Research on Factors Influencing the Cooperation between Operators
and Systems Integrators in IOT
Qianfan Zhang, Chengjue Wang and Xiaojuan Yu
School of Management, Huazhong University of Science and Technology, Wuhan, 430074, China
Keywords: IOT, Network Operator, Systems Integrator, Evolutionary Game.
Abstract: The internet of things (IOT) is called as another information industry revolution, which is following the
computer, Internet and mobile communication network. But there are many obstacles for the development of
our country’s IOT. Among them, the contradiction between fragmentation of the application requirements
and product supply scale is the bottleneck of our IOT development. And the contradiction’s root lies in the
unclear business model of IOT. Based on the existing IOT business model research, this paper has studied the
cooperation mode selection between operators and system integrators which were the two core enterprises of
IOT industry and analyzed various factors affecting competitive-cooperative relationship in detail to lay a
theoretical foundation for the establishment of IOT industry chain cooperation by pattern evolutionary game
theory. The result shows that within a certain extent, more shareable resources lead to larger probability of
cooperation. However, the probability of cooperation will gradually decrease if the distribution of shareable
resources exceeds the extent.
1 INTRODUCTION
Through decades of accumulation, the technology
development of domestic IOT industry has leaped
into front ranks in the world and now exerts
significant influence. Compared to other developed
countries, IOT in China possesses the first-mover
advantage. However, in order to fully implement IOT,
accelerating the promotion of our IOT products and
applications throughout the world, we are faced with
many technical problems and management issues.
Among these difficulties, one dominant and
imperative issue to tackle with in the development of
IOT industry is the conflict between fragmentation of
application requirements and product supply scale.
The challenge of the development of IOT lies not only
in technical problems, but also in market scale
applications. On the one hand, the deficiency of scale-
industry application seriously impedes the forming of
IOT in medicine industry and the breakthrough and
standardization of core technology, which results in
low participation and investment in every link of the
supply chain. On the other hand, only when scaling
supply is realized can we reduce cost. And finally it
helps to form a benign mechanism of development
and motivates market development. It takes a new
business model to resolve this conflict. The key to
breaking through the bottleneck of IOT development
lies in integrating fragmentation application and
achieving economic scale (Tan, 2010).
This paper investigates the innovation of IOT
business model from the perspective of operators and
system integrators, studying the cooperation between
them under the IOT environment. By analyzing
critical factors influencing cooperation, this paper
constructs a cooperative evolutionary game model.
The analysis of the result will provide a theoretical
basis for the strategic cooperation between operators
and system integrators, and also offer important
reference for scale of IOT.
2 STUDIES ON IOT
COOPERATION MODE
Domestic scholars investigate business cooperation
model of IOT from the angle of operators. Yongbo
Tang proposes that if the development of IOT agrees
with operators transition concept, it will effectively
promote the operators progress in the aspects such as
technique, product and customer development. By
analyzing domestic and foreign business models, the
IOT business models are classified into four
categories, namely Channel Model, Cooperation
Model, Proprietary Model, and Customized Model
(Yunxia Zhang, 2010). From the perspective of
407
Wang C., Zhang Q. and Yu X.
Research on Factors Influencing the Cooperation between Operators and Systems Integrators in IOT.
DOI: 10.5220/0006027404070413
In Proceedings of the Information Science and Management Engineering III (ISME 2015), pages 407-413
ISBN: 978-989-758-163-2
Copyright
c
 2015 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
407

operators, IOT business models are classified into
three categories, namely Cooperative Development,
Independent Promotion, Independent Development,
Cooperative Promotion and Customization (Pengfei
Fan, 2012); Another method classifies them into
Channel Model, Cooperative Development and
Promotion, Plat-form Operation Model, Applied
Service Model and Industry Union Model (Zhuoxian
Li, 2011). There are four modes of IOT: the
government BOT mode, channel accompanied with
cooperation mode, advertising mode and proprietary
mode (Pengfei Fan, 2012). In the network era, the
strategy operator business model should be more
diverse and flexible. Besides, the operation strategy
of business model for the telecom operators should be
divided into flow pipeline mode, optimized mode
with cooperation and independent open mode (Weijia
Zhu, 2014). On the basis of extensive market research
and summary of the research findings of available
literature, this paper summarizes the cooperative
modes in IOT into five categories: Channel Model,
Cooperative Development Model, Independent
Development Model, Independent Enterprise
implementation Model, Customized Model.
The first three models are principal while the other
two are auxiliary, coexisting with the principal
models. Among the three principal models, Channel
Model is a passive model which disagrees with the
strategy choices of game players. Therefore, this
paper mainly discusses the choice between
cooperative development and non-cooperative
development.
3 CONSTRUCTION OF
EVOLUTIONARY GAME
MODEL
3.1 Parameters and Model
Construction
This paper mainly considers the evolutionary game
relations between operators (denoted by 1) and
system integrators (denoted by 2). We assume that
there are only two strategies for both of them:
cooperate to develop new products or develop
independently. Furthermore, we assume the following
parameters:
2 


denotes normal revenue of operators
and system integrators without considering new
product development respectively.


denotes the
excessive return obtained when both of them choose
to cooperate in developing new products and succeed.

is the excessive revenue allocation coefficient,
which denotes the ratio of excessive revenue being
allocated to operators, thus the ratio of excessive
revenue being allocated to system integrators is

-1 .The account of

depends on both sides
resources input and revenue contribution.
'
'
2
1




denotes the revenue gained in independent
development of operators and system integrators.
i

(
i
=1, 2) denotes the shareable resources and abilities
of company
i
, thus the total input of resources during
the cooperation
iC



21


(
i
=1, 2)is the
learning coefficient of company
i , which describes
the ability of learning from the other side in
technologies, experiences and so on. Thus during the
cooperation operators and system integrators could
respectively obtain
i32


(
i
=1, 2) economic
values from learning. (
i
=1, 2) is betrayal benefit
which denotes the revenue company
i
obtains after
betraying cooperation;
f is penalty coefficient, by
assuming both sides in the games have the same
penalty coefficient, we come to their penalty cost for
betraying would be
if

.
3.2 The Solving of the Model
It is assumed that the probability of operators
choosing cooperation is
x
, thus the probability of
choosing noncooperation is
x1 . Therefore,
operators obtain
H
V
1
expected benefit when choosing
cooperation, and obtain
N
V
1
expected benefit when
choosing noncooperation and the average benefit is
1V . We assume the probability of system integrators
choosing cooperation is y, thus the probability of
choosing noncooperation is
y1 . Therefore, system
integrators obtain
H
V
2
expected benefit when choose
cooperation, and obtain expected benefit when
choose noncooperation, and the average benefit is
2V
. All of these functions are shown bellows:
)(
)()(
)1(
11
'
1221
11
'
1211
111
Effxy
fxfEy
VxxVV
NH






(1)
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408

))1((
)()(
)1(
22
'
2112
22
'
2122
222
Effxy
fyfEx
VyyVV
NH





(2)
According to the replicated dynamic equation of
evolutionary game theory, we can derive two
replicated dynamic equations shown as bellow.
Operators’ replicated dynamic equation:
)]()
)[(1(
)(
1
'
111
11221
11


fyE
ffxx
VVx
dt
dx
H



(3)
System integrators’ replicated dynamic equation:
)]()
)1()[(1(
)(
2
'
222
2112
22


fxE
ffyy
VVy
dt
dy
H



(4)
According to the stability theorem of differential
equation and the replicated dynamic equations of
evolutionary game theory, the following condition
must satisfy:
0
11
'
1221
 Eff 
(5)
0)1(
22
'
2112
 Eff 

(6)
Operators replicated dynamic equation indicates that
only when
11
'
1221
1
'
11
Effr
f
y





or
1,0x , there is a consistent ratio of operators to
total operators of the group using cooperation
strategy.
System integrators’ dynamic equation indicates
that only when
22
'
2112
2
'
22
Effr
f
x





or
1,0

y
,
there is a consistent ratio of operators to total system
integrators of the group using cooperation strategy.
Hence, the system has 5 partial equilibrium
points. According to Friedman’s method, when a
group’s dynamic evolutionary process is described by
a derivative equation system, the equilibrium point
can be analyzed by the Jacobian matrix obtained via
the system in order to find out the stability (Friedman,
1996). To make the equation clearer, we assume
M
N
Eff
f
x 



22
'
2112
2
'
22


(7)
G
H
Eff
f
y 



11
'
1221
1
'
11



(8)
The system’s Jacobian matrix is shown as
follows:









))(21(*)1(
*)1())(21(
NMxyMyy
GxxHGyx
J
(9)
The trace of this Jacobian matrix is:
))(21())(21( NMxyHGyxtrJ 




(10)
Then we analyze the stabilities of these equilibrium
points by using the partial stability analysis method
for Jacobian matrix, and find out the relationships
among impact factors. From the results shown above,
we can draw out the phase diagram describing the
evolutionary game on cooperation-noncooperation
choice between operators and system integrators.
Phase diagram of the evolutionary game between
operators and system integrators. The system has 5
equilibrium points. The broken line made up of
unsteady equilibrium point B (1, 0), D (0, 1) and
saddle point E indicates that the system converges to
various critical lines. When initial status falls in the
upper right corner of the broken line, the system
converges into (1, 1), which means both operators and
system integrators will choose the cooperation
strategy. If initial status falls in the lower left corner
of the broken line, the system converges into (0, 0),
which means both operators and system integrators
will choose the noncooperation strategy (Matthew
and Alison, 2002).
If
''
22 2 2 11 1 1
''
211 2 2 1 2 12 2 1 1 2 1
-f - -f - 1
--f+f-E --ff-E2
     
       



+
,
the system has identical probability converging into
both strategies. In other words, the areas to the right
and to the left of broken line BED are equal
(
BCDEABED
SS

).
If
''
22 2 2 11 1 1
''
211 2 2 1 2 12 2 1 1 2 1
-f - -f -
--f+f-E --ff-E
     
       


+
,
the probabilities of converging into two strategies are
different, the difference depends on many factors.
3.3 Factors Analyses
By analyzing the phase program, we come to these
following conclusions. The long-term stable statuses
throughout the evolutionary process between
operators and system integrators could be cooperation
or noncooperation; which ESS point the long-term
evolutionary process will converge to depends on the
Research on Factors Influencing the Cooperation between Operators and Systems Integrators in IOT
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409

initial status are given; the initial revenues for
operators and system integrators will not influence
the stable equilibrium of evolutionary process.
Nevertheless, different initial values and variations of
some parameters in the revenue functions will
converge the evolution system into different
equilibrium points in the long-term (Lee, 2007).
Although the Pareto optimality appears when both
operators and system integrators choose the
cooperation strategy, (1,1) and (0,0) are all stable
points, so which condition will the evolutionary result
converges to relies on the areas of area
I
and area
I
I
(
ABED
S
and
BCDE
S
, respectively ). If
BCDEABED
SS 
,
operators and system integrators have more
probability to choose cooperation than
noncooperation; if
BCDEABED
SS 
, operators and
system integrators have less probability to choose
cooperation than noncooperation; If
BCDEABED
SS

,
the system has the same probability for convergence
for both strategies. Apparently, we can easily derive
the areas equations for
III
SS ,
from Fig. 1 as follows:
)(
2
1
)
)1(
(
2
1
11
'
1221
1
'
11
2
'
2112
2
'
22
G
H
M
N
Eff
f
E
ff
f
SS
ABEDI












(11)
)(
2
1
)
)1(
)1(
(
2
1
11
'
1221
11221
2
'
2112
22112
G
HG
M
NM
Eff
Ef
E
ff
Ef
SS
BCDEII















We can analyze those factors influencing the
cooperation choice between operators and system
integrators by changing them into factors influencing
the areas of
I
I
, because the impact factors of areas of
I
I
share the same tendency with that of cooperation
strategy, and have opposite tendency with the impact
factors of noncooperation strategy. We find 11 impact
factors of area
I
I
from formula (11), so in the
following part will analyze their influences on the
stable strategies of evolutionary process.
3.3.1 Shareable Resources
Only if companies’ resources are complementary,
will they choose to cooperate in developing new
products. Thus under a certain condition, more
investment of resources means stronger desire to
cooperate.
According to formula (11), we derivate
II
S by
21
,  as follows:
2
22
1
1( +f)( )
[]
2
SMNGHf
MG





