This study conducts an original, data-driven bibliometric investigation into the evolution of construction risk management (CRM) research between 2002 and 2024. Drawing on a dataset of 778 peer-reviewed publications retrieved from the Web of Science Core Collection, the study utilizes VOSviewer and Biblioshiny to empirically map publication trends, thematic clusters, and regional distributions. A key contribution of the research is the development of a four-phase thematic evolution model that illustrates the transition from conventional risk classification approaches to integrated frameworks incorporating advanced technologies such as Artificial Intelligence (AI), Building Information Modelling (BIM), and Environmental, Social, and Governance (ESG) principles. The analysis reveals three dominant thematic axes: methodological innovations in risk assessment, technological advancement in digital integration, and socio-political dynamics in stakeholder governance. Despite the increasing academic emphasis on data-driven approaches, persistent regional disparities and implementation gaps remain, particularly in underrepresented regions such as Sub-Saharan Africa and South Asia. The study also uncovers the fragmented integration of ESG dimensions across literature, suggesting the need for more coherent and context-sensitive risk models. By offering a longitudinal and spatial synthesis of global knowledge production, this research enhances our understanding of how CRM has evolved and where critical research gaps persist. The findings aim to inform future efforts toward inclusive, resilient, and adaptable risk management strategies in the construction sector.
ESG integration Risk perception Thematic mapping Regional disparities Stakeholder governance Construction sector
Ethics committee approval was not required for this study because of there was no study on animals or humans.
I would like to thank Fatma KÜRÜM VAROLGÜNEŞ for her valuable support.
This study conducts an original, data-driven bibliometric investigation into the evolution of construction risk management (CRM) research between 2002 and 2024. Drawing on a dataset of 778 peer-reviewed publications retrieved from the Web of Science Core Collection, the study utilizes VOSviewer and Biblioshiny to empirically map publication trends, thematic clusters, and regional distributions. A key contribution of the research is the development of a four-phase thematic evolution model that illustrates the transition from conventional risk classification approaches to integrated frameworks incorporating advanced technologies such as Artificial Intelligence (AI), Building Information Modelling (BIM), and Environmental, Social, and Governance (ESG) principles. The analysis reveals three dominant thematic axes: methodological innovations in risk assessment, technological advancement in digital integration, and socio-political dynamics in stakeholder governance. Despite the increasing academic emphasis on data-driven approaches, persistent regional disparities and implementation gaps remain, particularly in underrepresented regions such as Sub-Saharan Africa and South Asia. The study also uncovers the fragmented integration of ESG dimensions across literature, suggesting the need for more coherent and context-sensitive risk models. By offering a longitudinal and spatial synthesis of global knowledge production, this research enhances our understanding of how CRM has evolved and where critical research gaps persist. The findings aim to inform future efforts toward inclusive, resilient, and adaptable risk management strategies in the construction sector.
ESG integration Risk perception Thematic mapping Regional disparities Stakeholder governance Construction sector
Ethics committee approval was not required for this study because of there was no study on animals or humans.
I would like to thank Fatma KÜRÜM VAROLGÜNEŞ for her valuable support.
| Primary Language | English |
|---|---|
| Subjects | Risk Analysis, Infrastructure Engineering and Asset Management, Numerical Modelization in Civil Engineering, System Identification in Civil Engineering, Civil Construction Engineering |
| Journal Section | Research Article |
| Authors | |
| Submission Date | July 3, 2025 |
| Acceptance Date | October 27, 2025 |
| Early Pub Date | November 12, 2025 |
| Publication Date | November 15, 2025 |
| Published in Issue | Year 2025 Volume: 8 Issue: 6 |