A robust design approach for enhancing the feeling quality of a product a car profile case study

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International Journal of Industrial Ergonomics 35 (2005) 445–460

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A robust design approach for enhancing the feelingquality of a product: a car profile case study

Hsin-Hsi Lai�, Yu-Ming Chang, Hua-Cheng Chang

Department of Industrial Design, National Cheng Kung University, No.1, Dasyue Rd., East District, Tainan City 701, Taiwan, ROC

Received 9 January 2004; received in revised form 27 August 2004; accepted 18 October 2004

Available online 20 December 2004

Abstract

A consumer’s feeling plays a key role in determining his or her affection for a product. However, estimating,

reviewing, and enhancing this feeling are difficult since (1) no suitable criteria are available to do so, (2) a variance exists

between different consumer’s evaluations, and (3) no practicable design process is available. This paper develops the

concept of ‘‘feeling quality’’ to concretize the feeling effects evoked by a product. A robust design method is applied to

enhance this quality by reducing the discrepancy between the actual consumer feeling and the target feeling, and by

reducing the feeling ambiguity induced by the highly individualized characteristics of the consumers. The proposed

robust design is verified in a case study concerning a passenger car profile. A target feeling is specified and three original

car shapes are redesigned on the basis of the optimal parameters identified by the robust design in order to minimize the

feeling discrepancy and the feeling evaluation variation. The results confirm that compared to the original profiles, the

redesigned profiles evoke an enhanced ‘‘feeling quality’’. Specifically, the feeling discrepancy and the feeling ambiguity

are reduced by 41.31% and 51.49%, respectively.

Relevance to industry

This paper presents a robust design approach, which assists designers in enhancing the feeling quality of their

products. The approach enables the optimal design parameters to be identified and overcomes the problem of consumer

differences through the use of a simple experimental and analysis procedure. Adopting the proposed method

substantially reduces the likelihood of generating faulty designs.

r 2004 Elsevier B.V. All rights reserved.

Keywords: Feeling quality; Robust design; Taguchi’s method; Product design; Kansei engineering

e front matter r 2004 Elsevier B.V. All rights reserve

gon.2004.10.008

ing author. Tel.: +886 6 2757575x54325; fax:

.

esses: [email protected] (H.-H. Lai),

.ncku.edu.tw (Y.-M. Chang),

[email protected] (H.-C. Chang).

1. Introduction

Modern consumers not only place importanceon a product’s physical quality, but also employ

d.

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H.-H. Lai et al. / International Journal of Industrial Ergonomics 35 (2005) 445–460446

their sentimental responses when deciding whetheror not to purchase a particular product (Holbrookand Hirschman, 1982). The latter phenomenon isparticularly evident in the case of mature con-sumer products such as cars, cell phones, electricaland electronic appliances, furniture, etc. It hasoften been shown (e.g. by Apple’s iMac computer)that if products possess superior feeling features,such as form and color, they can still sell well andbe well liked even if they lack obvious advancedtechnologies and functions. Accordingly, design-ing products with enhanced feeling qualities is avital means of gaining market advantages. How-ever, many problems still remain in developing anaffective design process.Firstly, the consumer’s feeling evoked by a

particular product is generally regarded as anabstract or uncontrollable product feature. Whendeveloping a product, designers are commonlysupplied with a target feeling generated on thebasis of market analysis. With this target in mind,the designer then employs his or her subjectiveexperiences to develop the physical product.However, under this approach, there are no targetfeeling criteria against which to test the success orotherwise of the finished design. Hence, the riskexists that the product is actually a failure before iteven enters the market. Therefore, it is clearlynecessary to develop scientific methods andprocedures to facilitate the estimation, reviewand improvement of the feeling qualities of adesign.Secondly, the existence of highly individualized

characteristics induces significant variances intothe feeling evaluation of a product. Previousresearch (Boote, 1981; Kolter, 1992) has shownthat when the consumers’ characteristics are moreuniform, their evaluation responses are likely to bebroadly similar. Therefore, maintaining a consis-tency of consumers’ characteristics is an importantaspect of marketing. Accordingly, analysts fre-quently employ demographic characteristics tosegment the total market into particular consumergroups comprising individuals with commoncharacteristics. Powerful psychological or beha-vioral individualized characteristics are generallyneglected since they tend to be very difficult toinvestigate reliably. However, the influences of

such characteristics are important since theyrepresent uncontrollable factors and may intro-duce significant variances into the feeling evalua-tions of a product. If it is infeasible to exclude theinfluence of such uncontrollable factors comple-tely, then it is clearly prudent to take steps to atleast reduce their influence.Additionally, fierce market competition now

compels product developers to meet very shortdevelopment cycle times and to address thedemands of highly diverse target markets. ManyKansei Engineering studies (e.g. Nagamachi, 1995;Tomio and Kiyomi, 1997; Ishihara et al., 1997)have proposed methods to infer a prototype whichwill generate the required consumer feeling. How-ever, these methods are generally based on theapplication of exact mathematical models andthese models tend to be highly complex and canonly be constructed over the long term. Complexanalysis and prediction models of this type do notyield sufficiently rapid results and, furthermore,lack the flexibility which allows them to be appliedto diverse markets.The purpose of this paper is to apply the

