American Journal of Operations Research, 2013, 3, 393-401
http://dx.doi.org/10.4236/ajor.2013.34037 Published Online July 2013 (http://www.scirp.org/journal/ajor)
Prediction Models for Total Customer Satisfaction Based
on the ISO/IEC9126 System Quality Model
Kazuhiro Esaki
Faculty of Science and Engineering, HOSEI University, Tokyo, Japan
Received March 14, 2013; revised April 14, 2013; accepted April 21, 2013
Copyright © 2013 Kazuhiro Esaki. This is an open access article distributed under the Creative Commons Attribution License, which
permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ABSTRACT
The profitability of the system product is decided on the sales of the product. Furthermore, a customer satisfaction for
products quality and a price have a big influence on the sales of the product. It spends limited financial resources effec-
tively to raise the profitability of the system product, and it is necessary to realize the high quality product correspond to
the customer needs as much as possible. There may be close relationship between cost of a product and an expense to
implement the individual inherent attribute of system product. For the purpose of improvement of the customer satisfac-
tion for quality of system product, the method of quantitative quality requirement and evaluation based on the
ISO/IEC9126 quality model that includes six quality characteristics is widely recognized. However, independency
among each quality characteristic has not been sure and the suitability of method for quality requirement of system
product by using these six quality characteristics could not certified statistically. In the precedent study, introduced the
requirements definition method for the quality of system product based on the system quality model defined in
ISO/IEC9126 and proposed the effectiveness of it statistically. This study have measured the customer satisfaction for
the system quality from the viewpoint of six quality characteristics quantitatively and confirmed the effectiveness of the
technique to evaluate. In this study, we have confirmed the relationship between inherent attributes of the product and
quantitative result of a measured value of total customer satisfaction from the view point of six quality characteristics
statistically. This study performed the trial to clarify the relations with the inherent attributes that quantitative result of a
measurement of the customer satisfaction based on six quality characteristics by the quality model of ISO/IEC9126. In
addition, this study performed the development of the prediction model to estimate the total customer satisfaction for
the system product from the view point of inherent attribute of the product. In this paper, we propose the effectiveness
of application of the estimated prediction model and possibility of improvement of the total customer satisfaction of a
system product.
Keywords: System; Software; Quality Requirement; Quality Evaluation; Quality Model; Quality Characteristic;
Inherent Attribute; Quality Measure; Prediction Model; Total Customer Satisfaction
1. Introduction
The profitability of system product is decided by sales of
products influenced by a price and an inherent attribute
of product. Furthermore, a customer satisfaction and
price of product have a big influence on sales of a prod-
uct. Furthermore, a customer satisfaction and a price of
product have a big influence to sales of a product. In or-
der to improve customer satisfaction of system product
successfully, it is very important to catch-up the cus-
tomer’s quality needs and high quality products should
be designed corresponding to the real customer needs
during possible early stage of development.
From the view point of expected profitability, financial
resources should be limited and the most suitable product
should be realized from the view point of customer need
and based on the consideration of reduction of cost.
But it is necessary to realize attractive and coseffective
product correspond to the real customer needs as much as
possible. Usually, relationship between sales and cus-
tomer satisfaction, which is caused by the inherent qual-
ity of product, may be recognized closely.
In order to realize suitable quality of product, it is
necessary to grasp the quality requirement of customers
for a system accurately, and the quantitative and concrete
inherent quality for targets system product should be
defined. After that, requirement of inherent attributes
should be described into the quality requirement specifi-
C
opyright © 2013 SciRes. AJOR

