Posted: March 16th, 2022

ORIGINAL RESEARCH

ORIGINAL RESEARCH
ORIGINAL RESEARCH
published: 10 January 2022
doi: 10.3389/fpsyg.2021.634911
Edited by:
Angel Alberto Valdés-Cuervo,
Instituto Tecnológico de Sonora
(ITSON), Mexico
Reviewed by:
Jose Ramon Saura,
Rey Juan Carlos University, Spain
Mohd Nazri Bin Abdul Rahman,
University of Malaya, Malaysia
Shuiqing Yang,
Zhejiang University of Finance
and Economics, China
*Correspondence:
Shih-Chih Chen
[email protected]
Athapol Ruangkanjanases
[email protected]
Kwanrat Suanpong
[email protected]
Specialty section:
This article was submitted to
Human-Media Interaction,
a section of the journal
Frontiers in Psychology
Received: 29 November 2020
Accepted: 19 November 2021
Published: 10 January 2022
Citation:
Lin X, Suanpong K,
Ruangkanjanases A, Lim Y-T and
Chen S-C (2022) Improving
the Sustainable Usage Intention
of Mobile Payments: Extended Unified
Theory of Acceptance and Use
of Technology Model Combined With
the Information System Success
Model and Initial Trust Model.
Front. Psychol. 12:634911.
doi: 10.3389/fpsyg.2021.634911
Improving the Sustainable Usage
Intention of Mobile Payments:
Extended Unified Theory of
Acceptance and Use of Technology
Model Combined With the
Information System Success Model
and Initial Trust Model
Xin Lin1, Kwanrat Suanpong2*, Athapol Ruangkanjanases3*, Yong-Taek Lim4 and
Shih-Chih Chen5*
1 Northeast Electric Power University, Jilin City, China, 2 Chulalongkorn Business School, Chulalongkorn University, Bangkok,
Thailand, 3 Chulalongkorn University, Bangkok, Thailand, 4 Kunsan National University, Gunsan, South Korea, 5 National
Kaohsiung University of Science and Technology, Kaohsiung City, Taiwan
Under the background of global cross-border mobile commerce (m-commerce)
integration, the importance of cross-border payment research is becoming increasingly
prominent and urgent. The important value of this study is to empirically research the
influence power of key elements in using two different mobile payment (m-payment)
platforms in Korea. The extended unified theory of acceptance and use of technology
(UTAUT2) has been widely applied in various studies because of its strong interpretive
power. In Korea, there are a few empirical studies on Chinese users. Based on a survey
of 908 Chinese participants (486 WeChat Pay’s Chinese users and 465 Kakao Pay’s
Korean users) in Korea, this study is one application extending UTAUT2 by incorporating
multi-group and multi-model constructs: UTAUT2, information system success (ISS)
model, and an initial trust model (ITM), considering a multi-group analysis with some
mediating variables (payment difference). By comparing the two different payment
platforms’ characters, this manuscript provides a set of targeted measures to ensure
Chinese WeChat Payment platform decision-makers create effective long-term strategic
policies for cross-border m-payments in Korea, and eventually, benefit cross-border
m-commerce and economic cooperation in Southeast Asia.
Keywords: information system success model (ISS), extending the unified theory of acceptance and use of
technology (UTAUT2), sustainable usage intention, mobile payment, initial trust model
INTRODUCTION
Under the background of the global mobile commerce (m-commerce) integration, mobile
payments (m-payments) have become increasingly popular in Southeast Asia. Compared to
traditional cash payment means such as credit cards, mobile digital payment systems can help
consumers to complete various types of online payments through digital terminal devices without
Frontiers in Psychology | www.frontiersin.org 1 January 2022 | Volume 12 | Article 634911
Lin et al. Usage Intention of Mobile Payment
having to be restricted by time and location (Liébana-Cabanillas
et al., 2018). As innovation in payment technology, m-payments
are defined as “any payment method that uses a digital device
to activate, authorize, and confirm the exchange of transaction
values in exchange for products and services” (Kim et al., 2009).
For example, the rapid expansion of m-payment transactions
in China has been attributed to WeChat Pay, and the Korean
m-payment market has benefited from “third-party m-payment
platforms” such as Kakao Pay (Shao et al., 2019).
Mobile payment has brought about a historic technological
revolution in the field of financial payments, with far-reaching
social and economic impacts on the payment ecosystem in
Southeast Asia and the world at large. With the high growth
level of m-payment penetration in many economic entities,
m-payment not only brings convenience to consumers but also
increases the volume of business for many companies and
improves the overall transaction payment level of the relevant
economic entities (Fan et al., 2018).
Despite the numerous benefits described above, consumers
are still limited by significant trust factors in accepting
m-payment usage scenarios. For example, many traditional
consumers continue to worry about the security and
reliability of m-payment usage scenarios, because m-payment
involves privacy information such as the user’s property
account, credit card number, ID number, and account
flow amount (Kim et al., 2010b). Previous relevant studies
mainly analyzed the influence of various quality factors and
initial trust (IT) on user behavior in m-payment scenarios
(Liebana-Cabanillas et al., 2015). But, few studies empirically
analyzed the antecedents of both various quality factors and
IT.
Previous studies investigated the different factors affecting
users’ adoption of m-payments, but there are still deficiencies
to be filled. First, the research on m-payments is mainly
concentrated in the financially developed countries, such as
China and the United States (Fan et al., 2018), South Korea and
the United States (Jung et al., 2015), China and South Korea
(Lin et al., 2019), and India and the United States (Queiroz
and Wamba, 2019), but these studies are still unsystematic and
scattered. Second, globally, m-payment is a new technology.
Relevant studies mainly focus on empirical analysis of
consumers’ early willingness to adopt this technology,
and few studies examine the stage after the use of new
technology (Lin and Wu, 2021; Lin X. et al., 2021; Lin X. C.
et al., 2021). Third, m-payment can be divided into two
categories: short-range payment system (to pay for products
or services by connecting electronic digital terminals to
4G network) and short-range payment system (to pay for
products face to face in physical stores through payment
technologies such as short-range communication and near-field
communication (NFC), and short-range payment through
mobile phones (Gerpott and Meinert, 2017). Previous relevant
manuscripts either only studied the willingness to adopt
m-payment with no difference between the two technologies
(de Kerviler et al., 2016) or only studied the willingness to
use a proximity payment system (Khalilzadeh et al., 2017;
Verma et al., 2020).
Among the above-mentioned major e-commerce
marketplaces, China’s e-commerce market was valued at
USD 633.9 billion in 2018, with m-payments being one of the
most popular payment modes. By 2023, total revenue is expected
to grow by 11.6%, reaching total revenue of USD 10,945 billion.
This means China’s e-commerce area is the fastest growing
economic region and will remain in a leading position until
2023. Clearly, the trend of the transfer of e-commerce purchasing
power from the European Free Trade Area, the United States
Economic Area to Asia-Pacific Economic Cooperation (APEC)
has begun. Due to the growth of APEC’s e-commerce purchasing
power and the popularity of the Internet, especially gaining access
to mobile devices, more consumers are utilizing m-payments
(CNNIC, 2019).
