ARTIFICIAL INTELLIGENCE AND ORGANIZATIONAL ADAPTATION: A BIBLIOMETRIC SCIENCE MAPPING ANALYSIS OF RESEARCH EVOLUTION (2000–2025)
TL;DR
The study proposes the AI-Driven Organizational Adaptation Knowledge Evolution Framework as an interpretive synthesis of intellectual, conceptual, thematic, and collaborative dimensions that provides a structured perspective for understanding the field’s evolution and supports future interdisciplinary research and evidence-informed managerial decision-making.
Abstract
Artificial intelligence (AI) is reshaping organizational adaptation, yet the evolution of research in this domain remains insufficiently understood. This study addresses this gap through a bibliometric science mapping analysis of 1,060 Scopus-indexed journal articles published between 2000 and 2025. Using VOSviewer, it examines annual scientific production, reference and source co-citation, bibliographic coupling, keyword co-occurrence, overlay visualization, and country co-authorship to map the field’s intellectual, conceptual, thematic, temporal, and collaborative structure. The results indicate that publication rates have been rising since 2020, that there are three intellectual traditions and five thematic clusters, and that generative AI, AI governance, human–AI collaboration, and organizational resilience have recently become important. Based on these findings, the study proposes the AI-Driven Organizational Adaptation Knowledge Evolution Framework as an interpretive synthesis of intellectual, conceptual, thematic, and collaborative dimensions. The framework provides a structured perspective for understanding the field’s evolution and supports future interdisciplinary research and evidence-informed managerial decision-making.