhaka Stock Exchange (DSE), has been collected from audited financial statements for the time period of 13 years, from 2010 to 2022. Data has been analyzed using different ratios of profitability, liquidity, capital adequacy, operational efficiency, and credit risk. The ANOVA test also has been used to make a comparison of profitability—the return on assets (ROA), and return on equity (ROE) of the banks of different generations. According to the results, the average profitability ratios in terms of ROA, and ROE of the second-generation banks showed their credibility than banks of other generations; while first generation banks kept higher liquidity, and capital adequacy with higher credit risk than the banks of other generations. The first-generation banks also showed a lower cost-expense ratio, and a higher efficiency in terms of operation, than the banks in the second and third generations. The research also discovered that the profitability of banks of different generations significantly varies from one another.