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What forms of the bancassurance
alliance model is customers’
preference?
Cheng-Ru Wu, Chin-Tsai Lin and Yu-Fan Lin
Yuanpei University, Hsinchu, Taiwan, Republic of China
Bancassurance
alliance model
207
Abstract
Purpose – The purpose of this paper is to identify and evaluate the relationships among the
flexibility enablers and to prepare a hierarchy of these enablers to know their influences over each
other in global supply chain. The framework suggests that the priority of enablers in supply chain
should be determined on the basis of their driving power and dependency.
Design/methodology/approach – Various enablers used by researchers and practitioners for
flexibility management of global supply chain have been identified. These enablers have been
classified as strategic, operational and performance-based enablers. Interpretive structural modeling
(ISM) is used to establish mutual relationships among the flexibility enablers and to prepare a
hierarchy-based model.
Findings – It has been observed that some enablers having high-driving power and low dependency
are of strategic importance. These enablers require more attention while other enablers based on
operations and performances are dependents of strategic enablers.
Practical implications – The index of enablers based on driving power and dependency provide an
insight for supply chain managers to make the entire supply chain highly flexible, that would help
them to respond to global uncertainties.
Originality/value – Presentation of enablers in the form of hierarchy using ISM and ranking them
into various driving power and dependent categories is a good effort to make flexible global supply
chain.
Keywords Banks, Insurance companies, Strategic alliances, Decision making,
Supply chain management, Taiwan
Paper type Research paper
1. Introduction
Bancassurance has enjoyed considerable success in Europe, but this concept is
relatively new for Asian countries, having emerged only in 2002. Furthermore, the
formation of strategic alliances has been a growing trend in the financial industry.
Nevertheless, Taiwan financial institutions are participating in the business activities
of other financial institutions since the Taiwan’s government deregulated its financial
restrictions. Especially, after enactment of the Financial Holding Company Law, the
bancassurance has escalated rapidly. Such an arrangement may benefit consumers
with a “one-stop shopping” option as well as create a business synergy through
cross-selling between banks and insurance companies.
In the present day, the literature regarding the financial alliances between banks
and insurance companies is limited. Wever (2000) refers to bancassurance as the
distribution of insurance products through banking networks; in other words, as the
collaboration between banks and insurers to distribute insurance products to bank
customers. Staikouras (2006) and Staikouras and Nurullah (2008) find that banking and
Journal of Modelling in Management
Vol. 3 No. 3, 2008
pp. 207-219
q Emerald Group Publishing Limited
1746-5664
DOI 10.1108/17465660810920564

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208
insurance entities have more similarities than differences, characteristics that may
favor joint production and business synergies. Through diversification, the
bancassurance approach reduces the resources required to manage risk, which in
turn results in lower costs (Hughes et al., 1999). Korhonen and Voutilainen (2006) and
Korhonen et al. (2006, 2005) applied the expert panels and the analytical hierarchy
process (AHP) to explore the most prefer alternative alliances between banks and
insurance companies from executive management perspectives, supervisory
authorities, and customers, respectively. Wu et al. (2008) adopt the modified Delphi
method to construct the framework of mutual fund performance and the AHP model to
design an assessment method for mutual fund performance.
According to the context of Taiwan’s current conditions with regard to finance and
financial systems, the aim of this paper is to searching for the preferable bancassurance
alliance model to customers. Both the banks and insurance companies involved in a
strategic alliance can be domestic or foreign with operations in the Taiwan area, and
insurers can be life or non-life companies with operations in the Taiwan area.
The selection of an alternative is a multi-criteria decision-making (MCDM) problem.
First, we defined three criteria and nine sub-criteria for use in evaluating six alternative
models of financial alliance. The criteria and sub-criteria were introduced at the
suggestion of the industry experts and the advanced or well-informed customers, and
other elements were based on the methodology of previous studies. Each expert was
interviewed individually. Then, the same experts were assembled as a panel to find the
preferable bancassurance alliance model to customers. As a decision support system,
we used the AHP developed by Saaty (1980).