concepts of quality engineering in developing amethod to concretize the feeling effects of pro-ducts, to enhance the feeling quality of products,and to minimize the influence of highly individua-lized characteristics.In the present context, the term ‘‘quality’’ refers

to the ability of a product to satisfy the consumers’requirements and expectations (Ishikwan, 1983).Since the purpose of affective design is to developa product which satisfies a certain set of consumerfeeling targets, consumer feelings also represent anaspect of quality which must be managed. There-fore, this study proposes the concept of ‘‘FeelingQuality’’ as a criterion for evaluating the perfor-mance of a particular product design. The robustdesign methodology (also referred to as ‘‘TaguchiQuality Engineering’’; Ross, 1988) provides themeans to minimize the variability of products andprocesses in order to improve their quality andreliability. This particular design methodology hasbeen successfully employed in a wide variety offields, including mechanical (Mauro, 1997), che-mical (Koolen, 1998), and material engineering(Khoei et al., 2002). Robust design employs a

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simple experimental approach to determine theoptimal design parameter settings by analyzingthe complex relationships among the controllablefactors (design parameters), the uncontrollablefactors (noise factors), and the quality perfor-mance. The optimal parameter settings minimizethe influence of the uncontrollable factors on theproduct, thereby reducing product variability andmaximizing its quality. The primary tools of theTaguchi method are orthogonal arrays (OA) andthe Signal-to-Noise (S/N) ratio. Use of the formerreduces the number of required experimentssubstantially, while the latter provides an indica-tion of the robustness and quality of the design(Taguchi and Clausing, 1990). It has been reportedpreviously that the robust design approach canusefully be applied to improve the feeling qualityof products. Accordingly, this study develops anapproach for measuring feeling quality andemploys a robust design process to improve thisfeeling quality for the particular case of apassenger car profile.

2. Feeling quality

It is always difficult to measure a consumer’sassessment of product quality objectively. Asses-sing the feeling quality aspects of a product isparticularly difficult. One meaning of productquality is the extent to which the product satisfiesthe consumer’s expectations (Ishikwan, 1983). Inaffective design, the consumer expectations areconcretized as a target feeling, and the feelingquality of the designed product is then assessed byconsidering the so-called ‘‘feeling discrepancy’’between this target feeling and the actual feeling.For example, the target feeling may be specified as‘‘luxurious’’, and the success of the design can beevaluated by testing whether or not the productactually evokes this feeling when revealed toconsumers, and if so, by determining the percen-tage of consumers who experience this samefeeling. A further indication of quality is the

DðO;TÞ ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiðX 1ðOÞ � X 1ðTÞÞ

2þ ð

q

extent to which different consumer’s evaluationsof the same product vary (Deming, 1982). Clearly,determining the feeling quality of a product musttake into account the evaluations of all consumerssince the product must meet the requirements ofthe entire market rather than just those of a singleconsumer. Each individual consumer possesses hisor her own particular set of feelings toward aproduct, and these feelings may well differ fromthose of other consumers. Hence, the present studyintroduces the concept of ‘‘feeling ambiguity’’ todenote the degree of consistency between thefeeling evaluations of different consumers.

2.1. Feeling discrepancy

The target feeling of most product designsusually involves more than one image aspect (e.g.a cell phone suitable for mature female consumersand a motorcycle which exudes both eleganceand vividness, etc.). Semantic differential scales(Osgood et al., 1957) provide an effective means ofdefining a consumer’s feeling, and have foundwidespread application (e.g. Chuang and Ma,2001; Piamonte et al., 2001). These approachesemploy individual semantic scales to evaluate thevarious product attributes (feelings) of interest tothe researchers. The values assigned on each scalethen represent one ingredient in the overall feelingevaluation space consisting of several semanticscales (or conversely, a position in the feelingevaluation space represents the attributes of theproduct on the corresponding semantic scales).Hence, this method can be used to determine thefeeling discrepancy between the planned feeling(i.e. the target feeling) of a product and the actualconsumer’s feeling (i.e. the output feeling) for thatproduct. This feeling discrepancy can be defined as

Feeling discrepancy ¼

Pni¼1DiðO;TÞ

n; (1)

where O is the output feeling, T is the targetfeeling, n is the number of output feelings, Di(O,T)

is the distance between the ith O and T values, andD is given by

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiX 2ðOÞ � X 2ðTÞÞ

2þ . . .þ ðX mðOÞ � X mðTÞÞ

2; (2)

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where m is the number of image scales (1, 2,y, m)and Xi is the value assigned on the ith imagescale.The feeling discrepancy parameter provides

an indication of how closely (or otherwise) thedesigned product matches the target feeling.Clearly, the value of this parameter is inverselyproportional to the ideal degree.

Fig. 1. Two situations of feeling ambiguity.

