Description
Case Study of Integrated Marketing Communication Competencies on Banking Performance: Analysis with Fuzzy Vikor Method:- Integrated Marketing Communication (IMC) is a term that emerged in the late 20th century regarding application of consistent brand messaging across myriad marketing channels. The term has varying definitions depending upon the source cited.
Case Study of Integrated Marketing
Communication Competencies on Banking
Performance: Analysis with Fuzzy Vikor
Method
Chain reaction of bank failures in advanced economies and the possibility of sovereign defaults
are still the major concerns as credit default swap spreads breaking new records high. Moreover, the
latest policy steps by banking authorities in advanced economies seem to have negative effect on bank
ing performance. Fierce competition at financial market with relatively little profit, plus the new with
drawal mechanism regulations for low performance banks have resulted in a limited growth of banks
at capital markets. IMC approach as a strategic tool aligns effective marketing strategies with suc
cessful corporate strategies. The result of fuzzy VIKOR analysis adapted in this study illustrate (i)
effective banking performance depends on financial and nonfinancial parameters, (ii) effective mar
keting activities enhance performance, (iii) IMC is a strategic kit for aligning marketing operations
and strategies, (iv) IMC approach with its competencies outperforms competing banks, (v) stock per
formance of the banks with IMC approach determines the banking position.
Keywords: performance evaluation, banking, IMC, strategy, fuzzy VIKOR.
1. Introduction. The global economic recession and subsequently the sovereign
crisis melt down global economic activity having impacts on growth of national
economies, banking operations, asset prices, profitability, sustainability of business
operations and so on (Economic Outlook, 2010; Conyon et al., 2011: 399 404; Naes et
al., 2011: 139 142; Rjoub, 2011:83 95). In the last quarter of 2011 the sovereign debt
crisis in the euro area reached its peak point. Chain reaction of bank failures in
advanced economies and the possibility of sovereign defaults were the major concerns
as credit default swap spreads breaking new records high, even sovereigns with rela
tively strong public finances were hit by illiquid market conditions in the euro zone
(GFSR, 1012: 17 23). Even though equity prices have recovered, there is a need to
strengthen capital structure of banks to increase banking performance with healthy
returns on equity. According to IMF's Global Financial Report Analysis, the pres sures
on European banking system has sparked a broader drive to reduce balance sheet size
shrinking by as much as EUR 2.6 trln through the end 2013, which is almost equal to
7% of total assets (GFSR, 1012: 17). The major objective of the poli cies attached to
the deleveraging process is to prevent future potential consequences of an unhealthy
condition of banking system which may damage asset prices, credit chain and economic
activity.
The deleveraging process in banking sector seem to have negative effects bank
ing performance (Dattels, et al. 2010:32 43; Reinhart & Rogoff, 2008:27 46;
Ruscher & Wolff, 2012:7 13). In this situation, effective marketing strategies must be
applied to increase banking performance. According to GFSR's estimation, with the
current policies scenario, aggregate leverage of the banks falls from 29 to 23, with the
majority of this decline achieved through retained earnings and the capital raised
through reduction in assets ahead of cutback in lending (GFSR, 2012: 33).
Top managers in retail banking must focus on competitive strategies increasing
banking performance to outperform competing banks whilst reducing balance sheet
size. According to Hung Yi Wu (2012), a fiercely competing financial market with
relatively little profit, plus the new withdrawal mechanism regulations for low per
formance banks has resulted in a limited growth of banks in emerging markets (Wu,
2012:303 320). She concludes that outperforming competing bank institutions, more
emphasis on internal operational performance is required (Wu et al., 2009:100135
1001 47). Effective performance of a bank depends on financial and
249
non financial parameters including marketing activities. The studies illustrate that
aligning operations with competitive strategies throughout effective marketing activ
ities enhances banking performance and profitability (Rhee and Mehra, 2006:505 515;
Lariviere and Poel, 2007:345 369; Wu, 2012: 303 320; Boot, 2011: 167 183; Samad,
2008: 181 193; Berger and Patti, 2006:1065 1102). Integrated Marketing
Communication (IMC) is a strategic kit for aligning marketing operations and strate
gies with corporate level strategies.
This study aims to identify the effect of IMC competencies on aligning opera
tions with corporate strategies affecting banking performance with Fuzzy VIKOR
method.
2. Literature Review. IMC has a critical role on enhancing effective banking
performance. IMC removes all the limits of communication items and creates
dynamism within organization (Pickton & Broderick, 2001: 9). IMC as strategic
business process helps banking organizations to develop and execute persuasive brand
communications programs covering customers, employees, associates, and other
targeted relevant internal and external audiences. Subsequently, IMC gener ates both
short term and long term shareholder value based on increasing financial returns,
customer loyalty and brand depth (Belch & Belch, 2009: 745 775; Laric &Lynagh,
2010; Kotler & Armstrong, 1996: 400 450). Studies illustrate that 8 core competencies
in IMC process contributes into achievement of aligning marketing operations with
competitive strategies which enhance banking performance and profitability. These are
the level of institutionalization, share of spending, level of visibility, effectiveness,
current image, discursive consistency, market share, and financial performance.
3. Fuzzy VIKOR Method. Decision makers often simultaneously evaluate their
progress in attaining one or limited number alternatives and thus need to know where
gaps in alternatives exist to minimize them. Traditional methods are unsuitable for
ranking these gaps because each alternative has its own criteria (Liou et al., 2011:
57). VIKOR method was developed by Opricovic in 1998 for multi criteria
optimization of complex systems. The method focuses on decision making and
selecting from a set of alternatives, and determines compromise solu tions for a
problem with conflicting criteria to reach a final decision (Opricovic, 2011: 12983;
Opricovic and Tzeng, 2007: 515). Decision matrix can be explained
as follows:
C1
A
1
X
11
C2
X
12
C3
X
13
.
L
Cn
X
1
n
A
2
X
21
X
22
X
23
L X
2
n
(1)
D=
A
3
X
31
X
32
X
33
L X
3
n
,
M M
M M O
M
A
m
X
m
1
X
m
2 X
m
3
L X
mn
where A1, A2, . . .,A
m
are possible alternatives among which decision makers have to
choose, C1,C2, . . .,Cn are the criteria with which alternative performance are meas
ured, X
ij
is the rating of alternative A
i
with respect to criterion C
j
(Chu, 2004: 154).
