An Empirical Analysis of Attributes Influencing Bank ...globalbizresearch.org/Dubai_Conference/pdf/pdf/D4115.pdfProceedings of the First Middle East Conference on Global Business,

Proceedings of the First Middle East Conference on Global Business, Economics, Finance and Banking

(ME14 DUBAI Conference) Dubai, 10-12 October 2014

ISBN: 978-1-941505-16-8 Paper ID_D4115

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An Empirical Analysis of Attributes Influencing Bank Selection

Choices by Customers in the UAE: The Dubai Context

Shirin KHaitbaeva,

Canadian University in Dubai,

School of Business Administration, UAE.

Abdulaziz Ahmed Al-Subaiey,



Chris I. Enyinda,



Email: [email protected]

___________________________________________________________________________

Abstract

This paper quantifies the determinants of banks selection attributes by university students-

customers in Dubai leveraging multi-attribute model algorithm. The motivation for this paper

is that there is little or no research that empirically examined retail bank selection attributes

important to university student-customers when choosing a bank in the UAE. Our paper fills

this gap by utilizing AHP algorithm to model determinants of bank selection and preference

by university student-customers. Leveraging effective marketing strategies to procure and

retain today’s savvy university student-customers is more ever imperative. Therefore, it

behooves forward looking C-level bank marketers to properly identify the requisite selection

attributes that matter the most to potential customers when deciding on a bank to patronize.

The study focuses on examining the bank selection attributes utilized by university student-

customers. A sum of 100 students of Canadian University of Dubai served as a sample for the

research. We used seven prominent bank selection attributes derived from relevant bank

marketing literature, interviews with some undergraduate students, and one attribute from

our own experience. Results indicate that three premier factors influencing student-

customers’ bank preference are service charge, proximity to location and ATM, and

convenience. The results also indicate that for the focal banks as a whole, ENBD is the most

preferred choice. Thus, results of this paper provide insightful and valuable information to

bank C-suit executives on the selection attributes that are important to nowadays university

student-customers.

_____________________________________________________________________

Key Words: Consumer behavior, Retail bank preference, Selection attributes, AHP algorithm

JEL Classification: M31, C83

mailto:[email protected]



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1. Introduction

Arguably, since the recent global financial crisis, bank customers have become

increasingly fastidious and savvy when selecting a bank that they can trust and can meet their

banking service needs. Indeed, retail banks must understand these hard facts and formulate

appropriate marketing strategies to regain their trust and offer services that can meet and

exceed both established and emerging value expectations. Unfortunately, growing list of retail

banks is yet to discern the most prominent service attributes that matter to customers. This

means that bank marketers must endeavor to understand consumer behavior of today‟s savvy

customers. The growing constituent is university students. Given the knowledge economy era,

university students are increasingly becoming a profitable and well informed segment of

customers for banks to target, attract, and retain. According to Chigamba and Fatoki (2011),

university students constitute a valuable target market for banks. According to Almossawi

(2001), “to plan an appropriate marketing strategy for attracting new customers, commercial

banks need to identify the criteria on which potential customers determine their bank selection

decision.” Arguably, retail banks in the UAE must leverage requisite marketing strategies to

target and serve student-customers. It also means that C-suit bank marketers must take into

account consumer selection attributes and preferences when formulating marketing strategies

that will meet and exceed their value expectations. Furthermore, because of the changing

landscape of competitive marketplace, it behooves forward looking banks to “obtain

information concerning customers‟ patronage factors towards a specific financial institution”

(Haron et al., 1994). Haron et al. (1994) attest that “the success and survival of the individual

commercial bank depends on the bankers‟ ability to understand customers‟ needs and to find

effective ways to satisfy these needs.”