2.2. Feeling ambiguity

The term ‘‘ambiguity’’ refers to the situation inwhich different consumers experience differentfeelings when presented with the same product. Itcan be further defined as the degree of consistencyof the n output feelings for the same product. Sincethe feeling discrepancy represents the average of n

distances between the consumer feeling and thetarget feeling, it is possible that the same feelingdiscrepancy can arise from different degrees offeeling ambiguity. Fig. 1 illustrates two feelingambiguity situations, where each dot representsthe output feeling of an individual consumer. InFig. 1(a), the outputs are concentrated, and henceindicate a reduced feeling ambiguity, i.e. theconsumers share similar feelings for the product.Conversely, in Fig. 1(b), the output feelings arecomparatively scattered, indicating a greater de-gree of feeling ambiguity. Higher ambiguitysuggests that the feeling discrepancy will be lowfor some consumers, but high for others. There-fore, the product will most likely satisfy no morethan a sub-set of the total consumers. The feelingambiguity represents the degree of concentrationof n outputs about their center and can beexpressed as

Feeling ambiguity ¼

Pni¼1DiðO;CÞ

n; (3)

where O is the output feeling, C is the centerof the output feeling, n is the number ofoutput feelings (1, 2, y, n), Di(O,C) is the distancebetween the ith O and C values, and D isgiven by

DðO;CÞ ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiðX 1ðOÞ � X 1ðCÞÞ

2þ ðX 2ðOÞ � X 2ðCÞÞ

2þ . . .þ ðX

q

where m is the number of image scales (1, 2,y, m)and Xi is the value assigned on the ith image scale.Xi(C) is given by

X iðCÞ ¼

Pnj¼1X iðOjÞ

n; (5)

where X iðOjÞ is the value assigned on the ith imagescale for the jth O.

3. Robust design for feeling quality

This study develops a robust design for thefeeling quality of a product. The Taguchi method

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffimðOÞ � X mðCÞÞ

2; (4)

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Table 1

Basic phases in robust design for feeling quality

Phase Description

1 Setting target feeling Identify crucial images and evaluation scales

Construct multidimensional feeling space

Select the position of target feeling

2 Taguchi experiment Identify control factors and setting levels

Identify uncontrollable factors and setting levels

Select inner and outer orthogonal array

Array the experiment and generate experimental

samples

Perform feeling evaluation experiment

3 Result analysis Calculate feeling discrepancy

Calculate S/N ratio

Select the setting optimal parameters

4 Improvement and verification Select powerful control factors by ANOVA

Redesign initial design

Predict the S/N ratio of the improved design

Perform verification experiment to confirm the

prediction


is employed to determine the optimal productdesign parameters in order to improve the feelingperformance of the product, while simultaneouslyreducing its susceptibility to highly individualizedcharacteristics. Table 1 illustrates the basic phasesof the robust design for feeling quality. Phase 1involves the use of preliminary market analysis tospecify the position of the target feeling in a feelingspace composed of various critical image scales. Inthe second phase, a Taguchi experiment isperformed using appropriate inner and outerorthogonal arrays. The inner OA is specifiedaccording to the number of control factors (i.e.product design parameters) and levels. The so-called ‘‘combinative samples’’ (i.e. experimentalproduct samples) are then separately generated inaccordance with the condition array of the innerOA. The outer OA is specified in accordance withthe number of uncontrollable factors (i.e. con-sumer characteristics) and levels. Estimator (con-sumer) groups are established, and eachcombinative sample is then evaluated by theindividual estimator groups using appropriateimage scales. Phase 3 analyzes the results of the

preceding Taguchi experiment to obtain theoptimal parameters for each factor. The feelingquality of each combinative sample is measuredusing the ‘‘smaller-the-better’’ S/N ratio since theideal affective design is the design which yields theminimum feeling discrepancy. The ‘‘smaller-the-better’’ S/N ratio, Z, is given by

S=N ratio ðsmaller-the-betterÞ

¼ Z ¼ �10 log101

n

Xn

i¼1

y2i

!; ð6Þ

where yi is the feeling discrepancy of the ith groupand n is the number of estimator groups in theouter OA. The final stage of Phase 3 is to identifythe optimal levels (parameters), which reduce thisS/N ratio to a minimum value for each factor. InPhase 4, ANOVA is employed to identify the mostsignificant factors, and the initial design is thenmodified accordingly. Superposition is then usedto predict the expected feeling discrepancy andS/N ratio of the redesigned product. Finally, averification experiment is performed to confirm theaccuracy of these predictions.

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4. Case study

The passenger car represents a typical exampleof a mature product. Since there is little to choosebetween the features, structures, and materials ofthis particular product nowadays, the relativedistinctiveness of the car profile is receivingincreasing emphasis in manufacturers’ marketingstrategies. Therefore, the present study adaptedthe case of a car profile to explore the feasibility ofthe proposed approach in improving the feelingquality of an affective product design.

4.1. Target feeling and initial design

The case study assumed that Company A wasconducting a new design project for a passengercar, which, according to market analysts, was to betargeted at consumers with the following char-acteristics: (1) Age 25–30, (2) White-Collar, (3)Married (for 1–8 years), (4) Parent, and (5) With aliking for outdoor life. Furthermore, the car wasto simultaneously evoke the following images:(1) Youthful, (2) Outdoor, and (3) Family. Thetarget feeling could then be accurately defined in afeeling domain comprised of three nine-pointimage scales, namely ‘‘young2mature’’ (T1),

Fig. 2. Target feeling.

Fig. 3. Initial designs of car profile genera

‘‘field2city’’ (T2), ‘‘personal2family’’ (T3).Furthermore, the relative target feeling could bedefined as T(1,2,3) ¼ [2, 2, 7], as shown in Fig. 2.With these targets in mind, three product designerswere requested to develop appropriate initialpassenger car profile designs (I1, I2, I3). Thecorresponding designs are illustrated in Fig. 3.

