中 華 大 學

博 士 論 文

題目：新模糊概念於二維品質模型之研究

A New Fuzzy Concept Approach for Kano’s Model

系 所 別：科 技 管 理 研 究 所學號姓名：D09403016 黃 勝 彥指導教授：李 友 錚 博 士

中 華 民 國 九 十 七 年 六 月

中 華 大 學

(博士論文授權書)

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論文題目： 新模糊概念於二維品質模型之研究指導教授： 李友錚 博士研究生姓名： 黃勝彥

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中 華 民 國 97 年 6 月 30 日

中華 大學博士 班研究生

論文 指導教授 推薦書

科技管理研究所碩士班黃勝彥君所提之論文新模糊概念於二維品質模型之研究，係由本人指導撰述，同意提付審查。

指導教授 (簽章)

中華民國九十七年六月

中華 大學博士 班研究生

論文 口試委員 會審定書

科技管理研究所碩士班黃勝彥君所提之論文新模糊概念於二維品質模型之研究，經本委員會審議，符合博士資格標準。

論文口試委員會 召集人 (簽章)

委 員 (簽章)

(簽章)

(簽章)

(簽章)

(簽章)

(簽章)

所 長 (簽章)

中華民國九十七年六月六日

謝辭

光陰荏苒，三年的博士班研究所的時光轉眼飛逝，回首三年歲月，在這一

路上要感恩答謝的人很多，在論文完成的時刻心中感激與敬意，在此向大家至

上最深的謝意與祝福。

首先誠摯的感謝指導教授李友錚博士，謝謝老師悉心的教導使我得以一窺

服務品質與顧客滿意領域的深奧，不論是研究學習或是生涯規劃，老師都不時

的討論並指點我正確的方向，並給予我相當大的啟發，讓我從當初的毛頭小子，

轉眼成為一位獨立且懂得自我要求的成熟學生，師恩浩瀚，謹記吾心，在此學

生願獻上最真摯的謝意與祝福。

口試期間承蒙許良僑博士、林淑萍博士、吳祥華博士、梁榮輝院長、黃廷

合院長、邱紹一研發長對於論文內容悉心批閱與不吝的指正，使我獲益良多，

在此由衷感謝。

同時要感謝的還有美力大方的肖琳學姊、衝勁十足的 PONY 同學、聰明友

善的鐵民大哥在理論及實務上給我的支援。因為有你們的體諒及幫忙，使得本

論文能夠更完整而嚴謹。此外感謝阿淦學長、雲瀚學長、綺華學姐、秀緩同學

們不厭其煩的指出我研究中的缺失，且總能在我迷惘時為我解惑。實驗室的仲

銘學弟、文登學弟、秋月姊、美蘭姊、筱琪學妹們當然也不能忘記，你們的幫

忙及歡樂的笑容使我銘感在心。

最後要感謝我的家人、我的父母、女友珞怡，謝謝你們的這一段時間的陪

伴與諒解，給予我生活中所需的溫暖，使我有的動力及自信去完成堅鉅的工作，

並在我有壓力時給予我最有效的舒解與鼓勵，謝謝你們，我愛你們。

回想這本論文的完成，的確仰賴了多人的協助、幫忙，再多的千言萬語，

也無法表示我此刻最深的感謝，僅以此項成果獻給我最敬愛的家人、師長以及

朋友，謝謝您們，祝福：主愛深深。

勝彥

于 民國九十七年六月

i

摘 要近年來由日本學者狩野紀昭博士所提出的二維品質模型，已被證實是分析

顧客需求的有力工具。此模型是利用正反向問卷方式取得顧客的感受，並以眾

數概念取得代表性的品質屬性，讓研究人員能對顧客的認知有更多的了解，進

而開發能滿足顧客需求的產品或服務。然而通常人們在做決策時，心中的思維

及感受很多時候都具有模糊、不確定的特性，因此傳統問卷的回答方式，在決

策環境越來越複雜的今日社會中似乎略顯不足。同樣的，忽略了人們感受具多

元化與模糊特性的傳統品質屬性判別方法，可能導致得到錯誤的分類結果，而

難以設計出真正滿足顧客所需的產品。

因此本研究欲提出以模糊理論適於處理模糊、認知不確定等問題的優點，

藉由隸屬度函數及允許存在多重感受的概念來設計模糊 Kano 問卷與模糊 Kano

眾數分類法。期望以更符合人類思維的問卷設計方式與判別方法，使 Kano 二維

模型的問卷內容能包含更多且更完整的顧客訊息，而有助於改善傳統判別上的

困難，得到更具代表性的判別結果。

關鍵字：二維品質模型、模糊理論、隸屬度函數、模糊 Kano 問卷、模糊 Kano

眾數

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ABSTRACTIn resent years, two-dimensional quality model proposed by Japanese scholar,

Dr. Kano has been proved as effective instrument for analyzing customer

requirement. The model is to reflect consumer feeling within functional and

dysfunctional questionnaires. In addition, conceptual work of mode is conducted to

substitute the representative instrument, quality attribute. This is to enhance

researcher to have advance understanding of consumer value and to develop product

and service to meet the need of consumer. However, consumption decision has the

character of blur and uncertain due to various mentality and affection. Traditional

questionnaire is not enough to deal with complicate decision making in modern

society. At mean while, the lack of consideration of diverse affection and blur in

traditional quality attribute methodology may cause misleading classification when

designing product for customer requirement.

This research is to conduct fuzzy theory, which has advantage to modify blur,

ambiguity and uncertainty questions. Moreover, membership function include the

concept to tolerant multiple affection when design Kano’s questionnaire and blur

Kano’s mode classification. It is to precede a questionnaire approaching human

mentality as well as its classification. As a result, the content of Kano’s

two-dimensional questionnaire could pass on more consumer information to amend

the difficulty in traditional classification in addition to be representative.

Keywords: Kano’s model、Fuzzy Theory、Membership function、Fuzzy Kano’s

Questionnaire、Fuzzy Kano’s mode.

iii

CONTEST摘 要........................................................................................................................... i

ABSTRACT................................................................................................................. ii

CONTEST...................................................................................................................iii

TABLE ........................................................................................................................ iv

FIGURE ........................................................................................................................ i

FIGURE ........................................................................................................................ i

CHAPTER.1 INTRODUCTION ............................................................................ 1

Section.1 Background ....................................................................................... 1

Section.2 Purpose.............................................................................................. 3

Section.3 Significance....................................................................................... 4

CHAPTER.2 LITERATURE REVIEW.................................................................. 6

Section.1 Review of Kano’s Model.................................................................. 6

Section.2 Review of Fuzzy Theory................................................................. 11

Section.3 Review of Fuzzy Theory Application in Kano’s Model................. 14

Section.4 Review of Fuzzy Questionnaire Design.......................................... 15

Section.5 Review of Fuzzy Mode ................................................................... 21

CHAPTER.3 METHODOLOGY ......................................................................... 25

Section.1 Fuzzy Kano’s Questionnaires (FKQ).............................................. 25

Section.2 Fuzzy Mode Results to Kano’s Classifications............................... 27

CHAPTER.4 CASE STUDY................................................................................ 31

Section.1 Fuzzy Kano’s two-dimensional quality questionnaire .................... 31

Section.2 Fuzzy Kano’s two-dimensional quality attribute judgment ............ 33

CHAPTER.5 CONCLUSION............................................................................... 46

REFERENCE............................................................................................................. 47

APPENDIX A ............................................................................................................ 51

iv

TABLETable. 1 Kano’s evaluation table.................................................................................. 8

Table. 2Schvaneveldt’s evaluation table ..................................................................... 9

Table. 3Matzler and Hinterhuber’s evaluation table.................................................. 9

Table. 4 Tontini‘s evaluation table............................................................................... 9

Table. 5 Result of traditional questionnaire and mode .............................................. 22

Table. 6 Result of fuzzy questionnaire and mode ....................................................... 23

Table. 7 Result of decision making for touring spot................................................... 23

Table. 8 Answer for TKQ and FKQ............................................................................ 32

Table. 9Kano’s evaluation table................................................................................ 36

Table. 10 Total membership grade for interviewee Ⅰ ............................................... 36

Table. 11 Total membership grade for interviewee Ⅱ ............................................... 37

Table. 12 Total membership grade for interviewee Ⅲ ............................................... 37

Table. 13 Total membership grade for interviewee Ⅳ ............................................... 38

Table. 14 Total membership grade for interviewee Ⅴ ............................................... 38

Table. 15 Total membership grade for interviewee Ⅵ ............................................... 39

Table. 16 Total membership grade for interviewee Ⅶ ............................................... 39

Table. 17 Total membership grade for interviewee Ⅷ ............................................... 40

Table. 18 Total membership grade for interviewee Ⅸ ............................................... 40

Table. 19 Total membership grade for interviewee Ⅹ ............................................... 41

Table. 20 Total level of quality attribute of ten interviewees ..................................... 41

Table. 21 Classification of ten interviewees common consensus 4.0 .................. 42

Table. 22 Comparison of traditional and fuzzy numeric classification ..................... 43

Table. 23 Kano’stwo-dimensional fuzzy classification-analysis of whole group ...... 45

i

FIGUREFig. 1Kano’smodel and five categories of quality attribute....................................... 7

Fig. 2 Triangle fuzzy membership function............................................................... 13

Fig. 3 Single Answer for TKQ………………………………………………………26

Fig. 4 Multiple Answers for FKQ………………… .................................................. 26

Fig. 5 Frequencies of traditional and fuzzy method .................................................. 44

1

CHAPTER.1 INTRODUCTION

Section.1 Background

Recently, it is proven that the two-dimensional quality model addressed by Dr.

