Determinants of customer satisfaction in commercial banks: A case study in Quang Nam province, Vietnam

AGU International Journal of Sciences – 2019, Vol. 7 (2), 42 – 55

DETERMINANTS OF CUSTOMER SATISFACTION IN COMMERCIAL BANKS: A
CASE STUDY IN QUANG NAM PROVINCE, VIETNAM
Huynh Tan Nguyen1, Nguyen Hoang Hung1
1

Dong Nai Technology University

Information:
Received: 18/09/2018
Accepted: 29/03/2019
Published: 11/2019
Keywords:
Customer Satisfaction,
Structural Equation Modelling
(SEM), Commercial Banks.

ABSTRACT
Currently, customer satisfaction is often considered as the sustainable and
long-term strategy to expand business operations for any banking
institutions. Hence, the purpose of this article is to explore and measure
factors affecting customer satisfaction in commercial banks via both
qualitative and quantitative approaches. To collect data, the questionnaire
was designed to officially survey 500 customers at nine commercial banks in
Quang Nam province, Vietnam. Next, the study used Structural Equation
Modelling (SEM) to test the proposed research hypotheses. It is found that
there are four factors influencing customer satisfaction, including Corporate
Image, Security, Service Price, Customer Expectations, Empathy. Based on
the findings, the paper proposed some managerial implications to enhance
customer satisfaction in the next period. Yet, the study had some limitations,

so more studies with alternative methods should be conducted to confirm the
results of this study for better policies and practices.

1. INTRODUCTION

al., 2017). However, today's competitive market
has forced banks to only focus on reducing
operating cost for gaining short-term high profit
instead of improving service quality and
enhancing customer satisfaction, which is
regarded as a long-term business strategy. That’s
why exploring determinants of customer
satisfaction is presently a popular research topic in
research as regards human behavior.

Doing business in the context of such competitive
environment nowadays, customer satisfaction is
considered as a main factor affecting business
operations of any commercial bank (Colm &
Parasuraman, 2017), in which the bank retains
customer interest and loyalty. Doing so will
provide opportunities to widen market share,
increase profit and develop business operation
sustainably (Hennig & Klee, 1997). Thus, the
commercial banks are shifting business strategies
towards improvement of customer satisfaction
(Yang & Peterson, 2004). Many empirical studies
have illustrated that profits and business
efficiency only come from satisfying customer
expectations and demands (Le Thị Thanh Giang,

2016; Lone, Aldawood, & Bhat, 2017; Wolter et

The empirical research of Sirdeshmukh, Deepak,
Singh, and Sabol (2002) demonstrates that
customer satisfaction is significantly different
between the state-owned commercial banks and
the private commercial banks with higher
satisfaction in the private banks. The reasons for
this phenomenon may stem from deliverability,
accessibility as well as transaction convenience,

42


AGU International Journal of Sciences – 2019, Vol. 7 (2), 42 – 55

which lead to the advancement of customer
service capacity. This finding is consistent with
the study of Janahi and Mubarak (2017) and
Sirdeshmukh et al., (2002).

2.2.1 Corporate image
Corporate image may be regarded as “the
perceptions of an enterprise” that exists lastingly
in consumers' memory, and also helps customers
to recognize the difference between the product
and service of the organization from their
competitors as well (Elgin & Nedunchezhian,
2012). In the opinion of Connor and Davidson
(1997), an enterprise with a positive corporate

image is likely to lead to attraction of new
customers and retain existing ones. Because of the
current competitive nature of the banking sector
nowadays, corporate image plays a crucial role in
seeking and attracting new customers, owing to
the fact that the more reputable banks will be easy
to appeal to new customers. Various empirical
evidence have suggested that corporate image is a
vital factor of customer satisfaction (Johnson et
al., 2001). Thus, it is hypothesized as follow:

There are presently numerous ongoing empirical
researches in the context of the Vietnamese
banking industry, yet, descriptive statistics of
customer satisfaction instead of testing the
relationship among research constructs is one of
the main limitations of these studies. Furthermore,
the other limitation is that the studies are only
conducted at one commercial bank, so the results
are generally difficult to apply for overall system
of commercial banking in Vietnam. Hence, this
research aims (1) to explore and measure the
factors influencing customer satisfaction in
commercial banks in Vietnam; (2) from that, to
propose effective recommendations to improve
service quality as well as boost customer
satisfaction.
2. LITERATURE
REVIEW
RESEARCH MODEL

H1: Corporate image has a positive influence on
customer satisfaction.

