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

1. International Journal of Industrial Ergonomics 35 (2005) 445–460
A robust design approach for enhancing the feeling
quality of a product: a car profile case study
Hsin-Hsi LaiÃ, 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
1. Introduction
Modern consumers not only place importance
on a product’s physical quality, but also employ
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ÃCorresponding author. Tel.: +886 6 2757575x54325; fax:
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E-mail addresses: hsinhsi@mail.ncku.edu.tw (H.-H. Lai),
ymchang@mail.ncku.edu.tw (Y.-M. Chang),
chang_huacheng@seed.net.tw (H.-C. Chang).

2. their sentimental responses when deciding whether
or not to purchase a particular product (Holbrook
and Hirschman, 1982). The latter phenomenon is
particularly evident in the case of mature con-
sumer products such as cars, cell phones, electrical
and electronic appliances, furniture, etc. It has
often 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 and
be well liked even if they lack obvious advanced
technologies and functions. Accordingly, design-
ing products with enhanced feeling qualities is a
vital means of gaining market advantages. How-
ever, many problems still remain in developing an
affective design process.
Firstly, the consumer’s feeling evoked by a
particular product is generally regarded as an
abstract or uncontrollable product feature. When
developing a product, designers are commonly
supplied with a target feeling generated on the
basis of market analysis. With this target in mind,
the designer then employs his or her subjective
experiences to develop the physical product.
However, under this approach, there are no target
feeling criteria against which to test the success or
otherwise of the finished design. Hence, the risk
exists that the product is actually a failure before it
even enters the market. Therefore, it is clearly
necessary to develop scientific methods and
procedures to facilitate the estimation, review
and improvement of the feeling qualities of a
design.
Secondly, the existence of highly individualized
characteristics induces significant variances into
the feeling evaluation of a product. Previous
research (Boote, 1981; Kolter, 1992) has shown
that when the consumers’ characteristics are more
uniform, their evaluation responses are likely to be
broadly similar. Therefore, maintaining a consis-
tency of consumers’ characteristics is an important
aspect of marketing. Accordingly, analysts fre-
quently employ demographic characteristics to
segment the total market into particular consumer
groups comprising individuals with common
characteristics. Powerful psychological or beha-
vioral individualized characteristics are generally
neglected since they tend to be very difficult to
investigate reliably. However, the influences of
such characteristics are important since they
represent uncontrollable factors and may intro-
duce significant variances into the feeling evalua-
tions of a product. If it is infeasible to exclude the
influence of such uncontrollable factors comple-
tely, then it is clearly prudent to take steps to at
least reduce their influence.
Additionally, fierce market competition now
compels product developers to meet very short
development cycle times and to address the
demands of highly diverse target markets. Many
Kansei Engineering studies (e.g. Nagamachi, 1995;
Tomio and Kiyomi, 1997; Ishihara et al., 1997)
have proposed methods to infer a prototype which
will generate the required consumer feeling. How-
ever, these methods are generally based on the
application of exact mathematical models and
these models tend to be highly complex and can
only be constructed over the long term. Complex
analysis and prediction models of this type do not
yield sufficiently rapid results and, furthermore,
lack the flexibility which allows them to be applied
to diverse markets.
The purpose of this paper is to apply the
concepts of quality engineering in developing a
method 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 develop
a product which satisfies a certain set of consumer
feeling targets, consumer feelings also represent an
aspect of quality which must be managed. There-
fore, this study proposes the concept of ‘‘Feeling
Quality’’ as a criterion for evaluating the perfor-
mance of a particular product design. The robust
design methodology (also referred to as ‘‘Taguchi
Quality Engineering’’; Ross, 1988) provides the
means to minimize the variability of products and
processes in order to improve their quality and
reliability. This particular design methodology has
been successfully employed in a wide variety of
fields, including mechanical (Mauro, 1997), che-
mical (Koolen, 1998), and material engineering
(Khoei et al., 2002). Robust design employs a
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3. simple experimental approach to determine the
optimal design parameter settings by analyzing
the complex relationships among the controllable
factors (design parameters), the uncontrollable
factors (noise factors), and the quality perfor-
mance. The optimal parameter settings minimize
the influence of the uncontrollable factors on the
product, thereby reducing product variability and
maximizing its quality. The primary tools of the
Taguchi method are orthogonal arrays (OA) and
the Signal-to-Noise (S/N) ratio. Use of the former
reduces the number of required experiments
substantially, while the latter provides an indica-
tion of the robustness and quality of the design
(Taguchi and Clausing, 1990). It has been reported
previously that the robust design approach can
usefully be applied to improve the feeling quality
of products. Accordingly, this study develops an
approach for measuring feeling quality and
employs a robust design process to improve this
feeling quality for the particular case of a
passenger car profile.