K. ESAKI 394
cation. If we take the wrong approach to requirement
specification based on the real needs of quality of system
product, it may cause a big loss for a purpose of invest- ment.
In recent years, we have been working on developing
the ISO/IEC25000 (SQuaRE) series [1-5] of standards
for quality requirements and evaluation for system and
software product for a long time in ISO/IEC JTC1 (Joint
Technical Committee 1 of the International Organization
for Standardization and the International Electro techni-
cal Commission) SC7WG 6 (software and systems engi-
neering under ISO technical committee, working group
six). As part of this project, we have also worked on the
developments of ISO/IEC9126-1 [6] (This standard has
revised to ISO/IEC25010:2011 [5]), which are the stan-
dards to provide supporting technology for above men-
tioned works and also we have developed the quality
characteristics.
Currently, the method of quantitative quality require-
ment definitions [2,7] based on ISO/IEC9126-1 [6] qual-
ity model is widely recognized and used in worldwide for
the purpose of specify the quality requirement and
evaluation of system/software product. ISO/IEC-9126-1
defines the six quality characteristics of the system and
software. These six quality characteristics are described
based on the model of Boehm [8] or McCall [9], or from
the view point of a stakeholder’s wide experience, which
are considered as necessary and de pendent from cus-
tomer’s point of view. This model introduced in ISO/
IEC9126-1 may be formulated with almost perfect qual-
ity target establishment and evaluation perspective of the
system. Through analyzing customer requirements based
on these six quality characteristics, it becomes possible to
perform complete and objective evaluation of customer
quality requirements for a system/ software product. Al-
though a certain level of improvement is expected in the
completeness of describing product quality objectives by
using the ISO/IEC9126-1 quality model.
However, for an evaluation of the customer satisfac-
tion about the system quality, the effectiveness of the
quality model application was not inspected because in-
dependency of each quality characteristic is not sure and
the suitability of method by using these six quality char-
acteristics for quality requirement and evaluation is not
certified statistically.
In recent years, customers are now able to purchase
products based on an increasing number of customer
reviews posted on the Internet web site. For example, an
online negative review may relate to a serious concern
that affects the operation of the laptop computer, or it
may relate to a relatively minor concern that does not
affect the operation of the system, but expresses person-
ally preference. Therefore, the degree of customer dis-
satisfaction may not be accurately obtained by simply
classifying online negative reviews into the six quality
characteristics. This study focuses on negative reviews of
Laptop Personal Computers (LPCs) posted by consumers
and this study uses the statistical analysis approach based
on the previous study of software product and process
improvement [10,11].
Above assumption, we tried to verify the validity and
effectiveness of quantitative quality requirement defini-
tion from the view point of six quality characteristics
andinspected requirements definition of the system qual-
ity based on the system quality model of ISO/IEC9126,
and the effectiveness of the evaluation in the precedent
study statistically, measured the customer satisfaction for
the system quality from the viewpoint of six quality
characteristics quantitatively and confirmed the effec-
tiveness of the technique to evaluate.
Based on the result of previous study, this study per-
formed the trial to clarify the relations with the inherent
attributes of product and quantitative result of a meas-
urement of the customer satisfaction based on six quality
characteristics defined by the quality model of ISO/
IEC9126. In addition, this study performed development
of the prediction model to estimate the total customer
satisfaction for the system product from the view point of
inherent attribute of product. Also, this paper propose the
result of investigation of influence of inherent attribute to
customer satisfaction, and the possibility of application
of estimated prediction model for improvement of the
total customer satisfaction of system product based on
the inherent attributes of the product.
2. Concepts
2.1. Concept of Prediction Model
Fig ure 1 shows the concept of system product implemen-
tation supported by ISO/IEC25000 (SQuaRE) series.
Customers have needs for the inherent attributes of
system product. In order to perform development, at first,
developers should specify a quality requirement from the
Figure 1. Concept of prediction model of customer satisfac-
tion for system product quality.

