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Chong, H-Y and Oon, C K (2016) A practical approach in clarifying legal drafting: Delphi and case study in Malaysia. Engineering, Construction and Architectural Management, 23(05), 610-21.

Ding, Z, Zuo, J, Wang, J and Zillante, G (2016) Searching for niche market for engineering consultants: Case of regional supervision systems in China. Engineering, Construction and Architectural Management, 23(05), 622-37.

Gambo, N, Said, I and Ismail, R (2016) Comparing the levels of performance of small scale local government contractors in northern Nigeria with international practice. Engineering, Construction and Architectural Management, 23(05), 588-609.

Hu, L and Wu, H (2016) Exploratory study on risk management of state-owned construction enterprises in China. Engineering, Construction and Architectural Management, 23(05), 674-91.

  • Type: Journal Article
  • Keywords: China; quantitative analysis; risk management; exploratory research; spss; state-owned construction enterprises
  • ISBN/ISSN:
  • URL: https://doi.org/10.1108/ECAM-05-2014-0064
  • Abstract:
    Purpose There is a relatively low risk management (RM) level and maturity in China’s state-owned construction enterprises (CSCEs). The purpose of this paper is to find the main factors impacting RM in practice to promote rapid, sound and sustained development in CSCEs. Design/methodology/approach There are a few state-owned CSCEs in China. Most enterprises know little about RM. Because of the limited number of RM departments in these enterprises, 200 questionnaires were sent to the enterprises to investigate the RM strategies employed by them. The research is quantitative and used a questionnaire survey to determine the important factors influencing RM practice. The collected data were analyzed with the Statistical Package for the Social Sciences to identify the most important factors affecting RM as well as the extent of influence of these factors, in order to facilitate further research. Findings The survey revealed the top eight factors (i.e. leaders’ support, personnel’s responsibility, comprehensiveness of identification, costs and benefits, risk appetite, understanding of language, frequency of training and performance management) that highly impact RM in CSCEs and the extent to which these factors impact RM. The data reveal that the average RM level is low. Some methods have been recommended to improve RM. Research limitations/implications The research lays the foundation for further RM development in CSCEs. The low RM level in CSCEs should encourage researchers to find better ways to improve RM. Some factors in the research will function as valuable guides for China’s private and public-private partnership enterprises. Practical implications A quantitative analysis methodology for RM has been developed for CSCEs that can reflect their RM level. In addition, the degree of impact of key factors on RM has been shown. The results can act as a reference to improve RM quantitatively, making the RM system more explicit in dealing with risks more accurately and instructively. Originality/value Structural RM research is utilized to evaluate RM in CSCEs by following an empirical method. With the continuous improvement in RM, CSCEs can cooperate well with construction enterprises of other countries for infrastructure projects and gain more benefits.

Karimidorabati, S, Haas, C T and Gray, J (2016) Evaluation of automation levels for construction change management. Engineering, Construction and Architectural Management, 23(05), 554-70.

Kwofie, T E, Amos-Abanyie, S and Afram, S O (2016) Principal component analysis of professional competencies of architects in the Ghanaian construction industry. Engineering, Construction and Architectural Management, 23(05), 571-87.

Lindhard, S and Larsen, J K (2016) Identifying the key process factors affecting project performance. Engineering, Construction and Architectural Management, 23(05), 657-73.

Mensah, I, Adjei-Kumi, T and Nani, G (2016) Duration determination for rural roads using the principal component analysis and artificial neural network. Engineering, Construction and Architectural Management, 23(05), 638-56.