Ⅱ
(12)
1
22
2
1Nf ( )( +f)
[]
2
SMGH
MG





Ⅱ

(13)
When
2
22
(+f)( )
M
NGHf
MG
 

,
1
0
S




Ⅱ
, thus
II
S is a monotone increasing function of
1
 . In
another word, the more resources operators share, the
larger
II
S will be and the more possible for this
system to converge into C(1,1); when
22
2
))((
G
HfG
M
NMf





, 0
1




II
S
, thus
II
S is a monotone decreasing function of
1
 . In
another word, the areas
I
I
will decrease as the
resources shared by operators increase. Similarly,
system integrators see the same condition.
3.3.2 Independent-Developing Revenue
(Noncooperation Revenues)
According to the analyses, we conclude that if the
excess return operators and system integrators earn in
cooperation be higher than the revenue they earn from
betraying, the more revenues they earn from
noncooperation, the larger the possibility of their
choosing cooperation will be. According to formula
(11), we derivate
II
S by
'
2
'
1
,   as follows:
+
12 2 1 1
'2 2
1
1f+-(+E)
×0
22
SGH
GG
    

 




(14)
21 1 2 2
'2 2
2
1+f+(1-)-(+E)
×0
22
SMN
MM
    

 





(15)
Generically, if the excessive return operators obtain
under cooperation strategy (that is total revenue of
learning from system integrators, punishing system
integrators’ betray and succeeding in cooperation) is
higher than the sum of its devotion costs and probable
betrayal revenue, in another word, if the opportunity
revenue is larger than the opportunity cost, or if
)(
11221
Ef   , we have
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0
'
1




II
S
, so
II
S is a monotone increasing
function of
'
1
  . At this time, the areas of
II
S will
increase with the increasing of probable
noncooperation revenue, and the system will have
larger probability to converge into C (1, 1), which
means operators are more likely to strengthen
cooperation. Or we can come to these conclusions:
the more revenue operators could obtain from
noncooperation, the more resources they need to
input, and the higher risks they will encounter. At this
time, operators could choose the optimal strategy no
matter what strategy the system integrators choose.
Similarly, system integrators see the same condition.
3.3.3 Learning Coefficient
If the devotion costs of operators (system integrators)
is larger than the sum of noncooperation revenue and
probable revenue from punishing system integrators’
(operators’) betrayal, the probability of operators
(system integrators) choosing cooperation will
increase as learning coefficient increases. Reasons are
shown as follows. According to formula (11), we
derivate
II
S by
21
,  :
'
22111
22
1
+f
×× 0
22
SH
GG



 

Ⅱ
（）
(16)
'
11222
22
2
+f
×× 0
22
SN
MM



 

Ⅱ
（）

(17)
If
1
'
11
 f , then 0
1




II
S
, so
II
S is a
monotone increasing function of
1
 . In another
word, the higher the learning coefficient is, the larger
areas of
II
S will be. It means that if the operators
resource input is larger than the sum of their
noncooperation revenue and probable revenues from
punishing system integrators’ betray penalty, they
tend to strengthen the cooperation to obtain excess
return because the cost of choosing betrayal strategy
is much higher. Hence, the higher operators’ learning
coefficient is, the higher the economic value
operators will obtain from their cooperative partners
and the quicker operators would recover their
investment, and the higher excess return they will
obtain. At this time, the operators will have larger
probability to strengthen cooperation, and this system
will have larger probability to converge into C (1, 1).
Similarly, the system integrators see the same
choices.
3.3.4 Betrayal Revenue Coefficient
Because of the asymmetric information, if one side in
the game chooses the cooperation strategy while the
other one chooses noncooperation, the probability of
the noncooperation side betraying will increase as his
probable betrayal revenue increases, and the increase
of betrayal revenue also leads to the decrease of
probability of cooperation establishment in the long-
term evolutionary game. Reasons are shown as
bellows. According to formula (11), we derivate
II
S
by
1
E and
2
E :
'
111
22
1
1+f
×0
22
SH
EG G
 



