Analyzing Patterns of Social Responsibility. A Clustering Approach to B Corporation Performance





Slay-Ramos, Rodolfo

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The study explores the use of hierarchical clustering techniques to assess the performance of B Corporations, which are businesses certified by B Lab for their commitment to social and environmental responsibility. The study aims to identify distinct patterns and clusters among these corporations based on their operational metrics and impact assessments.

The research involves a comprehensive analysis of a dataset from B Corporation impact assessments, applying various data science methodologies, including clustering and silhouette scoring, to validate the results. The findings highlight significant variations in performance and operational strategies among different clusters, with a particular emphasis on the influence of worker participation and ownership on overall company performance and sustainability.

Key insights from the study suggest that corporate governance structures, especially those involving employee ownership, play a crucial role in enhancing corporate performance and stakeholder impact. The thesis contributes to a broader understanding of how socially responsible business models can be effective and provides valuable implications for policymakers, corporate leaders, and researchers interested in sustainability and corporate governance.


Palabras clave

Social Responsability, Data Science, Stakeholder Impact, Worker Participation, Employee Ownership, Clustering


Slay-Ramos, R. (2024). Analyzing Patterns of Social Responsibility: A Clustering Approach to B Corporation Performance. Trabajo de obtención de grado, Maestría en Ciencia de Datos. Tlaquepaque, Jalisco: ITESO.