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研究生:王宗興
研究生(外文):Tsung-HsingWang
論文名稱:情感反應尺度篩選與產品外型特徵關係之研究
論文名稱(外文):A Study of Relation on Affective Responses Dimension Selection and Product Form Features
指導教授:謝孟達謝孟達引用關係
指導教授(外文):Meng-Dar Shieh
學位類別:博士
校院名稱:國立成功大學
系所名稱:工業設計學系碩博士班
學門:設計學門
學類:產品設計學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:英文
論文頁數:105
中文關鍵詞:感性工學系統情感反應尺度篩選集群分析普魯斯特分析數量化一類
外文關鍵詞:Kansei engineering systemAffective response dimension selectionCluster analysisProcrustes AnalysisQuantitative Theory Type I.
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本論文係基於感性工學系統(Kansei engineering system),對於消費者的情感反應(consumers’ affective responses)與產品外型特徵 (product form features)兩者之間的關係進行研究,近年來它已成為工業設計領域的重要研究議題。對於產品外型的呈現,消費者的情感反應是影響他們是否決定購買的主要原因。在產品設計領域,研究者經常會提出形容詞來闡述消費者主觀的心理感受,這些形容詞也代表了消費者的情感反應,然而在這些形容詞中卻存在著相似性與模糊性,這使得研究者在語意差異實驗中要挑選適合的形容詞有些困難。為了要挑選出代表性的形容詞來描述消費者的情感反應,本研究提出了因素分析(Factor analysis)、集群分析(Cluster analysis)、普魯斯特分析(Procrustes analysis)與KJ(Kawakita Jiro)方法。研究初期所蒐集的形容詞以語意差異法(semantic differential method)作為研究的前測實驗,來取得消費者情感反應之數據,因素分析用來找出潛在的主要因素構面,集群分析則用於萃取出代表性的形容詞;同時使用普魯斯特分析的排序規則來決定形容詞的優先性,另一方面,也使用KJ法來挑選形容詞。為了比較此三種方法的差異性,本研究以手機產品為例進行分析,並以自行車與數位相機的兩種例子加以說明;研究後期,則進行此三種方法的比較,從比較的結果顯示,普魯斯特分析是能夠挑選出代表性形容詞較適合的方法。此外為了萃取關鍵的產品外型特徵,形態分析法用於產品外型特徵的分類預測,數量化一類理論(Quantitative Theory Type I)則用於建構消費者情感反應與產品外型特徵兩者之間線性迴歸的關連性。在線性回歸方程式中,從產品外型特徵(product form features)的細目得點能夠反應出對於消費者情感反應(Consumers’ affective responses)的影響程度;對於產品外型特徵的相對重要性,也可以藉由分析外型特徵的權重來獲得。使用數量化一類理論,不僅可以挑選出關鍵的產品外型特徵,而這些外形特徵對消費者情感反應的影響也可以被分析出來,不管是使用形態分析法的分類模式或是線性迴歸模式中的關鍵外型特徵挑選,都對產品開發的過程有很大的幫助,同時藉由方法論的使用可擴及至產品設計領域的實際應用。
This study deals with the relationship between Consumers’ Affective Responses (CARs) and Product Form Features (PFFs) arising in the context of Kansei Engineering System (KES).This study deals with the relationship between consumers’ affective responses (CARs) and product form features (PFFs) arising in the context of Kansei engineering system (KES). In recent years, it has been an important research issue in the field of industrial design that CARs to the appearance of a product seems greatly influencing consumers’ purchasing decisions and interest rates. In the product design field, researchers often provide adjectives so that consumers can express their subjective feelings. Nevertheless, among the similarity and the vagueness of the chosen adjectives, it is unlikely to choose suitable adjectives for Semantic Differential (SD) experiments.In recent years, it has been an important research issue in the industrial design field. Consumers’ affective responses to the appearance of a product will greatly influence their purchasing decisions. In the product design field, researchers often provide adjectives so that consumers can express their subjective feelings. However, there exists both similarity and vagueness among these adjectives, which make it difficult to choose suitable adjectives for semantic differential (SD) experiments. In order to select distinguished adjectives for describing CARs, some methods based on Factor Analysis (FA), Cluster Analysis (CA), Procrustes Analysis (PA) and KJ method were adopted.In order to select representative adjectives for describing CARs, some method based on factor analysis (FA), cluster analysis (CA), Procrustes analysis (PA) and KJ method were proposed. Previously, Semantic Differential (SD) method has been applied to collect the CARs data. In this study, FA was used to decide latent factor dimensions. Then, CA was applied to extract the representative adjective vocabularies, as well as PA to decide adjective vocabularies according to the sorting rule. KJ method was also applied to select suitable adjective vocabularies. Previously, the selected adjectives can be used in the semantic differential (SD) method as the prior test experiment to obtain the CARs data. Then, FA was used to decide the latent factor dimensions, CA to extract the representative adjectives vocabulary. In the meanwhile, the PA was also used to decide adjective the priorities of these adjectives according to the sorting rule. On the other hand, the KJ method was also used to select the adjectives vocabulary. In order to compare the favorability among these three methods (FA/CA, FA/PA, KJ), this study has proposed an example of mobile phone for analysis. In addition, two examples of bicycle and digital camera were raised to illustrate. Finally, these three methods were used to compare the effectiveness.To compare the differences in these three methods, this study proposed an example of mobile phone for analysis. In addition, two examples of bicycle and digital camera were raised to illustrate. Finally, these three methods were used to compare the effectiveness. From the result of compared, PA is the most suitable method of selecting CARs. For selecting critical form features, a feature selection method, a shape analysis method is adapted to develop the classification based on PFFs approach. Quantitative Theory Type I (QT1) was used to create the liner regression relevance between CARs and PFFs. In linear regression equation, the category scores have reflected the degree of influence for CARs. For the relative importance of the product appearance, we can also analyze the characteristics of the shapes by the weight to obtain.From the results of with comparedcomparison, PA is the a suitable method of for selecting CARs. For selecting critical form features, a feature selection method, i.e. shape analysis method, is adapted to develop the classification based PFFs approach. Quantitative Theory Type I (QT1) was used to create the linear regression relevance between CARs and PFFs. In linear regression equation, the category score can reflect the degree of influence for CARs. To the relative importance of the product appearance, we can also analyze the characteristics of shape by the weight to get. By applying QT1, not only the critical PFFs can be selected but also the influence over producing specific CARs can be extracted. It is recognized that either the methodology of classification model or regression based on PFFs is beneficial to future products development process. Meanwhile, this methodology also extends the actual application in product design field.By Applied applying QT1, not only the critical PFFs can be selected but also their influence to produce specific CARs can be extracted. Either the methodology of classification model or the regression method based PFFs are is beneficial to the product development process. In the meanwhile, the use of the methodology, extends the actual application in product design field.
LIST OF CONTENTS

