Research on Effect of Customer Perceived Value on Quality Information
Dissemination
Peng Sun , Shu-lin Tang , Qiang Liu, Jing-ting Yuan
School of Economics and Management, Harbin Engineering University, Harbin 150001
(tangshulin00.student@sina.com)
Abstract - From the angle of customers on quality of
product's perceived value, the paper indicates that quality
information dissemination is a noisy and stochastic process,
which could lead to customer perceived value fluctuation,
and usually customer perceived value fluctuation is
regarded as the key indicator of product crisis. Take the
randomness of quality information appearance into account,
the uncertainty of information dissemination and perceived
quality fluctuation, the paper describes the changeable
disciplines of customer perceived value of different
customers at the same time and the same customers of
different times by adopting random walk theory in order to
recover customers perceived value fluctuation.
Keywords - Quality information, information
dissemination, customer perceived value, random walk
model
I. INTRODUCTION
With the development of economy and social
progress, enterprises must transcend the traditional
philosophy of “profit as the only goal”, concentrate on
customer perceived value. The philosophy indicates that
quality information generated during the production
process no longer remains within the enterprise, which
will deliver to customs, finally achieve purchase
behaviors of customers, whose essence is a process that
enterprises transmit product quality value to customers in
value style. 1
There are some researches on how to construct the
model of disseminating quality information. Previous
researches proved that quality information was a
description of continuous random signal and quality
information dissemination is a noisy, non-linear stochastic
process from the perspective of information theory[1].
Some researches presented the continuous tracking of
information model based on Bayes’ theorem which could
describe the effect of customer perceived value on quality
information dissemination in different areas. These
models could help enterprises make the best plan to sale
their products[2]. Several recent researches had recovered
that the dissemination model should contain three roles of
information sources, gatekeepers and receivers. However,
previous researches argued that there were four roles in
This research was supported by Technology Foundation of National
Defence(Grant No. GZ2011010) and by The National Soft Science
Research Program (Grant No. 2008GXQ6D152) and by soft science
research project of industrial and information technology Committee of
Heilongjiang Province of China(Grant No. GXW2010150).
quality information dissemination. Recently, some
researches indicated that the dissemination model
consisted
of
information
source,
information
dissemination space and perceived space[3]. Zhu et
al.(2010) indicated that negative quality information
dissemination could arise from the credit risk and reduce
customer perceived value, which was the important factor
in quality management. Due to the continuous effect of
negative quality information on product utility, customer
perceived value showed the nonlinear change[4]. It
suggested that information releasing system, recognition
mechanisms of publisher identity and reputation were
able to effectively control the situation of expanding
production risk caused by quality information
dissemination[5-6].
The paper suggests that quality information
dissemination model should be able to describe the
features of stochastic process. The random walk theory
can meet the requirements and have a visual function.
II. METHODOLOGY
A. Problem description
Quality information dissemination can be expressed
as variable multiply forms. Variables include: quality
information value V generated by point ( x0 , y0 ) , the
probability of customer on ( x, y) accepting the quality
information P(V , D( x, y),( x, y)) . The expression is:
(1)
V ( x, y)  V  P(V , D( x, y),( x, y))
B. Basic assumptions
The paper concentrates on the description of
determining space discrepancy of information flow value
dissemination and dissemination rules, thus the paper has
the following assumptions:
Assumption1. Information is disseminated in a specific
space and can not disseminate out of the space. Individual
information is regarded as a collection of infinite number
of discrete mediums, whose total volume is 1. It can be
expressed as:

l0
0
 (l )  
 l  0
P (l , 0)   (l )  
(2)
 
   (l )dl  1
Assumption2. Customer is a strictly rational person. If
customer perceived value is higher than product’s price,
customer will buy it. Otherwise, customer will refuse to
buy. If customers have the same perception of quality
information, they will adopt the same purchase
decision-making behaviors. The more the perception of
quality information they have, the greater the probability
of decision-making to accept quality information will
happen. The functions can be expressed as:
P( D  D,V )  P( D,V ) .