The continuous growth of Korea’s m-payments is the main
driving force for the continued growth of Korea’s, and even the
world’s economy. Faced with Chinese m-payment consumers’
exponential growth and driven by the geographical advantages
and high e-commerce penetration of APEC and China, Korean
m-payment service providers are gradually realizing simply
retaining existing Korean customers is not enough. To improve
the Chinese users’ usage attitude effectively and rapidly toward
Korean m-payment platforms, it is essential to concentrate on
vital elements affecting the usage intention of Chinese consumers
in using Korean m-payment systems.
Considering the above research gaps, this study developed
the extended unified theory of acceptance and use of technology
(UTAUT2) theoretical model integrating information system
success (ISS) model and ITM for m-payment usage attitudes
in China and Korea and tested the relationship between the
constructs of the related models with a sample of 486 Chinese
and 465 Koreans. This theoretical model not only studies the
antecedents such as trust, quality, and payment conditions but
also assumes the relationship between these constructs and their
impact on use intention. The main purpose of this study is to
fill the deficiencies in the following three aspects. First, this study
focuses on several key antecedents to enhance the willingness to
use third-party m-payment systems in China and South Korea.
Second, this study examines the mediating effect between various
antecedents and continuous intention of PE in the third-party
m-payment platforms of China and South Korea. Finally, this
study empirically analyzes the differences in the impact of crosscultural comparison between the UTAUT2 integrated ISS model
and ITM. The expected results of the manuscript can effectively
fill the shortcomings of the existing literature and provide a
more comprehensive theoretical and practical contribution to
the development of the third-party m-payment system in China
and South Korea.
The rest of this article is organized as follows: the second
part comprehensively introduces the concept of m-payments and
the existing research literature of the three information system
models (ISMs) in the article; the third part expounds on the
research theory and methods, research assumptions, puts forward
the corresponding UTAUT2 integration model, and theorizes
as to the potential relationship between facets; the fourth part
introduces the research methods, data collection, analysis, and
results, and discusses the results of statistical analysis; and the
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Lin et al. Usage Intention of Mobile Payment
fifth part summarizes the research results, research contributions,
practical impact, and suggestions for future research directions.
BACKGROUND AND LITERATURE
REVIEW
Mobile Payment
Mobile payment refers to any online transaction explicitly
initiated, granted, registered, and confirmed through mobile
terminal equipment (Venkatesh et al., 2012; Dahlberg et al.,
2015). Worldwide m-payment has stimulated a subversive
revolution in the socio-economic field and has had a profound
historical influence on the global payment system. The
m-payment transactions are usually completed over long
distances via network terminal electronic devices in the form of
short message delivery, wireless billing, cellular networks, useroperated bills, and credit cards. As a result, m-payment systems
can efficiently process m-economical transactions by various
wireless technologies. The m-payment has a high penetration
rate around the world, not only allowing convenient payment
by consumers but also bringing economic returns to reduce
transaction costs for companies that provide products and
services and rapidly improving the overall national service levels
for financial transactions (Phonthanukitithaworn et al., 2016).
According to the relevant literature, m-payment transactions
contributed 4.6% toward global GDP in 2018, with a specific
economic value of USD 3.9 trillion (CNNIC, 2019). Wang
et al.’s (2017) research showed that cross-border m-payment
between different countries played a significant role in promoting
international trade integration. In particular, the development of
cross-border m-payment platforms helped promote m-payment
transactions and payments in APEC. In 2020, China’s crossborder transactions’ revenue is expected to be USD1.71 trillion,
accounting for 37.6% of China’s foreign trade import and export
volume. In 2023, global m-payment technology will grow to
USD 4.8 trillion (4.8% of global GDP) (CNNIC, 2019). The
integration of m-payment businesses even makes the sustainable
development of the Southeast Asian cross-border consumer
market in the process of rapid integration possible.
From what has been mentioned above, it is not difficult
to make the following observations. First, in the context of
the large-scale popularization of global m-payment platforms
and rapid preemption of multinational markets, Korean
m-payment platform service providers increasingly feel simply
maintaining existing Korean customers is not enough. Second,
a comprehensive analysis of the elements affecting the Korean
users’ usage intention of Korean m-payment systems can
increase the willingness of more consumers to use Korean
m-payment platforms. Consequently, scholars have been trying
to determine the elements influencing the willingness to use
different m-payment systems. In the performance comparison
process between Kakao Pay and Naver Pay, Lee and Kim
(2017) revealed Kakao Pay showed limited applicability due
to insufficient alliance merchants and also revealed a lack of
reliability due to payment errors. The results showed Naver Pay
also needed to improve reliability and greatly reduce errors in the
early use and later payment process of the Naver Pay program.
Due to the need for a tedious payment operation and a large
amount of consumer personal information, it is easy to make
users feel weary. By analyzing the response of the international
cross-border consumer market of Kakao Pay and Samsung
Pay, Son and Kim (2018) discovered management strategies
can ensure the sustainable development of rapid technology.
By comparing the conversion intention between Samsung pay
and Kakao Pay, Lee et al. (2017) firmly believed the more
benefits obtained after the conversion of different m-payment
platforms, the stronger the convenience experience brought by
the conversion of different m-payment platforms, the safer the
conversion process, the stronger the conversion intention of
consumers between different m-payment tools will be.
Additionally, similar to Kakao Talk, WeChat Pay binds the
WeChat account to the user’s bank card and uses NFC or
QR for payment services. This can be easily viewed online
at any time through the APP via WeChat’s official account.
Other previous studies indicated some scholars used various
representative structural equation models as the basic structure
of the research, including technology acceptance model (TAM2),
UTAUT, and UTAUT2, and any element that might influence the
willingness to use m-payments (Alalwan et al., 2018; LiébanaCabanillas et al., 2018). This study’s conclusion further confirms
the research significance of this manuscript. In other words, it
is necessary for this manuscript to integrate the models and
analyze the Chinese and Korean m-payment platforms and to
better promote the win-win cooperation of Chinese and Korean
m-payments.
Extended Unified Theory of Acceptance
and Use of Technology Model
How to classify and explain the influencing elements affecting
the consumers’ voluntary usage intention of new m-payment
platforms has become the most important study area of
information technology systems (Swanson, 1988). Davis (1989)
and Venkatesh and Davis (1996) successively put forward
some theoretical structures of information technology, for
example, the TAM and UTAUT model, to explain the elements
directly affecting the willingness of a new m-payment platform.
Venkatesh et al. (2012) expanded UTAUT from a perspective
of new technology perception of users by absorbing price
value (PV), hedonic motivation, and habit and finally proposed
UTAUT2 that further improved the interpretation ability of
UTAUT. Correspondingly, other researchers (Martins et al.,
2014) also suggested in future research, a more complete
UTAUT2 model must be used for further analyzing the relevant
factors affecting the consumer usage intention of m-payment.
