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Comparison among three analytical methods for knowledge communities group-decision analysis

dc.citation.epage1024en_US
dc.citation.issue4en_US
dc.citation.spage1011en_US
dc.citation.volume33en_US
dc.citation.woscount56
dc.contributor.authorChu, Mei-Taien_US
dc.contributor.authorShyu, Josephen_US
dc.contributor.authorTzeng, Gwo-Hshiungen_US
dc.contributor.authorKhosla, Rajiven_US
dc.contributor.department科技管理研究所zh_TW
dc.contributor.departmentInstitute of Management of Technologyen_US
dc.date.accessioned2014-12-08T15:13:10Z
dc.date.available2014-12-08T15:13:10Z
dc.date.issued2007-11-01en_US
dc.description.abstractKnowledge management can greatly facilitate an organization's learning via strategic insight. Assessing the achievements of knowledge communities (KC) includes both a theoretical basis and practical aspect; however, a cautionary word is in order, because using improper measurements will increase complexity and reduce applicability. Group decision-making, the essence of knowledge communities, lets one considers multi-dimensional problems for the decision-maker, sets priorities for each decision factor, and assesses rankings for all alternatives. The purpose of this study is to establish the objective and measurable patterns to obtain anticipated achievements of KC through conducting a group-decision comparison. The three multiple-criteria decision-making methods we used, simple average weight (SAW), "Technique for Order Preference by Similarity to an Ideal Solution" (TOPSIS) and "VlseKriterijumska Optimizacija I Kompromisno Resenje" (VIKOR), are based on an aggregating function representing "closeness to the ideal point". The TOPSIS and VIKOR methods were used to highlight our innovative idea, academic analysis, and practical appliance value. Simple average weight (SAW) is known to be a common method to get the preliminary outcome. Our study provides a comparison analysis of the above-three methods. An empirical case is illustrated to demonstrate the overall KC achievements, showing their similarities and differences to achieve group decisions. Our results showed that TOPSIS and simple average weight (SAW) had identical rankings overall, but TOPSIS had better distinguishing capability. TOPSIS and VIKOR had almost the same success setting priorities by weight. However, VIKOR produced different rankings than those from TOPSIS and SAW, and VIKOR also made it easy to choose appropriate strategies. Both the TOPSIS and VIKOR methods are suitable for assessing similar problems, provide excellent results close to reality, and grant superior analysis. (c) 2006 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.eswa.2006.08.026en_US
dc.identifier.issn0957-4174en_US
dc.identifier.journalEXPERT SYSTEMS WITH APPLICATIONSen_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.eswa.2006.08.026en_US
dc.identifier.urihttps://ir.lib.nycu.edu.tw/handle/11536/10165
dc.identifier.wosnumberWOS:000246315200019
dc.language.isoen_USen_US
dc.subjectknowledge communities (KC)en_US
dc.subjectTechnique for Order Preference by Similarity to an Ideal Solution (TOPSIS)en_US
dc.subjectVlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR)en_US
dc.subjectmultiple criteria decision making (MCDM)en_US
dc.titleComparison among three analytical methods for knowledge communities group-decision analysisen_US
dc.typeArticleen_US

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