Analysis of Customization Strategy for E-Commerce Operation Based on Big Data

Abstract

In order to improve the efficiency of customization and reduce the cost of customization under Big data environment, this paper uses cost-sharing contract, pricing mechanism, Hotelling model, and game theory tools and research methods, for C2B Electronic Commerce (e-commerce) mode of Supply Chain Pricing Strategy for in-depth discussion. This paper first gives the architecture of the customization service system based on big data. The paper studies the game equilibrium of supply chain members under four scenarios: centralized decision-making, decentralized decision-making, C2B-dominated decision-making, and traditional enterprise-dominated decision-making in a supply chain composed of a supplier and C2B e-commerce enterprises with horizontal price competition, and examines the cross-price. Important parameters such as impact coefficient, impact coefficient of effort degree of personalized customization, and so on have an impact on variables such as effort degree of personalized customization, retail price, and profit of supply chain members of C2B e-commerce enterprises. Research shows that with the increase of cross-price impact coefficient, C2B e-commerce will enhance its personalized customization efforts in different situations in order to pursue higher profits.

Full text

Research Article
Analysis of Customization Strategy for E-Commerce Operation
Based on Big Data
Su Chen
School of International Trade and Economics, Ningbo University of Finance and Economics, Ningbo 315175, China
Correspondence should be addressed to Su Chen; suchen@nbufe.edu.cn
Received 3 October 2020; Revised 20 October 2020; Accepted 5 February 2021; Published 26 February 2021
Academic Editor: Hongju Cheng
Copyright © 2021 Su Chen. 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.
In order to improve the efficiency of customization and reduce the cost of customization under Big data environment, this paper
uses cost-sharing contract, pricing mechanism, Hotelling model, and game theory tools and research methods, for C2B Electronic
Commerce (e-commerce) mode of Supply Chain Pricing Strategy for in-depth discussion. This paper first gives the architecture of
the customization service system based on big data. The paper studies the game equilibrium of supply chain members under four
scenarios: centralized decision-making, decentralized decision-making, C2B-dominated decision-making, and traditional
enterprise-dominated decision-making in a supply chain composed of a supplier and C2B e-commerce enterprises with
horizontal price competition, and examines the cross-price. Important parameters such as impact coefficient, impact coefficient
of effort degree of personalized customization, and so on have an impact on variables such as effort degree of personalized
customization, retail price, and profit of supply chain members of C2B e-commerce enterprises. Research shows that with the
increase of cross-price impact coefficient, C2B e-commerce will enhance its personalized customization efforts in different
situations in order to pursue higher profits.
1. Introduction
With the continuous development and popularization of
Internet technology, e-commerce has begun to challenge
the traditional business transaction model and become an
important market force, affecting the development process
of the world economy [1, 2]. E-commerce is a new business
model based on the Internet as a trading platform. Since its
appearance in the 1990s, its development momentum has
been very rapid. Under the circumstance of making full use
of Internet resources, e-commerce has incomparable advan-
tages over the traditional business model [3, 4]. It can realize
cross-regional and all-weather business, with a complete
range of goods, easy retrieval, low cost, and can provide per-
sonalized services for consumers. All these are the unique
features of electronic commerce born on the Internet [5].
C2B module customization: this C2B model is mainly to
meet the personalized needs of specific groups formed a
model, with a strong market target orientation; usually, the
success rate of marketing will be relatively large [6]. Typi-
cally, such as Haier Home Appliances Customization Service,
Blue Orange Mobile Phone, etc. are trying this kind of ser-
vice. Consumers choose and combine the performance of
the components according to their own needs when placing
an order, and then, the manufacturer produces according to
the combination requirements in the order [7]. However,
the C2B mode of this module combination requires higher
production efficiency of the enterprise production line, and
this so-called function combination cannot really integrate
the opinions of consumers. Consumers’choice is just to
arrange and combine the existing functions within the range
given by the manufacturer [8]. Restrictions on objective con-
ditions of the needs of consumers [9, 10]. Mass customiza-
tion refers to the new mode of production produced by
enterprises in order to adapt to the new market environment.
With the diversification and customization of products
aggravating economic value and strategic advantages, the
demand of consumer personalized customization needs to
be urgently met [11]. The realization of mass customization
is facilitated. Mass customization is an important way to
enhance the competitiveness of enterprises, so more and
more supply chain enterprises also realize the importance
Hindawi
Wireless Communications and Mobile Computing
Volume 2021, Article ID 6626480, 11 pages
https://doi.org/10.1155/2021/6626480