The UTAUT2 model has been applied to analyze and test
influencing elements of m-commerce (Chopdar et al., 2018),
m-transactions (Farah et al., 2018), and m-banking (Khan
et al., 2017) usage intention. Studies also showed UTAUT2 was
an effective model for understanding the usage intention of
m-payment (Wu and Lee, 2017). The existing research focused
on the integration of various theoretical structures to study the
usage willingness of new information technology. Particularly,
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Lin et al. Usage Intention of Mobile Payment
numerous studies on the application of m-banking integrated
UTAUT2 with other theories (Lin et al., 2019), showing the
necessity and importance of using other theories to make up for
the theoretical gaps in information technology.
Although the majority of the literature mostly used age,
gender, and experience as moderating variables, few academics
attempted to improve the model with other structures to
improve its accuracy in the m-payment area (Baptista and
Oliveira, 2015). Therefore, recent research analysis (Oliveira
et al., 2014) suggested integrating distinctive models to obtain
a more complete view to accomplish their research goals. This
research will use UTAUT2 in combination with other important
information technology models as a theoretical framework to
evaluate the elements influencing consumer acceptance of the
m-payment platform in Korea. Consequently, the next study
should research integrating various information technology
models into UTAUT2 and then analyze which factors can
influence the m-payment platform users’ usage willingness.
DeLone and McLean’s Information
System Success Model
Information system success explains how system and information
qualities affect users’ usage willingness and user satisfaction (US),
leading to the influence of individual willingness (DeLone and
McLean, 1992). Moreover, DeLone and McLean (2003) improved
an upgraded model that incorporated quality of service. From
then on, the upgraded ISS was extensively applied to assess the
usage willingness of dissimilar m-payment systems. In our article,
ISS is applied to confirm some promoters of usage willingness
in the m-payment system. Tam and Oliveira (2017b) proposed
ISS moderated by the cross-cultural dimension, revealing the
relationship between personal performance and m-banking is
mediated by diverse-cultural effects on the usage of m-banking.
Hence, mobile bank managers were provided new insights
from the mediating influence of cultural effects, which is very
important to recollect former users and further attract potential
users by applying strategies.
Mobile payment is an advanced information payment
technology, which has rarely been studied by researchers,
especially the different groups of WeChat Pay’s and Kakao Pay’s
customers in Korea. Accordingly, ISS should be utilized and
generalized as a theoretical model of m-payment with other
information models.
Initial Trust Model
Initial trust emphasizes the “usage intention of the customer
to take advantage of trust in satisfying a demand without
pre-experience, or reliable, profound information” (Kim and
Prabhakar, 2004). Accessibility, adaptability, and potential profits
(the function of service utility) can be attributed to the
foundation of IT (Koufaris and Hampton-Sosa, 2004). Therefore,
Kim et al. (2009) found a model using ITM, whereby the IT of the
mobile bank could be explained by a structural guarantee, trust
tendency, and corporate reputation.
Some scholars (Kim, 2012; Zhou, 2014) contended IT has been
proven to be an important factor influencing the first adaptation
decision of consumers because the stable usage intention can be
formed only after the IT is established. Three categories of the
element are classified as follows: the first element is linked with
the features of m-payment. The usage intention of consumers
will depend on IT to a certain extent. Of course, structural
factors are effective in influencing IT (Lin, 2011). The second
element is closely related to the reputation of the company.
Corporate reputation is also an important factor affecting IT
because it reduces the risk of potential price information
asymmetry and forms an after-sales guarantee after completing
the m-payment transaction process (Li et al., 2008). The third
element is combined with users’ trust tendency. Personal trust
tendency reveals a psychological trend of users, which also has
an important impact on IT.
The initial trust model was used in various researches
to judge or predict the usage intention of the m-payment
system, for instance, m-shopping, m-banking, m-commerce, and
m-payments (Shankar and Jebarajakirthy, 2019).
Research Model
As mentioned above, originally suggested by DeLone and
McLean (1992), system quality is interpreted as the quality
expressed in the complete function of the system, therefore it
can be perceived by individuals (DeLone and McLean, 2003).
Furthermore, Venkatesh et al. (2012) interpreted performance
expectation (also performance expectancy, PE) as “the extent to
which technology will Help customers when completing certain
tasks.” Obviously, powerful navigation, a clear outline, and a
responsive interface may be crucial for adopting m-payments.
Thus, system quality directly affects PEs.
Bhattacherjee (2002) pointed out if consumers can experience
better system performance, it will significantly increase US and
thus generate continuous use intent. Notably, the better the
system quality, the greater it can markedly improve US, and
the more it can make up for the limitations of mobile device
size. Pyo and Kim (2014) found Kakao Talk users are more
interested in ease of use and high-speed accessibility, and system
quality significantly impacts satisfaction. Sharma (2019) pointed
out users may not trust the m-payment platform’s ability to
provide high-quality system services, which may make it harder
to use the device, according to a group of users who are not
able to meet the user’s expectations. The following hypotheses
are given:
H1a: System quality significantly influences user satisfaction.
H1b: System quality significantly influences performance
expectancy.
Information quality (IQ) includes comprehensibility,
accessibility, sufficiency, accuracy, feedback report, and other
characteristics (Sharma, 2019). Clearly, information quality
is a vital element to determine the usage intention toward
information mobile technology. DeLone and McLean (2003)
pointed out that information quality was also a crucial part
of the m-payment information platform, which was the most
basic communication ability of Internet buyers and sellers.
Interpreted as the inherent quality of the information itself,
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Lin et al. Usage Intention of Mobile Payment
such as accuracy, reliability, and completeness, information
quality significantly influences PE (Tam and Oliveira, 2016).
The m-payment consumers always want to gain the whole
transaction records, accurately, and timely. After all, users
will also be concerned with the m-payment transaction is
complete. If there is no receipt, the payer cannot obtain
proof of the payment transaction, so it is difficult to ask
for a refund when the goods are not ideal. The m-payment
consumers generally believe a lack of transaction-related
information is risky. They are not sure whether the payment
has occurred or not, and they are not sure about the
payment (Mallat, 2007). This extra difficulty of tracking
past payments may additionally make consumers feel service
suppliers do not provide sufficient functional investment in
m-payments. The m-payment consumers’ PE of consumers
will be influenced.
Information quality, on the other hand, may also affect
customer satisfaction. Existing studies reported how IQ affected
US, m-banking, and the virtual community (Elliot et al., 2013).
The following hypotheses are given:
H2a: Information quality significantly influences user
satisfaction.
H2b: Information quality significantly influences performance
expectancy.
Service quality (SQ) refers to some features of service aids
(such as responsiveness, credibility, simplicity, and technical
ability, etc.) obtained by consumers from the information transfer
department and technical support department (DeLone and
McLean, 2003). In addition, service quality will also affect the
US wireless business transactions (Gounaris et al., 2010), virtual
travel societies, and mobile instant messaging (Elliot et al., 2013).