AND

2.2.2 Security

2.1 Customer Satisfaction

Cyberattacks in the financial industry, especially
the banking sector, are recently increasing more
and more seriously when so many recent attacks
have become more complex than before (Amin,
2016). The reason may be explained as the quite
rapid increase of the quantity of financial
transactions and the continued advancement of
new technologies in the banking sector (Fabrigar
& Wegener, 2011). Therefore, security is an
extremely important factor for financial
operations. Although there is much technological
progress towards security namely cryptography,
electronic signatures, as well as multi-factor
authentication, consumers are still at risk in
monetary transactions, particularly online
transactions (Hazen et al., 2017). Consequently, if
the security is assured, customer satisfaction may
increase. Several previous studies in retail banks
also demonstrate that security is a significant
determinant of customer satisfaction. Therefore it

is hypothesized that:

Oliver (2014) states that satisfaction is the
understanding of the consumer comparison
between the expectation and performance. Kotler
et al. (2009) postulate that satisfaction is “a
person's feelings of pleasure or disappointment
that results from comparing a product's perceived
performance
or
outcome
with
his/her
expectations”. According to Fečiková (2004),
satisfaction is the feeling of customers originated
from the procedure of assessing what they obtain
and what they expect. Abdullah and Rozario
(2009) emphasize that customer satisfaction may
be affected by several various internal and
external elements. Hence, it's difficult to
determine client satisfaction. In the opinion of
Veloutsou et al. (2005), overall customer
satisfaction is a moving process and will change
the clients’ experience with the service and
products that they purchase.
2.2 Development of hypothesis
43


AGU International Journal of Sciences – 2019, Vol. 7 (2), 42 – 55

H2: Security has a positive influence on
customer satisfaction.

expectations. Thus, it is worth mentioning that the
commercial banks ought to identify customer
expectations and to seek to meet their
expectations with the aim of maximizing their
satisfaction (Jamal & Naser, 2002). Accordingly,
the following hypothesis is proposed as:

2.2.3 Service price
For a specific bank, the service price is one of the
factors of the marketing strategy. The marketing
perspectives evaluate the service price in the
banking sector, especially, from the customer’s
point of view. The service price is regarded as
what customers actually pay for their transactions
including the loan interest, the money transfer
cost, the other service fees. Many recent
researches have shown that service price is one of
the significant factors influencing service quality
and customer satisfaction in the banking industry
(Hazen, Boone, Wang, & Khor, 2017). Hence,
with this factor, the hypothesis is posited as:


H4: Customer expectations have a positive
impact on the customer satisfaction.
2.2.5 Empathy
Empathy refers to the possibility to understand

another person’s feelings and experiences within
their frame of reference (Kotler, Kartajaya, &
Setiawan, 2018). The different types of empathy
rely mainly on the range of emotional states,
which result in three types of empathy including
emotional empathy, somatic empathy as well as
cognitive empathy (Levesque & McDougall,
1996). Some researchers have demonstrated role
of empathy as an essential part of service quality
in any enterprise, and certainly affecting customer
satisfaction. Many prior studies have postulated
that empathy has a direct and considerable effect
on re-purchasing behavior of the clients (Tadic et
al., 2018; Yang, 2017). Empathy in the banking
sector is also reflected through free service
capacity, which relates to caring, listening and
careful compensation in case of any mistake (Ekiz
et al., 2006; Farooq, 2016). Hence, it is
hypothesized that:

H3: Service price has a positive effect on
customer satisfaction.
2.2.4 Customer expectations
Customer expectations are defined as the
perceived value or benefits that the customer
perceives before consuming goods or services
from providers. They may be derived from the
“experiencing” process and can also be shaped
immediately with even first impressions during
purchasing (Cronin Jr et al., 2000; Hult et al.,

2017). Once formed, these expectations are hardly
to change and are likely to considerably affect
clients’ decision-making processes. Furthermore,
customer expectations might be established from
customers' consumption experience in the past
and largely depend on the firm's product or
service quality as well as promotion strategies and
word-of-mouth information (Johnson et al., 2001).
Therefore, customers will evaluate the
performance efficiency of a bank via their

H5: Empathy has a positive impact on customer
satisfaction.
2.3 Proposed research model
Based on the literature review from the previous
studies, the proposed theoretical model includes
six constructs. These are depicted in Fig. 2.1
below.

