Sustainable Leadership and Corporate Social Responsibility: Strategies for Balancing Profit and Purpose

The connection between sustainable leadership (SL) and corporate societal responsibility (CSR) for balancing financial profit has been the subject of numerous studies in the past decade. Although earlier research has examined this relationship, objectively evaluating the performance of CSR and SL strategies has frequently been difficult. Nevertheless, more reliable evaluation metrics have been developed due to improvements in study design and the growth of ecological, societal, and governing (ESG) measuring variables. ESG values are the main benchmark variables used in these standards to evaluate the efficacy of CSR initiatives. To create CSR efficacy variables and forecast financial profit, this research will use ESG variables from the Indian Stock Market Corporation's (ISMC) ESG InfoHub webpage as standards. These markers include 15 elements from the ESG size. Since artificial intelligence has advanced so quickly in the past decade, deep learning (DL) and machine learning (ML) have found widespread use in a variety of areas. ML analysis of ESG data is still uncommon, though. Thus, using both categorization and regression techniques, this work uses ML algorithms to forecast financial profit depending on ESG efficiency. Two DL types, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), function better than different algorithms in regression and categorization activities, respectively, according to computational findings. As a result, DL methods are practical, efficient, and successful substitutes for forecasting the financial profit of businesses using ESG measures.