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<p>Digital Twin Technology (DTT) represents a cutting-edge paradigm enabling real-time digital replication of physical entities. The DTT research domain has seen exponential growth, particularly in the last decade, reflecting its wide-ranging applications across manufacturing, healthcare, smart cities, and beyond. This study aims to systematically map and visualise global research trends on DTT by identifying prolific authors, contributing institutions and countries, influential publications, and evolving keyword networks. It also explores the collaborative structure of research in this domain using advanced scientometric indicators. A longitudinal scientometric analysis was conducted on 30,327 cleaned and validated bibliographic records from the Scopus database using Biblioshiny and VOSviewer tools. The PRISMA 2020 protocol guided the data selection and cleaning process. Scientometric indicators like Degree of Collaboration (DC), Collaboration Index (CI), and Collaboration Coefficient (CC) were computed to assess collaboration patterns. The findings reveal a sharp rise in research output post-2015. China has emerged as a leading country, contributing nearly 25% of global publications. Collaboration metrics show a significant shift from single to multi-authored works, confirming DTT’s multidisciplinary nature. Zhihan LV, Tao Fei, and Weyrich Michael emerged as the most prolific authors, and Beihang University and RWTH Aachen University led among institutions. Conference papers dominate the publication types, and keywords such as “Digital Twin,” “Industry 4.0,” and “Artificial Intelligence” highlight the convergence of DTT with emerging technologies. This study is distinctive in its scale, time span, and application of enhanced scientometric indicators. It bridges existing research gaps by integrating collaboration metrics and offering a granular visual analysis of DTT scholarship globally, providing actionable insights for researchers, technologists, and policymakers seeking to understand the evolving landscape of digital twin technology. The analysis also comprises emerging research themes and potential gaps in the literature that could guide future research directions.</p>

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Mapping and visualisation of global research trend on digital twin technology (DTT): a scientometric study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月31日

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  • Ashok Kumar,
  • Pankaj Kumar,
  • Reetu Verma,
  • Amit Kumar

摘要

Digital Twin Technology (DTT) represents a cutting-edge paradigm enabling real-time digital replication of physical entities. The DTT research domain has seen exponential growth, particularly in the last decade, reflecting its wide-ranging applications across manufacturing, healthcare, smart cities, and beyond. This study aims to systematically map and visualise global research trends on DTT by identifying prolific authors, contributing institutions and countries, influential publications, and evolving keyword networks. It also explores the collaborative structure of research in this domain using advanced scientometric indicators. A longitudinal scientometric analysis was conducted on 30,327 cleaned and validated bibliographic records from the Scopus database using Biblioshiny and VOSviewer tools. The PRISMA 2020 protocol guided the data selection and cleaning process. Scientometric indicators like Degree of Collaboration (DC), Collaboration Index (CI), and Collaboration Coefficient (CC) were computed to assess collaboration patterns. The findings reveal a sharp rise in research output post-2015. China has emerged as a leading country, contributing nearly 25% of global publications. Collaboration metrics show a significant shift from single to multi-authored works, confirming DTT’s multidisciplinary nature. Zhihan LV, Tao Fei, and Weyrich Michael emerged as the most prolific authors, and Beihang University and RWTH Aachen University led among institutions. Conference papers dominate the publication types, and keywords such as “Digital Twin,” “Industry 4.0,” and “Artificial Intelligence” highlight the convergence of DTT with emerging technologies. This study is distinctive in its scale, time span, and application of enhanced scientometric indicators. It bridges existing research gaps by integrating collaboration metrics and offering a granular visual analysis of DTT scholarship globally, providing actionable insights for researchers, technologists, and policymakers seeking to understand the evolving landscape of digital twin technology. The analysis also comprises emerging research themes and potential gaps in the literature that could guide future research directions.