Assumption3. There are two kinds of rules in quality
information dissemination. One is incremental
dissemination, the other is attenuation dissemination.
Assumption4. The probability of the source of quality
information disseminates itself is 1.
C. Random walk model of quality information
dissemination
Assume that l is the distance from point ( x, y) to
P
2P
P
(5)
  (V ) 2   (V )
V
l
l
 (V ) ,  (V ) are the reciprocals of customer’s
perception of quality information in Equation (5). System
of differential equations including Equation (2) and
Equation (5) are processed by a Fourier transform. The
dynamic solutions are:
D( x, y ) l 4D
(6)
e
4
From Assumption 4, the boundary conditions is
P(V , D,0)  1 , then solve the equation. The answer is
2
P(V , D, l )  C 
the probability of customer on ( x, y) accepting the
quality information
can be
P(V , D( x, y),( x, y))
simplified as random walk process based on a
two-dimensional coordinate system ( L, D) to calculate,
which is shown in Figure 1.
In the model, assume that quality information whose
value is V existing in the market disseminating to point
C is a full probability event. The probabilities are p
and q respectively p  q  1 . The paper adopts the
symbol of P(l , D) to represent the probability of the
event that quality information disseminates to point (l , D) .
the relationship function can be expressed as follows:
P(l , D  D)  pP(l  l , D)  qP(l  l , D) (3)
D
V
D  D
C
D
A
B
L
O
l  l
l
l  l
Fig.1 The dissemination model of quality information based on Random
Walk theory
The equation (2) was developed by the formula of
Tailor. The result is:
P (l , D)
D
D
(4)
P (l , D) (l ) 2  2 P(l , D)
 l (q  p )


l
2
l 2
Assume that
  lim(( l ) / 2D)
2
l 0
D0
  lim [l ( p  q) / D]
l 0
D0
p q
Because customers in different locations have
different perceptions of quality information, so Equation
(4) can be expressed as:
4
D( x0 , y0 )
C
the source of quality information’s location ( x0 , y0 ) . So
Put the answer into Equation (6), the probability of
customer on ( x, y) accepting the quality information
can be represented as:
P(V , D, l ) 
l
D ( x, y )
e
D( x0 , y0 )
2
D ( x, y )
4
(7)
Take the time factor t into account, put Equation
(7) into Equation (1), then get the function of quality
information dissemination, it can be shown as:
Vt ( x, y )  Vt 
l
Dt ( x, y )
e
Dt ( x0 , y0 )
2
 Dt ( x , y )
4
(8)
Vt is the value of quality information created at
time t and Dt ( x, y) is the customer’s perception in
( x, y) of quality information at time t and Dt ( x0 , y0 )
is the customer’s perception in the source of quality
information whose coordinate is the ( x0 , y0 ) of quality
information at time t and l is the distance from point
( x, y) to the source of quality information’s
location ( x0 , y0 ) , it can be shown as:
l
 x  x0 
2
  y  y0  .
2
For quality information dissemination in multiple source
condition, the influence of a potential customer is equal to
the sum effect of each source disseminating its quality
information. Because V (A) , V (B) , … , V (N ) are
regarded as independent events, so the dissemination
function of quality information in multiple source
condition can be expressed as:
V
N
V (i)
(9)
i A
D. Index settings
In order to achieve the calculation approaches of the
above model, the paper constructs the index system which
consists of quality information source, quality
information dissemination space and customer perception
of quality information[7]. The summary of variables in the
dissemination model of quality information can be seen in
Table 1.
III. EMPIRICAL ANALYSIS
A. Data collection
The paper collects the panel date, quality and price
data of sea cucumber, related-region statistical yearbook
and customer survey from Zhangzidao Fishery Group Co.,
Ltd after two weeks when oil spill happen and research
on the changes in the sales market caused by the oil spill
combining with the quality information dissemination
model.