Clearly, information systems cannot be fully evaluated
effectively without reference to the quality of service. All quality
factors, including SQ, will help users to evaluate their PE
correctly. The following hypotheses are given:
H3a: Service quality significantly influences user satisfaction.
H3b: Service quality significantly influences
performance expectancy.
DeLone and McLean (2003) defined US as “the degree to
which an application platform can create value for internal
or external consumers.” This means US reflects consumers’
subjective feelings accumulated in full cooperation with mobile
suppliers (San-Martín et al., 2013). Furthermore, prior research
proposed satisfaction is a decisive determinant of the willingness
to use continuously (Zhou, 2014; Lee et al., 2015).
The US of the ISS model reveals the positive correlation
between PE and usage willingness (Au et al., 2008). According to
previous literature, PE is positively correlated with the relevant
models (DeLone and McLean, 1992). UTAUT2 also explained
that the influence of enhancing US to user’s usage willingness was
clearly significant, and US was also affected by PE. Therefore, the
increased PE will positively increase US and ultimately influence
the acceptance intention.
On this basis, Tam and Oliveira (2016) revealed an affirmative
correlation between US and m-banking service intention is
established by integrating UTAUT2 into ISS. They also confirmed
service quality directly affects the performance, and US by
confirming satisfaction is the consumers’ feeling from the
total qualities provided by m-payment provider qualities in
the wireless economic commerce environment. The following
hypotheses are proposed:
H4a: Performance expectancy significantly influences
user satisfaction.
H4b: User satisfaction significantly influences usage intention.
Structural assurance (SA) refers to the trust structure
framework based on institutions, which is determined by laws,
credit guarantees, and the industry regulations existing in a
certain environment (McKnight et al., 1998). Judged by the above
views, we conclude that IT comes from people’s sense of security
in the process of online banking transactions under the dual
effects of relevant social institutions, industry laws, government
supervision, contract and offer, and the online structure of
online banking. If the information of the counterparties is
incomplete, the role of these structural security measures is
essential to consumers’ IT.
In the m-payment transaction environment, a structural
guarantee ensures the reliability of financial transactions, the
protection of personal privacy, and transaction confidentiality.
With promises, deals, rules, contracts, laws, managed services,
and other forms of structural guarantees, the IT in a transactional
relationship can be enhanced (McKnight et al., 2004). It can
improve the user’s IT, considering that the user wants to
be guaranteed and avoid the risks and uncertainties affecting
information, finance, etc. (McKnight et al., 2002).
Of course, the structural guarantee has been proven to affect
the trust of the bank (Moin et al., 2015), electric commerce
(Alqatan et al., 2016), and m-banking (Yu and Asgarkhani, 2015),
which regards manuscript reports as structural guarantees. The
following hypothesis is suggested:
H5a: Structural assurance significantly influences initial trust.
Personal propensity to trust (PPT) refers to users’ natural
tendency to trust new technology. Consumers with a natural
trust tendency have a greater tendency to trust mobile
banks (McKnight et al., 2002). Personal trust tendency is
an attribute characteristic and experience formed by one’s
cultural background and psychology (Lee and Turban, 2001).
When people make judgments about services without prior
knowledge, those who are more inclined to trust may think
services are reliable. Many studies on the IT of online
banking transactions show the individual’s trust tendency may
positively influence the establishment of trust in m-banking
(Gefen, 2000).
Therefore, personal trust often does not have the experience
of dealing with the trustee and relies on trust expectations.
In the IT situation, personal trust significantly impacts IT.
Personal trust tendency is a kind of trust formed from
small to large. This kind of personal trust is generally
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Lin et al. Usage Intention of Mobile Payment
considered to be direct and dependent behavior (Kim and
Prabhakar, 2004). Research by Wu and Lee (2017) reveals
the personal trust tendency of customers will positively
influence adoption willingness when a company offers reliable
and accurate services. The following hypothesis is stated
as follows:
H5b: Personal propensity to trust significantly influences
initial trust.
Firm reputation (FR) refers to a company’s ability to provide
an effective service to its customers and the reliability of
customers’ participation in the company’s business (McKnight
et al., 1998). The company’s reputation improves consumers’ trust
in its new services and helps to comprehensively improve the
trust of potential consumers in new service transactions (Kim
et al., 2009). Therefore, enterprise reputation is one important
element of IT. It directly influences users’ willingness to adopt
related services.
The existing research shows that the most important thing is
customers initially trust the reputation of the enterprise, rather
than taking trust behavior according to the actual scale of the
enterprise (Wu and Lee, 2017). Therefore, many well-known
enterprises actively provide after-sales support for customers,
timely publicize, and improve the high-tech image of the
enterprise, and persuade consumers to believe that the wellknown enterprise has sufficient technical strength and core
competitive advantages, thus greatly improving the consumer
trust of the enterprise’s mobile operation platform. The following
hypothesis is submitted:
H5c: Firm reputation significantly influences initial trust.
Initial trust points out the user’s intention to bear unexpected
losses to meet the demand, without the use of experience or
reliable and referential information (McKnight et al., 1998; Kim
and Prabhakar, 2004). IT guarantee consumers eventually reach
the desired outcome (Pavlou et al., 2003). Especially in the
m-payment environment, the trust of consumers strengthens
the individual’s expectation of the product’s usefulness or
performance (Bock et al., 2012). Prior research also indicated that
trust would promote volunteering that influences the perceived
usefulness of the web platform (Saura et al., 2020). If the
service provider is not trusted to provide dependable m-payment
services, the positive adopters are more likely to suffer losses
after adopting m-payments because the service supplier is
speculative. Therefore, the IT factor may positively affect the
PE of consumers (Dishaw and Strong, 1999). The following
hypothesis is submitted:
H6a: Initial trust significantly influences performance
expectancy.
Kim et al. (2010a) studied the impact of IT on m-payment
usage acceptance. They confirmed the elements of IT, together
with the relative benefits of m-payments, structural guarantees,
corporate reputation, and users’ propensity to trust. Hung
et al. (2012) showed that trust played a critical role in mobile
shopping’s persistent intention. Due to the mobile networks’
vulnerability, the mistrust of mobile providers, and m-payment
systems, m-commerce has greater doubt and hazards. Viruses and
Trojans can also infect mobile platforms. Under the background
of m-payments, a purchase is influenced by safety and trust issues,
so more risk and more mistrust should be considered (Chen,
2013). M-payment consumers have reason to worry whether
the m-payment platform can safely transfer and store their own
credit card accounts, passwords, location privacy, and other
privacy information (Mamonov and Benbunan-Fich, 2015).
Therefore, we believe that IT may influence the sustainability of
m-transactions and thus make the hypothesis as follows:
H6b: Initial trust significantly influences usage intention.
Performance expectancy refers to the degree to which a
user considers adopting an m-platform contributes to his work
performance (Venkatesh et al., 2003). In previous literature, if
individuals figure out that the profit of using new technology
outweighs the disadvantages, they will be more inclined to accept
and continue to adopt the technology (Venkatesh et al., 2012).
