Jiménez-Casillas, Laura Elena
Main Affiliation
Preferred name
Jiménez-Casillas, Laura Elena
Official Name
Jiménez Casillas, Laura Elena
Scopus Author ID
60387983900
2 results
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Item type:Publication, Corporate Social Responsibility and Financial Performance: Evidence from Public Companies Listed on the Mexican Stock Exchange(Springer Nature Switzerland, 2026); ; The research on how corporate social responsibility (CSR) affects the financial performance (FP) of companies is limited, especially in the case of Mexico. There is a lack of studies that measure the financial impact of socially responsible strategies in Mexican companies. This study introduces an innovative approach by proposing a hybrid model that combines three quantitative methodologies: principal component analysis (PCA), cluster analysis, and an ordered logit model. The goal is to assess the impact of sustainability practices on financial performance. The study considers financial data from the past decade for 91 companies listed on the Mexican Stock Exchange. The results indicate that CSR practices have a positive effect on FP. The proposed hybrid model serves as a valuable methodological tool for evaluating the impact on FP of Mexican public companies listed on the BMV and holding the ESR seal granted by CEMEFI (Mexican Center for Philanthropy). This approach will help monitor the evolution of FP through the adoption of various CSR-related practices. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Evaluating the Financial Performance of CSR Strategies and Sustainable Operations in Mexican Companies: An Explainable Machine Learning ApproachResearch on how corporate social responsibility (CSR) practices linked to sustainable operations (SO) affect corporate financial performance (FP) is still limited. This study presents a novel methodological proposal to measure the individual impact of such practices on the profitability of companies listed on the Mexican Stock Exchange. The method employed consists of a Random Forest (RF) model complemented by Explainable Machine Learning (XML) techniques, namely Individual Conditional Expectation (ICE), Partial Dependence Plots (PDPs) and SHapley Additive exPlanations (SHAP), to calculate the individualized marginal effect in the return on assets (RoA), return on equity (RoE) and return on investment capital (ROIC) for each company, explained by the environmental, social, and governance scores provided by Bloomberg (Bloomberg Finance, L.P., New York, NY, USA), such as the market capitalization, debt-to-equity ratio, sales growth, and years since listing. The novelty of this model lies in the application of RF and XML, which offers a comprehensive and interpretable perspective on the CSR–FP relationship and the use of lagged explanatory variables to avoid endogeneity problems, overcoming the limitations of traditional analyses. The results indicate that environmental scores exhibit the most consistent contribution to FP, whereas social and governance effects are highly metric-dependent. The SHAP analysis reveals substantial heterogeneity in the drivers of firm FP, highlighting the relevance of XML methods. © The authors © MDPI41