- Email: [email protected]
Application studies to car interior of Kansei engineering
Industrial Ergonomics ELSEVIER
International Journal of Industrial Ergonomics 19 (1997) 105-114
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 improvement 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: [email protected].
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, regarding impressions of these units in styling, supports
0169-8141/96/$15.00 Copyright © 1996 Elsevier Science B,V. All rights reserved. PII S 0 1 6 9 - 8 1 4 1 ( 9 6 ) 0 0 0 0 7 - 8
106
T. Jindo, K. Hirasago / International Journal o['Industrial Ergonomics 19 (1997) 105 114
Knowledgedatabase
Input of evaluationadjectives [ Inference Searchfor corresp?ndingadjectives ~
A~tive
d ~
T
Determinationof designelements Checkfor contradictions Determinationof interiorparts ~daGraphic, k imag~ --. database.....J Fig. 1. Construction and flowchart of car interior styling support system. 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
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.
2. l. 1. Subjective eualuation of speedometers in isolation 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 design 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 impressions of the speedometer were used by 23 subjects, 14 men and 9 women, in making evaluations. The subjects assigned a numerical value to each of
Table 1 Design elements of analog meter
1. Meter layout 2. Meter types and number Speedometer Tachometer Fuel level gauge Water Ievel gauge etc.
3. Panel color and material Plastic Wood Leather 4. Meter shape
',.j Round Semicircular Quarter 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 orientation 9. Lettering
s ~ < ~
Horizontal 10. Indicator shape
~ e ~1 Centrifugal
12 Jindo, K. Hirasago / lnternational Journal of Industrial Ergonomics 19 (1997J 105-I 14
the eight adjective pairs according to a 7-point semantic 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 adjective 'clean-looking'. The other axis is interpreted as indicating a design factor, represented by the adjective '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 examine 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 adjective 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
7
. ...
~
Lettering
4
0123
Indicator shape Starting point
4. <.~---.~-~
~ ('-"
107
Fig. 2. Exampleof speedometersample.
strong effect on increasing the perceived feeling of luxury. The type-3 lettering in particular was effective in projecting a more luxurious impression. Similarly, the scale had a strong influence on the cleanlooking feeling. The type-3 scale with large graduations was noticeably effective in heightening the image of being clean-looking. 2.1.2. S u b j e c t i u e e u a l u a t i o n s o f m e t e r c l u s t e r l a y o u t
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 adjectives. 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 subjects, 18 men and 6 women, took part in the evaluations. 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 extracted, as in the subjective evaluations of speedometers. The factor axis interpretations were also identical, 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.
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T. Jindo, K. Hirasago / lnternational Journal of Industrial Ergonomics 19 11997) 105-114
2 meters
Design axis
Luxu!i°us•OElegant Playful •
~etro-looking
oSporty
/ • Likable
@
3 meters @
~
/
~
@
4 meters
5 meters
6 meters
7 meters
@
Clean-looking
Easy 1o understand
Easy -tounderstand axis
Fig. 3. Arrangement of evaluation adjectives. Fig. 4. Meter cluster layouts evaluated,
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
correlation between their perception of the meter cluster assemblies and the design elements. The photographs showed ten types of meter cluster assemblies 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, excluding 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 Partial I Cluttered Clean-looking correlanon[ Category [correlation Partialregression Partialregression element coefficienJt " ~ e f f i c i e n l 0.5 coetII coettmtentl coefficient 0.5 -0.243 ~-0.794~ l. L~=-J i Scale 0.124 2. ~ [ 0.49 0.91 I -0.353 m0.93 l ~ m -0..72 3.'' 'i i 0.035 ~-1.519i _ _ ~ .......... i 1. 0123 !-0.5671 -0.005 0.166 0.188 Letterin~ 3.2" 0Of.go.1 2 3 I 0.92 0.39 -0.248 ~"~'0.8001 m //.065 -0.066 4. 0 1 2 3 -0.044 I R 0.092 m 0.098 /I.067 2. ~ I 0.63 -0.043 -0.157~ 0.518 I , I m-0.489~ 0.100 0.198 0.52 0.053 4).276 -0.247 -0.318
T. Jindo, K. Hirasago / lnternational Journal of Industrial Ergonomics 19 (1997) 105-114 Table 4 Design elements and categories of each sample evaluated
sign Overall cluster layout (no. of meters) 3 4 5 or more meters meters meters
1. 2. s. 4.
