Journal of Service Science and Management, 2011, 4, 469-475
doi:10.4236/jssm.2011.44053 Published Online December 2011 (http://www.SciRP.org/journal/jssm)
469
Using TOPSIS & CA Evaluating Intentions of
Consumers’ Cross-Buying Bancassurance
Chiang Ku Fan1, Yu Hsuang Lee2, Li Tze Lee3, Wen Qin Lu4
1Department of Risk Management and Insurance, Shih Chien University, Taipei, Chinese Taipei; 2Department and Graduate Institu-
tion of Business Administration, Shih Chien University, Taipei, Chinese Taipei; 3Department of Accounting and Information, Over-
seas Chinese University, Taipei, Chinese Taipei; 4Department of Industrial Engineering and Management, Hsiuping University of
Science and Technology, Taipei, Chinese Taipei.
Received August 23th, 2011; revised October 12th, 2011; accepted November 16th, 2011.
ABSTRACT
The purpose of this study is to develop and assess an objective research model to weigh the factors that affect intention
of cross-buying insurance in banks. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was
conduct firstly for the sho rtlist selection of factors of cross-bu ying intention. Then , the factors’ weights of cross-buying
intention is also used as the evaluation criteria, and these are calculated effectively by employing Conjoint analysis
(CA). This study finding: The TOPSIS is an effective method to help decision makers for the shortlist selection of idea
factors of cross-buying intention. In order to collect data to identify and shortlist selection the intentions of
cross-buying insurance in banks by Delphi & TOPSIS, and develop an evaluation structure to weigh the intentions of
cross-buying insurance in banks, an interview protocol was designed.
Keywords: Cross-Buying Intention, TOPSIS, CA
1. Introduction
The banking industry in Taiwan has experienced tremen-
dous change and an increased growth in earnings from
selling insurance products. Banking networks represent
the major distribution channel for life insurance products.
According to the statistics reported by the Financial Su-
pervisory Commission of the Republic of China, as of
the third quarter of 2010, bancassurance accounted for
over 67% of the total first-year life insurance premium
income in Taiwan. Moreover, the number of insurance
sales representatives employed by agencies and brokera-
ges has tripled to approximately 142,000 people. The in-
creased number of agencies and brokerages affiliated with
banks account for 70% of all new entries, whereas the
growth rate of insurance premiums from these banking
agents now exceeds those from traditional direct writers
of insurers. In this context, competition in the bancassu-
rance industry is at an all-time high, which challenges pro-
viders to retain existing customers while attracting new
ones. Most banks are looking for the same things—better
ways to retain customers and to increase income. Simi-
larly, most insurers are looking for the same things—mo-
re efficient distribution channels to sell policies and to
expand premium incomes.
Therefore, financial firms try to stimulate the relation-
ship length and depth, and they are particularly focusing
their efforts on cross-selling, which increases the breadth
of the relationship with each customer (that is, the avera-
ge number of services sold to each individual) [1]. In spi-
te of cross-selling being associated with increased life-
time duration and value [2], prior studies have implied
that it is not easy to motivate customers to cross-buy ser-
vices or products from the same provider. Day [3] has
also found that cross-selling is unlikely to occur if cus-
tomers are not willing to buy the services or products that
they already have. In fact, not all customers are disposed
to engage in expanding their relationships with firms [4].
Customers in some service categories intrinsically tend to
develop a multibrand loyalty [5].
Unfortunately, the question of why customers decide to
cross-buy and to enhance their relationships with a bank
has received scant attention in the literature and has not
been appropriately investigated in prior studies [5]. Fur-
thermore, the major contributions of previous research
have only implied the relationships between the factors
of cross-buying intentions, and the weight of those fac-
tors that impact crossing-buying intentions in the deci-
sion-making process has not been confirmed by research