The notion of a fuzzy set was introduced by Zadeh in 1965. It provides a convenient
250
point of departure for the construction of a conceptual framework which parallels in
many respects the framework used in the case of ordinary sets, may prove to have a
much wider scope of applicability such as in the fields of pattern classification and
information processing, time series (Zadeh, 1965: 339; Petrovic, Xie and Burnham,
2006:1714; Girubha and Vinodh, 2012). According to the classical set theory, the
truth value of a statement can be shown by the membership function as
f
A
(X)
f
A
(X )= 1
0
ifx e A
ifx ? A
(2)
Table 1. Literature Review and Criteria Selection for Fuzzy Vikor Applied IMC
model evaluating banking performance
Subject Study
Dattels et al. (2010), Reinhart&
Rogoff (2008)
Ruscher & Wolff (2012)
Wu (2012), Wu et al. (2009)
Rhee and Mehra ( 2006)
Samad ( 2008), Berger & Patti
(2006)
Uhde and Heimeshoff (2009)
Mayer and Rowan (1977), Zucker
(1977),
Schuman (1997)
Belch & Belch, 2009, Laric &
Lynagh, 2010
Luo & Donthu (2005), Hill &
Rifkin (1999)
Varadarajan & Menon (1988)
Fombrun (1996), Abratt (1989)
Van Dijk (1993), Lord & Putrevu
(1993)
Samad (2008), Lariviere and Poel
(2007)
Zadeh, 1965
Opricovic (2011), Opricovic &
Tzeng (2007)
Liou (2011)
Petrovic et al. (2006), Girubha &
Vinodh (2012)
Wu et al. ( 2009), Chen & Wang
(2009)
Ma, Lu & Zhang ( 2010)
Chen & Wang, 2009; Chen &
Huang, 1992
Approach
Historical Data
Analysis
Panel Analysis
BSC, Fuzzy
AHP, Topsis
HP and Pattern
Analysis
SCP, SP, EH,
SE Models
Z-score
technique
Structural
Analysis
International
Review
Historical Data
Analysis
Content Analysis
International
Review
International
Review
Patern Analysis,
SCP
Fuzzy
Fuzzy-Vikor
Fuzzy-Vikor
Fuzzy-Vikor
Fuzzy-Vikor
Fuzzy-Vikor
Fuzzy-Vikor
Criteria
Risk Mapping, Crisis and
Performance
Balance sheet adjstment and
Performance
Permance and Strategy
Banking Strategy and
Performance
Structure, Performance and
Marketing
Financial Performance
Legitimacy and
Institutionalization
IMC
Share of spending
Visibility
Current Image
Discursive Consistency
Market share and
performance
Basics
Optimization of complex
system
Ranking gaps
Pattern classification
Triangular Fuzzy
Linguistic Methods
Triangular Fuzzy-Linguistic
Variables
Fuzzy numbers are a fuzzy subset of real numbers expressing the idea of a confi
dence interval. A triangular fuzzy number can be defined as a triplet
~
= (a
1
,
a
2
,
a
3
) A
of crisp numbers with a
1
< a
2
<a
3.
B
a
n
k
i
n
g
I
M
C
F
u
z
z
y
L
o
g
i
c
f
~
(x)
A
~
A
251
a
1
a
2
a
3
x
Source: Wu, Tzeng and Chen, 2009: 10138; Chen and Wang, 2009: 235; Ngai and Wat, 2005: 242.
Figure 1. Membership function of the triangular fuzzy number
Membership function f~ (x) of the fuzzy number
~
is presented by AA
0, x?a
1
(x ÷ a )/(a ÷ a ), a s x s a (3)
f~ (X )=
A
(a ÷ x1)/(a2 ÷ a1 ), a1 s x s a2
3
3 2 2 3
0,
x?a
3
Supposed any two positive positive triangular fuzzy numbers,
~
= (a
1,
a
2,
a
3
) and
A
~
B = (b
1,
b
2,
b
3
) and a positive real number r, the operational laws of these two tri
angular fuzzy numbers are as follows (Wu, Tzeng and Chen, 2009; Chen and Wang,
2009; Sanayei, Mousavi and Yazdankhah, 2010; Lin, Hsu and Sheen, 2007) :
Addition of two triangular fuzzy numbers ?:
~ ? B = (a + b ,a + b ,a + b )
A~
1 12 2 3 3
(4)
Multiplication of two triangular fuzzy numbers ? :
~ ? B = (a b ,a b ,a b A~
11 2 2 3 3
)
(5)
Multiplication of any real number r and a triangular fuzzy numbers ?:
r ? ~ = (ra
1
,ra
2
,ra
3
) for r>0 and ai>0, bi>0, ci>0 A (6)
Subtraction of two triangular fuzzy numbers O:
~OB = (a ÷ b ,a ÷ b ,a ÷ b ) for ai 0, bi 0, ci 0 A~
1 3 2 2 3 1
(7)
Division of two triangular fuzzy numbers (|):
~
(|)B = (a / b ,a / b ,a / b )
A~
1 3 2 2 3 1
(8)
Reciprocal of a triangular fuzzy numbers:
()
~
÷
1 = (1/ a ,1/ a ,1/ a )for ai 0, bi 0, ci 0
A
3 2 1
(9)
The Fuzzy VIKOR method built on integrated marketing communications and
related financial parameters in banking sector allows solving MCDM problems with
conflicting and non commensurable criteria and provides a solution that is the clos est
to the optimum. The compromise ranking algorithm Fuzzy VIKOR has 8 steps
according to the above mentioned ideas:
Step 1: Two set of appropriate linguistic variables are constructed to estimate the
importance weight of each criterion and the fuzzy rates of alternatives appointed by deci
sion makers.
252
f
~
(
x
)
A
~~
AB
~
C
x
Source: Wu, Tzeng and Chen, 2009: 10139; Chen and Wang, 2009: 236.
Figure 2. Three triangular fuzzy numbers
Subjective information with fuzziness is often expressed by fuzzy sets and is
processed by linguistic methods (Ma, Lu and Zhang, 2010: 24). In this study, linguis
tic variables defined by triangular fuzzy number for the important weight of criteria
are very low (0.00, 0.00, 0.25); low (0.00, 0.25, 0.50); medium (0.25, 0.50, 0.75); high
(0.50, 0.75, 1.00); very high (0.75, 1.00, 1.00). Linguistic scales for the rating of alter
native are worst (0.00, 0.00, 2.50); poor (0.00, 2.50, 5.00); fair ( 2.50, 5.00, 7.50);
good (5.00, 7.50, 10.00); best (7.50, 10.00, 10.00) (Chen and Wang, 2009; Chen and
Huang, 1992).
Step 2: it is taken from k decision makers' opinions to get the aggregated fuzzy
weights w
j
of each criterion, and aggregated fuzzy ratings
~
ijof alternatives and con
~ x
struct a fuzzy decision matrix (Chen and Klein, 1997: 51 52) .
w
j
= 1
¿w
j
e
, j=1,2,3,.,n n
~
k
e
=1
~
(10)
~
ij
= 1
¿ ~ij
e
, i=1,2,3,.,m n
x
k e=1
x
(11)
Step 3: Fuzzy weighted average is calculated and the normalized fuzzy decision
matrix is constructed.
C1 C2 C3 . Cn
A
1
X
11
X
12
X
13
~
~
~
~
~
~
L ~1
n
X
~ = A
2
X
21
X
22
X
23
L ~2n X
(12)
D A
3
~31 ~32 ~33
X X X
L
~3n X
M M M M O
M
A
m
~m1 ~m2 ~m3
X X X
L
~mn X
i=1,2,3,.,m; j=1,2,3,..n (13)
W ~1 ~2 ~ = w ,w
,....,w ~n
(14)
where ~
ij
is the rating of alternative Ai with respect to Cj, w is the importance weight
x
~ij
of the j th criterion holds, mentioned linguistic variables
~
ij and w
ij
can be approxi
x ~
mated by positive triangular fuzzy numbers.
Step 4:
~
It is calculated an aspired (fuzzy best value
~
j
*
) and tolerable level (fuzzy f
worst value f
j
÷
) of all criterion functions,
253
~
*
= max ~ , and
~
÷
= min ~ ,
f
J
i
x
ij
f
j
i
x
ij
(15)
Step 5: Mean group utility and maximal regret are calculated. The values are
computed by
(~
*
~ )
S
i
¿i=1
~ =
n
w
f
j
÷ x
ij
~j
(
(16)
~
*
÷
~÷f
j
f
j
)
~ = max w f
j
÷ x
ij
R
i
j
~j
(~
*
~ )
(17)
(~
*
÷ ~
÷
)
~
f
j
f
j
where w ij are the fuzzy weights of criteria,
~
expressing the decision makers' preference
as the relative importance of the criteria.S
i
is Ai with respect to all criteria calculated
~
by the total of the distance for the fuzzy best value, and R
i
is Ai with respect to the j
th criterion, calculated by maximum distance of the fuzzy best value.
