The Effect of Integrated Marketing Communication Competencies on Banking Performance: Analysis with Fuzzy VIKOR Method

Hasan Dincer1_{, Umit Hacioglu}2_{, Recep Yilmaz}3

THE EFFECT 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. Хасан Дінчер, Юміт Хаджіоглу, Реджеп Їлмаз ВПЛИВ РІВНЯ ІНТЕГРОВАНОЇ МАРКЕТИНГОВОЇ КОМУНІКАЦІЇ НА ПОКАЗНИКИ РОБОТИ БАНКІВ: АНАЛІЗ ЗА МЕТОДОМ FUZZY VIKOR У статті описано ланцюгову реакцію банківських банкрутств у розвинених економіках і можливість суверенних дефолтів при рекордних показниках кредитних дефолтів, при цьому вжиті урядом заходи призвели до негативного впливу на показники роботи банків. Жорстка конкуренція на фінансовому ринку з відносно невеликим прибутком і нові механізми регулювання банківської діяльності призвели до обмеження розвитку банків на ринках капіталу. Підхід інтегрованої маркетингової комунікації (IMC) зіставляє ефективні маркетингові і корпоративні стратегії. Результати аналізу за методом fuzzy VIKOR показали, що 1) ефективна робота банку залежить як від фінансових, так і від нефінансових параметрів; 2) ефективні маркетингові заходи покращують показники роботи; 3) IMC зв'язує маркетингові операції і стратегії; 4) підхід інтегрованої маркетингової комунікації дозволяє перевершити конкуруючі банки; 5) акційні показники банків із системою IMC визначають рейтинг банку. Ключові слова: оцінка показників роботи, банківські операції, інтегрована маркетингова комунікація, стратегія, fuzzy VIKOR. Форм. 19. Рис. 2. Табл.10. Літ.53. Хасан Динчер, Юмит Хаджиоглу, Реджеп Йилмаз ВЛИЯНИЕ УРОВНЯ ИНТЕГРИРОВАННОЙ МАРКЕТИНГОВОЙ КОММУНИКАЦИИ НА ПОКАЗАТЕЛИ РАБОТЫ БАНКОВ: АНАЛИЗ ПО МЕТОДУ FUZZY VIKOR В статье описана цепная реакция банковских банкротств в развитых экономиках и возможность суверенных дефолтов при рекордных показателях кредитных дефолтов, при 1

Faculty of Economics and Administrative Science, Beykent University, Istanbul, Turkey. 2

Faculty of Economics and Administrative Science, Beykent University, Istanbul, Turkey. 3

Beykent University, Istanbul, Turkey.

ˆ

,

..

этом предпринятые правительством меры привели к отрицательному влиянию на показатели работы банков. Жесткая конкуренция на финансовом рынке с относительно небольшой прибылью и новые механизмы регулирования банковской деятельности привели к ограничению развития банков на рынках капитала. Подход интегрированной маркетинговой коммуникации (IMC) сопоставляет эффективные маркетинговые и корпоративные стратегии. Результаты анализа по методу fuzzy VIKOR показали, что 1) эффективная работа банка зависит как от финансовых, так и от нефинансовых параметров; 2) эффективные маркетинговые мероприятия улучшают показатели работы; 3) IMC связывает маркетинговые операции и стратегии; 4) подход интегрированной маркетинговой коммуникации позволяет превзойти конкурирующие банки; 5) акционные показатели банков с системой IMC определяют рейтинг банка. Ключевые слова: оценка показателей работы, банковские операции, интегрированная маркетинговая коммуникация, стратегия, 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

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 C2 C3 … Cn

where A1, A2, . . .,A_mare possible alternatives among which decision
makers have to choose, C1,C2, . . .,Cn are the criteria with which alternative performance are meas
ured, X_ijis the rating of alternative A_iwith respect to criterion C_j(Chu, 2004: 154). The notion of a fuzzy set was introduced by Zadeh in 1965. It provides a convenient

, 3 2 1 3 33 32 31 2 23 22 21 1 13 12 11 3 2 1                 mn m m m n n n m X X X X X X X X X X X X X X X X A A A A L M O M M M L L L M

D=

(1)

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)

(2)

Table 1. Literature Review and Criteria Selection for Fuzzy Vikor Applied IMC model evaluating banking performance

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

of crisp numbers witha₁ < a₂ <a_3.