of pricing in the case of mass customization. Some
researchers study the product pricing problem of mass cus-
tomizer and distributor under the condition that product
price and lead time affect demand and analyze the lead time
strategy and optimal pricing strategy of supply chain enter-
prises under centralized decision, decentralized decision,
and mass customizer-led decision. Based on this, a commis-
sion contract is proposed. It can effectively coordinate the
lead time and pricing problem of mass customization supply
chain. Literature investigates the product pricing problem
under mass customization and analyzes the time problem
of price change from the perspective of internal operation
decision-making in order to achieve the goal of maximizing
profit [12]. Under the condition of certain mass customiza-
tion capability, how to analyze the individual demand of con-
sumers and determine the degree of customization effort of
products are discussed. E-commerce refers to the use of any
information and communication technology for any form
of business or management operations or information
exchange. The relationship between enterprise income and
customer satisfaction and customization degree is discussed.
The model and algorithm of optimizing output are estab-
lished, and the optimum degree of customization is deter-
mined to maximize profits [13].
In order to improve the efficiency of customization and
reduce the cost of customization under Big data environ-
ment, this paper uses cost-sharing contract, pricing mecha-
nism, Hotelling model, and game theory tools and research
methods, for C2B Electronic Commerce (e-commerce)
mode of Supply Chain Pricing Strategy for in-depth discus-
sion. This paper first gives the architecture of the customiza-
tion service system based on big data. The paper studies the
game equilibrium of supply chain members under four
scenarios: centralized decision-making, decentralized deci-
sion-making, C2B-dominated decision-making, and tradi-
tional enterprise-dominated decision-making in a supply
chain composed of a supplier and C2B e-commerce enter-
prises with horizontal price competition, and examines the
cross-price. Important parameters such as impact coeffi-
cient, impact coefficient of effort degree of personalized cus-
tomization, and so on have an impact on variables such as
effort degree of personalized customization, retail price, and
profit of supply chain members of C2B e-commerce enter-
prises. Research shows that with the increase of cross-price
impact coefficient, C2B e-commerce will enhance its person-
alized customization efforts in different situations in order to
pursue higher profits [14].
2. Electronic Commerce’s Customization
System Based on Network Big Data
2.1. The Big Data Platform for Electronic Commerce. The
implementation of C2B customization mode requires the
active participation of consumers, but the participation of
consumers requires enterprises to spend more energy to
achieve, which will inevitably cause a burden on the cost of
enterprises. However, under the influence of the big data
era, we can make full use of the collected large data to realize
the “big data customization”C2B mode [15]. This mode is
actually based on the full analysis of the collected massive
data, mining valuable information for enterprises, usually
consumers’consumption habits. Methods and characteris-
tics, and then the integration of various social resources
based on large data extracted from useful information for
product production. The intelligent tourism system architec-
ture based on big data technology is shown in Figure 1.
The data that can be used in the personalization of
e-commerce are mainly click traffic data of website and
mobile device data. In our daily life, we can also integrate
the personal data of e-commerce users through some records
with the characteristics of identifying users to form a com-
plete set of personalized e-commerce recommendation data.
For effective marketing and promotion, every click and its
time on the Internet are recorded, and with this data, service
providers can carefully analyze user access patterns to pro-
vide more targeted services. User personalized behaviour
data mainly include social network, logged-in sites, page stay
and news, search keywords, mobile phone click applications,
and LBS-based user behaviour data.
E-commerce data is becoming more and more huge, tra-
ditional databases will have query bottlenecks, whether in
storage or inquiry, there are performance bottlenecks, user
applications and analysis results show a trend of integration,
real-time and response time requirements are getting higher
and higher, the model used is becoming more and more com-
plex, and the amount of calculation increases exponentially,
traditional skills. Unable to deal with large data: mobile cus-
tomer data volume to TB level; Oracle database SQL state-
ments can get results, but hope to further improve efficiency.
According to the existing personalized recommendation
structure, the improved E-commerce can be divided into four
modules: data integration, data preprocessing, model algo-
rithm, and online recommendation of products. Starting
from the data integration, the user’s personal information is
collected from all aspects, and the user’s personalized data
is fully grasped from the data source. Then, the formatted
data are stored in the database through data preprocessing,
and the effective analysis data are extracted by e-commerce.
Using e-commerce personalized recommendation technol-
ogy, by matching the existing recommendation patterns, a
certain rule is stored in the database. When the user browses
the relevant information in the e-commerce website, the rec-
ommendation information is returned to the client through
the current session to complete an effective e-commerce per-
sonalized recommendation activity.
2.2. CBR Architecture of Customization Service in Electronic
Commence. CBR architecture of customization service in
electronic commerce is shown in Figure 2.
The theory of customization service is to adjust the infor-
mation organization mode to provide the best service accord-
ing to the specific consumption mode of each user. But the
convergence of the crowd consumption shows that it is feasi-
ble to divide the consumption behaviour mode according to
the user group. For large-scale e-commerce service providers,
it does not reduce the quality of customized service. On the
premise of quantity, this partition can greatly reduce infor-
mation load. CBR is precisely an idea to solve new problems
2 Wireless Communications and Mobile Computing