44


AGU International Journal of Sciences – 2019, Vol. 7 (2), 42 – 55

Corporate image
(F1)

(+)
Security (F2)

(+)
Service price (F3)

Customer
expectations (F4)

(+)
Customer satisfaction
(Y)

(+)
(+)

Empathy (F5)

Figure 2.1. Proposed research model

in which 1 represented as “strongly disagree”
while 5 reflected as “strongly agree”.

3. METHODOLOGY
3.1 Measures of the constructs

3.2 Sampling

The items used to establish constructs are mainly
adapted from prior studies to assure content
validity. Measures for the independent constructs
were taken from Lone et al. (2017), Shahin et al.
(2017), Verhoeven and Sha (2017), Fabrigar and

Wegener (2011), Hazen, Boone, Wang, and Khor
(2017), Hult et al. (2017). Measures of customer
satisfaction construct was taken from Fabrigar and
Wegener (2011), Lone et al. (2017), Verhoeven
and Sha (2017), Hazen, Boone, Wang, and Khor
(2017). This research used five-point likert scales
to collect customer responses for each construct,

Hair, Black, Babin, Anderson, and Tatham (2006)
suggest that minimum sample size in EFA is 50
observations, and preferably sample size should
be 100 or larger and observations/measured
variables is 5:1, more acceptable sample size
would have a 10:1 ratio. From this general rule,
this study surveyed 500 customers at 09
commercial banks in Quang Nam province,
Vietnam. Then, cleaning inappropriate responses,
official data was used to analyze as follow:

Table 1. The result of survey responses at each bank

Valid

Frequency

Percent

Vietcombank

39

8.46

BIDV

34

7.38

MHB

44

9.54

Dong A Bank

37

8.03

45


AGU International Journal of Sciences – 2019, Vol. 7 (2), 42 – 55

Frequency

Percent

Techcombank

53

11.5

Maritimebank

61

13.23

ACB

73

15.84

VPbank

33

7.16

LienViet Post Bank

87

18.87

461

100

Total
3.3 Data analysis methods
After finishing the phase of qualitative research,
the official questionnaires are delivered to clients
transacting at the commercial banks to collect
necessary information for the study. Then all data
is cleaned missing information, coded, and
analyzed through SPSS 22 and AMOS 22. Some
techniques are utilized for quantitative research,
consisting of:

analysis is to explore the most appropriate items
from a set of items for a specific construct, in
which their internal consistency has been
analyzed (Everitt and Dunn, 2001). Everitt and
Dunn (2001) and Esbensen et al. (2002) state that
the standard required in EFA comprising of: (1)
Eigenvalue ≥ 1, (2) total variance explained ≥
50%, (3) Kaiser-Meyer-Olkin (KMO) ≥ 0.5 and
sig. coefficient of the KMO test ≤ 5%, (4) factor
loadings of observed items ≥ 0.5, (5) difference
between the loading of two factors greater than
0.3.

Firstly, the study carries out descriptive statistic
technique to examine mean, standard deviation,

and minimum value and maximum value of
observed variables. Furthermore, this technique is
also exploited to test the difference between
controlled variables in proposed research model at
a particular significant level.

Fourthly, the study utilizes Confirmatory Factor
Analysis (CFA) to further confirm the
unidirectionality, scale reliability, convergence
value and distinctive value; next the Structural
Modelling Equation (SEM) is use to test research
hypothesis. According to Hair Jr et al. (2016) and
Everitt and Dunn (2001), a model is considered
consistent with market data if satisfying some
criterion, including: (1) significance value of Chisquare test is less than 5%, (2) CMIN/df ≤ 3 (if
CMIN/df ≤ 2 is demonstrated best), (3) GFI, CFI,
TLI ≥ 0.9. Furthermore, some scholars illustrate
that RMSEA ≤ 0.08, GFI should be greater than
0.8, composite reliability (CR) ≥ 0.6 and the
average variance extracted (AVE) ≥ 0.5
(Esbensen et al., 2002, Ho, 2006). Additionally, to
further confirm the exactness of estimated results,
the study utilizes the boostrap technique, which is
suggested by Yung and Bentler (1996).

