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To leave or retain? An interplay between quality digital banking services and

customer satisfaction

Article  in  International Journal of Bank Marketing · August 2021

DOI: 10.1108/IJBM-02-2021-0072

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To leave or retain? An interplay
between quality digital banking

services and customer satisfaction
Sulemana Bankuoru Egala

School of Management and Economics,
University of Electronic Science and Technology of China,
Centre for West African Studies (CWAS), Chengdu, China

Dorcas Boateng
Department of Information Technology, University of Professional Studies,

Accra, Ghana, and

Samuel Aboagye Mensah
Department of Operations and Management Information Systems,

University of Ghana Business School, Accra, Ghana

Abstract

Purpose – In this paper, the authors investigated the impact of quality digital banking services delivered
during the COVID-19 pandemic on customers’ satisfaction and retention intentions.
Design/methodology/approach – This study combined constructs drawn from the E-S-QUAL and BSQ
models to measure the impact of digital banking services on subscribers of digital banking services in Ghana.
The study utilized structural equationmodelingwith partial least squares (PLS-SEM) to analyze 395 responses.
Findings – Results revealed a significant direct effect between digital banking services satisfaction and
customer retention decision. The results also revealed that digital banking services quality dimensions such as
ease of use, efficiency, privacy/security and reliability impact customers’ satisfaction and retention intentions.
Research limitations/implications – Digital banking service portfolios and their quality dimensions vary
among banks. This offers an opportunity for banking institutions and other non-bank financial service
providers to be wary of the impact of quality service delivery on customers’ decisions. This paper makes
significant theoretical contributions and practical implications on the relevance of quality digital banking
services in customers’ retention strategies for competitive advantage.
Originality/value – This study has underlined the significance of quality digital banking services in
developing countries. The study underscored the need for banking and non-bank financial institutions to
embrace the much-anticipated quality service demanded by customers and the need for continuous service
improvement relative to the growing deployment of financial technologies.

Keywords COVID-19, Digital banking services, Customer satisfaction, Customer retention, Service quality

Paper type Research paper

1. Introduction
The raging spread of the novel human Coronavirus disease 2019 (COVID-19) pandemic
occasioned restrictions on themovement of people by national authorities to keep the virus at
bay. These restrictions consequently impacted the operations of most businesses in several
economic sectors including banking and finance, education, tourism and transportation
(Goodell, 2020; Seetharaman, 2020). Since the mode of spread of the virus is mainly human-to-
human, closed physical contact was discouraged placing an obligation on service-oriented
businesses to adopt a safer approach to protect themselves and clients. In the banking sector,
activities such as face-to-face customer service were affected culminating in a situation
referred to as “positive discontinuity” (KPMG, 2020). Given that the banking operations
largely rely on humanmobility, the sector especially those in developing economies came to a
near-standstill as a result of the restrictions on human movement (Chirisa et al., 2020). As a
remedy, banking institutions were required to deploy digitally enabled service platforms

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The current issue and full text archive of this journal is available on Emerald Insight at:

https://www.emerald.com/insight/0265-2323.htm

Received 26 February 2021
Revised 14 June 2021
Accepted 26 July 2021

International Journal of Bank
Marketing
Vol. 39 No. 7, 2021
pp. 1420-1445
© Emerald Publishing Limited
0265-2323
DOI 10.1108/IJBM-02-2021-0072

https://doi.org/10.1108/IJBM-02-2021-0072


such as online/Internet banking, mobile and telephone banking in order to reach their
customers. A phenomenon known as digital banking (DB) where banks digitalize customary
banking services (Cuesta et al., 2015). DB entails the delivery of banking services through
technologically mediated channels such as mobile phones, Internet and personal digital
assistants (Cuesta et al., 2015). Essentially, DB channels help bring banking services closer to
customers to foster a closer relationship between them and the banks (Ozili, 2018).

Although extant literature has highlighted on the impact of the COVID-19 pandemic on
the banking industry (Goodell, 2020; Lelissa, 2020), studies on customers’ expectations from
the digital services being provided are yet to emerge. The emergence of the COVID-19
pandemic gave some impetus to the acceptance and usage of digital platforms for customer
interactions. This consequently increased the reliance on digital banking application services
(DBSs) such as mobile and online payment particularly during the period of restriction of
human mobility (KPMG, 2020). Moreover, the increasing rate of deployment of DBSs by
banks in developing countries has not had a corresponding increase in literature (Abualsauod
and Othman, 2019). Furthermore, extant studies (Narteh, 2018; YuSheng and Ibrahim, 2019;
Ketema and Selassie, 2020; Raza et al., 2020) have explored the impact of DBSs on customer
satisfaction but neglected the consequences of the impact on customers retention intentions.
Our preliminary search reveals that studies on the impact of quality DBS on customers’
retention intentions are still underexplored even thoughAl-Ghraibah (2020) had established a
relationship between online sales services and customer retention. While customer retention
remains an important outcome of quality service delivery (Sinha et al., 2019), the onset of the
COVID-19 has not attracted research on factors that inherently influence customers
decisions. Hence, presenting a stream of lacunas in literature is yet to be addressed.

To fill these gaps, this study seeks to unearth the impact of quality DBS on customers’
satisfaction and retention intentions. The study pursues this objective leveraging on
constructs drawn from the electronic service quality (E-S-QUAL) model (Parasuraman et al.,
2005) and the banking service quality (BSQ) model (Bahia and Nantel, 2000). Specifically, the
study focuses on the DBSs offered by banking institutions to customers in Ghana during the
COVID-19 pandemic. To achieve the stated purpose, the following research questions would
be pursued:

RQ1. What latent factors in digital banking services impact customers’ satisfaction and
retention decisions?

This paper is arguably positioned as the first to explore quality DBS dimensions and their
impact on customers’ retention decisions in Ghana through a combination of constructs of the
E-S-QUAL and BSQ frameworks. Empirically, relying on a single theory that only considers
one component of an artefact will not reveal the complete story since some feature will be
prioritized above others, resulting in only partial knowledge of the phenomenon under study
(Nilsen, 2015). Therefore, we argue that given the evolving nature of digital banking
technologies, combining constructs from the E-S-QUAL and BSQ frameworks will help to
provide a more understanding of the phenomenon under study, yet it can also mask
contrasting assumptions made in literature.

Unlike other studies, it explored several factors relative to digital banking services and
ascertain customers’ level of satisfaction and how they impact on their retention decisions
during the COVID-19 era inGhana. A conceptualmodel was developed and tested against 395
bank customers in Ghana. Though a stark of revelations emerged, it was apparent that
quality DBS dimensions have varied consequences on customers’ intentions in times of
pandemic (Al-Ghraibah, 2020; Ketema and Selassie, 2020; Ul Haq and Awan, 2020). This
research makes significant contribution to both research and practice by espousing how
customers’ retention decision toward a satisfactory relative quality DBS. Furthermore, the
study demonstrates how DBS quality dimensions’ can be used to predict customer level of

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satisfaction and, as a result, influence their retention decisions. The understanding of the
primary impacting DBS quality dimensions on customer satisfaction and retention help
obtain a better understanding of banking institutions developmarketing strategies, maintain
customer intimacy and gain a competitive edge in the current competitive banking industry.

