Ethical leadership and responsible AI governance in digital organizations: A bibliometric analysis of emerging trends (2011–2025)

Résumé

Abstract

The growing integration of artificial intelligence (AI) into organizational processes has intensified concerns regarding ethical leadership and responsible AI governance. Despite the proliferation of ethical AI principles and regulatory initiatives, the intellectual structure of research at the intersection of ethical leadership and AI governance remains fragmented. This study provides a systematic and bibliometric analysis to map the evolution, thematic structure, and emerging directions of this field.

Drawing on 273 English-language articles and reviews indexed in Scopus between 2011 and 2025, the study combines descriptive performance analysis using the Bibliometrix R package with science mapping techniques implemented in VOSviewer. The review process follows established guidelines for systematic literature reviews (PRISMA 2020), ensuring transparency and reproducibility. The results reveal rapid scholarly growth, with an average annual publication increase of 40.6%, reflecting accelerated consolidation of the domain.

China leads in research productivity, while the United Kingdom and Norway demonstrate higher citation impact, indicating a distinction between output volume and scholarly influence. Thematic cluster analysis identifies four interconnected research streams: corporate social responsibility and AI ethics; governance, transparency, and accountability; sustainability-oriented leadership and digital transformation; and AI-driven innovation and risk management. These findings highlight the interdisciplinary convergence of management, ethics, and digital governance studies.

By mapping the intellectual landscape of ethical AI leadership research, this study contributes to the theoretical consolidation of responsible AI governance and underscores the central role of leadership in aligning AI systems with organizational values and societal expectations. The results offer strategic insights for scholars and practitioners seeking to develop transparent and sustainability-oriented AI governance frameworks.

Keywords:  Ethical leadership, AI governance, bibliometric analysis, thematic clusters, responsible AI.