Subject Study Approach Criteria

Ba n k in g

Dattels et al. (2010), Reinhart&

Rogoff (2008) Historical Data Analysis Risk Mapping, Crisis and Performance

Ruscher & Wolff (2012) Panel Analysis Balance sheet adjstment and Performance

Wu (2012), Wu et al. (2009) BSC, Fuzzy AHP, Topsis Permance and Strategy

Rhee and Mehra ( 2006) HP and Pattern Analysis Banking Strategy and Performance

Samad ( 2008), Berger & Patti (2006)

SCP, SP, EH, SE Models

Structure, Performance and Marketing

Uhde and Heimeshoff (2009)

Z-score

technique Financial Performance

Mayer and Rowan (1977), Zucker (1977), Schuman (1997) Structural Analysis Legitimacy and Institutionalization IM C

Belch & Belch, 2009, Laric & Lynagh, 2010

International

Review IMC

Luo & Donthu (2005), Hill & Rifkin (1999)

Historical Data

Analysis Share of spending

Varadarajan & Menon (1988) Content Analysis Visibility

Fombrun (1996), Abratt (1989) International Review Current Image

Van Dijk (1993), Lord & Putrevu

(1993) International Review Discursive Consistency

Samad (2008), Lariviere and Poel

(2007) Patern Analysis, SCP Market share and performance

Zadeh, 1965 Fuzzy Basics

Opricovic (2011), Opricovic &

Tzeng (2007) Fuzzy-Vikor Optimization of complex system F u zzy L o gi c

Liou (2011) Fuzzy-Vikor Ranking gaps

Petrovic et al. (2006), Girubha &

Vinodh (2012) Fuzzy-Vikor Pattern classification

Wu et al. ( 2009), Chen & Wang

(2009) Fuzzy-Vikor Triangular Fuzzy

Ma, Lu & Zhang ( 2010) Fuzzy-Vikor Linguistic Methods

Chen & Wang, 2009; Chen &

Huang, 1992 Fuzzy-Vikor Triangular Fuzzy-Linguistic Variables

( )

   =

∉

∈

A

fx

A

fx

i i X f_A 0 1

(

1, 2, 3

)

~ a a a A=

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 of the fuzzy number is presented by

(3)

Supposed any two positive positive triangular fuzzy numbers, = (a_1,a_2, a₃) and

= (b_1,b_2, b₃) 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 :

(4) Multiplication of two triangular fuzzy numbers :

(5) Multiplication of any real number r and a triangular fuzzy numbers :

for r>0 and ai>0, bi>0, ci>0 (6) Subtraction of two triangular fuzzy numbers :

for ai0, bi0, ci0 (7) Division of two triangular fuzzy numbers :

(8) Reciprocal of a triangular fuzzy numbers:

for ai0, bi0, ci0 (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. ( )x ~ A f x A~ 2 a a3 1 a ) ( ~ x f A A ~

( ) (

₍

) (

_{) (}

)

₎

       〉 ≤ ≤ − − ≤ ≤ − − 〈 = 3 3 2 2 3 3 2 1 1 2 1 1 ~ , 0 , / , / , 0 a x a x a a a x a a x a a a a x a x X f A A~ B~ ⊕

(

1 1, 2 2, 3 3

)

~ ~ b a b a b a B A⊕ = + + + ⊕

(

1 1, 2 2, 3 3

)

~ ~ b a b a b a B A⊗ = ⊕

(

1, 2, 3

)

~ ra ra ra A r⊗ = Θ

(

1 3, 2 2, 3 1

)

~ ~ b a b a b a B AΘ = − − −

( )

÷

( ) (

1/ 3, 2/ 2, 3/ 1

)

~ ~ b a b a b a B A÷ =

( )

(

3 2 1

)

1 / 1 , / 1 , / 1 ~ a a a A− =

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 of each criterion, and aggregated fuzzy ratings of alternatives and con
struct a fuzzy decision matrix (Chen and Klein, 1997: 51
52) .

, j=1,2,3,…,n (10)

, i=1,2,3,…,m (11)

Step 3: Fuzzy weighted average is calculated and the normalized fuzzy decision matrix is constructed.

C1 C2 C3 … Cn

i=1,2,3,…,m; j=1,2,3,….n (13)

(14) where is the rating of alternative Ai with respect to Cj, is the importance weight

of the j
th criterion holds, mentioned linguistic variables and can be approxi
mated by positive triangular fuzzy numbers.

