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Int. Journal of Business Science and Applied Management, Volume 18, Issue 1, 2023
Analysis of Customer Satisfaction and the Customer
Experience in Digital Payments: A Meta-Analysis Review
Shilpa Agarwal
Research Scholar, Amity College Commerce, and Finance, Amity University, Noida
Sector 125, Noida, Uttar Pradesh, 201301
Email: shilpa.agarwal@s.amity.edu
Priyanka Malik
Assistant Professor, Amity International Business School, Amity University, Noida
Sector 125, Noida, Uttar Pradesh, 201301
Email: pmalik2@amity.edu
Shalini Gautam
Associate Professor, Delhi Metropolitan Education, Noida
Block B, Sector 62, Noida, Uttar Pradesh, 201301
Email: s.gautam@dme.ac.in
Abstract
The meta-analysis of studies has become useful in the development of knowledge in the banking sector,
producing important theoretical contributions to future research agendas. To generate theoretical
contributions to the study of banking digital payment services, this research is a type of desk research
based on a literature review of secondary data. The present study provides a meta-analysis of the
generalizations in the relationships between the antecedents (functional quality, perceived value, trust,
perceived risk, and service quality) and consequences of customer experience and satisfaction with
digital payment services. The study conducted a weight analysis, in which the above -mentioned
antecedents were considered for meta-analysis to see the impact on customer experience and
satisfaction separately. According to the findings of this study, functional quality, perceived value,
trust, perceived risk, and service quality are significant antecedents of customer experience and
satisfaction toward digital payments in banks. Further, it has been found that the strongest indicator of
consumer satisfaction is service quality, while trust is essential for both a pleasant customer experience
and satisfaction. The study offers insights into how these antecedents improve the functionality of
customers, their experience, and satisfaction. This meta-analysis study contributes to the existing
literature by offering a set of empirical generalizations, including relationship coefficients and weight
analysis.
Keywords: functional quality, perceived value, trust, service quality, perceived risk
Int. Journal of Business Science and Applied Management / Business-and-Management.org
1. INTRODUCTION
ICT and digital innovation have both caused massive changes in our daily lives. This includes
financial transactions that have largely moved from cash to digital (Mohamad et al., 2009). According
to a survey conducted by Statista.com (2017), the global digital payments sector was worth over $3
trillion in 2017. Within the next two years, it was worth $4.7 trillion, and by 2021, the valuation stood
at a $6.6 trillion. Further, by 2027, the global digital payment market is expected to reach US$ 12.55
trillion, growing at a CAGR of 10.9% from 2021 to 2027 (businesswire.com, 2022)
Recognizing the growing importance of digital payments both the government and private service
providers have seized the opportunity. The government and business service providers have embraced
the change as they recognize the growing relevance of digital payments. Banks, for instance, have
constantly been using digital technology to establish new value streams, looking to enhance customer
service efficiency. However, it should be highlighted that although customers, banks, and financial
institutions have all benefited from using digital payments for completing their transactions, the
perception of risk is still considered a constant problem. Previously researchers have identified the
relationship between customer satisfaction and its antecedents and customer experience and its
antecedents separately. Several studies tried to identify these relationships and measure their magnitude
(Ojiagu et al., 2022; Ali bayad, 2021; Jacinda et al.,2021; Rana et al., 2020; Kar Arpan, 2020; Mbama,
2018; Alvarez, 2019; Goutam, 2018; Loi Leong et al.,2017; Elissavet et al., 2013). A study conducted
by (Kar Arpan (, 2020) has investigated how trust has a negative and significant impact on customer
satisfaction while trust is found to have a non-significant impact on customer experience, as per the
study conducted by Mbama, 2018. This means that often customers do not share their experiences
unless they are extremely delighted with a product. On the other hand, when customers are dissatisfied
they complain in the hope of a resolution of the complaint. Further, perceived risk is found to have a
non-significant impact on customer satisfaction (Kar Arpan et al., 2020), while it has a negative but
significant impact on customer satisfaction and customer experience (Kar et al., 2020; Mbama, 2018).
This implies that if the risk increases, as perceived by the consumer, there is a greater chance that the
consumer may not adopt or use the technology. Functional quality has been found to have a non-
significant impact on customer satisfaction (Elissavet, 2013), which occurred due to the customers’
notion that all banks provide the same level of functional quality. On the other hand, functional quality
has been found to have a significant impact on customer experience (Mbama, 2018). The functional
quality makes digital payment services accessible to people in remote areas without access to branches.
Further, service quality and perceived value has been found to have a significant impact on customer
satisfaction (Jacinda et al., 2021; Rana et al., 2020; Alvarez, 2019; Loi Leong et al., 2017; Goutam,
2018) and customer experience (Ali bayad, 2021; Mbama, 2018).
Further, customer experience and customer satisfaction with the digital payments provided by
banks appear to be fragmented in terms of both conceptual breadth and empirical results (Tjahjaningsih
et al., 2020; Mbama, 2018; Tandon et al., 2017; Elissavet, 2013). This fragmentation highlights the
need for a comprehensive model that organizes all antecedents and consequences. To completely
understand the implications produced by customer satisfaction and experience with bank-provided
digital payment systems, depending solely on the knowledge presented in each article separately is
insufficient.