K. ESAKI 395
view point of customer's needs. We should make speci-
fications that at first we confirm the real needs of the
customers at the beginning, and described the concrete
inherent attribute that you should realize with a product.
After development, developer and customer should
evaluate the target system product based on the quality
requirement specification in order to assure the quality of
developed product.
From Figure 1, ISO/IEC25030 provides the require-
ments and recommendations for specifying the quality
requirements from the view point of selected customer’s
needs. The specified quality requirements should be used
as the criteria of system and software product evaluation.
Quality requirements for system can be specified using
the process defined in ISO/IEC25030 [2] based on the
quality model include six quality characteristics de-
scribed in ISO/IEC9126-1. From Figure 1, system qual-
ity evaluation can be performed by using ISO/IEC25040
[3] and 25041 [4] based on the specified quality require-
ments, which are specified by using ISO/IEC25030 dur-
ing system design phase.
Table 1 is the example of inherent attributes of the
sample products which used in this study. A good prod-
uct is the product which is high quality in a sufficiency
degree of the product for the customer needs, and it is
thought that the satisfaction of quality of the product for
the customer is high. Customer satisfaction or negative
opinion of inherent attribute of the product may be as a
target of decision for the purchase. It is thought that the
customer satisfaction of the product depends on the in-
herent attribute of a product, which comes out from the
customer needs for the target product. The developer
should implement the inherent attribute of the product to
the target system based on the customer needs.
After completion of products development, concrete
inherent attribute of product and production cost is de-
cided. If the customer satisfaction for an inherent attrib-
ute and the price of product is high, as a result, the sales
of the product increase, and it is thought that profitability
may increase.
In this study, at first, I calculate the observed total
customer satisfaction for the product from the viewpoint
of six quality characteristics of the ISO/IEC9126 based
on the method that I showed for the beginning in a
precedent study. After that, I investigate the relations
between the inherent attribute of product and the ob-
served total customer satisfaction according to six quality
characteristics for the product.
Furthermore, I assume the observed total customer
satisfaction according to six quality characteristics as an
objective variable and assume the inherent attributes of
product as an explanation variable, and performed a mul-
tiple regression analysis. Also, this study inspects the
effectiveness of the prediction model that can estimate
total customer satisfaction judging from the viewpoint of
six quality characteristics of the product from the inher-
ent attributes of the products.
2.2. Concept of System and Software Quality
Model
Figure 2 shows the structure of the Software Quality
Model defined in ISO/IEC9126-1. Recently, ISO/ IEC-
9126-1 have replaced by ISO/IEC25010:2011, but ISO/
IEC9126-1 is widely recognized and actually used, then
this study focus on the ISO/IEC9126-1.
From Figure 2, this model includes the six quality
characteristics for system and software such as Function-
ality, Reliability, Usability, Efficiency, Portability and
Maintainability.
Functionality can provide the ultimate function for
supporting the improvement of work. And, Usability pro-
vides the ease of use of the system.
Reliability and Efficiency represents the capability
possessed in the system, and is the characteristic associ-
ated with sustaining the quality objectives determined by
the Functionality and Usability.
Maintainability and Portability provide the capability
of the system to adapt to changes in the system environ-
ment and the usage environment.
3. Summaries
This study first collects the customers complaints, i.e.,
expression of customer dissatisfaction posted on a review
of website where customers who actually purchased per-
sonal computer related products post their complaints as
shown in Table 2. The study, then, classifies the posted
customer complaints based on the six quality characteris-
tics defined in the ISO/IEC9126-1, obtains from ques-
tionnaires a weight for each quality characteristic to rep-
resent how important the quality characteristic is to the
customer, applies the weights to the six quality charac-
teristics, and quantifies the degree of customer satisfac-
tion for each quality characteristic as shown in Table 3.
The degree of total customer satisfaction is obtained
from the each customer satisfactions of six quality char-
acteristics for the each target system.
This study has calculated the observed total customer
satisfaction by using customer satisfaction of each six qual-
ity characteristics based on the result of precedent study [11].
Second, this study have collected inherent attributes of
target products, i.e., CPU, HDD capacity, Wait of body,
Drive time, as shown in Table 1. The study has per-
formed correlation analysis of the degree of inherent at-
tributes of LPCs and has confirmed the independency of
each inherent attributes or not. In addition, the study has
performed multiple-regression analyses and for perform-
ing the multiple regression analysis, has assigned the