Kano is an effective instrument to analyze the requirement of customer. Dr. Kano

uses functional and dysfunctional questionnaires and 5×5 evaluation table as

conducting instrument. He classified quality elements into 5 catalogs. These become

essential instruments for two-dimensional quality model and extensively applied in

numerous researches and case studies. Kano’s Model is to find out the customer

affection towards products and services through questionnaire and then based on the

information as references to improve the customer satisfaction.

The purpose of customer satisfaction research is to reflect the actual

circumstances. However, researchers applied Kano’s Model are always lack of

considering the fuzzy and uncertainty of mentality and affection when devising

questionnaire. Furthermore, external factors which influence decision making grow

more and more. Hence, the previous method which applied singular scale to fill in

the questionnaire and to catalog in tradition mode quality classification become

rough and insufficient in current complicated decision making society.

In past, there are scholars have bring up their own points to devise and evaluate

traditional questionnaire. Bohrnstedt (1970) had addressed on hypothesize of scale in

equidistance in traditional questionnaire. This hypothesize would cause the

measurement error in evaluation. Moreover, it will also attenuate the outcome of

researches, instead of over estimated. As a consequence, the outcome of the research

will be conservative. Olsson et al. (1982) found that hypothesize of scale in

equidistance in traditional questionnaire will derive underestimate of Pearson

relevant coefficient. It will also be conservative the outcome of analyzes. In addition,

the result being underestimated will be critical as the scale numbers of category

decreasing. Manski (1990) furthermore put statement that the process of human

2

mentality and behavior is with uncertainty, and the data in traditional questionnaire

are always over interpreted. Huang and Wu (1992) indicated in their research that

customer can not respond their multiple affections in singular answer. Nguyen and

Wu (2000) also show clearly that the experimental data are over used to cater

numeral in traditional questionnaire. Moreover, Hsu et al. (2001) considered that

traditional questionnaire neglects the dynamic factors which are related to real

circumstances to simplified and reduce the complexity of mathematic model.

However, traditional questionnaires are reflecting the result from a two-valued

logic world. To investigate the population, people’s opinions or the complexity of a

subjective event more accurately, it is suggested that we had better use the fuzzy

logic. Moreover, since Zadeh (1965) developed fuzzy set theory, its applications are

extended to traditional statistical inferences and methods in social sciences, including

medical diagnosis or a stock investment system.

There are more and more researches focus on the fuzzy questionnaires analysis

and applications in the social science fields, such as Pryor et al. (1989) brought up

computerized fuzzy graphic rating scale model. It is to select the most accurate

numerical point which could reflect self-feeling onto scale, and then to rank feelings

on linear intervals. This is to measure the attitude at work. Liang and Wang (1991)

quantified how importance of these indicators to respondents by using fuzzy

linguistic. It used triangular fuzzy numbers of 5 linguistic scales to represent the

ranking weight of indicators. Besides, Chen and Hwang (1992) also proposed an

application of fuzzy theory. The basic concept is to converse fuzzy number of

linguistic terms into crisp number. Chen and Hwang also put together of 8

conversion scales. Furthermore, there are 13 linguistic terms which can be collected.

Through conversion, these will be conversed as a continuously precise fraction

number in a close interval between [0,1]. Yeh (1998) also proposed an improved

fuzzy Delphi method. It was applied in policy decision-making study. It is found that

3

the information analyzed from fuzzy questionnaire is better than traditional

questionnaire. In addition, Chen (2000) used membership function in fuzzy theory to

measure the linguistic variable in questionnaires. He found the reliability of different

linguistic variable in membership function in this study. The reliability is higher than

in Likert scale. Wang and Wang (2000) addressed fuzzy questionnaire to study how

suitable between basketball coach and the team. It permits the respondents to fill in

the percentage in several items. This method could reflect the psychological

character of respondents.

In currently, respondents may have to simplify their own complicated feeling in

order to response questionnaire of Kano’s model base on binary logic which neglects

the fuzzy, uncertainty and multiple perception in the process of thinking. It is an

inappropriate approach to study accurate customer information. Furthermore, quality

attribution discriminablility base on traditional questionnaire may be convenient in

calculation, the output may be unpresentable or error may occur due to the lack of

information.

Section.2 Purpose

The main purposes of this research are as following：

1. To propose specific approach for the complexity of mentality on Kano’s

quality model.

2. To bring up fuzzy approach in designing questionnaire as solution for

incomplete information in Kano’s questionnaire.

3. To develop qualitative analysis in Kano’s model as fuzzy quality

classifiable approach.

4. To address easy and applicable method for fuzzy Kano’s quality model.

4

Section.3 Significance

The purpose of the research is to integrate fuzzy theory and Kano’s model.

Therefore, the innovation of this research is the application of fuzzy Kano’s

questionnaire and fuzzy Kano’s mode analysis. The significance of the research are

as following：

1. Questionnaire investigation is the common instrument of social science. It

is to measure the attitude, reorganization and referable potential

characteristics of respondents from the reflection in questionnaire. The

previous research found that human mentality and feelings are fuzzy and

uncertain, hence traditional Kano’s functional and dysfunctional

comparison questionnaire can not image the complexity of human

mentality.

2. The design of Kano’s functional and dysfunctional questionnaire is applied

by fuzziness concept which conducts discrete membership function. It

tolerates fuzzy and multiple feelings existing in customer which include

more information and customer perception. Complete and accurate

information is the foundation of quality attribution discriminablity.

3. Discriminate approach from mode statistic which base on traditional

questionnaire in addition with quality attribution classification, may be

convenient in calculation but also unpresentabe and invalid due to lack of

information.

4. Fuzzy Kano’s mode discriminablity base on concept of membership

function is an innovation which collects customer feeling as well as

appropriately connecting with quality attribution classification. Upon

common consensus level, conceptual work of agreement sets of confidence

level will present commonly accepted quality attribution classification.

5. It is lack of research around combination of fuzzy theory and Kano’s model

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in previous work. Little work with link up of the two approaches does not

amend the root of questionnaire though it agrees with conceptual work of

fuzzy theory application.

6. This research is differ from other work because it conducts concept of

discrete membership function to design questionnaire and discriminable

method instead of conducting continuous membership function and metric

scale. In theory, it fit in with Kano’s model which is classification criterion

of abstract issue. This makes the math question become reasonable and

simplify for researcher with non-related background.

Fuzzy statistical analysis grows as a new discipline from the necessity to deal

with vague samples and imprecise information caused by human thought in certain

experimental environments. Furthermore, the Kano’s Method is a powerful way to

classify a set of customer requirements. The results, however, can be difficult to

interpret. Thus, in this research we want to administer a Fuzzy Kano’s Questionnaire

along with a Kano’s questionnaire, interpreting the results becomes more faithful and

more reasonable.

This research consists of five sections. The next section introduces Kano’s

model. This is followed to understand accurately the voice of the customer, a process

model to develop the Fuzzy Kano’s Questionnaires and Fuzzy Mode by

incorporating Kano’s model is proposed in the next section.Thereafter are two case

examples showing the use of Fuzzy Kano’s Questionnaires (FKQ) and Fuzzy Kano’s

Mode (FKM) in Kano’s model.

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CHAPTER.2 LITERATURE REVIEW

Section.1 Review of Kano’s Model

In planning a product or service, one makes a list of potential customer needs

that the product or service should perhaps try to satisfy. Going to see current and

potential customer (“voice-of the-customer” visits) is one good way to getideas for

what should be on the list of potential customer requirements.

Recently, it is proven that two-dimensional quality model addressed by Dr.

Kano is an effective instrument. It meets the need of analyzing the requirement of

customer. Dr. Kano used functional and dysfunctional questionnaires and 5×5

evaluation table as conducting instrument. He classified quality elements into 5

catalogs. This has become a core instrument for two-dimensional quality model and

extensively applied in numerous researches and case studies.

The Kano’s model illustrates the relationship between customer satisfaction and

the performance of a product or a service. As Fig.1 shows, the extent to which a

quality element is provided is indicated on the x-axis. The more the arrow moves

towards the right, the greater the extent to which the quality element is provided,

while the more the arrow moves towards the left, the less the left, the less the extent

to which the quality element is provided. The customer satisfaction is indicated on

the y-axis. The higher the arrow, the higher the customer dissatisfaction, while the

lower the arrow, the higher the customer dissatisfaction. Based on these axes, the

following are the popularly named Kano’s customer requirement categories.

7

Fig. 1 Kano’smodel and five categories of quality attribute.

The Kano’s model divides product or service features into five distinct

categories, each of which affects customer in a different way. The five categories of

quality attributes proposed by Kano’s are:

I. Attractive quality attribute

When this element is sufficient, customer feel satisfactory, but still acceptable

if it is not sufficient.

II. One-Dimensional quality attribute

If this element is sufficient, customer feel satisfactory. Insufficiency of this

element results in non-satisfaction.

III. Must-Be quality attribute

It is taken for granted when this element is sufficient and will not result in more

satisfaction, but insufficiency of this element results in non-satisfaction.

IV. Indifferent quality attribute

This element will not result in satisfaction or not, whether it is sufficient or not.

V. Reverse quality attribute

Non-satisfaction comes when it is insufficient and on the contrary satisfaction

comes when it is sufficient.

8

Kano’s model developed a structured user questioning methodology to help

characterize different features and remove ambiguity by ensuring that categorization

is based on user research. The methodology is relatively straightforward, as outlined

below:

1. Devise Kano’s Questionnaire

The questionnaire aims to understand how potential customer would feel if a

feature was either present or not present. This is achieved by asking two questions

for each feature - a functional question (the feature is present) and a dysfunctional

question (the feature is not present), as shown in the diagram opposite.