Unambiguously, in a large number of m-payment scenarios, PEs
are found to directly affect the user’s usage intention of the
relevant information system (Baptista and Oliveira, 2015).
In the m-commerce environment, consumers will judge the
effectiveness of using the m-payment application platform to
help complete their business transactions. Clearly, PE is one
critical element in the process of consumer Assessment. Many
previous studies (Faqih and Jaradat, 2015) explicitly support
the positive influence of the willingness to use m-commerce.
In addition, more research results show that PE plays a critical
role in affecting the willingness to use m-payment platforms
(Morosan and DeFranco, 2016). The hypothesis is:
H7: Performance expectancy significantly influences usage
intention.
Effort expectancy’s (EE) definition (Venkatesh et al., 2003) is
“the degree of ease associated with using the system.” In a large
number of studies involving UTAUT2, the expected workload has
been generally considered a vital precondition for the expected
work (Venkatesh et al., 2003, 2012; Slade et al., 2015). That is,
the influencing factors of consumers’ willingness to accept a new
platform are not only the benefits of the platform itself, but also
the difficulty and effort of using the system. The ease of access to
a system tends to stimulate users to adopt it (Oliveira et al., 2014;
Dwivedi et al., 2019). Under the background of m-payments, the
EE will be regarded as the capability to carry out a certain mobile
business function with the least amount of work. Reasonable
work expectations can make consumers feel very comfortable
when they carry out the m-commerce transaction.
In addition, the particularity of m-banking also forces
system operators to have some basic finance knowledge and
related operational skills. Therefore, efforts are expected to
effectively influence determining customers’ willingness to
use an m-payment platform system (Alalwan et al., 2016).
Many m-banking studies demonstrated the factors captured
by effort expectations positively affect measuring customers’
usage intention of m-banking (Gu et al., 2009). The interaction
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Lin et al. Usage Intention of Mobile Payment
interface, function design, and computing power of m-banking
can directly influence the consumers’ willingness to adopt. The
interaction interface, function design, convenient navigation, and
the computing power of m-banking can directly affect the user’s
willingness to adopt (Venkatesh et al., 2003; Kim et al., 2009).
Therefore, the following hypothesis is given:
H8: Effort expectancy significantly influences usage intention.
Social influence (SI) indicates some extension to which
platform users’ important social relations (e.g., family, friends,
or leaders) have faith in the new m-payment system should
be adopted (Venkatesh et al., 2012; Tam and Oliveira, 2016).
Social impact reveals the impact of individuals on the adaptation
of technology by their social relatives. Users often consider
the opinions of others when choosing a new technology.
Supposing the attitude of his social relatives is active, users will
accept it; on the contrary, negative attitudes will affect users’
decision not to adopt.
The social preferences and values coming from family
relatives, friends, and neighbors often profoundly change users’
views and opinions (Rana et al., 2017). Especially when the
present user intends to change from using one technical service
to another technical service, the user’s willingness to change will
be easily influenced by peers and influence of family members
(Baptista and Oliveira, 2015; Dwivedi et al., 2019). Under the
background of mass media dominating the online world (Kapoor
et al., 2018), the impact of social relations may not only continue
the usage intention of old technology but also guide users to new
technologies recognized by social relations (Williams et al., 2015).
Through multicultural surveys between Australia and Thailand,
Mortimer et al. (2015) found that even in different cultures,
social impact can become a significant element in the usage
intention of a new platform. Moreover, under the background of
Saudi Arabia’s electrical commerce, prior research also proves the
active influence of SI on the adoption willingness of m-banking
(Al-Husein and Asad Sadi, 2015). The hypothesis is given:
H9: Social influence significantly influences usage intention.
Facilitation (FC) reflects a positive and significant impact
of the infrastructure related to organization and technology on
the use of online banking, such as consumer expertise, related
operational skills, and platform resources (Venkatesh et al., 2003).
In fact, the enhancement of the willingness to use m-payments
requires online banking to train users to have specific operation
skills, provide service resources and basic hardware and software
conditions of high matching financial systems (Alalwan et al.,
2015). The necessary knowledge reserve and skill accumulation
play many roles in adopting m-banking services, thus affecting
the usage intention. Previous literature studies pointed out
convenience significantly impacts usage willingness (Alalwan
et al., 2016). The hypothesis is:
H10: Facilitating conditions significantly influence usage
intention.
Hedonic motivation’s (HM) definition mentions a degree
of enjoyment in the process of adopting m-banking, which is
a vital pretest factor affecting consumer willingness to use a
new technology (Van der Heijden, 2004); hedonic behavior is
essentially a non-functional and personality emotional variable,
which is completely based on consumers’ emotional cognition
(Malik et al., 2013). In other words, the pleasure gained from
using new technologies significantly promotes usage intention
(Alalwan et al., 2015). The higher the degree of entertainment the
mobile platform brought, the greater willingness customers will
have (Zhang et al., 2012).
In addition, many previous studies revealed hedonic
motivation was positive in predicting usage intention in various
m-payment technology application scenarios (Morosan and
DeFranco, 2016; Alalwan et al., 2017). When consumers find the
existing m-payment technologies bring enough effective comfort,
satisfaction, and pleasure, they often do not switch their use
intention to other competitive payment platforms (Karjaluoto
et al., 2010). Therefore, many research conclusions have revealed
the satisfaction or pleasure obtained by consumers from using
a specific m-payment platform is an extremely vital element of
the willingness to use the technology (Morosan and DeFranco,
2016; Alalwan et al., 2017; Dwivedi et al., 2019). Accordingly, the
hypothesis is submitted:
H11: Hedonic motivation significantly influences usage
intention.
The price value is a cognitive balance comparing the profits
from m-banking platforms and the financial price of adopting
m-banking services that consumers experience (Venkatesh et al.,
2012), including m-payment service operator costs, equipment
costs, after-sales costs, purchase and sale costs, and other factors.
PV is optimistic because the profits of adopting m-banking
outweigh the associated currency costs.
Since the profits of adopting m-bank are larger than
related economic value, the PV is positively correlated. As
Alalwan et al. (2017) indicated, the higher the PV level, the
more motivated customers are to continue to adopt a certain
technology. Further, after UTAUT2 introduced PV, prior research
pointed out that there was an important positive correlation
between PV and behavioral intention (Alalwan et al., 2017).
Therefore, considering the potential profits of adopting the
electrical commerce apps, consumers may reassess whether the
relevant transaction costs are reasonable. If the potential revenue
is significantly greater than the foreseeable cost of adopting
m-payment applications, consumers are more inclined to adopt
m-commerce solutions. Otherwise, users who cannot afford
to continue using the upgraded technology will not express
interest in continuing to adopt it. Given the above situation, this
hypothesis can be given:
H12: Price value significantly influences usage intention.
