Predicting Corporate Social Responsibility Performance Using Machine Learning Models: Evidence from Bangladeshi Private Companies

This paper explores the use of machine learning (ML) models in forecasting the performance of Corporate Social Responsibility (CSR) using a sample of 50 Bangladeshi companies (Privately held) in 10 years (2016-2025). The research with a quantitative research methodology involving the secondary data analysis employs a well-crafted system of lagged predictor variables, namely such variables, as the financial indicators, the attributes of the governance and the environmental and social performance criteria, and the textual sentiment rating. Three major ML algorithms such as Random Forest, Gradient Boosting, and Artificial Neural Networks (ANN) were implemented and compared. Gradient Boosting Regressor became the best model with highest predictive accuracy with an RS of 0.7406 and Root Mean Square Error (RMSE) of 4.2607. The analysis of the feature importance showed that Employee Training Hours, workforce Diversity Index, and environmental Spending are the most significant variables when it comes to the prediction of a CSR score of a firm. These results show the great potential of ML to improve the presence and prediction of CSR behavior, providing a fact-based instrument to stakeholders, investors, and corporate sustainability managers in such emerging economies as Bangladesh. The findings highlight the forecasting capabilities of concrete social and environmental investments compared to conventional financial measures, which proves to be vital in the context of strategic choices of the corporation in South Asia.

Corporate Social Responsibility, Machine Learning, Bangladesh, Artificial Neural Networks

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