Meter shape
OC)
I
0
0 0 0
0 0 0 0
0
0
6.
0
7.
0
8. 9.
0 0
10.
0
0 0 0 0
] 1, l , I h,,,u,,I White Cellow Red
0
0 0 0
0
5.
Indicator color
Scale
© 0
0
(D ©
0
0
0 L--
0
0 0
, 0 _~
0
0
0
0
Table 5 Quantification I method analysis results for 'sporty'
Design element
Category
1.3 meters 1. Overall cluster layout 2.4 meters (no. of meters 3.5 or more meters 2. Meter shape l. R o u n ~
Partial Partial correlation regression coefficient coefficient 0,79
4. Indicator color
1.1 1 I 2.1~Itl 3. I~,1,,,[ 1. White 2. Yellow 3. Red
] 0.46
(1.5
i u
-0.181
l
@
0.155 -0.263
m m n m
0.052
Sporty I
-0.139 0.11
~-
-0.432 -0.749
0.49
2. Semioir~_ cular ~_~ 3. Scale
Not sporty -. -o;5
in
109
110
~ Jindo, K. Hirasago/ International Journal of Industrial Ergonomics 19 (1997) 105-114 ~-to-understand axis1 • easy-to-understand • clean-lookang
@ eElegant
• Retro-looking
Fig. 6. A sample of meter cluster.
•LikabIe •Luxurious
• Sporty • Playful
Fig. 5. Arrangement of evaluation adjectives and meter cluster samples. expressions used: easy-to-understand, practical, luxurious, sporty, subdued, enthusiast-oriented, appealing, clean-looking, elegant, well-laid-out, wellshaped, well-organized and likable. A total of 30 subjects participated, divided equally between men and women. The results obtained for the ten samples are summarized in Table 4 in relation to the design elements and their categories. Similar to the procedure employed for the subjective evaluations of speedometers, 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
layout having five or more meters, a round meter shape, a scale with medium-level graduations and yellow-colored indicators. 2.1.4. Conclusions q f speedometer study In addition to the subjective evaluations and analyses described above, other evaluations were also conducted under different test conditions using variable design elements. The results of those evaluations are also reflected in the following conclusions. I. Two independent factors that appear to influence the static impression conveyed by automotive instrumentation 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 interpreted in terms of these two factors. 2. Correlations were made between the design elements of analog meter clusters and the evaluation adjectives. It was confirmed that the correlations could be used in determining meter cluster specifications that would convey an intended image.
Table 6 Classification and definition of design elements and categories Design elements
Category
Definition
The numberof spokes
1.2 2.3 3.4
The numberof spokes is 2. The numberof spokes is 3. The numberof spokes is 4.
Pad surface shape
1. Flat 2. The secondorder curved surface 3. The third order curved surface
Pad surface is flat. Pad surface is of the second order curved surface. Pad surface is of the third order curved surface.
Area of pad
1. Large 2. Medium 3. Small
Pad area is large. Pad area is medium. Pad area is small.
Pad upper side shape
1. Sharp projectionshapeR in upper side is small. 2. Gentle projectionshape 3. Varied projection shape
R in upper side is large. R in upper side is varied.