(1) Quality information source
In order to avoid the stochastic volatility of stock
price, the paper uses the data of 7 days moving average of
stock price(MA7) as the indicator to measure quality
information value[8]. Vt is regard as the indicator of
quality information value and whose sign represents the
impact of quality information on products is good or bad,
which can be shown in Table 2.
(2) Dissemination space standardization
The range of the dissemination space, the location of
quality information source and sales store are given in
Fig.2.
(3) Initial value of customer perceived value
Fu et al.(1999) thought price was a convex function
of quality, defines it as P  aV 2 [9].
Types of Variables
Quality information source
Quality information dissemination
space
Customer perception of quality
information
date
Fig.2 Quality information’s diffusion space coordinate system
B. Simulation
Based on the above-mentioned statistics, the paper
adopts random walk model to simulate quality
information dissemination of products of Zhangzidao
Fishery Group Co., Ltd. The simulation results of quality
information dissemination are shown from Fig.3 to 5.
TABLE I
Variables in the dissemination model of quality information
Variable Level
Date Level
Value
The value of quality information about product creation in the market
Property
The impact of quality information on products is good or bad?
X of dissemination space
Coordinate system
Y of dissemination space
The source’s coordinate
The location where quality information is created
Sales store’s coordinate
The location of sales store
Customer’s coordinate
The location of customer who want to buy the product
Certified quality standards
Mass inertia
Sampling percent of pass
Invest in advertising
The accuracy of quality
The times of report about quality information
information obtained by customer
The objectivity of report about quality information
The ratio of Price and industry average price
consumption level on (x,y)
Customer preferences
The frequency of consumption behavior on (x,y)
The ratio of good review and bad review
TABLE 2
The data of quality information source
2011/9/14
2011/9/28
MA7
27.56
27
Vt
0
-0.56
item
(4) Customer perception of quality information
distribution
After handing out the questionnaire to customers
who live in the place as shown in Fig.2, the paper gets
customer perception of quality information distribution
through the analysis and fitting of the statistic data.
IV. DISCUSSION
The result shows that quality information
dissemination obeys a gaussian distribution, which will
generate a black hole of customer perceived value in
order to make customers refuse to buy when the quality
information is bad enough. The distance from customer to
the source of quality information plays a leading role in
the effect of the black hole on customer perceived value,
followed by customer perception of quality information.
In Fig.3, customer perceived value drops quickly and
generates a black hole of value in Dalian region. But in
the farther region like Beijing-Tianjin region, although
customer perception of quality information is higher than
that in Dalian region, the maximum of the black hole of
value in Beijing-Tianjin region is lower than that in
Dalian region along with the increase of the distance.
In the same distance region, customer perception of
quality information is the crucial factor in customer
perceived value. For example, in Fig.4, the distance from
Taiyuan to the source is the same as Harbin to the source
and the customer perception in Taiyuan is higher than that
in Harbin, but the maximum of the black hole of value
and the scope of influence is lager than that in Harbin.
Fig.5 illustrates customer perceived value drops
slowly and customers still have a higher willingness to
purchase because of their curiosity, expense inertia and
the accuracy of quality information in the region farther
from the source or the junction among several sales
stores.
V. CONCLUSIONS
The model can describe customer perceived value in
different locations and the results are consistent with real
data. It is quite effective in simulating quality information
dissemination using random walk model. The model can
partly explain the complex fluctuations of the customer
perceived value caused by quality information
dissemination.
There is a limitation for using the stock price to
represent the value of quality information. For example,
the irrational high/low price and the tendency variation
will happen in stock market, so the stock price does not
reflect the value of quality information [10].
Fig.3 Space distribution of quality information perceived value
Fig.4 Simulation for quality information dissemination
Fig.5 Regional distribution where customers refuse to buy products
ACKNOWLEDGEMENTS
This research was supported by Technology
Foundation of National Defence(Grant No. GZ2011010)
and by The National Soft Science Research Program
(Grant No. 2008GXQ6D152) and by soft science research
project of industrial and information technology
Committee of Heilongjiang Province of China(Grant No.
GXW2010150).
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