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Cited by 587

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Modelling global risk factors affecting construction cost performance

Open access
Baloi, Daniel, Price, Andrew D.F.·2003·International Journal of Project Management

Abstract

Abstract This paper discusses the core issues of global risk factors modelling, assessment and management. The research reported upon forms part of a larger study that aims to develop a fuzzy decision framework for contractors to handle global risk factors affecting construction cost performance at a project level. Major global risk factors affecting cost performance were identified through an extensive literature review and preliminary discussions with construction contractors. The main decision perspectives namely normative and behavioural were explored. Different decision-making technologies, both classical and emergent, such as classical management science techniques and DSSs, KBSs were explored and evaluated. Preliminary indications show that Fuzzy Set Theory is a viable technology for modelling, assessing and managing global risk factors affecting construction cost performance and thus a fuzzy decision framework for risk management can be successfully developed.

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Metadata

Authors
Baloi, D., & Price, A. D. F.
Venue
International Journal of Project Management
Publisher
Elsevier BV
Published
2003-05
Volume
21
Issue
4
Pages
261-269
ISSN
0263-7863 (ISSN-L)
OpenAlex
W2092066568
Cited by
587
References
0
FWCI
7.24
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Modelling global risk factors affecting construction cost performance. https://app.cochat.ai/papers/10.1016/s0263-7863(02)00017-0
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Baloi, D., & Price, A. D. F. (2003). Modelling global risk factors affecting construction cost performance. International Journal of Project Management. https://doi.org/10.1016/s0263-7863(02)00017-0

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.