Using TOPSIS & CA Evaluating Intentions of Consumers’ Cross-Buying Bancassurance
470
data. Most importantly, no satisfaction assessment me-
thod, such as factors shortlisting and factors weighting,
has been conducted sufficiently to understand the factors
that motive cross-buying intention.
The purpose of this study is to address this research
gap by developing and assessing an objective research
model to shortlist and weigh those factors that affect in-
tention of cross-buying insurance in banks that have been
suggested in previous studies.
2. Literature Review
Verhoef [7] were the first to introduce the term “cross-
buying” and defined it as the purchase of a number of di-
fferent services from the same provider. In other words,
cross-buying is the behavior expressed in buying various
products from the same provider [6-7]. In fact, cross-sell-
ing and its benefits can only be achieved if consumers are
willing to cross-buy [8]. Therefore, cross-buying is com-
plementary to cross-selling, which pertains to the sup-
plier’s efforts to increase the number of products or ser-
vices that a customer uses within a firm [9].
A number of factors that may impact bank customers’
cross-buying intentions have been proposed in previous
research studies. The findings of these prior studies are
presented in Table 1.
3. Methodology
The purpose of this study is to address this research gap
by developing and assessing an objective research model
to weigh those factors that affect intention of cross-buy-
ing insurance in banks that have been suggested in pre-
vious studies by conjoint analysis, especially the full-pro-
file conjoint analysis. But it’s impossible to select all fa-
ctors of cross-buying intentions, Hair et al.[19] and Sid-
diqui & Awan [20] figure out the conjoint analysis is u-
seful for measuring up to about six attributes.
In the first phrase adopts the TOPSIS for the shortlist
selection of factors of cross-buying intention, then the
CA approach is employed to compute factors’ weights of
cross-buying intention in the second phrase.
This study selected 23 financial advisers who were em-
ployed by different model banks and have many years of
experience working with bancassurance. The interviews
explored more fully the perceptions of experts about
these factors that affect every customer to cross-buy in-
surance products in a bank.
The major methods include two parts. The first part is
TOPSIS and the second is CA, stated below:
3.1. The TOPSIS Methodology
Developed by Hwang & Yoon [21], TOPSIS attempts to
define the ideal solution and the negative ideal solution.
The ideal solution maximizes the benefit criteria and mi-
nimizes the cost criteria, whereas the negative ideal solu-
tion maximizes the cost criteria and minimizes the bene-
fit criteria. The optimal alternative is the closest to the
ideal solution and the farthest from the negative ideal so-
lution. Alternatives in TOPSIS are ranked based on “the
relative similarity to the ideal solution”, which avoids
having the same similarity for both ideal and negative
ideal solutions. The method is calculated as follows:
3.1.1. Establishing the Performance Matrix
11 1211 1
21 2222 2
12
12
,
jn
jn
iiij in i
mmmj mn m
XX XX
A
XX XX
A
DXX XX
A
XX XX
A





























(1)
where Xij is the performance of attribute Xj for alternative
Ai, for 1, 2, 1, 2,im jn

.
3.1.2. Normalize the Performance Matrix
Normalizing the performance matrix is an attempt to uni-
fy the unit of matrix entries.


, ,
ij
X
ij (2)
where Xij is the performance of attribute i to criterion j.
3.1.3. Create the Weighted Normalized
Performance Matrix
TOPSIS defines the weighted normalized performance
matrix as


V,
,
ij
ijj ij
Vij
Vwrij


,
(3)
where wj is the weight of criterion j.
Table 1. The factors impact cross-buy i ng intention for bancassurance.
Factors Impact Cross-Buying Intention References Factors Impact Cross-Buying Intention References
Image [5,6] Payment Equity [7,17]
Service Convenience [5,10,11] Experience [5,7]
Interpersonal Relationships [12,13] Pricing [7,13]
Trust [6,11,14-16] Product Variety [13,18]

Using TOPSIS & CA Evaluating Intentions of Consumers’ Cross-Buying Bancassurance471
3.1.4. Determine the Ideal Solution and
Negative Ideal Solution
The ideal solution is computed based on the following
equations:
 

|, max '
1,2, , m
ij ij
VmaxVjJ VjJ
i
 


,
(4)



min , min ',
1,2, , m
ij ij
VVjJVj
i
 


J
(5)
where

1,2, , n belongs to benefit criteria ,jj j 

'1,2, , n belongs to cost criteria .jj j 

3.1.5. Calculate the Distance between Idea Solution
and Negative Ideal Solution for Each
Alternative, Using the N-Dimensional
Euclidean Distance

2
1
1, 2, , m ,
n
iijj
j
SVVi


 
 (6)

2
1
1, 2,, m ,
n
iijj
j
SVVi


 
 (7)
3.1.6. Calculate the Relative Closeness to the Ideal
Solution of Each Alternative
*
* 1, 2, , m .
i
i
ii
S
Ci
SS