Secondly, the reliability of overall measurement
scale is tested by Cronbach's alpha, which is a
statistical test used to estimate the reliability or
internal consistency of a composite scale,
originates from the paradigm of (Cronbach,

1951). According to Nunnally and Bernstein
(1994) and Hair Jr et al. (2016), a scale is
considered reliable if Cronbach's Alpha
coefficient greater than 0.7 and observed variables
have corrected item-total correlation greater than
0.3.
Thirdly, exploratory factor analysis (EFA) is
carried out to identify trends in the sample data
and reduce a set of observed variables by
grouping them together (Ho, 2006, Hair et al.,
2015). In addition, the aim of applying factor

46


AGU International Journal of Sciences – 2019, Vol. 7 (2), 42 – 55

4. RESULTS

and the corrected item-total correlations are
greater than 0.3. Hence, six constructs and
relevant items (exempt from "BM_3", which
corrected item-total correlations are smaller than
0.3) are accepted and included in the next
analysis.

4.1 Scale reliability tests
Table 4.1 briefly summarizes the results of scale
reliability tests through Cronbach’s Alpha
coefficients. The overall Cronbach’s Alpha

coefficients of all constructs are greater than 0.7

Table 4.1. Scale reliability tests

Construct

Cronbach's Alpha

Corporate image

0.815

Security

0.880

Service price

0.810

Customer expectations

0.786

Empathy

0.871

Customer satisfaction

0.862

4.2 Exploratory factor analysis (EFA)

value of 0.000; meaning that, EFA is used
appropriately in this study. Additionally, the
factor loadings are greater than 0.5 and the weight
differences among the loadings are greater than
0.3, demonstrating that these observed variables
are acceptable for further analysis (exempt from
“VC_7”).

4.2.1 EFA for independent constructs
Table 4.2 shows that the items of independent
constructs are converged on original five
constructs with the total variance explained of
61.05%; KMO = 0.946 with the significance

Table 4.2. Exploratory factor analysis

Independent Constructs
Observed variables

Corporate
image

DA_2

0.758

DA_1

0.749

DA_4

0.694

DA_3

0.693

DA_5

0.616

DA_6

0.575

Security

VC_1

0.701

VC_2

0.636

VC_4

0.611

VC_3

0.608

47

Service
price

Customer
expectations

Empathy


AGU International Journal of Sciences – 2019, Vol. 7 (2), 42 – 55

Independent Constructs
Observed variables

Corporate
image

Security

VC_5

0.575

VC_6

0.519

Service
price

Customer
expectations

Empathy

VC_7
TL_1

0.694

TL_2

0.685

TL_3

0.679

TL_4

0.631

TL_6

0.592

TL_5

0.578

GDV_1

0.799

GDV_2

0.698

GDV_3

0.670

GDV_4

0.534

BM_4

0.737

BM_1

0.654

BM_2

0.552

BM_5

0.535

Eigenvalues

11.156

1.7071

1.488

1.1142

1.0194

% of variance

41.317

6.3227

5.5114

4.1267

3.7754

Kaiser-Meyer-Olkin Measure of
Sampling Adequacy.
Bartlett’s Test of
Sphericity

.946

Approx.
Chi-Square

6434.638

df

351

Sig.

.000

4.2.2 EFA for “Customer Satisfaction”
As shown in Table 4.3, the results of EFA for the scale “Customer Satisfaction” with 5 items illustrates
that using EFA in the scale is also appropriate and the items can be used for the next analysis.

48


AGU International Journal of Sciences – 2019, Vol. 7 (2), 42 – 55
Table 4.3. EFA for construct “Customer Satisfaction”

Customer Satisfaction
SHL_3

0.847

SHL_4

0.809

SHL_2

0.798

SHL_5

0.786

SHL_1

0.77

Eigenvalue

3.221

Total variance

64.442

KMOs

0.859

Approx. Chi-Square

982.507

df

10

Sig.

0.000

4.3 Confirmatory factor analysis (CFA)

Specifically, Chi-square = 927.132 with 332
degree of freedom; p = 0,000; CMIN/df = 2,793;
GFI = 0,875, TLI = 0,9; CFI = 0,912; RMSEA =
0,062.

The results from CFA model is assessed via three
criteria including unidirectionality, convergence

values, composite reliability (CR) and average
variance extracted (AVE).