2. Feeling quality
It is always difficult to measure a consumer’s
assessment of product quality objectively. Asses-
sing the feeling quality aspects of a product is
particularly difficult. One meaning of product
quality is the extent to which the product satisfies
the consumer’s expectations (Ishikwan, 1983). In
affective design, the consumer expectations are
concretized as a target feeling, and the feeling
quality of the designed product is then assessed by
considering 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 be
evaluated by testing whether or not the product
actually evokes this feeling when revealed to
consumers, and if so, by determining the percen-
tage of consumers who experience this same
feeling. A further indication of quality is the
extent to which different consumer’s evaluations
of the same product vary (Deming, 1982). Clearly,
determining the feeling quality of a product must
take into account the evaluations of all consumers
since the product must meet the requirements of
the entire market rather than just those of a single
consumer. Each individual consumer possesses his
or her own particular set of feelings toward a
product, and these feelings may well differ from
those of other consumers. Hence, the present study
introduces the concept of ‘‘feeling ambiguity’’ to
denote the degree of consistency between the
feeling evaluations of different consumers.
2.1. Feeling discrepancy
The target feeling of most product designs
usually involves more than one image aspect (e.g.
a cell phone suitable for mature female consumers
and a motorcycle which exudes both elegance
and vividness, etc.). Semantic differential scales
(Osgood et al., 1957) provide an effective means of
defining a consumer’s feeling, and have found
widespread application (e.g. Chuang and Ma,
2001; Piamonte et al., 2001). These approaches
employ individual semantic scales to evaluate the
various product attributes (feelings) of interest to
the researchers. The values assigned on each scale
then represent one ingredient in the overall feeling
evaluation space consisting of several semantic
scales (or conversely, a position in the feeling
evaluation space represents the attributes of the
product on the corresponding semantic scales).
Hence, this method can be used to determine the
feeling discrepancy between the planned feeling
(i.e. the target feeling) of a product and the actual
consumer’s feeling (i.e. the output feeling) for that
product. This feeling discrepancy can be defined as
Feeling discrepancy ¼
Pn
i¼1DiðO;TÞ
n
; (1)
where O is the output feeling, T is the target
feeling, n is the number of output feelings, Di(O,T)
is the distance between the ith O and T values, and
D is given by
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DðO;TÞ ¼
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
ðX1ðOÞ À X1ðTÞÞ2
þ ðX2ðOÞ À X2ðTÞÞ2
þ . . . þ ðXmðOÞ À XmðTÞÞ2
q
; (2)
H.-H. Lai et al. / International Journal of Industrial Ergonomics 35 (2005) 445–460 447

4. where m is the number of image scales (1, 2, y, m)
and Xi is the value assigned on the ith image
scale.
The feeling discrepancy parameter provides
an indication of how closely (or otherwise) the
designed product matches the target feeling.
Clearly, the value of this parameter is inversely
proportional to the ideal degree.
2.2. Feeling ambiguity
The term ‘‘ambiguity’’ refers to the situation in
which different consumers experience different
feelings when presented with the same product. It
can be further defined as the degree of consistency
of the n output feelings for the same product. Since
the feeling discrepancy represents the average of n
distances between the consumer feeling and the
target feeling, it is possible that the same feeling
discrepancy can arise from different degrees of
feeling ambiguity. Fig. 1 illustrates two feeling
ambiguity situations, where each dot represents
the output feeling of an individual consumer. In
Fig. 1(a), the outputs are concentrated, and hence
indicate a reduced feeling ambiguity, i.e. the
consumers share similar feelings for the product.
