دانلود رایگان مقاله ارزیابی رویکرد Kansei بر اساس تکنیک محاسبات لغات

Abstract

Kansei evaluation plays a vital role in the implementation of Kansei engineering; however, it is difficult to quantitatively evaluate customer preferences of a product’s Kansei attributes as such preferences involve human perceptual interpretation with certain subjectivity, uncertainty, and imprecision. An effective Kansei evaluation requires justifying the classification of Kansei attributes extracted from a set of collected Kansei words, establishing priorities for customer preferences of product alternatives with respect to each attribute, and synthesizing the priorities for the evaluated alternatives. Moreover, psychometric Kansei evaluation systems essentially require dealing with Kansei words. This paper presents a Kansei evaluation approach based on the technique of computing with words (CWW). The aims of this study were (1) to classify collected Kansei words into a set of Kansei attributes by using cluster analysis based on fuzzy relations; (2) to model Kansei preferences based on semantic labels for the priority analysis; and (3) to synthesize priority information and rank the order of decision alternatives by means of the linguistic aggregation operation. An empirical study is presented to demonstrate the implementation process and applicability of the proposed Kansei evaluation approach. The theoretical and practical implications of the proposed approach are also discussed.

6. Conclusions

Kansei studies refer to an interdisciplinary research field that focuses on understanding what Kansei is, how the Kansei process works, and how designers/engineers can apply valid domain knowledge to relevant Kansei implementation. This study aimed at implementing the psychological measures of Kansei responses via a mathematical model. In this paper, a Kansei evaluation approach based on computing with words (CWW) was presented to assess customer Kansei attribute preferences of products. Kansei preferences were modeled by positively worded items with 7 levels of semantic labels defined by fuzzy, interval, and cardinal numbers for establishing Kansei priorities. Kansei attributes were extracted from a set of collected Kansei words using fuzzy relation-based clustering associated with a cluster validation index (CVIÞ. A linguistic aggregation model was used as a CWW engine to synthesize Kansei priority information and rank the order of product alternatives. The implementation process and applicability of the proposed Kansei evaluation approach were illustrated through a product evaluation example of USB flash drives