Proposal of Method to Make Product Design Concepts
by Evaluation Structure
Katsuo INOUE*, Mitsuo HIROKAWA**, Kouki ITOH***, Masayuki SAKAI**** Hiroshima International University, Faculty of Psychological Science, 555-36 Kurose-Gakuendai, Higashi Hiroshima
Hiroshima 739-2695 Japan** Tokai University Takanawa Junior College, 2-3-23 Takanawa, Minato-ku, Tokyo, 108-8619, Japan*** Takushoku University, 815-1 Tate-machi, Hachiouji-shi, Tokyo, 193-0985, Japan****Sapporo City University, School of Design, 1Geijyutsu-no-mori Minami-ku Sapporo Hokkaido 005-0864 JapanAbstract: Recently the necessity of making a product design concept is increasing as the methodology of a product design
so that the design process becomes transparent. However, from result of our reference research in both scientific fields andcompany design fields, we found that there were few studies about the method to make the product design concept.
Therefore, we proposed a method to make a new concept by transforming user's cognitive evaluation structure which wasconstructed from the result of the fact finding about products in the market by quantitative analysis. Concretely, after weextracted user's attitudes such as cool or interior matching and impressions of those products by Evaluation grid method,
we obtained the cognitive evaluation structure consisted of attitudes in higher rank, impressions in the middle rank andrecognition parts in the low rank by multiple regression analysis and Rough set theory. Thus we could make a new designconcept by rearranging these impressions contributed to a specific attitude mentioned above. This method has theadvantage that a new design concept can be made from various view points. We considered the availability of the methodby the case study of a light instrument design on the latter half of the research. Specifically, after we assumed a new designconcept made by some impressions extracted from light instrument samples now in use, we asked a designer to draw
sketches based on the new design concept. We confirmed the validity of it by comparing impressions of light instrument
samples with those of sketches by user evaluation.
Key words: Product design, Design concept, Rough sets

specific, evaluation terms such as attitudes andimpressions were experimentally extracted by theLaddering method used in the Evaluation Grid Method[2], and were used in a survey experiment. The
relationships between attitude and impressions wereanalyzed with the data from the survey by multiple
regression analysis, and then the relationships between
impressions and “recognition parts” were determinedusing Rough Sets [3] and a decision rule analysis [4].

1. Introduction
We propose a method with concrete procedures formaking design concepts by reconstructing humancognitive evaluation structure for a product in design
development. We have verified and discussed the above
method using lighting fixtures, which are being produced
in a wide variety but in a limited quantity, as a casestudy.
2. Analytical Methods
We have employed an analytical method with a humanevaluation structure based on Personal Construct Theory[1], a base theory of cognitive psychology, where“attitudes,” upper most abstract notions which vary
greatly by individual, were hierarchically resolved, via“impressions” in the middle layer, into the lowermost“(cognitive) geometrical elements” (physical quantity),in the reverse direction to that of behaviors. To be3. Extraction of Evaluation Terms and RecognitionParts
An interview survey experiment was conducted with 10
university students as subjects using photographs of 45
different interior lighting fixtures (Oct. 2005). We
extracted evaluation terms in the experiment, and thenclassified them into three attitudes and 12 impressions.
Table 3 shows the results. We also determined three1

5.1 Determination of Decision Classes
To analyze the relationships between impressions,which have strong influences on the three attitudes, andrecognition parts, the decision classes of Rough Sets
have to be determined. Conventionally, decision classes
have been determined by simple methods such as equal
divisions of two or three using averaged values. These,
however, brought cases where clear characteristics could
not be obtained. We, therefore, employed a method to
divide data into three decision classes using thefrequency distributions for a five-grade evaluation toreflect the data contents. For example, to decide a classof Y=3 (unique), Y=2 (neutral) or Y=1 (not unique),seven different frequency patterns had been prepared inadvance and correlated with the data to classify the databy the correlation coefficients.
Seven frequency patterns, as in Fig.1, comprise fivetypical patterns (pattern 1 to 5), a pattern having twopeaks (double peaks) and a pattern having a flat plateau(no peak), totaling seven. When the correlation
coefficient with pattern 1 or 2 gives the maximum value,then the decision is Y=1, when the coefficient withpattern 3 or the pattern with double peaks or no peaks
gives the maximum, Y=2, and when the coefficient withthe pattern 4 or 5 gives the maximum, Y=3. If a humandecision was required, an analyzer gave the decision.
Since many decision rules obtained for Rough Sets
brought difficulties in examining results, we applied adecision rule analysis method proposed by us to calculate
data for easy examination. Table 2 shows the results.

