This study aims to evaluate the evolution and impact of Building Information Modelling (BIM) research in the context of the construction industry’s digital transformation, by assessing key indicators such as scientific productivity, citation impact, collaboration networks, and emerging themes, and from the lens of cumulative capability development theory (CCDT).
This study utilises a comprehensive bibliometric analysis to investigate research productivity in the area of BIM. An iterative search strategy identifies the most productive BIM authors and examines their productivity over a 6-year period (2018–2024) using Elsevier’s SciVal tool and Ucinet software. The study explores the quantitative and qualitative performance of publications at the country (macro), institutional (meso), and individual (micro) levels.
Drawing on the results from different bibliometric analyses, at the macro level, national BIM capability development is driven by governmental initiatives, industry maturity, and cutting-edge research centers. At the meso level, specialised academic ecosystems in leading universities foster BIM knowledge and capability development. At the micro level, prominent researchers' leadership and international collaborations enhance routine and evolutionary learning capabilities. By applying the CCDT to macro, meso, and micro-level analyses, the study reveals how BIM research drives capability accumulation and supports the construction industry’s digital transformation.
This study presents the first multi-level bibliometric analysis of BIM research not confined to a specific journal, laying the groundwork for understanding the knowledge structure in this field. The evolution of BIM research themes highlights the integration of innovative approaches and the importance of interdisciplinary and international collaboration. The findings provide valuable insights into the scientific trajectory of BIM, benefiting researchers, professionals, and decision-makers in the construction sector.