~
Step 6: The index value (Q) is calculated, the value can be counted by i
~~ ~ ~ ~~ ~ ~
Q~ = v S ÷ S
*
S
÷
÷ S
*
+ (1÷ v )R ÷ R
*
R
÷
÷ R
*
,
where
i
(
i
~
)(
~
) (
i
)( )
(18)
S
*
= minS
i
; i
~ ~
S
÷
= max S
i
; i
~ ~
R
*
= minR ; ii
R
÷
= max R
i
~
i
~
and v is presented as the weight of the strategy of maximum group utility, whereas 1
v is the weight of individual regret (Kaya and Kahraman, 2010: 2521 2522).
~
Step 7: Defuzzify triangular fuzzy number Q
i
and rank the alternatives, sorting
by the value Qi. In this study, the method of maximizing set and minimizing set to
defuzzify triangular fuzzy number is used (Chen, 1985).
Step 8: The alternatives are ranked or improved for a compromise solution. The
values S, R and Q in decreasing order are sorted. Propose a compromise solution the
alternative (?
(1)
) which is the best ranked by the measure Q (minimum) when the two
conditions are satisfied:
C1. Acceptable Advantage:
Q A
(
2
)
÷ Q A
(
1
)
>1/(j ÷1), ()()
where ? is the second position in the alternatives ranked by Q (minimum). (2)
C2. Acceptable stability in decision making:
(19)
The alternative ?
(1)
must also be the best ranked by S or/and R. This compromise
solution is stable within a decision making process, which could be the strategy of
maximum group utility (when v > 0.5 is needed), or ''by consensus'' v ~ 0.5, or ''with
veto'' (v < 0.5). If one of the conditions is not satisfied, a set of compromise solutions
is selected. The compromise solutions are composed of (1) Alternatives ?
(1)
and ?
(2)
254
if only condition C2 is not satisfied, or (2) Alternatives ?
(1)
, ?
(2)
. . . ,?
(M)
if condition
C1 is not satisfied. ?
(M)
is calculated by the relation
Q A
(
M
)
÷ Q A
(
1
)
< 1/(j ÷1) ()()
for maximum M (the positions of these alternatives are close) (Wang and Tzeng, 2012:
5608; Opricovic and Tzeng, 2007: 515 516; Bazzazi, Osanloo and Karimi, 2011:
2551; Shemshadi et al. 2011: 12164; Yucenur and Demirel, 2012: 3704 3705).
4. Empirical Study.
4.1. Research Goal and Analysis. In this study we aim to identify the effect of
IMC competencies on aligning operations with corporate strategies affecting banking
performance with Fuzzy VIKOR method. Our critical question focuses on the per
formance evaluation criteria attached to IMC competencies. How does IMC affect a
banking performance in comparison with other competitors? To testify our proposi
tion, according to IMC criteria, we have selected 12 major banks at Istanbul Stock
Exchange (ISE). We have also selected 3 important decision makers at high rank from
major institutions and ask them to determine the priorities.
Table 2. Description of Proposed IMC performance Criteria
No. IMC Criteria Description
I: IMC
1 (C1) Level of Institutionalization Legitimacy
2 (C2) Share of Spending Marketing Expenditures
3 (C3) Level of Visibility Activity
4 (C4) Effectiveness Achievement 5
(C5) Current Image Reputation
6 (C6) Discursive Consistency Trust
7 (C7) Market Share Power& deterrence at the market 8
(C8) Financial Performance Profitability
The proposed banking performance model has been applied to the banks traded at
ISE based on IMC approach. Main parameters for evaluating banking perform ance
are listed in Table 2.
4.2. Analyses and Results. To measure the actual ranks of banks in accordance
with the selection criteria generated by the experts, Fuzzy VIKOR method has been
conducted. The fuzzy VIKOR method is built on integrated marketing communica
tions and related financial parameters in banking. In this scope, the linguistic impor
tance of each criteria in the judgment of the experts has been examined. Firstly, 3
experts, D1; D2 and D3, helped to define the main criteria for evaluating banks based
on IMC competencies, eqs. (1). Also, they define the linguistic weights according to
the study of Chen and Huang (1992) to assess the importance of each criteria (Table
3). The linguistic evaluations have been converted into triangular fuzzy numbers.
On the basis of generated 8 evaluation criteria (C) and 3 decision makers and
feasible 12 banking trading at ISE (alternatives), corresponding triangular fuzzy num
bers have been defined. Then, the important fuzzy weight of the criteria is aggregated
and also, the weighted normalized fuzzy decision matrix is determined from the lin
guistic rating of each alternative under each criterion according to the equations (10)
(14).
255
Table 3. The linguistic importance weight of criteria
C1
C2
C3
C4
C5
C6
C7
C8
D1
H
H
H
VH
M
H
VH
H
D2
H
H
H
VH
H
M
H
H
D3
VH
H
M
H
H
VH
VH
H
Table 4. The aggregate fuzzy weight of each criterion
(The importance weight of each criteria in the judgment of experts)
fuzzy weight
C1
C2
C3
C4
C5
C6
C7
C8
0.5833
0.5
0.4167
0.6667
0.4167
0.5
0.6667
0.5
0.8333
0.75
0.6667
0.9167
0.6667
0.75
0.9167
0.75
1
1
0.9167
1
0.9167
0.9167
1
1
Table 5. The weighted normalized Fuzzy Decision Matrix
A1 A2 A3 A4
DM1 DM2 DM3 DM1 DM2 DM3 DM1 DM2 DM3 DM1 DM2 DM3
C1 5.00 7.50 10.00 7.50 10.00 10.00 5.00 7.50 10.00 7.50 10.00 10.00
C2 5.00 7.50 10.00 7.50 10.00 10.00 5.00 7.50 10.00 5.00 7.50 10.00
C3 5.83 8.33 10.00 7.50 10.00 10.00 6.67 9.17 10.00 6.67 9.17 10.00
C4 5.83 8.33 10.00 7.50 10.00 10.00 5.83 8.33 10.00 5.00 7.50 10.00
C5 7.50 10.00 10.00 7.50 10.00 10.00 5.83 8.33 10.00 7.50 10.00 10.00
C6 5.00 7.50 10.00 7.50 10.00 10.00 6.67 9.17 10.00 5.00 7.50 10.00
C7 5.83 8.33 10.00 7.50 10.00 10.00 7.50 10.00 10.00 5.00 7.50 10.00
C8 7.50 10.00 10.00 5.83 8.33 10.00 5.83 8.33 10.00 3.33 5.83 8.33
A5 A6 A7 A8
DM1 DM2 DM3 DM1 DM2 DM3 DM1 DM2 DM3 DM1 DM2 DM3
C1 5.00 7.50 10.00 3.33 5.83 8.33 4.17 6.67 9.17 2.50 5.00 7.50
C2 2.50 5.00 7.50 2.50 5.00 7.50 3.33 5.83 8.33 0.83 3.33 5.83
C3 5.00 7.50 10.00 2.50 5.00 7.50 3.33 5.83 8.33 2.50 5.00 7.50
C4 3.33 5.83 8.33 2.50 5.00 7.50 2.50 5.00 7.50 2.50 5.00 7.50
C5 5.83 8.33 10.00 3.33 5.83 8.33 3.33 5.83 8.33 2.50 5.00 7.50
C6 5.00 7.50 9.17 2.50 5.00 7.50 2.50 5.00 7.50 2.50 5.00 7.50
C7 2.50 5.00 7.50 5.00 7.50 10.00 2.50 5.00 7.50 2.50 5.00 7.50
C8 3.33 5.83 8.33 3.33 5.83 8.33 2.50 5.00 7.50 5.00 7.50
10.00
A9 A10 A11 A12
DM1 DM2 DM3 DM1 DM2 DM3 DM1 DM2 DM3 DM1 DM2 DM3
C1 3.33 5.83 8.33 2.50 5.00 7.50 0.00 2.50 5.00 0.00 0.00 2.50
C2 2.50 5.00 7.50 0.00 2.50 5.00 0.00 0.00 2.50 0.00 0.00 2.50
C3 3.33 5.83 8.33 5.00 7.50 10.00 0.00 2.50 5.00 0.00 0.00 2.50
C4 2.50 5.00 7.50 0.00 2.50 5.00 0.00 2.50 5.00 0.00 0.00 2.50
C5 0.83 3.33 5.83 2.50 5.00 7.50 0.00 0.00 2.50 0.00 0.00 2.50
C6 0.83 3.33 5.83 2.50 5.00 7.50 0.00 2.50 5.00 0.00 0.00 2.50
C7 2.50 5.00 7.50 1.67 4.17 6.67 0.00 2.50 5.00 0.00 2.50 5.00
C8 2.50 5.00 7.50 0.00 2.50 5.00 0.00 0.00 2.50 0.00 0.00 2.50
256
Table 6. The linguistic rating of each alternative under each criterion
A1 A2 A3 A4
DM1 DM2 DM3 DM1 DM2 DM3 DM1 DM2 DM3 DM1 DM2 DM3
C1 G G G B B B G G G B B B
C2 G G G B B B G G G G G G
C3 B G G B B B B G B B G B
C4 B G G B B B B G G G G G
C5 B B B B B B B G G B B B
C6 G G G B B B B B G G G G
C7 B G G B B B B B B G G G
C8 B B B B G G B G G G F F
A5 A6 A7 A8
DM1 DM2 DM3 DM1 DM2 DM3 DM1 DM2 DM3 DM1 DM2 DM3
C1 G G G G F F G F G F F F
C2 F F F F F F G F F F P P
C3 G G G F F F F F G F F F
C4 G F F F F F F F F F F F
C5 B G G G F F F F G F F F
C6 B G F F F F F F F F F F
C7 F F F G G G F F F F F F
C8 G F F G F F F F F G G G
A9 A10 A11 A12
DM1 DM2 DM3 DM1 DM2 DM3 DM1 DM2 DM3 DM1 DM2 DM3
C1 F F G F F F P P P W W W
C2 F F F P P P W W W W W W
C3 F F G G G G P P P W W W
C4 F F F P P P P P P W W W
C5 P P F F F F W W W W W W
C6 P P F F F F P P P W W
WC7 F F F P F F P P P P P
P
C8 F F F P P P W W W W W W
Table 7. Fuzzy Best and Worst Value
Fuzzy Best Value (
f
j
~
*
)
Fuzzy Wors Value (
f
j
~
÷
)
C1 7.5 10 10 0 0 2.5
C2 7.5 10 10 0 0 2.5
C3 7.5 10 10 0 0 2.5
C4 7.5 10 10 0 0 2.5
C5 7.5 10 10 0 0 2.5
C6 7.5 10 10 0 0 2.5
C7 7.5 10 10 0 2.5 5
C8 7.5 10 10 0 0 2.5
In the following step, the best and the worst values of all the criterion ratings have
been determined, based on eq.(15), as seen Table 7.
~~ ~
The values of S
i
, R
i
and Q are calculated for all banks as Table 8 using equations
i
~
(16) (18). In the calculations, v is assumed to be 0.5.Q
i
values are defuzzified, and
ranking of the alternative banks by Qi, Ri and Si in decreasing order is listed in Table
10. Bank A2 is the most prominent for IMC competencies by potential investors and
customers. Also, the conditions C1 and C2 are satisfied (QA3 QA2)> 1/(12 1) and
A2 is also the best by the value of R and S.
257
~~ ~
Table 8. The values of S
i
, R
i
and Q
i
Alternatives ~ ~ ~S
i
R
i
Q
i
A1 0.9167 1.0509 0.0000 0.1944 0.2083 0.0000 0.1723 0.1282 0.0000
A2 0.1111 0.1250 0.0000 0.1111 0.1250 0.0000 0.0000 0.0000 0.0000
A3 0.8148 0.9028 0.0000 0.1944 0.2083 0.0000 0.1600 0.1161 0.0000
A4 1.1019 1.2778 0.2222 0.2778 0.3125 0.2222 0.2697 0.2125 0.1254
A5 2.0185 2.3542 1.3796 0.4444 0.6111 0.5000 0.5304 0.4890 0.3390
A6 2.4444 2.7847 1.9259 0.4444 0.4583 0.3333 0.5819 0.4276 0.2909
A7 2.5556 2.9653 2.2130 0.4444 0.6111 0.5000 0.5953 0.5389 0.3928
A8 2.7778 3.2153 2.6389 0.4444 0.6111 0.5556 0.6221 0.5593 0.4480
A9 2.9259 3.3889 2.9444 0.4444 0.6111 0.5093 0.6400 0.5735 0.4446
A10 3.3241 3.8171 3.6111 0.6667 0.7130 0.6667 0.8881 0.6727 0.5663
A11 4.2500 5.4583 6.4722 0.6667 0.9167 1.0000 1.0000 0.9354 0.9176
A12 4.2500 6.2500 7.7500 0.6667 0.9167 1.0000 1.0000 1.0000 1.0000
Table 9. Ranking of each alternative by Qi values
Alternatives
A1
A2
A3
A4
A5
A6
A7
A8
A9
A10
A11
A12
Qi
0.1002
0.0000
0.0920
0.2026
0.4528
0.4335
0.5090
0.5432
0.5527
0.7091
0.9510
1.0000
Ranking of alternatives
A2
A3
A1
A4
A6
A5
A7
A8
A9
A10
A11
A12
Table 10. Ranking by Qi, Ri and Si values
Rank
1
2
3
4
5
6
7
8
9
10
11
12
Ranking by Q
i
A2
A3
A1
A4
A6
A5
A7
A8
A9
A10
A11
A12
Ranking by S
i
A2
A3
A1
A4
A5
A6
A7
A8
A9
A10
A11
A12
Ranking by R
i
A2 A1
A3
A4
A6
A5
A7
A9
A8
A10
A11
A12
When we examine Table 7 for the fuzzy best and worst value and Table 10 illus
trating rankings by Qi, Ri and Si values, it can be seen that the 8 competencies have
significant effect on both banking performance and competitiveness. Rankings by Qi
illustrates that A2 bank has superior competitiveness in comparison with other banks
whilst ranked at top by S
i
and R
i
values.
Conclusion. Risks and pressures on banking system in advanced and emerging
economies still remain. Pressures on European banking system have sparked a broad er
drive for policy makers to reduce balance sheet size shrinking by as much as EUR
258
2.6 trillion by the end of 2013, which is almost equal to 7% of their total assets
(GFSR, 1012: 17). To overcome negative effect of the latest deleveraging process,
major strategies are required for boosting banking performance, having more empha sis
on internal operations (Wu et al., 2009). Top managers in retail banking must focus on
competitive strategies to increase banking performance whilst outperforming
competing banks. The Fuzzy VIKOR method in our analysis is built on integrated
marketing communications competencies and related financial parameters in bank ing
sector.
As a conclusion, the new deleveraging process at capital markets has negative
effects on banking performance. IMC approach as a strategic tool aligns effective
marketing strategies with successful corporate strategies. The findings of our study are:
(i) effective banking performance depends on financial and nonfinancial param eters,
(ii) effective marketing activities enhance performance, (iii) IMC is a strategic kit for
aligning marketing operations and strategies, (iv) IMC approach with its com petencies
outperforms competing banks, (v) stock performance of the banks with IMC approach
determines the banking position.