This paper is organized as follows: Section 2 briefly describes the AHP
methodology. Sections 3 applying the AHP and the sensitivity analytic develop an
evaluation method. Finally, we conclude the paper with general remarks and
suggestions for further research.
2. Analytic hierarchy process methodology
As a decision method that decomposes a complex MCDM problem into a hierarchy
(Saaty, 2000), AHP is also a measurement theory that prioritizes the hierarchy and
consistency of judgmental data provided by a group of decision makers. AHP
incorporates the evaluations of all decision makers into a final decision, without
having to elicit their utility functions on subjective and objective criteria, by pair-wise
comparisons of the alternatives (Saaty, 1990), with the calculation procedure as
follows.
2.1 Establish the hierarchy structure
When deals the complex issue, we can be performed decompose the hierarchy
structure. Based on the human cannot comparison above the seven kinds things at
the same time, so we must assume each elements of the hierarchy not suitably
surpasses seven elements. Under this limited condition, it may carry on the reasonable
comparison and easier ensure the consistency (Saaty, 1980). The first hierarchy of the
structure that is we want to achieve goal. Under the end hierarchy is the choice projects
or replacement alternatives, as well as the each middle hierarchies are the appraisal
factor or criteria.

2.2 Various hierarchies’ elements weight computation
2.2.1 Establishment of pair-wise comparison matrix A. Based on an element of the
upper hierarchy is an evaluating standard, going on the pair-wise comparison to
each elements. If has the n elements must make nðn 2 1Þ=2 elements of the
pair-wise comparison. Let C 1 ; C 2 ; ...; C n denote the set of elements, while a ij
represents a quantified judgment on a pair of elements C i , C j . In the AHP, paired
comparison judgments from a fundamental scale of absolute numbers are entered in
a reciprocal matrix. Their numerical values and corresponding intensities are: 1 –
equal, 3 – moderately dominant, 5 – strongly dominant, 7 – very strongly
dominant and 9 – extremely dominant, along with intermediate values for
compromise and reciprocals for inverse judgments and even using decimals to
compare homogeneous elements whose comparison falls within one unit. This yields
an n £ n matrix A as follows:
Bancassurance
alliance model
209
A ¼½a ij Š¼
C 1 C 2 ··· C n
0
1
C 1 1 a 12 ··· a 1n
C 2
1=a 12 1 ··· a 2n
; ð1Þ
.
. . .
. . .
.
B
. . C
@
A
C n 1=a 1n 1=a 2n ··· 1
where a ij ¼ 1 and a ij ¼ 1=a ji ; i; j ¼ 1; 2; ...; n. In matrix A, the problem becomes
one of assigning to the n elements C 1 ; C 2 ; ...; C n a set of numerical weights
W 1 ; W 2 ; ...; W n that reflects the recorded judgments. If A is a consistency matrix, the
relations between weights W i and judgments a ij are simply given by W i =W j ¼ a ij
ðfor i; j ¼ 1; 2; ...; nÞ and matrix A as follows:
A ¼
C 1 C 2 ··· C n
2
3
C 1 w 1 =w 1 w 1 =w 2 ··· w 1 =w n
C 2
w 2 =w 1 w 2 =w 2 ··· w 2 =w n
. .
.
. . . . . ; ð2Þ
6
7
4
5
C n w n =w 1 w n =w 2 ··· w n =w n
2.2.2 Eigenvalue and eigenvector calculation. Matrix A multiply the elements weight
vector (x) equal to nx, that is ðA 2 nIÞx ¼ 0, the x is the eigenvalue (n) of eigenvector.
Owing to a ij be makers’ subjective judgment give comparison and appraisal, with the
truly value ðW i =W j Þ have the some level degree difference, so that Ax ¼ n · x cannot
to be set up. Saaty (1990) suggested that the largest eigenvalue l max be:
l max ¼ Xn
j¼1
a ij
W j
W i
:
ð3Þ

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If A is a consistency matrix, eigenvector X can be calculated by:
ðA 2 l max IÞX ¼ 0:
ð4Þ
210
2.2.3 Consistency test. Saaty (1990) proposed utilizing consistency index (CI) and
consistency ratio (CR) to verify the consistency of the comparison matrix. CI and RI are
defined as follows:
CI ¼ l max 2 n
n 2 1 ; ð5Þ
CR ¼ CI
RI ;
ð6Þ
where RI represents the average CI over numerous random entries of same order
reciprocal matrices. If CR # 0:1, the estimate is accepted: otherwise, a new comparison
matrix is solicited until CR # 0:1.
2.3 Overall hierarchy weight computation
After weight computation for the various hierarchies and elements, then compiles the
overall hierarchy weight computation, and finally to decide our goal of the most
suitable plan.
3. Evaluation process for the preferable bancassurance alliance model
In this paper, bancassurance alliance models are classified into six categories stand on
the bank whether direct cross-operation insurance business activities by the current
status and business model of bancassurance in Taiwan. We introduce the following
alliance models.
3.1 Direct cross-operation
.
Financial holding company (FHC). An FHC is a financial conglomerate that
engages in at least two different financial industries as a single entity, including
the banking, security, and insurance industries (Li, 2006). For the purposes of
this study, FHC means a company that is established in accordance with
Taiwan’s “Financial Holding Company Act” and has a controlling interest in a
bank or an insurance company.
.
Holding shareholding (HS). The bank makes equity investment in insurance
companies. It becomes an insurance companies’ limited liability shareholder.
.
Establishing a new joint venture (JV). A bank and an insurance company jointly
establish another, new insurance company.
3.2 Indirect involvement
.
Precompetitive alliances (PA). For purposes of this study, indirect involvement
means that the agreement or the cooperative relationship is usually established
according to the terms of a written contract. Moreover, it has a specified duration
and does not establish another, new company.

.
Establishing insurance intermediaries. A bank invests to establish an insurance
“agency” or “brokerage” (IA or IB) company and then an agent or a broker
contract with the insurance companies. At the same time, the bank sells products
of these insurance companies and gains commissions. The difference between
insurance agents and insurance brokers is that agents can be a substitute for
insurance companies to issue policies, insurance brokers cannot.
The proposed evaluation model aimed at determining the preferable bancassurance
alliance model for Taiwan’s customers through the following steps.
3.2.1 Step 1: define the evaluative criteria and sub-criteria for determining the
preferable bancassurance alliance model e and establish a hierarchical framework. Here,
the modified Delphi method (Murry and Hammons, 1995) is applied to define the
evaluation criteria and sub-criteria. About 12 experts contain two financiers, two
academicians, and eight the advanced or well-informed customers are then issued a
preliminary questionnaire in which three evaluation criteria and nine evaluation
sub-criteria are incorporated and are described in the definition of operation type for
each criterion. Finally, based on the modified Delphi method, there was a general
consensus among experts that a hierarchical structure should be established. The
preferable choices for a bancassurance alliance model can be selected and assessed
based on three evaluation criterion, nine evaluation sub-criterion and, finally, the
alternatives (Figure 1).
Bancassurance
alliance model
211
3.3 Executive aspect (C 1 )
Customers will consider the financial institution’s operating conditions, financial
products and sales channels before they purchase insurance products. And then select
a sound and stable bancassurance alliance to be traded:
.
Product development (SC 1 ). Financial institutions should grasp the different
needs of the customers to develop new financial products for them, such as
endowment insurance, credit insurance and investment-linked product.
.
Economies of scale (SC 2 ). Here is to mean the service network rationalization. In
other words, the service networks of a financial institution should be average
distribution in the Taiwan area for customers’ convenience.
.
Stability of the operation (SC 3 ). For customers, it is very important that financial
institutions whether have sufficiency of capital, solvency and solidity. Because of
this may affect customers’ rights and interests.
