Bibliometric analysis of digital twin literature: a review of influencing factors and conceptual structure

ABSTRACT

This article systematically summarises the digital twin technology in the existing scientific field using the bibliometric method, puts forward the critical areas and research directions at different stages of the life cycle of the digital twin research topic. Using R-Studio ‘Biblioshiny’ to measure scientifically, we visually analyze 514 articles related to digital twin in the Web of Science's core set from 2014 to 2021 (including SCI-Expanded and SSCI database). Firstly, we analyzed the core journals, articles, authors, institutions, relevant countries to determine the vital influence of digital twin-related literature. Secondly, we use theme maps to draw the research topics of digital twin-related literature as icons on the conceptual structure and offer four cluster parts (Motor themes, highly developed and isolated themes, Emerging themes, and Basic themes). Finally, we concluded by summarising the limitations of our study and subsequently highlighted future research directions. The study shows that monitoring, maintenance, and computer vision are research topics with good development momentum. Disturbance observer, reinforcement learning, and friction are relatively small research topics with a good development foundation, and scheduling, process modelling, and deep reinforcement learning are emerging research directions.

KEYWORDS:

• Bibliometric analysis
• biblioshiny
• digital twin
• influencing factors
• conceptual structure
• topic maps
• life cycle

Disclosure statement

No potential conflict of interest was reported by the author.

Additional information

Funding

This work was supported by the Fundamental Research Funds for the Central Universities (grant number 2020ZDPYSK02).

Notes on contributors

Jing Wang

Wang Jing, associate research librarian of China University of mining and technology library, is currently applying for a Ph.D. in management science and engineering of China University of mining and technology. Her research fields include information measurement, scientific evaluation and knowledge management.

Xinchun Li

Li Xinchun, professor and doctoral supervisor of School of economics and management of China University of mining and technology. He has published more than 50 papers and 4 books in authoritative journals at home and abroad.

Peng Wang

Wang Peng holds a Ph.D. in engineering, deputy director of scientific research support department of China University of mining and technology library. His research fields include information measurement, scientific evaluation, and intellectual property research.

Quanlong Liu

Liu Quanlong holds a Ph.D. in management, associate Professor and master's supervisor in school of economics and management, China University of mining and technology. His research fields include enterprise safety management and risk pre-control.