GLOBAL RESEARCH TRENDS IN HUMAN-MACHINE COLLABORATION: A BIBLIOMETRIC ANALYSIS

Abstract

Human–Machine Collaboration (HMC) has become an important research area due to rapid advances in  artificial intelligence, robotics, and intelligent systems. This study aims to identify global research trends,  major contributors, collaboration networks, and emerging topics in HMC through a bibliometric analysis.  Data were collected from the Scopus database, covering publications from 1994 to 2026. Bibliometric  methods, including publication trend analysis, co-authorship networks, country collaboration, keyword co occurrence, and citation analysis, were employed to examine the development of the field. Visualization  and network mapping were conducted using VOSviewer and the Bibliometrix package in R. A total of 1,088  publications were included in the analysis. The results indicate a significant growth in HMC research,  particularly during the last decade, reflecting increasing academic and industrial interest in human centered intelligent technologies. China emerged as the leading contributor, followed by the United States,  India, Germany, and Japan. More than 3,800 authors participated in the publications, demonstrating  extensive international collaboration. Keyword analysis identified major research themes such as artificial  intelligence, human–robot collaboration, machine learning, intelligent manufacturing, and Industry 4.0.  Citation analysis further revealed a strong and expanding knowledge base within the field. Overall, the  findings highlight the rapid development and interdisciplinary nature of HMC research and provide valuable  insights into current trends and future directions for human-centered intelligent systems.