Conversely, in Fig. 1(b), the output feelings are
comparatively scattered, indicating a greater de-
gree of feeling ambiguity. Higher ambiguity
suggests that the feeling discrepancy will be low
for some consumers, but high for others. There-
fore, the product will most likely satisfy no more
than a sub-set of the total consumers. The feeling
ambiguity represents the degree of concentration
of n outputs about their center and can be
expressed as
Feeling ambiguity ¼
Pn
i¼1DiðO;CÞ
n
; (3)
where O is the output feeling, C is the center
of the output feeling, n is the number of
output feelings (1, 2, y, n), Di(O,C) is the distance
between the ith O and C values, and D is
given by
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
XiðCÞ ¼
Pn
j¼1XiðOjÞ
n
; (5)
where XiðOjÞ is the value assigned on the ith image
scale for the jth O.
3. Robust design for feeling quality
This study develops a robust design for the
feeling quality of a product. The Taguchi method
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Fig. 1. Two situations of feeling ambiguity.
DðO;CÞ ¼
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
ðX1ðOÞ À X1ðCÞÞ2
þ ðX2ðOÞ À X2ðCÞÞ2
þ . . . þ ðXmðOÞ À XmðCÞÞ2
q
; (4)
H.-H. Lai et al. / International Journal of Industrial Ergonomics 35 (2005) 445–460448

5. is employed to determine the optimal product
design parameters in order to improve the feeling
performance of the product, while simultaneously
reducing its susceptibility to highly individualized
characteristics. Table 1 illustrates the basic phases
of the robust design for feeling quality. Phase 1
involves the use of preliminary market analysis to
specify the position of the target feeling in a feeling
space composed of various critical image scales. In
the second phase, a Taguchi experiment is
performed using appropriate inner and outer
orthogonal arrays. The inner OA is specified
according to the number of control factors (i.e.
product design parameters) and levels. The so-
called ‘‘combinative samples’’ (i.e. experimental
product samples) are then separately generated in
accordance with the condition array of the inner
OA. The outer OA is specified in accordance with
the number of uncontrollable factors (i.e. con-
sumer characteristics) and levels. Estimator (con-
sumer) groups are established, and each
combinative sample is then evaluated by the
individual estimator groups using appropriate
image scales. Phase 3 analyzes the results of the
preceding Taguchi experiment to obtain the
optimal parameters for each factor. The feeling
quality of each combinative sample is measured
using the ‘‘smaller-the-better’’ S/N ratio since the
ideal affective design is the design which yields the
minimum feeling discrepancy. The ‘‘smaller-the-
better’’ S/N ratio, Z, is given by
S=N ratio ðsmaller-the-betterÞ
¼ Z ¼ À10 log10
1
n
Xn
i¼1
y2
i
!
; ð6Þ
where yi is the feeling discrepancy of the ith group
and n is the number of estimator groups in the
outer OA. The final stage of Phase 3 is to identify
the optimal levels (parameters), which reduce this
S/N ratio to a minimum value for each factor. In
Phase 4, ANOVA is employed to identify the most
significant factors, and the initial design is then
modified accordingly. Superposition is then used
to predict the expected feeling discrepancy and
S/N ratio of the redesigned product. Finally, a
verification experiment is performed to confirm the
accuracy of these predictions.
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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
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6. 4. Case study
The passenger car represents a typical example
of a mature product. Since there is little to choose
between the features, structures, and materials of
this particular product nowadays, the relative
distinctiveness of the car profile is receiving
increasing emphasis in manufacturers’ marketing
strategies. Therefore, the present study adapted
the case of a car profile to explore the feasibility of
the proposed approach in improving the feeling
quality of an affective product design.
4.1. Target feeling and initial design
The case study assumed that Company A was
conducting a new design project for a passenger
car, which, according to market analysts, was to be
targeted 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 a
liking for outdoor life. Furthermore, the car was
to simultaneously evoke the following images:
(1) Youthful, (2) Outdoor, and (3) Family. The
target feeling could then be accurately defined in a
feeling domain comprised of three nine-point
image scales, namely ‘‘young2mature’’ (T1),
‘‘field2city’’ (T2), ‘‘personal2family’’ (T3).
Furthermore, the relative target feeling could be
defined as T(1,2,3) ¼ [2, 2, 7], as shown in Fig. 2.
With these targets in mind, three product designers
were requested to develop appropriate initial
passenger car profile designs (I1, I2, I3). The
corresponding designs are illustrated in Fig. 3.
4.2. Taguchi experiment
4.2.1. Control factors
In the present case study, the Taguchi control
factors included the various profile variables of the
passenger car. Most previous car profile studies
have focused upon manufacturing issues, and yield
little in the way of useful information for the
current investigation regarding the impact of a
car’s profile upon consumers’ feelings. Conse-
quently, this study commenced by compiling
profile images of 125 existing passenger cars.