 (8)
where That is, an alternative i is closer to 0
i
C 1. A

as approaches to 1.
i
C
3.1.7. Rank the Preference Order
A set of alternatives can be preferentially ranked accord-
ing to the descending order of .
i
C
3.2. The Conjoint Analysis Methodology
The concept of conjoint analysis is introduced in this
section, as well as the determined formula of the utility
with the conjoint analysis. The final part in this section
discusses the process of data analysis with conjoint
analysis.
CA has been employed in research for many years.
Panda & Panda [22] have described CA as a “what if”
experiment in which buyers are presented with different
possibilities and asked which product they would buy. In
other words, CA is a multivariate technique used spe-
cifically to understand how respondents develop prefe-
rences for products or services [19]. Sudman & Blair [23]
emphasized that CA is not a data analysis process, such
as cluster analysis or factor analysis; it can be regarded as
a type of “thought experiment,” designed to display how
various elements, such as price, brand, and style, can be
used to predict customer preferences for a product or
service.
The basic CA model was computed with the ordinary
least squares (OLS) regression parametric mathematic al-
gorithm [24] using dummy variable regression. This ba-
sic model can be represented as follows [25-26].

11
UX
mki
ij ij
ij






where
U(X) = Overall utility (importance) of an attribute; αij
= Overall utility of the j level of the i attribute.
1, 2, 1, 2,i im jk


Xij = 1, if the jth level of the ith attribute is present, or
Xij = 0, otherwise.
According to the CA basic model, Churchill & Iaco-
bucci [27] presented a six-stage model that is based on
the more critical decision points in a conjoint experiment.
3.2.1. Select Attri bu tes
The attributes are those that the company can do something
about and which are important to consumers. In other
words, the company has the technology to make changes
that might be indicated by consumer preferences.
3.2.2. Determine Attribute Levels
The number of levels for each attribute has a direct bear-
ing on the number of stimuli that the respondents will be
asked to judge.
3.2.3. Determine Attribute Combinations
This will determine what the full set of stimuli will look
like.
3.2.4. Select Form of Presentation of Stimuli and
Nature of Judgments
Typically, three approaches can be used: a verbal descri-
ption, a paragraph description, and a pictorial representa-
tion. One method for characterizing judgments is to ask
respondents to rank the alternatives according to prefer-
ence or intention to buy. Another method that is gaining
popularity among researchers is to use rating scales.
3.2.5. Decide on Ag greg a ti on of Jud gmen ts
This step basically involves the decision as to whether
the responses from consumers or groups of consumers
will be aggregated.
3.2.6. Select Anal ysi s Technique
The final step is to select the technique that will be used
to analyze the data. The choice depends largely on the
method that was used to secure the input judgments from
the respondents.