Convergence values: Figure 4.1 also shows that
the weights of CFA meet required standards that
is greater than 0.5.

Unidirectionality: Figure 4.1 illustrates that CFA
model is suitable with the market data.

Table 4.4. Confirmatory factor analysis

Scale

Notation

Numbers
of items

CR

AVE

Conclusion

Corporate image

F1

6

0.865543

0.518827

Accepted

Security

F2

5

0.849550

0.531231

Accepted

Service price

F3

6

0.816107

0.525269

Accepted

Customer
expectations

F4

4

0.797496

0.496602

Accepted

Empathy

F5

4

0.785127

0.528049

Accepted

Customer
satisfaction

Y

3

0.817978

0.500277

Accepted

49


AGU International Journal of Sciences – 2019, Vol. 7 (2), 42 – 55

Composite Reliability and Average Variance Extracted: The calculation of CR and AVE in the Table
4.4 indicates that the scales meet criteria in terms of Composite Reliability and Variance Extracted.

Figure 4. 1. The CFA results of the saturated model

4.4

Analysis of Structural Equation Modelling

The results of SEM in Figure 4.2 show that the proposed research model is consistent with the market
data. Particularly, Chi-square = 845.819; df = 306; p = 0,000; CMIN/df = 2.764; GFI = 0.881; TLI =
0.904; CFI = 0.916; RMSEA = 0.062.

50

AGU International Journal of Sciences – 2019, Vol. 7 (2), 42 – 55

Figure 4. 2. Standardized SEM Model

The results of hypothesis testing shown in Table 4.5 clearly illustrate that 4 out of research hypotheses
are accepted with significant of 5% apart from the third hypothesis. Moreover, Boostrap testing with
N=1000 also indicates the appropriateness of the results from SEM (Table 4.6).

51


AGU International Journal of Sciences – 2019, Vol. 7 (2), 42 – 55
Table 4.5. Coefficients from SEM

Relationship

Coefficient

S.E.

C.R.

P

Conclusion

Y <--- F1

0.172

0.056

3.056

0.002

H1 supported

Y <--- F2

0.282

0.115

2.461

0.014

H2 supported

Y <--- F3

0.074

0.089

0.828

0.408

H3 rejected

Y <--- F4

0.388

0.102

3.81

***

H4 supported

Y <--- F5

0.305

0.114

2.679

0.007

H5 supported

Table 4.6. Boostrap testing with N=1000

Relationship

SE

SE-SE

Mean

Bias

SE-Bias

Y<---F1

0.073

0.002

0.167

-0.005

0.002

Y<---F2

0.168

0.004

0.269

-0.013

0.005

Y<---F3

0.128

0.003

0.067

-0.006

0.004

Y<---F4

0.134

0.003

0.395

0.008

0.004

Y<---F5

0.191

0.004

0.329

0.024

0.006

5. CONCLUSION AND
IMPLICATIONS

MANAGERIAL

exploratory factor analysis, confirmatory factor
analysis, structural equations modelling (5)
Bootstrap testing with the help of SPSS 16 and
Amos 22. It is found that there are four factors
significantly affecting customer satisfaction,
consisting of “Corporate Image”, “Security”,
“Customer Expectations”, “Empathy”. Service
Price failed to have a positive impact on customer
satisfaction.

5.1 Conclusion
To improve the competitive capacity in the
context of the extremely challenging business
environment, the only way for any organization to
do so especially the commercial banks, is to

satisfy customer’s expectations and demands via
effective marketing strategies. Therefore, this
research aims (1) to explore and measure the
factors influencing customer satisfaction in
commercial banks in Vietnam; (2) from that, to
propose effective recommendations to improve
service quality as well as boost customer
satisfaction. To obtain the objectives, research
procedure was proposed including (1) propose the
theoretical model based on the literature review;
(2) apply a qualitative approach to perfect the
scale measurement of the constructs and to
establish the official questionnaire; (3) survey 500
customers at 09 commercial banks in Quang Nam
province of Vietnam; (4) perform data analysis
through four techniques referenced from Hair et
al., (2006) namely Cronbach's Alpha testing,

