Prediction and Ranking of Corporate Diversity in European and American Firms

Iñigo Martín-Melero (), Felipe Hernández-Perlines, Raúl Gómez-Martínez and María Luisa Medrano-García
Additional contact information
Iñigo Martín-Melero: Business Administration Department, University of Castilla-La Mancha, 45071 Toledo, Spain
Felipe Hernández-Perlines: Business Administration Department, University of Castilla-La Mancha, 45071 Toledo, Spain
Raúl Gómez-Martínez: Business Economics Department, Rey Juan Carlos University, 28933 Madrid, Spain
María Luisa Medrano-García: Business Economics Department, Rey Juan Carlos University, 28933 Madrid, Spain

Administrative Sciences, 2025, vol. 15, issue 11, 1-42

Abstract: Currently, corporate social responsibility and environmental/social/governance topics are gaining more relevance in business and finance. Attention to corporate diversity in boards and the workforce is included in this trend. Although most studies focus on executive boards and objective scores, the perception of diversity by employees and its rankability are not fully understood or researched. In this paper, we analyze corporate diversity rankings from the perspective of predictive and prescriptive analytics. Inside predictive analytics, the perceived diversity of a sample of 350 European diversity leader companies is predicted by using three different feature sets (raw financial data, ratios and objective diversity variables) and three machine learning algorithms (K Nearest Neighbors, Logistic Regression, Decision Tree). The best performing algorithm is the Decision Tree, and all three feature sets outperform one random dummy algorithm; the best performing set is the financial ratios set. Inside prescriptive analytics, several rankings involving American companies are intersected and compared in three exercises (studying diversity categorization, ethnic origin and comparing diversity with other unrelated metrics). From these, global rankings were built to search for the best possible agreement among the rankings. These results with both predictive and prescriptive analytics encourage managers to strategize and include diversity in management, as well as employ new technologies in their decision-making processes.

Keywords: corporate governance; diversity and inclusion; machine learning; operations research; human resources management (search for similar items in EconPapers)
JEL-codes: L M M0 M1 M10 M11 M12 M14 M15 M16 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2076-3387/15/11/406/pdf (application/pdf)
https://www.mdpi.com/2076-3387/15/11/406/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jadmsc:v:15:y:2025:i:11:p:406-:d:1776653

Access Statistics for this article

Administrative Sciences is currently edited by Ms. Nancy Ma

More articles in Administrative Sciences from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().