major divisions and 19 items and 58 categories in Fig.2
based on recognition parts extracted in the survey
experiment. We determined three attitudes because it
would be natural for consumers to reach a purchasedecision based on not only purchase desire attitude but
also other attitudes comprehensively.
4. Relationship Analysis between Attitudes andImpressions
A questionnaire survey was conducted with 30
university students as subjects using the above
photographs of the 45 lighting fixtures with a five-grade
evaluation by the SD method (Oct. 2005). The evaluation
terms used for attitude and impressions were the 15
terms extracted in the previous experiment. Using thesurveyed data, we analyzed the relationships between
attitudes and impressions with multiple regression
analysis (Table 1). The partial regression coefficients forthe three attitudes in Table 1 indicate that the attitudes
are strongly influenced by the following five
impressions: “interest retaining,” “mature” “playful”“posh” and “useful.”
Table 1

Relationship Analysis Results between Attitudes andImpressions(attitude)

Want-to-buy

CoolInterior matching

Multi-correl. coeff.

0.878

0.893

0.849

1. Posh2. Cute
3. Mature
4. Simple
5. Presence
6. Interest retaining
7. Japanese
8. Playful
9. Warm feeling
10. Unique
11. Stately
12. Useful

-0.15
0.13
0.40
0.01
0.16
0.56
0.06
0.230.01
-0.14
-0.10
0.21

-0.05
0.01
0.43
-0.08
0.13
0.53
-0.01
0.30
-0.11
-0.09
-0.12
0.08

0.19
0.17
0.27
-0.06
-0.20
0.15
0.03
0.48
0.05
-0.20
-0.05
0.18

* Decision class extraction method by frequency distribution( Correlation coefficient between frequency distribution and frequency pattern. )
1
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16

5. Relationship Analysis between Impressions andRecognition Parts
In relationship analysis between impressions andrecognition parts, quantification theories have been usedwith the categorical data of recognition parts. This,
however, involves problems of multicollinearity. To
avoid the problems, we used a method using Rough Sets,
for which application studies have started recently, toanalyze the relationships between impressions andrecognition parts.

2

9 6
1 72 111 9
5 100 20 8
2 1
0 1
0 0
1 0
8 3
2 2
2 10 1
1 7

345

2 3 0
4 3 5
3 8 6
0 0 0
2 3 0
3 9 6
4 8 0
3 6 8
2 6 11
1 10 92 11 64 4 1
3 7 6
2 12 33 5 114 6 2

Avg.
1.95
3.2
3.75
1.45
2.15
3.95
3
3.85
4.35
109
4.4
8
4.05
76
2.35
5
3.65
4
3.65
32
4.3
1
3.05
03
4
5
6
7
8
9
10
12
13
14
16
17
18
21
22
1

Fig.1

1
0.9
-0.3
-0.9
0.9
0.7
-0.9
-0.2
-0.9
-0.8
-0.9
-0.8
0.8
-0.9
-0.6
-0.8
-0.2
2

2
0.8
0.3
-0.9
0.9
0.9
-0.8
0.2
-0.9
-0.8
-0.9
-0.8
0.4
-0.9
-0.6
-0.8
0.2
3Freq.
3
4-0.3 -0.7
0.3 -0.1-0.1 1.0-0.4 -0.8
0.1 -0.7
0.1 1.0
0.6 0.2-0.3 0.9-0.3 0.8-0.2 0.9-0.1 1.0-0.1 -0.5-0.1 1.0
0.1 0.8-0.3 0.7
0.6 0.2
4

Double peaks
No peaks

5
-0.9
0.1
0.9
-0.8
-0.8
0.8
-0.2
1.0
1.0
0.9
0.8
-0.7
0.9
0.5
1.0
-0.2

6
0.0
0.4
0.1
0.0
0.6
0.4
1.0
-0.2
-0.2
0.1
0.3
-0.2
0.1
0.5
-0.3
1.07
-0.2
0.4
0.0
-0.3
0.2
0.2
0.8
-0.3-0.3
-0.2
0.0
-0.2
-0.1
0.2
-0.3
0.8

Decision classdetermination

1231132333313332

5

Determination of Decision Classes (a part of outcomes)

5.2 Examination of Analysis Results
We made Rough Sets with five impressions obtained
through the examination of the results from multiple
regression analysis in the previous section. To be specific,
2