K. ESAKI 396
Figure 2. System and software product quality model -ISO/IEC9126-1:2001 [6].
Table 1. Example of the inherent attribute of LPCs.
Inherent Attributes
CPU
HDD capacity (GB)
GPU
Resolution (Dot)
Wait of body (Kg)
Drive time (sec)
Number of USB port
Number of memory
slot
Aproduction country
Cock speed (GHz) Cash Memory size
(MB)
Liquid crystal size
(MB)
Memory capac-
ity(GB)
Capacity of SSD
Memory (GB)
a
i bi ci di ei fi gihi ii ji ki mi ni oi
S1 Core i5 2410M 0 Intel HD
Graphics 3000 1366 × 7681.0000 13.000031 J 2.3 3 10.1 2 128
S2 Core i5 460M 0 Intel HD
Graphics 1366 × 7681.2050 12.000031 J 2.53 3 10.1 2 128
S3 Core i3 2310M 500 RADEON HD
6470M 1366 × 7681.7200 8.5000 31 J/ C 2.1 3 13.3 4 0
S4 Core i3 380M 160 Intel HD
Graphics 1366 × 7681.1850 7.5000 31 J 2.53 3 10.1 2 0
S5 Pentium Dul-Core
B940 640 Intel HD Graphics 1366 × 7682.4000 2.1000 32 T/C 2 2 15.6 4 0
S6 Core i 5 2410M 750 Intel HD
Graphics 3000 1366 × 7682.4000 2.3000 22 T/C 2.3 3 15.6 4 0
S7 Core i5 2520M 500 Intel HD
Graphics 3000 1280 × 8001.3300 15.500031 J 2.5 3 12.1 4 0
S8 Core i5 2520M 640 Intel HD
Graphics 3000 1280 × 8001.3400 16.500031 J 2.5 3 12.1 4 0
S9 Core i3 380M 320 Intel HD
Graphics 1366 × 7682.5000 5.2000 32 C 2.53 3 15.6 2 0
S10 Core i7 2630QM 640 GeForce
GT 540M 1920 × 10803.2000 2.5000 33 J/C 2 6 16 8 0
Si: Example of target laptop personal computers (i: Number of sample product (i = 1 - 35)).

K. ESAKI 397
Table 2. Example of negative review from web-site.
Number of complaints (count)
View point of Six
quality characteristics Category of negative review S1S2S3S4S5S6 S
7 S 8 S 9 S 10 Si
Number of Built-in application softwarea1i0 0 1 0 0 0 0 0 0 0 0
functionality A Kind of OS a2i0 0 1 0 0 0 0 1 0 0 0
Easiness in seeing screen b1i143 113 3 11 2 0 2 6 3
Easiness to use Keyboard b2i6 3 5 6 2 12 3 5 1 124 usability
Wait of body b3i5 0 8 0 2 1 0 1 2 1 0
Number of fault c1i1 0 1 1 0 4 0 0 0 0 0
A production country c2i3 0 4 5 0 7 0 0 1 2 1 readability
Capacity of Battery c3i6 1 1 120 0 0 0 7 9 1
Transaction speed d1i2 0 3 3 0 1 1 4 0 0 1
efficiency Drive time d2i130 0 8 0 3 0 2 1 1 0
portability Number of USB port e1i7 1 2 0 1 0 1 0 0 3 3
maintainability Customer support f1i1 1 0 0 0 0 1 0 1 1 0
Total number of Review RCi58 2238 41 26 78 23 19 21 3532
Si: Example of target laptop personal computers (i: Number of sample product (i = 1 - 35)).
Table 3. Importance of customerneeds by six quality characteristics.
Samples of Customers
View point of Six
quality characteristics Category of Questions U1U2U3U4U5Un Weight: Importance Ratio
Number of Built-in application softwareSa1n15101010 8 15
functionality A Kind of OS Sa2n9 4 3 9 1312 M 0.6350
Easiness in seeing screen Sb1n4 2 6 3 5 6
Screen Size Sb2n5 3 148 6 5
Easiness to use Keyboard Sb3n6 9 15124 7
usability
Wait of body Sb4n8 6 9 7 1111
N 0.7260
Number of fault Sc1n3 5 4 1 128
A production country Sc2n121212 5 1510 readability
Capacity of Battery Sc3n2 8 7 4 9 2
O 0.7610
Transaction speed Sd1n7 7 8 3 104
efficiency Drive time Sd2n1 1 1 2 3 3 P 1.0000
Number of USB port Se1n13111311 7 14
portability Number of Memory Slot Se2n10141113 2 13 Q 0.4440
maintainability Customer support Sf1n1413 5 1414 9 R 0.5480
Un: Example of customers (n : Number of customers (n = 1 - TN), TN = 61).
degree of total customer satisfaction as an objective
variable and assigned the degrees of inherent attributes as
explanatory variables. Furthermore, the study has devel-
oped the model that actually predicts the degree of total
customer satisfaction of the target products from the de-
gree inherent attributes of product. The possibility of
whether or not the degree of observed total customer
satisfaction corresponding to the specified products could
be derived from the degree of inherent attributes corre-
sponding to the target product during the system design
phase was verified.
Finally, the study discusses the validity of estimated
prediction models based on the significance of the de-
veloped prediction model and possibility of application
of proposed prediction model and approaches.
In recent years, due to the explosion of the Internet,
purchasing behaviours of customers have significantly
changed. For example, an increasing number of custom-
ers can order a product directly from an electric com-
merce site without visiting brick-and-motor shops while
remaining at home. The degree of customer satisfaction
is aindicators used in marketing that represents how a
product or service produced by a company meets cus-
tomer expectation.
This study focuses on online reviews posted on the
Internet, an effective alternative to face-to-face inter-
views of customers, and uses the online negative reviews
of a system product as the data of investigation. This
study collects and uses online reviews of products posted
at a web-site, kakaku.com [12] as customer’s expression
of his/her dissatisfaction of system products. Table 1
shows the part of collection data concerning negative
review from web-site, which total number of type of
LPCs is 35 and total number of review is 457.
From Table 2, this study counts the number of online
negative reviews for each concrete category of interest of