This paper offers a systematic framework based on a meta-analytical approach to distinguish
different types of antecedents of customer satisfaction and experience of digital payments in the
banking industry. To meet the demand for academic studies on digital payments services provided by
banks, this research presents a discussion between interpretive and quantitative research on customer
experience and satisfaction with digital payments in the banking industry. The development of this
paper is specifically driven by three main objectives: (i) to build a model that incorporates the
antecedents of customer experience and satisfaction in banking services through a review of the
significant contributions to the field, (ii) to empirically test the model through a quantitative meta-
analysis of existing research, (iii) to contribute to the existing literature by offering a set of empirical
generalizations, including relationship coefficients and weight analysis.
2. LITERATURE REVIEW
Several researchers have argued that customers’ intentions, attitudes, trust, and perceived risk are
all important factors in ultimately influencing their decisions (Bélanger & Carter, 2008; Kim &
Benbasat, 2006; Lopez-Nicolas & MolinaCastillo, 2008; McKnight & Chervany, 2002; Shen & Chiou,
2010). Several studies in the past have focused on perceived risk and found that perceived risk has a
substantial influence on customer satisfaction with e-banking services (Cunningham et al.,2005;
Shilpa Agarwal, Priyanka Malik and Shalini Gautam
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Ramezani et al., 2016; McKnight and Chervany, 2002; Corbitt et al., 2003; Fernandes, 2016; Kar
Arpan, 2020). On the other hand, Tandon et al. (2017) found that perceived risk does have a negative
relationship with consumer satisfaction, especially in the case of ‘online shopping.' They recommended
banks enhance their service quality, and thereby lower perceived risk. (Trivedi et al., 2019) dealt with
the customer experience of using chatbots and found that perceived risk has a considerable impact on
customer experience. Moreover, there is a significant influence of perceived risk on perceived value
according to some researchers, as the higher the perceived quality, the lower the perceived risk (Batra
& Sinha, 2000; Beneke, 2013). Therefore, another major component that have a significant impact in
determining customer satisfaction and experience, especially using digital payments is the perceived
value (K. Johanis,2017; Goutam,2018; Sweeny and Webb, 2007).
Further, the researchers investigated whether there is a significant relationship between perceived
value and consumer trust, because if consumers feel that the value of a product is higher, their trust
increases and they are more likely to buy a product, which impacts customer satisfaction and
experience (Chang and Chen, 2008; Zulfikar and Mayvita, 2018). (Mbama, 2018) found that trust has a
non-significant relationship with customer experience.
Since functional quality forms functional value, the extant literature also discussed customers’
view of functional value, which may be explained as the individuals' rational and economic valuations.
For instance, responsiveness, flexibility, empathy, and price are factors that are directly related to
functional value (Parasuraman et al, 1988; Lapierre 2000). Other studies have firmly demonstrated that
functional value has the strongest impact on consumer satisfaction (Jahanshahi et al, 2011; Orose &
Boonchai, 2012; Hur et al, 2013; Yousif & Hassan, 2015; Monferrer et al,2016; Kaisiri,2017;
Sukaisih,2015).
Notably, functional quality refers to how bank services are delivered (e.g., the responsiveness and
professionalism of the bank staff) (Grönroos, 1982, 1990b). However, Elissavet (2013) found that
functional quality did not influence consumer satisfaction. On the other hand, (Mbama, 2018, Garg et
al., 2014; Monferrer-Tirado et al.m 2016; Sukaish et al., 2015) found that functional quality has a
significant impact on customer satisfaction and experience. Most of the literature also agrees that
functional quality has a significant effect on the perception of overall service quality.
As per the earlier studies, service quality was found to be the most important antecedent and has a
significant effect on customer experience and satisfaction in the context of online customers (Farooqi,
2017; Hummoud et al.,2018; Suleiman & Warda,2017; Mbama,2018; Raza et al., 2020; Amin,2016;
Tjahjaningsih et al.,2020; Desiyanti, 2018; Jacinda et al.,2021 Trivedi et al.,2019; Paulo Rita et
al.,2019; Al-Hawary et al., 2017; Azevedo, 2015; Ali bayad, 2021; Alam, 2017; Goutam,2018). It must
be noted that service quality, being one of the most important predictors and most frequently used
relationships, has been measured with several dimensions in previous studies. Therefore, in the instance
of service quality, only antecedents of e-service quality were considered, to see their impact on
customer experience and satisfaction.
Based on the literature review, the study affirms and acknowledges the fact that customer
satisfaction with digital payments has been studied previously; only a few studies seem to have looked
at the impact of digital payments on customer experience. This is possible because, despite the
numerous benefits of digital payments extended by banks, individuals still view them only as an
‘alternative’, and therefore do not utilize them frequently (microsave.net, 2020). Secondly, this study
serves as a guide for organizations that provide digital payment services to customers, assisting them in
identifying the factors that make the entire process of digital payment transactions smooth for the
customers. Thirdly, no previous work seems to have been conducted as a systematic literature review
or meta-analysis of these dimensions in connection to customer satisfaction and experience together
with digital payment services. The goal of this study, therefore, is to conduct a meta-analysis to better
understand the overall impact of some of the theoretical constructs (specifically, functional quality,
perceived value, trust, perceived risk, and service quality) on customer satisfaction with and experience
of digital payments.
