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Article: Using the principal component analysis method as a tool in contractor pre-qualification

TitleUsing the principal component analysis method as a tool in contractor pre-qualification
Authors
KeywordsContractor Pre-Qualification
Neural Networks
Principal Component Analysis
Issue Date2005
PublisherRoutledge. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/01446193.asp
Citation
Construction Management and Economics, 2005, v. 23 n. 7, p. 673-684 How to Cite?
AbstractContractor pre-qualification can be regarded as a complicated, two-group, non-linear classification problem. It involves a variety of subjective and uncertain information extracted from various parties such as contractors, pre-qualifiers and project teams. Non-linearity, uncertainty and subjectivity are the three predominant characteristics of the contractor pre-qualification process. This makes the process more of an art than a scientific evaluation. In addition to non-linearity, uncertainty and subjectivity, contractor pre-qualification is further complicated by the large number of contractor pre-qualification criteria (CPC) used in current practice and the multicollinearity existing between contractor attributes. An alternative empirical method using principal component analysis (PCA) is proposed for contractor pre-qualification in this study. The proposed method may alleviate the existing amount of multicollinearity and largely reduce the dimensionality of the pre-qualification data set. The applicability and potential of PCA for contractor pre-qualification has been examined by way of two data sets: (1) 73 pre-qualification cases (37 qualified and 36 disqualified) collected in England and (2) 85 (45 qualified and 40 disqualified) pre-qualification cases relating to 10 public sector projects in Hong Kong. The PCA-based results demonstrated that strong and positive inter-correlations existed between most of the qualifying variables, with the minimum correlation coefficient being 0.121 and the maximum being 0.899, and that qualified and disqualified contractors could be satisfactorily separated. © 2005 Taylor & Francis.
Persistent Identifierhttp://hdl.handle.net/10722/150326
ISSN
2015 SCImago Journal Rankings: 0.967
References

 

DC FieldValueLanguage
dc.contributor.authorLam, KCen_US
dc.contributor.authorHu, TSen_US
dc.contributor.authorNg, TSTen_US
dc.date.accessioned2012-06-26T06:03:22Z-
dc.date.available2012-06-26T06:03:22Z-
dc.date.issued2005en_US
dc.identifier.citationConstruction Management and Economics, 2005, v. 23 n. 7, p. 673-684en_US
dc.identifier.issn0144-6193en_US
dc.identifier.urihttp://hdl.handle.net/10722/150326-
dc.description.abstractContractor pre-qualification can be regarded as a complicated, two-group, non-linear classification problem. It involves a variety of subjective and uncertain information extracted from various parties such as contractors, pre-qualifiers and project teams. Non-linearity, uncertainty and subjectivity are the three predominant characteristics of the contractor pre-qualification process. This makes the process more of an art than a scientific evaluation. In addition to non-linearity, uncertainty and subjectivity, contractor pre-qualification is further complicated by the large number of contractor pre-qualification criteria (CPC) used in current practice and the multicollinearity existing between contractor attributes. An alternative empirical method using principal component analysis (PCA) is proposed for contractor pre-qualification in this study. The proposed method may alleviate the existing amount of multicollinearity and largely reduce the dimensionality of the pre-qualification data set. The applicability and potential of PCA for contractor pre-qualification has been examined by way of two data sets: (1) 73 pre-qualification cases (37 qualified and 36 disqualified) collected in England and (2) 85 (45 qualified and 40 disqualified) pre-qualification cases relating to 10 public sector projects in Hong Kong. The PCA-based results demonstrated that strong and positive inter-correlations existed between most of the qualifying variables, with the minimum correlation coefficient being 0.121 and the maximum being 0.899, and that qualified and disqualified contractors could be satisfactorily separated. © 2005 Taylor & Francis.en_US
dc.languageengen_US
dc.publisherRoutledge. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/01446193.aspen_US
dc.relation.ispartofConstruction Management and Economicsen_US
dc.subjectContractor Pre-Qualificationen_US
dc.subjectNeural Networksen_US
dc.subjectPrincipal Component Analysisen_US
dc.titleUsing the principal component analysis method as a tool in contractor pre-qualificationen_US
dc.typeArticleen_US
dc.identifier.emailNg, TST: tstng@hkucc.hku.hken_US
dc.identifier.authorityNg, ST=rp00158en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1080/01446190500041263en_US
dc.identifier.scopuseid_2-s2.0-25844517320en_US
dc.identifier.hkuros119075-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-25844517320&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume23en_US
dc.identifier.issue7en_US
dc.identifier.spage673en_US
dc.identifier.epage684en_US
dc.publisher.placeUnited Kingdomen_US
dc.identifier.scopusauthoridLam, KC=35324530300en_US
dc.identifier.scopusauthoridHu, TS=15759762900en_US
dc.identifier.scopusauthoridNg, ST=7403358853en_US

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