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

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.

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

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

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

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,

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)

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

RESEARCH MODEL

2.1 Customer Satisfaction

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

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:

H1: Corporate image has a positive influence on customer satisfaction

2.2.2 Security

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:

H2: Security has a positive influence on

customer satisfaction

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:


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

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:

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:

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

Figure 2.1 Proposed research model

3 METHODOLOGY

3.1 Measures of the constructs

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,

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

3.2 Sampling

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

(+)

(+)

(+)

(+) (+)

Corporate image

(F1)

Security (F2)

Service price (F3)

Customer

expectations (F4)

Empathy (F5)

Customer satisfaction

(Y)

Frequency Percent

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:

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

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

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

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 Chi-square 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)

4 RESULTS

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

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

Table 4.1 Scale reliability tests

4.2 Exploratory factor analysis (EFA)

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

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”)

Table 4.2 Exploratory factor analysis

Observed variables

Independent Constructs Corporate

image Security

Service price

Customer expectations Empathy

DA_2 0.758

DA_1 0.749

DA_4 0.694

DA_3 0.693

DA_5 0.616

DA_6 0.575

VC_1 0.701

VC_2 0.636

VC_4 0.611

VC_3 0.608

Observed variables

Independent Constructs Corporate

image Security

Service price

Customer expectations Empathy

Kaiser-Meyer-Olkin Measure of

Bartlett’s Test of

Sphericity

Approx

Chi-Square 6434.638

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

Table 4.3 EFA for construct “Customer Satisfaction”

4.3 Confirmatory factor analysis (CFA)

The results from CFA model is assessed via three

criteria including unidirectionality, convergence

values, composite reliability (CR) and average

variance extracted (AVE)

Unidirectionality: Figure 4.1 illustrates that CFA

model is suitable with the market data

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

Convergence values: Figure 4.1 also shows that

the weights of CFA meet required standards that

is greater than 0.5

Table 4.4 Confirmatory factor analysis

Customer

Customer

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

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 (Table4.6)