Uncertainties exist in both the human mentality and the languages. Simultaneously, modes of acknowledgement
exhibited by the interviewees vary from one another. Feelings associated with the tone of the linguistic
implications in response to the questionnaire are also vague. The expression in one single numeric value is sometimes
not suitable. Therefore, Zadeh (1973) believed the application of the Fuzzy linguistic in the uncertain
issues will be a relatively better tool. Questionnaire interviewees apply the linguistic remarks such as never,
rarely, sometimes, frequently, and always to express their degree of acknowledgement of the issues. However,
the Fuzzy linguistic is able to evaluate the interviewee’s response to the linguistic remark one step further based
on the strength of feeling exhibited by the evaluation at different linguistic implication interval, which is much
more reflective of the factual circumstances and is enabled to acquire much more accurate results.
As a consequence, this study will use Fuzzy mode to improve the subjective linguistic scale in Kano’s twodimensional
quality element. Our method allows respondents to express themselves about the extent of correct
attribution by membership and any numeric value. With this method, it can pass on the overall correct mentality
in reasonable sense under unknown circumstances.
Suppose U is a universe of discourse, ffsgk
si¼1 and fdtgk
t¼1 are the Kano’s functional and dysfunctional questions
located in U. k is the item number of linguistic variable. fehgn
h¼1 is the respondent of one set Kano’s
Fuzzy mode questionnaire. Meanwhile, by using Fuzzy set relations, we have defined respondent eh normalized
membership fmhs; ð
Pk
s¼1mhs ¼ 1Þ and dmht; ð
Pk
t¼1mht ¼ 1Þ corresponding to linguistic variable fs and dt to
calculate two-dimensional Fuzzy relation perception level. The possible classification will be defined by
Kano’s two-dimensional quality element according to references. All the quality element classification meeting
the significant level will also be calculated under the a cut significant membership level. Among all,
eM
;eO
;eA
;eI
;eR
; and eQ
with maximum value Ss in Kano’s Fuzzy quality element classification is defined as
Kano’s Fuzzy mode (KFM). If there are more than two sets of Kano’s Fuzzy quality element classification
with the same value ySs, then this set of data is called with multi-Fuzzy mode or multi-consensus. If the final
scoring is equal, the greatest impact on the product is determined in the following order: M > O > A > I
(CQM, 1993). The procedure of Kano’s Fuzzy mode classification in this study is shown as follows: