AUTHOR=Mazher Khwaja Mateen 

TITLE=A semi-automated systematic review of literature reviews in construction engineering and management research

JOURNAL=Frontiers in Built Environment

VOLUME=Volume 11 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/built-environment/articles/10.3389/fbuil.2025.1582475

DOI=10.3389/fbuil.2025.1582475

ISSN=2297-3362

ABSTRACT=Review studies are critical in all knowledge domains to benchmark the state-of-the-art at a given point in time and to identify possible future research directions. Due to the fragmented and ever evolving nature of the construction industry, research in the field of construction engineering and management (CEM) is growing exponentially. Researchers across the world are publishing reviews in CEM and the body of knowledge of reviews in this field has become large and significant. While considering literature reviews in CEM as a separate class of CEM research, there has been no attempt to date to analyze and document publication trends and to summarize methods and approaches being utilized in drafting these reviews. Moreover, there is no documented reference that tracks or highlights the concentration or scarcity of literature reviews in various domains and sub-domains of CEM research. Following the PRISMA protocol, this systematic literature review (SLR) aims to benchmark the existing reviews in the field of CEM and to chart the growth of interest of researchers in publishing reviews. A total of 549 review studies were obtained from the Scopus database, as of 20 December 2024, based on relevance, accessibility, and other inclusion and exclusion criteria adopted for this review. Bibliometric analysis shows an exponential annual growth in review studies with Australia, China, US, Hong Kong, and the UK leading this growth. According to the metrics, the journal of Automation in Construction has published the largest number of reviews in the field of CEM. Reviews focusing on applications of robots, automation, and digital technologies in construction constitute about 52% of the reviews published in CEM. Part of this review employed various models of ChatGPT for data extraction from shortlisted articles, therefore risk of bias was minimized by using the tool for simple tasks only. This review is one of its kind and the analysis and findings presented herein are expected to assist researchers in conducting more focused reviews in CEM in the future.