Ⅱ
(18)
'
222
22
2
1+f
×0
22
SN
EM M
 



Ⅱ

(19)
we have
0
1



E
S
II
because 0H , so
II
S is a
monotone decreasing function of
1
E . In other words,
with the betrayal revenues
1
E increase, areas of
II
S
will decrease, and the probability of this system to
evolution into A (0, 0) will increase. It means that if
the sum of operators’ noncooperation revenue and
probable revenue from punishing system integrators’
betrayal is less than the resources they input
throughout cooperation, the increase of betrayal
revenue coefficient will lead to increase of operators’
betrayal benefit, then further increase the probability
of operators choosing the betrayal strategy.
3.3.5 Betrayal Penalty Coefficient
To maintain cooperation, both sides in the game will
choose to consistently raise the betrayal penalty
coefficient to lower the probability of betraying.
Hence, the betrayal penalty coefficient is set up
corresponding to betrayal revenue. The higher
betrayal penalty coefficient is, the higher betray costs
will be, and the less probability for both sides to
choose betray strategy is, then both sides in the game
will have larger probability to choose cooperation
strategy. Reasons are shown as follows. According to
formula (11), we derivate
II
S by f :
Research on Factors Influencing the Cooperation between Operators and Systems Integrators in IOT
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Research on Factors Influencing the Cooperation between Operators and Systems Integrators in IOT
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121212
22
1221
22
1(-) (-)
[]
2
1- -
= [ ] 0
2
SM NG H
fM G
MN N GH H
MG


 




Ⅱ
（） （）
(20)
From the relation of M, N, G and H we can conclude
that
0


f
S
II
, so
II
S is a monotone increasing
function of f. In another word, the higher the betrayal
penalty coefficient is, the larger areas of
II
S will be,
and the larger the probability of system to evolution
into C (1, 1), so both sides will have larger probability
to choose the cooperation strategy. In the multi-stage
game, any betrayal behaviour of either side will lower
the probability of cooperation establishing in the next
stage. However, because the market requirements are
changing all the time, there is always cooperation
necessity for operators and system integrators to
launch new product to satisfy new requirements of
customers, and obtain excess return. So the only
choice for them is to establish cooperation by raising
the betrayal penalty coefficient and increasing the
betray cost so that significantly decrease the betrayal
possibility.
3.3.6 Excessive-revenue Allocation
Coefficient
Keep the other coefficients unchanged, there is an
optimal excessive-allocation coefficient who can
achieve double-wins between operators and system
integrators, where the probability of both sides
choosing cooperation strategy will reaching
maximum. According to formula (11), we derivate
II
S by

:
22
1* *
[]
2
SNH
MG



 


Ⅱ
(21)
Apparently, there is no monotonic relation between

and
II
S
. Hence, we second-order derivate
II
S
by

:
222
233
**
]0
SN H
MG



  

Ⅱ
(22)
Let
0



II
S
, namely
22
**
G
H
M
N





,
II
S
has maximum value. At this time, the system has the
largest probability to evolution into C (1, 1), thus
operators and system integrators have the largest
probability to establish cooperation.
4 CONCLUSIONS
The main contribution of this paper is that it applies
the evolutionary game theory to the study of
cooperation model selection between operators and
system integrators, and it constructs an evolutionary
game model on cooperation strategy and
noncooperation strategy between operators and
system integrators. The result shows that within a
certain extent, more shareable resources lead to larger
probability of cooperation, but, the probability of
cooperation will gradually decrease if the distribution
of shareable resources exceeds the extent. It also
shows that the independent-developing coefficient,
learning coefficient and penalty coefficient are all
proportional to the probability of cooperation. That is
to say, increasing any value of these coefficients
could promote the cooperation. On the other hand,
betrayal revenue is inversely proportional to the
probability of cooperation. Therefore, the higher the
betrayal revenue is, the lower the probability of long-
term cooperation establishment will be. Last but not
least, there is an optimal excessive return allocation
coefficient to maximize the probability of
cooperation. It means that a fair and reasonable
revenue allocation mechanism will promote the
cooperation establishment in this system.
ACKNOWLEDGEMENTS
Project supported by the Humanities and Social
Sciences of Ministry of Education Planning Fund of
China (No. 14YJA630091).
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