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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 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.