Enhancing the Knowledge of Construction Business Failure: A Social Network Analysis Approach

Abstract

The failure of construction companies is quite crucial because it results in unfinished projects and responsibilities that in turn result in many losses to governments, economies, owners, creditors, and surety companies. Previous research on the prediction of construction business failure utilized insolvency causes that were either arbitrary or based on the availability of data. Many scholars have shown the absence of, and need for, a holistic framework for the identification of the causes of construction business failure. As such, this paper reviews previous literature incorporating construction business failure applications, with the objective of identifying existing knowledge, current gaps, and needed future research directions on the different failure factors in a comprehensive approach. To this end, the authors (1) performed a meta-analysis of previous research work for a 30-year period spanning from 1988 to 2018; (2) identified and defined failure factors that impact the business operations of construction firms; and (3) utilized social network analysis to quantitatively identify the overlooked and missing construction business failure factors. Research results indicate that there are 20 factors that could collectively contribute to business failures of construction firms. It is also shown that there is a dire need for future research to better explore the impacts of some understudied critical factors, including the effect of inadequate company organizational structure and human capital on construction business failure. Another important finding is the absence of models that include a holistic incorporation of all 20 construction business failure factors. The findings herein are a significant contribution to the body of knowledge on construction business failure because they integrate the outcomes of previous works and use them to provide robust foundations for knowledge advancement. The presented guidelines are believed to close areas where an abundance of research work occurs and to unveil areas where additional research is necessary.