These images were then reviewed with six experts
in the field of car profile design to establish the
profile variables which would most likely influence
consumer feeling. Fig. 4 presents the 13 profile
variations and the three corresponding levels
finally selected in accordance with the following
principles:
The integer of all selected factors must be
capable of explaining most variations in the
passenger car profile.
The relationship between any two factors must
be independent such that the variation of any
single variable has no influence upon the
variation of the other variables.
Each factor contains three levels: the maximum
level depends on the maximum parameter of
the 125 original samples, the minimum level
depends on the minimum parameter, and the
middle level represents the average of the
maximum and minimum parameters.
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Fig. 2. Target feeling.
Fig. 3. Initial designs of car profile generated from traditional design process.
H.-H. Lai et al. / International Journal of Industrial Ergonomics 35 (2005) 445–460450

7. 4.2.2. Uncontrollable factors
This study adopted four psychological or
behavioral individualized characteristics as un-
controllable factors, namely involvement, personal
trait, peer relation, and social support, and applied
three level settings to each, as shown in Table 2.
These factors were then employed as the basis
for selecting estimators in the follow-up experi-
mental processes. ‘‘Involvement’’ indicates that
the consumer expresses concern for, or participates
in situations on the basis of inherent needs,
worth, and interest. This study employed the
Personal Involvement Inventory indicator (PII;
Zaichkowsky, 1994) to differentiate between the
involvements of different estimators regarding a
car. ‘‘Personal Trait’’ refers to the phenomenon in
which consistent personalities tend to express
similar attitudes when confronting a common
situation. In the present study, the Eysenck
Personality Questionnaire (EPQ; Eysenck, 1975)
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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
H.-H. Lai et al. / International Journal of Industrial Ergonomics 35 (2005) 445–460 451

8. was applied to differentiate between the person-
ality traits of different estimators. ‘‘Peer Relation’’
describes the degree to which an individual shares
his values, experiences, and lifestyle with his peer
group, and was measured for different estimators
in the current study using the indicator proposed
by Bearden et al. (1989). Finally, ‘‘Social Support’’
indicates the degree of support and assistance
received by an individual from his social network
of family and friends. In the present study, the
social support of each estimator was again
assessed using a measure proposed by Bearden
et al. (1989).
4.2.3. Experimental design
The present experimental design was composed
of the inner (shape) and outer (estimator group)
arrays shown in Fig. 5. An L27 (313
) array was
adopted for the inner array since the control
factors contain thirteen three-level factors, and 27
different combinative samples are generated, as
shown in Fig. 6. Meanwhile, an L9 (34
) array was
employed for the outer array since there are four
uncontrollable factors, each with three levels, and
a total of nine estimator groups.
4.2.4. Feeling evaluation
Estimators: A total of 27 estimators were
assigned equally across nine estimator groups
(G1–G9) according to the respective conditions
of each group. Samples: (1) three initial designs
and (2) 27 combinative designs. Each car profile
was displayed on individual A4-sized cards.
Evaluation: Each estimator evaluated their feeling
for the initial and combinative car profile samples
using 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 the
three initial designs and the corresponding feeling
evaluation results. The shape factor levels of the
initial designs are decided by the most approx-
imate parameter, and the feeling discrepancy of
each estimator group represents the average
evaluation of the three estimators within that
particular group. Even though previous studies
(Boote, 1981; Kolter, 1992) have suggested that
consumers with similar characteristics are likely to
provide similar responses, it is still necessary to
cater for the influence of unanticipated estimator
characteristics. Hence, the average approach was
employed in the present case study to reduce the
possible influences of unexpected estimator char-
acteristics in each individual estimator group.
Each standard deviation in the estimator group
was then checked by a criterion which prescribed
that the standard deviation was only acceptable if
its value did not exceed 1. Table 3 shows each
standard deviation and indicates that all values are
acceptable.
