Payment Issues in China’s Construction Industry: Nature, Causes, and a Predictive Model
Publication: Journal of Construction Engineering and Management
Volume 149, Issue 1
Abstract
The aim of this paper is to understand the formation mechanism of and establish a predictive model for payment issues faced by contractors in China’s construction industry. Factors critical to such issues were first identified under different categories from literature and verified by a Delphi survey. This paper identified critical factors under three categories, i.e., culture, client’s financial management, and interactions and processes. A focus group was then conducted to understand the roles played by each category of factors. Specifically, the cultural factors explained the origination of payment. By taking an analogy, the current unhealthy culture acted as pathogens of payment issues as a disease in the body of the construction industry; the financial management capability of the client acted as the defense system; and the interactive and process factors played the role as trigger and catalyst. It was followed by a logical deduction assisted by another Delphi survey to understand the associations among the identified factors. The model structure was hence constructed upon the factors and their associations and was then quantified by Bayesian belief network parameter learning with quantitative data collected from a questionnaire survey. As such, the formation mechanism of payment issues was investigated, based on which a Bayesian classifier was established from the perspective of Chinese contractors. The model demonstrated a high accuracy rate of more than 90%. This paper systematically investigated the formation mechanism of payment issues in China’s construction industry and revealed that root causes of payment issues could not be eliminated in a short term. The predictive model endows contractors with advantages in proactively evaluating if they are capable of tackling the risks of potential payment issues without disturbing the current power balance of the industry.
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Data Availability Statement
Some data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request, such as the final predictive model, the distribution of the survey results, and the distribution of conditional probabilities. Some data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions, such as the direct survey results. The respondents were guaranteed confidentiality during the surveys.
Acknowledgments
This project receives financial support from the National Natural Science Foundation of China (Grant No. 52108322) and Jiangsu Ocean University Youth Foundation (Grant No. KQ20024).
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Journal of Construction Engineering and Management
Volume 149 • Issue 1 • January 2023
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© 2022 American Society of Civil Engineers.
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Received: Jan 6, 2022
Accepted: Aug 10, 2022
Published online: Oct 25, 2022
Published in print: Jan 1, 2023
Discussion open until: Mar 25, 2023
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