5.2 Managerial Implications
This research yields several practical implications
for the banking sector, policy-makers as well as
practitioners. Findings from this study posit that
Vietnamese commercial banks should focus on
the service-oriented strategy for improving
customer satisfaction. Considering the context of
the somewhat rapid growth of banking services in
Vietnam, findings of this research are likely to be
actually useful for policy-makers and practitioners
for better understanding regarding dimensions of
the independent constructs. In addition,

commercial banks ought to educate and train their
staff to provide better services with higher
empathy, which is illustrated to be of greater
52


AGU International Journal of Sciences – 2019, Vol. 7 (2), 42 – 55

importance on banking customers. The banks
need to represent a personal warmth, customized
touch in their financial service provision.
Therefore, the banks should focus on advancing
the quality of corporate image along with security.
Additionally, we highly recommend that
commercial banks should ensure the safety of
online monetary transfer services, expand their
forms of customer service and further upgrade
technical maintenance in order to build image of a
reliable and safe business unit in the eyes of the
customers.

Journal of Security and Sustainability Issues.
doi: />Colm & Parasuraman. (2017). When Service
Customers Do Not Consume in Isolation: A
Typology of Customer Copresence Influence
Modes (CCIMs). Journal of Service Research,
20(3),
223-239.
doi:
/>10.1177%2F1094670517690025

Fabrigar, L. R., & Wegener, D. T. (2011).
Exploratory factor analysis: Oxford University
Press. ISBN: 01-99-81-35-15
Ha Thi Thanh Hai. (2016). Improve customer
service quality satisfaction at Saigon
Commercial Joint Stock Bank - Phu Dong
Branch. Finance-Banking. University of
Economics Ho Chi Minh City. Retrieved from
/>6

5.3 Limitations And Further Research
Although this study sheds light on a number of
important issues in the banking sector of Vietnam,
but some limitations should be considered
carefully for further research. Firstly, all
constructs as well as observed variable were
mainly adapted from the prior studies in both
Vietnam and worldwide. Secondly, owing to
limitation of resources and time, this study only
employed a convenience sampling technique that
regards for slightly biased results (Tadic et al.,
2018; Verhoeven & Sha, 2017); thus, future
researchers should avoid this limitation by
utilizing other sampling methods for better results
(e.i. Cluster Sampling, Systematic Sampling,
Stratified Random Sampling). Finally, sample
size of this research was fairly small as compared
to the population of the commercial banking
industry.

Hair, J. F., Black, W. C., Babin, B. J., Anderson,
R. E., & Tatham, R. L. (2006). Multivariate
data analysis (Vol. 6): Upper Saddle River,
NJ: Pearson Prentice Hall.
Hazen, B. T., Boone, C. A., Wang, Y., & Khor, K.
S. (2017). Perceived quality of remanufactured
products: construct and measure development.
Journal of Cleaner Production, 142, 716-726.
doi:
/>Hennig‐Thurau, T., & Klee, A. (1997). The
impact of customer satisfaction and
relationship quality on customer retention: A
critical reassessment and model development.
Psychology & Marketing, 14(8), 737-764.
/>
6. REFERENCES
Amin, M. (2016). Internet banking service quality
and its implication on e-customer satisfaction
and e-customer loyalty. International Journal
of Bank Marketing, 34(3), 280-306. doi:
/>
Hill, N., & Brierley, J. (2017). How to measure
customer satisfaction: Routledge.

Bachelet, D. (1995). Customer satisfaction
research. Measuring Satisfaction, or the Chain,
the Tree and the Nest.

Hult, G. T. M., Morgeson, F. V., Morgan, N. A.,
Mithas, S., & Fornell, C. (2017). Do managers

know what their customers think and why?

Belás, J., Korauš, M., Kombo, F., & Korauš, A.
(2016). Electronic banking security and
customer satisfaction in commercial banks.
53


AGU International Journal of Sciences – 2019, Vol. 7 (2), 42 – 55

Journal of the Academy of Marketing Science,
45(1), 37-54.

Shahin, A., Mohammadi, S., Harsij, H., & Rahbar
Qazi, M. R. (2017). Revising satisfaction and
dissatisfaction indexes of the Kano model by
reclassifying indifference requirements: A case
study of the presidential elections. The TQM
Journal,
29(1),
37-54.
doi:
/>
Jamal & Naser. (2002). Customer satisfaction and
retail banking: an assessment of some of the
key antecedents of customer satisfaction in
retail banking. International Journal of Bank
Marketing,
20(4),
146-160.

doi:
/>
Singhal, N., Bala, K., & Sarawgi, S. (2018). An
empirical study of employee engagement and
customer satisfaction in banking sector.
Journal of Business Thought, 8, 24-41.