2. Evaluation according to frequencies

An overview of the requirement categories of the individual product

requirements is gained from the table of results. The simplest method is evaluation

and interpretation according to the frequency of answers. Part of the power of the

Kano’s model is that it does not simply provide an average response from a customer

survey.

Based upon the responses, Dr. Kano and his colleagues believe that the

“One-dimensional”,“Attractive”, “Must-be”, “Indifferent”and “Reverse”quality

attributes can be classified through a customer questionnaire. See for Kano’s et al.

(1984); Schvaneveldt et al. (1991); Matzler and Hinterhuber (1998); Tontini (2000)

evaluation Table.1 to Table.4.

Table. 1

Kano’s evaluation tableDysfunctional

Like Must-be Neutral Live-with Dislike OtherLike I I A A O QMust-be I I I I M QNeutral R R I I M QLive-with R R R I I QDislike R R R I I Q

Functional

Other Q Q Q Q Q QSource By: Kano et al., 1984, p.39-48.

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Table. 2

Schvaneveldt’s evaluation tableDysfunctional

Like Must-be Neutral Live-with DislikeLike Q A A A OMust-be Q I I I MNeutral Q I I I MLive-with Q I I I M

Functional

Dislike R Q Q Q QSource By: Schvaneveldt et al., 1991, p.149-161.

Table. 3

Matzler and Hinterhuber’s evaluation tableDysfunctional

Like Must-be Neutral Live-with DislikeLike Q A A A OMust-be R I I I MNeutral R I I I MLive-with R I I I M

Functional

Dislike R R R R QSource by: Matzler and Hinterhuber, 1998, p.25-38.

Table. 4

Tontini‘s evaluation tableDysfunctional

VeryLike Like Must-be Neutral Live-with Dislike Very

DislikeVery Like - A A VA VA O OLike - - A A A O OMust-be - - - I I M VMNeutral I I I I I M VMLive-with R R R I I M VMDislike R R R R R - -

Functional

Very Dislike R R R R R - -Source By: Tontini, 2000, p.728-734

Matzler and Hinterhuber (1998) consider the advantage of quote Kano’s model：

1. Kano’s model offer valuable help on product development despite of technique

and finances doesn’t achieve certain requirement. However, it can find out the

10

key influencing factor on customer satisfaction.

2. It is able to understand the requirement of product in the aim of delivering better

definition on product quality with customer satisfaction. It is helpful for

developing new product. For example, it is not only to upgrade the existing

demand in customer satisfaction, but also improving the one-dimensional and

attractive demand of existing product.

3. To find attractive demand can offer various probability in product diversity..

4. The difference of must demand, one-dimensional demand and attractive demand

will suggest customer division. It can meet the requirement of different

customer.

5. Kano’s model can be applied alone with quality function development.

To summarize the above description, Kano’s two-dimensional model breaks

down the traditional mentality which is one-dimension. It energize the latent customer

demand as well as encourage innovation and minimize customer complaint. It also

corrects the old-fashion mentality that zero complaint equals to customer satisfaction.

This assists enterprise to sequence their priority to enhance customer satisfaction. This

research positively recommends Kano’s two-dimensional model can be applied in

service quality. The approach is within customer perception to achieve service quality

classification in advance to draw enhancement drills.

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Section.2 Review of Fuzzy Theory

The term of fuzzy theory appeal to solve the fuzzy phenomenon exists in daily

life. It was firstly emerged in the development by Professor Lotti. A. Zadeh, a master

of automatic control engineering, of University of California at Berkeley in 1965. It

is to illustrate abstract concept in mathematical approach which is application of

membership function concept. The purpose is to express ambiguity, vagueness and

blur which occurs in daily life in indicating the level of adjective in human linguistic.

Membership function in fuzzy theory is the membership grade of fuzzy set in

elements. The range is between 0 to 1. The higher numeral of element means higher

membership grade which also indicate membership of element is also higher in the

set. In addition, definition of membership function can be defined as discretization

membership function and continuous membership function.

Traditional crisp set is rooted from binary logic. It means correlation of one

element x and one set A leading to two probability“x belongs to A”or“x does

not belong to A”. It is a choice of 0 or 1 {0,1}. Therefore, it will expand to any

choice between 0 to 1 [0,1] to give numeric between 0 to 1 according to grade of

membership.

Zadeh defined that “the grade of a element under a set can be express by a

numeric between 0 to 1.”

( ) : [0,1]A x U → ， Uxxux A ，)( (1)

U：domain, each subject in domain is called “element”

：fuzzy subset A upon domain U, x U indicates any element x in domain

U. a real number ( ) [0,1]A x between 0 and 1 is to present membership of x to A .

( )A x is the membership function of A .

When U is countable set of finite set or element (discrete), A is illustrated as：

A =1

( ) /n

A i ii

x x (2)

= 1 1 2 2( ) / ( ) / ... ( ) /A A A n nx x x x x x

12

When U is uncountable set of infinite element (continuous), A is illustrated as：

A = ( ) /A i iUx x (3)

Assume A and B are the two fuzzy set of domain U . Mathematic calculation of

any two fuzzy sets us are followed：

1. Union

( ) ( ) ( ) [ ( ), ( )]A B i A i B i A i B ix x x Max x x (4)

2. Interaction

( ) ( ) ( ) [ ( ), ( )]A B i A i B i A i B ix x x Min x x (5)

Membership function is to present the grade of the element in fuzzy set. The

higher grade of the element membership, the higher grade of the membership in the

set. See the figure 2.

1. Normality of A Fuzzy Subset.

2. Convex Fuzzy Subset.

3. Piecewise Continuous.

Fuzzy set A is normality of A fuzzy subset when the height of fuzzy set A is 1. It

means at least existing an element 1)(max xu AUx which membership grade is 1.

Fuzzy set A is a convex fuzzy subset when any real number set is universal set

including fuzzy set A in term that any real number x y z and exists

( ) ( ) ( )A A Ay x z . Formula definition of triangle fuzzy number as (6)：

( )A x =

0,

0,

x ax a

a x bb ac x

b x cc b

x c

(6)

13

x

1

a b c

( )A x

Fig. 2 Triangle fuzzy membership function

Though fuzzy theory is extensively applied in engineering, social science also

conduct fuzzy statistic, fuzzy questionnaire and fuzzy decision in developing fuzzy

multi-goal decision making, fuzzy multi-criterion discrimination and fuzzy linear

planning. The common predict instrument in public policy making is also related to

fuzzy theory, such as：fuzzy Delphi method and fuzzy analytic hierarchy method. In

general, fuzzy theory application is inclusive as describe in following:

1. Image control：applied in medical disease distinguish, identification of

handwriting, printing character, sounds, fingerprint and etc…

2. Automatic control：applied in various household electronic control, temperature

control, industrial power control, robot control, car driving control and etc…

3. Application of database management, teaching evaluation, psychoanalysis, and

financial management.

14

Section.3 Review of Fuzzy Theory Application in

Kano’s Model

Fuzzy theory is extensively applied in social science in recent years because it

effectively better the issue of mentality complexity. However, there was lack of

research publish in international journal around the combination in fuzzy theory and

Kano’s model until 2006. There are two articles around the issue published.

The first was from Foldesi et al.(2007) who conducts fuzzy expansion theory in

Kano’s model. This research is oriented from the conceptual work of continuous

membership function. Fuzzy expansion theory is to quantify the scores of functional

and dysfunctional comparison questionnaire in accordance with quality attribution

discrimination. The second article was by Cheng and Chiu (2007) who integrated

Kano’s model, QFD and fuzzy theory. Despite of presenting functional and

dysfunctional comparison questionnaire by QFD house of quality, triangle-shape

grad of membership function is to quantify the fuzzy linguistic scale in functional

and dysfunctional questionnaire and identify the membership function of 5 quality

attribution. It is to calculate the possible quality attribution of customer’s demand in

the questionnaire.

The two methods in above description may reflect the concern of fuzzy and

uncertainty in mentality, however, its questionnaire design does not reflect the same

purpose. It emphasizes on linguistic quantification and it can not provide effective

base to conclude the quality attribution classification due to its questionnaire design

base on nominal scale instead of numerical scale. Therefore, it is recommended to

develop an unique approach both concern of Kano’s model and fuzzy theory.

15

Section.4 Review of Fuzzy Questionnaire Design

Questionnaire investigation is the common instrument of social science. It is to

measure the attitude, reorganization and referable potential characteristics of

respondents from the reflection in questionnaire. The previous research found that

human mentality and feelings are fuzzy and uncertain, hence traditional Kano’s

functional and dysfunctional questionnaire can not image the complexity of human

mentality. There are more and more researches focus on the fuzzy questionnaires

analysis and applications in the social science fields, such as :

Wu, Cheng and Tseng (2002) claimed that trend of fuzzy statistic and fuzzy

correlation is the result of social science which can not be explained by traditional

numerical model. Furthermore, psychology phenomenon is with uncertainty and blur.

Traditional numeric tends to be over applied or over interpretive. Application of

fuzzy numeric data has the capability to avoid such doubt. Fuzzy theory analysis is

more practical than “probability”of traditional statistic in term of analyzing social

science data.

Ho (2001) discussed scale design of fuzzy semantic differential, questionnaire

fill in approach and data processing model in overlapping zone. They conducted

“Career cognitive scale”to collect the viewpoints of primary students on six kinds of

career. It appeals to be six scales as “poor-rich”, “smart-not smart”,

“indifference-enthusiasm”,“cowardly-brave”,“depreciate-respect”and“hard-relax”.

