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1. ELSEVIER International Journal of Industrial Ergonomics 19 (1997) 105-114
Industrial
Ergonomics
Application studies to car interior of Kansei engineering
Tomio Jindo *, Kiyomi Hirasago
Vehicle Research Laboratory, Nissan Research Center. Nissan Motor Co., Lid. 1. Natsushima-cho, Yokosuka 237. ,lapan
Abstract
This paper describes studies of styling or design specification of passenger car interiors as examples of application of
Kansei engineering, especially regarding the speedometer and steering wheel of a passenger car. These units require easy
operation or good visibility, but we did not consider these functions here and treated only the styling impression. Subjective
evaluations were carried out by semantic differential methods, then analyzed by using multivariate analyses. We gathered the
results concerning the relationship between an impression and characteristics of styling to understand the conditions which
create a desired impression.
Relevance to industry
These studies were carried out to improve the cabins of passenger cars. We can also apply similar methods of styling to
other industry products.
Kevwords: Kansei engineering: SD method; Automobile design: Multivariate analysis
1. Introduction
According to a recent passenger car's improve-
ment of basic functions, the user's demand for a car
shifted from functional aspects to a total ambience
including styling. Therefore, when designing a car
interior, Kansei engineering is used to grasp vague
demands of the consumer, and develop the car based
on the user's words. As an example, there is support
for expert systems relating styling to car interiors.
This was based upon the analysis data showing a
relationship between human impression and interior
design. Fig. 1 shows a rough construction and flow
:' Corresponding author. Tel.: +81 468-67-5158, Fax: +81
468-65-5699, E-mail: jindo@nova.lab.nissan.co.jp.
chart. For example 'sporty' is the input and this
system shows the suitable graphics for that adjective
on the CRT.
However, this system included many car interior
parts like seats, a speedometer, a steering wheel,
switches and so on. Detailed information about each
part's design elements could not be gained because it
was impossible to carry out actual experiments using
so many samples in order to analyze detail design
elements of each part.
Therefore, we studied only one interior unit with
detail design elements and analyzed the relationships
between design elements and impressions. By doing
this, more practical knowledge was gained than by
conventional experiments which study many interior
units. Adding this data to conventional data, regard-
ing impressions of these units in styling, supports
0169-8141/96/$15.00 Copyright © 1996 Elsevier Science B,V. All rights reserved.
PII S0169-8141(96)00007-8

2. 106 T. Jindo, K. Hirasago / International Journal o['Industrial Ergonomics 19 (1997) 105 114
Inputofevaluationadjectives [
Inference
Searchforcorresp?ndingadjectives
T
Determinationofdesignelements
Checkforcontradictions
Determinationofinteriorparts
Knowledgedatabase
~ A~tive d ~
~ k
daGraphic,
imag~
--. database.....J
Fig. 1. Constructionand flowchart of car interior styling support
system.
digital ones will not be used widely which is why
they are not considered in this paper.
A survey was made of existing analog meter
clusters and their constituent design elements were
extracted and classified. Typical design elements are
listed in Table 1. A series of subjective evaluations
were made of those elements. This paper describes
the results of subjective evaluations and subsequent
analyses that were made of speedometers alone,
meter cluster layouts, and meter cluster assemblies
using photographs of actual meter clusters.
expert systems about car interiors. Car interior styling
support systems which also have detailed data of
design elements will become possible in the future.
2. Overview of research work
2.1. A study of speedometer on Kansei engineering
The scope of this research was limited to analog
speedometers. Digital ones have too many degrees of
freedom in styling. They are too complicated to
study and analyze. The number of actual digital
speedometers is small, so the result of a study on
2. l. 1. Subjective eualuation of speedometers in isola-
tion
The design elements of the speedometer that were
chosen for analysis were the scale, lettering, types of
indicators and starting point of the indicator. The
types of design elements examined are shown in
Table 2. Using different combinations of these de-
sign elements, 24 speedometer samples were created
by computer graphics for subjective evaluation. Fig.
2 shows an example of one of the samples. Eight
pairs of adjectives thought to describe typical im-
pressions of the speedometer were used by 23 sub-
jects, 14 men and 9 women, in making evaluations.
The subjects assigned a numerical value to each of
Table 1
Designelementsof analog meter
1. Meter layout
2. Meter types and number
Speedometer
Tachometer
Fuel level gauge
Water Ievelgauge
etc.