Step 4: It is calculated an aspired (fuzzy best value ) and tolerable level (fuzzy worst value ) of all criterion functions,

( )

x

~

A

f

x

A

~ B~ C~

    =

∑

= n e e j j w k w 1 ~ 1 ~ j w~ xij ~     =

∑

= n e e ij ij x k x 1 ~ 1 ~                 mn m m m n n n m X X X X X X X X X X X X X X X X A A A A ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 3 2 1 3 33 32 31 2 23 22 21 1 13 12 11 3 2 1 L M O M M M L L L M = D~ (12) n w w w W~= ~₁,~₂,....,~ ij x ~ ij w~ ij x ~ ij w~ * ~ j f − j f ~

Step 5: Mean group utility and maximal regret are calculated. The values are computed by

(16)

(17)

where are the fuzzy weights of criteria, expressing the decision
makers' preference as the relative importance of the criteria. is Ai with respect to all criteria calculated

by the total of the distance for the fuzzy best value, and is Ai with respect to the j

th criterion, calculated by maximum distance of the fuzzy best value. Step 6: The index value ( ) is calculated, the value can be counted by

(18) where

and ν is presented as the weight of the strategy of maximum group utility, whereas 1
ν is the weight of individual regret (Kaya and Kahraman, 2010: 2521
2522).

Step 7: Defuzzify triangular fuzzy number 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 (А(1)_{) which is the best ranked by the measure Q (minimum) when the two}

conditions are satisfied:

C1. Acceptable Advantage:

(19) where А(2)_{is the second position in the alternatives ranked by Q (minimum).}

C2. Acceptable stability in decision making:

The alternative А(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 А(1)and А(2)

, ~ max ~* ij i J x f = ~ min~_ij, i j x f−= and ₍₁₅₎

(

)

(

)

∑

= − − − = n i j j ij j j i f f x f w S 1 * * ~ ~ ~ ~ ~ ~

(

)

(

)

         − − = ₋ j j ij j j j i f f x f w R _{~ ~} ~ ~ ~ max ~ * * ij w~ i S~ i R~ i Q~

(

~ ~

)(

~ ~

)

( )

1

(

~ ~

)(

~ ~

)

, ~ * * * * R R R R v S S S S v Q_i= _i− −− + − _i− −− ; ~ min ~* i i S S = ; ~ max ~ i i S S−= ; ~ min ~* i i R R = i i R R~ =− max~ i Q~

( ) ( )

A(2) −Q A(1) ≥1/

( )

j−1, Q ≈

if only condition C2 is not satisfied, or (2) Alternatives А(1), А(2). . . ,А(M) if condition C1 is not satisfied. А(M)_{is calculated by the relation}

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

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).

( ) ( )

_A( ) _{−Q A}(1) _<1/

( )

_j−1

Q M

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

Table 3. The linguistic importance weight of criteria

Table 4. The aggregate fuzzy weight of each criterion (The importance weight of each criteria in the judgment of experts)

Table 5. The weighted normalized Fuzzy Decision Matrix

D1 D2 D3 C1 H H VH C2 H H H C3 H H M C4 VH VH H C5 M H H C6 H M VH C7 VH H VH C8 H H H fuzzy weight C1 0.5833 0.8333 1 C2 0.5 0.75 1 C3 0.4167 0.6667 0.9167 C4 0.6667 0.9167 1 C5 0.4167 0.6667 0.9167 C6 0.5 0.75 0.9167 C7 0.6667 0.9167 1 C8 0.5 0.75 1 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

Table 6. The linguistic rating of each alternative under each criterion

Table 7. Fuzzy Best and Worst Value

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 , and are calculated for all banks as Table 8 using equations (16)
(18). In the calculations, v is assumed to be 0.5. 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.

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 W C7 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

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 i S~ R~_i Qi ~ i Q~ ≥

Table 8. The values of , and

Table 9. Ranking of each alternative by Qi values

Table 10. Ranking by Qi, Ri and Si values

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_iand R_ivalues.

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

i S~ R~_i Q~_i Alternatives i

S

~

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

Alternatives Qi Ranking of alternatives

A1 0.1002 A2 A2 0.0000 A3 A3 0.0920 A1 A4 0.2026 A4 A5 0.4528 A6 A6 0.4335 A5 A7 0.5090 A7 A8 0.5432 A8 A9 0.5527 A9 A10 0.7091 A10 A11 0.9510 A11 A12 1.0000 A12

Rank Ranking by Qi Ranking by Si Ranking by Ri

1 A2 A2 A2 2 A3 A3 A1 3 A1 A1 A3 4 A4 A4 A4 5 A6 A5 A6 6 A5 A6 A5 7 A7 A7 A7 8 A8 A8 A9 9 A9 A9 A8

10 A10 A10 A10

11 A11 A11 A11

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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