3.4 Supervisor aspect (C 2 )
The customers will evaluate that the supervisory authorities whether effectively
supervise the financial alliance before they purchase financial products. If supervisory
authorities cannot supervise effectively, the customer may choose in the traditional
sector to transact:
.
System risk control (SC 4 ). Systemic risk is usually caused by environmental
factors, such as the overall political, economic, and social conditions. The
characteristic of the system risks is that if one business operator fails, so happens
to a second and a third one, etc. It was stated that efficient supervision is the way
to prevent the realization of system risks (Korhonen et al., 2005).

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Figure 1.
Hierarchical structure to
select and evaluate the
preferable bancassurance
alliance model for
customers’ perspectives
Level 1: Ultimate goal Level 2: Criteria Level 3: Sub-criteria Level 4: Alternatives
Product development
Financial holding
company
Economies of scale
Execituve aspect
Holding shareholding
Stability of the operation
Establishing a new joint
venture
System risk control
Supervisory authorities has better resources
Supervisor aspect
Precompetitive alliances
Choosing a preferable
bancassurance alliance
model alternatives
Division of risks and benefits between
alliance members
Establishing insurance
agent company
Service convenience
Establishing insurance
broker company
Sufficiency of product information
Customer aspect
Equal treatment of customers

.
Supervisory authorities has better resources (SC 5 ). Supervisory authorities for
the financial institutions’ mutual cooperation should have proper supervision
policies to prevent the risks and conflicts of the financial alliance, for example,
improper insider trading may affect the interests of consumers.
.
Division of risks and benefits between alliance members (SC 6 ). Financial
institutions cross-operating with other financial enterprises may mismanage
financial products and thus affect their original banking or insurance business.
Accordingly, the risks and benefits should be clearly divided and avoid mutual
circulation among the members of alliance.
Bancassurance
alliance model
213
3.5 Customer aspect (C 3 )
Customers receive the benefits and rights among a bancassurance alliance:
.
Service convenience (SC 7 ). It means that a customer is offered as many bank and
insurance products as possible at one place during one customer service event.
The objective is full customer service at one stop, keeping customers and
deepening consumer loyalty (Voutilainen, 2005).
.
Sufficiency of product information (SC 8 ). Customers will base on the
transparency of product information as a purchase reference. Therefore,
the higher the transparency of product information, the more easily obtain the
consumers’ trust.
.
Equal treatment of customers (SC 9 ). For example, the customers in an insurance
company gain treatment in claims handling whether better than the customers
who to a bank purchase insurance products.
3.5.1 Step 2: establishment each factors of the pair-wise comparison matrix. This
purposive sampling study is applied to twelve experts representing financiers,
academicians and the advanced or well-informed customers. The weights of level 2
criteria and level 3 sub-criteria are then determined for a sample group of twelve
individuals matching the above characteristics, with each expert making a
pair-wise comparison of the decision elements and assigning them relative scores.
At the same time, each expert also makes a pair-wise comparison of the selection of six
alliance structure models under nine subjective sub-criteria and, then, assigns those
relative scores. The relative scores provided by the 12 experts are aggregated using
the geometric mean method. We used the equations (1) and (2) to calculate the
aggregate pair-wise comparison matrix. The pair-wise comparisons matrix of
the criteria and sub-criteria and the corresponding value scores for the various alliance
models are presented in Tables I-III, respectively.
3.5.2 Step 3: determine the eigenvalue and eigenvector. The comparison matrices in
Tables I-III are used to calculate the eigenvectors using equation (3) and (4). Table IV
summarizes the results of eigenvectors for the criteria, sub-criteria, and six alliance
structure models.
3.5.3 Step 4: consistency ratio. According to equations (5) and (6), each hierarchy’s
criteria comparison matrix of CR is calculated, as shown in Tables I-III. This result
shows that the CR of the each hierarchy’s criteria comparison matrix is below 0.1,
indicating “consistency.”