3. RESEARCH METHODOLOGY
3.1 Literature Search
In our endeavour to conduct the meta-analysis, first, we used a keyword-based search to find
relevant empirical work on digital payment customer satisfaction through customer experience. The
keywords included are "Digital Payment" OR "E- Payment" OR “Electronic Payment” AND
“Customer Satisfaction” OR “Customer Experience”. We conducted this keyword search in several
electronic databases, including Science Direct, ISI Web of Science, Scopus, Emerald, Springer, Taylor
& Francis, and Google Scholar.
Int. Journal of Business Science and Applied Management / Business-and-Management.org
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The second step encompassed the literature selection procedure. This was based on a method
propounded by Urbach et al., (2009). It included three stages: (1) specification of an analytical period;
(2) selection of literature sources; and (3) selection of articles to be evaluated. We identified about
1060 articles that discussed the usage and adoption of digital payments, customers’ intention to
continue and usage satisfaction, and customers’ overall experience.
To access the usefulness of the 1060 articles, a rigorous set of criteria was developed, and 179
articles were identified based on the following parameters for the meta-analysis. While searching it was
found by the researcher that prior to 2013 most journals were showing the impact of digital payments
on adoption intention (Adeoti et al., 2012; Khairun & Yasmin, 2010; Muhamad et al., 2009; Odi &
Richard, 2013; Tran et al., 2014). Furthermore, the period 2013-2021 encompasses the period during
which digital payments increased and contributed to customer satisfaction and experience (Ramezani et
al., 2016; Trivedi et al., 2019; Tandon et al., 2017; Kar Arpan, 2020; Jacinda et al.,2021; Ali Bayad,
2021; Lu, 2021). This period shifted from digital payments adoption to the satisfaction and experience
of digital payments in banks. The criteria are as follows: (i) the time frame taken from 2013 to 2021, (ii)
the type of analysis had to be Quantitative, (iii) the unit of analysis had to be the individual level, (iv)
the studies to be included in the inclusion criteria need to provide the path coefficient and p-value
between related variables used in supporting the theoretical model, (v) the studies had to provide a
research model depicting the antecedents (vi) the studies having selected variables have been
considered, other studies having other variables have been excluded, (vii) In case of customer
experience, only the antecedents selected for customer satisfaction have been included, (viii) For
service quality, only e-service quality antecedents were included in the study. The flow chart
explaining the process of shortlisting the papers as discussed above is shown in figure 1.
Figure 1: Search Process for shortlisting papers
Shilpa Agarwal, Priyanka Malik and Shalini Gautam
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On completing the process of shortlisting the papers, we calculated the weights of the most
frequently utilized relationships and considered them in the study.
3.2 Weight Analysis
Weight analysis is a technique used to determine the predictive value of a predictor (in this case,
the independent variables) in a particular relationship (Jeyaraj et al., 2006). We calculated the weights
of the 26 most frequently utilized relationships. Notably, a relationship's weight significance is
calculated by dividing the number of times it has been statistically significant by the total number of
studies that have utilized it. For instance, weight 1 (one) denotes that the association between the two
constructs is significant across all studies, whereas 0 (zero) denotes that it is not significant across all
studies (Jeyaraj et al., 2006).
In recent years, several researchers have looked at the acceptability of digital payments. Many
quantitative studies have used a range of theoretical models, assumptions, and constructs, each with its
own set of conclusions; thus, it is appropriate to look at their combined results. We began our
investigation with the most effective predictors of the associations found, assuming the higher the
effect size, the greater the probability that it would be significant in the meta-analysis. Considering all
the studies, the most effective predictors of customer satisfaction with digital payments in the banking
sector include (i) Functional Quality, (ii) Perceived Value, (iii) Trust, (iv) Perceived Risk, and (v)
Service Quality, which are both the best predictors in the weight analysis and statistically significant in
the meta-analysis, as shown in Table- 1.