with the help of past experience. It is embodied in custom-
ized services, that is, to provide a single user with the infor-
mation services necessary to complete network consumption
behaviour by using the known classification of consumer
behaviour patterns. The mature CBR technology provides a
complete set of solutions for the realization of customized
services in e-commerce environment. On this basis, we give
the client/server structure of customized service CBR, as
shown in Figure 2. The “customization”is the abbreviation
of customization service, and the algorithm is the abbrevia-
tion of rewriting algorithm.
Due to the uncertainty of the number of online cus-
tomers, the scalability of the system becomes very important.
The client/server architecture is used to meet this need. The
client is a plug-in (such as Java Applet) that can run on a
standard WWW browser, and the server includes two server
modules, the control centre and the customized service (cor-
responding to the retrieval and rewriting of the traditional
CBR, respectively), which can be distributed on multiple
computers. The typical customization service process is as
follows: the client application starts, establishes a connection
between the customized client and the case control centre,
requests the product customization service, a control centre
for customer verification, and returns the product informa-
tion. The customized client submits the product information
to the customized server and requests the case to rewrite the
customized server. According to the product information,
the algorithm server chooses the corresponding mechanism
from the algorithm library to rewrite. The customized client
interacts with the customized client repeatedly until the user
stops the loop by accepting or rejecting the result of rewrit-
ing. The customized client returns the final result recognized
by the user to the control centre for restarting.
3. C2B Electronic Commerce Pricing Strategy
considering Customized Effort
In today’s information economy era, customers need person-
alized products and services more and more. However, some
enterprises set the same price instead of charging different
consumers different prices according to their perceived con-
sumption value, thus losing a lot of potential benefits. In fact,
Mobile data Navigation
data Web log
Model
algorithm
Database
Layer
Perceptive
layer
Data reduction Data
integration Data conversion
Keyword
search Application
Cloud platform
Data mining Data
visualization
Machine
learning Data analysis
Data
Pre-processing
Model
establishment Knowledge rule Storage medium
Application
Layer
Customization
APP Web Search engine
Figure 1: The intelligent tourism system architecture based on Big data technology.
3Wireless Communications and Mobile Computing

different customers have a variety of needs, C2B electronic
commerce (e-commerce) enterprises should adopt personal-
ized customized pricing strategy according to actual needs; in
order to improve consumer value, further expand market
demand, thus greatly improving economic benefits.
Most of the traditional supply chain enterprises are
affected by other supply chain members. By pricing and
inventory flow, suppliers always dominate the supply chain.
But with the rapid development of the economy, manufac-
turers and retailers are shifting their positions in the transac-
tion, leading retailers like Carrefour, Best Buy, and Wal-Mart
are rising, and their power structure in the supply chain will
change accordingly. In order to survive and develop in the
fierce competition, supply chain enterprises must analyze
the behaviour of their competitors and make reasonable deci-
sions according to the actual situation in order to win a place
for themselves.
3.1. Mathematical Strategy Model Analysis and Symbol
Description. Consider a two-echelon supply chain structure
model consisting of a supplier and two manufacturers with
horizontal price competition (a C2B e-commerce enterprise
and a traditional enterprise). The supplier and C2B e-
commerce enterprises as well as the traditional enterprises
are risk-neutral, and the market demand and cost parameters
are also their common information. The supplier provides
general parts to C2B e-commerce enterprises and traditional
enterprises at wholesale price w. The C2B e-commerce enter-
prises deliver the products to customers after individualized
customization. The traditional enterprises assemble the gen-
eral parts into standard products and then ship them. The
retail price of individualized customized products is pc, and
the market needs it. Quantity is Dc, the retail price of stan-
dardized products is ps, and the market demand is Ds,Dc
and Dswhich constitute the whole consumer market. Sup-
pose w<pc,ps, otherwise, the profits of suppliers and C2B
business enterprises and traditional enterprises will be zero.
The C2B pricing model is shown in Figure 3.
In the demand function constructed in this paper, the
market demand is fixed. The retail price pcof customized
products has a negative impact on the demand Dcof person-
alized products, a positive impact on the demand Dsof stan-
dardized products, a negative impact on the demand Dsof
standardized products, a positive impact on the demand Dc
of personalized products, and a positive impact on the per-
sonality of products. The degree of customization effort e
has a positive impact on the demand for customized products
and a negative impact on the demand for standardized prod-
ucts. The following demand functions is built as
Dc=1−pc+θps+γe
Ds=1−ps+θpc−γe
(:ð1Þ
In which, θ∈ð0, 1Þrepresents the cross-price influence
coefficient, and γ∈ð0, 1Þis the individualized customization
effort influence coefficient. From the above demand func-
tion, we can see that the market demand Dcis positively cor-
related with the degree of customization effort eand the
retail price psof standardized products, and negatively corre-
lated with the retail price pcof customized products. Market
demand Dsis positively correlated with the degree of cus-
tomization effort eand retail price pcof customized products
but negatively correlated with the retail price psof standard-
ized products. The cost function of C2B business enterprise
customization effort degree is
ck=k
2e2
:ð2Þ
Custom graphical
interface
Graphical interface
control algorithm
Custom server
Custom
graphical
Algorithm server Algorithm
adjustment
Case control center
XML XML
TCP/IP TCP/IP
Database
XML
Sever 1
Sever 2
Figure 2: CBR architecture of customization service in electronic commerce.
4 Wireless Communications and Mobile Computing

In which, k>0,ckfor personalized customization effort
cost parameter. ∂ck/∂e>0,∂2ck/∂2e>0.
3.2. Pricing Model under Centralized Decision and Its
Solution. Members of the supply chain want to achieve the
goal of maximizing the profit of the supply chain. When
the supply chain adopts the centralized decision-making
mode, the decision-maker sets the optimal degree of custom-
ization effort from the angle of maximizing the profit of the
whole supply chain and, then, determines the retail price of
C2B e-commerce enterprises and traditional enterprises.
The total profit function of C2B supply chain is as follows:
π=pc−w
ðÞ
1−pc+θps+γe
ðÞ
+ps−w
ðÞ
1−ps+θpc−γe
ðÞ
−k
2e2
:
ð3Þ
The first derivative pcand pscan be obtained:
1‐2pc+2θps+1−θ
ðÞ
w+γe=0
1‐2ps+2θpc+1−θ
ðÞ
w−γe=0
(:ð4Þ
In the centralized decision-making situation, the optimal
pricing pcd
cand pcd
sof C2B supply chain and the optimal
degree of customization effort ecd are as follows:
pcd
c=1
21−θ
ðÞ
+1
2w
pcd
s=1
21−θ
ðÞ
+1
2w
ecd =0
8
>
>
>
>
>
>
<
>
>
>
>
>
>
:
:ð5Þ
In the centralized situation, the decision-maker has no
incentive to improve the profit of the whole supply chain
through the strategy of individualized customization effort.
Although the profit of the whole supply chain has been
maximized, consumers cannot buy customized products. In
addition, we can conclude that the greater the cross-price
influence coefficient, the higher the final pricing of C2B
e-commerce enterprises and traditional enterprises. Sec-
ondly, there is a positive correlation between the price of
products and the wholesale price of suppliers. This is because
the higher the wholesale price of suppliers, the higher the
purchasing cost of C2B e-commerce enterprises and tradi-
tional enterprises will be, so the supply chain enterprises will
increase the price in order to achieve considerable profits. In
the sense of management, C2B supply chain enterprises
should not only meet the individual needs of consumers
but also make the cost control of enterprises within a certain
range, so as to ensure the necessary profits.
Dcd
c=1
2+1
2wθ−1
ðÞ
Dcd
s=1
2+1
2wθ−1
ðÞ
πcd =1
2
1
1−θ−w

1+wθ−1
ðÞ½ðÞ
8
>
>
>
>
>
>
>
<
>
>
>
>
>
>
>
:
:ð6Þ
It shows that the market demand of individualized cus-
tomized products is the same as that of standardized prod-
ucts when the centralized decision-making model is
adopted. The market demand is positively correlated with
wholesale price, and the influence coefficient of cross-price
is also positively correlated. When the influence coefficient
of cross-price rises, the market demand of products will
increase. The profit of a supply chain is increased.
3.3. Pricing Model and Solution under Decentralized
Decision-Making. When decentralized decisions are made,
supply chain members are independent individuals. Different
from centralized decision-making, C2B e-commerce enter-
prises and traditional enterprises have different interests in
decentralized decision-making. C2B e-commerce enterprises
first determine the degree of individual customization effort e
, and then, the two enterprises determine the retail prices of
two different products psand pcat the same time. In this case,
it can be concluded that the profit function of C2B business
enterprise and traditional enterprise are
Supplier S
C2B enterprise
Standardized
production
enterprises
Customer
w
w
e, p
c
p
s
D
c
D
s
Figure 3: C2B pricing model.
5Wireless Communications and Mobile Computing

πnl
c=pc−w
ðÞ
1−pc+θps+γe
ðÞ
−k
2e2
πnl
s=ps−w
ðÞ
1−ps+θpc−γe
ðÞ
8
>
<
>
:
:ð7Þ
Under the independent and simultaneous decision-
making of C2B e-commerce enterprises and traditional
enterprises, the optimal pricing pnl
cand pnl
sof supply chain
members and the optimal degree of customization effort enl
are as follows:
pnl
c=w+1
ðÞ
k2+θ
ðÞ
2−2γ

+2γ21+θw−w
ðÞ
2−θ
ðÞ
k2+θ
ðÞ
2−2γ

pnl
s=w+1
ðÞ
k2+θ
ðÞ
2−2γ

−2γ21+θw−w
ðÞ
2−θ
ðÞ
k2+θ
ðÞ
2−2γ

enl =2γ1+θw−w
ðÞ
2+θ
ðÞ
2−θ
ðÞ
k2+θ
ðÞ
2−2γ

8
>
>
>
>
>
>
>
>
>
>
<
>
>
>
>
>
>
>
>
>
>
:
:ð8Þ
3.4. Pricing Model and Solution of C2B Business Enterprise
Dominated Decision-Making. Considering that C2B e-
commerce enterprises which provide customized products
play a dominant role, they make decisions first, while tradi-
tional enterprises which provide standardized products to
consumers follow suit, and the two enterprises make deci-
sions individually. In the model of this section, C2B e-
commerce enterprises and traditional enterprises constitute
a three-stage sequential game model. In the first stage, C2B
e-commerce enterprises determine the degree of effort to
customize the product personalized in the second stage,
C2B e-commerce enterprises according to the actual situa-
tion to determine the price of customized products pc;in
the third stage, traditional enterprises determine the price
of standardized products ps. From this, we get the pricing
decision timing of the C2B supply chain. In this case, it can
be concluded that the profit function of C2B business enter-
prise and traditional enterprise are
πcs
c=pc−w
ðÞ
1−pc+θps+γe
ðÞ
−k
2e2
πcs
s=ps−w
ðÞ
1−ps+θpc−γe
ðÞ
8
<
:
:ð9Þ
C2B e-commerce enterprises first determine the degree of
personalized effort of the product ecs, and then according to
the actual situation to determine the price of personalized
customized products pc, and finally the traditional enterprise
to determine the price of standardized products to con-
sumers ps. In this paper, the C2B supply chain model is
solved by the inverse induction method of dynamic game.
The following conclusions can be drawn. Under the domi-
nant decision-making of C2B business enterprises, the degree
of individualized customization of supply chain members is
ecs =2−θ
ðÞ
2+θw+1
ðÞ½
γ−w2−θ
ðÞ
2−θ2

γ
4k2−θ2

−2−θ
ðÞ
2γ
:ð10Þ
3.5. The Pricing Model and Solution of Traditional Enterprise
Dominated Decision-Making. Considering the dominance of
traditional enterprises providing standardized products, C2B
e-commerce enterprises that provide customized products to
consumers first make decisions, and then, the two enterprises
make decisions individually. In the model of this section,
C2B e-commerce enterprises and traditional enterprises con-
stitute a three-stage sequential game model. In the first stage,
C2B enterprises determine the degree of individualized effort
e; in the second stage, the traditional enterprises determine
the price PS of standardized products; in the third stage,
C2B e-commerce enterprises determine the price PC of
personalized customized products according to the actual
situation.
In this case, it can be concluded that the profit function of
C2B business enterprise and traditional enterprise are
πts
c=pc−w
ðÞ
1−pc+θps+γe
ðÞ
−k
2e2
πts
s=ps−w
ðÞ
1−ps+θpc−γe
ðÞ
8
<
:
:ð11Þ
Firstly, C2B e-commerce enterprises determine the
degree of individual effort ets; then, the traditional enterprises
determine the price psof standardized products they provide
to consumers, and finally, C2B e-commerce enterprises
determine the price pcof customized products according to
the actual situation. Reverse induction is still used to solve
the C2B supply chain model.
The customization effort ets of supply chain members can
be expressed as
ets =2USγ+θ−2
ðÞ
wγST
4kT2−2S2γ2,ð12Þ
where S=−θ2−2θ+4,T=4−2θ2,U=−θ2+2θ+4+
θ2w.
4. Simulation Results and Discussion
Because the expression of equilibrium decision of supply
chain members is very complicated in some situations, it
is difficult to get intuitive conclusions. In order to further
analyze the relationship between the variables, this section
simplifies the model by assuming that the degree of custom-
ization effort eis a parameter. Comparing four different
decision situations of supply chain members under C2B sce-
nario is conducted in this section.
4.1. The Influence of Cross-Price Influence Coefficient on
Decision Equilibrium. Based on the basic model proposed
in this paper, the simulation is carried out in the subsection.
Considering eas the decision variable, the parameters are
assigned as follows according to the actual situation of the
enterprise: w=0:5,γ=1, and k=1.
The study did not affect the degree of customization
effort e. Figure 4 shows that in three different scenarios,
with no increase, that is, the more intense price competition,
C2B e-commerce enterprises personalized customization
6 Wireless Communications and Mobile Computing

efforts have increased. This is very understandable, C2B e-
commerce enterprises perceive fierce price competition, will
set a higher degree of customization efforts to achieve prod-
uct differentiation, in order to obtain customer orders. It is
worth noting that under the C2B e-commerce-dominated
situation, C2B e-commerce enterprise personalized customi-
zation efforts to improve faster, because in this situation, C2B
e-commerce enterprises have the dominant power in price
competition, which can better use personalized customiza-
tion to win the favour of customers.
4.2. The Impact of θon Standardized Price and
Customization Price. Consider the impact of θon product
prices. From Figures 5 to 6, we can see that the price of
customized products and standardized products will increase
with the increase of cross-price impact coefficient. There is
no positive correlation between the price of the product
and the increase in the price of θ. The price of standardized
products under centralized decision-making mode is the
highest, and the price under decentralized decision-making
and e-commerce-led mode is higher than that under
traditional enterprise-led mode, respectively. The price of
standardized products is the lowest under traditional
enterprise-dominated situation. The reason for this phenom-
enon may be that traditional enterprises want to take advan-
tage of their dominant advantages and adopt low price. The
competitive strategy of price marketing has won the favour
of more consumers.
4.3. The Impact of θon Standardized Demand and
Customization Demand. Consider the impact of θon product
demand. From Figures 7 to 8, we can see that with the
increase of cross-price impact coefficient, the demand for
personalized customized products and standardized prod-
ucts will increase. From Figure 7, we can see that the demand
of customized products is the biggest when traditional enter-
prises are dominant. The demand of products increases with
the increase of θ. The larger θ, the faster the demand is rising.
From Figure 8, we can see that the demand for standardized
products is the smallest when traditional enterprises are
dominant and the demand for standardized products
increases slowly when centralized decision-making is taken.
This shows that the change of 0 at this time has a weak
impact on product demand. When the cross-price influence
coefficient is higher than a certain threshold (θ>0:425), the
demand for products in decentralized decision-making sce-
nario is the largest, and when it is not in a certain interval
(0<θ<0:425), the demand for products in centralized
decision-making scenario is the largest. When it is higher
than a certain threshold (θ>0:67), when C2B e-commerce
enterprises dominate, the market demand of standardized
products is larger than that of centralized decision-making
situation, which shows that θhas a greater driving force for
product demand in this context.
4.4. The Impact of θon Profit of e-Commerce Enterprises and
Traditional Enterprises. As shown in Figure 9, with the
increase of x, the profits of C2B e-commerce enterprises grow
rapidly in three different situations, but they are the most
profitable under the traditional enterprise-led situation,
which is very interesting. This shows that, although C2B e-
commerce enterprises cannot bring high profits by dominat-
ing price competition, they are more profitable to become
followers in pricing. This is because, although the price of
0.2 0.3 0.4 0.5 0.6 0.7 0.8
0.5
0.55
0.6
0.65
0.7
0.75
0.8
Eort of customization e
Cross price inuence coecient sita
ts
cs
nl
Figure 4: Influence of cross-price influence coefficient θon decision equilibrium.
7Wireless Communications and Mobile Computing

personalized products is higher and the unit profitis
increased under the C2B-dominated situation, the demand
falls greatly, so the profit is smaller than that under the tradi-
tional enterprise-dominated situation.
As is shown in Figure 10, the profit of traditional enter-
prises is positively correlated with X. When traditional enter-
prises are dominant, the profit of standardized products is
the largest, while the profit of traditional enterprises is the
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
1
1.5
2
2.5
3
3.5
4
4.5
Customization price Pc
Cross price inuence coecient sita
ts
cs
nl
Figure 5: The impact of θon standardized price.
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
Strandardized price Pc
Cross price inuence coecient sita
cd
nl
cs
ts
Figure 6: The impact of θon the price of customization price.
8 Wireless Communications and Mobile Computing

lowest under decentralized decision-making. And the bigger
the X, the more profitable it will be to promote the growth of
traditional business profits.
It is worth noting that, compared with other scenarios,
the profits of C2B e-commerce enterprises and traditional
enterprises are lower in both independent and simultaneous
decision-making scenarios, which show that when choosing
reasonable pricing strategies, enterprises should also pay
attention to cooperation and competition, rather than relying
solely on their own single enterprise to compete with many
enterprises. After all, in the fierce market competition, the
dominant situation on one side will not last long. In order
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
Customized demand Dc
Cross price inuence coecient sita
cd
nl
cs
ts
Figure 7: The impact of θon standardized demand.
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Standardized demand Ds
Cross price inuence coecient sita
cd
nl
cs
ts
Figure 8: The impact of θon the price of customization demand.
9Wireless Communications and Mobile Computing

to remain invincible in the cruel competition, enterprises and
competitors should maintain the spirit of both competition
and mutual learning and cooperation, and constantly inno-
vate to meet the needs of more consumers.
5. Conclusion
The continuous development and expansion of e-commerce
market has brought many business opportunities to the
0.2 0.3 0.4 0.5 0.6 0.7 0.8
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
e prots of C2B e-commerce enterprises
Cross price inuence coecient sita
ts
cs
nl
Figure 9: The impact of θon the profits of C2B e-commerce enterprises.
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0
0.5
1
1.5
2
2.5
3
3.5
e prots of traditional enterprises
Cross price inuence coecient sita
ts
cs
nl
Figure 10: The impact of θon the profits of traditional enterprises.
10 Wireless Communications and Mobile Computing

market, and also brought different degrees of negative
impact. In an age of computer development is very rapid,
large data network technology to every aspect of life brings
a lot of change, this article discussed the electronic commerce
strategy research based on the technology of network data,
through the study of game balance four members of the sup-
ply chain scenario: centralized decision-making and decen-
tralized decision-making, C2B-dominated decision-making,
and traditional enterprise-dominated in the enterprise appli-
cations; finally, it is concluded that the e-commerce market
of big data based on Internet can develop healthier, whether
the data storage and analysis of the research can be more effi-
cient. This method can bring important development func-
tion to the electronic commerce market.
Data Availability
All data are true and reliable, and all data can be obtained by
contacting the author.
Conflicts of Interest
The authors declare that they have no conflicts of interest.
References
[1] W. Liu, Y. Yang, S. Wang, and E. Bai, “A scheduling model of
logistics service supply chain based on the time windows of the
FLSP’s operation and customer requirement,”Annals of Oper-
ations Research, vol. 257, no. 1-2, pp. 183–206, 2017.
[2] Q. Tu, M. A. Vonderembse, and T. S. Ragu-Nathan, “The
impact of time-based manufacturing practices on mass cus-
tomization and value to customer,”Journal of Operations
Management, vol. 19, no. 2, pp. 201–217, 2001.
[3] Y. Wang, E. Zio, X. Wei, D. Zhang, and B. Wu, “A resilience
perspective on water transport systems: the case of eastern
star,”International Journal of Disaster Risk Reduction,
vol. 33, no. 1, pp. 343–354, 2019.
[4] Z. Chen, Y. Zhang, C. Wu, and B. Ran, “Understanding indi-
vidualization driving states via latent Dirichlet allocation
model,”IEEE Intelligent Transportation Systems Magazine,
vol. 11, no. 2, pp. 41–53, 2019.
[5] R. Aron, A. Sundararajan, and S. Viswanathan, “Intelligent
agents in electronic markets for information goods: customiza-
tion, preference revelation and pricing,”Decision Support Sys-
tems, vol. 41, no. 4, pp. 764–786, 2006.
[6] Y. Wang and D. Li, “Testing the moderating effects of toolkits
and user communities in personalization: the case of social
networking service,”Decision Support Systems, vol. 55, no. 1,
pp. 31–42, 2013.
[7] S. Rosaci and G. M. L. Sarné, “Recommending multimedia web
services in a multi-device environment,”Information Systems,
vol. 38, no. 2, pp. 198–212, 2013.
[8] Z. Ning, X. Kong, F. Xia, W. Hou, and X. Wang, “Green and
sustainable cloud of things: enabling collaborative edge com-
puting,”IEEE Communications Magazine, vol. 57, no. 1,
pp. 72–78, 2018.
[9] S. Afshin Mansouri, D. Gallear, and M. H. Askariazad, “Deci-
sion support for build-to-order supply chain management
through multiobjective optimization,”International Journal
of Production Economics, vol. 135, no. 1, pp. 24–36, 2012.
[10] J. H. Park and S. C. Park, “Agent-based merchandise manage-
ment in business-to-business electronic commerce,”Decision
Support Systems, vol. 35, no. 3, pp. 311–333, 2003.
[11] K. Li and T. C. Du, “Building a targeted mobile advertising sys-
tem for location-based services,”Decision Support Systems,
vol. 54, no. 1, pp. 1–8, 2012.
[12] S. Kortmann, C. Gelhard, C. Zimmermann, and F. T. Piller,
“Linking strategic flexibility and operational efficiency: the
mediating role of ambidextrous operational capabilities,”Jour-
nal of Operations Management, vol. 32, no. 7-8, pp. 475–490,
2014.
[13] I. G. Dino and R. Stouffs, “Evaluation of reference modeling
for building performance assessment,”Automation in Con-
struction, vol. 40, no. 4, pp. 44–59, 2014.
[14] M. Zhang, D. Zhang, F. Goerlandt, X. Yan, and P. Kujala, “Use
of HFACS and fault tree model for collision risk factors analy-
sis of icebreaker assistance in ice-covered waters,”Safety Sci-
ence, vol. 111, pp. 128–143, 2019.
[15] T. Yuan and O. Joon, “Research on credit scoring of Chinese
listed SMEs based on DEA method,”Credit, vol. 32, no. 6,
pp. 52–56, 2014.
11Wireless Communications and Mobile Computing