Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/94107
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Building and Real Estate | en_US |
| dc.creator | Saka, AB | en_US |
| dc.creator | Chan, DWM | en_US |
| dc.creator | Wuni, IY | en_US |
| dc.date.accessioned | 2022-08-11T01:07:09Z | - |
| dc.date.available | 2022-08-11T01:07:09Z | - |
| dc.identifier.issn | 0926-5805 | en_US |
| dc.identifier.uri | http://hdl.handle.net/10397/94107 | - |
| dc.language.iso | en | en_US |
| dc.publisher | Elsevier | en_US |
| dc.rights | © 2022 Elsevier B.V. All rights reserved. | en_US |
| dc.rights | © 2022. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/. | en_US |
| dc.rights | The following publication Saka, A. B., Chan, D. W. M., & Wuni, I. Y. (2022). Knowledge-based decision support for BIM adoption by small and medium-sized enterprises in developing economies. Automation in Construction, 141, 104407 is available at https://doi.org/10.1016/j.autcon.2022.104407. | en_US |
| dc.subject | Building information modelling | en_US |
| dc.subject | Decision support system | en_US |
| dc.subject | Decision-making factors | en_US |
| dc.subject | Developing economies | en_US |
| dc.subject | Small and medium-sized enterprises | en_US |
| dc.title | Knowledge-based decision support for BIM adoption by small and medium-sized enterprises in developing economies | en_US |
| dc.type | Journal/Magazine Article | en_US |
| dc.identifier.volume | 141 | en_US |
| dc.identifier.doi | 10.1016/j.autcon.2022.104407 | en_US |
| dcterms.abstract | BIM 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.accessRights | open access | en_US |
| dcterms.bibliographicCitation | Automation in construction, Sept 2022, v. 141, 104407 | en_US |
| dcterms.isPartOf | Automation in construction | en_US |
| dcterms.issued | 2022-09 | - |
| dc.identifier.scopus | 2-s2.0-85132361853 | - |
| dc.identifier.eissn | 1872-7891 | en_US |
| dc.identifier.artn | 104407 | en_US |
| dc.description.validate | 202208 bcch | en_US |
| dc.description.oa | Accepted Manuscript | en_US |
| dc.identifier.FolderNumber | a1586, a2319 | - |
| dc.identifier.SubFormID | 45534, 47496 | - |
| dc.description.fundingSource | RGC | en_US |
| dc.description.fundingSource | Others | en_US |
| dc.description.fundingText | from a2319 47496: RGC. | en_US |
| dc.description.fundingText | from: a1586 45534 self-funded | en_US |
| dc.description.fundingText | from 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.pubStatus | Published | en_US |
| dc.description.oaCategory | Green (AAM) | en_US |
| Appears in Collections: | Journal/Magazine Article | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Saka_Knowledge-Based_Decision_Support.pdf | Pre-Published version | 3.04 MB | Adobe PDF | View/Open |
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