The result indicated that the analyzing model is capable to interpret linguistic space

interval of unilateral overlapping interval and to evaluate the priority.

Liang and Wang (1991) applied triangle fuzzy numeric of 5 points linguistic

scale to present index weight through fuzzy linguistic score. Research focusing on

regional choice emphasis on significance of index from respondents.

Lee (1996) issued that application of fuzzy logic in control area is well-known.

However, foundation work “fuzzy expression”and “fuzzy clustering”in fuzzy set is

16

often ignored. It is oriented from similarity of fuzziness which discuss fuzzy

aggression, fuzzy linguistic in approximation.

Law (1996) and Yen (1996) utilized definition of fuzzy membership function to set

up mathematic education index system.

Yamashita (1997) believed that people does not fully aware of their own

motivation in decision making. Therefore, he developed a career decision making

scale for senior high school students to decide carrying on education or enroll into

employment according to fuzzy deduction and fuzzy structure model.

Bagnoli and Smith (1998) applied fuzzy theory in decision making and

evaluation process in human being in order to state the uncertain behavior.

Herrera, López, Mendaña, and Rodriguez (2001) attempted to set up a linguistic

decision making model for human resource management on assigning employee in

enterprise. It is to minimize unwell job assignment risk on human resource

management under uncertain decision making policy.

Lin (2002) shows that it is doubted to quantify score by conducting integer

numeric in equidistance from Linkert scale and semantic differential scale in order to

process data.：

1. It is too simple and lack of theoretical evident to transfer semantic variable

into equidistance variable.

2. For different respondent, similar semantic terms may refer to different level

of affection.

3. It is inappropriate to transfer semantic variable into accurate numeric due to

its characteristic of qualify, subjective, uncertainty and blur.

Chen (2001) pointed that traditional questionnaire design conducts Linkert scale

for respondent easily find their semantic terms and express in singular accurate

numeric. However, conceptual work in social science always involve with

uncertainty. Respondents may face situation in an equivocal way within these

17

questions. Accurate numeric is inappropriate to solve these problems. Disadvantages

of traditional sampling analysis are：

1. Human mentality and behavior is process of blur, hence traditional

questionnaire numeric is often being over interpreted.

2. To meet need of accurate numeric, empirical data is often being over

interpreted.

3. To simplify and minimize the complexity of mathematical model, dynamic of

actual condition is disregard.

4. Unitary logic does not suggest respondent with multi-feelings to follow.

Tseng and Shih (1997) considered most statement of questionnaire scale adopts

5-point semantic sequence scale which can not sufficiently cope with uncertainty

opinion from respondents. Error occurs when transferring score and leads to

instability of its result. Therefore, theory of integration evaluation principal, fuzzy

comprehensive distinction and clustering analysis was developed to deal with

concept of uncertainty as well as new solution for fuzzy relation equation. It is to

frame model which can be applied in engineering vocational education.

Chen (1998) applied fuzzy relation and fuzzy transformation to transfer fuzzy

evaluation of respondents into semantic fuzzy space which is similar to evaluator.

This enable the result of the investigation gathered statistic under equivalent standard.

It shows“different standard of evaluator and different respondent will cause different

analysis result with the same questionnaire which effects the reliability of data”

Therefore, fuzzy transformation of questionnaire into identical evaluation standard

before undertaking statistic will avoid issue of diverse evaluation standard.

Wang and Wang (2000) studies the match between basketball coach and team

through fuzzy questionnaire investigation. The research allows respondents to fill in

percentage in several items in fuzzy questionnaire to amend the deficiency of

traditional questionnaire which requires respondents mark only one answer in several

18

items. This dichotomy of “either this or that”twists the data from truth. Therefore,

fuzzy questionnaire reflect the psychology traits of respondents.

Liu and Wang (1992) address that questionnaire and scale is an evaluation of

psychology affection. Hence, subjective feeling is difficult to reflect within singular

numeric. It is only suitable for fuzzy numeric to undertake mentality assessment.

Lin and Lin (2001) analyzed the issue of tiredness of primary school teacher

with fuzzy theory in addition to compare difference of fuzzy theory and test theory in

assessment. The research is base on questionnaire investigation and fuzzy

comprehensive distinction and the result shows fuzzy theory application is a practical

and valuable method in assessment.

Wu (2004) believed unitary logic in traditional questionnaire does not suggest

respondent with multi-affection to follow.

Chen and Ding (2000) conducted membership function of fuzzy theory into

semantic variable in questionnaire which is guarded as measurement instrument. In

empirical work, reliability of membership function in different type of semantic

variable is higher than score in Linkert scale.

Yeh and Hong (1998) proposed refine fuzzy Delphi method. It conducted on

policy research and proved that information analysis of fuzzy questionnaire is better

than traditional questionnaire analysis.

Wu and Yang (1997) utilized a fuzzy statistic method in analyzing fuzzy

questionnaire base on fuzzy logic and fuzzy measure. It is base on fuzzy theory to

apply in questionnaire design and related investigation which proves the value of

fuzzy theory application.

Wang (2000) investigated the required ability of quality control staff from

enterprise. Trapezoid fuzzy numeric represents the significance of these abilities. The

result shows that it is more rational comparing to definite numeric in traditional

questionnaire.

19

Wang and Weng (2001) engaged in research of scale score and distribution on

factor loading estimation. It is believed that factor analysis model is to assume

observation variable as continuous equidistance variable. However, hypothesize is

seriously violated in data analysis. Score in each item generally measures the roughly

react of respondents when response scale score is limited.

Lin (2001) undertook research to find the reliability of traditional score and

fuzzy linguistic variable score under the influencing of number of question items,

fuzziness of linguistic and internal consistence by data simulation. The simulation

result showsαcoefficient of fuzzy linguistic variable score is significant higher than

αcoefficient of traditional score. This proves feasibility of fuzzy linguistic variable

score.

Lin (2003) established fuzzy parameter of fuzzy linguistic scale by object

function algorithm of fuzzy set. The empirical work approved that reliability and

validity of fuzzy linguistic variable score is better than traditional score.

Yan (2003) discussed different attitude score and number of question item

whether or not cause difference in score statistic between fuzzy linguistic score and

Linkert scale. The result shows when attitude score at point 4, 5 and 6, the score of

fuzzy linguistic scale is more accurate on estimating the inner latent character of

respondent comparing to Likert scale. Increasing with number of question item,

accuracy of fuzzy linguistic is increasing than Linkert scale.

Bohrnstedt (1970)；Olsson, Drasgrow and Dorans (1982): showed hypothesize

of interval scale would underestimate relative coefficient of Pearson. It will lead the

result tending to be conservation. The phenomenon of underestimate will become

worse follow the decline number of scale category.

Pryor, Hesketh and Gleitzman (1989) addressed model of computerize fuzzy

graph score which applied in measuring different vocational attitude in vocation

consultant. The model appeals to respondents choosing a numeric on scale to reflect

20

their thought and expressing their feeling on linear interval.

Discretization membership function is presented by vector which directly

identify membership grade of each element in finite fuzzy set. It is appropriate to

apply in categorical scale or nominal scale. Continuous membership function is to

present fuzzy set by infinite element and its membership grade. There are a few

common model in continuous membership function to describe the summery of

fuzzy set, such as S-function, Z-function, π-function, triangle function, trapezoid

function, gauss function(or exponential function). It is appropriate for ordinal scale,

interval scale and ratio scale.

Kano’s model is a linguistic questionnaire of categorical scale in the mean of

find the quality attribution from the functional and dysfunctional comparison

questionnaire, therefore, it suits for discretization membership function to deal with

fuzzy question. However, it will not reflect its quantified characteristic if only use

numeral(1,2,3,4,5) as continuous membership function to express“like”or“dislike”

in traditional questionnaire. Calculation of these numeral is not interpretive for the

condition of classification.

This research will conduct discretization membership function base on Kano’s

model to consider how to reflect customer feeling on single or multi- categorical

scale. It will also offer vector grade for each scale upon personal perception, as

shown in figure 1. It shows traditional Kano’s questionnaire restrict the response of

customer with multi-feelings. On the other hand, fuzzy Kano’s questionnaire

designed by discretezation membership function has more concern of information

related to customer feeling.

21

Section.5 Review of Fuzzy Mode

Traditional mode in statistic means the opinion of majority of population.

However, each individual has unclear views on universe of discourse U . For

example, the perception of indoor air conditioner can be“a bit cold, but not too cold”,

or the description of appearance can be “a bit pretty, but not that much”and etc.

Such situation is very common. As a consequence, it becomes a very important issue

to obtain statistic consensus under fuzzy concept. When the item is fuzzy and the

factors of universe of discourse can be cataloged into several types, it can use

discrete fuzzy mode to obtain the consensus of the issue. Wu and Sun (2004) has

addressed the fuzzy mode. The definition of discrete fuzzy mode is as follows:Let U be the universal set (a discussion domain), mkkkK ,,, 21 be a set

of m-linguistic variables onU , and { , 1,2 , }iFS i n be a sequence of random fuzzy

sample on U . For each sample iFS , assign a linguistic variable jL a normalized

membership )1(1

m

jijij mm , let

1

n

j iji

S m mj ,,2,1, . Then, the maximum

value of jS (with respect to jK ) is called the fuzzy mode ( FM ) of this sample.

A ballot or random sampling is common application in social science to

evaluate public opinion on specific issue. However, we are forced to make one

choice when facing more than two options in previous ballots. It also occurs in

questionnaire investigation that traditional sampling investigation forces respondents

to accept binary logic and to respond with one answer. Therefore, it ignores the

diverse of human mentality and the multi-dimension of issue. Such questions can not

effectively express the mentality of individual or the whole group without accurate

measuring instrument for solution.