4.2. Taguchi experiment

4.2.1. Control factors

In the present case study, the Taguchi controlfactors included the various profile variables of thepassenger car. Most previous car profile studieshave focused upon manufacturing issues, and yieldlittle in the way of useful information for thecurrent investigation regarding the impact of acar’s profile upon consumers’ feelings. Conse-quently, this study commenced by compilingprofile images of 125 existing passenger cars.These images were then reviewed with six expertsin the field of car profile design to establish theprofile variables which would most likely influenceconsumer feeling. Fig. 4 presents the 13 profilevariations and the three corresponding levelsfinally selected in accordance with the followingprinciples:

�

ted

The integer of all selected factors must becapable of explaining most variations in thepassenger car profile.

�
The relationship between any two factors mustbe independent such that the variation of anysingle variable has no influence upon thevariation of the other variables.
�
Each factor contains three levels: the maximumlevel depends on the maximum parameter ofthe 125 original samples, the minimum leveldepends on the minimum parameter, and themiddle level represents the average of themaximum and minimum parameters.
from traditional design process.

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Fig. 4. Profile factors and levels.

Table 2

Uncontrollable factors and their respective levels

Factor Description Level 1 Level 2 Level 3

W Involvement Low Medium High

X Personal trait Introvert Medium Extrovert

Y Peer relation Aloof Medium Intimate

Z Social support Scanty Medium Abundant


4.2.2. Uncontrollable factors

This study adopted four psychological orbehavioral individualized characteristics as un-controllable factors, namely involvement, personaltrait, peer relation, and social support, and appliedthree level settings to each, as shown in Table 2.These factors were then employed as the basisfor selecting estimators in the follow-up experi-mental processes. ‘‘Involvement’’ indicates thatthe consumer expresses concern for, or participatesin situations on the basis of inherent needs,worth, and interest. This study employed thePersonal Involvement Inventory indicator (PII;Zaichkowsky, 1994) to differentiate between theinvolvements of different estimators regarding a

car. ‘‘Personal Trait’’ refers to the phenomenon inwhich consistent personalities tend to expresssimilar attitudes when confronting a commonsituation. In the present study, the EysenckPersonality Questionnaire (EPQ; Eysenck, 1975)

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was applied to differentiate between the person-ality traits of different estimators. ‘‘Peer Relation’’describes the degree to which an individual shareshis values, experiences, and lifestyle with his peergroup, and was measured for different estimatorsin the current study using the indicator proposedby Bearden et al. (1989). Finally, ‘‘Social Support’’indicates the degree of support and assistancereceived by an individual from his social networkof family and friends. In the present study, thesocial support of each estimator was againassessed using a measure proposed by Beardenet al. (1989).

4.2.3. Experimental design

The present experimental design was composedof the inner (shape) and outer (estimator group)arrays shown in Fig. 5. An L27 (313) array wasadopted for the inner array since the controlfactors contain thirteen three-level factors, and 27different combinative samples are generated, asshown in Fig. 6. Meanwhile, an L9 (3

4) array wasemployed for the outer array since there are fouruncontrollable factors, each with three levels, anda total of nine estimator groups.

4.2.4. Feeling evaluation

Estimators: A total of 27 estimators wereassigned equally across nine estimator groups(G1–G9) according to the respective conditionsof each group. Samples: (1) three initial designsand (2) 27 combinative designs. Each car profilewas displayed on individual A4-sized cards.Evaluation: Each estimator evaluated their feelingfor the initial and combinative car profile samplesusing the three nine-point semantic scales pre-sented previously in Section 4.1.

4.3. Analysis of results

4.3.1. Evaluation of initial design results

Table 3 presents the profile factor levels of thethree initial designs and the corresponding feelingevaluation results. The shape factor levels of theinitial designs are decided by the most approx-imate parameter, and the feeling discrepancy ofeach estimator group represents the averageevaluation of the three estimators within that

particular group. Even though previous studies(Boote, 1981; Kolter, 1992) have suggested thatconsumers with similar characteristics are likely toprovide similar responses, it is still necessary tocater for the influence of unanticipated estimatorcharacteristics. Hence, the average approach wasemployed in the present case study to reduce thepossible influences of unexpected estimator char-acteristics in each individual estimator group.Each standard deviation in the estimator groupwas then checked by a criterion which prescribedthat the standard deviation was only acceptable ifits value did not exceed 1. Table 3 shows eachstandard deviation and indicates that all values areacceptable.

4.3.2. Analysis of Taguchi experimental results

Table 4 presents the feeling evaluation resultsand the corresponding mean values and S/N ratiosfor each of the 27 combinative shapes. Meanwhile,Table 5 indicates the individual S/N ratios for eachlevel of every shape factor, and the correspondingfactor effects. The S/N ratio measures the influ-ence of a particular level upon the feeling quality.Specifically, a greater S/N ratio implies a higherfeeling quality. The ‘‘effect of factor’’ parameterrepresents the difference in the S/N ratio betweenthe maximum level and the minimum level ofa single factor. A greater effect indicates that thefactor has a more significant influence uponthe feeling quality. For ease of comprehension,the data of Table 4 are also illustrated graphicallyin Fig. 7. It can be seen that the sequence ofinfluence of the individual factors (i.e. most toleast influential) is given by A4E4F4C4K4L4M4H4G4B4D4I4J. The ‘‘optimal set-ting’’ can be obtained by selecting the maximumlevel of each factor, i.e. A3, B3, C1, D1, E2, F2, G1,H3, I1, J2, K2, L3, and M3. Fig. 8 presents thecorresponding optimal car profile and its para-meters.Although establishing the optimal settings facil-

itates the design of a car profile which closelymatches the target feeling, it is known that somefactors are of high influence, while others are oflesser significance. The purpose of the improve-ment stage of the Taguchi approach is not torenovate all the design factors, but simply to