We proposed a multi-attribute decision making algorithm developed by Saaty (1980) to

model bank selection attributes by student-customers attending Canadian University of Dubai,

UAE. Eliashberg (1980) attests that “the modeling of preferences for multiattribute

alternatives has received an increased attention in marketing (consumer behavior) and

management science (decision analysis)”. Bank selection attributes are qualitative and

quantitative in nature, and selecting bank options is equally conflict. As a multi-attribute

decision making process, the AHP enables decision makers or a group of decision makers to

set priorities and deliver the best decision when both quantitative and qualitative aspects of a

decision must be considered. The AHP encompasses three basic functions, including

structuring complexity, measuring on a ratio scale, and synthesizing. It is a powerful

operational research methodology useful in structuring complex multi-attribute problems or

decisions in many fields such as marketing (consumer behavior), operations and supply chain

management, engineering, education, economics, and host of other areas. Furthermore, the



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AHP advantages includes its reliance on easily derived expert opinion data, ability to

reconcile differences (inconsistencies) in expert judgments and perceptions, and the existence

of Expert Choice Software that implements the AHP (Calantone et al. 1989).

The purpose of this paper is to add to the current body of literature by empirically

quantifying the determinants of bank selection by university student-customers and the choice

of bank. Essentially, we will evaluate the relative importance of multiple selection attributes

and bank choice or options. Thus, the AHP algorithm is utilized to rank the focal banks based

on some attributes of student-customer preference. The remainder of the paper is structured

into the following sections, beginning with the review of relevant literature, methodology.

Next, the case study, data collection and analysis are presented. Finally, the empirical results

and discussions, and conclusions and managerial implications are presented.

2. Literature Review

A growing list of studies using diverse research methodologies and approaches to

investigate factors influencing customers‟ selection of banks and financial institutions has

graced the bank marketing literature (e.g., Aregbeyen, 2011; Maiyaki, 2011; Lee, J., and J.

Marlowe, 2003; Haque et al., 2009; Khazeh and Decker, 1992; Anderson et al. 1976; Denton

and Chan, 1991; Turnbull and Gibbs, 1989; Javalgi et al., 1989). Sayani and Miniaoui (2013)

used multiple discriminate analyses to identify the most important determinants of bank

selection for Islamic and conventional banks in the UAE. Bank selection determinants used in

their study were bank products, service quality, profit, reputation, cultural and religious

factors, and demographic attributes. Their study suggests religious preferences are considered

the most important attributes when choosing between Islamic and conventional banks.

Specifically, extant literature exist that examined how students select the bank to do business

with (e.g., Hinson et al., 2013; Cicic et al., 2004; Mokhlis et al., 2008; Almossawi, 2001; Ta

and Har, 2000; Gerrard and Cunningham, 2001; Chigamba and Fatoki, 2011; Sharma and

Rao, 2010; Narteh and Owusu-Frimpong, 2011). Almossawi (2001) findings reveal that the

primary factors determining college students‟ bank selection include bank‟s reputation,

availability of parking space near the bank, friendliness of bank personnel, availability and

location of automated teller machines (ATM). Moreover, Almossawi (2001) and Lenka et al.

(2009) note the importance of technology in commercial bank selection.

Dabone et al. (2013) in their study considered factors that determine customer choice of

bank including occupation of customers, proximity and convenience, and safety of deposit.

Their findings suggest that proximity and convenience is the most important factor in

influencing customers‟ bank choice. The most important factors considered important in bank

selection by both Muslims and Non-Muslims were fast and efficient services, speed of

transactions, friendliness of bank personnel, confidentiality of banks, reputation and image of



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bank, knowledgeable about their business, lower interest charges on loans, and parking

facilities and accessibilities (Haron, 1994). Chigamba and Fatoki (2011) identified six crucial

factors influencing choice of commercial banks, including easy of opening an account,

availability of ATM in several locations, availability of ATM 24 hours, provision of fast and

efficient service, convenient branch location, and appropriate range of service offered.

Maiyaki (2011) asserts that customers‟ consider size of bank total assets, nearness of the bank

to your office or house (residence), convenient access to bank location, personal security of

customer, and ease of procedures of account opening as most important in bank selection.

