PurposeOperational performance is critical for the banking sector for both managers and other stakeholders as it strongly affects the overall performance of the banking system. Traditional performance measures such as ratio analysis encountered certain shortcomings. At this juncture, data envelopment analysis (DEA) approaches are increasingly applied in bank efficiency studies. However, basic DEA models ignored the interactions between consecutive terms and focused primarily on measuring performance independently for each study period. All this is required to develop an operational performance model that can enable the long-term decision model.Design/methodology/approachAn attempt has been made to develop a dynamic DEA within a non-radial category to measure interconnection activities considering non-performing loans as an undesirable link. This study uses the Indian banking dataset from 2015 to 2019. The study's research design directs three directions: ‘comparison of the dynamic DEA with the traditional static DEA model, areas of inefficiencies that are investigated for each factor using the factor efficiency index and the robustness results highlighting the performance difference between bank categories.'FindingsComparing with static DEA results, the study confirms that the dynamic model best measures long-term operational performance due to the linkage between consecutive terms. The efficiency analysis concludes that the input factor that requires the most improvement is ‘fixed assets' and ‘deposits'. The output factor that needs the most progress is ‘non-interest income'. The robustness of the developed model is proven by ownership categories present within the Indian banking system. At a significance level of 10%, the result of both the separate and dynamic model for privately owned banks is significantly better than that of publicly owned banks.Originality/valueThis paper proposes an operational efficiency model for Indian banks in line with undesirable output. The mean factor efficiency analysis related to non-radial DEA modelling enhances managerial flexibilities in determining improvement initiatives.

Non-performing loans (NPLs) is a critical constituent that impacts the operational performance of banks. Rising level of risk leads to poor operational performance, especially when it is beyond the bank’s capabilities to control the increasing bad assets. This calls for real-time performance assessment coupled with futuristic decision making to support banking managers. This observation motivates the authors of this article to develop a two-stage performance prediction assessment model. Accordingly, a hybrid approach combining data envelopment analysis (DEA) and artificial neural network (ANN) is developed to measure and predict the operational efficiency scores of banks. DEA effectively explores the operational performance as well as improvable areas of inefficient banks. The training of ANN model is dependent on estimated operational DEA efficiency scores with the objective to estimate the efficiency scores. Domain for the validation of this study includes dataset derived from Indian banks. The validation result shows that trained ANN model has the prediction capacity with minimum error and maximum accuracy. Finally, the outcome of this study is significantly directed towards business managers who can rely on predictions based on empirical findings of this proposed hybrid modelling.

Background: Mental illnesses including depression and anxiety are very common across all age groups. Even individuals with minimal or undetectable COVID-19 symptoms have felt the effects of this burden, which the COVID-19 pandemic has intensified. Methods: A cross-sectional study was conducted on 346 COVID-19 patients with asymptomatic or mild illness. Depression and anxiety were measured using the Hospital Anxiety and Depression Scale (HADS), and perceived social support was measured using the Multidimensional Scale of Perceived Social Support (MSPSP). Version 16 of SPSS was utilised to analyse the data. We employed ANOVA, Pearson's rank correlation, independent t-tests, and multivariate linear regression. P values less than 0.05 were considered statistically significant. Results: 13.8 percent patients had comorbid depression and 32 percent, anxiety. 35.2 percent patients did not have enough support. Significant correlation between depression and anxiety, depression and social support, and anxiety and social support was observed. Regression analysis showed age, marital status, covid case in family, and hypertension had significant association with both anxiety and depression. Association of diabetes mellitus with anxiety was also observed. Perceived social support was found to be significantly associated with age, covid case in family, and presence of diabetes mellitus and hypertension. Conclusion:Significant number of COVID-19 patients displayed signs of anxiety, depression and lack of social support. Clinico-social factors found associated with anxiety, depression and perceived social support should be better taken care of in a future crisis like COVID-19.

No abstract

No abstract