Table 1: Weight analysis
Independent
variable
Dependent
Variable
Studies
Significant
Non-
significant
Total
Weight
Perceived
Usefulness
CS
9
7
2
9
0.778
Perceived ease of
use
CS
10
8
2
10
0.800
Service Quality
CS
19
17
2
19
0.894
Perceived Value
CS
10
10
0
10
1.000
Trust
CS
12
11
1
12
0.916
Perceived Risk
CS
6
5
1
6
0.833
Functional Quality
CS
5
4
1
5
0.810
Innovation
CS
4
3
1
4
0.750
Accessibility
CS
3
2
1
3
0.667
Social Influence
CS
4
3
1
4
0.750
Customer
Experience
CS
3
3
0
3
1.000
Trust
CX
2
1
1
2
0.500
Perceived value
CX
4
4
0
4
1.000
Perceived Risk
CX
3
2
1
3
0.667
Functional quality
CX
3
3
0
3
1.000
Service quality
CX
4
4
0
4
1.000
Efficiency (eff)
SQ
10
9
1
10
0.900
System availability
(sys)
SQ
10
10
0
10
1.000
Privacy (pri)
SQ
10
10
0
10
1.000
Fulfilment (ful)
SQ
10
9
1
10
1.000
Site Organisation
SQ
3
3
0
3
1.000
Tangibility
SQ
7
7
0
7
1.000
Reliability
SQ
7
7
0
7
1.000
Assurance
SQ
7
7
0
7
1.000
Empathy
SQ
7
7
0
7
1.000
Responsiveness
SQ
7
7
0
7
1.000
Int. Journal of Business Science and Applied Management / Business-and-Management.org
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[Legends: CS: Customer satisfaction; CX: Customer Experience; FQ: Functional Quality; PV: Perceived Value;
TR: Trust; PR: Perceived Risk; SQ: Service Quality]
Based on the above weight analysis, 36 publications were selected out of 179 publications. While
calculating the weights, predictors with weights larger than 0.8 are being studied for customer
satisfaction and utilized for customer experience as well. It has been noticed that customer experience
with digital payments has received less attention. Out of 26 relationships as shown in Table- 1, only 11
relationships were chosen for the meta-analysis and weight analysis. The criterion for choosing these
studies is that they had been considered at least 3 times in the literature in the case of customer
satisfaction. However, for customer experience, the selected antecedents (i.e., functional quality,
perceived value, trust, perceived risk, and service quality) of customer satisfaction are studied, and it is
found that these antecedents have received limited attention in relation to customer experience with
digital payments. As a result, in the weight analysis, the antecedents which have been chosen for
customer experience included both direct and indirect impact (Ladeira et al., 2016; Valipour et al.,
2021; Goncalo & Tiago, 2016; Patil et al., 2018). However, for calculating meta-analysis results this
study has considered only the direct impact on customer satisfaction and customer experience, as
specified in Table 3. The dimensions of antecedents have been considered in various studies to test the
meta- analysis (Goncalo & Tiago, 2016), but we have considered the direct impact of antecedents
instead of dimensions in this study. Furthermore, service quality is one of the most important predictors
and most frequently used relationships, and this has been measured with several dimensions in previous
studies. Therefore, in the instance of service quality, only the antecedents of e-service quality were
considered to check their impact on customer experience and satisfaction. The average cumulative
value was determined for each of the 11 relationships using the path coefficients gathered between each
pair of constructs from the various studies. The meta-analysis results are further generated using the
Comprehensive Meta-Analysis software program, as supported by these values, which were merged
with the total sample sizes of the investigations (www.meta-analysis.com). Based on the above
discussion, the conceptual framework depicting the 11 relationships is shown in figure 2.
Figure 2: Conceptual Model
3.3 Descriptive Review
We studied literature in the fields of online payments, mobile payments, and digital payments with
emphasis on the influence on customer satisfaction and experience (Patil et al., 2017; Slade et al., 2013
& 2014; Oliveira et al., 2016; Abdullah et al., 2016). As shown in Table 2, antecedents, such as
functional quality, perceived value, trust, perceived risk, and service quality have all been empirically
studied. The descriptive review includes 36 research studies that looked at the impact of each
independent construct on customer experience and customer satisfaction with digital payment services,
as shown in Table- 2.
Shilpa Agarwal, Priyanka Malik and Shalini Gautam
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Table 2: Existing studies which have utilized functional quality, perceived value, trust, perceived
risk, and service quality as antecedents of Customer Experience and Satisfaction
Independent
Variable
Dependent
Variable
Significant
Non- Significant
Context
Respondent
Types
Functional
Quality (FQ)
CX
CS
Mbama,2018
Garg et al., 2014
Monferrer-
Tirado et
al.,2016
Kasiri, 2017
Sukaisih et al.,2015
Elissavet et al.,
2013
UK
India
Spain
Malaysia
Greece
Indonesia
Bank Employees
Bank Customers
Hotel customers
Perceived Value
(PV)
CX
CS
Mbama, 2018
K. Johanis et al., 2017
Loi Leong et al.,2019
Alvarez, 2019
Goutam,2018
Rana et al., 2020
Hsin-
Fan &
Chen,2019
UK
Indonesia
Taiwan
Spain
India
Turkey
Bank customers
Bank employees
Automobile
Customers
Media users
Trust (TR)
CX
CS
Mbama, 2018
Fernandes, 2016
Kundu & Dutta, 2015
Dehghanpouri,2020
Beyari,2020
Kar Arpan,2020
Sukru & Beykan,
2019
Marion Garaus,2021
Nitesh & Sanjeev,
2013
Geraldine & Ebong,
2018
Mbama, 2018
UK
USA
Portugal
India
Iran
Saudi
Arabia
India
Turkey
Austria
India
Cyprus
Bank Employees
Online
customers
Bank Customers
Mobile phone
users
Perceived Risk
(PR)
CX
CS
Mbama,2018
Trivedi et al., 2019
Ramezani et al., 2016