A number of studies have argued that friendliness of staff, hours of operations,

convenience of location, low service charges and efficiency of banking services are the main

selection criteria of a specific bank (e.g., Holstius and Kayank, 1995; Yue and Tom, 1995;

Mylonakis et. al., 1998; Coyle, 1999; Driscoll, 1999; and Moosawi, 2001). Krisnanto (2011)

notes selecting a bank tend to be based on the secondary data such as recommendations of

friends and family members. When selecting a bank, it is not only the price of the serves or

how fast a transaction can be done, but also friendliness of tellers (Loukas, 2012; Channon,

1986; Krisnanto, 2011; Frangos, 2012; Aregbeyen, 2011; Mokhlis 2009; Fulcher, &

Anderson, 1976; and Goiteom, 2011). According to Caratelli (2013), customers look at the

reputation of a bank before other attributes such as technology and prices. Customers consider

the opinion of their families and friends as important when selecting a bank (Caratelli, 2013;

Channon 1986; Aregbeyen, 2011; Fulcher and Anderson, 1976; and Goiteom, 2011). Renman

and Ahmed (2008) opine that the location is one of the most important criteria influencing

customer choices among other factors such as customer services, online banking facilities,

and overall bank environment. Mokhlis (2009) argues that customers tend to emphasis on

electronic services (ATM) which gives them quick and convenient access to the bank service.

A convenient ATM services can save customers time (Saleh, 2013; Channon 1986; Mokhlis,

Hazima, & Safrah, 2008; Marimuthu, Jing, & Ping, 2010; Hinson Osarenkhoe and Okoe

2013; Sayuti, & Rahimiu, 2013; Frangos, 2012 and Aregbeyen, 2011). Security of banks or

deposits is an important criterion for selecting a bank given the recent global financial

meltdown (Channon 1986; Mokhlis, Hazima, & Safrah, 2008; Frangos, 2012; and Aregbeyen,

2011). Other bank selection factors important to savvy customers are technology and mobile

banking services which includes remote deposit capture, two-way text banking, apps for

location branches and ATMs using GPS, and bill payment (Sayuti and Rahimiu, 2013;

Frangos, 2012; and Aregbeyen, 2011). Tables 1 and 2 report on the university student and

non-student-consumer selection attributes, respectively.

Table 1: University Student-Consumer Selection (Preference) Attributes

Attributes Author/Year

Bank reputation, service provision, proximity of location Sivula, M. W. (2013)



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and ATM, student‟s special programs or benefits,

recommendation, secure feeling, and online service

Service charge, reputation, availability of parking space

near the bank, friendliness of bank personnel, availability

and location (proximity) of automated teller machines

(ATM)

Almossawi (2001)

Reliability, convenience, accessibility, responsiveness Rao and Sharma (2010)

Price, proximity, service, attractiveness, recommendations,

marketing

Chigamba and Fatoki (2011)

Image, attitude and behavior of staff, core service delivery,

technology-related factors

Narteh and Owusu-Frimpong (2011)

Secure feeling, proximity of branch and ATM, bank‟s

reputation, service fee, online service, service provision,

student‟s benefits, recommendation

Tai and Zhu (2013)

Service charge, financial benefits, close proximity to home

and work, price

Cicic et al. (2004)

Hinson et al. (2009)

Table 2: Non-student-consumer Preference attributes

Criteria of consumer preferences Author/s

Friends` recommendations; Reputation of the bank

Availability of credit; Friendliness of staff; Service charges

on accounts

Goiteom, (2011).

Reliability; Responsiveness; Assurance; Empathy

Dependent variable; Customer satisfaction

Yaseen, & Abraheem, (2011).

Bank image; friends' recommendations; reputation;

friendliness;

Fulcher, & Anderson, (1976).

Friendliness of bank personnel; recommendations of

friends and relatives; service provision; branch

locationsecure feeling; free gifts for customers; ATM

service; low interest rates on loans

Mokhlis (2009).

customer services; convenience (location); online banking

facilities and overall bank environment; Safety of Funds;

secured ATMs; ATMs availability; reputation,

personal attention; pleasing manners; confidentiality;

closeness to work; timely service; friendly staff willing to

work; rate of return; low fees

Aregbeyen, (2011)

Pricing; service quality; friendly and efficient staff

convenience of bank and ATM location; internet banking;

security and reliability

Frangos, (2012)

Recommendation from a friend; the bank‟s reputation

availability of loans; friendly employees; appropriate

charges

Krisnanto, (2011)

Reliability; responsiveness; value added service;

convenience and assurance; convenient ATM locations; 24

hours availability of ATM services; internet banking

facility; number of branches

Sayuti, & Rahimiu, (2013)

Convenience; bank staff-customer relations; and banking

services/financial benefits

Hinson, Osarenkhoe and Okoe (2013)

Cost-benefits; service delivery; convenience;

friends/relatives‟ influence

Marimuthu, Jing, & Ping, (2010)

Secure feelings; ATM service; financial benefits

service provision; proximity; branch location

Mokhlis, Hazima, & Safrah, (2008)

Speed; security; convenience; high quality operating

services; friendly Staff; flexibility; quality in foreign

exchange; international money transfer services;

officers knowledgeable about international services;

attractive pricing; global branch network; knowledge of US

Channon (1986).



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financial conditions; reputation.

3. Methodology

3.1 Research Objectives

The premier objective of this research is to determine the factors considered important

and rank choice of retail banks leveraging AHP algorithm based on student-consumers‟

preference. Specific objectives include

1. Ranking of attributes influencing consumers‟ preference.

2. Ranking of retail banks under different attributes of consumers‟ preference.

4. Analytic Hierarchy Process Algorithm

Bank selection attributes and bank choice are typical multi-attribute decision making

problem that involves multiple attributes that can be both qualitative and quantitative. The

AHP is an example of multi-attribute decision making selected to model bank selection

attributes and bank choice. The proposed AHP allows decision-makers to model a complex

problem in a hierarchical structure, showing the relationships of the overall goal, attributes

(objectives), and alternatives. Due to its usefulness, the AHP has been widely used in

research. Some studies that have used AHP include supply chain management (e.g., Gaudesi

and Borghesi 2006; Enyinda et al, 2009), marketing (e.g., Dyer and Forman 1991; Enyinda, et

al., 2011), and retail bank marketing (e.g. Enyinda, 2014; Ta and Har, 2000; Jantan and

Kamaruddin, 1998).

4.1 Application of AHP to Bank Selection Attributes and Bank Preference

A typical AHP is composed of four-phases. 1) The construction of the hierarchy which

describes the problem. The overall goal depicted at the top of the structure, with the main

attributes on a level below. The „parent‟ attributes can be sub-divided on the lower-levels. 2)

Deriving the weights for the lowest level attributes. This can be done by performing a series

of pair-wise comparisons in which each attribute on each level is compared with its family

members in relation to their significance to the parent. However, to compute the overall

weights of the lowest level, matrix arithmetic is required. 3) The options available to the

decision-maker are scored with respect to the lowest level attributes or utilizing the pair-wise

comparison method. 4) Adjusting the options‟ scores to reflect the weights given to the

attributes, and adding the adjusted scores to produce a final score for each optimum (Roper-

lowe and Sharp 1990). The hierarchy structure is consist of bank selection attributes,

including friendliness of staff (FOS), proximity of location and ATM (PLA),

recommendations (REC), service charge (SCH), service quality (SQU), social medial

presence (SMP), convenience (CON), and reputation (REP). The alternatives are the

consumers‟ bank preference. Following are the four of the leading Banks in the UAE,



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including Abu Dhabi Commercial Bank (ADCB), Emirates National Bank of Dubai (ENBD),

Union National Bank (UNB), and Mashreq Bank (MB).

Figure 1: Bank Selection Attributes and Preference Decision Hierarchy

4.2 AHP steps

1. Define an unstructured problem and determine the overall goal. According to Simon

(1960), the methodology of decision-making process encompasses identifying the problem,

generating and evaluating alternatives, designing, and obtaining actionable intelligence. The

overall goal of the focal firm is in the first level of the hierarchy, shown in Figure 1.