T. Jindo, K. Hirasago / International Journal of Industrial Ergonomics 19 (1997) 105- 114 2.2. A study o f steering wheel on Kansei engineering 2.2.1. Subjective evaluation on steering wheel
After the research on speedometers we investigated steering wheels. We carried out subjective evaluation tests on steering wheels to relate their impressions to physical 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 emphasized 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 o f 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 Multiple correlation coefficient=0.7605
Design Elements
Category
The nurnbe 1.2 2.3 o f spoe,es 3.4
0.728
surface
1. Flat 2. The second order curved surface
shape
3. Thethirdorder
Pad
Partial correlation coefficient
Area of pad
1. Large 2. Medium 3. Small
Pad upper side shape
1.Sharp projection shape 2. Gentle projection shape 3. Varied projection shape
Not sporty .1.o
-~
~ Category score
Sporty 1.o
-1.296 /
0.410
-0.146 •
0.693
0.694 -0.518 1
curved surface
0.618
111
-0.212 1 -0.265 1
-0.369 1 -0.242 1
Fig. 7. Results of quantitative analysis.
0.685
112
T. Jindo, K. Hirasago / lnternational Journal of Industrial Ergonomics 19 (1997) 105-114
of adjective In'P-put dImage --ata
processing ~ - - ~
Inference of priority o f ~ . _ ~ sample candidates ~ ~
Pad ondorder surface ]r"ese curved surface
shape 15. ]:hesecondorder . . . . ---4 curvedsurface _ areao~ I!:L~e ~ pad ~ 2 . Medium
2 O
Q) --
I
t I
i) / c'
~ of s a m p l e s ~ - - ~ l•Graphic display Fig. 8. System flowchart.
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 coefficient) 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
Fig. 9. Sample design database.
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 (partial 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 category score) are found. Then, calculated image scores
Fig. 10. Output screen of 'serene' steering wheel.
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.
2.2.3. Conclusions of steering wheel study
Ishihara et al., 1993; Jindo et al., 1990; Jindo and Nagamachi, 1991; Jindo et al., 1994a,b,1995; Nagamachi, 1989, 1986; Shimizu et al., 1989; Yanagishima and Nagamachi, 1988.
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 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 important units constituting car interiors will be achieved. However, there is the problem where an evaluation 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 'excellent' 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-
4. For further reading
Acknowledgements This study was conducted collaboratively by Nissan Motor Co., Ltd. and Mr. Nagamachi's office of Hiroshima University. Students of Hiroshima University helped us in evaluating the film samples in this study. We wish to express our gratitude to all of the persons concerned.
References lshihara, S., Hatamoto, K., Nagamachi, M. and Matsubara, Y., 1993. An automatic builder for kansei engineering expert system using self organizing neural networks. In: C. Gu and H. Osaki (Eds.), Proc. Second China-Japan International Symposium on Industrial Management. International Academic Publishers, Beijing, pp. 497-502. Jindo, T., Yanagishima, T. and Shimizu, Y., 1990. An evaluation structure of automobile interiors using multivariate analysis. 23rd Fisita Congress Technical Paper, pp. 635-641. Jindo, T. and Nagamachi, M., 1991. The development of car interior image system incorporating knowledge engineering and computer graphics. In: Y. Queinnec and F. Daniellou (Eds.), Proc. llth Congress of the International Ergonomics Association, pp. 625-627. Jindo, T. et al., 1994a. A study of kansei engineering method applying to car interior. Human Factors in Organizational and Management, IV: 479-484. Jindo, T. et al., 1994b. A study of kansei engineering on steering wheel of passenger cars. In: Proc. 1994 Japan-U.S.A. Symposium on Flexible Automation, Vol. 2, pp. 545-548. Jindo, T., Hirasago, K. and Nagamachi, M., 1995. Development of a design support system for office chairs using 3-D graphics. International Journal of Industrial Ergonomics, 15(1): 49-62. Nagamachi M., 1989. Sensory engineering approach to automobile. Journal of the Society Automotive Engineers of Japan, 43(1): 94-100.
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Nagamachi, M., 1986. Emotion technology and its application. The Japanese Journal of Ergonomics, 22(6): 319-324. Shimizu, Y., Nagamachi, M. and Jindo, T., 1989. Analyses of automobile interiors using a semantic differential method. In: A.D. Keller (Ed.), Proc. Human Factors Society 33rd Annual Meeting, Vol. 1, pp. 620- 624.
Yanagishima, T. and Nagamachi, M., 1988. Sensory engineering approach to automobile interior. The Japanese Journal of Ergonomics, 24(6), 38-40.