The rest of the paper was structured as follows: research background and theoretical
foundation, research model with hypotheses development, research methodology, data
analysis presentation and results. The remaining sections were findings, study implications
and recommendations for future research directions followed by the conclusion.

2. Background and theoretical foundation
2.1 Digital banking concept
DB involves the transition to technologically based banking where banking services are
administered to customers through an array of open and customized channels such as
automated teller machine (ATM) and mobile, online/Internet platforms. These channels
present a ubiquitous opportunity for banks to deliver to their customers’ services such as
mobile and online banking, text alerts, electronic statements and bill payments. With DB,
customers for instance have the opportunity to perform their banking needs without
traveling to their physical branch (Kimenyi and Ndung’u, 2009). Further, DB enables
seamless communication between banks and their customers through autonomous channels
thereby creating customer intimacywhile increasing banks investments by reducing the cost
associated with the provision of physical infrastructure (Hoffmann and Birnbrich, 2012).

The onset of the COVID-19 pandemic and the consequent restrictions on human mobility
reinforced yet another opportunity for banking institutions to exploit the affordances of
digital banking for service delivery (Lelissa, 2020). Following the shift to DB, a corresponding
transition from traditional services to innovative services has been observed among banking
institutions globally. Fundamentally, these streams of DBSs have only improved on the
delivery of traditional tasks during the COVID-19 and also helped introduce new business
models for banks (Goodell, 2020). For instance, banking has nowbecome a 24/7 activitywhich
was not the case in most developing countries like Ghana (Boateng and Molla, 2006). Again,
the need for paper works such as demand for drafts and cash cheques or pay-in slips have
gradually being replaced with DBSs (KPMG, 2020). Earlier, Deloitte’s banking and capital
markets outlook reports that about 50% of banks are making digital banking their priority in
a bid to match consumers’ expectations (Srinivas et al., 2019). Recent advances in ATMs such
as deposit cash, utility bills payment andmobile wallet have made DBSmore exciting (Hasan
et al., 2013). Furthermore, mobile banking, through which customers use their mobile phones
to check their account balance, make payment and transfer funds from their bank accounts to
other mobile wallet accounts, gained prominence during the COVID-19 era due to the
convenience it provided to users (Ho et al., 2020). In effect, DBSs during the pandemic have
been phenomenal and this paper seeks to investigate the consequences of these services on
customers’ retention intentions.

2.2 Digital banking and quality service
Given the prevalence of new digital service delivery modes especially in the wake of the
pandemic, the need for banks to gain a better understanding of howcustomers assess the quality
of services they obtain through electronic means is significant (Zeithaml et al., 2002). Note that
unlike tangible goods which can be objectively assessed for their quality, the same cannot be
attributed to intangible customer service (Dhurup et al., 2014). Hence, assessing quality
consumer services is elusive and complex. It is in this vein that institutions often evaluate quality
consumer services based on their perceptions, trust and satisfaction (Amin, 2016). Further, the

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assessment of customers’ DBSs quality perceptions has, thus, become increasingly important
and strategic for banks (Huei et al., 2018). That notwithstanding, prior studies on DBS have
unearthed quality dimensions that drive customers’ satisfaction such as include security/trust,
design, availability, convenience, reliability and cost (Zavareh et al., 2012; Dhurup et al., 2014;
Addai et al., 2015; Chen et al., 2016; Narteh, 2018). Addai et al. (2015) for instance aver that a
positive relationship exists betweenDBS convenience and customer satisfactionwhich results in
maintaining customer intimacy and loyalty. Yet, as extant literature suggests, assessment of the
impact DBS quality on the customers’ intention to retain is still underexplored (Murali et al.,
2016). Arguably, research conducted on the affordances of DBS has largely concentrated on the
use and customer satisfactionwith limited focus on its impact on customers’ retention intentions.

Table 1 presents a selection of some recent research on the impact of DBS on customers
summarized according to theoretical underpinnings, the context of the study and quality
dimensions mostly studied. These studies focused largely on the impact of quality DBS
on customer satisfaction (Ketema and Selassie, 2020; Narteh, 2018; Raza et al., 2020;
Shahabi et al., 2020) and mostly drawing on SERVQUAL and E-S-QUAL models.
Unfortunately, studies on DBS during the COVID-19 (Al-Ghraibah, 2020; Ketema and
Selassie, 2020; Shahabi et al., 2020; Ul Haq and Awan, 2020) did not sufficiently explore
the consequences on customers’ retention intentions. Thus, this study focus on the
delivery of quality DBSs during the COVID-19 and its impact on customers’ satisfaction
and retention intentions.

2.3 Electronic service quality model
According to Parasuraman et al. (1988), service quality is the difference between customer
expectations of what a firm should provide and perceived service performance. Empirically,
the SERVQUAL framework by Parasuraman et al. (1988) has been the predominantmetric for
assessing perceived service quality. The original SERVQUAL framework had ten quality
dimensions but was later decomposed into five namely reliability, responsiveness,
communication, credibility and tangibles, However, critiques of the SERVQUAL avers that
given the exponential rate at which electronic services are deployed, the model could not
explicitly measure the quality of digital services (Zeithaml, 2002). This birthed a revised
model; E-S-QUAL (Parasuraman et al., 2005) comprising a 22-item scale on four dimensions
namely efficiency, fulfilment, system availability and privacy. However, Zeithaml (2002)
content that the last three attributes are often relevant when online customers are faced with
challenges although Zeithaml (2000) avers that efficiency, reliability, fulfilment, privacy,
responsiveness, compensation are sufficient attributes to measure e-service quality.

Extant studies have utilized the E-S-QUAL to measure the satisfaction of DBS being
delivered to customers (Javed et al., 2018; Ketema and Selassie, 2020; Madavan and
Vethirajan, 2020; Mujinga, 2020; Raza et al., 2020). Yet, none attempted to explore the impact
on customers’ retention intention. Mujinga (2020) for instance noted that since technology
drives the competitive strategy of banks, the need for cutting-edge technology to provide
customers with the minimum convenience is essential. Further, Ketema and Selassie (2020)
argue that in the wake of the COVID-19 pandemic, a great deal of challenges has befallen
financial institutions leading to aggressive use of mobile banking services. The latter’s study
found that in Ethiopia, DBS quality dimensions such as security, reliability and ease of use
greatly influence customer satisfaction. Mujinga (2020) however laments that the E-S-QUAL
scale needs improvement since electronic services quality has evolved. Hence, in this study,
we complement the E-S-QUAL framework with BSQ (Bahia and Nantel, 2000).