3. Panel color and material
Plastic
Wood
Leather
4. Meter shape
',.j
Round SemicircularQuarter Ova[
5. Outside scale, '~
inside scale Q!J 2
Outside scale insidescale
6. Starting point
7.Scale type ~t'l L~J~~0 40 > 4o
20 40 2{) 40 20 40
I ~ J L~_! krcrt2Lzz]
8.Number s ~ ~
orientation < e
9. Lettering ~ ~1
Horizontal Centrifugal
10. Indicator shape

3. 12Jindo, K. Hirasago / lnternational Journal of Industrial Ergonomics 19 (1997J 105-I 14 107
the eight adjective pairs according to a 7-point se-
mantic differential (SD) scale.
A factor analysis was then performed on the
scores assigned to each evaluation adjective pair, and
two factors were extracted. The evaluation adjectives
were then positioned on the two factor axes in terms
of their factor loading. The resulting arrangement of
the adjectives is shown in Fig. 3. One of the factor
axes in the figure is seen to indicate a feeling of
being easy to understand, represented by the adjec-
tive 'clean-looking'. The other axis is interpreted as
indicating a design factor, represented by the adjec-
tive 'luxurious', In addition, the adjective 'likable' is
positioned where both the easy-to-understand and
design factors have positive values. These results
suggest that easy-to-understand and the design were
the two major impressions on which the subjects
based their evaluation of the speedometer samples.
Thus, it can be assumed that an instrumentation
design which satisfies these two aspects should be
favorably received.
A multiple regression analysis technique known
as the Quantification I method was then used to
analyze the relationships between the subjective
evaluation scores and meter cluster design elements.
This technique is commonly used in Japan to exam-
ine the relationship between quantitative data (the
scores in this work) and qualitative data (the design
categories of the samples evaluated). The analytical
results obtained for the two adjectives 'luxurious'
and 'clean-looking' are shown in Table 3.
The partial correlation coefficients in the table
indicate the extent to which each design element
contributes to an explanation of the evaluation adjec-
tive concerned. The bar graph results in the table
show that the lettering of the speedometer had a
Table 2
Design elements of evaluation test
scale type Lettering Indicator shape Starting point
7 ..... ~ 4 0123 4. <.~---.~-~ ~ ('-"
Fig. 2. Exampleof speedometersample.
strong effect on increasing the perceived feeling of
luxury. The type-3 lettering in particular was effec-
tive in projecting a more luxurious impression. Simi-
larly, the scale had a strong influence on the clean-
looking feeling. The type-3 scale with large gradua-
tions was noticeably effective in heightening the
image of being clean-looking.
2.1.2. Subjectiue eualuations of meter cluster layout
Meter cluster samples having two to seven meters
were prepared and evaluated for the purpose of
examining the correlation between the number of
meters in the same cluster and the evaluation adjec-
tives. Fig. 4 shows the six types of meter layouts
used, which were identical in size to actual meter
clusters. Each meter cluster was given a design
resembling an actual meter assembly like the sample
shown in Fig. 1. The same eight pairs of evaluation
adjectives were used as in the subjective evaluations
of speedometers. Evaluation scores were assigned
according to a 7-point SD scale. Twenty-four sub-
jects, 18 men and 6 women, took part in the evalua-
tions.
The evaluation scores were averaged for each
adjective and layout sample, and a factor analysis
was performed on the average scores found for the
adjectives. As a result, two factor axes were ex-
tracted, as in the subjective evaluations of speedome-
ters. The factor axis interpretations were also identi-
cal, i.e., an easy-to-understand factor and a design
factor. The adjectives were arranged on the factor
axes in terms of their factor loading, as shown in
Fig. 5. The meter layout samples have also been
arranged in the figure on the basis of their factor
scores. The following observations can be made
from the results.

4. 108 T. Jindo, K. Hirasago / lnternational Journal of Industrial Ergonomics 19 11997) 105-114
Playful•
2 meters 3 meters
Design axis
Luxu!i°us•OElegant / @ @ ~ / ~ @ @
~etro-looking
• Likable
oSporty Clean-looking
Easy-to-
understand
axis
Easy 1ounderstand
Fig. 3. Arrangement of evaluation adjectives.
1. Meter clusters with a smaller number of meters
(2-4) projected a stronger feeling of being easy
to understand.
2. Meter clusters having an even number of meters
(4 and 6) were evaluated highly in terms of
design and tended to be liked by the subjects.