It is assumed that 10 students would like to decide their graduation tour by

conducting questionnaire investigation. They will pick up the final decision with

most numerous votes. There are four touring spots are , , ,ⅠⅡⅢⅣ and data of

22

returned questionnaire shows in Table.5.

Table. 5

Result of traditional questionnaire and mode

SpotRespondents

Ⅰ Ⅱ Ⅲ Ⅳ

1 v2 v3 v4 v5 v6 V7 v8 v9 v10 v

If traditional questionnaire and mode analysis is the decision making of

graduation tour spot, the statistic of Table.5 shows spot Ⅱ with 4 votes will be the

decision. It is because spotⅡ wins the highest votes. However, there are 6 students

do not want to go spot Ⅱ even there are 4 students choose the venue. The 6

students means more than half of the students may not enjoy the trip. In the end, they

may give up the plan due to the lack of consensus.

If we consider the blur and multi-feelings in real life when design questionnaire,

students are allowed to write down the grade of the 4 proposed spots. This will not

only collect more information close to more thoughts of students, but also find out

consensus accepted by most students. Table.6 shows application of fuzzy

questionnaire and fuzzy mode investigation.

23

Table. 6

Result of fuzzy questionnaire and modeSpot

RespondentⅠ Ⅱ Ⅲ Ⅳ

1 0.4 0.62 0.6 0.43 0.4 0.64 0.55 0.455 0.4 0.5 0.16 0.45 0.557 0.4 0.68 0.5 0.4 0.19 0.45 0.5510 0.45 0.55

Comparing to traditional mode approach to force respondent to make one

decision, fuzzy questionnaire does not restrict students from any possible response.

Respondents may reflect the grade of their preference. Consequence, fuzzy theory is

to reflect the grade of multi-preference which occur commonly in daily life.

At this point, this questionnaire sufficiently indicates the most wanted touring

spot is spotⅠ that there are 9 students would like to visit. In other words, it means 9

students accept to visit the spot. Furthermore, different significant levels of cut

show different results. The result shows fuzzy mode expresses more willingness of

majority than traditional mode. It guides to a commonly accepted touring spot under

certain accepted level. See the Table.7.

Table. 7

Result of decision making for touring spot

TraditionalMode

Fuzz y Mode

55.0

Fuzzy Mode

45.0

Fuzz y Mode4.0

Ⅰ 2 1 5 9Ⅱ 4 4 4 4Ⅲ 3 2 4 5Ⅳ 1 1 1 2

24

The traditional statistic is random sampling and gain singular numerical data or

definite equidistance scale. However, it can not reflect the ideas of each individual. If

the respondents can express the extent of real feeling by utilizing membership and

interval numeric out of their feelings, the questionnaire will fully show the mentality

of human. Therefore, it is more reasonable to apply fuzzy mode in social science

research.

25

CHAPTER.3 METHODOLOGY

The traditional Kano’s survey forces people to choose one answer from the

survey, but it ignores the uncertainty of human thinking. For instance, when people

need to choose the answer from the survey which lists five choices including "I like

it that way," "It must be that way," "I am neutral," "I can live with it that way," "I

dislike it that way," traditional survey become quite exclusive. There are many tools

to help interpret Kano’s results; this article introduces a new one. It is nature for us to

propose the fuzzy statistics, such as Fuzzy Kano’s Questionnaires (FKQ) and Fuzzy

Kano’s Mode (FKM), to fit the modern requirement. In this section we demonstrate

the definitions for FKQ and FKM generalized from the traditional statistics. The

discrete case is simpler than the continuous one’s.

Section.1 Fuzzy Kano’s Questionnaires (FKQ)

Traditional Kano’s Questionnaire (TKQ) deals single answer or certain range of

the answer through sampling survey, and unable to sufficiently reflect the complex

thought of an individual. If people can use the membership function to express the

degree of their feelings based on their own choices, the answer presented will be

closer to real human thinking. For instance, when people process a pollution

assessment, they classify the distraction into two categories: pollution and

non-pollution. This kind of classification is not realistic, since the pollution is a fuzzy

concept (degree) and can hardly be justified by the true-false logic. Therefore, to

compute the information based on the fuzzy logic should be more reasonable.

Therefore, to collect the information based on the fuzzy mode should be the first

step to take. Since a lot of times, the information itself embedded with uncertainty

and ambiguity. (e.g. Hsu et al., 2001; Huang & Wu, 1992; Manski, 1990; Nguyen &

Wu, 2000; Sun & Wu, 2007, Wu & Sun, 2004)

26

Wu and Sun (2004; 2007) was address the advantages of evaluation with fuzzy

questionnaire include: (i) Evaluation process becomes robust and consistent by

reducing the degree of subjectivity of the evaluator. (ii) Self-potentiality is

highlighted by indicating individual distinctions. (iii) Provide the evaluators with an

encouraging, stimulating, self-reliant guide that emphasizes on individual

characteristics. While the drawback is that the calculating process will be a little

complex than the conventional one.

Both TKQ and FKQ use functional and dysfunctional model to ask customer of

product feeling. The largest difference is TKQ only one answer will be permitted;

interviewee who select ignore or forget may have various answers or an equivocal

way as shown in Fig.3. FKQ makes usage of flexible way to allow interviewee with

personalized standard to answer questions, as well as other answers of same issue.

Therefore, it is more reasonably express customer real ideas to the issue (Fig.4).

Fig. 3 Single Answer for TKQ. Fig. 4 Multiple Answers for FKQ.

Kano’s model is a linguistic questionnaire of categorical scale in the mean of

find the quality attribution from the functional and dysfunctional comparison

questionnaire, therefore, it suits for discretization membership function to deal with

fuzzy question. However, it will not reflect its quantified characteristic if only use

numeral(1,2,3,4,5) as continuous membership function to express“like”or“dislike”

in traditional questionnaire. Calculation of these numeral is not interpretive for the

27

condition of classification.

We will conduct discretization membership function base on Kano’s model to

consider how to reflect customer feeling on single or multi- categorical scale. It will

also offer vector grade for each scale upon personal perception, as shown in figure 4.

It shows traditional Kano’s questionnaire restrict the response of customer with

multi-feelings. On the other hand, fuzzy Kano’s questionnaire designed by

discretezation membership function has more concern of information related to

customer feeling.

Section.2 Fuzzy Mode Results to Kano’s

Classifications

The Kano’s method of characterizing customer requirements is an effective tool.

Based on the customer's response to pairs of functional and dysfunctional questions

about each requirement, it is classified as one of six discrete groups for that customer.

In general, the mode, or most frequent observation, of the sample set of responses is

considered the best descriptor of the requirement. When the responses are almost

evenly distributed between two or more classifications, however, the proper

characterization of the requirement is more difficult to determine.

Therefore, to collect the information based on the fuzzy mode should be the first

step to take. Since a lot of times, the information itself embedded with uncertainty

and ambiguity. It is nature for us to propose the fuzzy statistics, such as fuzzy mode

and fuzzy median, to fit the modern requirement. The definition of discrete Fuzzy

Kano’s Mode (FKM) is as follows:

Definition: Fuzzy Kano’s Mode (FKM)Let U and V be the universal set of positive and negative questions,

28

pk PPPP ,,, 21 and nk NNNN ,,, 21 be the sets of p and n linguistic

variables on U and V respectively which jointly construct a np evaluation sheet

of two-dimensional quality model, and rkFSk ,,2,1, be a sequence of random

fuzzy sample on U and V.

For each sample kFS , assign linguistic variables iP and jN normalized

membership kiPm )( where

p

ikiPm

1

)1)(( and kjNm )( where

n

jkjNm

1

)1)(( , and

to structure Fuzzy Relationship Set

p

i

n

jkjkikij NmPmS

1 1

)()(~

, then calculate

kijS~

belonged to the hth quality attribute in the evaluation sheet, and get the total

membership grade of each quality attribute QRIAOMTk~

,~

,~

,~

,~

,~

.

Then, the maximum value of khT is called the Fuzzy Kano’s Mode (FKM)

of this quality element and is a significant classification level.

If there are more than two sets of Kano’s fuzzy quality attribute classification

with the same value hT , then this set of data is called with multi-fuzzy mode or

multi-consensus. If the final scoring are equal, the greatest impact on the product

using the following ordering: M > O > A > I (CQM,1993).

The procedure of Kano’s fuzzy mode classification in this study is shown as

follow:

Step1. To assure the number of respondents and establish the initiating feeling set

NP ~,~ in functional and dysfunctional question:

rkk

～

n

jjk

piik

FSFS

NN

PP

1

1

1~

~

, (7)

np, : The scale used in the questionnaire;

rk ,,2,1 :The sequence of fuzzy sample.

29

Step2. To normalize the initiating evaluation value:

n

jkj

kjkj

p

iki

kiki

N

NNm

P

PPm

1

1

)(

)(

, (8)

Where rk ,,2,1 ;

)(),( NmPm are the fuzzy evaluation after normalized.

Step3. To structure two-dimensional fuzzy relationship set S~

:

p

i

n

jkjkikij NmPmS

1 1

)()(~

(9)

Where rk ,,2,1

Step4. To define Kano’s two-dimensional quality element classification set

QRIAOM~

,~,~,~

,~

,~ :

Sxxuxc

QRIAOMcC

ici

mii

~,0)(|supp

~,

~,

~,

~,

~,

~~1

, (10)

Where SQRIAOM~~~~~~~ ,

QRIAOM~~~~~~

、、、、、 are represent the Kano’s

two-dimensional quality element fuzzy classification

“Must-Be”, “One-Dimensional”, “Attractive”, “Indifferent”,

“Reverse”and“Questionable”result.