Habit (HA) reveals various outcomes of past practice, and
the frequency of previous acceptance behavior is reflected to
be one main element of current behavior (Ajzen, 2002). The
empirical research of Eriksson et al. (2008) reveals that a positive
correlation between online banking and customer habits is an
important factor for US users to accept online m-commerce.
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Lin et al. Usage Intention of Mobile Payment
Pavlou and Fygenson (2006) contended habits could be the
principal determinants of prior behavior.
Consumers are less likely to change acquired habits and
may resist any new and unfamiliar interactions with m-banks
(Chemingui, 2013); this research further leads consumers to
hesitate to adopt new apps or platforms, for instance m-payments
(Antón et al., 2013). Actually, as an unconscious factor, previous
experience, and habits largely hinder consumers’ intention to use
a new platform, because they tend to depend on past experience
rather than cognitive reasoning when making decisions (Zhang
et al., 2017). In this research, following the structure of UTAUT2,
habits have taken a positive role in motivating online banks to
take actions. Therefore, the hypothesis postulates:
H13: Habit significantly influences usage intention.
Khan et al. (2017) finally determined the moderating
effect of cultural factors by revealing a certain relationship
between UTAUT2 variables. Through an adjustment analysis and
segmentation test, Zhang et al. (2012) argues the use intention
model of m-payment and divides it into two cultures to determine
the regulatory role of different ethnic PVs in m-commerce.
The results reveal UTATU2 is in good agreement with the
data. Lee et al. (2017) found in the UTAUT model, the role
of government has an adjusting effect on the determinants
of adoption willingness. According to the above results, we
examined the moderating effect of different ethnic consumers
in an integrated UTAUT2 model. We assume there are some
dissimilarities, which adjust the influence of these determinants
on users’ intention to use. In the empirical investigation
of this manuscript, it is necessary to compare and analyze
WeChat payment in China and Kakaopay, the largest m-payment
platform in South Korea to study the subjective and objective
factors affecting the sustainable development of Kakao Pay as
comprehensively as possible. In the near future, Kakao Pay
and WeChat payment will provide remote payment services,
respectively, to support the m-commerce payment of up to 450
million Chinese and Korean consumers. Facing the growing
Chinese consumer group, the successful survival of the Korean
m-payment system is mainly affected by important core factors,
such as US, PE, compatibility between technical characteristics
and task requirements, etc. The cultural differences between
China and Korea will certainly affect the usage intention of
m-payment services by different consumers to some extent.
In the process of globalization of m-payment technology, the
importance of cross-cultural research is obvious. Thus, we test
the hypotheses:
H14: The impact of initial trust on usage intention differs
between WeChat Pay and Kakao Pay Chinese consumers.
H15: The impact of user satisfaction on usage intention differs
between WeChat Pay and Kakao Pay Chinese consumers.
H16: The influence of performance expectancy on usage
intention differs between WeChat Pay and Kakao Pay
Chinese consumers.
H17: The influence of effort expectancy on usage
intention differs between WeChat Pay and Kakao Pay
Chinese consumers.
H18: The effect of social influence on usage intention differs
between WeChat Pay and Kakao Pay Chinese consumers.
H19: The impact of facilitating conditions on usage
intention differs between WeChat Pay and Kakao Pay
Chinese consumers.
H20: The impact of hedonic motivation on usage
intention differs between WeChat Pay and Kakao Pay
Chinese consumers.
H21: The impact of price value on usage intention differs
between WeChat Pay and Kakao Pay Chinese consumers.
H22: The impact of habit on usage intention differs between
WeChat Pay and Kakao Pay Chinese consumers.
According to the above hypotheses development, we proposed
the research model for this study (as shown in Figure 1).
DATA COLLECTION AND RESULTS
The constructs in this study are measured using 7-point Likert
scales drawn and revised from existing studies (e.g., DeLone
and McLean, 2003; Venkatesh et al., 2003, 2012; Koufaris
and Hampton-Sosa, 2004). We employed the back-translation
procedure suggested by Brislin (1970), using focus groups
to ensure a match between the original wordings and their
translation. Subsequently the measurement items were translated
into English and double-checked for veracity of meaning from
Chinese to English by two native English speakers.
From the beginning of July to the end of August 2019,
through face-to-face interviews with experienced users coming
from Chinese students in Seoul University and local Kakao Pay’s
Korean experienced users, the survey was conducted for 8 weeks.
We used the two-process method (Anderson and Gerbing,
1988) to evaluate the collected data. In the first step,
convergence and discriminant validity are examined. Second,
the measurement model between the integrated structures is
evaluated. To test the fitting of the measured values and
structure modeling, the integration structure was tested using
all of the WeChat Pay and Kakao Pay’s Korean consumer data.
Hooper et al. (2008) offered model fitting cooperation as a fitting
index and proposed the following indexes as fitting indexes.
As the m-payment industry of the Seoul cultural business
circle and many universities is far more mature than other
regions in South Korea, we quickly obtained ideal samples.
Meanwhile, to ensure the measurement followed the direct
behavioral practice of the object, some participants not using
WeChat Pay and Kakao Pay were strictly excluded. Moreover, by
modifying the ambiguous part of the questionnaire, this research
guarantees each respondent can fully comprehend every question
of the questionnaire.
A survey of 1,200 questionnaires was issued and 1,143 copies
were collected (response rate 86.46%). After 192 responses
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Lin et al. Usage Intention of Mobile Payment
H4a
System quality
Information quality
Service quality
Structural assurances Personal
propensity to trust Firm reputation
Initial trust
User
satisfacition
Performance
expectancy
Effort
expectancy
Social
influence
Facilitating
conditions
Hedonic
Motivation
Price Value
Habit
Usage intention
Moderator:
Payment Difference
ISS
ITM
UTAUT2
FIGURE 1 | Research model.
were discarded, 951 samples (85.33%) were eventually used for
deterministic analysis (486 WeChat Pay’s Chinese consumers
465 and Kakao Pay’s Korean consumers) because of missing
key data or lack of corresponding m-payment experience.
The final data is sufficient to define the difference in the
middle of the two object groups, although the sample sizes are
not identical.
Every item was asked on a five-point Likert’s scale (Table 1).
For instance, “strongly reject” to 5, “strongly agree.” In the
subsequent empirical analysis, Cronbach’s a was applied to
calculate the reliability of the measurement method using
IBM SPSS 24.0 (IBM Corp., Armonk, NY, United States), and
construct validity was evaluated by examining the factor structure
and intrinsic relevance of each construct. To test the research
hypotheses, we used IBM Amos 24.0 (IBM Corp., Armonk,
NY, United States) and determined the causal relationship
between core variables through a significance value and standard
coefficient. For testing each hypothesis, the entire sample is
used to analyze the integration model before the hypothesis
verification test.
Reliability, Validity, and Measurement
Model
Three steps are required to evaluate the convergence effectiveness
of a measurement object against its related structures. First, the
reliability of each index is evaluated by means of standardized
load. Second, as a measuring item, Cronbach’s a and CR are
used to measure the reliability of composites. Third, extracted
average variance (AVE) measures the variance of variables caused
by measurement error relative to the variance.