3. The 4-meter cluster was rated highly as being
easy to understand, and the 6-meter cluster was
perceived as being sporty.
2.1.3. Subjective evaluations using photogaraphs of
actual meter clusters
Subjects were asked to evaluate photographs of
actual meter clusters for the purpose of analyzing the
4 meters
6 meters
5 meters
7 meters
Fig. 4. Meter cluster layouts evaluated,
correlation between their perception of the meter
cluster assemblies and the design elements. The pho-
tographs showed ten types of meter cluster assem-
blies used in Japanese passenger cars. An example is
shown in Fig. 6. Thirteen pairs of adjectives were
used and scores were assigned according to a 7-point
SD scale.
The evaluation adjectives consisted of the words
used in the previous two subjective evaluations, ex-
cluding expressions that were thought to be difficult
to evaluate, and words that the design department
asked to have included. The following is a list of the
Table 3
Results of analysis ('luxurious', 'clean looking')
Design Partial Not luxurious Luxurious
Category [correlation Partialregression
element coefficienJt "~efficienl 0.5 coetII
l. L~=-J i
Scale
2. ~ [ 0.49
3.'' 'i
_ _ ~ .......... i
1. 0123
Letterin~ 3.2"0Of.go.1
2 3 I 0.92
4. 0123
2. ~ I 0.63
-0.243
-0..72
0.124
i 0.035
!-0.5671
0.166
~"~'0.8001
-0.066
-0.044 I
m 0.098
-0.157~
m-0.489~
0.100
0.053
-0.247
Partial I
correlanon[
coettmtentl
0.91 I
0.39
0.52
Cluttered Clean-looking
Partialregression
coefficient 0.5
~-0.794~
-0.353
~-1.519i
-0.005
0.188
-0.248
m //.065
R 0.092
/I.067
-0.043
0.518 I , I
0.198
4).276
-0.318
m0.93l~m

5. T. Jindo, K. Hirasago/ lnternational Journal of Industrial Ergonomics 19 (1997) 105-114
Table 4
Design elements and categories of each sample evaluated
109
sign Overall cluster layout
(no. of meters)
3 4 5 or more
meters meters meters
1. 0
2. 0
s. 0
4. 0
5. 0
6. 0
7. 0
8. 0
9. 0
10. 0
Meter shape
OC)
0
0
0
0
0
0
0
0
0
0
I
0
0
0
0
L--
, 0
_~ 0
Scale
] 1, l, I h,,,u,,I
0
0
0
0
Indicator color
White Cellow Red
0
©
0
(D
©
0
0
0
0
0
Table 5
Quantification I method analysis results for 'sporty'
Design Category
element
1. Overall
cluster layout
(no. of meters
2. Meter shape
3. Scale
4. Indicator
color
1.3 meters
2.4 meters
3.5 or more
meters
l. Roun~
2. Semioir~_
cular ~_~
1.1 1 I
2.1~Itl
3. I~,1,,,[
1. White ]
2. Yellow
3. Red
Partial Partial
correlation regression
coefficient coefficient
0,79 -0.432
0.49
0.11
0.46
-0.749
-0.139
-0.181
@
0.155
-0.263
0.052
Not sporty -.
-o;5
u
l
n
i
m
m
m
in
~- Sporty
(1.5
I

6. 110 ~ Jindo, K. Hirasago/ InternationalJournalof IndustrialErgonomics19 (1997) 105-114
~-to-understandaxis1
• easy-to-understand
• clean-lookang
• Retro-looking
@
eElegant
•LikabIe
•Luxurious
• Sporty
• Playful
Fig. 5. Arrangementof evaluation adjectives and meter cluster
samples.
expressions used: easy-to-understand, practical, luxu-
rious, sporty, subdued, enthusiast-oriented, appeal-
ing, clean-looking, elegant, well-laid-out, well-
shaped, well-organized and likable. A total of 30
subjects participated, divided equally between men
and women.
The results obtained for the ten samples are sum-
marized in Table 4 in relation to the design elements
and their categories. Similar to the procedure em-
ployed for the subjective evaluations of speedome-
ters, the Quantification I method was used to analyze
the relationships between the evaluation scores and
the design element categories in Table 4. Typical
analytical results obtained for the adjective 'sporty'
are shown in Table 5. The results in Table 5 indicate
that a sporty impression was conveyed by an overall
Fig. 6. A sampleof metercluster.
layout having five or more meters, a round meter
shape, a scale with medium-level graduations and
yellow-colored indicators.
