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Title: Models for predicting project performance in China using project management practices adopted by foreign AEC firms
Authors: Ling, F.Y.Y. 
Low, S.P. 
Wang, S.
Egbelakin, T.
Keywords: China
Construction management
Foreign projects
International commissions
Performance characteristics
Issue Date: 2008
Citation: Ling, F.Y.Y., Low, S.P., Wang, S., Egbelakin, T. (2008). Models for predicting project performance in China using project management practices adopted by foreign AEC firms. Journal of Construction Engineering and Management 134 (12) : 983-990. ScholarBank@NUS Repository.
Abstract: China is a new market to many international architectural, engineering, and construction (AEC) firms and it is not known what would be the likely project outcomes, based on different project management (PM) practices adopted. This research developed and tested five models to predict the likely project success levels, based on PM practices adopted by foreign AEC firms in China. Based on data obtained from 33 projects, multiple linear regression (MLR) models for predicting the performance of foreign managed projects in China were constructed. The models were tested against 13 new cases, and the results show that they are able to predict project outcomes with some level of accuracy. The models show that certain scope management practices can be used to predict owner satisfaction, profit margin, and cost and quality performance of the project. Construction industry practitioners who are managing projects in China may benefit from the findings by focusing more on upstream management, like managing project scope, in order to ensure project success. It is recommended that construction industry practitioners use the MLR models to make preliminary assessment of the possibility of project success based on the type of PM practices they intend to adopt in China. From the results, they can then decide if they should change their practices or abort the project. © 2008 ASCE.
Source Title: Journal of Construction Engineering and Management
ISSN: 07339364
DOI: 10.1061/(ASCE)0733-9364(2008)134:12(983)
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