4.3.2. Analysis of Taguchi experimental results
Table 4 presents the feeling evaluation results
and the corresponding mean values and S/N ratios
for each of the 27 combinative shapes. Meanwhile,
Table 5 indicates the individual S/N ratios for each
level of every shape factor, and the corresponding
factor 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 higher
feeling quality. The ‘‘effect of factor’’ parameter
represents the difference in the S/N ratio between
the maximum level and the minimum level of
a single factor. A greater effect indicates that the
factor has a more significant influence upon
the feeling quality. For ease of comprehension,
the data of Table 4 are also illustrated graphically
in Fig. 7. It can be seen that the sequence of
influence of the individual factors (i.e. most to
least influential) is given by A4E4F4C4K4
L4M4H4G4B4D4I4J. The ‘‘optimal set-
ting’’ can be obtained by selecting the maximum
level of each factor, i.e. A3, B3, C1, D1, E2, F2, G1,
H3, I1, J2, K2, L3, and M3. Fig. 8 presents the
corresponding optimal car profile and its para-
meters.
Although establishing the optimal settings facil-
itates the design of a car profile which closely
matches the target feeling, it is known that some
factors are of high influence, while others are of
lesser significance. The purpose of the improve-
ment stage of the Taguchi approach is not to
renovate all the design factors, but simply to
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9. redesign those factors which have a significant
influence upon the feeling quality. In other words,
the intention is to obtain the greatest improvement
in feeling quality through the minimum of redesign
activity. Therefore, it is necessary to identify which
of the 13 profile factors have the most significant
influence upon the feeling quality. As recom-
mended by Taguchi, the present study identified
these factors using ANOVA and the ‘‘contribution
percentage’’ parameter (calculation details pro-
vided in Taguchi, 1987). The corresponding
ANOVA results are presented in Table 6, which
indicates that factors A, C, E, F, H, K, L, and M
are the most influential factors in this particular
case study. The optimal settings of these factors
are: A1, C3, E1, F1, H2, K3, L3, and M2.
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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 3321
2 1 1 1 1 2 2 2 2 2 2 2 2 2 1111 1222 1333 2123 2231 2312 3132 3213 3321
3 1 1 1 1 3 3 3 3 3 3 3 3 3 1111 1222 1333 2123 2231 2312 3132 3213 3321
4 1 2 2 2 1 1 1 2 2 2 3 3 3 1111 1222 1333 2123 2231 2312 3132 3213 3321
5 1 2 2 2 2 2 2 3 3 3 1 1 1 1111 1222 1333 2123 2231 2312 3132 3213 3321
6 1 2 2 2 3 3 3 1 1 1 2 2 2 1111 1222 1333 2123 2231 2312 3132 3213 3321
7 1 3 3 3 1 1 1 3 3 3 2 2 2 1111 1222 1333 2123 2231 2312 3132 3213 3321
8 1 3 3 3 2 2 2 1 1 1 3 3 3 1111 1222 1333 2123 2231 2312 3132 3213 3321
9 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 3321
11 2 1 2 3 2 3 1 2 3 1 2 3 1 1111 1222 1333 2123 2231 2312 3132 3213 3321
12 2 1 2 3 3 1 2 3 1 2 3 1 2 1111 1222 1333 2123 2231 2312 3132 3213 3321
13 2 2 3 1 1 2 3 2 3 1 3 1 2 1111 1222 1333 2123 2231 2312 3132 3213 3321
14 2 2 3 1 2 3 1 3 1 2 1 2 3 1111 1222 1333 2123 2231 2312 3132 3213 3321
15 2 2 3 1 3 1 2 1 2 3 2 3 1 1111 1222 1333 2123 2231 2312 3132 3213 3321
16 2 3 1 2 1 2 3 3 1 2 2 3 1 1111 1222 1333 2123 2231 2312 3132 3213 3321
17 2 3 1 2 2 3 1 1 2 3 3 1 2 1111 1222 1333 2123 2231 2312 3132 3213 3321
18 2 3 1 2 3 1 2 2 3 1 1 2 3 1111 1222 1333 2123 2231 2312 3132 3213 3321
19 3 1 3 2 1 3 2 1 3 2 1 3 2 1111 1222 1333 2123 2231 2312 3132 3213 3321
20 3 1 3 2 2 1 3 2 1 3 2 1 3 1111 1222 1333 2123 2231 2312 3132 3213 3321
21 3 1 3 2 3 2 1 3 2 1 3 2 1 1111 1222 1333 2123 2231 2312 3132 3213 3321
22 3 2 1 3 1 3 2 2 1 3 3 2 1 1111 1222 1333 2123 2231 2312 3132 3213 3321
23 3 2 1 3 2 1 3 3 2 1 1 3 2 1111 1222 1333 2123 2231 2312 3132 3213 3321
24 3 2 1 3 3 2 1 1 3 2 2 1 3 1111 1222 1333 2123 2231 2312 3132 3213 3321
25 3 3 2 1 1 3 2 3 3 1 2 1 3 1111 1222 1333 2123 2231 2312 3132 3213 3321
26 3 3 2 1 2 1 3 1 1 2 3 2 1 1111 1222 1333 2123 2231 2312 3132 3213 3321
27 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.