2.1.4. Conclusions qf speedometer study
In addition to the subjective evaluations and anal-
yses described above, other evaluations were also
conducted under different test conditions using vari-
able design elements. The results of those evalua-
tions are also reflected in the following conclusions.
I. Two independent factors that appear to influence
the static impression conveyed by automotive in-
strumentation are the design and a feeling of
being easy to understand. In nearly every case,
the results of the series of subjective evaluations
conducted in the present work could be inter-
preted in terms of these two factors.
2. Correlations were made between the design ele-
ments of analog meter clusters and the evaluation
adjectives. It was confirmed that the correlations
could be used in determining meter cluster speci-
fications that would convey an intended image.
Table6
Classificationand definitionof designelementsand categories
Designelements Category Definition
The numberof spokes 1.2
2.3
3.4
Pad surfaceshape 1. Flat
2. The secondordercurvedsurface
3. The thirdordercurvedsurface
Areaof pad 1. Large
2. Medium
3. Small
Pad uppersideshape 1. SharpprojectionshapeRin uppersideis small.
2. Gentleprojectionshape
3. Variedprojectionshape
The numberof spokesis 2.
The numberof spokesis 3.
The numberof spokesis 4.
Pad surfaceis flat.
Pad surfaceis of the secondordercurvedsurface.
Pad surfaceis of the thirdordercurvedsurface.
Pad area is large.
Pad areais medium.
Pad area is small.
R in uppersideis large.
R in uppersideis varied.

7. T. Jindo, K. Hirasago/ InternationalJournal of IndustrialErgonomics 19 (1997) 105- 114 111
2.2. A study of steering wheel on Kansei engineering
2.2.1. Subjective evaluation on steering wheel
After the research on speedometers we investi-
gated steering wheels.
We carried out subjective evaluation tests on
steering wheels to relate their impressions to physi-
cal features. In these tests, samples of 59 types of
steering wheels, shown by projector films, are ranked
by 50 persons according to SD 5-grade method
regarding 41 pairs of evaluation terms.
We prepared those test samples by putting each
photograph of automobile steering wheels on the
market into Macintosh computers using a scanner,
and then masking the backgrounds and eliminating
corporate marks on the steering wheels by graphic
processing of 'Photo Shop', an image processing
software.
Prior to the analysis of the test data, we classified
the steering wheel designs to investigate how the
ratings of the evaluation terms are influenced by
such design items as the number of spokes and pad
size. In this classification, we chose 13 design items
which are supposed to influence human impression,
and established 34 categories consisting of two or
three categories for each design item. Table 6 shows
a part of this item category classification.
We analyzed the correlation level between the
rating results obtained from the tests and the item
category classification shown in Table 6 by Quantifi-
cation 1 method. This analyzing method is used for
categorical data including external criteria. In this
case, the dependent variables are the ratings for 41
pairs of evaluation terms as external criteria and the
descriptive variables are the design categories as
categorical data.
Fig. 7 shows the result of the analysis of the
impression evaluation concerning 'sporty' or 'not
sporty'. The multiple correlation coefficient in this
figure indicates to which degree a certain item and
its category classification which are chosen as design
elements can explain the evaluation result of the
relevant term. The partial correlation coefficient
shows the correlation between each item and the
evaluation, and the category score shows the one
between each category and the evaluation. To give
an example from Fig. 7, a sporty image is empha-
sized by the following design items: three or four
spokes, the second order curved surface of the pad
and a smaller pad area.
Conducting this analysis for every evaluation term,
relating the results of the evaluation to each physical
feature (design), and storing these results in a
database will allow us to acquire the knowledge to
incorporate human sensitivity into products.
2.2.2. Construction of image retrieval system
This section describes the image retrieval system
for steering wheels we have constructed into which
analysis data of the test results are incorporated as a
Design Category
Elements
The nurnbe 1.2
2.3
of spoe,es 3.4
1. Flat
Pad 2. The second order
surface curved surface
shape 3.Thethirdorder
curved surface
1.Large
Area of 2.Medium
pad 3. Small
Pad upper
side shape
1.Sharp projection shape
2. Gentle projection shape
3. Varied projection shape
Multiple correlation coefficient=0.7605
Partial
correlation
coefficient
Not sporty -~ ~ Sporty
.1.o Category score 1.o
0.728
0.693
0.618
-1.296
-0.146 •
-0.518 1
-0.212 1
-0.265 1
-0.369 1
-0.242 1
/ 0.410
0.694
0.685
Fig. 7. Resultsofquantitativeanalysis.

