Numerous studies in academic literature investigated the impact of socially responsible activities on the financial performance of companies; those are committed to social events relative to companies that do not meet the socially responsible criteria. The existing theoretical and empirical research has supported both contradictory positions. We have chosen a different approach to provide an alternative dimension to existing literature. We have taken voluntary CSR disclosure as the dependant variable and attempted to find how the past financial performances of companies influence CSR activities. We test this hypothesis with 100 Indian companies included in BSE 100 index. The director’s report in the latest annual reports of companies were analysed to get voluntary disclosure of CSR activities. The study includes different financial performance variables: ROA, ROE, ROCE, debt to equity ratio, market capitalization and ownership as independent variables for analysis. Several binary classifier models are used for our empirical analysis. The binary model performances are validated with different performance measurement techniques such as F-measure, accuracy rates, balance error rate (BER), Matthews correlation coefficient (MCC), Kappa coefficient and AUROC. The model performance results show a better accuracy while comparing between predicted and actual values.

The study attempts to investigate the effect of employee stock option plans (ESOPs) on the financial performance of Indian non-finance companies. The study employed the quantile regression (QR) model to examine the effect of ESOP on the financial performance of sample companies. The empirical findings suggest that the effect of equity-based payment is positive at the higher performance levels. This indicates that the firms adopted stock-based compensation schemes in their early stage of growth may cause a declining financial performance in compared to the matured firms. Moreover, the findings indicate that the industry plays a significant role in deciding the equity-based compensation and depict a positive impact of ESOP on firm performance. The employee based compensation is also found to be positively associated with the company performance, while the performance is measured through market measures. The findings may be attributed due to the direct linkage of equity-based option schemes to the market performance measures.

The COVID‐19 pandemic inflicted multiple threats to individuals' physical, mental, and financial health conditions. The pandemic‐related restrictive behaviors pose serious consequences for public health and increase the risk of mental illness among individuals, particularly among older citizens. The combination of their pre‐existing illnesses, social isolation, COVID fear, and financial adversity frequently aggravates their condition and leads to depression and mental illness. Thus, the present study investigates the mental health status and the determinants of depressive symptoms among older adults of Bhubaneswar during the COVID pandemic context. The study used the Geriatric Depression Scale (GDS‐15) to measure their depressive symptoms. The social isolation parameter is measured with the De Jong Gierveld Loneliness Scale. Financial self‐efficacy, COVID‐19‐related psychological fear, and comorbidity health status are other determinants considered. A chi‐square test and multinomial logistic regression (MLR) models are adopted to find the probable risk factors that may influence depressive symptoms among older people. The results indicate that comorbidity health conditions, a social isolation mindset, and financial efficacy issues are the significant determinants that drive an older person towards different depression categories. The improvement of these influential factors can lead senior citizens to avoid any health emergency like COVID pandemic. In the event of a public health emergency, such as COVID pandemic, the government could use the study's findings to devise methods for assisting the elderly. Society as a whole should be aware of these findings, which can lead to depressive symptoms, and offer support to the elderly. Future research may concentrate on identifying the causes of depressive symptoms in different age groups or in the presence of specific comorbidity health conditions. Future research may also investigate the factors influencing depressive symptoms in a specific occupation.

Numerous studies are available in the academic literature that investigates the customer perception under different contexts. In the present research the researcher tries to investigate the customer perception towards the Indian Government-sponsored social programme from the slum dwellers’ prospective. The author believes that the customer perception towards the government-lead liquefied petroleum gas intervention programme is influenced by multiple functional factors. The functional factors include both process or delivery variables and the outcome factors. In order to test the hypothesis, machine learning binary classifiers like logit, support vector machine, linear discriminant analysis, quadratic discriminant analysis and artificial neural network models are adopted. The binary classifier model efficiencies are analysed with multiple performance measurement parameters like accuracy rate, error rate, F-score, precision, kappa coefficient, Matthews correlation coefficient and area under receiver operating characteristic. While evaluating between the degree of accuracy between actual and predicted cases, the model efficiency results indicate a better predictive power of the classifier models. In relative performance of classifier models, artificial neural network outperformed the other models adopted in the empirical research.

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