2. Build the hierarchy from the top through the intermediate levels (criteria on which

subsequent levels depend on) to the lowest level contains the list of alternatives.

3. Construct a set of pair-wise comparison matrices for each of the lower levels. The pair-wise

comparison is made such that the attribute in row i (i = 1, 2, 3, 4…n) is ranked relative to each

of the attributes represented by n columns. The pair-wise comparisons are done in terms of

which element dominates another (i.e. based on the relative importance of each elements).

These judgments are expressed as integer values 1 to 9 in which aij = 1 means that i and j are

equally important; aij = 3 signifies that i is moderately more important than j; aij = 5 suggests

that i is strongly more important than j; aij = 7 indicates that i is very strongly more important

than j; aij = 9 signifies that i is extremely more important than j.

4.3 Establishment of Pairwise comparison matrix A

The pair-wise comparisons are accomplished in terms of which element dominates or

influences the order. The AHP is then used to quantify these opinions that can be represented

in an n-by-n matrix as follows:

Bank Selection Attributes and Preference

FOS PLA REC SCH SQU SMP

ADCB ENBD UNB MB

CON REP



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A=[aij]=wi/wj =

nnnn

n

n

wwwwww

wwwwww

wwwwww

/...//

....

....

....

/...//

/...//

21

22212

12121

(1)

4.4 Eigenvalue and Eigenvector

Saaty (1990) recommended that the maximum eigenvalue, λmax, can be determined as

λmax =

n

j

ija1

Wj/Wi. (2)

Where λmax is the principal or maximum eigenvalue of positive real values in judgment

matrix, Wj is the weight of jth factor, and Wi is the weight of i

th factor.

If A represents consistency matrix, eigenvector X can be determined as

(A - λmaxI)X = 0 (3)

4.5 Consistency test

Consistency index (CI) and consistency ration (CR) are used to check for consistency

associated with the comparison matrix. A matrix is assumed to be consistent if and only if aij

* ajk = ajk i jk (for all i, j, and k). When a positive reciprocal matrix of order n is consistent,

the principal eigenvalue possesses the value n. Conversely, when it is inconsistent, the

principal eigenvalue is greater than n and its difference will serve as a measure of CI.

Therefore, to ascertain that the priority of elements is consistent, the maximum eigenvector or

relative weights/λmax can be determined. Specifically, CI for each matrix order n is determined

by using (3):

CI = (λmax – n)/n – 1. (4)

Where n is the matrix size or the number of items that are being compared in the matrix.

Based on (3), the consistency ratio (CR) can be determined as:

CR = CI/RI = [(λmax – n)/n – 1]/RI. (5)

Where RI represents average consistency index over a number of random entries of same

order reciprocal matrices shown in Table 1. The CR is acceptable, if its value is less than or

equal to 0.10. If it is greater than 0.10, the judgment matrix will be considered inconsistent.

To rectify a judgment matrix that is inconsistent, decision-makers‟ judgments should be

reviewed and improved.

Table 3: Average RI for Different Numbers of n

N 2 3 4 5 6 7 8 9 10

RI 0.00 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49



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4.6 Synthesized Matrix

To synthesize the pair-wise comparison matrix in (1), divide each element of the matrix

by its column total. The priority vector is derived by dividing the sum of the rows associated

with the synthesized matrix by the sum of the columns. Alternatively, the priorities of the

elements can be obtained by finding the principal eigenvector w of the matrix A (Saaty,

1980). For the priorities of the alternative ai, the priorities then are aggregated as follows:

P(ai) = ∑kwkPk(ai). (6)

Where wk is the local priority of the element k and Pk(ai) is the priority of alternative ai

with respect to element k of the upper level.