Using TOPSIS & CA Evaluating Intentions of Consumers’ Cross-Buying Bancassurance
472
4. Results
Based on the TOPSIS, a general consensus among ex-
perts can be reached to rate their level of agreement to-
ward factors of cross buying intention for CA. Those re-
sults are in Table 2.
The numerical illustration follows the procedure pre-
viously discussed.
1). Sample 23 attitude tendency toward cross-buying
intentions are graded based upon 23 Delphi panelists’
opinions (see Table 3).
2). Calculate the normalized performance matrix and
calculate the weighted normalized performance matrix,
using formulae (1) and (2). Table 4 summarizes those
results.
3). Determine the distance of the ith alternative from
the ideal and negative-ideal solutions, using formulae (6)
and (7). Table 5 displays those results.
4). Calculate the relative closeness to the ideal solution
and rank the preference order.
5). Calculate the relative closeness to the ideal solution
of each alternative, , using formulae (8) and rank the
preference order (Table 6).
*
i
C
Table 2. Descriptive statistics of expert attitude toward factors of cross-buying intention.
Factors of Cross-Buying Intention SA A UD D SD N Mean Std. Deviation
Image 21 2 0 0 0 23 4.91 0.29
Service Convenience 15 6 2 0 0 23 4.57 0.66
Interpersonal Relationships 12 11 0 0 0 23 4.52 0.51
Trust 8 8 7 0 0 23 4.04 0.82
Payment Equity 4 9 10 0 0 23 3.74 0.75
Experience 0 6 17 0 0 23 3.26 0.45
Pricing 0 0 0 3 20 23 1.13 0.34
Product Variety 0 0 0 16 7 23 1.70 0.47
Note: strongly agree = 5, agree = 4, undecided = 3, disagree = 2, and strongly disagree = 1.
Table 3. 23 Attitude tendency toward cross-buying intentions.
Experts Attitude Tendency
Cross-Buying Intentions EPT
01
EPT
02
EPT
03
EPT
04
EPT
05
EPT
06
EPT
07
EPT
08
EPT
09
EPT
10
EPT
11
EPT
12
EPT
13
EPT
14
EPT
15
EPT
16
EPT
17
EPT
18
EPT
19
EPT
20
EPT
21
EPT
22
EPT
23
Image 5 55 5 5 555555445555 5 5 5 555
Service Convenience 5 5 5 4 5 5 44545355555 4 5 3 455
Interpersonal Relationship 4 55 4 4 454545455455 4 5 4 455
Trust 3 45 3 5 343434555445 4 3 5 435
Payment Equity 3 44 3 5 353533434444 3 3 5 434
Experience 3 34 3 4 333433333443 3 3 3 334
Pricing 1 11 1 1 112111211111 1 2 1 111
Product Variety 2 2 1 2 1 1 22222212222 2 1 2 211
Note: EPT = Expert.
Table 4. Summary of data normalization.
EPT
01
EPT
02
EPT
03
EPT
04
EPT
05
EPT
06
EPT
07
EPT
08
EPT
09
EPT
10
EPT
11
EPT
12
EPT
13
EPT
14
EPT
15
EPT
16
EPT
17
EPT
18
EPT
19
EPT
20
EPT
21
EPT
22
EPT
23
Image 0.56 0.51 0.50 0.59 0.52 0.57 0.54 0.58 0.51 0.59 0.51 0.46 0.41 0.49 0.54 0.51 0.49 0.57 0.53 0.56 0.57 0.54 0.50
Service Convenience 0.56 0.51 0.50 0.47 0.52 0.57 0.43 0.46 0.51 0.47 0.51 0.35 0.52 0.49 0.54 0.51 0.49 0.45 0.53 0.34 0.45 0.54 0.50
Interpersonal Relationship 0.45 0.51 0.50 0.47 0.41 0.46 0.54 0.46 0.51 0.47 0.51 0.46 0.52 0.49 0.43 0.51 0.49 0.45 0.53 0.45 0.45 0.54 0.50
Trust 0.34 0.41 0.50 0.36 0.52 0.34 0.43 0.35 0.41 0.36 0.41 0.58 0.52 0.49 0.43 0.41 0.49 0.45 0.32 0.56 0.45 0.32 0.50
Payment Equity 0.34 0.41 0.40 0.36 0.52 0.34 0.54 0.35 0.51 0.36 0.31 0.46 0.31 0.39 0.43 0.41 0.39 0.34 0.32 0.56 0.45 0.32 0.40
Experience 0.34 0.31 0.40 0.36 0.41 0.34 0.32 0.35 0.41 0.36 0.31 0.35 0.31 0.29 0.43 0.41 0.29 0.34 0.32 0.34 0.34 0.32 0.40
Pricing 0.11 0.10 0.10 0.12 0.10 0.11 0.11 0.23 0.10 0.12 0.10 0.23 0.10 0.10 0.11 0.10 0.10 0.11 0.21 0.11 0.11 0.11 0.10
Product Variety 0.22 0.20 0.10 0.24 0.10 0.110.21 0.23 0.20 0.24 0.20 0.23 0.10 0.20 0.21 0.20 0.20 0.23 0.11 0.22 0.23 0.11 0.10
Note: EPT = Expert.

Using TOPSIS & CA Evaluating Intentions of Consumers’ Cross-Buying Bancassurance473
Table 5. Resultant of i S

