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Developing TOPSIS method using statistical normalization for selecting knowledge management strategies
dc.contributor.author | Sarraf, Amin Zadeh |
dc.contributor.author | Mohaghar, Ali |
dc.contributor.author | Bazargani, Hossein |
dc.date.accessioned | 2014-01-07T11:59:39Z |
dc.date.available | 2014-01-07T11:59:39Z |
dc.date.issued | 2013-12 |
dc.identifier.citation | Sarraf, Amin Zadeh; Mohaghar, Ali; Bazargani, Hossein. Developing TOPSIS method using statistical normalization for selecting knowledge management strategies. "Journal of Industrial Engineering and Management", Desembre 2013, vol. 6, núm. 4, p. 860-875. |
dc.identifier.issn | 2013-0953 |
dc.identifier.uri | http://hdl.handle.net/2099/14135 |
dc.description.abstract | Purpose: Numerous companies are expecting their knowledge management (KM) to be performed effectively in order to leverage and transform the knowledge into competitive advantages. However, here raises a critical issue of how companies can better evaluate and select a favorable KM strategy prior to a successful KM implementation. Design/methodology/approach: An extension of TOPSIS, a multi-attribute decision making (MADM) technique, to a group decision environment is investigated. TOPSIS is a practical and useful technique for ranking and selection of a number of externally determined alternatives through distance measures. The entropy method is often used for assessing weights in the TOPSIS method. Entropy in information theory is a criterion uses for measuring the amount of disorder represented by a discrete probability distribution. According to decrease resistance degree of employees opposite of implementing a new strategy, it seems necessary to spot all managers’ opinion. The normal distribution considered the most prominent probability distribution in statistics is used to normalize gathered data. Findings: The results of this study show that by considering 6 criteria for alternatives Evaluation, the most appropriate KM strategy to implement in our company was ‘‘Personalization’’. Research limitations/implications: In this research, there are some assumptions that might affect the accuracy of the approach such as normal distribution of sample and community. These assumptions can be changed in future work. Originality/value: This paper proposes an effective solution based on combined entropy and TOPSIS approach to help companies that need to evaluate and select KM strategies. In represented solution, opinions of all managers is gathered and normalized by using standard normal distribution and central limit theorem. |
dc.format.extent | 16 p. |
dc.language.iso | eng |
dc.publisher | School of Industrial and Aeronautic Engineering of Terrassa (ETSEIAT). Universitat Politècnica de Catalunya (UPC) |
dc.rights | Attribution-NonCommercial 3.0 Spain |
dc.rights.uri | http://creativecommons.org/licenses/by-nc/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Economia i organització d'empreses::Gestió del coneixement |
dc.subject.lcsh | Knowledge management -- Statistical methods |
dc.subject.other | Knowledge management |
dc.subject.other | Strategy |
dc.subject.other | TOPSIS |
dc.subject.other | Normal distribution |
dc.subject.other | Entropy |
dc.title | Developing TOPSIS method using statistical normalization for selecting knowledge management strategies |
dc.type | Article |
dc.subject.lemac | Gestió del coneixement -- Mètodes estadístics |
dc.identifier.dl | B-28744-2008 |
dc.description.peerreviewed | Peer Reviewed |
dc.rights.access | Open Access |
local.citation.author | Sarraf, Amin Zadeh; Mohaghar, Ali; Bazargani, Hossein |
local.citation.publicationName | Journal of Industrial Engineering and Management |
local.citation.volume | 6 |
local.citation.number | 4 |
local.citation.startingPage | 860 |
local.citation.endingPage | 875 |
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2013, vol. 6, núm. 4 [26]