5. Sampling and Data Collection

We used a case study methodology to achieve an in depth knowledge of bank selection

attributes and bank choice of the focal banks. Yin (1994) popularized the use of case study

methodology. Indeed, a case study can be a relevant approach to investigate a phenomenon in

its own natural environment where complex links and underlying meanings can be pursued as

well as to enable the researcher study the entire supply chains (Miles and Huberman 1994;

Yin, 1994). According to Oke and Gopalakrishnan (2009), a case study is also relevant “where

existing knowledge is limited because it generates in-depth contextual information which may

result in a superior level of understanding.” We selected our sample from Canadian

University of Dubai students (CUD). The sample was given to a wide spectrum of majors in

CUD. We collected the data through self-administered questionnaire distributed to a sample

of 100 full-time students. Convenience sample method was utilized. The data collection

period spans from June – July 2014. Out of the 100 questionnaires distributed 97 were

received, among which 82 were considered valid. Thus, we able to achieve a response rate of

82%. Table 4 summarizes the sampling design.

Table 4: Summary of sampling Design

Target Population: Elements: Student-customers of the focal Banks

Sample size: 100

Sampling units: Focal Retail Banks

Extent: Dubai, UAE

June-July 2014

Sampling Technique Non-probability; Judgmental Sampling.

Scaling Technique AHP 1-9 scale.

The collected data were through literature review and validated by interviews with subject

matter experts to determine factors that tend to influence bank selection and bank preference.

We then developed the questionnaire from the hierarchy tree depicted in Figure 1 to facilitate

pair-wise comparisons between all the bank selection attributes and bank alternatives at each

level in the hierarchy using Saaty‟s 1-9 scale. Finally, CUD students responded to several

pairwise comparisons with respect to the goal and the major attributes. We used the survey



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questionnaire results as input for the AHP. It took 28 judgments (i.e., 8(8-1)/2) to complete

the pairwise comparisons shown in Table 2. The other entries are ones along the diagonal as

well as the reciprocals of the 28 judgments. We used the data shown in the matrix to derive

the criteria priorities. The priorities provide a measure of the relative importance of each

attribute. The bank selection attribute priorities can be determined manually or automatically

(i.e., using the AHP Expert Choice Software).

6. Empirical Results and Discussion

Both Figures 2a and 2b report on the bank selection attribute priority scores. Service

charge is considered the most important bank selection attribute by student-customers,

followed by proximity of location and ATM, convenience, and reputation, respectively.

Table 5 reports the summary synthesis with respect to goal. With respect to individual

selection attributes ADCB is known for friendliness of staff, recommendations, service

quality, social media presence, convenience, and better reputation. For ENBD, it has better

proximity of location and ATM, recommendations, little or zero service charge, ties with

ADCB in service quality and social media presence.

Figure 2a: Major Bank Selection Attribute Priority

Figure 2b: Bank Selection Attribute Priority

Model Name: Bank Selection Determinants and Consumer Preference

Priorities with respect to:

Goal: Bank Selection Attributes and Bank Preference

Service Charge .278

Proximity of Location and ATM .237

Convenience .171

Reputation .108

Service Quality .087

Friendliness of Staff .050

Recommendations .035

Social Media Presence .033

Inconsistency = 0.07

with 0 missing judgments.

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Treeview

Goal: Bank Selection Attributes and Bank Preference

Friendliness of Staff (L: .050)

Proximity of Location and ATM (L: .237)

Recommendations (L: .035)

Service Charge (L: .278)

Service Quality (L: .087)

Social Media Presence (L: .033)

Convenience (L: .171)

Reputation (L: .108)

Alternatives

ADCB .331

ENBD .361

UNB .120

MB .188

* Ideal mode

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Table 5: Summary Synthesis with respect to Goal

6.1 Prioritizing for Decision Attributes

The overall priority is determined using equation (6). For a given alternative bank

preference, priority is determined for each of the eight bank selection attribute. Specifically,

multiply each bank choice row by the corresponding selection attribute priority. The output of

each row is summed together to determine the bank choice or preference overall score. As

mentioned previously, data for pairwise comparisons were analyzed utilizing the Expert

Choice Software. The pair comparisons made by the subject matter experts or respondents

were consistent. The consistency ratio of the comparisons were less than or equal 0.10. They

are acceptable for the questionnaires administered to CUD student-customers. Table 5 depicts

the priority weights determined for the bank preference or choice using the pairwise

comparison data for the aggregate sample and for the bank selection attributes. The results

show that for the bank as a whole, ENBD is the most important bank with a mean weight or

priority of 0.387. The priorities for ADCB and MB are 0.312 and 174, respectively.