2.4 The bank service quality model
The BSQ model Bahia and Nantel (2000) is used to measure the quality of retail banking
services (Shayestehfar and Yazdani, 2019). Derived from the SERVQUAL, the BSQ is

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References Theoretical position Focus and context
Quality DBS service dimension
used

Ketema and
Selassie (2020)

E-S-QUAL The study explored the impact
of mobile banking quality
service on customers in Ethiopia
based on 240 bank respondents

Reliability
Efficiency
Privacy/security
Ease of use
Responsiveness
Empathy

Shahabi et al.
(2020)

TAM and diffusion
of innovation
theory (DOI)

The study investigated the
effect of COVID-19 on the
acceptance of e-banking
services in Iran

Ease of use, perceived utility,
attitude to use, willingness,
awareness, habit, trust and
satisfaction, support, regulations

Raza et al.
(2020)

E-S-QUAL The study explored the services
quality of Internet banking and
its impact on customer
satisfaction and loyalty in Saudi
Arabia

Site organization
Reliability
Responsiveness
User friendliness
Personal need
Efficiency

YuSheng and
Ibrahim
(2019)

Conceptual The paper explored the role of
innovative service delivery on
customers’ satisfaction and
customer loyalty in Ghana

Services innovation
Service delivery
Customer satisfaction
Customers loyalty

Narteh (2018) SERVQUAL and
banking service
quality (BSQ)

The paper examined the quality
of retail banking services on
customers’ satisfaction in
Ghana

Tangibles
Reliability
Assurance
Empathy
Price

Pooya et al.
(2020)

Conceptual The study examines customers
technology readiness and the
effect of quality electronic
services on their satisfaction in
Iran

Technology readiness
Perceived quality of self-service
Value
Trust
Satisfaction

Geebren et al.
(2021)

Conceptual The paper investigated the
mechanism of customer
satisfaction in Libya

Assurance
Trust
Task characteristics

Arcand et al.
(2017)

Conceptual The paper investigated the
impact of mobile banking
service quality on customers’
commitment, trust and
satisfaction in Canada

Security
Privacy
Experience
Design
Sociality
Enjoyment

Mujinga
(2020)

E-S-QUAL The paper investigated
e-banking service quality
among customers in South
Africa

Efficiency
Fulfilment
Privacy
System availability

Amin (2016) Conceptual The paper examined the
implications of Internet banking
service quality on customer
satisfaction and loyalty in
Malaysia

Site organization
User friendliness
Efficiency

Ul Haq and
Awan (2020)

Cognitive-
motivational
relational theory

Investigated the impact of
e-banking service quality on
customer loyalty through
customer satisfaction

Reliability
Privacy and security
Site design
Service support

(continued )

Table 1.
Summary of studies on
digital banking
services

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comprised of six key quality dimensions decomposed from the original 31 items namely
effectiveness and assurance, access, price, tangibles, services portfolio and reliability. The BSQ
offers a better assessment of banking services than the SERVQUAL since its scale
encompasses different perceptions and expectations (Bahia and Nantel, 2000). Again, the BSQ
is considered to be an assessment tool designed specifically tomeasure diverse services around
the banking ambience (Bahia and Nantel, 2000). Shayestehfar and Yazdani (2019) for instance
used theBSQ to compare customer service quality between a bank’s branches in Iran and found
that access, assurance and effectiveness of services influence customers’ satisfaction. Similarly,
Sumardiningsih et al. (2012) found the reliability dimension as key to influencing customers’
satisfaction. Narteh (2018) also found that since electronic banking comes with the associated
cost, charges on DBSs significantly influence customers’ satisfaction.

Extant studies have explored several dimensions of the E-S-QUAL and BSQ models in
their assessment of DBSs. Narteh (2018) combined SERVQUAL and BSQ and found that
reliability, price, tangibility, assurance significantly influence customers’ satisfaction. Given
that the BSQ proffers additional dimension to measure banking services, it will complement
understanding why customers would want to retain or leave after receiving a service. Hence,
this study draws on E-S-QUAL and BSQ models to determine if quality DBS influences
customers’ retention intentions.

3. Research model and hypothesis development
Based on the review of literature on the evaluation of services quality guided by the adopted
E-S-QUAL and BSQmodels, this paper argues that a combination of these twomodels would
complement to give a holistic understanding of varied dimensions which influence
customers’ retention intentions through satisfaction. Hence, this study adopts the
following constructs: Ease of Use, Efficiency, Interoperability, Privacy/Security,
Responsiveness from E-S-QUAL and, Reliability, Service Portfolios and Service Charges,
from the BSQ model. The choice of efficiency, privacy/security, responsiveness drawn from
the E-S-QUAL (Parasuraman et al., 2005) and the extension to include ease of use and
interoperability was informed by the fact that these features uniquely describe digital
platforms and serves as key determinants in electronic banking service quality (Hoehle et al.,
2012). Similarly, reliability, service charge and service portfolios dimensions drawn from
Bahia and Nantel (2000) have framed the quality of digital banking service quality (Narteh,
2018). Again, not only do these factors influence the quality of services delivered through
digital channels, they also have several consequences on customers’ perception and
expectations (Osei et al., 2016; Rita et al., 2019) such as satisfaction and intention to retain as
shown in Figure 1. The current model was necessitated particularly given the varied
outcomes that have emerged in DBS literature and the assertion that DBS quality assessment
has gone beyond the E-S-QUAL dimensions (Mujinga, 2020).

References Theoretical position Focus and context
Quality DBS service dimension
used

Al-Ghraibah
(2020)

Social exchange
theory and TAM

Examine the predictors of online
customers retention during the
COVID-19 in Saudi Arabia

Attitude
Ease of use
Responsiveness

Murali et al.
(2016)

SERVQUAL The paper investigated the
relationship between after-sales
service quality and customer
satisfaction, retention and
loyalty in India

Reliability
Responsiveness
Assurance
Empathy
Tangibles Table 1.

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services and

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1425



3.1 Ease of use
Ease of use explains how user friendly a digital application is to a user (Parasuraman et al.,
2005). Venkatesh et al. (2003) note that user friendliness is an essential factor in human-
computer interaction and it significantly generates user adoption and continual usage.
Moreover, the varying complexities in digital applicationsmake ease of use an essential factor
in assessing its use. In their assessment of banking application during the COVID-19
pandemic, Ketema and Selassie (2020) found that ease of use significantly influences usage
and customer satisfaction. Similarly, prior studies (Simon and Senaji, 2016; Zavareh et al.,
2012) conclude that user friendliness of e-services affects customers’ satisfaction. Given the
disparities in user experience and demographics, digital platforms must be designed to meet
the expectations of all users especially in times of pandemic. In this regard, Amin (2016) found
that e-banking ease of use positively correlates with Customers’ Satisfaction. Hence, this
study hypothesizes that:

H1. Ease of use of DBS will influence customers’ satisfaction and retention intentions.

3.2 Efficiency
A digital service platform is efficient if it synchronizes properly with customers request, and
requires the least information to be provided by the users (Parasuraman et al., 2005; Sharma
and Malviya, 2011). Efficiency also defines how quickly a platform can offer customers
versatile financial help and the convenience with which the digital platform responds to
requests for customer services (Ariff et al., 2013). Ankrah (2012) opined that improved service
quality based on operational efficiencies in the Ghanaian banking industry has the potential
to deliver strategic benefit such as customer retention. Similarly, John and Rotimi (2014)
suggest that efficiency remains a key factor in the banking industry in Nigeria which fosters
satisfaction among e-customers. Furthermore, previous studies show that efficiency
significantly influences e-customers satisfaction and retention intentions (Amin, 2016;
Raza et al., 2020). Given these perceptions, we argue that efficiency remains a significant
quality dimension in the delivery of DB services and hypothesize that:

H2. The efficiency of DBS will significantly influence customers’ satisfaction and
retention intentions.

