A new MCDM method combining QFD with TOPSIS for knowledge management system selection from the user's perspective in intuitionistic fuzzy environment

Knowledge management system (KMS) is crucial for organization knowledge management. In order to help the evaluation and selection of KMS from the user’s perspective, a new multiple criteria decision making (MCDM) method combining quality function deployment (QFD) with technique for order preference by similarity to an ideal solution (TOPSIS) in intuitionistic fuzzy environment is proposed. In the method, the customer criteria and system criteria for KMS selection are required. These two kinds of criteria are established from the user’s perspective and the designer’s perspective respectively. Customers give their linguistic opinions about the importance of the customer criteria and the rating of alternatives with respect to the customer criterion. Analysts give their linguistic opinions about the relationship between the customer criteria and the system criteria, and the correlation between the system criteria. OPSIS After the aggregation of linguistic opinions in intuitionistic fuzzy environment, the customers’ opinions are transformed into the rating of the weight of system criteria and rating of the alternatives concerning the system criteria by the QFD. Afterwards the alternatives are ranked according to system criteria by TOPSIS method in intuitionistic fuzzy environment and the best alternative is determined. In the end an example is provided to illustrate the applicability of the proposed method. 38 39 40 41 42 43 44 45 46 47 48 49 50 51 . Introduction Knowledge management system (KMS) refers to the computer nformation system employed to better retain and utilize orgaizational knowledge, as well as support knowledge utilization ithin and between organizations [1,2]. Organizations are devotng considerable resources to implement KMS to assist knowledge anagement. However, customer requirements for the KMS are arious in organizations [1,3–7], many of such investments end in ess than desirable outcomes possibly due to a mismatch between he KMS and the customer requirements [5]. Therefore, the selecion of the best KMS for knowledge management is the crucial ask [1,8,9]. Since the evaluation of KMS from various aspects is he complex task, many approaches have been proposed to assist Please cite this article in press as: M. Li, et al., A new MCDM agement system selection from the user’s perspective in intu http://dx.doi.org/10.1016/j.asoc.2014.03.008 he decision makers in the evaluation and selection of KMS. For xample, Wang [10] and Wang and Jiang [11] proposed the interated evaluation method for the KMS based on linguistic symbol ∗ Corresponding author. Tel.: +86 18010028109. E-mail addresses: [email protected] (M. Li), [email protected] (L. Jin), [email protected] (J. Wang). ttp://dx.doi.org/10.1016/j.asoc.2014.03.008 568-4946/© 2014 Published by Elsevier B.V. 52 53 54 55 © 2014 Published by Elsevier B.V. operators. In the methods, the criteria are constructed from the performance, function, cost, environment and humanity aspects. Liu and Peng [12] and Ngai and Chan [13] use the fuzzy AHP method to evaluate KMS. In the former research the KMS are evaluated from function, value, benefit, operation and performance aspects. In the latter research the KMS are evaluated from the cost, functionality and vendor aspects. Yu [14] evaluated the KMS from the performance, function, application and value perspectives. In the method, matter element model is extended to compare the alternatives. These researches facilitate the evaluation and selection of KMS. Most criteria such as full text search, version control [10–14] and agent [12] are constructed from the designer’s perspectives. Designers consider how to fulfill the functions with information technology (IT). Their perspectives focus more on IT and IT related parameters. The criteria well reflect the inherent characteristics of KMS and are fit for the decision makers skilled at IT. However, most decision makers especially the customers are not familiar with IT. They only concern their requirements. Their perspective method combining QFD with TOPSIS for knowledge manitionistic fuzzy environment, Appl. Soft Comput. J. (2014), focuses more on what extent their requirements are fulfilled by the KMS. For example, customers pay more attention to whether the knowledge can be found easily but does not care how to achieve it. Analysts concern more on the rationality of the knowledge map and 56 57 58 59