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3.5.4 Step 5: computation of each levels relative weight of the elements. The
aggregate-related scores provided by all decision makers using simple additive
weighting and the results for each level’s relative weight of the elements are shown in
Table IV.
3.5.5 Step 6: overall level hierarchy weight calculation for selecting the preferable
bancassurance alliance model. The composite value scale for the alliance structure
alternatives was found by computing the weighted sums for each alternative. The
separate value scores were multiplied by the scaled scores of the criteria. Table V
shows the priorities of the six alliance models:
FHCð0:325Þ . JVð0:183Þ . PAð0:175Þ . IAð0:121Þ . IBð0:117Þ . HSð0:079Þ:
This result shows that FHC is preferable to customers and compares to obtain the
customers’ trust.
3.5.6 Step 7: sensitivity analysis of six alliance structure models under three criteria.
The final prioritization of the alternatives is heavily dependent on the weights attached
to the criteria. Sensitivity analysis thus can be performed using scenarios that reflect
various future developments or different views regarding the relative importance of the
criteria. Increasing or decreasing the weightings of individual criterion can illustrate
the resulting changes in the priorities and the ranking of the alternatives. It turns out
that one can give (separately) each criterion whatever value from the interval [0, 1], and
FHC still remains the most preferable alliance model to customers.
Goal C 1 C 2 C 3
Table I.
The pair-wise
comparison matrix of the
criteria
C 1 1 3.130 0.485
C 2 0.319 1 0.325
C 3 2.061 3.077 1
l max ¼ 3.11; CR ¼ 0.095
Table II.
Criteria for the pair-wise
comparison matrix of the
sub-criteria
C 1 SC 1 SC 2 SC 3
SC 1 1 0.452 0.207
SC 2 2.214 1 0.266
SC 3 4.819 3.761 1
l max ¼ 3.060; CR ¼ 0.052
C 2 SC 4 SC 5 SC 6
SC 4 1 2.449 4.141
SC 5 0.408 1 1.682
SC 6 0.241 0.595 1
l max ¼ 3.00; CR ¼ 0.00
C 3 SC 7 SC 8 SC 9
SC 7 1 1.377 1.681
SC 8 0.726 1 2.213
SC 9 0.595 0.452 1
l max ¼ 3.080; CR ¼ 0.069

FHC HS JV PA IA IB
SC 1
FHC 1 3.364 2.711 2.000 4.606 4.356
HS 0.297 1 0.452 0.302 0.658 0.760
JV 0.369 2.214 1 1.519 2.943 2.783
PA 0.500 3.311 0.658 1 2.632 2.632
IA 0.217 1.520 0.340 0.380 1 0.841
IB 0.230 1.316 0.359 0.380 1.189 1
l max ¼ 6.10; CR ¼ 0.016
SC 2
FHC 1 3.130 2.449 2.546 4.054 3.984
HS 0.319 1 0.500 0.452 0.707 0.783
JV 0.408 2.000 1 1.682 2.590 2.913
PA 0.393 2.214 0.595 1 1.682 2.060
IA 0.247 1.414 0.386 0.595 1 1.414
IB 0.251 1.278 0.343 0.485 0.707 1
l max ¼ 6.10; CR ¼ 0.016
SC 3
FHC 1 3.310 2.913 1.968 2.449 3.027
HS 0.302 1 0.537 0.562 0.707 0.748
JV 0.343 1.862 1 1.565 2.449 2.590
PA 0.508 1.779 0.639 1 2.913 3.310
IA 0.408 1.414 0.408 0.343 1 1.565
IB 0.330 1.337 0.386 0.302 0.639 1
l max ¼ 6.20; CR ¼ 0.032
SC 4
FHC 1 3.500 3.130 1.968 2.913 3.027
HS 0.286 1 0.537 0.523 0.632 0.707
JV 0.319 1.862 1 1.565 2.783 3.130
PA 0.508 1.911 0.639 1 2.378 2.783
IA 0.343 1.581 0.359 0.420 1 1.565
IB 0.330 1.414 0.319 0.359 0.639 1
l max ¼ 6.20; CR ¼ 0.032
SC 5
FHC 1 3.310 3.000 2.632 3.663 4.120
HS 0.302 1 0.686 0.553 0.669 0.748
JV 0.333 1.457 1 2.060 2.632 2.991
PA 0.380 1.807 0.485 1 2.590 2.711
IA 0.273 1.495 0.380 0.386 1 1.565
IB 0.243 1.337 0.334 0.369 0.639 1
l max ¼ 6.20; CR ¼ 0.032
SC 6
FHC 1 3.224 2.632 2.515 4.356 4.527
HS 0.310 1 0.707 0.903 0.970 0.955
JV 0.380 1.414 1 2.449 2.913 3.310
PA 0.398 1.107 0.408 1 2.515 2.913
IA 0.230 1.030 0.343 0.398 1 1.565
IB 0.221 1.047 0.302 0.343 0.639 1
l max ¼ 6.150; CR ¼ 0.024
(continued)
Bancassurance
alliance model
215
Table III.