8. 112 T. Jindo, K. Hirasago / lnternational Journal of Industrial Ergonomics 19 (1997) 105-114
In'P-put
of adjective
Image
d--ata processing ~ - - ~
Inference of priority o f ~ . _ ~
sample candidates ~ ~
• ~ samples~--~
lGraphic display of
Fig. 8. System flowchart.
Pad r"eseondorder 2 t
surface ] curvedsurface
shape 15.]:hesecondorder ....
---4 curvedsurface _ Q) i)
areao~ I!:L~e ~ -- I I / c'
pad ~2. Medium O
Fig. 9. Sampledesign database.
database. This system, constructed on Macintosh
computers using THINK C, connects the evaluation
terms used in the tests with the steering wheel
images, making it possible to retrieve and check
concrete samples in response to inputted evaluation
terms. Fig. 8 displays the system flow chart.
The image database includes a partial correlation
coefficient and category score (partial regression co-
efficient) of design items in each evaluation term
which are obtained from the analysis of the test
result. There are three types of database because the
data analysis was carried out for three subject groups
consisting of male, female and combined.
The sample design database is built as a design
item classification table regarding the evaluated
steering wheels. Fig. 9 shows a part of the table.
The sample PICT database is a PICT-type storage
of the image files of steering wheels used for the
tests. The processing in the system is conducted as
follows. When an adjective is entered, the data (par-
tial correlation coefficient and category score) for
such adjectives is read from each image database.
Each sample's item category information is read
from the sample design database. (See Fig. 9.) Image
scores of each sample (the sum of each item's partial
correlation coefficient multiplied by each item's cat-
egory score) are found. Then, calculated image scores
Fig. 10. Output screen of 'serene' steering wheel.

9. T. Jindo, K. Hirasago / lnternational Journal of lndustrial Ergonomics 19 (1997) 105-114 113
of each sample are arranged in descending order to
be regarded as the candidate priority. After reading
the steering wheel PICT images according to this
candidate priority, the graphic display is started. Fig.
lO shows an example of the output when 'serene' is
inputted.
tion adjective and decide on unit styling from the
unit impressions database when an operator inputs
total evaluation adjectives.
4. For further reading
2.2.3. Conclusions of steering wheel study
In this study, subjective evaluations about the
relation of the impressions of steering wheels to their
features were carried out. Then we analyzed the
results of the evaluation, quantitized that relation,
and finally constructed an image retrieval system
which output the best fit photograph on the CRT
according to the adjective input. In other words, we
proposed an environment where the persons from the
design department and styling department are able to
discuss the evaluation of the steering wheel with
each other using common adjectives.
3. Conclusions
Ishihara et al., 1993; Jindo et al., 1990; Jindo and
Nagamachi, 1991; Jindo et al., 1994a,b,1995; Naga-
machi, 1989, 1986; Shimizu et al., 1989; Yanag-
ishima and Nagamachi, 1988.
Acknowledgements
This study was conducted collaboratively by Nis-
san Motor Co., Ltd. and Mr. Nagamachi's office of
Hiroshima University. Students of Hiroshima Uni-
versity helped us in evaluating the film samples in
this study. We wish to express our gratitude to all of
the persons concerned.
In order to get more detailed impression data
indicating the relation of evaluation scores to its
styling features, experiments and analyses of small
units of passenger car interiors rather than whole
units were carried out. As a result, the detailed
impression data also seemed to be useful for actual
styling.
Impression studies of interior units will proceed in
the future. Accumulating impression data of impor-
tant units constituting car interiors will be achieved.
However, there is the problem where an evalua-
tion adjective of a certain unit is not always used in
the same meaning or nuance as in a whole interior
evaluation.
For example, the car interior that is evaluated
as'comfortable' must have an 'easy-to-understand'
speedometer, ' not oppressed' dash shape, and 'ex-
cellent' seating.
Exactly, evaluation adjectives of each unit may
not have the same meaning if the same evaluation
adjective is used in whole interior evaluations.
The future task involves examining relations of
total evaluation and unit evaluation adjectives.
We will reconstruct styling support systems aimed
at whole car interiors which select each unit evalua-
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