Step5. To calculate the total membership grade of each quality attribute kT~

:

QRIAOMTk~

,~

,~

,~

,~

,~

(11)

30

Step6. To defuzzificate Kano’s significant classification level( cut ):

iff,0iff,1~

kh

khkh T

TT , (12)

where h is quality attribute in the evaluation sheet. Such as

QRIAOM~~~~~~

、、、、、

Step7. To obtain Kano’s fuzzy mode set (KFM):

)~(1

r

kkhTMaxKFM (13)

If there are more than two sets of Kano’s fuzzy quality attribute classification

with the same KFM, then this set of data is called with multi-fuzzy mode or

multi-consensus. If the final scoring are equal, the greatest impact on the product

using the following ordering: M > O > A > I (CQM,1993).

31

CHAPTER.4 CASE STUDY

This case uses “Taiwan district theme park service quality survey”as a case

study to confer the difference of traditional Kano’s model and combination of fuzzy

theory. Two stages have been divided: questionnaire and quality attribute category.

Section.1 Fuzzy Kano’stwo-dimensional quality

questionnaire

The questionnaire design is based on Haahti, A. and Yavas, U. (2004) and

Taiwan Tourism Bureau (2005) 18-item issue of the theme park research report.

Among 47 theme park island-wide, one can be represented the theme park to

undertake issuance of questionnaire from north, central and southern Taiwan

respectively. From July 4 through September 8, 2006, 450 copies have been issued

and 384 copies of effective retrieved (85%).

In order to confer the difference of traditional Kano’s model and combination of

fuzzy theory, Traditional Kano’s Questionnaires (TKQ) and fuzzy Kano’s

Questionnaire (FKQ) questionnaire reviews interviewee feeling of the theme park

service quality. The comparison of Interviewee Ⅰ to Ⅹ to issue of 「Whether or not

to have theme souvenir?」in Table.8 is listed.

32

Table. 8

Answer for TKQ and FKQ.TKQ FKQ

Whether or not to have themesouvenir?

Like

Must-be

Neutral

Live-w

ith

Dislike

Like

Must-be

Neutral

Live-w

ith

Dislike

Functional IntervieweeⅠ

Dysfunctional Functional 70% 30%

IntervieweeⅡDysfunctional 60% 40%Functional 100%

IntervieweeⅢDysfunctional 40% 60%Functional 100%

IntervieweeⅣDysfunctional 60% 40%Functional

IntervieweeⅤDysfunctional Functional 80% 20%

IntervieweeⅥDysfunctional 30% 70%Functional

IntervieweeⅦDysfunctional Functional 80% 20%

IntervieweeⅧDysfunctional 60% 40%Functional 50% 50%

IntervieweeⅨDysfunctional 20% 80%Functional 60% 20% 20%IntervieweeⅩDysfunctional 40% 60%

In comparison of Table.8, it is found out that when interviewee idea appeared

multi-feeling, TKQ single answer way will form interviewee indecisive and has to

select ignore of partial existed feeling.

FKQ questionnaire enables interviewee more completely present ideas and

solution of usually faced and fit it with humanity thinking model, even a little feeling

or ideas, service provider will know through questionnaire. Therefore, Kano’s Model

and follow-up quality attribute classification will be comparative object and real.

33

Section.2 Fuzzy Kano’stwo-dimensional quality

attribute judgment

Based on interviewee toⅠ Ⅹ questionnaires, Definition will be used to

illustrate Fuzzy Kano’s two-dimensional attribute judgment process.

① To establish sample set and two-dimensional fuzzy set.

The effective sample of 384 copies means fuzzy sample 384,,3,2,1 ～

FS . If

we want to know 「Whether or not to have theme souvenir?」 to interviewee ⅠtoⅩ

belonging to which kind of quality attribute, its fuzzy idea result (Fig.4) will be

presented with combination of positive questions (functional) and negative questions

(dysfunctional). Pre-preparation of quantity is needed, (1) means “Yes” and (0)

means “No”.

1,1,0,0,0

~0,0,0,0,1

~

40,60,0,0,0~

0,0,0,0,100~

60,40,0,0,0~

0,0,0,0,100~

40,60,0,0,0~

0,0,0,30,70~

1,1,1,0,0~

0,0,0,0,100~

Ⅴ

Ⅴ

Ⅳ

Ⅳ

Ⅲ

Ⅲ

Ⅱ

Ⅱ

Ⅰ

Ⅰ

N

P

N

P

N

P

N

P

N

P

0,0,0,60,40

~20,20,60,0,0

~

80,20,0,0,0~

0,0,0,50,50~

40,60,0,0,0~

0,0,0,20,80~

1,1,0,0,0~

0,0,0,1,1~

70,30,0,0,0~

0,0,0,20,80~

Ⅹ

Ⅹ

Ⅸ

Ⅸ

Ⅷ

Ⅷ

Ⅶ

Ⅶ

Ⅵ

Ⅵ

N

P

N

P

N

P

N

P

N

P

34

② To normalize the initiating evaluation value.

In addition, no matter of questionnaire filling, through standard model,

difference and level will be completely presented. Interviewee Ⅰto Ⅹ feeling are:

50.0,50.0,00.0,00.0,00.0

~00.0,00.0,00.0,00.0,00.1

~

40.0,60.0,00.0,00.0,00.0~

00.0,00.0,00.0,00.0,00.1~

60.0,40.0,00.0,00.0,00.0~

00.0,00.0,00.0,00.0,00.1~

40.0,60.0,00.0,00.0,00.0~

00.0,00.0,00.0,30.0,70.0~

33.0,33.0,33.0,00.0,00.0~

00.0,00.0,00.0,00.0,00.1~

Ⅴ

Ⅴ

Ⅳ

Ⅳ

Ⅲ

Ⅲ

Ⅱ

Ⅱ

Ⅰ

Ⅰ

Nm

Pm

Nm

Pm

Nm

Pm

Nm

Pm

Nm

Pm

00.0,00.0,00.0,60.0,40.0

~20.0,20.0,60.0,00.0,00.0

~

80.0,20.0,00.0,00.0,00.0~

00.0,00.0,00.0,50.0,50.0~

40.0,60.0,00.0,00.0,00.0~

00.0,00.0,00.0,20.0,80.0~

50.0,50.0,00.0,00.0,00.0~

00.0,00.0,00.0,50.0,50.0~

70.0,30.0,00.0,00.0,00.0~

00.0,00.0,00.0,20.0,80.0~

Ⅹ

Ⅹ

Ⅸ

Ⅸ

Ⅷ

Ⅷ

Ⅶ

Ⅶ

Ⅵ

Ⅵ

Nm

Pm

Nm

Pm

Nm

Pm

Nm

Pm

Nm

Pm

③ To structure Fuzzy Relationship Set.

Use matrix multiplication, NmPm~

'~ will obtain a 5x5 Kano’s

two-dimensional fuzzy relation combination S~

as:

35

0000000000000000000050.050.0000

~

0000000000000000000040.060.0000

~

0000000000000000000060.040.0000

~

00000000000000012.018.000028.042.0000

~

0000000000000000000033.033.033.000

~

Ⅴ

Ⅳ

Ⅲ

Ⅱ

Ⅰ

S

S

S

S

S

00012.008.000012.008.000036.024.00000000000

~

00000000000000040.010.000040.010.0000

~

00000000000000008.012.000032.048.0000

~

000000000000000025.025.000025.025.000

~

00000000000000014.006.000056.024.0000

~

Ⅹ

Ⅸ

Ⅷ

Ⅶ

Ⅵ

S

S

S

S

S

④ To define Kano’s two-dimensional quality element classification set

QRIAOM~

,~,~,~

,~

,~ .

After S~

being obtained, through previous literature or experience,

two-dimensional attribute classification will be obtained based on Matzler and

Hinterhuber (1998) model as shown in Table.9.

36

Table. 9

Kano’s evaluation tableDysfunctional

Like Must-be Neutral Live-with DislikeLike Q11 A12 A13 A14 O15

Must-be R21 I22 I23 I24 M25

Neutral R31 I32 I33 I34 M35

Live-with R41 I42 I43 I44 M45

Functional

Dislike R51 R52 R53 R54 Q55

Source by: Matzler and Hinterhuber, 1998, p.25-38.

11~

54535251413121~

444342343332242322~

141312~

15~

453525~

~,0)(|

~supp

,,,,,,~

,0)(|~supp

,,,,,,,,~

,0)(|~

supp

,,~

,0)(|~

supp

~,0)(|

~supp

,,~

,0)(|~

supp

SSxxuxQ

SSSSSSSSxxuxR

SSSSSSSSSSxxuxI

SSSSxxuxA

SSxxuxO

SSSSxxuxM

Q

R

I

A

O

M

⑤ To calculate the total membership grade of each quality attribute ⅠT~

to . Ⅹ~T

Regarding to setp3 and setp4, interviewee Ⅰ has 0.333 feeling of this issue

belonging to one-dimensional quality attribute while 0.666 of attractive quality

attribute. The total membership grade for ⅠT~

to. Ⅹ~T please see the Table.10 to

Table.19.