H.-H. Lai et al. / International Journal of Industrial Ergonomics 35 (2005) 445–460 453

10. 4.4. Improvement and verification
To verify the conclusions presented above, the
original profiles were modified accordingly, and a
verification experiment was performed. Initially,
the three original car profiles were modified in
accordance with the optimal factor levels identified
above, 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 indicates
the corresponding profile parameters of the
redesigned profiles (R1, R2, R3) and Fig. 9
presents the effects of the profile modification
pictorially for each original profile. Subsequently,
a process of superposition (Eqs. (7)–(10)) was
employed to predict the S/N ratios of the optimal
profile and those of the redesigned profiles.
The corresponding results were determined to be
ZðOÞ ¼ À1:45; ZðR1Þ ¼ À4:45; ZðR2Þ ¼ À3:06; and
ZðR3Þ ¼ À2:71:
ZðOÞ ¼ T þ ðA1 À TÞ þ ðB3 À TÞ þ ðC3 À TÞ
þðD3 À TÞ þ ðE1 À TÞ þ ðF1 À TÞ þ ðG1 À TÞ
þ ðH2 À TÞ þ ðI2 À TÞ þ ðJ1 À TÞ
þ ðK3 À TÞ þ ðL3 À TÞ þ ðM2 À 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 À 12T
¼ ðÀ13:12Þ þ ðÀ14:89Þ þ ðÀ14:07Þ
þ ðÀ14:7Þ þ ðÀ13:66Þ þ ðÀ14:05Þ
þ ðÀ14:79Þ þ ðÀ14:44Þ þ ðÀ14:96Þ
þ ðÀ14:95Þ þ ðÀ13:96Þ þ ðÀ14:23Þ
þ ðÀ14:6Þ À 12ðÀ15:41Þ
¼ À 1:45; ð7Þ
ARTICLE IN PRESS
Fig. 6. Twenty-seven combinative designs for Taguchi experiment.
H.-H. Lai et al. / International Journal of Industrial Ergonomics 35 (2005) 445–460454

11. ZðR1Þ ¼ ðA1Þ þ ðB2Þ þ ðC3Þ þ ðD3Þ þ ðE1Þ
þ ðF1Þ þ ðG2Þ þ ðH2Þ þ ðI1Þ þ ðJ2Þ
þ ðK3Þ þ ðL3Þ þ ðM2Þ À 12T
¼ À 4:45; ð8Þ
ZðR2Þ ¼ ðA1Þ þ ðB1Þ þ ðC3Þ þ ðD3Þ þ ðE1Þ
þ ðF1Þ þ ðG1Þ þ ðH2Þ þ ðI3Þ þ ðJ2Þ
þ ðK3Þ þ ðL3Þ þ ðM2Þ À 12T
¼ À 3:06; ð9Þ
ARTICLE IN PRESS
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 À17.12
2 6.24 5.58 6.04 5.87 6.44 6.82 5.97 6.24 6.55 6.19 À15.85
3 8.13 6.05 6.45 8.04 6.47 5.77 5.34 6.57 5.24 6.45 À16.29
4 1.44 1.68 1.47 2.04 1.76 1.56 1.74 1.84 1.08 1.62 À4.319
5 6.24 7.54 5.47 6.88 5.44 6.57 6.08 5.97 6.57 6.31 À16.04
6 7.05 6.75 7.42 7.83 6.75 7.16 6.27 7.05 6.87 7.02 À16.94
7 1.84 2.36 2.05 2.45 1.69 1.88 2.52 2.33 2.04 2.13 À6.637
8 3.25 3.58 2.97 3.23 3.08 2.44 3.69 3.97 3.01 3.25 À10.3
9 6.24 4.23 5.67 7.05 4.33 5.12 6.57 3.81 4.33 5.26 À14.61
10 6.24 5.77 5.63 6.86 5.84 5.28 5.61 5.24 5.08 5.73 À15.2
11 7.41 4.35 5.87 4.28 6.57 5.17 4.83 4.71 7.54 5.64 À15.21
12 8.57 5.34 7.56 5.48 6.55 7.05 4.87 5.27 6.37 6.34 À16.18
13 2.45 2.66 2.04 3.02 2.55 2.93 2.82 2.07 2.95 2.61 À8.409
14 8.94 7.25 5.21 5.36 8.48 9.02 7.25 5.68 8.24 7.27 À17.4
15 7.34 6.84 7.44 6.81 7.54 6.24 5.88 7.68 8.54 7.15 À17.13
16 6.54 7.05 6.57 5.28 4.87 5.22 6.48 6.95 4.57 5.95 À15.59
17 8.57 8.47 8.21 7.94 7.83 8.54 7.63 8.57 6.57 8.04 À18.13
18 8.63 5.27 4.92 5.38 8.33 5.34 7.22 7.68 5.24 6.45 À16.39