According to Table 2, Cronbach’s a and CR are above
0.70 (Nunnally, 1994), indicating the optimal validity measures
explain the structure and higher level of comprehensive
consistency. Moreover, convergent validity was measured by
three dimensions of indicators: the standardized loadings
signifying the relationship between some underlying elements
and every indicator was statistically above 0.7, the Cronbach’s a
values were significant at greater than 0.7 for the reliability of the
integrated construct (Nunnally, 1994; Hair et al., 1998); each AVE
value was greater than 0.5 (Fornell and Larcker, 1981).
In Table 3, discriminant validity indicates the difference
between one principle and its related indicators and the second
principle and its related indicators (Bagozzi et al., 1991). Fornell
and Larcker (1981) found discriminant validity must be tested
by evaluating the square root of each variables’ AVE in each
construct and their correlation coefficients among other models.
Table 3 shows for each data compared with the correlation
between one structure and another, the variance between
structures and each AVE’s square root is larger than any related
correlation coefficient, pointing to good discriminant validity
of each criterion (Fornell and Larcker, 1981). The correlation
between constructs was exceeded by the diagonal values, proving
the satisfactory construct validity of our measurement tool.
The IBM Amos 24.0 program (IBM Corp., Armonk, NY,
United States) was utilized to evaluate the measurement and
structural models of this research. The $2/d.f.s are 1.299 and
1.408, GFIs are 0.927 and 0.919, AGFIs are 0.917 and 0.910,
Frontiers in Psychology | www.frontiersin.org 9 January 2022 | Volume 12 | Article 634911
Lin et al. Usage Intention of Mobile Payment
TABLE 1 | Sample characteristics (entire samples).
Characteristics Frequency Percentage (%) WeChat Pay’s Kakao Pay’s
Gender Male 433 45.53 219 45.06% 214 46.02%
Female 518 54.47 267 54.93% 251 53.98%
Age Below 20 93 9.80 55 11.30% 38 8.20%
20–30 391 41.11 183 37.65% 208 44.73%
30 –40 351 36.90 182 37.45% 169 36.34%
40 –50 65 6.80 35 7.20% 30 6.50%
Over 50 51 5.40 31 6.40% 20 4.30%
Education High school student/resident 91 9.60 51 10.50% 40 8.60%
College student/student 417 43.80 207 42.60% 210 45.20%
Graduate school or higher 443 46.60 228 46.90% 215 46.20%
Experience Yes 951 100.00 486 100.00% 465 100.00%
NFIs are 0.949 and 0.943, CFIs are 0.988 and 0.983, IFIs are
0.988 and 0.983, RFIs are 0.944 and 0.939, PGFIs are 0.816 and
0.824, PCFIs are 0.898 and 0.910, PNFIs are 0.862 and 0.873,
RMRs are 0.050 and 0.061, and RMSEAs are 0.018 and 0.021.
The results from the measurement and structural models support
this association for each model. Twenty research hypotheses
presented in this manuscript were tested by scanning electron
microscope (SEM). For the parsimonious fitting index, the
acceptable fitness minimum is exceeded here, which is a standard
value. All the fitting indexes show the fitting results of the
analyzed samples and the integrated model are satisfactory.
Hypothesis Verification
After determining the measurement suitability and organization
of the combined model, the structure was analyzed with Chinese
samples and the Chinese path coefficient was evaluated as shown
in Table 4. Judging by the p-value, 4 paths of the 20 paths (H3a,
H3b, H6b, and H8; p-value of > 0.05) were rejected, and the other
16 paths proved statistically positive.
TABLE 2 | Convergent validity and reliability (entire samples).
Construct Indicators Standardized
loading
Cronbach’s
a
Composite
reliability
AVE
SYQ SYQ 1-4 0.768–0.888 0.889 0.890 0.669
IQ IQ 1-4 0.821–0.858 0.900 0.901 0.694
SEQ SEQ 1-4 0.799–0.866 0.903 0.904 0.702
US US 1-4 0.805–0.861 0.899 0.900 0.691
PE PE 1-4 0.777–0.865 0.894 0.896 0.683
EE EE 1-4 0.810–0.871 0.905 0.906 0.706
SI SI 1-4 0.772–0.873 0.901 0.902 0.699
FC FC 1-4 0.802–0.862 0.898 0.899 0.690
HM HM 1-4 0.808–0.848 0.894 0.896 0.683
PV PV 1-4 0.809–0.853 0.897 0.897 0.686
HA HA 1-4 0.810–0.864 0.896 0.897 0.686
SA SA 1-4 0.816–0.862 0.906 0.907 0.710
PPT PPT 1-4 0.807–0.868 0.903 0.904 0.701
FR FR 1-4 0.818–0.836 0.900 0.901 0.696
IT IT 1-4 0.844–0.890 0.918 0.926 0.757
UI UI 1-4 0.760–0.860 0.889 0.889 0.667
Chinese consumers’ usage intention revealed by ITM
(b = 0.057), US (b = 0.351), PE (b = 0.291), EE (b = 0.020),
SI (b = 0.189), FC (b = 0.107), HMM (b = 0.269), PV
(b = 0.079), and HA (b = 0.285) explain 75.1% of the variance in
adoption willingness. The influence on Chinese users shows the
antecedent variables of the ITM, ISS model, and UTAUT2 model
account for 61.7, 62.6, and 68.1% of the variance, respectively,
which are related to the 75.1% explanatory ability of the
comprehensive structure on use willingness. The measurement
and structural model results are given, and a comprehensive
model analysis is carried out with Kakao pay’s experience
consumers sample as an example. Kakao Pay path coefficient
between the basic hypotheses of the comprehensive model
was properly evaluated. Judging by their respective p-values,
5 paths of these 15 paths (H3a, H3b, H4b, H7, and H8;
p-value > 0.05) were unqualified, and the rest of the paths are
statistically positive.
Kakao Pay consumers’ usage intention predicted ITM
(b = 0.509), US (b = 0.061), PE (b = 0.050), EE (b = 0.040), SI
(b = 0.213), FC (b = 0.278), HMM (b = 0.099), PV (b = 0.330),
and HA (b = 0.097) explain Kakao Pay’s Korean consumers’ usage
willingness for 76.7% of the explained variance. The influence of
Kakao Pay’s Korean consumers shows the prerequisites of ITM,
ISS, and UTAUT2 can explain the variance of 60.0, 64.1, and
66.8%, respectively, and the explanatory power of these three
variables for the comprehensive model is 76.7%.
The analysis outcomes of the whole dataset are shown in
Table 5. There are four paths (H3a, H3b, H6b, and H8),
p-value > 0.05) not supported, and the other paths are
significantly below the 0.05 level. Table 5 lists the features of the
causal path, including the coefficients of this integrated model
and the hypothesis test results. Table 5 demonstrates the entire
dataset supports this integrated structure.