and i S

.
Image Service Convenience Interpersonal RelationshipTrust Payment EquityExperience Pricing Product Variety
i
S 0.007 0.019 0.016 0.029 0.033 0.041 0.087 0.075
i
S 0.086 0.079 0.077 0.068 0.061 0.050 0.005 0.018
Note: EPT = Expert.
Table 6. Summary of the TOPSIS .
*
i
C
Cross-Buying Intentions Image Service ConvenienceInterpersonal RelationshipTrust Payment EquityExperience Pricing Product Variety
*
i
C 0.927 0.809 0.825 0.698 0.647 0.549 0.050 0.190
Rank 1 2 3 45 6 8 7
Note: EPT = Expert.
Figure 1. Factors affect inte ntion of c ross-buying insurance in a bank.
From Table 6, this study decided the TOPSIS was fol-
lowing C*1 > C*2 > C*3 > C*4 > C*5 > C*6 > C*8 > C*7.
In other words, after conducting the TOPSIS, this re-
search showed the experts’ attitude tendency toward the
8 cross-buying intentions from the most important to the
least important as followings: (1) Image, (2) Service
Convenience, (3) Interpersonal Relationship, (4) Trust, (5)
Payment Equity, (6) Experience, (7) Product Variety, and
(8) Pricing. However, according to Table 3, most of ex-
perts graded “Product Variety” and “Pricing” 1 or 2.
Therefore, this study decides to choose top six cross-
uying intentions including [19]: Image (0.927), Service
Convenience (0.809), Interpersonal Relationship (0.825),
Trust (0.698), Payment Equity (0.647), and Experience
(0.549) as factors of cross-buying intentions. The ad-
justed cross-buying intentions by TOPSIS used in this
study are reported in Figure 1.
For a formal analysis, the different attribute levels ha- ve
to be dummy-encoded in a binary manner. The lowest a-
bute level serves as a reference point and gets a binary code
of 0 [28]. For any other attribute level, a binary digit of 1 is
given if the level is present, and 0 is given if it is not.
Due to s of the attributes having two levels, the total
number of possible combinations is 26 = 64 alternatives
(stimuli). This is far too many possible combinations to
be evaluated by any decision maker. Therefore, we had
to construct a design of the inquiry that defined a re-
stricted set of stimuli to be considered and the pairs of
these stimuli to be compared.
Starting with a basic orthogonal plan generated by A-
delman [29], 8 stimuli were determined (see Table 7).
Using the stimuli of the orthogonal array, a difference
design was constructed by a randomized procedure fol-
lowing the principles given by Hausruckinger & Herker
[30].
The CA questionnaire was developed on the basis of
some of the literature and shortlist select by TOPSIS
methodology, planned with an orthogonal design, and
distributed to 300 customers. 269 questionnaires were
completed in the survey.
According to the CA report (see Table 8), the most
important factor was payment equity (relative importance
= 31.352%), the second most important factor was image
(relative importance = 23.827%) and the third most im-
portant factor was interpersonal relationships (relative
importance = 14.352 %).

Using TOPSIS & CA Evaluating Intentions of Consumers’ Cross-Buying Bancassurance
474
Table 7. Attribute level and orthogonal plan card of cross-buying intentions.
Card No.
Factors Attribute Level
123 4 5 6 78
Image 1 Good 0 Not Good 0 1 1 0 1 1 0 0
Services Convenience 1 Convenience0 Not Convenience 0 0 1 0 1 0 1 1
Interpersonal Relationships 1 Good 0 Not Good 0 1 0 1 1 0 1 0
Trust 1 High 0 Low 1 0 1 1 1 0 0 0
Payment Equity 1 Equal 0 Not Equal 1 1 0 0 1 0 0 1
Experience 1 Good 0 Not Good 0 0 0 1 1 1 0 1
Table 8. Relative importance of factors affecting intention of cross-buying insurance in a bank.
Factors Variable Part-Worth Utility Relative Importance
1 Good 0.513
Image 0 Not Good 0.000 0.23827
1 Convenience 0.174
Services Convenience 0 Not Convenience 0.000 0.08082
1 Good 0.309
Interpersonal Relationships 0 Not Good 0.000 0.14352
1 High 0.229
Trust 0 Low 0.000 0.10636
1 Equal 0.675
Payment Equity 0 Not Equal 0.000 0.31352
1 Good 0.253
Experience 0 Not Good 0.000 0.11751
Total Utility 2.153
5. Conclusions & Recommendations
Since 1964, conjoint analysis study are issued firstly by
conjoint measure study of Luce & Tukey [31], and used
many years. Since 1998, Hair et al. [19] suggest the con-
joint analysis is useful for measuring up to about 6 at-
tributes, but no research provides the method of shortlist
selections, this study find the TOPSIS is an useful
method to help this study to shortlist these attributes.
In order to collect data to identify and shortlist selec-
tion the intentions of cross-buying insurance in banks by
TOPSIS, and develop an evaluation structure to weigh
the intentions of cross-buying insurance in banks, an in-
terview protocol was designed. The interview question
was initially developed based on intentions found in prior
studies and shortlist selection by TOPSIS. Moreover, the
finalization of the interview question was enabled by
means of qualitative research.
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