Tandon et al.,2016
K&J, 2014
Ozer et al,2013
Geraldine & Ebong,
2018
KarArpan,2020
UK
India
Iran
India
Sri Lanka
India
Turkey
Cyprus
Bank customers
Bank employees
Passengers
Service Quality
(SQ)
CX
CS
Mbama,2018
Raza et al., 2020
Amin,2016
Tjahjaningsih et
al.,2020
Desiyanti, 2018
Jacinda et al.,2021
Trivedi et al.,2019
Rita et al.,2019
Al-Hawary et al., 2017
(de Aguiar Mala
Azevedo, 2015)
Ali bayad, 2021
Alam A, 2017
Goutam,2018
Phyo, 2020
UK
Pakistan
Australia
Malaysia
Mauritius
Indonesia
Indonesia
India
Indonesia
Jordan
Portugal
Kurdistan
Indonesia
India
Thailand
Online
customers
Bank Customer
Customer
Experience (CX)
CS
Mbama,2017
Chahal & Dutta,2014
Tjahjaningsih et
al.,2020
UK
India
Indonesia
Bank Customers
Bank Employees
[Legend: CS: Customer satisfaction; CX: Customer Experience; FQ: Functional Quality; PV: Perceived Value;
TR: Trust; PR: Perceived Risk; SQ: Service Quality]
Int. Journal of Business Science and Applied Management / Business-and-Management.org
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Five previous studies (Mbama, 2018; Ruchi et al., 2014; Monferrer-Tirado et al., 2016; Elissavet
et al., 2013; Sukaisih et al., 2015) investigated the role of functional quality in determining customer
experience and customer satisfaction with digital payments in banks in both developed (UK, Spain,
Greece) and developing countries (India, Indonesia). One study (Elissavet et al., 2013) found that
functional quality did not influence customer satisfaction. This conflicting view suggests that a
synthesis of existing results through meta-analysis is possibly required to determine whether
‘functional quality’ in and of itself is indeed a more relevant construct for investigating issues related to
digital payment experience and customer satisfaction.
Six studies revealed a strong influence of perceived value on customer experience and customer
satisfaction (Mbama, 2018; K. Johanis et al., 2017; Loi Leong et al.,2017; Alvarez, 2019; Goutam,
2018; Rana et al., 2020). Notably, the influence of perceived value has been considered in both
developed (UK, USA, Taiwan, Turkey, and Spain) and developing countries (India and Indonesia).
This may justify our use of this construct (perceived value) for additional investigation of developing
digital payment customer experience and customer satisfaction across diverse settings, provided that a
substantial cumulative impact size is demonstrated across all current studies.
The role of trust as a determinant of customer experience and customer satisfaction with digital
payments has been investigated by ten studies, with nine reporting significant influences in the contexts
of the United States, India, Iran, and Portugal, and one reporting a non-significant effect on customer
experience in the context of the United Kingdom (Mbama, 2018). Again, given the discrepancy of the
findings relating to this construct, we thought it is suitable to use a meta-analysis technique to evaluate
the overall effect size and importance of this construct. Eight studies evaluated the impact of perceived
risk on customer experience and customer satisfaction with digital payment systems in developed (UK,
Spain) and developing (India, Sri Lanka) nations (Mbama,2018; Trivedi et al., 2019; Ramezani et al.,
2016; Tandon et al., 2016; K&J, 2014; Ozer et al., 2013; Geraldine & Ebong, 2018; Kar Arpan, 2020).
The findings of all these studies show that perceived risk has a significant influence on their digital
payment customer experience and customer satisfaction. However, one study showed a significant
negative impact on customer satisfaction (Tandon et al., 2017). This may provide a rationale for
applying this construct for further research into enhancing the digital payment experience and pleasure
in a variety of scenarios, provided that a significant cumulative impact size is demonstrated across all
prior studies.
Literature on service quality in banks has shown its importance in affecting customer experience
and customer satisfaction formation for a range of systems in diverse situations. Fourteen studies
explored the role of service quality in determining customer experience and satisfaction with digital
payment systems in various geographic contexts. They include the United Kingdom, Pakistan,
Australia, Malaysia, Mauritius, Finland, Oman, Indonesia, and Jordan (Mbama,2018; Raza et al.,2020;
Amin,2016; Tjahjaningsih et al.,2020; Desiyanti, 2018; Jacinda et al.,2021; Trivedi et al.,2019; Rita et
al.,2019; Al-Hawary et al., 2017; Azevedo, 2015; Ali bayad,2021). Largely, all these studies have
suggested that service quality does consistently exert a significant influence both on customer
experience and satisfaction with digital payment systems, albeit under various contexts.
Three studies have found a strong effect of customer experience on customer satisfaction; they
were conducted in the United Kingdom, India, and Indonesia (Mbama, 2017; Chahal & Dutta, 2014;
Tjahjaningsih et al., 2020). This shows that customer experience is a rather resilient and relevant
antecedent that should be considered in customer satisfaction research, along with other related areas.
As a result, it is appropriate to use meta-analysis to determine its cumulative impact size.
Thus, it can be concluded that the research has been conducted across the globe with a minimum
sample size of 45 respondents to a maximum of 2301 respondents, as shown in Figure 3.
Shilpa Agarwal, Priyanka Malik and Shalini Gautam
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Figure 3: World distribution and coverage of research studies included in the meta-analysis.