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Outer (estimator group) array

Condition L9(34)

Factor 1 2 3 4 5 6 7 8 9 W 1 1 1 2 2 2 3 3 3 X 1 2 3 1 2 3 1 2 3 Y 1 2 3 2 3 1 3 1 2 Z 1 2 3 3 1 2 2 3 1

Inner (shape) array

Factor L27(313) Estimator groups

Cond.A B C D E F G H I J K L M G1 G2 G3 G4 G5 G6 G7 G8 G9

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1111 1222 1333 2123 2231 2312 3132 3213 33212 1 1 1 1 2 2 2 2 2 2 2 2 2 1111 1222 1333 2123 2231 2312 3132 3213 33213 1 1 1 1 3 3 3 3 3 3 3 3 3 1111 1222 1333 2123 2231 2312 3132 3213 33214 1 2 2 2 1 1 1 2 2 2 3 3 3 1111 1222 1333 2123 2231 2312 3132 3213 33215 1 2 2 2 2 2 2 3 3 3 1 1 1 1111 1222 1333 2123 2231 2312 3132 3213 33216 1 2 2 2 3 3 3 1 1 1 2 2 2 1111 1222 1333 2123 2231 2312 3132 3213 33217 1 3 3 3 1 1 1 3 3 3 2 2 2 1111 1222 1333 2123 2231 2312 3132 3213 33218 1 3 3 3 2 2 2 1 1 1 3 3 3 1111 1222 1333 2123 2231 2312 3132 3213 33219 1 3 3 3 3 3 3 2 2 2 1 1 1 1111 1222 1333 2123 2231 2312 3132 3213 3321

10 2 1 2 3 1 2 3 1 2 3 1 2 3 1111 1222 1333 2123 2231 2312 3132 3213 332111 2 1 2 3 2 3 1 2 3 1 2 3 1 1111 1222 1333 2123 2231 2312 3132 3213 332112 2 1 2 3 3 1 2 3 1 2 3 1 2 1111 1222 1333 2123 2231 2312 3132 3213 332113 2 2 3 1 1 2 3 2 3 1 3 1 2 1111 1222 1333 2123 2231 2312 3132 3213 332114 2 2 3 1 2 3 1 3 1 2 1 2 3 1111 1222 1333 2123 2231 2312 3132 3213 332115 2 2 3 1 3 1 2 1 2 3 2 3 1 1111 1222 1333 2123 2231 2312 3132 3213 332116 2 3 1 2 1 2 3 3 1 2 2 3 1 1111 1222 1333 2123 2231 2312 3132 3213 332117 2 3 1 2 2 3 1 1 2 3 3 1 2 1111 1222 1333 2123 2231 2312 3132 3213 332118 2 3 1 2 3 1 2 2 3 1 1 2 3 1111 1222 1333 2123 2231 2312 3132 3213 332119 3 1 3 2 1 3 2 1 3 2 1 3 2 1111 1222 1333 2123 2231 2312 3132 3213 332120 3 1 3 2 2 1 3 2 1 3 2 1 3 1111 1222 1333 2123 2231 2312 3132 3213 332121 3 1 3 2 3 2 1 3 2 1 3 2 1 1111 1222 1333 2123 2231 2312 3132 3213 332122 3 2 1 3 1 3 2 2 1 3 3 2 1 1111 1222 1333 2123 2231 2312 3132 3213 332123 3 2 1 3 2 1 3 3 2 1 1 3 2 1111 1222 1333 2123 2231 2312 3132 3213 332124 3 2 1 3 3 2 1 1 3 2 2 1 3 1111 1222 1333 2123 2231 2312 3132 3213 332125 3 3 2 1 1 3 2 3 3 1 2 1 3 1111 1222 1333 2123 2231 2312 3132 3213 332126 3 3 2 1 2 1 3 1 1 2 3 2 1 1111 1222 1333 2123 2231 2312 3132 3213 332127 3 3 2 1 3 2 1 2 2 3 1 3 2 1111 1222 1333 2123 2231 2312 3132 3213 3321

Fig. 5. Experimental design.


redesign those factors which have a significantinfluence upon the feeling quality. In other words,the intention is to obtain the greatest improvementin feeling quality through the minimum of redesignactivity. Therefore, it is necessary to identify whichof the 13 profile factors have the most significantinfluence upon the feeling quality. As recom-mended by Taguchi, the present study identified

these factors using ANOVA and the ‘‘contributionpercentage’’ parameter (calculation details pro-vided in Taguchi, 1987). The correspondingANOVA results are presented in Table 6, whichindicates that factors A, C, E, F, H, K, L, and M

are the most influential factors in this particularcase study. The optimal settings of these factorsare: A1, C3, E1, F1, H2, K3, L3, and M2.

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Fig. 6. Twenty-seven combinative designs for Taguchi experiment.


