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

Abdulaziz Ahmed Al-Subaiey

Canadian University in Dubai

School of Business Administration

Chris I. Enyinda*

Canadian University in Dubai

School of Business Administration

Christian@cud.ac.ae

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

1.0 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 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.

2.0 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 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 and ATM, student’s special programs or

benefits, recommendation, secure feeling, and online

service

Sivula, M. W. (2013)

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

Aregbeyen, (2011)

willing to work; rate of return; low fees

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 financial conditions; reputation.

Channon (1986).

3.0 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.

4.0 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, 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

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.

Bank Selection Attributes and Preference

FOS PLA REC SCH SQU SMP

ADCB ENBD UNB MB

CON REP

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:

A=[aij]=wi/wj =

nnnn

n

n

wwwwww

wwwwww

wwwwww

/...//

....

....

....

/...//

/...//

21

22212

12121

(1)

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)

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

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.

5.0 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

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).

6.0 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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Model Name: Bank Selection Determinants and Consumer Preference

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

ratio (CR)

0.01 0.09 0.02 0.10 0.01 0.02 0.06 0.04

Model Name: Bank Selection Determinants and Consumer Preference

Synthesis: Details

Level 1 Alts Prty

Friendliness of Staff (L: .050)

ADCB .020

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

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

Friendliness of Staff (L: .050)

MB .005

Proximity of Location and ATM (L: .237)

ADCB .027

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

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

Proximity of Location and ATM (L: .237)

MB .014

Recommendations (L: .035)

ADCB .014

Recommendations (L: .035)ENBD .014

Recommendations (L: .035)UNB .005

Recommendations (L: .035)

MB .002

Service Charge (L: .278)

ADCB .096

Service Charge (L: .278)ENBD .115

Service Charge (L: .278)UNB .026

Service Charge (L: .278)

MB .041

Service Quality (L: .087)

ADCB .035

Service Quality (L: .087)ENBD .033

Service Quality (L: .087)UNB .012

Service Quality (L: .087)

MB .007

Social Media Presence (L: .033)

ADCB .013

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

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

Social Media Presence (L: .033)

MB .002

Convenience (L: .171)

ADCB .063

Convenience (L: .171)ENBD .029

Convenience (L: .171)UNB .016

Convenience (L: .171)

MB .063

Reputation (L: .108)

ADCB .043

Reputation (L: .108)ENBD .017

Reputation (L: .108)UNB .009

Reputation (L: .108)

MB .039

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7.0 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.

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.

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