International Journal of Industrial Ergonomics 19 (1997) 105-114
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 improvement 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: [email protected].
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, regarding impressions of these units in styling, supports
0169-8141/96/$15.00 Copyright © 1996 Elsevier Science B,V. All rights reserved. PII S 0 1 6 9 - 8 1 4 1 ( 9 6 ) 0 0 0 0 7 - 8
106
T. Jindo, K. Hirasago / International Journal o['Industrial Ergonomics 19 (1997) 105 114
Knowledgedatabase
Input of evaluationadjectives [ Inference Searchfor corresp?ndingadjectives ~
A~tive
d ~
T
Determinationof designelements Checkfor contradictions Determinationof interiorparts ~daGraphic, k imag~ --. database.....J Fig. 1. Construction and flowchart of car interior styling support system. 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
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.
2. l. 1. Subjective eualuation of speedometers in isolation 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 design 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 impressions of the speedometer were used by 23 subjects, 14 men and 9 women, in making evaluations. The subjects assigned a numerical value to each of
Table 1 Design elements of analog meter
1. Meter layout 2. Meter types and number Speedometer Tachometer Fuel level gauge Water Ievel gauge etc.
3. Panel color and material Plastic Wood Leather 4. Meter shape
',.j Round Semicircular Quarter 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 orientation 9. Lettering
s ~ < ~
Horizontal 10. Indicator shape
~ e ~1 Centrifugal
12 Jindo, K. Hirasago / lnternational Journal of Industrial Ergonomics 19 (1997J 105-I 14
the eight adjective pairs according to a 7-point semantic 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 adjective 'clean-looking'. The other axis is interpreted as indicating a design factor, represented by the adjective '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 examine 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 adjective 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
7
. ...
~
Lettering
4
0123
Indicator shape Starting point
4. <.~---.~-~
~ ('-"
107
Fig. 2. Exampleof speedometersample.
strong effect on increasing the perceived feeling of luxury. The type-3 lettering in particular was effective in projecting a more luxurious impression. Similarly, the scale had a strong influence on the cleanlooking feeling. The type-3 scale with large graduations was noticeably effective in heightening the image of being clean-looking. 2.1.2. S u b j e c t i u e e u a l u a t i o n s o f m e t e r c l u s t e r l a y o u t
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 adjectives. 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 subjects, 18 men and 6 women, took part in the evaluations. 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 extracted, as in the subjective evaluations of speedometers. The factor axis interpretations were also identical, 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.
108
T. Jindo, K. Hirasago / lnternational Journal of Industrial Ergonomics 19 11997) 105-114
2 meters
Design axis
Luxu!i°us•OElegant Playful •
~etro-looking
oSporty
/ • Likable
@
3 meters @
~
/
~
@
4 meters
5 meters
6 meters
7 meters
@
Clean-looking
Easy 1o understand
Easy -tounderstand axis
Fig. 3. Arrangement of evaluation adjectives. Fig. 4. Meter cluster layouts evaluated,
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
correlation between their perception of the meter cluster assemblies and the design elements. The photographs showed ten types of meter cluster assemblies 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, excluding 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 Partial I Cluttered Clean-looking correlanon[ Category [correlation Partialregression Partialregression element coefficienJt " ~ e f f i c i e n l 0.5 coetII coettmtentl coefficient 0.5 -0.243 ~-0.794~ l. L~=-J i Scale 0.124 2. ~ [ 0.49 0.91 I -0.353 m0.93 l ~ m -0..72 3.'' 'i i 0.035 ~-1.519i _ _ ~ .......... i 1. 0123 !-0.5671 -0.005 0.166 0.188 Letterin~ 3.2" 0Of.go.1 2 3 I 0.92 0.39 -0.248 ~"~'0.8001 m //.065 -0.066 4. 0 1 2 3 -0.044 I R 0.092 m 0.098 /I.067 2. ~ I 0.63 -0.043 -0.157~ 0.518 I , I m-0.489~ 0.100 0.198 0.52 0.053 4).276 -0.247 -0.318
T. Jindo, K. Hirasago / lnternational Journal of Industrial Ergonomics 19 (1997) 105-114 Table 4 Design elements and categories of each sample evaluated
sign Overall cluster layout (no. of meters) 3 4 5 or more meters meters meters
1. 2. s. 4.