3.3 Interoperability
Interoperability is defined as the ability of a DB technology to link and interact with other
electronic banking systems (Gupta et al., 2017). This study adopts this construct given its

Customer
Retention 
Intentions

H9

Service Charges Service portfolios
H

8

BSQ Dimension

DBS Customer 
Satisfaction

Ease of Use

Efficiency

Interoperability

Privacy/Security  

Responsiveness

Reliability  

E
-S

-Q
U

A
L

D
im

e
n
si

o
n

H6

H3

Figure 1.
Research framework

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relevance in the emergence of interorganizational platforms specifically in the banking sector.
Banks are now integrating their digital services into several platforms where customers can
seamlessly transact services across disparate services providers. For instance, in Ghana,
banks have integrated their digital systems into the national mobile money interoperability
infrastructure to enable customers to make a transfer between two disparate mobile or bank
accounts (GSMA, 2020). Financial information exchange will remain a proprietary silo
without interoperability. Moreover, financial information exchange across disparate digital
platforms has become a competitive strategy for banks (Wu et al., 2006). Given the restrictions
placed on individuals during the COVID-19 pandemic, bank customers are obliged to
leverage on available platforms to perform multiple financial transactions (Kelecic, 2020).
Moreover, Bourreau and Valetti (2015) opined that interoperability significantly reduces
consumer switchover and associated costs since e-customers no longer have to switch from
one digital platform to the other to perform a similar activity. These studies (Bourreau and
Valetti, 2015; Kelecic, 2020) found a positive and significant relationship between
interoperability and customer satisfaction. Hence, we hypothesize that:

H3. Interoperability of DBS application will significantly influence customers’
satisfaction and retention intentions.

3.4 Privacy/security
Privacy/security dimension measure how banks digital platform protects consumers’
personal and financial information (Parasuraman et al., 2005). This dimension of DB platform
may include attributes such as integrity, nonrepudiation, authenticity and confidentiality
(Aboobucker and Bao, 2018; Pooya et al., 2020). According to Ketema and Selassie (2020),
security was a key factor in customers’ perception of service quality during the COVID-19 era
in Ethiopia. This assertion is true since the restrictions imposed on individuals forced them to
depend on their DBS applications for their financial transactions inviting a plethora of
security issues. Ketema and Selassie (2020) study concluded that the security being offered by
mobile banking platforms significantly impacts on customers trust and satisfaction. Note
that secured DBSs foster trust which eventually generates consumer confidence satisfaction
and loyalty (Mujinga, 2020). Ketema and Selassie’s (2020) study shows a significant and
proportional relationship between DBS privacy/security, and customer satisfaction and
retention. To this end, we hypothesize that:

H4. Privacy/security of DBS application will influences customers’ satisfaction and
retention intentions.

3.5 Responsiveness
Responsiveness describes the willingness to help customers and to provide prompt service
(Akinyemi et al., 2010; Parasuraman et al., 2005). This quality dimension requires that a DBS
application provides prompt and expedite services to customers especially in the era of
COVID-19 when individuals largely rely on digital platforms. This dimension is significant
because promptly resolving customers’ complaints positively influence their perception of
service quality (Narteh, 2018). Often, when a bank’s digital service downtime or failure occurs,
the ability to recover quickly and with professionalism can create a very positive perception
of quality (Zavareh et al., 2012). Consequently, Narteh (2018) concludes that in Ghana, the
responsiveness of a bank’s service quality impacts on customer satisfaction. This implies that
banks are enjoined to ensure that digital platforms and customer e-services staff provide
prompt and expedite response to queries and also willing to help customers. Prior studies
found that DBS responsiveness influences customer satisfaction and retention (Narteh, 2018;
Al-Ghraibah, 2020). Therefore, in this study, we hypothesize that:

Banking
services and

customer
satisfaction

1427



H5. Responsiveness of DBS will influence customers’ satisfaction and retention
intentions.

3.6 Reliability
Reliability defines the ability of DBS platform to perform its functions based upon correctly
defined task characteristics without failure (Bahia and Nantel, 2000; Parasuraman et al.,
2005). Reliability according to Parasuraman et al. (1988) is one of the most important factors
towards quality service because a customer would like to ensure that services subscribed can
perform the promised service adequately and accurately (Parasuraman et al., 2005). Given
that reliability results in the provision of quality services Raza et al. (2020), Narteh (2018)
found that the reliability of DBS positively influences customers’ satisfaction and loyalty.
Note that a satisfied customer would remain loyal and retain (Al-Ghraibah, 2020). Hence, in
this study, we hypothesize that:

H6. The reliability of DBSwill influence customers’ satisfaction and retention intentions.

3.7 Service charges
Service charge is the cost involved in accessing or utilizing a DBS (Bahia and Nantel, 2000).
According to Abouraia and Othman (2017), cost involved in utilizing a DBS significantly
determines their continual usage. Senyo and Osabutey (2020) observed that mobile banking
service users in Ghana are concerned about how much they pay for every transaction they
conduct and conclude that reduced service charges significantly improve customer
satisfaction. Huei et al. (2018) also submit that service charges significantly drives an
individual’s motivation towards adoption and use of digital banking application.
Furthermore, the study carried out by Narteh (2018) shows service charges significantly
influence customer satisfaction. Hence in this study, we hypothesize that:

H7. Service charges on DBSs will significantly influence customers’ satisfaction and
retention intentions.

3.8 Service portfolios
Service portfolio according to Bahia and Nantel (2000) describes a portmanteau of innovative
products and services which are consistent with its task characteristics. Following the
imposed restrictions amidst the COVID-19 pandemic, banks moved most customarily
services onto their digital platforms. Customers on the other hand are anxious in anticipation
of bundled services on a convergent platform from which they can access (Mersha and
Worku, 2020). The banking industry in Ghana has been competitive mainly due to the
constant deployment of newer product and services (Addai et al., 2019). The competition has
intensified in the wake of the COVID-19 pandemic heightening the need to offer a convergent
of customer-oriented quality services on digital platforms in a bid to satisfy and retain their
customers (Agudze-Tordzro et al., 2014). It was evident that service portfolio significantly
predicts customer satisfaction and retention intentions (Petridou et al., 2007; Narteh, 2018).
Hence, this study hypothesizes that:

H8. Service portfolios of DBS application will significantly influence customer
satisfaction and retention intentions.

3.9 Digital banking customer service satisfaction and retention intension
Customer satisfaction is hypothesized as the assessment of opinions about a product or
services provided by an organization (Gustafsson et al., 2005). Customer satisfaction is
considered as an indicator for predicting customer purchase intention and loyalty (Chang and

IJBM
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1428



Chen, 2009). Chang and Chen (2009) submit that electronic services satisfaction could be as a
result of the expression of feelings in response to the features of the service. This impacts on
customers’ continual use of financial digital services (Wang et al., 2019). That
notwithstanding, overall satisfaction of a quality digital service could be as a result of an
accumulated impact of the electronic device since service quality impacts on e-service
customer satisfaction and loyalty (Raza et al., 2020).