K. ESAKI 398
LPCs from the view point of the six quality characteris-
tics. This study collects and classifies online negative
reviews from the view point of the six quality character-
istics in this manner. Furthermore, for each product, this
study obtains the degree of importance for each of the six
quality characteristics taking into account the interest of
attribute of LPCs (i.e., weight for) by the six quality
characteristics as shown in Table 3.
Table 3 shows the example of questionnaires and the
obtained result of importance of quality needs by each
six quality characteristics obtain from questionnaires.
For example, the questionnaires have asked to the
customers, “in purchasing a LPC, what attributes are
important?” and the customers have assigned the numeric
order number between 1 and 15 based on the importance.
The meaning of order number 1 is the most important
attribute for the quality characteristic, and the weights for
the six quality characteristics have normalized in the
range from 0 to 1.
This study quantitatively calculatesthe importance of
customer needs for each quality characteristics.
For example, theimportance of functionality as M is
obtained from the following Equations (1) and (2).
 
11
11
'2
TN TN
n
n
ON SaON
TN
M  


12
n
n
Sa

 (1)
'
', ',', ')max(',',
M
M
M
NO
PQR (2)
M: Importance ratio of Functionality (weight);
M’: Importance ratio of Functionality (Un-Normalised);
Sa1n: Order number of importance (Sa1n =1 ~ ON);
Sa2n: Order number of importance (Sa2n =1 ~ ON);
n: Number of customers (n = 1 ~ TN);
ON: Maximum order number (ON = 15);
TN: Total number of customers (TN = 61).
From Table 2, TN is the total number of customers,
which total number of customersis 61, and pis the weight
for the efficiency determined from the questionnaires.
This study focused on laptop computers (LPCs).
Reasons for choosing LPCs are, at first, LPCs have
at-tribute and characteristics that correspond to the six
quality characteristics, and there is a large amount of data
available on the non-functionality and non-quantitatively
requirements on online review web-sites. Table 1 shows
the part of collection data concerning inherent attributes
of LPCs from web-site, which total number of type of
LPCs is 35. Inherent attribute refers to the degree to
which attributes of target product have the intrinsic po-
tential to satisfy stated and implied needs when LPCs is
used under specified conditions. From the inherent point
of view, Attribute of LPCs is product itself, in par ticu-
larto shown in Table 1.
Above consideration, price is not inherent attribute but
assigned attribute. If we wish to evaluate the customer
satisfaction from the view point of product quality, we
should use only inherent attribute of target product itself.
Then, this study use only inherent attributes of target
product without price.
Degreeof Customer Satisfaction
Table 3 shows the parts of result about degree of the
observed customer satisfaction for each quality charac-
teristics. By applying the weight for each of the six qual-
ity characteristics, this study has quantitatively calculated
the degree of customer satisfaction for each six quality
characteristics. For example, the degree of customer sat-
isfaction for efficiency as SDi is obtained as following
Equations (3) and (4)
1
1i
ii
dp
dd RC