ABSTRACT(ENGLISH).......................................................................Ⅰ
ABSTRACT(CHINESE).......................................................................Ⅲ
ACKNOWLEDGEMENTS...................................................................Ⅴ
LIST OF CONTENTS............................................................................Ⅵ
LIST OF TABLES..................................................................................Ⅸ
LIST OF FIGURES.................................................................................X
ABBREVIATIONS AND SYMBOLS.................................................XII

1. INTRODUCTION................................................................................1
1.1 RESEARCH BACKGROUND.........................................................................1
1.2 RESEARCH MOTIVATION.............................................................................4
1.3 RESEARCH OBJECTIVES..............................................................................6
1.4 RESEARCH SCOPE AND LIMITATIONS......................................................8
1.5 ORGANIZATION OF THE STUDY................................................................10

2. LITERATURE REVIEW...................................................................12
2.1 THE RELATIVE RESEARCH ABOUT PRODUCT AFFECTIVE AND IMAGE..............................................................................................................12
2.2 THE RESEARCH STATE FOR ARD SELECTION USING METHODOLOGY...........................................................................................13
2.2.1 Semantic Differential Method.................................................................13
2.2.2 Factor Analysis........................................................................................14
2.2.3 Cluster Analysis.......................................................................................15
2.2.4 Procrustes Analysis..................................................................................16
2.3 THE REPRESENTATION OF PRODUCT FORM FEATURE.......................17

3. THEORETICAL BACKGROUND..................................................20
3.1 VARIABLES EXTRACTION FOR METHODOLOGY..................................20
3.1.1 Extracting Latent Factor using Factor Analysis.......................................21
3.1.2 Extracting variables using Cluster Analysis............................................23
3.1.3 Comparing Data Matrix via Procrustes Analysis....................................24
3.2 FEATURE EXTRACTION FOR PRODUCT SHAPE....................................25
3.3 QUALITATIVE CLASSIFICATION MODEL FOR KJ METHOD................26
3.4 LINEAR REGRESSION MODEL FOR QUANTITATIVE THEORY TYPE 1.........................................................................................................................27