3.4 Meta-Analysis
Meta-analysis is a way of quantitatively measuring the degree to which a given discovery has been
successfully repeated by analyzing an area of the scientific literature. By clarifying and statistically
combining previous studies' findings, meta-analysis offers the possibility of integrating findings, thus
producing a generalizable understanding of the phenomenon (Eden, 2002). It has become widely
recognized as an essential tool for statistically integrating knowledge gleaned from several empirical
investigations on a given issue (Eden, 2002; He et al., 2008). The broad use of meta-analysis in the
literature on technology adoption demonstrates its expanding importance in this subject as a tool for
integrating collected information, explaining conflicting findings, and identifying gaps in the literature
for future study.
The present research studies the statistical impact of various independent variables on customer
experience and customer satisfaction. Table 3 summarizes the data (path coefficients (b), significance
(p), and sample size) utilized in a meta-analysis of the relationships between independent variables
(functional quality, perceived value, trust, and perceived risk) and customer experience and customer
satisfaction with digital payments from 36 prior studies. It also shows that the sample size was fewer
than 300 in several studies (1, 9, 12, 13, 15, 17, 18, 20, 22, 23, 27, 28), which is typically recommended
as a minimum threshold for theory testing, particularly for studies that employed SEM as a theory
testing technique. Further, it shows that some research has indicated a significant association, while
other studies have found non-significant associations, resulting in inconsistency, and preventing
generalization.
Table 3: Details of existing studies that have utilized the direct impact of functional quality,
perceived value, trust, perceived risk, and service quality as antecedents
S.No.
Study
Year
IV
DV
Beta
P-value
Sample
size
1.
Cajetan
2018
FQ
PV
TR
PR
SQ
CX
CX
CS
0.31
0.14
0.09
−0.10
0.12
0.63
0.01
<0.05
ns
0.05
0.05
0.01
206
2.
Garg et al.
2014
FQ
CX
0.83
<0.01
624
3.
Monferrer-Tirado et al.
2016
FQ
CS
0.53
<0.03
634
4.
Elissavet et al.
2013
FQ
CS
0.298
Ns
304
5.
Raza et al.
2020
SQ(eff)
CS
0.330
0.01
500
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6.
Amin
2016
SQ(eff)
CS
0.810
0.001
520
7.
Chahal & Dutta
2014
CX
CS
0.85
0.05
180
8.
Tjahjaningsih et al.
2020
CX
SQ
CS
0.683
0.534
0.00
0.01
631
9.
Desiyanti
2018
SQ
CS
0.794
0.01
2301
10.
Jacinda et al.
2021
SQ
CX
0.794
0.05
200
11.
Ramezani et al.
2016
PR
CS
-0.64
0.05
776
12.
Trivedi et al.
2019
SQ
PR
CX
0.368
0.343
0.001
0.001
277
13.
Rita et al.
2019
SQ (ful&pri)
CS
0.791
0.01
355
14.
Al-Hawary et al.
2017
SQ(eff) SQ(pri)
CS
0.085
0.163
0.05
0.05
208
15.
Fernandes
2016
TR
CX
0.230
0.000
290
16.
de Aguiar Mala
Azevedo
2015
SQ
CS
0.760
0.000
308
17.
Ali bayad
2021
SQ(eff)
SQ(sys)
SQ(ful)
SQ(pri)
CS
0.74
0.61
0.64
0.63
0.01
0.01
0.01
0.01
129
18.
Alam
2017
SQ(eff)
SQ(sys)
SQ(ful)
SQ(pri)
CS
0.129
0.219
-0.43
0.372
0.01
0.000
0.029
0.000
385
19.
Sukaisih et al.
2015
FQ
CS
0.456
0.000
312
20.
K. Johanis et al.
2017
PV
CS
0.610
0.001
45
21.
Beyari
2020
TR
CS
0.61
0.000
314
22.
Dehghanpour
2020
TR
CS
0.42
0.001
378
23.
Tandon et al.
2017
PR
CS
-0.689
0.001
729
23.
K & J
2014
PR
CS
0.695
0.000
64
24.
Kundu & Dutta
2015
TR
CS
0.604
0.001
100
25.
Loi Leong et al.
2019
PV
CS
0.493
0.001
502
26.
Alvarez
2019
PV
CS
0.90
0.001
763
27.
Kar Arpan
2020
TR
PR
CS
-0.395
-2.15
0.013
0.501ns
100
28.
Sukrun & Beykan
2019
TR
CS
0.074
0.05
362
29.
Marion Garaus,2021
2021
TR
CS
0.677
0.001
103
30.
Ozer et al, 2013
2013
PR
CS
0.07
0.000
1000
31.
Goutam,2020
2020
PV
SQ
CS
0.16
0.67
0.05
0.001
937
32.
Rana et al., 2020
2020
PV
CS
0.583
0.000
604
33.
Nitesh & Sanjeev, 2013
2013
TR
CS
0.301
0.000
172
34.
Phyo, 2020
2020
SQ
CS
0.478
0.001
235
35.
Geraldine & Ebong,
2018
2018
PR
TR
CS
0.126
0.510
0.01
0.01
191
36.
Kasiri et al., 2017
2017
FQ
CS
0.69
0.000
400
[Legends: IV= Independent variables; DV= Dependent variables; FQ= Functional quality; PV= Perceived value;
TR= Trust; PR= Perceived risk; SQ= Service quality; CX= Customer experience; CS= Customer satisfaction;
eff= efficiency; pri= privacy; sys= system availability; ful= fulfilment]
The publication trend of the 36 studies identified was from 2013 to 2021. This is shown in figure
4.