4.4. Improvement and verification

To verify the conclusions presented above, theoriginal profiles were modified accordingly, and averification experiment was performed. Initially,the three original car profiles were modified inaccordance with the optimal factor levels identifiedabove, i.e. A1, C3, E1, F1, H2, K3, L3, and M2,while the remaining factors (B, D, G, I, and J)retained their original settings. Table 7 indicatesthe corresponding profile parameters of theredesigned profiles (R1, R2, R3) and Fig. 9presents the effects of the profile modificationpictorially for each original profile. Subsequently,a process of superposition (Eqs. (7)–(10)) wasemployed to predict the S/N ratios of the optimalprofile and those of the redesigned profiles.The corresponding results were determined to beZðOÞ ¼ �1:45; ZðR1Þ ¼ �4:45; ZðR2Þ ¼ �3:06; and

ZðR3Þ ¼ �2:71:

ZðOÞ ¼ T þ ðA1 � TÞ þ ðB3 � TÞ þ ðC3 � TÞ

þðD3 � TÞ þ ðE1 � TÞ þ ðF1 � TÞ þ ðG1 � TÞ

þ ðH2 � TÞ þ ðI2 � TÞ þ ðJ1 � TÞ

þ ðK3� TÞ þ ðL3 � TÞ þ ðM2 � TÞ

¼ ðA1Þ þ ðB3Þ þ ðC3Þ þ ðD3Þ þ ðE1Þ

þ ðF1Þ þ ðG1Þ þ ðH2Þ þ ðI2Þ þ ðJ1Þ

þ ðK3Þ þ ðL3Þ þ ðM2Þ � 12T

¼ ð�13:12Þ þ ð�14:89Þ þ ð�14:07Þ

þ ð�14:7Þ þ ð�13:66Þ þ ð�14:05Þ

þ ð�14:79Þ þ ð�14:44Þ þ ð�14:96Þ

þ ð�14:95Þ þ ð�13:96Þ þ ð�14:23Þ

þ ð�14:6Þ � 12ð�15:41Þ

¼ � 1:45; ð7Þ

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Table 3

Evaluation result of initial designs

Initial design Level of shape parameter Feeling discrepancy

A B C D E F G H I J K L M G1 G2 G3 G4 G5 G6 G7 G8 G9 Total mean Variance

I1 3 2 2 3 3 2 2 2 1 2 3 2 2 Mean 2.33 5.48 3.12 4.85 6.18 3.44 3.86 3.61 4.16 4.11 1.2913

SD 0.44 0.57 0.40 0.68 0.55 0.55 0.52 0.33 0.57

I2 3 1 3 3 3 1 1 2 3 2 3 1 3 Mean 3.58 1.49 5.42 4.58 4.33 2.14 1.85 4.77 2.27 3.38 1.9065

SD 0.52 0.35 0.58 0.45 0.74 0.62 0.80 0.48 0.74

I3 2 2 2 2 3 2 3 1 2 2 3 2 3 Mean 2.47 2.14 3.26 2.47 3.64 2.26 4.68 1.57 3.23 2.86 0.7849