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doc_363161166.docx
Case Study of Integrated Marketing Communication Competencies on Banking Performance: Analysis with Fuzzy Vikor Method:- Integrated Marketing Communication (IMC) is a term that emerged in the late 20th century regarding application of consistent brand messaging across myriad marketing channels. The term has varying definitions depending upon the source cited.
Case Study of Integrated Marketing
Communication Competencies on Banking
Performance: Analysis with Fuzzy Vikor
Method
Chain reaction of bank failures in advanced economies and the possibility of sovereign defaults
are still the major concerns as credit default swap spreads breaking new records high. Moreover, the
latest policy steps by banking authorities in advanced economies seem to have negative effect on bank
ing performance. Fierce competition at financial market with relatively little profit, plus the new with
drawal mechanism regulations for low performance banks have resulted in a limited growth of banks
at capital markets. IMC approach as a strategic tool aligns effective marketing strategies with suc
cessful corporate strategies. The result of fuzzy VIKOR analysis adapted in this study illustrate (i)
effective banking performance depends on financial and nonfinancial parameters, (ii) effective mar
keting activities enhance performance, (iii) IMC is a strategic kit for aligning marketing operations
and strategies, (iv) IMC approach with its competencies outperforms competing banks, (v) stock per
formance of the banks with IMC approach determines the banking position.
Keywords: performance evaluation, banking, IMC, strategy, fuzzy VIKOR.
1. Introduction. The global economic recession and subsequently the sovereign
crisis melt down global economic activity having impacts on growth of national
economies, banking operations, asset prices, profitability, sustainability of business
operations and so on (Economic Outlook, 2010; Conyon et al., 2011: 399 404; Naes et
al., 2011: 139 142; Rjoub, 2011:83 95). In the last quarter of 2011 the sovereign debt
crisis in the euro area reached its peak point. Chain reaction of bank failures in
advanced economies and the possibility of sovereign defaults were the major concerns
as credit default swap spreads breaking new records high, even sovereigns with rela
tively strong public finances were hit by illiquid market conditions in the euro zone
(GFSR, 1012: 17 23). Even though equity prices have recovered, there is a need to
strengthen capital structure of banks to increase banking performance with healthy
returns on equity. According to IMF's Global Financial Report Analysis, the pres sures
on European banking system has sparked a broader drive to reduce balance sheet size
shrinking by as much as EUR 2.6 trln through the end 2013, which is almost equal to
7% of total assets (GFSR, 1012: 17). The major objective of the poli cies attached to
the deleveraging process is to prevent future potential consequences of an unhealthy
condition of banking system which may damage asset prices, credit chain and economic
activity.
The deleveraging process in banking sector seem to have negative effects bank
ing performance (Dattels, et al. 2010:32 43; Reinhart & Rogoff, 2008:27 46;
Ruscher & Wolff, 2012:7 13). In this situation, effective marketing strategies must be
applied to increase banking performance. According to GFSR's estimation, with the
current policies scenario, aggregate leverage of the banks falls from 29 to 23, with the
majority of this decline achieved through retained earnings and the capital raised
through reduction in assets ahead of cutback in lending (GFSR, 2012: 33).
Top managers in retail banking must focus on competitive strategies increasing
banking performance to outperform competing banks whilst reducing balance sheet
size. According to Hung Yi Wu (2012), a fiercely competing financial market with
relatively little profit, plus the new withdrawal mechanism regulations for low per
formance banks has resulted in a limited growth of banks in emerging markets (Wu,
2012:303 320). She concludes that outperforming competing bank institutions, more
emphasis on internal operational performance is required (Wu et al., 2009:100135
1001 47). Effective performance of a bank depends on financial and
249
non financial parameters including marketing activities. The studies illustrate that
aligning operations with competitive strategies throughout effective marketing activ
ities enhances banking performance and profitability (Rhee and Mehra, 2006:505 515;
Lariviere and Poel, 2007:345 369; Wu, 2012: 303 320; Boot, 2011: 167 183; Samad,
2008: 181 193; Berger and Patti, 2006:1065 1102). Integrated Marketing
Communication (IMC) is a strategic kit for aligning marketing operations and strate
gies with corporate level strategies.
This study aims to identify the effect of IMC competencies on aligning opera
tions with corporate strategies affecting banking performance with Fuzzy VIKOR
method.
2. Literature Review. IMC has a critical role on enhancing effective banking
performance. IMC removes all the limits of communication items and creates
dynamism within organization (Pickton & Broderick, 2001: 9). IMC as strategic
business process helps banking organizations to develop and execute persuasive brand
communications programs covering customers, employees, associates, and other
targeted relevant internal and external audiences. Subsequently, IMC gener ates both
short term and long term shareholder value based on increasing financial returns,
customer loyalty and brand depth (Belch & Belch, 2009: 745 775; Laric &Lynagh,
2010; Kotler & Armstrong, 1996: 400 450). Studies illustrate that 8 core competencies
in IMC process contributes into achievement of aligning marketing operations with
competitive strategies which enhance banking performance and profitability. These are
the level of institutionalization, share of spending, level of visibility, effectiveness,
current image, discursive consistency, market share, and financial performance.
3. Fuzzy VIKOR Method. Decision makers often simultaneously evaluate their
progress in attaining one or limited number alternatives and thus need to know where
gaps in alternatives exist to minimize them. Traditional methods are unsuitable for
ranking these gaps because each alternative has its own criteria (Liou et al., 2011:
57). VIKOR method was developed by Opricovic in 1998 for multi criteria
optimization of complex systems. The method focuses on decision making and
selecting from a set of alternatives, and determines compromise solu tions for a
problem with conflicting criteria to reach a final decision (Opricovic, 2011: 12983;
Opricovic and Tzeng, 2007: 515). Decision matrix can be explained
as follows:
C1
A
1
X
11
C2
X
12
C3
X
13
.
L
Cn
X
1
n
A
2
X
21
X
22
X
23
L X
2
n
(1)
D=
A
3
X
31
X
32
X
33
L X
3
n
,
M M
M M O
M
A
m
X
m
1
X
m
2 X
m
3
L X
mn
where A1, A2, . . .,A
m
are possible alternatives among which decision makers have to
choose, C1,C2, . . .,Cn are the criteria with which alternative performance are meas
ured, X
ij
is the rating of alternative A
i
with respect to criterion C
j
(Chu, 2004: 154).