Table. 10

Total membership grade for interviewee ⅠDysfunctional

Like Must-be Neutral Live-with DislikeLike 0/Q 0/A 0.333/A 0.333/A 0.333/O

Must-be 0/R 0/I 0/I 0/I 0/MNeutral 0/R 0/I 0/I 0/I 0/M

Live-with 0/R 0/I 0/I 0/I 0/MFunctional

Dislike 0/R 0/R 0/R 0/R 0/QClassification M O A I R Q

Degree 0 0.333 0.666 0 0 0

37

QRIAOM

T0

,0

,0

,666.0

,333.0

,0

Ⅰ

Table. 11

Total membership grade for interviewee ⅡDysfunctional

Like Must-be Neutral Live-with DislikeLike 0/Q 0/A 0/A 0.42/A 0.28/O

Must-be 0/R 0/I 0/I 0.18/I 0.12/MNeutral 0/R 0/I 0/I 0/I 0/M

Live-with 0/R 0/I 0/I 0/I 0/MFunctional

Dislike 0/R 0/R 0/R 0/R 0/QClassification M O A I R Q

Degree 0.12 0.28 0.42 0.18 0 0

QRIAOM

T0

,0

,18.0

,42.0

,28.0

,12.0

Ⅱ

Table. 12

Total membership grade for interviewee ⅢDysfunctional

Like Must-be Neutral Live-with DislikeLike 0/Q 0/A 0/A 0.4/A 0.6/O

Must-be 0/R 0/I 0/I 0/I 0/MNeutral 0/R 0/I 0/I 0/I 0/M

Live-with 0/R 0/I 0/I 0/I 0/MFunctional

Dislike 0/R 0/R 0/R 0/R 0/QClassification M O A I R Q

Degree 0 0.6 0.4 0 0 0

QRIAOM

T0

,0

,0

,4.0

,6.0

,0

Ⅲ

38

Table. 13

Total membership grade for interviewee ⅣDysfunctional

Like Must-be Neutral Live-with DislikeLike 0/Q 0/A 0/A 0.6/A 0.4/O

Must-be 0/R 0/I 0/I 0/I 0/MNeutral 0/R 0/I 0/I 0/I 0/M

Live-with 0/R 0/I 0/I 0/I 0/MFunctional

Dislike 0/R 0/R 0/R 0/R 0/QClassification M O A I R Q

Degree 0 0.4 0.6 0 0 0

QRIAOM

T0

,0

,0

,6.0

,4.0

,0

Ⅳ

Table. 14

Total membership grade for interviewee ⅤDysfunctional

Like Must-be Neutral Live-with DislikeLike 0/Q 0/A 0/A 0.5/A 0.5/O

Must-be 0/R 0/I 0/I 0/I 0/MNeutral 0/R 0/I 0/I 0/I 0/M

Live-with 0/R 0/I 0/I 0/I 0/MFunctional

Dislike 0/R 0/R 0/R 0/R 0/QClassification M O A I R Q

Degree 0 0.5 0.5 0 0 0

QRIAOM

T0

,0

,0

,5.0

,5.0

,0

Ⅴ

39

Table. 15

Total membership grade for interviewee ⅥDysfunctional

Like Must-be Neutral Live-with DislikeLike 0/Q 0/A 0/A 0.24/A 0.56/O

Must-be 0/R 0/I 0/I 0.06/I 0.14/MNeutral 0/R 0/I 0/I 0/I 0/M

Live-with 0/R 0/I 0/I 0/I 0/MFunctional

Dislike 0/R 0/R 0/R 0/R 0/QClassification M O A I R Q

Degree 0.14 0.56 0.24 0.06 0 0

QRIAOM

T0

,0

,06.0

,24.0

,56.0

,14.0

Ⅵ

Table. 16

Total membership grade for interviewee ⅦDysfunctional

Like Must-be Neutral Live-with DislikeLike 0/Q 0/A 0.25/A 0.25/A 0/O

Must-be 0/R 0/I 0.25/I 0.25/I 0/MNeutral 0/R 0/I 0/I 0/I 0/M

Live-with 0/R 0/I 0/I 0/I 0/MFunctional

Dislike 0/R 0/R 0/R 0/R 0/QClassification M O A I R Q

Degree 0 0 0.5 0.5 0 0

QRIAOM

T0

,0

,5.0

,5.0

,0

,0

Ⅶ

40

Table. 17

Total membership grade for interviewee ⅧDysfunctional

Like Must-be Neutral Live-with DislikeLike 0/Q 0/A 0/A 0.48/A 0.32/O

Must-be 0/R 0/I 0/I 0.12/I 0.08/MNeutral 0/R 0/I 0/I 0/I 0/M

Live-with 0/R 0/I 0/I 0/I 0/MFunctional

Dislike 0/R 0/R 0/R 0/R 0/QClassification M O A I R Q

Degree 0.08 0.32 0.48 0.12 0 0

QRIAOM

T0

,0

,12.0

,48.0

,32.0

,08.0

Ⅷ

Table. 18

Total membership grade for interviewee ⅨDysfunctional

Like Must-be Neutral Live-with DislikeLike 0/Q 0/A 0/A 0.1/A 0.4/O

Must-be 0/R 0/I 0/I 0.1/I 0.4/MNeutral 0/R 0/I 0/I 0/I 0/M

Live-with 0/R 0/I 0/I 0/I 0/MFunctional

Dislike 0/R 0/R 0/R 0/R 0/QClassification M O A I R Q

Degree 0.4 0.4 0.1 0.1 0 0

QRIAOM

T0

,0

,1.0

,1.0

,4.0

,4.0

Ⅸ

41

Table. 19

Total membership grade for interviewee ⅩDysfunctional

Like Must-be Neutral Live-with DislikeLike 0/Q 0/A 0/A 0/A 0/O

Must-be 0/R 0/I 0/I 0/I 0/MNeutral 0.24/R 0.36/I 0/I 0/I 0/M

Live-with 0.08/R 0.12/I 0/I 0/I 0/MFunctional

Dislike 0.08/R 0.12/R 0/R 0/R 0/QClassification M O A I R Q

Degree 0 0 0 0.48 0.52 0

QRIAOM

T0

,52.0

,48.0

,0

,0

,0

Ⅹ

⑥ To defuzzificate Kano’s significant classification level( cut ).

Being repeated above-mentioned steps, quality attribute views will be obtained.

If former ten interviewees Ⅰ to Ⅹ to total level of each quality attribute, it is shown

in Table.20.

Table. 20

Total level of quality attribute of ten interviewees

M O A I R Q

Ⅰ 0 0.333 0.666 0 0 0Ⅱ 0.12 0.28 0.42 0.18 0 0Ⅲ 0 0.6 0.4 0 0 0Ⅳ 0 0.6 0.4 0 0 0Ⅴ 0 0.5 0.5 0 0 0Ⅵ 0.14 0.56 0.24 0.06 0 0Ⅶ 0 0 0.5 0.5 0 0Ⅷ 0.08 0.32 0.48 0.12 0 0Ⅸ 0.4 0.4 0.1 0.1 0 0Ⅹ 0 0 0 0.48 0.52 0

In order to find more satisfactory and identification, the cut common

42

consensus standard concept is used to get khT . The threshold value of 4.0 as

an example, when total quality attribute level is large than or over, “1”will be

represented; while is lower than, “0”will be shown Table.21. It is agreed quality

attribute can be classified to Attractive, while under 4.0 , interviewee I

simultaneously identifies quality attribute can be classified to Must-Be and

One-Dimensional quality attribute.

Table. 21

Classification of ten interviewees common consensus 4.0M O A I R Q

Ⅰ 0 0 1 0 0 0Ⅱ 0 0 1 0 0 0Ⅲ 0 1 1 0 0 0Ⅳ 0 1 1 0 0 0Ⅴ 0 1 1 0 0 0Ⅵ 0 1 0 0 0 0Ⅶ 0 0 1 1 0 0Ⅷ 0 0 1 0 0 0Ⅸ 1 1 0 0 0 0Ⅹ 0 0 0 1 1 0

Frequency 1 5 7 2 1 0

⑦ To obtain Kano’s fuzzy mode set (KFM).

Finally, the largest identification frequency of quality attribute Fuzzy Kano’s

Mode; FKM) is the result agreed by majority. When 4.0 , ten interviewees’

largest fuzzy identification is “Attractive”, in other words, seven identify or accept

the result of“Attractive”.

Comparison of traditional Kano’s questionnaire, numeric statistics, fuzzy

Kano’s questionnaire and fuzzy mode is shown in Table.22.The quality attribute of

traditional numeric is “Indifferent”. However, among 384 interviewees, only 117

agreed (28%=107/384). In other words, 69% disagree to “Indifferent”quality

43

attribute. The quality attribute of 5.0 and 4.0 two-dimensional fuzzy

numeric is “Attractive”. Making usage of fuzzy questionnaire of 4.0

two-dimensional numeric standard, the largest fuzzy common consensus quality

attribute classification is “Attractive”and being agreed (57%=218/384). Therefore,

under 4.0 standard, result will be agreed by majority.

Table. 22

Comparison of traditional and fuzzy numeric classification

Traditional Mode Fuzz y Mode 5.0 Fuzzy Mode 4.0

M 74 63 83O 80 82 102A 97 162 218I 107 94 102R 26 15 17Q 0 0 0

Through case study, it is found that most interviewees have not only single

feeling but also have multi-feeling with difference. Therefore, current Kano’s Model

binary logic questionnaire will be rough and insufficient as single answer under

complex decision environment. Besides, traditional numeric classification is

convenient calculation but too simple, classification will show difference or result

which is unable to obtain half agreement. Thus, classification has not only possible

incorrect, but it is difficultly agreed. Through cut standard, answer agreed and

accepted by majority of interviewees. It is more reasonable to seek for majority idea

of society technology (Fig.5).