SD 0.37 0.42 0.72 0.61 0.61 0.69 0.52 0.71 0.55

Table 4

Evaluation results of combinative shapes

L27 G1 G2 G3 G4 G5 G6 G7 G8 G9 Total mean S/N ratio

1 6.21 7.63 7.22 6.85 7.49 7.82 7.29 6.23 7.68 7.16 �17.12

2 6.24 5.58 6.04 5.87 6.44 6.82 5.97 6.24 6.55 6.19 �15.85

3 8.13 6.05 6.45 8.04 6.47 5.77 5.34 6.57 5.24 6.45 �16.29

4 1.44 1.68 1.47 2.04 1.76 1.56 1.74 1.84 1.08 1.62 �4.319

5 6.24 7.54 5.47 6.88 5.44 6.57 6.08 5.97 6.57 6.31 �16.04

6 7.05 6.75 7.42 7.83 6.75 7.16 6.27 7.05 6.87 7.02 �16.94

7 1.84 2.36 2.05 2.45 1.69 1.88 2.52 2.33 2.04 2.13 �6.637

8 3.25 3.58 2.97 3.23 3.08 2.44 3.69 3.97 3.01 3.25 �10.3

9 6.24 4.23 5.67 7.05 4.33 5.12 6.57 3.81 4.33 5.26 �14.61

10 6.24 5.77 5.63 6.86 5.84 5.28 5.61 5.24 5.08 5.73 �15.2

11 7.41 4.35 5.87 4.28 6.57 5.17 4.83 4.71 7.54 5.64 �15.21

12 8.57 5.34 7.56 5.48 6.55 7.05 4.87 5.27 6.37 6.34 �16.18

13 2.45 2.66 2.04 3.02 2.55 2.93 2.82 2.07 2.95 2.61 �8.409

14 8.94 7.25 5.21 5.36 8.48 9.02 7.25 5.68 8.24 7.27 �17.4

15 7.34 6.84 7.44 6.81 7.54 6.24 5.88 7.68 8.54 7.15 �17.13

16 6.54 7.05 6.57 5.28 4.87 5.22 6.48 6.95 4.57 5.95 �15.59

17 8.57 8.47 8.21 7.94 7.83 8.54 7.63 8.57 6.57 8.04 �18.13

18 8.63 5.27 4.92 5.38 8.33 5.34 7.22 7.68 5.24 6.45 �16.39

19 5.84 5.36 8.54 7.24 5.29 5.67 6.73 8.45 6.22 6.59 �16.52

20 8.65 8.24 8.87 7.54 7.68 8.97 5.67 8.56 8.64 8.09 �18.22

21 7.85 5.27 6.78 8.45 7.56 8.16 5.54 8.56 7.58 7.31 �17.38

22 9.84 8.67 9.54 9.64 9.77 8.64 8.94 9.48 9.88 9.38 �19.45

23 6.53 4.08 7.56 7.54 4.35 5.39 4.87 5.67 5.02 5.67 �15.26

24 9.57 9.67 8.92 7.94 9.25 9.67 10.27 9.18 9.76 9.36 �19.44

25 9.57 8.27 10.27 9.86 9.74 10.38 9.78 9.68 9.14 9.63 �19.69

26 6.35 4.86 7.64 5.24 5.97 4.87 5.64 5.21 5.08 5.65 �15.14

27 8.04 4.57 8.64 9.73 5.27 5.26 7.58 8.67 7.88 7.29 �17.49


ZðR1Þ ¼ ðA1Þ þ ðB2Þ þ ðC3Þ þ ðD3Þ þ ðE1Þ



¼ � 4:45; ð8Þ


þ ðF 1Þ þ ðG1Þ þ ðH2Þ þ ðI3Þ þ ðJ2Þ


¼ � 3:06; ð9Þ

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Fig. 7. Response graphs for S/N ratios of shape factors.

Table 5

Response table for S/N ratio of shape factors

Level A B C D E F G H I J K L M

1 �13.12 �16.44 �17.06 �16.06 �13.66 �14.05 �14.79 �16.21 �16.26 �14.95 �16.22 �16.43 �16.41

2 �15.51 �14.93 �15.13 �15.5 �15.73 �15.08 �16.4 �14.44 �15.04 �15.01 �16.08 �15.6 �14.6

3 �17.62 �14.89 �14.07 �14.7 �16.87 �17.14 �15.07 �15.61 �14.96 �16.07 �13.96 �14.23 �15.25

Effect 4.498 1.556 2.992 1.358 3.214 3.092 1.604 1.773 1.302 1.118 2.269 2.193 1.805

Fig. 8. Car profile optimized for target feeling.





¼ � 2:71: ð10Þ

Finally, a verification experiment was performedby the estimators involved in the original profileand combinative sample assessment activity. Usingthe same set of nine-point image evaluation scales,the estimators evaluated the optimal profile andthe three redesigned profiles. The correspondingresults are presented in Table 8. It is noted that theactual S/N ratios for the four profiles do notmatch the predicted S/N ratios. The discrepancybetween the two sets of values can be attributed totwo possible causes: (1) The total percentagecontribution of the eight significant profile factorsis only 61.99%. Hence, the influence of thecombined error (including the other profile factorsand unknown factors) contributes 38.11%. There-fore, it is possible that some influential factors may

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Table 6

ANOVA table

Source fd SS MS F Partial SS Contribution percentage

A 2 30.41 15.21 8.69 62.02 22.53

Ba 2 4.71 2.35 1.34

C 2 13.80 6.90 3.94 23.72 8.62

Da 2 2.80 1.40 0.80

E 2 15.92 7.96 4.55 28.62 10.40

F 2 14.87 7.44 4.25 26.21 9.52

Ga 2 4.40 2.20 1.26

H 2 4.88 2.44 1.39 3.17 1.15

Ia 2 3.19 1.60 0.91

Ja 2 2.42 1.21 0.69

K 2 9.68 4.84 2.77 14.24 5.17

L 2 7.36 3.68 2.10 8.89 3.23

M 2 5.02 2.51 1.43 3.50 1.27

Error (10) (17.52) (1.75) (45.52) (38.11)

Total 26 119.45 119.45 100.00

aFactor combines into error.

Table 7

Profile parameters for redesigned profiles

Redesign Shape parameters

Aa B Ca D Ea Fa G Ha I J Ka La Ma

R1 1 2 3 3 1 1 2 2 1 2 3 3 2

R2 1 1 3 3 1 1 1 2 3 2 3 3 2

R3 1 2 3 2 1 1 3 2 2 2 3 3 2

aLevel changed from original setting to optimal setting.


not have been correctly identified. (2) The inter-actions among profile factors could representinfluential elements of the feeling difference, butare not considered in the present experiment.Although the predictions are not exact, the

redesigns nevertheless yield significant improve-ments in the feeling quality, as shown in Table 9.Regarding the feeling discrepancy, the meanachieved reduction is 41.31%, with individualreductions of R1 ¼ 48:19%; R2 ¼ 39:35%; andR3 ¼ 35:66%: Therefore, each of the redesignedprofiles is significantly closer to the target feelingthan its original version. As regards the feel-ing ambiguity, the mean achieved reduction is

51.49%, with individual reductions of R1 ¼

47:13%; R2 ¼ 58:32%; and R3 ¼ 49:02%: There-fore, each of the redesigned profiles has asignificantly lower sensitivity to the characteristicsof individual consumers than their original coun-terparts. These results verify the feasibility ofemploying the proposed robust design method toimprove the feeling quality of a car profile.