Janahi, M. A., & Al Mubarak, M. M. S. (2017).
The impact of customer service quality on
customer satisfaction in Islamic banking.
Journal of Islamic Marketing, 8(4), 595-604.
doi:
/>
Sirdeshmukh, D., Singh, J., & Sabol, B. (2002).
Consumer trust, value, and loyalty in relational
exchanges. Journal of marketing, 66(1), 15-37.
doi:
/>
Kotler, P., & Gertner, D. (2002). Country as
brand, product, and beyond: A place marketing
and brand management perspective. Journal of
brand
management,
9(4),
249-261.
/>
Stein, C., Morris, N., & Nock, N. (2012).
Structural equation modeling Statistical
Human Genetics (pp. 495-512) Berlin (pp.
495-512): Springer.

Le Thi Thanh Giang. (2016). Factors affecting
customer satisfaction about the quality of
personal credit services at Techcombank Can
Tho. Tay Do University. Retrieved from
http://115.74.210.103:81/tailieuso/handle/1234
56789/185
Le

Tabachnick, B. G., & Fidell, L. S. (2007). Using
multivariate statistics (5th ed). Boston, MA, :
Allyn & Bacon/Pearson Education.
Tadic, D., Aleksic, A., Mimovic, P., Puskaric, H.,
& Misita, M. (2018). A model for evaluation
of customer satisfaction with banking service
quality in an uncertain environment. Total
Quality Management & Business Excellence,
29(11-12),
1342-1361.
doi:
/>05

Van Hai. (2016). Improve customer
satisfaction on service quality of Big C-Thanh
Hoa supermarket. Hue University of
economics.
Retrieved
from
:8080/dspace/handle/
DHKTHue_123456789/4352

Levesque, T., & McDougall, G. H. (1996).
Determinants of customer satisfaction in retail
banking. International Journal of Bank
Marketing,
14(7),
12-20.
doi:
/>
Tran Duc Thuc & Pham Thi Lan Huong. (2017).
Factors affecting customer satisfaction with
ATM card services at Vietnam Joint Stock
Commercial Bank for Foreign Trade of
Vietnam Vung Tau branch. Ba Ria-Vung Tau
University.
Retrieved
from
/>06

Lone, F. A., Aldawood, E., & Bhat, U. (2017).
Comparative
Analysis
of
Customer
Satisfaction towards Islamic and Conventional
Banking: An Empirical Study from Saudi
Arabia. 7(1), 273-280. Retrieved from
/>
Tran Trong Khue & Le Thuy Kieu. (2017).
Factors affecting customer satisfaction on

retail banking service quality at the Bank for
54


AGU International Journal of Sciences – 2019, Vol. 7 (2), 42 – 55

Investment and Development of Vietnam Phu My Branch. Ba Ria-Vung Tau University.
Retrieved
from
/>VT/16208

/>.2.563.85678&rep=rep1&type=pdf
Wolter, J. S., Bock, D., Smith, J. S., & Cronin Jr,
J. J. (2017). Creating ultimate customer loyalty
through loyalty conviction and customercompany identification. Journal of Retailing,
93(4),
458-476.
doi:
/>
Ullman, J. B., & Bentler, P. M. (2012). Structural
equation modeling. Handbook of Psychology,
Second
Edition,
2.
/>2023

Yang, Z., & Peterson, R. T. (2004). Customer
perceived value, satisfaction, and loyalty: The
role of switching costs. Psychology &
Marketing,

21(10),
799-822.
doi:
/>
Verhoeven, P., & Sha, N. (2017). A study based
on Kano model to improve satisfaction of
students at university libraries in Sweden.
Retrieved
from
/>a-33534

Youssef, M. A., Youssef, E. M., Anadol, Y., &
Zahrani, A. A. (2017). Modelling customer's
intention to use e-banking in Saudi Arabia: an
empirical study. International Journal of
Business Innovation and Research, 14(2), 239258.

Webb, I. P., & Martin, H. D. (2017). The effect of
banking and insurance on the growth of capital
and output (Doctoral dissertation, Georgia
State
University).
Retrieved
from

55