“interest retaining” impression can be expressed by thefollowing necessary conditions: a cylindrical and linear
shade, multiple posts, and a large or small round base(Fig.2). Similarly, other impressions can be examined.
The impressions having strong influence on the attitude“cool” are “interest retaining,” “mature” and “playful,”
according to the analysis results in Section 4. Thus
examination of the attitude on Table 2 with the three
impressions will help us determine the present
recognition parts that express the attitude “cool.”
Similarly, recognition parts for the other attitudes canalso be determined respectively. As indicated above,
Table 2 represents the cognitive evaluation structure of
the designs of present lighting fixtures.
We determined the combination patterns for fiveindividual impressions relating to the three attitudes, anda part of them is shown in Table 3. For example, the firstcombination pattern from the left in the column of theimpression “playful” indicates only one attribute(characteristic), i.e., “the number of lights is one (F1),”by the filled circle. The next pattern to the right indicatesa combination of the two attributes, i.e., “the material of
the shade is special (D3)” and “the style of the shade iscurved (E2).” The eighth pattern including open circles
indicates the three combinations: the one represented by
the filled circles, i.e., “the size of the shade is standard(A2)” and “the material of the post is a metal (M1),”
followed by each one of the open circles, which are “A2M1 Q3,” “A2 M1 R3” and “A2 M1 S3.”

we calculated the decision rules (low approximation) ofRough Sets by creating decision tables with therecognition parts as conditional attributes and theimpressions as decision attributes, which we obtained inSection 3. Then, we obtained standardized column scores(Table 2) with the above-mentioned decision rule
analysis method. The table contains only standardized
column scores more than or equal to 0.5 for ease ofreading.
Table 2Analysis Results of Rough Sets
InterestretainingLarge
A1
Standard
A2
Small
A3
LengthHigh
B1
Standard
B2
Short
B3
Shape
Cylindrical
C1
Cone
C2
Square prism
C3
OthersC4
Material Plastic
D1
Japanese paper
D2
Special (bamboo etc)
D3
Style
LinearE1
CurvedE2
F1Light Number One
of lights Multiple
F2
Position High (top open)
G1
High (bottom open)
G2
High (same for high &G3low /special)
High to middle
G4
Top to bottom entirely G5
Mounting Hanging
H1
Fixed to post
H2
Fixed to baseH3
To branches etc.
H4
Mobility Yes
I1
NoI2
LengthLong
J1
PostStandard
J2
Short
J3
None
J4
Thickness Standard
K1
Thin
K2
None
K3
Cylindrical
Shape
L1
Square prism
L2
None
L3
Material Metal
M1
Wood
M2
Metal + Wood
M3
None
M4
Number One
N1
Multiple
N2
None
N3
Presence No
O1of feetYes
O2LinearStyle
P1
CurvedP2Standard
Size
Q1
Base
Large/Small
Q2
None
Q3
ShapeRound
R1
Rectangular
R2
None
R3
Material Metal
S1
WoodS2None
S3Want-to-buy(Attitude)
Cool
Interior matching

ShadeSize

MaturePlayfulPosh

Useful

2.04

1.54

2.22

1.13
4.27
0.68
2.50
0.90

0.94

2.94
1.53
0.74
0.79

0.77

2.94

1.62

1.10

1.92

5.94

0.94
2.95
0.78

1.63

3.97
1.41

0.58
2.99
0.76

1.88
1.35
2.09

0.97

1.13
0.94
1.02

1.79
0.55

0.83
2.02
2.34

1.58

0.94

1.43
1.34

1.39

6. Method for Making Design Concept
The analysis results in the previous section do notprovide creativity themselves. Thus new design concepts
have to be made based on the analysis results. One of theways to do this is to reconstruct the evaluation structureby, for example, changing the order of the importance of
the impressions in the middle layer, emphasizing some
impressions, or introducing new impressions fromoutside. We call this “reconstruction of the evaluation
structure.” For example, there may be a conceptemphasizing “playful,” which is placed at a low order in
the attitude “cool,” or a concept adding the impression“useful” for the attitude “want-to-buy” to “cool.” The
modification of the evaluation structure is up to thecreativity of designers and planners.
One of the procedures for making design concepts is touse the combination patterns in Table 3. For example, thecharacteristic impressions for “want-to-buy” and1.18
1.41
0.77

2.37

1.35

1.88

0.54

0.78

0.54
1.83

1.15
2.69

0.98

1.84
4.74
2.94

1.47

0.52

0.72
1.10
0.94

0.58

0.77

0.79
0.94

0.58
0.51

0.94
0.23
0.3
0.48

0.58

0.77

0.69
0.56
0.53

0.34
0.43
0.27

0.21
0.19

Focusing on high scores in the standardized column of“interest retaining,” for example, we find that the3entirely, and a round base (large or small) to which thelights are fixed.” In the process, we need to select thecombination patterns in Table 3 where individual
attributes do not conflict with one another.
This idea is an expanded version of “merger” proposedby Mori [5]. The idea of Mori only refers to elements
within any of the impressions in Table 3. This can beapplicable between impressions provided no above
conflicts occur. We call it “expanded merger.” Mori
mentioned that merger was a way to create an idea, andthis matches the creativity of the design concept.