The notion of a fuzzy set was introduced by Zadeh in 1965. It provides a convenient
250
point of departure for the construction of a conceptual framework which parallels in
many respects the framework used in the case of ordinary sets, may prove to have a
much wider scope of applicability such as in the fields of pattern classification and
information processing, time series (Zadeh, 1965: 339; Petrovic, Xie and Burnham,
2006:1714; Girubha and Vinodh, 2012). According to the classical set theory, the
truth value of a statement can be shown by the membership function as
f
A
(X)
f
A
(X )= 1
0
ifx e A
ifx ? A
(2)
Table 1. Literature Review and Criteria Selection for Fuzzy Vikor Applied IMC
model evaluating banking performance
Subject Study
Dattels et al. (2010), Reinhart&
Rogoff (2008)
Ruscher & Wolff (2012)
Wu (2012), Wu et al. (2009)
Rhee and Mehra ( 2006)
Samad ( 2008), Berger & Patti
(2006)
Uhde and Heimeshoff (2009)
Mayer and Rowan (1977), Zucker
(1977),
Schuman (1997)
Belch & Belch, 2009, Laric &
Lynagh, 2010
Luo & Donthu (2005), Hill &
Rifkin (1999)
Varadarajan & Menon (1988)
Fombrun (1996), Abratt (1989)
Van Dijk (1993), Lord & Putrevu
(1993)
Samad (2008), Lariviere and Poel
(2007)
Zadeh, 1965
Opricovic (2011), Opricovic &
Tzeng (2007)
Liou (2011)
Petrovic et al. (2006), Girubha &
Vinodh (2012)
Wu et al. ( 2009), Chen & Wang
(2009)
Ma, Lu & Zhang ( 2010)
Chen & Wang, 2009; Chen &
Huang, 1992
Approach
Historical Data
Analysis
Panel Analysis
BSC, Fuzzy
AHP, Topsis
HP and Pattern
Analysis
SCP, SP, EH,
SE Models
Z-score
technique
Structural
Analysis
International
Review
Historical Data
Analysis
Content Analysis
International
Review
International
Review
Patern Analysis,
SCP
Fuzzy
Fuzzy-Vikor
Fuzzy-Vikor
Fuzzy-Vikor
Fuzzy-Vikor
Fuzzy-Vikor
Fuzzy-Vikor
Criteria
Risk Mapping, Crisis and
Performance
Balance sheet adjstment and
Performance
Permance and Strategy
Banking Strategy and
Performance
Structure, Performance and
Marketing
Financial Performance
Legitimacy and
Institutionalization
IMC
Share of spending
Visibility
Current Image
Discursive Consistency
Market share and
performance
Basics
Optimization of complex
system
Ranking gaps
Pattern classification
Triangular Fuzzy
Linguistic Methods
Triangular Fuzzy-Linguistic
Variables
Fuzzy numbers are a fuzzy subset of real numbers expressing the idea of a confi
dence interval. A triangular fuzzy number can be defined as a triplet
~
= (a
1
,
a
2
,
a
3
) A
of crisp numbers with a
1
< a
2
<a
3.
B
a
n
k
i
n
g
I
M
C
F
u
z
z
y
L
o
g
i
c
f
~
(x)
A
~
A
251
a
1
a
2
a
3
x
Source: Wu, Tzeng and Chen, 2009: 10138; Chen and Wang, 2009: 235; Ngai and Wat, 2005: 242.
Figure 1. Membership function of the triangular fuzzy number
Membership function f~ (x) of the fuzzy number
~
is presented by AA
0, x?a
1
(x ÷ a )/(a ÷ a ), a s x s a (3)
f~ (X )=
A
(a ÷ x1)/(a2 ÷ a1 ), a1 s x s a2
3
3 2 2 3
0,
x?a
3
Supposed any two positive positive triangular fuzzy numbers,
~
= (a
1,
a
2,
a
3
) and
A
~
B = (b
1,
b
2,
b
3
) and a positive real number r, the operational laws of these two tri
angular fuzzy numbers are as follows (Wu, Tzeng and Chen, 2009; Chen and Wang,
2009; Sanayei, Mousavi and Yazdankhah, 2010; Lin, Hsu and Sheen, 2007) :
Addition of two triangular fuzzy numbers ?:
~ ? B = (a + b ,a + b ,a + b )
A~
1 12 2 3 3
(4)
Multiplication of two triangular fuzzy numbers ? :
~ ? B = (a b ,a b ,a b A~
11 2 2 3 3
)
(5)
Multiplication of any real number r and a triangular fuzzy numbers ?:
r ? ~ = (ra
1
,ra
2
,ra
3
) for r>0 and ai>0, bi>0, ci>0 A (6)
Subtraction of two triangular fuzzy numbers O:
~OB = (a ÷ b ,a ÷ b ,a ÷ b ) for ai 0, bi 0, ci 0 A~
1 3 2 2 3 1
(7)
Division of two triangular fuzzy numbers (|):
~
(|)B = (a / b ,a / b ,a / b )
A~
1 3 2 2 3 1
(8)
Reciprocal of a triangular fuzzy numbers:
()
~
÷
1 = (1/ a ,1/ a ,1/ a )for ai 0, bi 0, ci 0
A
3 2 1
(9)
The Fuzzy VIKOR method built on integrated marketing communications and
related financial parameters in banking sector allows solving MCDM problems with
conflicting and non commensurable criteria and provides a solution that is the clos est
to the optimum. The compromise ranking algorithm Fuzzy VIKOR has 8 steps
according to the above mentioned ideas:
Step 1: Two set of appropriate linguistic variables are constructed to estimate the
importance weight of each criterion and the fuzzy rates of alternatives appointed by deci
sion makers.
252
f
~
(
x
)
A
~~
AB
~
C
x
Source: Wu, Tzeng and Chen, 2009: 10139; Chen and Wang, 2009: 236.
Figure 2. Three triangular fuzzy numbers
Subjective information with fuzziness is often expressed by fuzzy sets and is
processed by linguistic methods (Ma, Lu and Zhang, 2010: 24). In this study, linguis
tic variables defined by triangular fuzzy number for the important weight of criteria
are very low (0.00, 0.00, 0.25); low (0.00, 0.25, 0.50); medium (0.25, 0.50, 0.75); high
(0.50, 0.75, 1.00); very high (0.75, 1.00, 1.00). Linguistic scales for the rating of alter
native are worst (0.00, 0.00, 2.50); poor (0.00, 2.50, 5.00); fair ( 2.50, 5.00, 7.50);
good (5.00, 7.50, 10.00); best (7.50, 10.00, 10.00) (Chen and Wang, 2009; Chen and
Huang, 1992).
Step 2: it is taken from k decision makers' opinions to get the aggregated fuzzy
weights w
j
of each criterion, and aggregated fuzzy ratings
~
ijof alternatives and con
~ x
struct a fuzzy decision matrix (Chen and Klein, 1997: 51 52) .
w
j
= 1
¿w
j
e
, j=1,2,3,.,n n
~
k
e
=1
~
(10)
~
ij
= 1
¿ ~ij
e
, i=1,2,3,.,m n
x
k e=1
x
(11)
Step 3: Fuzzy weighted average is calculated and the normalized fuzzy decision
matrix is constructed.
C1 C2 C3 . Cn
A
1
X
11
X
12
X
13
~
~
~
~
~
~
L ~1
n
X
~ = A
2
X
21
X
22
X
23
L ~2n X
(12)
D A
3
~31 ~32 ~33
X X X
L
~3n X
M M M M O
M
A
m
~m1 ~m2 ~m3
X X X
L
~mn X
i=1,2,3,.,m; j=1,2,3,..n (13)
W ~1 ~2 ~ = w ,w
,....,w ~n
(14)
where ~
ij
is the rating of alternative Ai with respect to Cj, w is the importance weight
x
~ij
of the j th criterion holds, mentioned linguistic variables
~
ij and w
ij
can be approxi
x ~
mated by positive triangular fuzzy numbers.
Step 4:
~
It is calculated an aspired (fuzzy best value
~
j
*
) and tolerable level (fuzzy f
worst value f
j
÷
) of all criterion functions,
253
~
*
= max ~ , and
~
÷
= min ~ ,
f
J
i
x
ij
f
j
i
x
ij
(15)
Step 5: Mean group utility and maximal regret are calculated. The values are
computed by
(~
*
~ )
S
i
¿i=1
~ =
n
w
f
j
÷ x
ij
~j
(
(16)
~
*
÷
~÷f
j
f
j
)
~ = max w f
j
÷ x
ij
R
i
j
~j
(~
*
~ )
(17)
(~
*
÷ ~
÷
)
~
f
j
f
j
where w ij are the fuzzy weights of criteria,
~
expressing the decision makers' preference
as the relative importance of the criteria.S
i
is Ai with respect to all criteria calculated
~
by the total of the distance for the fuzzy best value, and R
i
is Ai with respect to the j
th criterion, calculated by maximum distance of the fuzzy best value.