Sub-criteria (SC 1 -SC 9 ) for
the pair-wise comparison
matrix of the alternatives

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Table III.
FHC HS JV PA IA IB
SC 7
FHC 1 2.340 1.861 1.316 1.862 1.667
HS 0.427 1 0.420 0.325 0.319 0.266
JV 0.537 2.378 1 0.903 1.565 1.377
PA 0.760 3.082 1.107 1 1.245 1.075
IA 0.537 3.131 0.639 0.803 1 0.903
IB 0.600 3.761 0.726 0.930 1.107 1
l max ¼ 6.10; CR ¼ 0.016
SC 8
FHC 1 3.464 3.834 2.783 1.456 1.524
HS 0.289 1 0.707 0.473 0.334 0.330
JV 0.261 1.415 1 1.000 1.107 1.145
PA 0.359 2.115 1.000 1 1.190 1.230
IA 0.687 2.991 0.903 0.840 1 0.903
IB 0.656 3.027 0.874 0.813 1.107 1
l max ¼ 6.150; CR ¼ 0.024
SC 9
FHC 1 3.310 3.000 2.632 3.663 3.834
HS 0.302 1 0.760 0.707 0.760 0.748
JV 0.333 1.316 1 2.060 2.280 2.632
PA 0.380 1.415 0.485 1 2.783 2.632
IA 0.273 1.316 0.439 0.359 1 1.316
IB 0.261 1.337 0.380 0.380 0.760 1
l max ¼ 6.20; CR ¼ 0.032
4. Conclusions
AHP applies widely in MCDM problems. One of the main advantages of this method is
that it can effectively manage tangible and intangible or qualitative and quantitative
factors. This study attempts to find the most preferable bancassurance alliance model
for customers’ perspective, which is a problem that involves complex MCDM.
Therefore, using the AHP method seems to be a successful approach in finding a
solution to the finance alliance problem.
The composite priority of the six alliance structure models beneath the three criteria
is: executive aspect (0.331); supervisor aspect (0.135) and customer aspect (0.533).
Customer aspect ranked highest in the hierarchy. From this result knows that
customers will first consider themselves benefits while they purchase financial
products. Moreover, we discovered at customers care about not only themselves
benefits (SC 7 ,SC 8 and SC 9 ) but also system risk control (SC 4 ) and stability of financial
institutions operation (SC 3 ) (Table VI). Summarizing the results, the FHC received
higher overall value scores largely because it had highest scores according to criteria
executive aspect, supervisor aspect and customer aspect.
