Evaluating the Knowledge Management Practices of Construction Firms by Using Importance-Comparative Performance Analysis Maps

dc.contributor.author Kale, Serdar
dc.contributor.author Karaman, Erkan A.
dc.coverage.doi 10.1061/(ASCE)CO.1943-7862.0000369
dc.date.accessioned 2017-02-28T06:43:29Z
dc.date.available 2017-02-28T06:43:29Z
dc.date.issued 2011
dc.description.abstract The emergence of the effective management of knowledge resources as a key factor in gaining and sustaining competitive advantage presents new challenges to construction firms. Evaluating knowledge management practices is considered one of the most important challenges facing firms in today's business environment. This paper proposes a model for evaluating the knowledge management practices of construction firms. The proposed model incorporates knowledge management concepts and multilayer perceptron (MLP) neural networks to construct an importance-comparative performance analysis (ICPA) map, a simple visual tool that can provide powerful diagnostic information to executives of construction firms. The model evaluates a firm's knowledge management practices, identifies its competitive advantages and disadvantages in each knowledge management practice, and sets priorities for managerial actions to improve knowledge management practices. A real-world case study was conducted by administering a survey to 105 construction firms operating in Turkey and is presented to illustrate the implementation and utility of the proposed model. The case study findings provided preliminary support for the validity of the proposed model. en_US
dc.identifier.citation Kale, S., and Karaman, E. A. (2011). Evaluating the knowledge management practices of construction firms by using importance-comparative performance analysis maps. Journal of Construction Engineering and Management, 37(12), 1142-1152. doi:10.1061/(ASCE)CO.1943-7862.0000369 en_US
dc.identifier.doi 10.1061/(ASCE)CO.1943-7862.0000369 en_US
dc.identifier.doi 10.1061/(ASCE)CO.1943-7862.0000369
dc.identifier.issn 0733-9364
dc.identifier.issn 0733-9364
dc.identifier.issn 1943-7862
dc.identifier.scopus 2-s2.0-84855932421
dc.identifier.uri http://doi.org/10.1061/(ASCE)CO.1943-7862.0000369
dc.identifier.uri https://hdl.handle.net/11147/4918
dc.language.iso en en_US
dc.publisher American Society of Civil Engineers (ASCE) en_US
dc.relation.ispartof Journal of Construction Engineering and Management - ASCE en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.subject Artificial neural networks en_US
dc.subject Construction firm en_US
dc.subject Construction industry en_US
dc.subject Knowledge management en_US
dc.subject Organizations en_US
dc.title Evaluating the Knowledge Management Practices of Construction Firms by Using Importance-Comparative Performance Analysis Maps en_US
dc.type Article en_US
dspace.entity.type Publication
gdc.author.institutional Kale, Serdar
gdc.bip.impulseclass C4
gdc.bip.influenceclass C4
gdc.bip.popularityclass C4
gdc.coar.access open access
gdc.coar.type text::journal::journal article
gdc.collaboration.industrial false
gdc.description.department İzmir Institute of Technology. Architecture en_US
gdc.description.endpage 1152 en_US
gdc.description.issue 12 en_US
gdc.description.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
gdc.description.scopusquality Q1
gdc.description.startpage 1142 en_US
gdc.description.volume 137 en_US
gdc.description.wosquality Q1
gdc.identifier.openalex W2106321790
gdc.identifier.wos WOS:000299133600013
gdc.index.type WoS
gdc.index.type Scopus
gdc.oaire.accesstype BRONZE
gdc.oaire.diamondjournal false
gdc.oaire.impulse 7.0
gdc.oaire.influence 3.6862045E-9
gdc.oaire.isgreen true
gdc.oaire.keywords 690
gdc.oaire.keywords Organizations
gdc.oaire.keywords Artificial neural networks
gdc.oaire.keywords Performance
gdc.oaire.keywords Knowledge management
gdc.oaire.keywords Construction firm
gdc.oaire.keywords Construction industry
gdc.oaire.keywords Construction İndustry
gdc.oaire.keywords Knowledge Management
gdc.oaire.keywords Construction Firm
gdc.oaire.keywords Artificial Neural Networks
gdc.oaire.popularity 9.428809E-9
gdc.oaire.publicfunded false
gdc.oaire.sciencefields 05 social sciences
gdc.oaire.sciencefields 0211 other engineering and technologies
gdc.oaire.sciencefields 02 engineering and technology
gdc.oaire.sciencefields 0502 economics and business
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gdc.opencitations.count 26
gdc.plumx.crossrefcites 23
gdc.plumx.mendeley 105
gdc.plumx.scopuscites 31
gdc.scopus.citedcount 31
gdc.wos.citedcount 29
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