19 5.84 5.36 8.54 7.24 5.29 5.67 6.73 8.45 6.22 6.59 À16.52
20 8.65 8.24 8.87 7.54 7.68 8.97 5.67 8.56 8.64 8.09 À18.22
21 7.85 5.27 6.78 8.45 7.56 8.16 5.54 8.56 7.58 7.31 À17.38
22 9.84 8.67 9.54 9.64 9.77 8.64 8.94 9.48 9.88 9.38 À19.45
23 6.53 4.08 7.56 7.54 4.35 5.39 4.87 5.67 5.02 5.67 À15.26
24 9.57 9.67 8.92 7.94 9.25 9.67 10.27 9.18 9.76 9.36 À19.44
25 9.57 8.27 10.27 9.86 9.74 10.38 9.78 9.68 9.14 9.63 À19.69
26 6.35 4.86 7.64 5.24 5.97 4.87 5.64 5.21 5.08 5.65 À15.14
27 8.04 4.57 8.64 9.73 5.27 5.26 7.58 8.67 7.88 7.29 À17.49
H.-H. Lai et al. / International Journal of Industrial Ergonomics 35 (2005) 445–460 455

12. ZðR3Þ ¼ ðA1Þ þ ðB2Þ þ ðC3Þ þ ðD2Þ þ ðE1Þ
þ ðF1Þ þ ðG3Þ þ ðH2Þ þ ðI2Þ þ ðJ2Þ
þ ðK3Þ þ ðL3Þ þ ðM2Þ À 12T
¼ À 2:71: ð10Þ
Finally, a verification experiment was performed
by the estimators involved in the original profile
and combinative sample assessment activity. Using
the same set of nine-point image evaluation scales,
the estimators evaluated the optimal profile and
the three redesigned profiles. The corresponding
results are presented in Table 8. It is noted that the
actual S/N ratios for the four profiles do not
match the predicted S/N ratios. The discrepancy
between the two sets of values can be attributed to
two possible causes: (1) The total percentage
contribution of the eight significant profile factors
is only 61.99%. Hence, the influence of the
combined error (including the other profile factors
and unknown factors) contributes 38.11%. There-
fore, it is possible that some influential factors may
ARTICLE IN PRESS
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 À13.12 À16.44 À17.06 À16.06 À13.66 À14.05 À14.79 À16.21 À16.26 À14.95 À16.22 À16.43 À16.41
2 À15.51 À14.93 À15.13 À15.5 À15.73 À15.08 À16.4 À14.44 À15.04 À15.01 À16.08 À15.6 À14.6
3 À17.62 À14.89 À14.07 À14.7 À16.87 À17.14 À15.07 À15.61 À14.96 À16.07 À13.96 À14.23 À15.25
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.
H.-H. Lai et al. / International Journal of Industrial Ergonomics 35 (2005) 445–460456

13. not have been correctly identified. (2) The inter-
actions among profile factors could represent
influential elements of the feeling difference, but
are 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 mean
achieved reduction is 41.31%, with individual
reductions of R1 ¼ 48:19%; R2 ¼ 39:35%; and
R3 ¼ 35:66%: Therefore, each of the redesigned
profiles is significantly closer to the target feeling
than 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 a
significantly lower sensitivity to the characteristics
of individual consumers than their original coun-
terparts. These results verify the feasibility of
employing the proposed robust design method to
improve the feeling quality of a car profile.