 (3)
22
112
iii
SDdd dd (4)
SDi: Customer satisfaction of efficiency;
dd1i: Ratio of un-satisfaction of efficiency by each
category;
d1i: Number of negative review of efficiency;
i: Number of sample product (i = 1 - 35);
P: Importance ratio of efficiency (weight);
RCi: Total number of online reviews of a given product.
This study has quantitatively calculated the degree of
observed total customer satisfaction from the view point
of six quality characteristic included in the ISO/IEC9126
quality model.
The degree of observed total customer satisfaction is
obtained based on the consideration of independency
among six quality characteristics as following Equation
(5) as shown in Table 4.
22 222 2
iiiiiii
TSSA SBSCSD SESF
01 2iiini
PSrr arbro
(5)
TSi: Observed total customer satisfaction.
This study has predicted the degree of total customer
satisfaction. The degree of predicted total customer sat-
isfactions is obtained by using inherent attributes of
LPCs as following Equation (6).
(6)

   
PSi: Predicting total customer satisfaction;
rn: partial regression coefficient (n = 1 - 14).
4. Verification
4.1. Correlation Matrix between Observed Total
Customer Satisfaction and Attributes
Table 5 shows the result of correlation analysis among

K. ESAKI 399
Table 4. Degree of observed customer satisfaction.
Degree of observed customer satisfaction
Functionality Usability Reliability Efficiency Portability Maintainability Total
Number of
Sample
products
SA SB SC SD SE SF TS
S1 1.0000 0.7993 0.9110 0.7732 0.9464 0.9906 2.2234
S2 1.0000 0.8600 0.9654 1.0000 0.9798 0.9751 2.3627
S3 0.9764 0.7231 0.9150 0.9211 0.9766 1.0000 2.2618
S4 1.0000 0.8812 0.7580 0.7916 1.0000 1.0000 2.2311
S5 1.0000 0.8849 1.0000 1.0000 0.9829 1.0000 2.3977
S6 1.0000 0.8482 0.9213 0.9595 1.0000 1.0000 2.3428
S7 1.0000 0.8862 1.0000 0.9565 0.9807 0.9762 2.3696
S8 0.9666 0.8052 1.0000 0.7646 1.0000 1.0000 2.2732
S9 1.0000 0.8963 0.7438 0.9524 1.0000 0.9739 2.2830
S10 1.0000 0.7209 0.7995 0.9714 0.9619 0.9843 2.2354
Si 1.0000 0.8866 0.9664 0.9688 0.9584 1.0000 2.3615
Si: Example of target laptop personal computers (i: Number of sample product (i = 1 - 35)).
Table 5. Correlation matrix between TS and inherent attributes of LPCs.
Total customer
satisfaction
CPU
HDD capacity
GPU
Resolution
Wait of body
Drive time
Number of USB
port
Number of
memory slot
Aproduction
country
Clock speed (GHz)
Cash Memory size
(MB)
Liquid crystal
size (MB)
Memory capacity
(GB)
Capacity of SSD
Memory
TS a b c d e f g h i j K m n o
r
1 r 2 r 3 r 4 r 5 r 6 r 7 r 8 r 9 r 10 r 11 r 12 r 13 r 14
TS 1.000
a 0.292 1.000
b −0.122 0.316 1.000
c −0.056 0.347 0.304 1.000
d −0.125 0.137 0.217 0.346 1.000
e −0.140 0.262 0.679 0.229 0.442 1.000
f 0.234 0.088 −0.468 −0.042 −0.210 −0.7441.000
g 0.077 0.110 0.326 −0.118 0.180 0.501 −0.2641.000
h −0.435 0.040 0.513 0.246 0.352 0.691−0.7210.1461.000
i 0.148 0.115 −0.189 −0.166 0.196 −0.357 0.4600.227−0.4851.000
j 0.193 0.394 −0.092 −0.359 −0.252 −0.0360.181−0.025 −0.3370.2121.000
k 0.042 0.589 0.446 0.527 0.513 0.541−0.2130.2020.209 −0.014 −0.0641.000
m −0.128 0.270 0.727 0.211 0.340 0.947−0.724 0.3490.677 −0.4220.0070.454 1.000
n 0.166 0.577 0.646 0.422 0.496 0.560−0.2460.3320.370 −0.058 −0.1560.672 0.493 1.000
o 0.199 0.085 −0.623 −0.108 −0.082 −0.416 0.413−0.119 −0.483 0.3250.170 −0.007 −0.503 −0.266 1.000
rn: partial regression coefficient (n = 1 - 14). TS: Observed total customer satisfaction.
inherent attributes and TSi (Obtained total customer sat-
isfaction from Equation (5)) of LPCs.
Since the correlation coefficient, a, f, h, j, o are high,
there is a correlation among obtained total customer sat-
isfaction from the view point of concerning inherent at
tributes and dependency of each are recognized.

