Artificial Intelligence in Management Studies (2021-2025): A Bibliometric Mapping of Themes, Trends, and Global Contributions

TL;DR

This bibliometric study maps the evolution of AI research in management from 2021 to 2025, highlighting publication trends, key contributors, and thematic shifts towards sustainability and decision-making.

Contribution

It provides a comprehensive bibliometric analysis of AI in management, revealing evolving themes, global contributions, and the decline in publications in 2025.

Findings

Publication growth peaked in 2024 and declined in 2025

China, India, and the US lead in publications

Shift from technical AI applications to sustainability and decision-making

Abstract

AI has become one of the most influential research areas over the past decade, with growing applications across multiple disciplines. In management studies, artificial intelligence is increasingly recognized as a driver of innovation, sustainability, and decision-making support. This bibliometric study examines the evolution of AI-related research in management between 2021 and 2025. Data were collected from the Scopus database and analyzed using the Bibliometrix R package, with visualizations generated through VOSviewer. The dataset consisted of 5,624 documents filtered by subject area, document type, and language. The analysis included annual scientific production, country and institutional contributions, leading journals, co-authorship networks, keyword co-occurrence, and thematic mapping. Results reveal a strong increase in publications from 2021 to 2024, followed by a decline in 2025. China, India, and the United States lead in publication output, while the United Kingdom shows higher citation impact. Thematic analysis indicates a shift from technical applications of AI to broader concerns such as sustainability, digital transformation, and decision-making processes. These findings highlight a changing landscape in AI research within management, where technological innovation, social responsibility, and organizational performance converge to shape future directions.