Customer retention implies the commitment to maintain a long-term relationship between
the customers and the firm (Weinstein, 2002). Customer retention is increasingly becoming an
important dimension especially when economic endeavors are saturated amidst a lower rate
of new customers (Kelecic, 2020). Since DBS drives the competitive edge of banks (Oliveira
et al., 2002) and enhances customer intimacy and loyalty (Raza et al., 2020), digital customers
tend to stay (Al-Ghraibah, 2020). Note that customers’ satisfaction has a relationship with
their loyalty and subsequent stay onto the product or service (Ankit, 2011). Hasan et al. (2013)
established that quality ATM services significantly influence customers retention decisions.
Similarly, Al-Ghraibah (2020) found that online consumers in Saudi Arabia during the
COVID-19 will retain if satisfied with the online services received. Essentially, a reduction in
customer churn due to enhanced service delivery most especially when they are satisfied
(Ul Haq and Awan, 2020) significantly maximizes returns of businesses investments (Murali
et al., 2016). Along this line Mahmoud (2019) and Al-Ghraibah (2020) found a directly
proportional relationship between quality DBS customer satisfaction and retention
intentions. Hence, we hypothesize that:

H9. Quality DBS satisfaction will significantly influence customers’ retention intentions.

4. Research context and methodology
4.1 Research context
Ghana is recorded to have the second largest economy in theWest African sub-region with
a current population estimated about 30.42 million. The banking and telecommunication
sectors over decades now have become major drivers of Ghana’s economy. This stems
from the Government’s resolve to aggressively transition Ghana from the over-reliance on
physical cash to a cashless economy driven by digital technologies (Ozyurt and Beck,
2019). As a result, banks are leveraging on the capabilities of the telecom sector to drive
this strategy. For instance, mobile banking and Internet banking, a major feature in digital
banking have become pervasive and arguably inevitable since all banks in Ghana
currently have deployed multiple platforms enabled by mobile or Internet medium
(Sarpong and Agbeko, 2020). Though this development is not anew, the rate increased
exponentially in the wake of the COVID-19 pandemic. This follows a declaration of a
lockdown on some major cities in the country by the government of Ghana on March 28,
2020 intended to curb the spread of the virus. Banks subsequently advised their customers
to use their digital platforms for their routine banking needs. Hence, the surge in the
deployment of varied digital platforms with a portfolio of services to serve customers
during the period.

Presently, there is keen competition among banks in Ghana since the BOG enhanced its
reforms to strengthen the sector. This has caused the revocation of some banking licensing
and in some cases takeovers from other banks (Obuobi et al., 2020). Hence, banks are making
all efforts to improve on their services aimed at attaining operational excellence and retain
their customers to increase their market share for a competitive advantage (Agudze-Tordzro
et al., 2014). Yet, extant studies relative to the Ghanaian context have remained silent on DBS
dimensions which influence customer retention intentions through the satisfaction of the
DBSs received.

Banking
services and

customer
satisfaction

1429



4.2 Measurements scales
The measurement items for all constructs presented in Table 2 were adopted from literature.
These items guided us in the construction of our conceptual framework (Figure 1). Constructs
adopted were empirically grounded in literature. Eight constructs were adopted: Four (ease of
use, efficiency security/privacy and responsiveness) were drawn from the E-S-QUAL
(Parasuraman et al., 2005), one (interoperability) from (Gupta et al., 2017) and three
(reliability, service charges and service portfolio) from the BSQ (Bahia and Nantel, 2000).
Column 8 of Table 2 indicates the source where each of the constructs was adopted.

4.3 Sampling and data collection
The research questionnaire used in the data collection was structured into three parts: the
first section comprises respondent’s demographic information and the second part comprised
respondents’ answers to their assessment of the quality service variables in the model. The
third section sought to know customers’ satisfaction with the DBS and retention intentions
based on the variables in the second section. The second and third sections were structured
based on the constructs of the model questions using a five-point Likert scale ranging from 1
to 5 representing strongly disagree to strongly agree. A pilot study was initially conducted
among 50 participants to ascertain the validity of the questions for their internal consistency
and no issue of multicollinearity was recorded. Due to the prevalence of the COVID-19, we
distributed the online survey link to respondents through a snowballing approach between
the period May and December 2020. Snowballing technique helps in the recruitment of the
respondents through a chain of referral (Allen, 2017). Cognizance of possible duplication in
the online self-administration questionnaire, constraint was placed in the Google Forms to
prevent multiple responses. All questions were mandatory, and hence, no missing data were
recorded. At the end of the survey, 395 responses were received.

4.4 Profile of respondents
The demographic information was analyzed using IBM Statistical Package for Social Science
(SPSS version 26). The descriptive characteristics of respondents are shown inAppendix. Out
of the 395 responses 56.7% were females and 43.3% males. Age groups between 18 and 29
years were 58.7%, 30 and 39 years were 30.4%, 40 and 49 years were 5.6%, 50 and 58 years
were 2.8 and 2.5% were above 60 years. Relative to their education level, high school leavers
were 5.8%, Certificate/diploma 8.8%, professional certifications 2.0%, first degree holders
were 52.9%, master degree 29.4 and 1% doctorate holders. On the frequently used DB
channel during the COVID-19, 53.4% frequently used mobile channels, 31.9% ATM, 10.1%
online/Internet and 4.6% telephone banking. Relative to the frequency of access of the DBS
during the COVID-19, a few times in a week was 44.6%, daily 26.6%, few times in a month
16.7 and 12.2% accessional users.

5. Results
Further, we evaluated the reliability and validity of the constructs in the conceptual model as
recommended by Hair et al. (2014) and there was no ambiguity in the questions. The study
employed a structural model analysis using partial least square structural equation model
(PLS-SEM) to test the structural model. Hair et al. (2017) submit that PLS-SEM is appropriate
for predicting the structural relationship of latent variables. The SmartPLS software version
3.2.7 was used for the measurement and structural analysis.

5.1 Assessment of the measurement model
A three-stage measurement models namely factor loadings, convergent and discriminant
validitywas used to examine the relationship between the latent variables. To produce a good

IJBM
39,7

1430



C
on
st
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ct
s

It
em

s
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s

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oa
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om

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s

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se

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ay

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r

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n
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ly

0.
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7

0.
92
2

0.
92
2

0.
74
8

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an

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00
5)

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2

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ca
n
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n
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d
en
tl
y
n
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ig
at
e
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y
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0.
86
4

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3

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y
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p
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es

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e

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0.
88
9

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4

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fe
el
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m
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le
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y
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n
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y
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’s

d
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it
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p
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tf
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m

d
u
ri
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g

0.
89
6

E
ff
ic
ie
n
cy

E
F
F
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y
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k
’s
d
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p
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97
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0.
88
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0.
89
2

0.
73
5

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ar
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00
5)
,

S
h
ar
m
a
an
d
M
al
v
iy
a,
(2
01
1)

E
F
F
I2

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y
b
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k
’s
d
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se
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77
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h
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p
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0.
80
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In
te
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il
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1

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0.
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93
9

0.
93
9

0.
83
8

B
ou
rr
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d
V
al
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01
5)
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G
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l.
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01
7)

IN
T
E
2

M
y
b
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k
’s
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ca
n
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ed

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ir
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-p
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it
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s

0.
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5

IN
T
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3

M
ak
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g
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ay
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an
d
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an
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er

to
ot
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er

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ir
d
-

p
ar
ty

p
la
tf
or
m
s
w
it
h
m
y
b
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sy

0.
92
1

P
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v
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C
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1

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tr
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y
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81
8

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9

0.
60
2

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5)

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2

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re

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0.
78
2

S
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3

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y
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/