4. RESEARCH METHOD FOR ARD SELECTION.........................29
4.1 PLANNING AND PREPARATION OF EXPERIMENTAL FOR ARD SELECTION......................................................................................................29
4.1.1 Preparation for initial adjectives and experimental samples.....................29
4.1.2 The experimental planning and proceeding..............................................30
4.2 ANALYZING INITIAL ARDS USING FACTOR ANALYSIS........................31
4.3 EXTRACTION OF REPRESENTATIVE CARs USING TWO-STEP CLUSTER ANALYSIS......................................................................................33
4.3.1 Hierarchical Cluster Analysis....................................................................33
4.3.2 K-means Analysis......................................................................................34
4.4 SELECTION CARs USING PROCRUSTES ANALYSIS...............................34

5. THE EXPERIMENTAL RESULTS FOR CA AND PA....................36
5.1 RESULT OF FACTOR ANALYSIS...................................................................36
5.2 RESULT OF HIERARCHICAL CLUSTER ANALYSIS..................................38
5.3 RESULT OF K-MEANS ANALYSIS................................................................40
5.4 RESULT OF PROCRUSTES ANALYSIS.........................................................44
5.4.1 The Selected Process and RSSD Value of ARD selection for PA.............44
5.4.2 Results of the Adjective Ranking..............................................................48
5.5 RESULT OF SELECT AFFECTIVE RESPONSE DIMENSION USING KJ METHOD ..........................................................................................................50
5.6 COMPARISON AND INTEGRATION OF THE METHOLOGY....................................................................................................52

6. THE ANALYSIS FOR DIFFERENT CASE STUDY......................55
6.1 THE CASE STUDY FOR MOBILE PHONES.................................................55
6.2 THE CASE STUDY FOR BICYCLES..............................................................60
6.3 THE STUDY CASE FOR DIGITAL CAMERAS.............................................66

7. CREATE THE PREDICTION MODE OF CARs AND PFFs FOR KE........................................................................................................75
7.1 IMPLEMENTATION PROCEDURES..............................................................75
7.2 EXPERIMENTAL RESULTS............................................................................78
7.3 CREATE RELEVANCE BETWEEN CARs AND PFFs...................................85
7.4 THE APPLICATION OF REGRESSION MODEL...........................................86

8. CONCLUSIONS.................................................................................90

REFERNCES..........................................................................................92


APPENDIX.............................................................................................99
A. MOBILE PHONE DESIGN SAMPLES.............................................................99
Figure A. Complete list of 60 mobile phone design samples.............................101
B. MOBILE PHONE DESIGN SAMPLES OF VERIFICATION........................102
Figure B. Mobile phone design samples of verification....................................102
C. QUESTIONNAIRE INTERFACE....................................................................103
Figure C. A questionnaire interface for SD evaluation......................................103
D. BICYCLE DESIGN SAMPLES.......................................................................104
Figure D. Complete list of 40 bicycle design samples.......................................104
E. DIGITAL CAMERA DESIGN SAMPLES.......................................................105
Figure E. Complete list of 100 digital camera design samples..........................105


LIST OF TABLES

Table 4.1 The initial affective dimension.....................................................................30
Table 4.2 The background subjects..............................................................................31
Table 5.1 The result of rotated factor for factor analysis.............................................37
Table 5.2 Cluster combined and coefficients...............................................................41
Table 5.3 The coefficients increase ratio......................................................................42
Table 5.4 The distance from every affective dimension to Seed point.........................43
Table 5.5 Results of adjective ranking using PA..........................................................49
Table 5.6 The clustering result of the adjective using KJ method................................51
Table 5.7 Comparisons of the adjectives using CA and PA and KJ methods...............52
Table 6.1 The initial affective dimension for bicycle...................................................60
Table 6.2 The results for factor analysis of bicycle......................................................61
Table 6.3 The results for K-means analysis of bicycle.................................................62
Table 6.4 The results of adjective ranking using PA for bicycle..................................63
Table 6.5 The initial affective dimension for digital camera........................................67
Table 6.6 The results for factor analysis of digital camera...........................................68
Table 6.7 The results for K-means analysis of digital camera.....................................67
Table 6.8 The results of adjective ranking using PA for digital camera.......................70
Table 7.1 Complete list of mobile phone form feature used in the study.....................77
Table 7.2 The results of Quantitative Theory Type 1...................................................80
Table 7.3 The correspond table of Conservative-avant grade and Element attributes......................................................................................................82
Table 7.4 The correspond table of Masculine-feminine and Element attributes......................................................................................................83
Table 7.5 The correspond table of Unoriginal-creative and Element attributes......................................................................................................84
Table 7.6 Average value and predicate value...............................................................88
Table 7.7 Error absolute value and Error average value..............................................88
Table 7.8 Result of T-test.............................................................................................89
LIST OF FIGURES