Meter shape
OC)
I
0
0 0 0
0 0 0 0
0
0
6.
0
7.
0
8. 9.
0 0
10.
0
0 0 0 0
] 1, l , I h,,,u,,I White Cellow Red
0
0 0 0
0
5.
Indicator color
Scale
© 0
0
(D ©
0
0
0 L--
0
0 0
, 0 _~
0
0
0
0
Table 5 Quantification I method analysis results for 'sporty'
Design element
Category
1.3 meters 1. Overall cluster layout 2.4 meters (no. of meters 3.5 or more meters 2. Meter shape l. R o u n ~
Partial Partial correlation regression coefficient coefficient 0,79
4. Indicator color
1.1 1 I 2.1~Itl 3. I~,1,,,[ 1. White 2. Yellow 3. Red
] 0.46
(1.5
i u
-0.181
l
@
0.155 -0.263
m m n m
0.052
Sporty I
-0.139 0.11
~-
-0.432 -0.749
0.49
2. Semioir~_ cular ~_~ 3. Scale
Not sporty -. -o;5
in
109
110
~ Jindo, K. Hirasago/ International Journal of Industrial Ergonomics 19 (1997) 105-114 ~-to-understand axis1 • easy-to-understand • clean-lookang
@ eElegant
• Retro-looking
Fig. 6. A sample of meter cluster.
•LikabIe •Luxurious
• Sporty • Playful
Fig. 5. Arrangement of evaluation adjectives and meter cluster samples. expressions used: easy-to-understand, practical, luxurious, sporty, subdued, enthusiast-oriented, appealing, clean-looking, elegant, well-laid-out, wellshaped, well-organized and likable. A total of 30 subjects participated, divided equally between men and women. The results obtained for the ten samples are summarized in Table 4 in relation to the design elements and their categories. Similar to the procedure employed for the subjective evaluations of speedometers, 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
layout having five or more meters, a round meter shape, a scale with medium-level graduations and yellow-colored indicators. 2.1.4. Conclusions q f speedometer study In addition to the subjective evaluations and analyses described above, other evaluations were also conducted under different test conditions using variable design elements. The results of those evaluations are also reflected in the following conclusions. I. Two independent factors that appear to influence the static impression conveyed by automotive instrumentation 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 interpreted in terms of these two factors. 2. Correlations were made between the design elements of analog meter clusters and the evaluation adjectives. It was confirmed that the correlations could be used in determining meter cluster specifications that would convey an intended image.
Table 6 Classification and definition of design elements and categories Design elements
Category
Definition
The numberof spokes
1.2 2.3 3.4
The numberof spokes is 2. The numberof spokes is 3. The numberof spokes is 4.
Pad surface shape
1. Flat 2. The secondorder curved surface 3. The third order curved surface
Pad surface is flat. Pad surface is of the second order curved surface. Pad surface is of the third order curved surface.
Area of pad
1. Large 2. Medium 3. Small
Pad area is large. Pad area is medium. Pad area is small.
Pad upper side shape
1. Sharp projectionshapeR in upper side is small. 2. Gentle projectionshape 3. Varied projection shape
R in upper side is large. R in upper side is varied.
T. Jindo, K. Hirasago / International Journal of Industrial Ergonomics 19 (1997) 105- 114 2.2. A study o f steering wheel on Kansei engineering 2.2.1. Subjective evaluation on steering wheel
After the research on speedometers we investigated steering wheels. We carried out subjective evaluation tests on steering wheels to relate their impressions to physical 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 emphasized 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 o f 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 Multiple correlation coefficient=0.7605
Design Elements
Category
The nurnbe 1.2 2.3 o f spoe,es 3.4
0.728
surface
1. Flat 2. The second order curved surface
shape
3. Thethirdorder
Pad
Partial correlation coefficient
Area of pad
1. Large 2. Medium 3. Small
Pad upper side shape
1.Sharp projection shape 2. Gentle projection shape 3. Varied projection shape
Not sporty .1.o
-~
~ Category score
Sporty 1.o
-1.296 /
0.410
-0.146 •
0.693
0.694 -0.518 1
curved surface
0.618
111
-0.212 1 -0.265 1
-0.369 1 -0.242 1
Fig. 7. Results of quantitative analysis.
