The Nexus between Construction Contract Risk Assessment and Big Data in China

Authors

  • Deng Zihao School of Housing, Building, and Planning, Universiti Sains Malaysia, George Town 11800, Malaysia
  • Khoo Terh Jing School of Housing, Building, and Planning, Universiti Sains Malaysia, George Town 11800, Malaysia
  • Ha Chin Yee School of Housing, Building, and Planning, Universiti Sains Malaysia, George Town 11800, Malaysia
  • Shi Yangle School of Housing, Building, and Planning, Universiti Sains Malaysia, George Town 11800, Malaysia
  • Zhou Zilin School of Housing, Building, and Planning, Universiti Sains Malaysia, George Town 11800, Malaysia
  • Chen Siyao School of Housing, Building, and Planning, Universiti Sains Malaysia, George Town 11800, Malaysia
  • Sun Hui School of Housing, Building, and Planning, Universiti Sains Malaysia, George Town 11800, Malaysia
  • Li Yao School of Housing, Building, and Planning, Universiti Sains Malaysia, George Town 11800, Malaysia

DOI:

https://doi.org/10.37934/araset.33.1.441448

Keywords:

Contractual risks, Construction projects, Risk identification, Risk assessment, China, Big data

Abstract

The construction market in China is currently showing an increasing trend in terms of project size, complexity, and technical difficulty. With large initial investments and long capital payback periods, construction projects are vulnerable to the external environment during development and risk control is particularly important. Contracts are at the heart of project management and every step of project development revolves around the content of the contract signed by the contractor. Signing contracts to protect the rights of both parties has become the most effective way of controlling project risk. And with big data technology being rapidly developed and widely used, and the amount of data created and accumulated by the internet growing exponentially, big data has the potential to be used for analysis, forecasting and decision making in the construction industry. The aim of this study is therefore to develop a risk identification list for construction project contracts in China, to score the risks within the list according to their incidence and importance, and to find practical applications of big data technology in contract risk assessment. To achieve this goal, this study used qualitative research and in-depth interviews with experts within the Chinese construction engineering industry. The results of this study form a list of risks in Chinese construction contracts, indicating that late payment and government intervention are the most important risks in Chinese construction contracts. This study also identifies TianYanCha, a commonly used big data enterprise risk identification platform in China, which provides big data support in three areas: evaluating owner integrity, enterprise risk identification, and owner relationship knowledge. At the practical level, this study helps contractors to assess project risks and improve contractual contents during the preparation phase of construction projects, thus improving the success rate of projects. At the theoretical level, this study fills the gap in contractual risk assessment for construction projects in China and provides insights for subsequent risk assessment of construction projects based on the contractor's perspective.

Downloads

Download data is not yet available.

Author Biographies

Deng Zihao, School of Housing, Building, and Planning, Universiti Sains Malaysia, George Town 11800, Malaysia

dengzihao1949@gmail.com

Khoo Terh Jing, School of Housing, Building, and Planning, Universiti Sains Malaysia, George Town 11800, Malaysia

terhjing@usm.my

Ha Chin Yee, School of Housing, Building, and Planning, Universiti Sains Malaysia, George Town 11800, Malaysia

chinyeeha@yahoo.com

Shi Yangle, School of Housing, Building, and Planning, Universiti Sains Malaysia, George Town 11800, Malaysia

eliaukihs@gmail.com

Zhou Zilin, School of Housing, Building, and Planning, Universiti Sains Malaysia, George Town 11800, Malaysia

zhou.aron@foxmail.com

Chen Siyao, School of Housing, Building, and Planning, Universiti Sains Malaysia, George Town 11800, Malaysia

Chensiyao@student.usm.my

Sun Hui, School of Housing, Building, and Planning, Universiti Sains Malaysia, George Town 11800, Malaysia

sunhui@student.usm.my

Li Yao, School of Housing, Building, and Planning, Universiti Sains Malaysia, George Town 11800, Malaysia

liyao_0512@student.usm.my

Published

2023-10-30

Issue

Section

Articles

Most read articles by the same author(s)

1 2 > >>