Original scientific paper
https://doi.org/10.31297/hkju.26.1.2
Exploring the Relationship Between Machine Learning, Good Governance, and Organizational Performance in the Moroccan Public Sector
Saida Ifiss
; The National School of Business and Management, Mohammed First University
Abstract
This innovative study uses Machine Learning algorithms to analyse the impact of good governance principles on the performance of Moroccan public organizations. The results show that transparency and accountability as essential pillars of governance are directly correlated with substantial improvements in the efficiency and quality of public services, with extremely low p-values underlining their importance. Leadership, employee motivation, and sustainable development also appear to be important levers, although their impact is often more diffuse. Predictive models, such as XGBoost and CNN, can extract complex relationships between these practices and organizational performance, providing decision-makers with robust tools for optimizing public reforms. In other words, the use of Machine Learning in public management is proving to be a major breakthrough, enabling a more enlightened governance, focused on transparency, accountability, and innovation.
Keywords
good governance; machine learning; transparency; accountability; leadership; innovation; organizational performance
Hrčak ID:
345918
URI
Publication date:
30.3.2026.
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