~
Step 6: The index value (Q) is calculated, the value can be counted by i
~~ ~ ~ ~~ ~ ~
Q~ = v S ÷ S
*
S
÷
÷ S
*
+ (1÷ v )R ÷ R
*
R
÷
÷ R
*
,
where
i
(
i
~
)(
~
) (
i
)( )
(18)
S
*
= minS
i
; i
~ ~
S
÷
= max S
i
; i
~ ~
R
*
= minR ; ii
R
÷
= max R
i
~
i
~
and v is presented as the weight of the strategy of maximum group utility, whereas 1
v is the weight of individual regret (Kaya and Kahraman, 2010: 2521 2522).
~
Step 7: Defuzzify triangular fuzzy number Q
i
and rank the alternatives, sorting
by the value Qi. In this study, the method of maximizing set and minimizing set to
defuzzify triangular fuzzy number is used (Chen, 1985).
Step 8: The alternatives are ranked or improved for a compromise solution. The
values S, R and Q in decreasing order are sorted. Propose a compromise solution the
alternative (?
(1)
) which is the best ranked by the measure Q (minimum) when the two
conditions are satisfied:
C1. Acceptable Advantage:
Q A
(
2
)
÷ Q A
(
1
)
>1/(j ÷1), ()()
where ? is the second position in the alternatives ranked by Q (minimum). (2)
C2. Acceptable stability in decision making:
(19)
The alternative ?
(1)
must also be the best ranked by S or/and R. This compromise
solution is stable within a decision making process, which could be the strategy of
maximum group utility (when v > 0.5 is needed), or ''by consensus'' v ~ 0.5, or ''with
veto'' (v < 0.5). If one of the conditions is not satisfied, a set of compromise solutions
is selected. The compromise solutions are composed of (1) Alternatives ?
(1)
and ?
(2)
254
if only condition C2 is not satisfied, or (2) Alternatives ?
(1)
, ?
(2)
. . . ,?
(M)
if condition
C1 is not satisfied. ?
(M)
is calculated by the relation
Q A
(
M
)
÷ Q A
(
1
)
< 1/(j ÷1) ()()
for maximum M (the positions of these alternatives are close) (Wang and Tzeng, 2012:
5608; Opricovic and Tzeng, 2007: 515 516; Bazzazi, Osanloo and Karimi, 2011:
2551; Shemshadi et al. 2011: 12164; Yucenur and Demirel, 2012: 3704 3705).
4. Empirical Study.
4.1. Research Goal and Analysis. In this study we aim to identify the effect of
IMC competencies on aligning operations with corporate strategies affecting banking
performance with Fuzzy VIKOR method. Our critical question focuses on the per
formance evaluation criteria attached to IMC competencies. How does IMC affect a
banking performance in comparison with other competitors? To testify our proposi
tion, according to IMC criteria, we have selected 12 major banks at Istanbul Stock
Exchange (ISE). We have also selected 3 important decision makers at high rank from
major institutions and ask them to determine the priorities.
Table 2. Description of Proposed IMC performance Criteria
No. IMC Criteria Description
I: IMC
1 (C1) Level of Institutionalization Legitimacy
2 (C2) Share of Spending Marketing Expenditures
3 (C3) Level of Visibility Activity
4 (C4) Effectiveness Achievement 5
(C5) Current Image Reputation
6 (C6) Discursive Consistency Trust
7 (C7) Market Share Power& deterrence at the market 8
(C8) Financial Performance Profitability
The proposed banking performance model has been applied to the banks traded at
ISE based on IMC approach. Main parameters for evaluating banking perform ance
are listed in Table 2.
4.2. Analyses and Results. To measure the actual ranks of banks in accordance
with the selection criteria generated by the experts, Fuzzy VIKOR method has been
conducted. The fuzzy VIKOR method is built on integrated marketing communica
tions and related financial parameters in banking. In this scope, the linguistic impor
tance of each criteria in the judgment of the experts has been examined. Firstly, 3
experts, D1; D2 and D3, helped to define the main criteria for evaluating banks based
on IMC competencies, eqs. (1). Also, they define the linguistic weights according to
the study of Chen and Huang (1992) to assess the importance of each criteria (Table
3). The linguistic evaluations have been converted into triangular fuzzy numbers.
On the basis of generated 8 evaluation criteria (C) and 3 decision makers and
feasible 12 banking trading at ISE (alternatives), corresponding triangular fuzzy num
bers have been defined. Then, the important fuzzy weight of the criteria is aggregated
and also, the weighted normalized fuzzy decision matrix is determined from the lin
guistic rating of each alternative under each criterion according to the equations (10)
(14).
255
Table 3. The linguistic importance weight of criteria
C1
C2
C3
C4
C5
C6
C7
C8
D1
H
H
H
VH
M
H
VH
H
D2
H
H
H
VH
H
M
H
H
D3
VH
H
M
H
H
VH
VH
H
Table 4. The aggregate fuzzy weight of each criterion
(The importance weight of each criteria in the judgment of experts)
fuzzy weight
C1
C2
C3
C4
C5
C6
C7
C8
0.5833
0.5
0.4167
0.6667
0.4167
0.5
0.6667
0.5
0.8333
0.75
0.6667
0.9167
0.6667
0.75
0.9167
0.75
1
1
0.9167
1
0.9167
0.9167
1
1
Table 5. The weighted normalized Fuzzy Decision Matrix
A1 A2 A3 A4
DM1 DM2 DM3 DM1 DM2 DM3 DM1 DM2 DM3 DM1 DM2 DM3
C1 5.00 7.50 10.00 7.50 10.00 10.00 5.00 7.50 10.00 7.50 10.00 10.00
C2 5.00 7.50 10.00 7.50 10.00 10.00 5.00 7.50 10.00 5.00 7.50 10.00
C3 5.83 8.33 10.00 7.50 10.00 10.00 6.67 9.17 10.00 6.67 9.17 10.00
C4 5.83 8.33 10.00 7.50 10.00 10.00 5.83 8.33 10.00 5.00 7.50 10.00
C5 7.50 10.00 10.00 7.50 10.00 10.00 5.83 8.33 10.00 7.50 10.00 10.00
C6 5.00 7.50 10.00 7.50 10.00 10.00 6.67 9.17 10.00 5.00 7.50 10.00
C7 5.83 8.33 10.00 7.50 10.00 10.00 7.50 10.00 10.00 5.00 7.50 10.00
C8 7.50 10.00 10.00 5.83 8.33 10.00 5.83 8.33 10.00 3.33 5.83 8.33
A5 A6 A7 A8
DM1 DM2 DM3 DM1 DM2 DM3 DM1 DM2 DM3 DM1 DM2 DM3
C1 5.00 7.50 10.00 3.33 5.83 8.33 4.17 6.67 9.17 2.50 5.00 7.50
C2 2.50 5.00 7.50 2.50 5.00 7.50 3.33 5.83 8.33 0.83 3.33 5.83
C3 5.00 7.50 10.00 2.50 5.00 7.50 3.33 5.83 8.33 2.50 5.00 7.50
C4 3.33 5.83 8.33 2.50 5.00 7.50 2.50 5.00 7.50 2.50 5.00 7.50