Shilpa Agarwal, Priyanka Malik and Shalini Gautam
11
Figure 4: Publication Trend
The sample size and path coefficients of each paper for each relationship have been collected for
conducting the meta-analysis in the study. The meta-analysis calculator (https://www.meta-
mar.com/corr) was used to explore the various relationships, as shown in Table 4. It includes the total
sample size (TSS) for relationships across different studies, effect size (β), 95 percent lower (L(β)) and
upper U(β) confidence intervals, and significance level for effect size (β) (i.e., p(ES)) (Dwivedi et al.,
2017). According to Cohen (1998, 1992), the effect size is low if the value is around 0.1, medium if the
value is around 0.3, and large if the value is more than 0.5.
Table 4: Meta-analysis results
[Legend: IV= Independent variables; DV= Dependent variables; L(b)= Lower beta; U(b)= Upper beta; p(ES)= p-
value (effect size)]
As depicted in table 4, functional quality is significant for customer experience (β = 0.75, p <
0.001) and customer satisfaction (β= 0.46, p < 0.001). Perceived value has a significant relationship
with customer experience (β= 0.14, p < 0.005) and customer satisfaction (β=0.60, p < 0.001). Trust also
has a significant influence on customer experience (β= 0.17, p < 0.001) and customer satisfaction (β=
0.38, p < 0.001). The perceived risk has a significant relationship with both customer experience (β=
0.16, p < 0.001) and customer satisfaction (β= -0.35, p < 0.001). Lastly, service quality is significantly
associated with both customer experience (β= 0.47, p < 0.001) and customer satisfaction (β=0.60, p <
0.001). The studies also indicate that there is a significant relationship between customer experience
and customer satisfaction (β= 71, p < 0.001).
4. DISCUSSION AND CONCLUSION
The study focuses on key attributes that influence customer experience and customer satisfaction
(functional quality, perceived value, trust, perceived risk, and service quality). The findings of the
IV
DV
TSS
STUDIES
Effect size (β)
95% L(β)
95% U(β)
p(ES)
FQ
FQ
CX
CS
830
1016
2
3
0.75
0.46
0.71
0.41
0.77
0.50
0.000
0.000
PV
PV
CX
CS
206
2851
1
5
0.14
0.60
0.01
0.57
0.27
0.62
0.044
0.000
TR
TR
CX
CS
496
1720
2
8
0.17
0.38
0.08
0.34
0.25
0.42
0.000
0.000
PR
PR
CX
CS
483
2860
2
6
0.16
-0.35
0.07
-0.35
0.24
-0.29
0.000
0.000
SQ
SQ
CX
CS
683
6509
3
11
0.47
0.60
0.40
0.58
0.52
0.61
0.000
0.000
CX
CS
1017
3
0.71
0.68
0.74
0.000
Int. Journal of Business Science and Applied Management / Business-and-Management.org
12
present study build on and relate to findings in the literature resulting in new insights. The meta-
analysis included 36 publications from the 179 articles available in the literature between 2013 and
2021. According to the meta-analysis, 8 out of 11 relationships were statistically significant. According
to the results of the meta-analysis, functional quality, perceived value, trust, perceived risk, and service
quality have a positive and significant impact on customer experience in relation to the digital payment
services provided by banks. As far as customer satisfaction is concerned, functional quality, perceived
value, trust, service quality, and customer experience have a positive and significant impact on
customer satisfaction, while perceived risk has a negative but significant impact on customer
satisfaction with the digital payment services provided by banks. Future research assessing customer
experience and satisfaction using intention-based theories/models should include these important
predictors as antecedents alongside other commonly found antecedents in the literature.
Digital payments improve customer satisfaction and experience by making it more convenient, as
well as providing additional insights. As depicted in Table- 2, perceived value influences the digital
payment experience and satisfaction in both developed and developing countries (Mbama, 2018; Ruchi
et al., 2014; Monferrer-Tirado et al., 2016; Keisidou et al., 2013; Sukaisih et al., 2015), providing
theoretical and marketing insights across countries. Service quality influences customer experience and
satisfaction among online customers in Pakistan, Australia, Malaysia, and India, as well as bank
customers in the United Kingdom, Jordan, Indonesia, and Thailand (Mbama, 2018; Syed Ali, 2020;
Amin, 2016; Tjahjaningsih et al., 2020; Desiyanti, 2018; Jacinda et al., 2021; Trivedi et al., 2019; Rita
et al., 2019; Al-Hawary et al., 2017; de Aguiar Mala Azevedo, 2015; Ali bayad). Perceived Risk has a
negative impact on digital payment customer experience and satisfaction, extending the findings of a
study that found security to be a barrier to digital payment adoption and an increase in perceived risk
decreases customer satisfaction (Kar Arpan, 2020; Mbama, 2018; Trivedi et al., 2019; Ramezani et al.,
2016; Tandon et al., 2016; K&J, 2014; Ozer et al, 2013; Geraldine & Ebong, 2018). Trust influences
customer experience and customer satisfaction (Mbama, 2018; Fernandes, 2016; Kundu & Dutta, 2015;
Dehghanpouri, 2020; Beyari, 2020; Kar Arpan, 2020; Sukru & Beykan, 2019; Marion Garaus, 2021;
Nitesh & Sanjeev, 2013; Ebong, 2018). It has gained prominence since the financial crisis. Functional
quality affects customer experience and satisfaction in banks in the UK, India, Spain, Malaysia, and
Greece (Mbama, 2018; Garg et al., 2014; Monferrer-Tirado et al., 2016; Kasiri, 2017; Sukaisih et al.,
2015) in both online and offline activities. It is imperative to look holistically at all the studies done
across the globe to get a bird's-eye view of the important antecedents.