Figure 1.1 The approach architecture of this study......................................................11
Figure 2.1 The table shape for different style (Chen, 1995)........................................18
Figure 2.2 The computer design process of PFFs representation (Wallace, 1991)......18
Figure 2.3 The style profile of the Krups products (modified from Chen, K. H. 1995)............................................................................................................19
Figure 2.4 Morphological analysis based PFFs representation (Hsiao & Chen, 1997)............................................................................................................19
Figure 3.1 The 3D data structure for semantic differential experiment (Osgood, 1957)............................................................................................................20
Figure 3.2 The flowchart of two-step cluster analysis (Punj, G. & Stewart, D)...........24
Figure 4.1 The semantic space (Osgood, 1957)...........................................................33
Figure 4.2 Complete procedure for selecting representative CARs using PA.............35
Figure 5.1 Dendrogram of the Ward method for hierarchical cluster analysis............39
Figure 5.2 The increase ratio of combined coefficients...............................................41
Figure 5.3 The RSSDs value of adjective calculated in (a) step1,(b) step2, (c) step9,(d) step11,(e) step15, (f) step18, (g) step20 and (h) step21, during the PA backward elimination process....................................................................48
Figure 5.4 The RSSDs value of each adjective for all the elimination steps...............50
Figure 5.5 The concept chart of the comparison and integrated and application for ARDS..........................................................................................................54
Figure 6.1 The adjective exhibited for mobile phone for FA & CA :(a) factor1-factor2
and (b) factor1-factor3...............................................................................58
Figure 6.2 The adjective exhibited for mobile phone for FA & PA :(a) factor1-factor2
and (b) factor1-factor3...............................................................................59
Figure 6.3 The adjective exhibited for bicycle for FA & CA :(a) factor1-factor2
and (b) factor1-factor3...............................................................................65
Figure 6.4 The adjective exhibited for bicycle for FA & PA:(a) factor1-factor2
and (b) factor1-factor3...............................................................................66
Figure 6.5 The adjective exhibited for digital camera for FA & CA:(a) factor1-factor2
and (b) factor1-factor3...............................................................................72
Figure 6.6 The adjective exhibited for digital camera for FA & PA :(a) factor1-factor2
and (b) factor1-factor3...............................................................................73
Figure 7.1 The verification method and process..........................................................87


ABBREVIATIONS AND SYMBOLS

AHP Analytical Hierarchy Process
APD Affective product design
AR Affective Response
ARD Affective Response Dimension
AS Affective science
CA Cluster Analysis
CARs Consumers’ Affective Responses
CFA Confirmatory Factor Analysis
EFA Exploratory Factor Analysis
FA Factor Analysis
GMA Generalized Morphological Analysis
GPA Generalized Procrustes analysis
HCA Hierarchical Cluster Analysis
KE Kansei Engineering
KES Kansei Engineering System
KJ Kawakita Jiro method
KMO Kaiser-Meyer-Olkin value
MDS Multidimensional scaling
NHCA Nonhierarchical Cluster Analysis
PA Procrustes Analysis
PAM Pre-purchase Affect Model
PARAFAC Parallel Factor Analysis
PCA Principal Component Analysis
PFFs Product Form Features
QT1 Quantification Theory Type 1
RA Regression Analysis
RSSD Residual Sum of Squared Difference
SD Semantic Differential
SMC Squared Multiple Correlation value
SVD Singular Value Decomposition


Number of consumers
Number of affective dimensions
Number of product samples
Number of latent factors extracted from factor analysis
an Factor score coefficient
Feature vector of the ith product sample
Value of the ith affective response of a product sample
Data matrix of factor loadings obtained from factor analysis
Xn Independent variance
Y Dependent variance
Residual sum of squared difference of two data matrices and
obtained from Procrustes analysis
Y(C-A) Dependent variance for conservative-avant-garde adjective pairs
Y(M-F) Dependent variance for masculine-feminine adjective pairs
Y(U-C) Dependent variance for unoriginal-creative adjective pairs
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