A Generative Artificial Intelligence (Generative AI) Bibliometric Review of Journal of Management History (JMH) from 2006 to 2022

Abstract

Study presented in this paper represents the first Artificial Intelligence (AI) driven bibliometric study in  management and organizational history. It reviews literature published in the Journal of Management  History from 2006 to2022 and identifies patterns in authorship, productivity, geographic influence, and  collaboration. Articles were gathered through Harzing's Publish or Perish, Google Scholar, and Scopus,  and analyzed with ChatGPT 4o, following guidance from leading journals. Authors validated the results  via manual sampling and team reviews. As the use of artificial intelligence (AI) in (management) research  is emerging, we offer a transparent, ethical, replicable, AI-driven bibliometric method enhancing analytical  rigor. Findings show that North American scholars dominate collaborations, with Milorad Novićević, John  Humphreys, and Albert Mills leading total output. Based on adjusted publications metrics, Jeffrey Muldoon,  Novićević and Mills are the journal’s top contributors.