Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/94107
DC FieldValueLanguage
dc.contributorDepartment of Building and Real Estateen_US
dc.creatorSaka, ABen_US
dc.creatorChan, DWMen_US
dc.creatorWuni, IYen_US
dc.date.accessioned2022-08-11T01:07:09Z-
dc.date.available2022-08-11T01:07:09Z-
dc.identifier.issn0926-5805en_US
dc.identifier.urihttp://hdl.handle.net/10397/94107-
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectBuilding information modellingen_US
dc.subjectDecision support systemen_US
dc.subjectDecision-making factorsen_US
dc.subjectDeveloping economiesen_US
dc.subjectSmall and medium-sized enterprisesen_US
dc.titleKnowledge-based decision support for BIM adoption by small and medium-sized enterprises in developing economiesen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume141en_US
dc.identifier.doi10.1016/j.autcon.2022.104407en_US
dcterms.abstractBIM is often considered to be unsuitable and there are no systematic approaches to guiding suitability decisions in construction SMEs, especially in developing economies. Thus, this paper evaluated key decision-making factors (DMFs) for BIM-based projects in SMEs. Data was collected through interviews, a Delphi survey and analysed with Fuzzy Synthetic Evaluation (FSE). The result revealed that contractual factors, client requirements and project features are the most important DMF categories. Suitability Decision Support Index (SDSI) was computed, and action plans were compiled. These serve as a decision support engine and a knowledge base in developing a Knowledge-Based Decision Support System (KBDSS). The KBDSS validation showed that it is a useful tool for providing reliable decision support for SMEs. The findings provide solid empirical support for the evaluation of BIM in SMEs from a decision-making perspective. It has significant implications for policy and research and provides ground for technology suitability theory.en_US
dcterms.accessRightsembargoed accessen_US
dcterms.bibliographicCitationAutomation in construction, Sept 2022, v. 141, 104407en_US
dcterms.isPartOfAutomation in constructionen_US
dcterms.issued2022-09-
dc.identifier.scopus2-s2.0-85132361853-
dc.identifier.eissn1872-7891en_US
dc.identifier.artn104407en_US
dc.description.validate202208 bcchen_US
dc.description.oaNot applicableen_US
dc.identifier.FolderNumbera1586, a2319-
dc.identifier.SubFormID45534, 47496-
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextfrom a2319 47496: RGC.en_US
dc.description.fundingTextfrom: a1586 45534 self-fundeden_US
dc.description.fundingTextfrom publisher pdf: This research work was fully supported through the funding of the full-time PhD research studentship under the auspice of the Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong.en_US
dc.description.pubStatusPublisheden_US
dc.date.embargo2024-09-30en_US
Appears in Collections:Journal/Magazine Article
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Embargo End Date 2024-09-30
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