Research trends analysis using text mining in construction management: 2000–2020

Abstract

Purpose

This study aims to identify the trends that have changed in the field of construction management over the last 20 years.

Design/methodology/approach

In this study, 3,335 journal articles published in the years 2000–2020 were collected from the Web of Science database in construction management. The authors applied bibliometric analysis first and then detected topics with the latent Dirichlet allocation (LDA) topic detection method.

Findings

In this context, 20 clusters from cluster analysis were found and the topics were extracted in clusters with the LDA topic detection method. The results show “building information modeling” and “information management” are the most studied subjects, even though they have emerged in the last 15 years “building information modeling,” “information management,” “scheduling and cost optimization,” “lean construction,” “agile approach” and “megaprojects” are the trend topics in the construction management literature.

Research limitations/implications

This study uses bibliometric analysis. The authors accept that the co-citation and co-authorship relationship in the data is ethical. They accept that honorary authorship, self-citation or honorary citation do not change the pattern of the construction management research domain.

Originality/value

There has been no study conducted in the last 20 years to examine research trends in construction management. Although bibliometric analysis, systematic literature reviews and text mining methods are used separately as a methodology for extracting research trends, no study has used enhanced bibliometric analysis and the LDA topic detection text mining method.

Citation

Bilge, E.Ç. and Yaman, H. (2022), "Research trends analysis using text mining in construction management: 2000–2020", Engineering, Construction and Architectural Management, Vol. 29 No. 8, pp. 3210-3233. https://doi.org/10.1108/ECAM-02-2021-0107