K. ESAKI 400
Also, correlation between 1) CPU and 2) Clock speed
is recognized.
4.2. Multiple Regressions Analysis among
Observed Total Customer Satisfaction and
Attributes
Table 6 shows the three types of developed prediction
models based on the consideration about the result of
correlation analysis. From Table 6, multiple-regression
analysis between observed customer satisfaction and
concerning inherent attributes of the each types of pre-
diction model shows that the value of multiple-regression
coefficients and the determination coefficients are 0.5610
and 0.3147, respectively as prediction model type 2. In
addition, maximum value of F-test is 3.443. Since it is
higher than 5% significance level F0 = 2.689, this study
confirmed that there is significance in predicting the de-
gree of total customer satisfaction.
The cause and effect relationship between the degree
of observed total customer satisfaction and those corres-
ponding inherent attributes of LPCs could observe.
Based on the consideration of above result, this study
confirmed that causal relationship among the observed
total customer satisfaction and concerning inherent at-
tributes of sample products.
This study verified the validity of the introduced pre-
diction models of quantitatively predicting total customer
satisfaction using the inherent attributes of target LPCs.
Table 7 shows the parts of the result of degree of pre-
dicted total customer satisfaction, which predicted by
each types of prediction models.
5. Concluding Remarks
Based on the results of this study, realized entire cus-
tomer satisfaction of system product can be predicted and
compared by using implemented inherent attributes of
target system products during design stage of develop-
ment. Proposed prediction models and application may
be very useful for estimate the total customer satisfaction
of developing product, and compare with the configure-
tion of alternative candidate products, and provide the
solutions for taking the higher customer satisfaction of
the product. Also, proposed prediction models may be
used as the following:
To predict influence on customer satisfaction of the
inherent attribute of system products to implement at a
Table 6. Result of multiple regression analysis.
Result
F0 (m, 35, 0.05)
Prediction Models for PS
PS: Predicting total customer Satisfaction R: Multiple
correlation
coefficient
R2: coefficient of
determination F Value
m F0
Type 1 = r0 + r1 × a + r6 × f + r8 × h + r10 × j + r15 × o 0.5757 0.3314 2.8753 5 2.5336
Type 2 = r0 + r1 × a + r6 × f + r8 × h + r15 × o 0.5610 0.3147 3.4436 4 2.6896
Consider only
inherent attributes
Type 3 = r0 + r6 × f + r8 × h + r10 × j + r15 × o 0.4513 0.2036 1.9177 4 2.6896
Table 7. Predictingvalue of total customer satisfaction.
Result of prediction
Type 1 Type 2 Type 3
Observed customer satisfaction
Sample products
PSi PSi PSi TSi
S1 2.2871 2.3033 2.3040 2.2234
S2 2.3005 2.3043 2.2980 2.3627
S3 2.2783 2.2850 2.2853 2.2618
S4 2.2754 2.2643 2.2958 2.2311
S5 2.2706 2.2738 2.2946 2.3977
S6 2.2921 2.2970 2.2932 2.3428
S7 2.3123 2.3146 2.3048 2.3696
S8 2.2929 2.3010 2.3023 2.2732
S9 2.2955 2.3106 2.3088 2.2830
S10 2.3113 2.3138 2.3042 2.2354
Si 2.3455 2.3336 2.2951 2.3615
Si: Example of target laptop personal computers (i: Number of sample product (i = 1 - 35)).

K. ESAKI 401
design stage and may be able to simulate it;
Could realize the most suitable combination of parts in
to the target system;
The most suitable design of the product can be
performed and may be able to realize the cost effective
high quality product;
With the same cost, a product having higher customer
satisfaction may come out;
The possibility of the application to other systems.
In the future study, the author plans to study the influ-
ence of price and application of proposed models to each
six quality characteristics.
6. Acknowledgements
The authors are grateful to members of production sys-
tem research office at Graduate School of Factory of
Sciece and Engineering HOSEI University who their
contributions and support to make the discussion.
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