Structuring the Debate on Algorithmic Bias and AI Governance: A Bibliometric Analysis (2021–2025)
Keywords:
Artificial Intelligence; Strategic Decision-Making; Bibliometric Analysis; Mechanistic Gap; Machine Learning; Future Research AgendaAbstract
This study provides a comprehensive bibliometric analysis to map the intellectual structure, thematic evolution, and social configuration of the literature at the intersection of Artificial Intelligence (AI) and Strategic Decision-Making (SDM). Driven by the recognition of AI's disruptive strategic potential, the field demonstrates an explosive growth rate of 32.98% in scientific production. Utilizing a dataset of 623 documents extracted from the Scopus database (2005–2024), we employed advanced scientometric techniques including Thematic Map and Collaboration Network analysis. The results reveal a critical mechanistic gap: while strategic outcomes such as "Competitive Advantage" and "Decision Making" are established Motor Themes, the foundational technological tools like "Machine Learning" and "Corporate Strategy" remain in the Emerging Themes quadrant. The thematic evolution confirms a shift toward technological specificity (e.g., Deep Learning) and expansion into functional areas (e.g., Supply Chain, HRM). Furthermore, the social structure is highly fragmented with limited author collaboration, although the analysis identifies Europe as a crucial global collaboration hub connecting the US and China. This paper confirms the literature's rapid but structurally unintegrated growth. It contributes by providing an objective, data-driven framework and proposes a Future Research Agenda to bridge the mechanistic gap. We recommend research focusing on testing specific Machine Learning algorithms on core strategic processes, developing formal AI Governance frameworks, integrating classic strategic theories, and conducting cross-cultural studies in emerging markets to achieve theoretical consolidation.
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