“interior matching” are “interest retaining” and“playful,” respectively (Table 2, bottom row). These two
impressions are greatly different each other, but wedetermined that the trial design concept was “a lighting
fixture having ‘playful’ and ‘interest retaining’impressions” to emphasize the present results of theanalysis. To make a design to express the design concept
as a trial, we selected, for example, three combinations
from the impression “interest retaining,” which is to beemphasized most, and one from “playful,” which is thenext to be emphasized, as represented by the rectangularframes in Table 3.
Table 3

7. Experimental Confirmation of Design Concept
To confirm the effectiveness of the trial design concept,
we used an active designer to make a design proposal as
an experiment. The concrete processes were as follows:
The designer was presented with the sample photographsof lighting fixtures used in the analysis followed byexplanation of the design concept determined in theprevious section. The designer was then instructed todraw idea sketches based on the explanation. Thedesigner drew several designs and then selected
himself/herself the one that expressed the design concept
the most accurately as the finished proposal (Fig.2,left).The experiment was conducted in January 2007.

Combination Patterns Indicating Characteristics ofIndividual Impressions
Interest retaining
Shade Size
LengthShapeMaterial

Style

Light Numberof lights
Position

Mounting

Mobility

Post

LengthThickness

Shape

Material

NumberPresenceof feet
Style

Base

Size

Shape

Material

A1A2
A3
B1
B2
B3
C1C2C3C4
D1D2D3E1E2
F1F2G1G2G3G4G5H1H2H3H4I1I2J1J2J3J4K1K2K3L1L2L3M1M2M3M4N1N2N3O1O2P1P2Q1Q2Q3R1R2R3S1S2S3Playful●
● ○●
●

●
●
●

●
●
●

● ●

○

●
●
●
●

Welch official approval●Interest retaining :●t（39.01）=3.5、p < 0.005●●

Playful :● ○t（33.29）=3.6、p < 0.005
●

●

Fig.2
● ● ●

●oftheExperimental

Confirmation

andSignificance Test Results○ ●●Sketch●○ ●To confirm how much the two impressions adopted inthe sketch, i.e., “interest retaining” and “playful,” wererealized, we conducted a questionnaire survey (Feb.
2007) with a five-grade evaluation by the SD methodwith 30 university students as subjects using theproposed sketch as well as the same photographs of 45○ ●To be specific, it represents “a lighting fixture
consisting of a shade made of a special material with acurved shape, lights distributed from the top to bottom
4lighting fixtures used in the previous survey for multiple
regression analysis. The results were analyzed using thenotion of Welch official approval (Fig.2, right).
The values of the “Significant probability (p)” aresmaller than those of the “Significance test (0.005)” forboth the impressions “interest retaining” and “playful.”Thus the sketch is considered to be emphasizing theimpressions analyzed.
8. Conclusions
We have quantitatively obtained a cognitive evaluation
structure of consumers for lighting fixture design using
multiple regression analysis and Rough Sets. We
proposed a method for making design concepts byreconstructing the resulting evaluation structure. We
demonstrated the effectiveness of the proposal throughexperimental confirmation in which we made a design
sketch based on the method.
Acknowledgments
We wish to express our thanks to students of Kansei
Design Department at Hiroshima International
University for their cooperation. We are grateful toProfessor Hirokawa of Tokai University for giving us
valuable advice.
References
1. Architectural Institute of Japan: Introduction toEnvironmental Physiology Survey Methods. Gihodo
Shuppan Co., Japan, pp 13 (2000).
2. “ibid” Evaluation Grid Method. Section 3-2-1, pp
57-64.
3. Mori N, Tanaka H, Inoue K: Rough Sets and Kansei.
Kaibunndo, (2004).
4. Inoue K, Hirokawa M: Proposal of Rough Sets
Method for Analyzing Relationship between
Recognitive Parts and Evaluation Words. Journal ofJapan Society of Kansei Engineering, vol. 5/No.1,
pp 43-52 (2004).
5. Mori N, Tanaka H, Inoue K. Rough Sets and Kansei.
Kaibunndo, pp 43-50 (2004).

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