5. Discussion and conclusion
This paper has presented a robust design
approach to enhance the feeling quality of a
ARTICLE IN PRESS
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
a
Factor 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
a
Level changed from original setting to optimal setting.
H.-H. Lai et al. / International Journal of Industrial Ergonomics 35 (2005) 445–460 457

14. product. The Taguchi experimental design method
has been employed to obtain the optimal design
parameters which cause the consumers’ feelings
induced by the redesigned products to be closer to
the target feeling than the initial products, while
simultaneously reducing the influence of the
consumers’ highly individualized characteristics.
The proposed approach has been tested for the
case of a car profile design. It has been shown that
the approach successfully enhances the feeling
quality of the car profiles, i.e. the feeling dis-
crepancy and the feeling ambiguity are reduced by
41.31% and 51.49%, respectively.
Generally, a major difference between the
proposed approach and previous Kansei Engineer-
ing approaches (e.g. Nagamachi, 1995; Ishihara et
al., 1997; Chuang and Ma, 2001) lies in their
respective objectives. The purpose of the approach
presented in this study is to obtain a set of useful
product design parameters for achieving the target
feeling, whereas the intention of Kansei Engineer-
ing approaches is to construct an accurate model
to describe the anticipated consumers’ response to
a product. Therefore, the current approach offers
greater advantages in developing an affective
design. First, compared to Kansei Engineering
approaches, which depend upon a large number of
samples to ensure their accuracy, the robust design
approach requires fewer experimental frequencies
and a lesser number of experimental scales. Hence,
the present robust design method reduces the time
and cost required to complete the feeling evalua-
tion study. Moreover, the proposed approach can
yield the optimal design parameters of the product
directly. Second, the robust design approach is
suitable for a diverse range of applications. The
approach can be applied to global or regional
ARTICLE IN PRESS
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 À1.686 À1.45 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 À6.718 À4.45 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 À6.42 À3.06 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 À5.496 À2.71 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%
H.-H. Lai et al. / International Journal of Industrial Ergonomics 35 (2005) 445–460458

15. design problems since the target feeling or experi-
mental parameters can be assigned according to
the particular practical requirements. Addition-
ally, using the proposed approach, the obtained
optimal design parameters are broadly indepen-
dent of the influence of consumers’ highly indivi-
dualized characteristics since these characteristics
are already taken into consideration in the
Taguchi experiment. These advantages enable the
robust design method to support many tasks
involved in developing an affective design by
proving a systematical approach which is both
flexible and efficient. For example, this approach
can be employed to redesign existing products in
order to enhance their feeling quality, to modify
existing products in order to transform or expand
the originally targeted consumer group (e.g. to
expand the target consumer groups of a car from
middle-aged males to young males), or to develop
new products in order to accommodate existing
familial style (e.g. to develop a new generation
series of cars which still maintains the BMW
familial style).
Nevertheless, robust design has certain limita-
tions. The fundamental point in applying robust
design is to develop the means to identify the
controllable and uncontrollable factors which have
a significant and powerful influence on quality. In
other words, the appropriate selection of the
controllable and uncontrollable factors, and their
levels, has a crucial influence on the efficiency of
the robust design. For example, in the present case
study, 13 attributes of the car profile were selected
as controllable factors in accordance with the
opinions of six design experts. Using the ANOVA
statistical approach, it was found that these
controllable factors have a different influence on
the feeling quality. If factors with a lesser influence
had been selected, it is possible that the feeling
quality might not have been enhanced substan-
tially through the robust design approach. Clearly,
it is also possible that more powerful factors than
those actually selected might exist. If this were
indeed the case, it would be reasonable to assume
that the feeling quality might be enhanced further.
Consequently, when wishing to exploit the power
of the robust design approach, it is first advisable
to conduct a thorough pilot study to identify the
most influential factors with some certainty before
actually 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 to
review and improve the product design. Further-
more, robust design represents a feasible approach
for systematically improving the feeling quality of
a product. This study has demonstrated the
application of the robust design technique to the
development of a car profile, and has shown that
this simple experimental approach enables the
development of a product whose associated feeling
quality approaches that of the target quality. The
feeling quality of products is becoming an
increasingly important aspect of consumption
tendencies. Therefore, product developers and
designers are faced with the challenge of creating
and redesigning products which cater to consu-
mers of all types and preferences. The approach
presented within this study will surely assist them
in doing so.
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