A systematic review of construction labor productivity studies: Clustering and analysis through hierarchical latent dirichlet allocation

Abstract

The field of construction labor productivity (CLP) has witnessed a remarkable growth in scholarly research, presenting both opportunities and challenges due to the diverse focus and exponential increase in literature. This study aims to systematically review the burgeoning body of CLP literature, proposing an approach to tackle the complexity of the domain. Utilizing the text mining technique of Hierarchical Latent Dirichlet Allocation (HLDA), an automatic clustering method was developed to analyze and categorize the corpus of CLP research. The methodology involved a comprehensive extraction of 591 scholarly articles from scientific databases. These articles, spanning from 1973 to 2023, were subjected to HLDA topic modeling. This process generated a detailed three-layer, tree-like topic model, comprising three primary topics and 26 sub-topics, organized through the nested Chinese restaurant process (nCRP). The study advances theoretical and practical understanding by applying hierarchical topic modeling to construction project management literature and identifying key industry challenges.

Keywords

• Construction labor productivity
• Text mining
• Topic modeling
• Information extraction
• Hierarchical latent Dirichlet allocation
• Research trends