5. Discussion and conclusion

This paper has presented a robust designapproach to enhance the feeling quality of a

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Table 8

Results of verification experiment

Feeling discrepancy Actual S/N ratio Predictive S/N ratio Difference

G1 G2 G3 G4 G5 G6 G7 G8 G9 Mean Variance

O 0.94 1.53 1.32 1.09 1.54 0.84 0.97 1.14 1.34 1.19 0.0654 �1.686 �1.45 0.236

R1 2.84 2.02 1.75 1.05 2.59 2.64 1.58 1.97 2.43 2.10 0.1967 �6.718 �4.45 2.268

R2 2.31 2.46 2.48 1.35 1.66 2.38 1.89 1.42 2.47 2.05 0.1712 �6.42 �3.06 3.36

R3 1.53 1.84 2.35 1.97 2.08 2.55 1.28 1.39 1.54 1.84 0.3008 �5.496 �2.71 2.786

Fig. 9. Modification of original profiles in accordance with Taguchi analysis.

Table 9

Effects of improvements

Feeling discrepancy Feeling ambiguity

O 1.19 0.8682

I1 4.11 2.3352

R1 2.10 1.2347

Reduction 48.91% 47.13%

I2 3.38 2.6523

R2 2.05 1.1054

Reduction 39.35% 58.32%

I3 2.86 2.0104

R3 1.84 1.0249

Reduction 35.66% 49.02%

Mean reduction 41.31% 51.49%


product. The Taguchi experimental design methodhas been employed to obtain the optimal designparameters which cause the consumers’ feelingsinduced by the redesigned products to be closer tothe target feeling than the initial products, whilesimultaneously reducing the influence of theconsumers’ highly individualized characteristics.The proposed approach has been tested for thecase of a car profile design. It has been shown thatthe approach successfully enhances the feeling

quality of the car profiles, i.e. the feeling dis-crepancy and the feeling ambiguity are reduced by41.31% and 51.49%, respectively.Generally, a major difference between the

proposed approach and previous Kansei Engineer-ing approaches (e.g. Nagamachi, 1995; Ishihara etal., 1997; Chuang and Ma, 2001) lies in theirrespective objectives. The purpose of the approachpresented in this study is to obtain a set of usefulproduct design parameters for achieving the targetfeeling, whereas the intention of Kansei Engineer-ing approaches is to construct an accurate modelto describe the anticipated consumers’ response toa product. Therefore, the current approach offersgreater advantages in developing an affectivedesign. First, compared to Kansei Engineeringapproaches, which depend upon a large number ofsamples to ensure their accuracy, the robust designapproach requires fewer experimental frequenciesand a lesser number of experimental scales. Hence,the present robust design method reduces the timeand cost required to complete the feeling evalua-tion study. Moreover, the proposed approach canyield the optimal design parameters of the productdirectly. Second, the robust design approach issuitable for a diverse range of applications. Theapproach can be applied to global or regional

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design problems since the target feeling or experi-mental parameters can be assigned according tothe particular practical requirements. Addition-ally, using the proposed approach, the obtainedoptimal design parameters are broadly indepen-dent of the influence of consumers’ highly indivi-dualized characteristics since these characteristicsare already taken into consideration in theTaguchi experiment. These advantages enable therobust design method to support many tasksinvolved in developing an affective design byproving a systematical approach which is bothflexible and efficient. For example, this approachcan be employed to redesign existing products inorder to enhance their feeling quality, to modifyexisting products in order to transform or expandthe originally targeted consumer group (e.g. toexpand the target consumer groups of a car frommiddle-aged males to young males), or to developnew products in order to accommodate existingfamilial style (e.g. to develop a new generationseries of cars which still maintains the BMWfamilial style).Nevertheless, robust design has certain limita-

tions. The fundamental point in applying robustdesign is to develop the means to identify thecontrollable and uncontrollable factors which havea significant and powerful influence on quality. Inother words, the appropriate selection of thecontrollable and uncontrollable factors, and theirlevels, has a crucial influence on the efficiency ofthe robust design. For example, in the present casestudy, 13 attributes of the car profile were selectedas controllable factors in accordance with theopinions of six design experts. Using the ANOVAstatistical approach, it was found that thesecontrollable factors have a different influence onthe feeling quality. If factors with a lesser influencehad been selected, it is possible that the feelingquality might not have been enhanced substan-tially through the robust design approach. Clearly,it is also possible that more powerful factors thanthose actually selected might exist. If this wereindeed the case, it would be reasonable to assumethat the feeling quality might be enhanced further.Consequently, when wishing to exploit the powerof the robust design approach, it is first advisableto conduct a thorough pilot study to identify the

most influential factors with some certainty beforeactually performing the design task.The concept of feeling quality is a valuable

criterion for estimating the psychological perfor-mance of a product, and provides a means toreview and improve the product design. Further-more, robust design represents a feasible approachfor systematically improving the feeling quality ofa product. This study has demonstrated theapplication of the robust design technique to thedevelopment of a car profile, and has shown thatthis simple experimental approach enables thedevelopment of a product whose associated feelingquality approaches that of the target quality. Thefeeling quality of products is becoming anincreasingly important aspect of consumptiontendencies. Therefore, product developers anddesigners are faced with the challenge of creatingand redesigning products which cater to consu-mers of all types and preferences. The approachpresented within this study will surely assist themin doing so.

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