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n
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et
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er

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ir
d
-

p
ar
ty

in
st
it
u
ti
on
s

0.
79
4

(c
on
ti
n
u
ed

)

Table 2.
Results of factor

loadings, constructs
reliability and validity

Banking
services and

customer
satisfaction

1431



C
on
st
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ct
s

It
em

s
M
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s

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R
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R
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92
4

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4

0.
75
3

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(2
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5)

R
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om

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3

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1

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2

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8

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86
7

S
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1

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on
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66
7

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ah
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0)

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2

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os
t
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tf
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m

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R
em

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ed

S
C
H
A
3

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os
t
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su
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ri
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ti
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to
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y
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is
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0.
82
9

S
er
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ic
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p
or
tf
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io
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S
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P
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1

M
y
b
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k
d
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it
al
p
la
tf
or
m

p
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v
id
es

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ar
ie
d

fi
n
an
ci
al
se
rv
ic
es

0.
75
2

0.
82
4

0.
82
4

0.
61
0

B
ah
ia
an
d
N
an
te
l
(2
00
0)

S
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P
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2

M
y
b
an
k
p
ro
v
id
es

m
u
lt
ip
le
se
rv
ic
es

av
ai
la
b
le

on
it
s
d
ig
it
al
p
la
tf
or
m

0.
80
4

S
E
P
O
3

M
y
b
an
k
co
n
si
st
en
tl
y
u
p
d
at
es

it
s
d
ig
it
al

p
la
tf
or
m

w
it
h
n
ew

er
se
rv
ic
es

0.
78
6

(c
on
ti
n
u
ed

)

Table 2.

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39,7

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C
on
st
ru
ct
s

It
em

s
M
ea
su
re
s

L
oa
d
in
g
s

C
ro
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b
ac
h
’s

al
p
h
a

C
om

p
os
it
e

re
li
ab
il
it
y

A
V
E

R
ef
er
en
ce
s

D
B
S
sa
ti
sf
ac
ti
on

D
S
A
T
1

I
w
il
lr
ec
om

m
en
d
m
y
b
an
k
’s
d
ig
it
al
se
rv
ic
es

to
ot
h
er

cu
st
om

er
s

0.
76
2

0.
88
4

0.
88
4

0.
65
6

C
h
an
g
an
d
C
h
en

(2
00
9)
,

G
u
st
af
ss
on

et
a
l.
(2
00
5)

D
S
A
T
2

I
am

sa
ti
sf
ie
d
w
it
h
th
e
w
ay

d
ig
it
al
b
an
k
in
g

se
rv
ic
es

is
d
el
iv
er
ed

b
y
m
y
b
an
k

0.
82
1

D
S
A
T
3

I
am

sa
ti
sf
ie
d
w
it
h
th
e
q
u
al
it
y
of

d
ig
it
al

b
an
k
in
g
se
rv
ic
es

re
ce
iv
ed

0.
87
0

D
S
A
T
4

O
v
er
al
l,
Ia
m
sa
ti
sf
ie
d
w
it
h
th
e
q
u
al
it
y
of
d
ig
it
al

b
an
k
in
g
se
rv
ic
es

re
ce
iv
ed

fr
om

m
y
b
an
k

d
u
ri
n
g
th
e
C
O
V
ID
-1
9

0.
78
3

C
u
st
om

er
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88
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88
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(2
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,

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(2
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k
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0.
77
9

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I3

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n
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to

st
ay

b
ec
au
se

m
y
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k
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ro
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es

0.
73
5

R
T
N
I4

I
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te
n
d
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st
ay

an
d
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m
m
en
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k
’s

d
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rv
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es

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s

0.
81
8

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T
N
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O
v
er
al
l,
I
am

sa
ti
sf
ie
d
w
it
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th
e
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al
it
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m
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O
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9

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st
ay

0.
80
4

Table 2.

Banking
services and

customer
satisfaction

1433



fit, factor loadings were first computed and all factors below the acceptable 0.7 (Hair et al.,
2017) were removed from the final measurement. Final factor loadings (shown in Table 2).
Second, other reliability measures, internal consistency and convergent reliability were also
computed. The internal consistency was computed using the composite reliability (CR) and
Cronbach’s alpha. The convergent validity of each constructwas computed using the average
variance extracted (AVE) (Hair et al., 2019). Given the suggested threshold on the accepted
reliability value above 0.7 for Cronbach’s alpha (Hair et al., 2017), all constructs of the model
met this criterion indicating sufficient construct reliability internal consistency. Similarly, the
threshold for the accepted convergent validity of 0.5 (Hair et al., 2014) was also achieved
which indicates a good convergent validity (Table 2).

Relative to the discriminant validity, we employed the Fornell and Larcker and
Heterotrait-Monotrait (HTMT). The Fornell and Larcker (1981) criteria are one of the
traditional metrics for measuring discriminant validity. The method compares the square
root AVE of each construct with the inter-construct correlation of the same and all other
constructs in the structural model. As presented in Table 3, the bivariate correlation between
any construct in the model is lesser than the square root of the AVE indicating a strong
uniqueness between the constructs. The Fornell and Larcker metric has however been
critiqued for not providing adequate discriminant validity assessment. For instance, Henseler
et al. (2015) content that the metric lacks the ability to establish uniqueness between
constructs. Hence, the proposition of the HTMT ratio of the correlations. The HTMTachieves
higher specificity and sensitivity rates (Henseler et al., 2015). The HTMT threshold value
proposed by Henseler et al. (2015) is 0.9. However, in this study, we adopted the 0.85 threshold
proposed by Henseler et al. (2015) when constructs are conceptually more distinct. Table 4
presents the result of the HTMT values for each pair of the construct is between 0.019 and
0.553. This implies no discriminant validity problem existed between constructs.

5.2 Assessment of the structural model and hypothesis testing
Third, we measured the structural model of the study which entails computing (1) the
coefficient of determination (R2), (2) cross-validation redundancy measure Q2 and (3) testing
of hypotheses (Hair et al., 2019). Before that, wemeasured the collinearity among the predictor
constructs for their variance and the variance inflation factor (VIF) values were between the
threshold of 1 and 2.85 indicating the absence of possible biases in the path coefficients
consistent with Hair et al. (2017) proposition.We proceeded to determine theR2 andQ2 values
which is a measure of the explanatory power and high predictive ability (Hair et al., 2019). We
applied the SmartPLS blindfolding procedure in determining the Q2. The R2 values and Q2

are shown in Table 4.
The final data analysis was to test for hypotheses and the result is presented in Table 5.

The mediation analysis was to test the indirect effect in the conceptual model by the
independent variables on customer retention intention through DBS satisfaction. The result
as illustrated in Table 5 shows that ease of use had a positive and statistically significant
relationshipwithDBS satisfaction and customer retention intentions. Hence, hypothesis H1 is
supported. Similarly, efficiency, privacy/security and reliability of DBSs had a statistically
significant relationship with DBS satisfaction and retention intentions. This implies
hypotheses H2, H4 and H6were supported. On the contrary, interoperability, responsiveness,
service charges and services portfolio did not indicate any statistically significant
relationship with DBS satisfaction and retention intentions. Hence, hypotheses H3, H5, H7
and H8 were rejected. Furthermore, the final phase of the hypothesis was to ascertain the
impact of quality DBSs satisfaction on the customers’ retention intentions. The direct effect
path analysis shows a statistically significant relationship between quality DBS satisfaction
and customers’ retention intentions, which indicates the acceptance of hypothesis H9 (See
Figure 2).

