Key Performance Indicators for Regional Construction Supervision Systems in China

Abstract

This article aims to build a model to identify key performance indicators for regional supervision system performance in China. A quantitative model consisting of performance indicators using discriminant analysis method was developed using regional data from 2005 to 2009. Validity of the identified key performance indicators was verified using classification analysis and group membership prediction based on 2010 data. The results show, first, that the differences between groups arise from the scale, structure, and human resources of regional supervision systems. Second, regional supervision systems are labor intensive instead of knowledge intensive, no matter which group they belong to. Third, it may be appropriate to merge developing and underdeveloped regions. This implies that practicing engineering managers could play an even more significant role in these markets. Practicing engineering managers could provide knowledge-intensive services, such as design supervision and life-cycle cost supervision so that global consulting competitors could enter the Chinese supervision market and sustain a competitive edge. There are also lessons from China that there is a need for professional supervision engineers, which should be distinct from other engineering managers.

Keywords:

• Classification Function
• Discriminant Analysis
• Discriminant Function
• Supervision Engineers
• Supervision System

EMJ Focus Areas:

• Engineering Management Profession
• Organizational & Performance Assessment

Funding

This research was conducted with the support of the National Science Foundation for Young Scholars of China (Grant No. 71202101); Social Science Research Support Grant (No. 17QNFC34), Shenzhen University; Humanities and Social Science Research Funding, Ministry of Education of P.R.C (No. 10YJCZH025); Scientific Planning Research Grant, Ministry of Housing and Urban-Rural Development of P.R.C (Nos. 2009-K4-17 and 2011-K6-24).

Additional information

Funding

This research was conducted with the support of the National Science Foundation for Young Scholars of China (Grant No. 71202101); Social Science Research Support Grant (No. 17QNFC34), Shenzhen University; Humanities and Social Science Research Funding, Ministry of Education of P.R.C (No. 10YJCZH025); Scientific Planning Research Grant, Ministry of Housing and Urban-Rural Development of P.R.C (Nos. 2009-K4-17 and 2011-K6-24).

Notes on contributors

Zhikun Ding

Zhikun Ding, PhD is an associate professor in the College of Civil Engineering, Shenzhen University. His main research interests include engineering management, sustainable construction, and building information modeling (BIM).

Jiayuan Wang

Jiayuan Wang is a professor in the College of Civil Engineering, Shenzhen University. His main research interests are focused on risk management, sustainable construction, and building information modeling (BIM).

Jian Zuo

Jian Zuo, PhD is an associate professor in the School of Architecture and Built Environment at the University of Adelaide. His main research interest is to achieve sustainable built environment through stakeholder engagement.

Wenyan Gong

Wenyan Gong is a MPhil student in the Department of Construction Management and Real Estate, College of Civil Engineering, Shenzhen University. Her main research interest is focused on sustainable construction.