Through adjustment of cut standard, different quality attribute

classification can be obtained. Over large cut is easy to cause issue of less

samples and decrease representation of inference population. On the other hand,

cut is over small, lower sample subordination and over happy threshold will be

formed. Therefore, based on experience and in light of different type theme to

44

appropriate adjust cut set, it is able to obtain quality attribute representation

and agreed by majority. The finial result of 18-item issue of the theme park research

report, please see the Table.23.

Fig. 5 Frequencies of traditional and fuzzy method

45

Table. 23

Kano’s two-dimensional fuzzy classification-analysis of whole group（ 0.4cut ）

M O A I R Qclassification %

1. The theme of facility, building and shows oftheme amusement park. 118 193 85 73 2 0 O 50%

2. Interior design of theme amusement park. 137 183 112 78 1 0 O 48%3. Service attitude of staff in theme amusement

park. 295 103 38 102 1 0 M 77%4. The sphere of activities in theme amusement

park. 70 187 118 56 2 0 O 49%5. Route indication sign in theme amusement

park. 92 47 143 284 5 0 I 74%6. Transportation convenience of theme

amusement park. 114 209 92 36 1 0 O 55%7. Facility indication sign and illustration in

theme amusement park. 108 72 126 178 2 0 I 47%

8. Restaurant quality in theme amusement park. 126 173 64 92 3 0 O 45%

9. Information of theme amusement park. 175 91 46 212 4 0 I 55%10. The cost after entering theme amusement

park. 324 55 37 70 0 0 M 84%

11. Price and facility in theme amusement park. 308 71 66 57 0 0 M 80%12. Professional knowledge of staff in theme

amusement park. 84 75 116 168 5 0 I 44%

13. Souvenir of theme amusement park. 83 102 218 102 17 0 A 57%14. Amusement facility in theme amusement

park. 177 234 82 54 3 0 O 61%15. The level of green in the theme amusement

park 270 129 74 93 1 0 M 70%

16. Sanitary of theme amusement park. 244 162 54 108 0 0 M 64%

17. Facility safety in theme amusement park. 335 53 38 19 0 0 M 87%18. Tourist service venue and its ratio(such as

toilet, square and etc..) 136 74 232 120 4 0 A 61%

In currently, respondents may have to simplify their own complicated feeling in

order to response questionnaire of Kano’smodel base on binary logic which neglects

the fuzzy, uncertainty and multiple perception in the process of thinking. It is an

inappropriate approach to study accurate customer information. Furthermore, quality

attribution discriminablility base on traditional questionnaire may be convenient in

calculation, the output may be unpresentable or error may occur due to the lack of

information.

46

CHAPTER.5 CONCLUSION

Recently, many scholars present Kano’s model improvement, but have not done

questionnaire design and numeric method and fuzzy theory combination research.

This study has used fuzzy questionnaire to enhance the deficient of typical Kano’s

two-dimensional quality attribution in questionnaire linguistic scale. It assists

respondents to express the correct extent of their feelings in questionnaire items by

using membership and any numeric with their own wills. With this method, the

accurate mentality can be fully reasonable reflected in unknown circumstances.

Indeed, because of developing evaluation flow chart for Kano’s

two-dimensional fuzzy mode quality classification, the study has discovered that

under certain confidence level by using fuzzy mode classification is far more

objective than using traditional mode classification. It is more capable to get a

consensus solution for the whole respondents.

The mode developed by this study is more fit to humanity thinking phenomenon,

and assist researcher to get more and complete consumer’s real voice. In addition,

Kano’s model combination of fuzzy theory is not replaced formerly binary logic

questionnaire, but make up its insufficiency. Through this study fuzzy questionnaire

and analysis, within the limit to appropriately express interviewee real idea and assist

researcher to get more complete customer information.

47

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APPENDIX A問卷調查

選項說明：本問卷每題填答分為傳統問卷與模糊問卷，傳統問卷的部份依照程度不同分為五個等級，只要勾選「一個」最適合您的答案即可。模糊問卷則延續傳統問卷五個不同等級的程度，如果您的心理特質不是剛好落在某一個程度上，則請填答者在每一陳述後面，選擇符合你狀況的選項，可以是一項、兩項、三項、四項或五項，並以『100%』做比例分配。填答範例：

例如對於「主題樂園的地點交通便利」，您認為當此項服務出現時會感到滿意，有時也會覺得是必備的，所以您在右側選項中選擇「喜歡」及「理所當然」，而在比例分配的部份您可依據您心中對此二選項的比重填入符合該選項比重之百分比，如可能會填「喜歡」80% 「理所當然」20%。

『喜歡』：當左側說明〈品質要素〉出現時，會讓您感到滿意。『理所當然』：當您覺得當左側說明〈品質要素〉出現是必要的、必備的。『沒感覺』：當左側說明〈品質要素〉出現時，有沒有都沒有差別。『能忍受』：當左側說明〈品質要素〉出現，雖然還沒到不喜歡的程度，但還

可以忍受。『不喜歡』：當左側說明〈品質要素〉出現時，會讓您感到不滿意。

親愛的受訪者，您好：這是一份純粹學術性的研究問卷，研究目的在於探討『主題樂園服

務品質』，為使我們更瞭解受訪者目前對主題樂園服務品質的「認知」態度與其重視的服務品質屬性為何。本研究結果將可作為提昇主題樂園服務品質之依據。

本問卷採不記名方式作答，且僅作學術用途，您的寶貴意見將是本研究成果的重要關鍵。敬請安心填答。

我們非常感謝您的合作與支持，並敬祝您身體健康，萬事如意!

中華大學科技管理研究所指導教授：李友錚 博士學 生：黃勝彥 敬上

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第一部份 正向問卷傳統問卷 模糊問卷

1. 主題樂園內之設施、建築物及表演節目均富有主題特色__ __ __ __ __ __%__%__%__%__%

2. 主題樂園內空間設計優良 __ __ __ __ __ __%__%__%__%__%3. 主題樂園內服務人員服務態度佳 __ __ __ __ __ __%__%__%__%__%4. 主題樂園活動範圍較大 __ __ __ __ __ __%__%__%__%__%5. 主題樂園內路線標示清楚 __ __ __ __ __ __%__%__%__%__%6. 主題樂園的地點交通便利 __ __ __ __ __ __%__%__%__%__%7. 主題樂園內之設施標示及符號說明清楚 __ __ __ __ __ __%__%__%__%__%8. 主題樂園內所提供之餐飲品質佳 __ __ __ __ __ __%__%__%__%__%9. 主題樂園提供之各項資訊充足 __ __ __ __ __ __%__%__%__%__%10.入園後花費物超所值 __ __ __ __ __ __%__%__%__%__%11.主題樂園票價合理(票價和環境設施成正比)

__ __ __ __ __ __%__%__%__%__%12.主題樂園內服務人員具專業知識 __ __ __ __ __ __%__%__%__%__%13.主題樂園內販賣主題性紀念商品 __ __ __ __ __ __%__%__%__%__%14.主題樂園內之遊樂設施具多樣化 __ __ __ __ __ __%__%__%__%__%15.主題樂園綠化程度高 __ __ __ __ __ __%__%__%__%__%16.主題樂園內整齊清潔 __ __ __ __ __ __%__%__%__%__%17.主題樂園內遊樂設施之安全性佳 __ __ __ __ __ __%__%__%__%__%18.遊客服務設施地點佳比例高(如廁所、醫療站、休憩廣場)

__ __ __ __ __ __%__%__%__%__%

不喜歡

能忍受

沒感覺

理所當然

喜歡

不喜歡

能忍受

沒感覺

理所當然

喜歡

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第二部份 反向問卷傳統問卷 模糊問卷

1. 主題樂園內之設施、建築物及表演節目未具備主題特色__ __ __ __ __ __%__%__%__%__%

2. 主題樂園內空間設計拙劣 __ __ __ __ __ __%__%__%__%__%3. 主題樂園內服務人員服務態度較差 __ __ __ __ __ __%__%__%__%__%4. 主題樂園活動範圍較小 __ __ __ __ __ __%__%__%__%__%5. 主題樂園內路線標示較不清楚 __ __ __ __ __ __%__%__%__%__%6. 主題樂園的地點交通較不便利 __ __ __ __ __ __%__%__%__%__%7. 主題樂園內之設施標示及符號說明模糊不清

__ __ __ __ __ __%__%__%__%__%8. 主題樂園內所提供之餐飲品質差 __ __ __ __ __ __%__%__%__%__%9. 主題樂園提供之各項資訊較缺乏 __ __ __ __ __ __%__%__%__%__%10.入園後花費較不值得 __ __ __ __ __ __%__%__%__%__%11.主題樂園票價較不合理(票價和環境設施未成正比)

__ __ __ __ __ __%__%__%__%__%12.主題樂園內服務人員未具備專業知識 __ __ __ __ __ __%__%__%__%__%13.主題樂園內未販賣主題性紀念商品 __ __ __ __ __ __%__%__%__%__%14.主題樂園內之遊樂設施較單調 __ __ __ __ __ __%__%__%__%__%15.主題樂園綠化程度較低 __ __ __ __ __ __%__%__%__%__%16.主題樂園內凌亂骯髒 __ __ __ __ __ __%__%__%__%__%17.主題樂園內遊樂設施之安全性較差 __ __ __ __ __ __%__%__%__%__%18.遊客服務設施地點差比例低(如廁所、醫療站、休憩廣場)

__ __ __ __ __ __%__%__%__%__%

不喜歡

能忍受

沒感覺

理所當然

喜歡

不喜歡

能忍受

沒感覺

理所當然

喜歡