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C
on
st
ru
ct
s

E
O
U

E
F
F

IN
T
E

P
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A

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E
as
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0.
86
5

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32
9

0.
85
7

In
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0.
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6

P
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33
0

0.
52
4

0.
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1

0.
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6

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b
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0.
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3

0.
09
3

0.
25
7

�0
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86
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55
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2

0.
83
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41
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7

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8

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et
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4

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5

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77
7

0.
77
2

Table 3.
Result of discriminant
validity (Fornell and

Larcker criterion)

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services and

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satisfaction

1435



C
on
st
ru
ct
s

E
O
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IN
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5

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n
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0.
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8

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0.
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9

0.
01
9

P
ri
v
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ty

0.
33
1

0.
52
5

0.
04
7

R
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0.
07
2

0.
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4

0.
25
6

0.
05
6

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s

0.
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4

0.
55
3

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8

0.
33
2

S
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0.
29
1

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41
8

0.
02
1

0.
53
3

0.
04
6

0.
06
7

S
er
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p
or
tf
ol
io

0.
19
7

0.
41
2

0.
04
9

0.
50
5

0.
06
3

0.
04
0

0.
34
7

D
B
S
sa
ti
sf
ac
ti
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0.
48
5

0.
62
4

0.
02
5

0.
58
9

0.
13
3

0.
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1

0.
44
8

0.
36
1

0.
65

0.
40

C
u
st
om

er
re
te
n
ti
on

in
te
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s

0.
38
7

0.
71
5

0.
05
7

0.
68
4

0.
08
5

0.
04
5

0.
49
7

0.
58
5

0.
77
5

0.
61

0.
43

Table 4.
Result of discriminant
validity (HTMT
criterion)

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6. Discussion
6.1 Key findings
In this study, we explored the impact of digital banking services provided by banks in Ghana
during the COVID-19 on customers’ satisfaction and retention intentions. This study was
motivated by the impetus given to the acceptance and usage of DBS as a result of the
unfolding COVID-19 pandemic and the multiplicity of factors that could affect the quality of

Hypotheses

Original
sample
(O)

Sample
mean
(M) SD

t-
statistics

p-
values Interpretation

H1 Ease of use → DBS
satisfaction → customer
retention intention

0.190 0.190 0.042 4.511 0.000 Accepted

H2 Efficiency → DBS
satisfaction → customer
retention intention

0.280 0.276 0.062 4.538 0.000 Accepted

H3 Interoperability → DBS
satisfaction → customer
retention intention

0.026 0.027 0.044 0.603 0.547 Rejected

H4 Privacy/Security → DBS
satisfaction → customer
retention intention

0.223 0.233 0.074 2.996 0.003 Accepted

H5 Responsiveness → DBS
satisfaction → customer
retention intention

�0.079 �0.079 0.045 1.770 0.077 Rejected

H6 Reliability→ DBS
satisfaction → customer
retention intention

0.088 0.086 0.036 2.418 0.016 Accepted

H7 Service charge → DBS
satisfaction → customer
retention intention

0.063 0.061 0.052 1.210 0.227 Rejected

H8 Service portfolio → DB
satisfaction → customer
retention intention

�0.010 �0.009 0.061 0.163 0.871 Rejected

H9 DBS satisfaction → customer
retention intentions

0.777 0.777 0.041 18.844 0.000 Accepted

Note(s): *Significant level at 0.05, **Significant at 0.01 level, ***Significant at 0.001 level

Customer Retention 
Intentions
(R2 = 0.61)

0.777***

Service Charges Service portfolios

n.s.

DBS Customer 
Satisfaction

(R2 = 0.65)

Ease of Use

Efficiency

Interoperability

Privacy/Security  

Responsiveness

Reliability  

Note(s): ***p < 0.001. **p < 0.01. *p < 0.05

Table 5.
Results of hypotheses
testing (specific effect)

Figure 2.
Structural model result

Banking
services and

customer
satisfaction

1437



services offered during the period. Given that digital banking service has become
indispensable toward conforming to the established COVID-19 protocols especially for
banks in Ghana, understanding the impact of the services provided was eminent.

The predictive accuracy of themodel showed a coefficient of determinationR2 values of 65
and 61%of variations in DBS satisfaction and retention intention respectively. Given that the
rule of thumb regarding the acceptable R2 value are 0.75, 0.50 and 0.25 representing
substantial, moderate and weak predictive accuracy respectively (Hair et al., 2014), we argue
that a strong exploratory power has been obtained as compared to previous studies by
Al-Ghraibah (2020) who obtained 43.5% variations in customer retention.

Eventually, the study result revealed that ease of use, efficiency, privacy/security and
reliability of DBSs have a significant impact on customers’ retention intentions through
satisfaction. As a result, H1, H2, H4 and H6 which indicate that DBSs’ ease of use, efficiency,
privacy/security and reliability influence customers’ satisfaction and retention intentions are
supported. This outcome is consistent with earlier studies (Al-Ghraibah, 2020; Ankrah, 2012;
Ketema and Selassie, 2020) which hypothesized that quality DBS impacts on customer
satisfaction and retention decisions. Additionally, Narteh (2018) found that reliability of retail
banking services positively predicts customer satisfaction. Raza et al. (2020) further add that
quality dimensions such as user friendliness, reliability and efficiency of Internet services will
positively impact on customer satisfaction and loyalty. While Ul Haq and Awan (2020) found
the privacy and security of digital applications to have an association with consumer
satisfaction, it is contingent on the privacy and security the platform provides. More
significant about the study findings was that Ketema and Selassie (2020) in a related study
found the ease of use of DBS applications to have a positive relationship with customer
satisfaction.

Additionally, the study found a direct effect between DBS customers’ satisfaction and
retention intentions. The test revealed that the customer’s decision to retain is directly
associated with their level of satisfaction with DBS provided by their banks during the
COVID-19. This result is consistent with Murali et al. (2016) and Al-Ghraibah (2020) who
found a relationship with quality digital services on consumers satisfaction and retention
intentions. This in essence proves how sensitive customers were to services delivered during
the COVID-19 pandemic.

On the contrary, results of H3, H5, H7 and H8 indicating the influence of DBS quality
dimensions, interoperability, responsiveness, service charges and service portfolios on
consumer satisfaction and retention intentions had no impact. This result is inconsistent
with prior studies which found a significant impact of DB on consumers’ decisions (Narteh,
2018; Raza et al., 2020). Relative to service charges, Narteh (2018) found banking services to be a
positive and significant influence on customer satisfaction. Again, given that interoperability
offered DB customers’ unlimited access to make transfers from their account to other account
held by another service provider, the feature would have been a significant quality dimension.
Be that as it may, from the customers’ perspective, service charges and interoperability of the
digital banking services will not influence their satisfaction level and retention intentions.