5. THEORETICAL AND MANAGERIAL IMPLICATIONS
In recent years, many quantitative researchers have used a variety of theoretical models,
hypotheses, and constructs, each with their significance, making it appropriate to investigate their
combined results to investigate the acceptability of digital payments. Preliminary research indicates
that no previous work has conducted a meta-analysis that provides generalizations on the relationships
between the antecedents and consequences of customer experience of and satisfaction with digital
payments services (specifically, functional quality, perceived value, trust, perceived risk, and service
quality) concerning customer experience of and customer satisfaction with digital payment services. As
a result, combining the meta-analysis with the weight analysis improves the work's credibility by
presenting different perspectives on the importance of the predictors (functional quality, perceived
value, trust, perceived risk, and service quality) on the dependent variables (customer experience and
customer satisfaction). The study started the investigation with the most effective predictors in the
weight analysis as shown in Table- 1. Based on the studies included in our work and the results
presented, the most effective predictors of the intention to use digital payment services are service
quality and trust.
Banks and other financial institutions will comprehend the significance of digital payments and
the important factors to consider when designing digital payment services. Functional quality and
service quality are better for acquiring customers, while perceived value and trust are better for
retaining customers, allowing banks to provide services to customers through appropriate channels.
These channels allow banks to provide value-added digital payment services (such as payment history,
balance inquiry, and so on), which they should consider, giving customers a reason to use digital
payment services.
Functional Quality determines digital payment effectiveness; therefore, banks should design
digital payment services with interactivity and accessibility features in mind. Customers are demanding
digital payment services because of their perceived value. They save time, distress, and cost from
visiting branches. As a result, giving customers value, improving their experience, and making them
happy should be the marketing goal of digital payments. Trust improves customer experience and
satisfaction, implying that banks can retain customers and increase profitability by providing
Shilpa Agarwal, Priyanka Malik and Shalini Gautam
13
trustworthy and high-quality digital payment services. Perceived Risk influences the digital payment
experience and satisfaction; therefore, investing to mitigate risk, educate customers on security
challenges, prevent fraud, and protect and maintain customers' trust is critical. Service Quality
influences the digital payment experience and satisfaction, demonstrating that customers are satisfied
when their expectations are met. As a result, when designing digital payment services, improving
service and functional quality should be a top priority. All the above factors are important
considerations for banks to provide a positive digital payment customer experience and satisfaction,
demonstrating their impact on digital payments. Banks can help with customer acquisition and
retention, as well as developing better digital payment service design and customer insights. The
outcome reinforces the notion that a poor digital payment experience can lead to customer
dissatisfaction. The study investigated the phenomenon and developed a digital payment model, which
has managerial as well as future research implications.
6. CONCLUSION, LIMITATIONS AND FUTURE RESEARCH DIRECTIONS
While evaluating the findings of this study, there are a few caveats to keep in mind. The time
frame considered in the study is from 2013 to 2021. None the studies which were conducted prior to
this period have been considered. It is a remote possibility that a few of the important antecedents
might have been missed. In the future, researchers can expand the time frame of their study to include
more papers. In the present study, the antecedent needs to be reflected in at least three studies for
further investigation. It is a possibility that some upcoming antecedent which has not been studied
extensively would not have been considered because of this reason. The studies with only quantitative
data have been considered. As a result, factors that may have been discovered in any exploratory
research would have been missed by the authors. Future research can also study the antecedents which
have been investigated in qualitative studies. The study has only considered the research papers which
are present online and could not consider the offline studies published in the journals. The study has
taken the existing studies into account. Therefore, the inherent biases, regarding sampling, in those
papers cannot be ruled out completely.
In the banking sector, very few constructs and relationships with digital payments have been
identified. Any future researcher may investigate more constructs and their relationships could be
considered for analyzing the impact on digital payment. Another interesting thing that could be done is
to divide the meta-analysis study according to the continents where the studies were conducted, and
then the results can be compared. Since the usage of digital payments has increased (businesswire.com,
2022) future research may include a meta-analysis of customer loyalty towards digital payments in
banks and their impact on the financial performance of banks.
Future researchers can also study the impact of cross-cultural differences in the satisfaction and
experience of digital payments. A comparison between the different continents or between developed
and developing countries regarding customer satisfaction and experience of digital payments can also
be done. The other socio-demographic factors like income levels, education levels, age, gender, etc can
also be studied in the future. Digital payments are a mix of internet banking, mobile banking, RTGS etc.
Future studies can compare these different modes and how the acceptance levels differed in all the
cases.
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