0.685
112
T. Jindo, K. Hirasago / lnternational Journal of Industrial Ergonomics 19 (1997) 105-114
of adjective In'P-put dImage --ata
processing ~ - - ~
Inference of priority o f ~ . _ ~ sample candidates ~ ~
Pad ondorder surface ]r"ese curved surface
shape 15. ]:hesecondorder . . . . ---4 curvedsurface _ areao~ I!:L~e ~ pad ~ 2 . Medium
2 O
Q) --
I
t I
i) / c'
~ of s a m p l e s ~ - - ~ l•Graphic display Fig. 8. System flowchart.
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 coefficient) 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
Fig. 9. Sample design database.
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 (partial 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 category score) are found. Then, calculated image scores
Fig. 10. Output screen of 'serene' steering wheel.
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.
2.2.3. Conclusions of steering wheel study
Ishihara et al., 1993; Jindo et al., 1990; Jindo and Nagamachi, 1991; Jindo et al., 1994a,b,1995; Nagamachi, 1989, 1986; Shimizu et al., 1989; Yanagishima and Nagamachi, 1988.
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 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 important units constituting car interiors will be achieved. However, there is the problem where an evaluation 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 'excellent' 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-
4. For further reading
Acknowledgements This study was conducted collaboratively by Nissan Motor Co., Ltd. and Mr. Nagamachi's office of Hiroshima University. Students of Hiroshima University helped us in evaluating the film samples in this study. We wish to express our gratitude to all of the persons concerned.
References lshihara, S., Hatamoto, K., Nagamachi, M. and Matsubara, Y., 1993. An automatic builder for kansei engineering expert system using self organizing neural networks. In: C. Gu and H. Osaki (Eds.), Proc. Second China-Japan International Symposium on Industrial Management. International Academic Publishers, Beijing, pp. 497-502. Jindo, T., Yanagishima, T. and Shimizu, Y., 1990. An evaluation structure of automobile interiors using multivariate analysis. 23rd Fisita Congress Technical Paper, pp. 635-641. Jindo, T. and Nagamachi, M., 1991. The development of car interior image system incorporating knowledge engineering and computer graphics. In: Y. Queinnec and F. Daniellou (Eds.), Proc. llth Congress of the International Ergonomics Association, pp. 625-627. Jindo, T. et al., 1994a. A study of kansei engineering method applying to car interior. Human Factors in Organizational and Management, IV: 479-484. Jindo, T. et al., 1994b. A study of kansei engineering on steering wheel of passenger cars. In: Proc. 1994 Japan-U.S.A. Symposium on Flexible Automation, Vol. 2, pp. 545-548. Jindo, T., Hirasago, K. and Nagamachi, M., 1995. Development of a design support system for office chairs using 3-D graphics. International Journal of Industrial Ergonomics, 15(1): 49-62. Nagamachi M., 1989. Sensory engineering approach to automobile. Journal of the Society Automotive Engineers of Japan, 43(1): 94-100.
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Nagamachi, M., 1986. Emotion technology and its application. The Japanese Journal of Ergonomics, 22(6): 319-324. Shimizu, Y., Nagamachi, M. and Jindo, T., 1989. Analyses of automobile interiors using a semantic differential method. In: A.D. Keller (Ed.), Proc. Human Factors Society 33rd Annual Meeting, Vol. 1, pp. 620- 624.
Yanagishima, T. and Nagamachi, M., 1988. Sensory engineering approach to automobile interior. The Japanese Journal of Ergonomics, 24(6), 38-40.