6.2 Theoretical implications
From the theoretical viewpoint, this study added empirical evidence to the existing knowledge
on quality digital banking services. Thus, the study confirms DBS dimensions such as ease of
use, efficiency, privacy/security and reliability significantly influences customer satisfaction
and retention intentions. Although the study confirms the influence of some dimensions of
DBSs, other attributes such as interoperability, responsiveness, service charge and service
portfolio did not show any significant influence on customer satisfaction and retention.
Fundamentally, the study model has a crucial implication to literature particularly with the
combination of the two models and the introduction of other unique variables to complements

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DBS quality attributes. This unique approach offers a broader understanding of customers’
retention decision relative to their assessment of DBS being offered by banks. Essentially, the
consequences of DBSs delivered during the COVID-19 era on customer retention dimensions
through customer satisfaction is still in their infancy (Al-Ghraibah, 2020; Ketema and Selassie,
2020; Ul Haq and Awan, 2020). Moreover, the impact of the service quality dimensions on the
customers’ retention intentions has not been extensively explored particularly in sub-Saharan
Africa andmost especially the Ghanaian context (Narteh, 2018). The study does not only fill the
gaps in this context, but also add to the streams of research in the banking industry and setting
the stage for further studies in other economic jurisdictions.

6.3 Practical implications
Furthermore, this study goes beyond the impact of digital banking to improving the
knowledge on management practices aimed at identifying effective service quality metrics
for measuring customer services, especially how to measure improved customers’
satisfaction and predict their retention intentions. The deployment, acceptance and usage
of DBS have evolved in a developing economy like Ghana. Thus, demand effective and
sustainable strategies to pursue quality banking services strategies.While this does not come
at a cheaper cost, the most perhaps cost-effective strategy is to provide excellent services.
Particularly, when it has been suggested that it cost less retaining existing customers than
attracting new ones (Weinstein, 2002). This is important because the profitability of banks is
largely dependent on their ability tomaximize gains from their share of customers. Given that
sustainable quality DBS services increase market share through an increase in the customer
base, the focus should be on improving the digital banking platforms reliability and efficiency
consistent with the operations of disparate digital financial technologies. Regardless, service
portfolios on banks digital platforms should be responsive and accessed at minimal charges
to foster customer retention. Along this line, effective customer-retention strategies such as
building loyalty through quality service delivery have to be embraced.

Essentially, this study has proven through several electronic banking several quality
dimensions that customers will react in diverse ways based on the quality of services
received. While it is important to provide services with much usability, reliability and
efficiency, the platforms should also be user friendly and offer many assurances in terms of
privacy/security for customers of different competency levels to easily navigate on the digital
platforms. The result of this study, will serve as a blueprint to banks when developing digital
service applications. For instance, emphases should be given to quality attributes such as
ease of use, efficiency, reliability and privacy/security since they influence customers’
satisfaction and retention intentions. This implies consideration should be given to the
security and protection of customers’ digital financial asset and information on banks
financial technologies platforms. In the light of the result, this implication is important given
the rise in cyber threats among mobile and Internet banking users in Ghana (Kolog et al.,
2020). Furthermore, the study results serve as a guideline to banks and other allied financial
institutions to derive customer-centric service quality criteria to enhance decision making
based on customers’ preferences. It should also be mention that the findings will ultimately
guide financial institutions in the implementation of digital systems aimed at achieving
operational excellence. Furthermore, the findingswill inform banks of customers’ preferences
for quality services in order to tailored-made DBS for their competitive advantage.

6.4 Limitations and future research directions
First, even though this study provided an empirical assessment of the impact of quality DBS
on customer’ retention intentions, it highlighted on the customers’ retention through
satisfaction. Our study was based on the Ghanaian context which invariably is beset with
several challenges relative to its services sector (YuSheng and Ibrahim, 2019). A comparative

Banking
services and

customer
satisfaction

1439



study between a developed and developing economy in future will offer more generalized
results. Further, digital banking services encompasses several processes. This study
meanwhile focused on a combination of DB service quality dimensions. This approach
though novel may not be instructive since digital banking services vary based on the bank,
delivery channel and application. Other service quality frameworks could also be used by
future studies and or extend the existing frameworks to come out with similar profound
findings. Again, other moderating factors such as economic status and trust could also be
used coupled with other models to determine what informs customers to stay on their banks
DBS during the pandemic. A preference elicitation technique could also be used to
understand consumers preferred quality dimension in times of pandemic in future studies.

7. Conclusion
This paper investigated the impact of quality DBS on the customers’ satisfaction and retention
intentions leveraging on the E-S-QUAL and BSQ models. Specifically, the decision was based
onDBSs received during the COVID-19 era among customers of Ghanaian Banks.We collected
395 responses and at the end of the result analysis, it emerged that DBS satisfaction directly
impacts on customers’ retention intentions. Specifically, the study based on eight quality
dimensions namely ease of use, efficiency, interoperability, privacy/security, responsibility,
reliability, service charges andportfolio. It emerged that ease of use, efficiency, privacy/security
and reliability of DBS impacts on customers’ retention decisionwhen satisfied. On the contrary,
interoperability, responsiveness, service charge and portfolios of DBSs did not influence DBS
customers’ satisfaction and retention intentions.Moreover, although someportions of the study
findings were inconsistent with prior studies, it offers an opportunity for further assessment in
contribution to a broader knowledge of quality baking services.

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Further reading

Ayo, C.K., Oni, A.A., Adewoye, O.J. and Eweoya, I.O. (2016), “E-banking users’ behaviour: e-service
quality, attitude, and customer satisfaction”, International Journal of Bank Marketing, Vol. 34
No. 3, pp. 347-367, doi: 10.1108/IJBM-12-2014-0175.

Robinson, T. (2000), “Internet banking: still not a perfect marriage”, Informationweek, No. 782,
pp. 104-105.

Appendix

Corresponding author
Sulemana Bankuoru Egala can be contacted at: sbankuoru@gmail.com

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Demographics Frequency (N 5 395) Percent (%)

Gender
Female 224 56.7
Male 171 43.3

Age
18–29 232 58.7
30–39 120 30.4
40–49 22 5.6
50–58 11 2.8
60 and above 10 2.5

Educational
High school 23 5.8
Certificate/diploma 35 8.9
Professional 8 2.0
First degree 209 52.9
Master’s degree 116 29.4
Doctorate degree 4 1.0

Digital banking access channels frequently used
ATM 126 31.9
Mobile 211 53.4
Internet/online 40 10.1
Telephone 18 4.6

Frequency of digital banking service use
Rarely 48 12.2
Daily 105 26.6
A few times in a week 176 44.6
A few times in a month 66 16.7

Table A1.
Demographic
distribution of
participants

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satisfaction

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	To leave or retain? An interplay between quality digital banking services and customer satisfaction
	Introduction
	Background and theoretical foundation
	Digital banking concept
	Digital banking and quality service
	Electronic service quality model
	The bank service quality model

	Research model and hypothesis development
	Ease of use
	Efficiency
	Interoperability
	Privacy/security
	Responsiveness
	Reliability
	Service charges
	Service portfolios
	Digital banking customer service satisfaction and retention intension

	Research context and methodology
	Research context
	Measurements scales
	Sampling and data collection
	Profile of respondents

	Results
	Assessment of the measurement model
	Assessment of the structural model and hypothesis testing

	Discussion
	Key findings
	Theoretical implications
	Practical implications
	Limitations and future research directions

	Conclusion
	References
	Further reading
	Appendix