Through consultation with financiers, academicians, and the advanced or well
informed customers, we learned that Taiwan’s consumers in the purchase of financial
products will consider many factors regarding the executive, supervisor and customer
aspects, including company’s scale and operation condition, customer’s benefits, risk
and so forth. But the most important factors are convenience, customer’s benefits and

Criteria C 1 C 2 C 3
Weights of criteria 0.331 0.135 0.533
Sub-criteria SC 1 SC 2 SC 3 Global priority SC 4 SC 5 SC 6 Global priority SC 7 SC 8 SC 9 Global priority
Weights of Sub-criteria 0.116 0.214 0.670 0.606 0.247 0.147 0.421 0.373 0.206
FHC 0.371 0.372 0.335 0.347 0.350 0.378 0.377 0.361 0.252 0.321 0.376 0.303
HS 0.073 0.084 0.086 0.084 0.082 0.088 0.106 0.087 0.065 0.071 0.096 0.074
JV 0.204 0.210 0.200 0.202 0.207 0.202 0.217 0.207 0.180 0.135 0.193 0.166
PA 0.192 0.155 0.195 0.186 0.179 0.162 0.148 0.170 0.187 0.152 0.161 0.169
IA 0.078 0.098 0.102 0.099 0.101 0.094 0.084 0.097 0.148 0.160 0.092 0.141
IB 0.082 0.082 0.082 0.082 0.082 0.076 0.070 0.078 0.169 0.162 0.082 0.149
Bancassurance
alliance model
217
Table IV.
Eigenvectors and weights
of six alliance structure
models under nine
sub-criteria

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Table V.
C 1 C 2 C 3 Aggregate score Rank
0.331 0.135 0.533
FHC 0.347 0.361 0.303 0.325 1
HS 0.084 0.087 0.074 0.079 6
JV 0.202 0.207 0.166 0.183 2
PA 0.186 0.170 0.169 0.175 3
IA 0.099 0.097 0.141 0.121 4
IB 0.082 0.078 0.149 0.117 5
Note: The composite priority vector for application AHP decision model to select the alliance
structure models result
Criteria Sub-criteria Global priority Rank
Table VI.
Global priority of six
alliance structure models
under nine sub-criteria
C 1 SC 1 0.038 7
SC 2 0.071 6
SC 3 0.222 2
C 2 SC 4 0.082 5
SC 5 0.033 8
SC 6 0.020 9
C 3 SC 7 0.225 1
SC 8 0.199 3
SC 9 0.110 4
risk control. Therefore, the results of this study correspond with the realities of
Taiwan’s current situation.
This study provides an evaluation criterion for determining the optimal alliance
structure for Taiwan’s emerging bancassurance sector, and the proposal evaluation
criteria provides high-level management of financial institutions and academicians
with recommendations for future development.
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Bancassurance
alliance model
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About the authors
Cheng-Ru Wu is Head of the Graduate Institute of Business and Management at Yuanpei
University. He holds PhD from Ming Chuan University. He is the member of the Operations
Research Society of Taiwan. His major interest is in decision science, real options, mathematical
finance and mathematical economics. His papers appeared in Applied Stochastic Models in
Business and Industry, Asia-Pacific Journal of Operational Research, Building and Environment,
International Journal of Technology Management, Quality and Quantity, Computers & Industrial
Engineering, International Journal of Advanced Manufacturing Technology, Software Quality
Journal, Production Planning and Control, Expert Systems with Applications, Information
Sciences, Waste Management and others. Cheng-Ru Wu is the corresponding author and can be
contacted at: alexru00@ms41.hinet.net
Chin-Tsai Lin is in the Graduate Institute of Business and Management and is head of
Yuanpei University. He holds PhD in the Institute of Management Science, National Chiao-Tung
University. He is the member of the Operations Research Society of Taiwan. His current research
interests are in the area of decision science, grey system, and optimization theory and
applications. He has published more than 190 research papers in International journals. His
papers appeared in Journal of the Operational Research Society, Asia-Pacific Journal of
Operational Research, Journal of Grey System, Information Sciences, International Journal of
Advanced Manufacturing Technology, Frontiers in Bioscience, Statistics and Computing and
others.
Yu-Fan Lin is a Master in the Graduate Institute of Business and Management at Yuanpei
University. Her major interest is in decision science, bancassurance, performance evaluation and
mathematical finance.
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