Essentially, ENBD > ADCB > MB > UNB. Thus, ENBD is judged as the overall best or

preferred bank.

Table 6: Attribute priorities (weights) used in the ranking model

Attribute FOS

0.050

PLA

0.237

REC

0.035

SCH

0.278

SQU

0.087

SMP

0.033

CON

0.171

REP

0.108

Priority

ADCB 0.413 0.115 0.390 0.346 0.400 0.390 0.368 0.399 0.312

ENBD 0.360 0.622 0.390 0.415 0.379 0.390 0.169 0.159 0.387

UNB 0.120 0.202 0.152 0.093 0.140 0.152 0.096 0.081 0.127

MB 0.106 0.060 0.068 0.146 0.80 0.068 0.368 0.360 0.174

Consistency 0.01 0.09 0.02 0.10 0.01 0.02 0.06 0.04


Synthesis: Details

Level 1 Alts Prty


ADCB .020

Friendliness of Staff (L: .050)ENBD .018

Friendliness of Staff (L: .050)UNB .006


MB .005


ADCB .027

Proximity of Location and ATM (L: .237)ENBD .147

Proximity of Location and ATM (L: .237)UNB .048


MB .014


ADCB .014

Recommendations (L: .035)ENBD .014

Recommendations (L: .035)UNB .005


MB .002


ADCB .096

Service Charge (L: .278)ENBD .115

Service Charge (L: .278)UNB .026


MB .041


ADCB .035

Service Quality (L: .087)ENBD .033

Service Quality (L: .087)UNB .012


MB .007


ADCB .013

Social Media Presence (L: .033)ENBD .013

Social Media Presence (L: .033)UNB .005


MB .002


ADCB .063

Convenience (L: .171)ENBD .029

Convenience (L: .171)UNB .016


MB .063


ADCB .043

Reputation (L: .108)ENBD .017

Reputation (L: .108)UNB .009


MB .039

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ratio (CR)

7. Conclusion and Managerial Implication

This paper examined the most important criteria in selecting the best Bank in the UAE.

All banks face a wide variety of competition in order to be chosen between all those local and

international Banks. And they compete on different criteria to attract the special customers.

For Banks to achieve sustainable success in today‟s consumer market-centric environment,

anticipating criteria of consumers is more compulsory than ever. Thus, competing and

winning in today‟s global marketplace requires the ability of firms to identify and understand

the most important selection attributes that matter the most for student-customers and the

knowledge of how to formulate and implement better market strategies to attract and retain

them. Indeed, banks that leverage the most important selection attributes and marketing

strategies will gain competitive advantage and grow wallet share.

The proposed AHP can be used to support retail bank marketers interested in determining

factors that can influence student-customers to patronize their banks. We evaluated the

importance of each bank selection attribute to determine the best bank choice. This research

offers a number of contributions. It empirically derived bank selection attributes to be

considered bank management decisions. It provides an added support that AHP can be used in

modeling bank selection attribute and help today‟s savvy university student-customers to

choose the best bank of their choice. Finally, the paper extends the streams of research in

retail bank selection and preference by demonstrating that AHP can be used to support this

important and difficult decision.

7.1 Limitations of the study

The study was restricted to focal university students in Dubai. The study concentrated

only on one university among other higher institutions in the UAE. Thus, caution must be

exercised in generalizing the results of this study to the rest of UAE. The study focused on a

single segment of the customer-students. However, it did not take into account other

customers.

References

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Nigerian retail banking. International Journal of Business and Management 6(1), January,

253-257.

Almossawi, M., 2001, Bank selection criteria employed by college students in Bahrain: An

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