Fuzzy weighted average as a fuzzified aggregation operator and its properties

Pavlačka, Ondřej; Pavlackova, M.; Hetfleiš, Vladislav

doi:10.14736/kyb-2017-1-0137

Cited by 1 publication

(2 citation statements)

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“…Because the measurement of knowledge quality involves imprecise information that cannot be quantified, and the quality of knowledge must be evaluated by a group of experts and several attributes at the same time, it is suitable to use the fuzzy multi-criteria group decision making method [29]. Past studies mostly used the average value as the final group decision [26,[30][31][32][33]; however, average of opinion cannot accurately reflect the overall judgment. Therefore, Hsu and Chen proposed a similarity aggregation method (SAM) [34,35], and Lee developed an optimal aggregation method (OAM), to help obtain the consistence of fuzzy opinion [1,36].…”

Section: Fuzzy Set and Group Decision Makingmentioning

confidence: 99%

“…Based on the fuzzy weight average (FWA) method [30][31][32][41][42][43][44][45], this study proposes a knowledge quality fuzziness index (KQFI) to help make the Go/No go decision of the knowledge proposal. KQFI can be described as Equation (10), where r i and w i are the fuzzy assessment and fuzzy weight of the knowledge proposal, respectively, and i denotes the criteria for evaluating the knowledge proposal.…”

Section: Knowledge Quality Fuzziness Indexmentioning

confidence: 99%

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Assessing Knowledge Quality Using Fuzzy MCDM Model

Wei,

Tai,

Lee

et al. 2023

Mathematics

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The purpose of knowledge management is to excavate the tacit knowledge accumulated by each enterprise member through the knowledge proposal system. Each knowledge proposal must be assessed, and after passing the quality assessment, the knowledge proposal will be stored in the knowledge repository and shared with other employees who need the knowledge at work. In the long run, the capabilities of all employees will gradually enhance and the competitiveness of enterprises will naturally increase. The correct assessment of knowledge quality is the key to the success of knowledge management. Some scholars propose to use the AHP (analytical hierarchical process) to determine the quality of knowledge. The problem with this approach is that the AHP can only obtain the relative quality of all knowledge, not the actual quality of knowledge. Therefore, this study proposes a fuzzy assessment model to measure knowledge quality, which includes a knowledge quality fuzziness index (KQFI) and a checking gate. First, experts conduct linguistic evaluation on the weight of criteria and knowledge quality. All linguistic evaluations are then integrated into a knowledge quality fuzziness index (KQFI), which is compared with a fuzzy threshold (FT); then, the level of goodness of KQFI to FT is obtained. If it is greater than 0.5, it means that the quality of the knowledge proposal is qualified; otherwise, it means that the quality of the knowledge proposal is unqualified. This study uses a case including five experts and nine knowledge proposals to demonstrate the applicability of the method. The results show that the method finally judges six knowledge instances as qualified and three as unqualified. The results show that the proposed method can indeed assist enterprises to effectively screen knowledge proposals.

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