C5 5.83 8.33 10.00 3.33 5.83 8.33 3.33 5.83 8.33 2.50 5.00 7.50
C6 5.00 7.50 9.17 2.50 5.00 7.50 2.50 5.00 7.50 2.50 5.00 7.50
C7 2.50 5.00 7.50 5.00 7.50 10.00 2.50 5.00 7.50 2.50 5.00 7.50
C8 3.33 5.83 8.33 3.33 5.83 8.33 2.50 5.00 7.50 5.00 7.50
10.00
A9 A10 A11 A12
DM1 DM2 DM3 DM1 DM2 DM3 DM1 DM2 DM3 DM1 DM2 DM3
C1 3.33 5.83 8.33 2.50 5.00 7.50 0.00 2.50 5.00 0.00 0.00 2.50
C2 2.50 5.00 7.50 0.00 2.50 5.00 0.00 0.00 2.50 0.00 0.00 2.50
C3 3.33 5.83 8.33 5.00 7.50 10.00 0.00 2.50 5.00 0.00 0.00 2.50
C4 2.50 5.00 7.50 0.00 2.50 5.00 0.00 2.50 5.00 0.00 0.00 2.50
C5 0.83 3.33 5.83 2.50 5.00 7.50 0.00 0.00 2.50 0.00 0.00 2.50
C6 0.83 3.33 5.83 2.50 5.00 7.50 0.00 2.50 5.00 0.00 0.00 2.50
C7 2.50 5.00 7.50 1.67 4.17 6.67 0.00 2.50 5.00 0.00 2.50 5.00
C8 2.50 5.00 7.50 0.00 2.50 5.00 0.00 0.00 2.50 0.00 0.00 2.50
256
Table 6. The linguistic rating of each alternative under each criterion
A1 A2 A3 A4
DM1 DM2 DM3 DM1 DM2 DM3 DM1 DM2 DM3 DM1 DM2 DM3
C1 G G G B B B G G G B B B
C2 G G G B B B G G G G G G
C3 B G G B B B B G B B G B
C4 B G G B B B B G G G G G
C5 B B B B B B B G G B B B
C6 G G G B B B B B G G G G
C7 B G G B B B B B B G G G
C8 B B B B G G B G G G F F
A5 A6 A7 A8
DM1 DM2 DM3 DM1 DM2 DM3 DM1 DM2 DM3 DM1 DM2 DM3
C1 G G G G F F G F G F F F
C2 F F F F F F G F F F P P
C3 G G G F F F F F G F F F
C4 G F F F F F F F F F F F
C5 B G G G F F F F G F F F
C6 B G F F F F F F F F F F
C7 F F F G G G F F F F F F
C8 G F F G F F F F F G G G
A9 A10 A11 A12
DM1 DM2 DM3 DM1 DM2 DM3 DM1 DM2 DM3 DM1 DM2 DM3
C1 F F G F F F P P P W W W
C2 F F F P P P W W W W W W
C3 F F G G G G P P P W W W
C4 F F F P P P P P P W W W
C5 P P F F F F W W W W W W
C6 P P F F F F P P P W W
WC7 F F F P F F P P P P P
P
C8 F F F P P P W W W W W W
Table 7. Fuzzy Best and Worst Value
Fuzzy Best Value (
f
j
~
*
)
Fuzzy Wors Value (
f
j
~
÷
)
C1 7.5 10 10 0 0 2.5
C2 7.5 10 10 0 0 2.5
C3 7.5 10 10 0 0 2.5
C4 7.5 10 10 0 0 2.5
C5 7.5 10 10 0 0 2.5
C6 7.5 10 10 0 0 2.5
C7 7.5 10 10 0 2.5 5
C8 7.5 10 10 0 0 2.5
In the following step, the best and the worst values of all the criterion ratings have
been determined, based on eq.(15), as seen Table 7.
~~ ~
The values of S
i
, R
i
and Q are calculated for all banks as Table 8 using equations
i
~
(16) (18). In the calculations, v is assumed to be 0.5.Q
i
values are defuzzified, and
ranking of the alternative banks by Qi, Ri and Si in decreasing order is listed in Table
10. Bank A2 is the most prominent for IMC competencies by potential investors and
customers. Also, the conditions C1 and C2 are satisfied (QA3 QA2)> 1/(12 1) and
A2 is also the best by the value of R and S.
257
~~ ~
Table 8. The values of S
i
, R
i
and Q
i
Alternatives ~ ~ ~S
i
R
i
Q
i
A1 0.9167 1.0509 0.0000 0.1944 0.2083 0.0000 0.1723 0.1282 0.0000
A2 0.1111 0.1250 0.0000 0.1111 0.1250 0.0000 0.0000 0.0000 0.0000
A3 0.8148 0.9028 0.0000 0.1944 0.2083 0.0000 0.1600 0.1161 0.0000
A4 1.1019 1.2778 0.2222 0.2778 0.3125 0.2222 0.2697 0.2125 0.1254
A5 2.0185 2.3542 1.3796 0.4444 0.6111 0.5000 0.5304 0.4890 0.3390
A6 2.4444 2.7847 1.9259 0.4444 0.4583 0.3333 0.5819 0.4276 0.2909
A7 2.5556 2.9653 2.2130 0.4444 0.6111 0.5000 0.5953 0.5389 0.3928
A8 2.7778 3.2153 2.6389 0.4444 0.6111 0.5556 0.6221 0.5593 0.4480
A9 2.9259 3.3889 2.9444 0.4444 0.6111 0.5093 0.6400 0.5735 0.4446
A10 3.3241 3.8171 3.6111 0.6667 0.7130 0.6667 0.8881 0.6727 0.5663
A11 4.2500 5.4583 6.4722 0.6667 0.9167 1.0000 1.0000 0.9354 0.9176
A12 4.2500 6.2500 7.7500 0.6667 0.9167 1.0000 1.0000 1.0000 1.0000
Table 9. Ranking of each alternative by Qi values
Alternatives
A1
A2
A3
A4
A5
A6
A7
A8
A9
A10
A11
A12
Qi
0.1002
0.0000
0.0920
0.2026
0.4528
0.4335
0.5090
0.5432
0.5527
0.7091
0.9510
1.0000
Ranking of alternatives
A2
A3
A1
A4
A6
A5
A7
A8
A9
A10
A11
A12
Table 10. Ranking by Qi, Ri and Si values
Rank
1
2
3
4
5
6
7
8
9
10
11
12
Ranking by Q
i
A2
A3
A1
A4
A6
A5
A7
A8
A9
A10
A11
A12
Ranking by S
i
A2
A3
A1
A4
A5
A6
A7
A8
A9
A10
A11
A12
Ranking by R
i
A2 A1
A3
A4
A6
A5
A7
A9
A8
A10
A11
A12
When we examine Table 7 for the fuzzy best and worst value and Table 10 illus
trating rankings by Qi, Ri and Si values, it can be seen that the 8 competencies have
significant effect on both banking performance and competitiveness. Rankings by Qi
illustrates that A2 bank has superior competitiveness in comparison with other banks
whilst ranked at top by S
i
and R
i
values.
Conclusion. Risks and pressures on banking system in advanced and emerging
economies still remain. Pressures on European banking system have sparked a broad er
drive for policy makers to reduce balance sheet size shrinking by as much as EUR
258
2.6 trillion by the end of 2013, which is almost equal to 7% of their total assets
(GFSR, 1012: 17). To overcome negative effect of the latest deleveraging process,
major strategies are required for boosting banking performance, having more empha sis
on internal operations (Wu et al., 2009). Top managers in retail banking must focus on
competitive strategies to increase banking performance whilst outperforming
competing banks. The Fuzzy VIKOR method in our analysis is built on integrated
marketing communications competencies and related financial parameters in bank ing
sector.
As a conclusion, the new deleveraging process at capital markets has negative
effects on banking performance. IMC approach as a strategic tool aligns effective
marketing strategies with successful corporate strategies. The findings of our study are:
(i) effective banking performance depends on financial and nonfinancial param eters,
(ii) effective marketing activities enhance performance, (iii) IMC is a strategic kit for
aligning marketing operations and strategies, (iv) IMC approach with its com petencies
outperforms competing banks, (v) stock performance of the banks with IMC approach
determines the banking position.
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