Explore publications.

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 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. 

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.