In Table 4, the comprehensive structure was examined by
WeChat Pay’s Chinese consumer samples in Korea, showing
the integrated model is supported. According to the result of
Table 4, four paths (H3a, H3b, H6b, and H8; p-value > 0.05)
are not supported, and the rest of the paths are significant
below the 0.05 level. Table 4 normalizes the path coefficients,
lists the causal path’s characteristics, and confirms the results
of the hypothesis model. Moreover, the comprehensive model
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Lin et al. Usage Intention of Mobile Payment
TABLE 3 | Discriminant validity (entire sample).
SYQ IQ SEQ US PE EE SI FC HM PV HA SA PPT FR IT UI
SYQ 0.82
IQ 0.29 0.83
SEQ 0.27 0.37 0.84
US 0.54 0.62 0.36 0.83
PE 0.56 0.55 0.32 0.69 0.83
EE 0.11 0.19 0.14 0.19 0.11 0.84
SI 0.14 0.19 0.12 0.20 0.14 0.44 0.84
FC 0.16 0.20 0.17 0.25 0.16 0.46 0.42 0.83
HM 0.16 0.16 0.11 0.19 0.13 0.41 0.41 0.49 0.83
PV 0.08 0.18 0.10 0.19 0.15 0.39 0.40 0.48 0.38 0.83
HA 0.11 0.24 0.16 0.22 0.17 0.44 0.42 0.50 0.40 0.45 0.83
SA 0.22 0.28 0.22 0.32 0.37 0.22 0.24 0.27 0.20 0.22 0.30 0.84
PPT 0.16 0.17 0.16 0.22 0.30 0.22 0.20 0.24 0.20 0.20 0.24 0.51 0.84
FR 0.21 0.23 0.18 0.26 0.34 0.21 0.24 0.26 0.23 0.23 0.29 0.53 0.50 0.83
IT 0.26 0.26 0.22 0.36 0.55 0.23 0.23 0.28 0.25 0.25 0.30 0.70 0.67 0.68 0.87
UI 0.22 0.28 0.14 0.44 0.45 0.38 0.45 0.48 0.44 0.49 0.47 0.28 0.25 0.25 0.44 0.82
TABLE 4 | Results of hypotheses tests (WeChat Pay’s Chinese consumer’s
sample in Korea).
Hypothesis Route T-Value Path coefficients
H1a SYQ ! US 5:237 0:260***
H1b SYQ ! PE 10:498 0:482***
H2a IQ ! US 6:525 0:324***
H2b IQ ! PE 10:269 0:459***
H3a SEQ ! US 0:641 0:025
H3b SEQ ! PE 0:213 0:008
H4a PE ! US 7:006 0:415***
H4b US ! UI 5:589 0:351***
H5a SA ! IT 7:597 0:347***
H5b PPT ! IT 7:719 0:360***
H5c FR ! IT 7:410 0:338***
H6a IT ! PE 10:417 0:438***
H6b IT ! UI 1:350 0:057
H7 PE ! UI 4:162 0:291***
H8 EE ! UI 0:535 0:020
H9 SI ! UI 5:168 0:189***
H10 FC ! UI 2:645 0:107**
H11 HM ! UI 7:023 0:269***
H12 PV ! UI 2:148 0:079*
H13 HA ! UI 7:082 0:285***
*p-value < 0.05; **p-value < 0.01; and ***p-value < 0.001.
was analyzed with Kakao Pay’s Korean consumer samples in
Korea (Table 6).
Table 6 normalizes the path coefficients, lists the causal path
characteristics, and confirms the results of the hypothesis model.
The empirical analysis results of Kakao Pay’s Korean consumer
samples are shown in Table 6, confirming the existence of the
comprehensive model. Taking Kakao Pay’s Korean consumers’
samples as an example, five paths (H3a, H3b, H4b, H7, and
H8) are not supported, and the rest of the paths are significant
below the 0.05 level.
TABLE 5 | Results of hypotheses tests (all samples).
Hypothesis Route T-Value Path coefficients
H1a SYQ ! US 7:400 0:237***
H1b SYQ ! PE 13:084 0:392***
H2a IQ ! US 10:469 0:351***
H2b IQ ! PE 12:512 0:377***
H3a SEQ ! US 1:609 0:042
H3b SEQ ! PE –0:270 –0:007
H4a PE ! US 10:129 0:395***
H4b US ! UI 2:351 0:108*
H5a SA ! IT 12:539 0:375***
H5b PPT ! IT 11:623 0:332***
H5c FR ! IT 11:217 0:331***
H6a IT ! PE 13:441 0:370***
H6b IT ! UI 2:549 0:087*
H7 PE ! UI 4:332 0:225***
H8 EE ! UI –0:122 –0:004
H9 SI ! UI 5:144 0:168***
H10 FC ! UI 2:330 0:088*
H11 HM ! UI 4:246 0:143***
H12 PV ! UI 6:257 0:211***
H13 HA ! UI 3:858 0:136***
*p-value < 0.05; **p-value < 0.01; and ***p-value < 0.001.
Analysis of the Differences in Path
Coefficients Between WeChat Pay’s and
Kakao Pay’s Korean Groups
The research also studies the mediating effect between WeChat
Pay’s Chinese users and Kakao Pay’s Korean users’ groups
in Korea. There are two advantages in comparing these two
consumer groups. First, WeChat Pay’s Chinese consumers
(Chinese tourists in Korea) and Kakao Pay’s Korean consumers
(Local Korean in Korea) represent two distinct and distinct
demographic features according to income levels, purchasing
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Lin et al. Usage Intention of Mobile Payment
TABLE 6 | Results of hypotheses tests (Kakao Pay’s Korean consumers
sample in Korea).
Hypothesis Route T-Value Path coefficients
H1a SYQ ! US 5:007 0:233***
H1b SYQ ! PE 9:275 0:387***
H2a IQ ! US 7:319 0:357***
H2b IQ ! PE 9:704 0:417***
H3a SEQ ! US 1:154 0:045
H3b SEQ ! PE 1:284 0:048
H4a PE ! US 6:989 0:381***
H4b US ! UI 1:171 0:061
H5a SA ! IT 7:888 0:369***
H5b PPT ! IT 7:602 0:332***
H5c FR ! IT 6:677 0:304***
H6a IT ! PE 11:099 0:460***
H6b IT ! UI 11:203 0:509***
H7 PE ! UI 0:854 0:050
H8 EE ! UI 1:053 0:040
H9 SI ! UI 5:520 0:213***
H10 FC ! UI 6:747 0:278***
H11 HM ! UI 2:568 0:099*
H12 PV ! UI 8:161 0:330***
H13 HA ! UI 2:566 0:097*
*p-value < 0.05 and ***p-value < 0.001.
power, and total accepted qualities of Chinese users. Second,
Chinese users play an extremely vital role in m-payments. While
Kakao Pay consumers are made up of local Korean users who
are more familiar with Kakao Pay than their Chinese tourists’
counterparts, WeChat Pay’s usage model is quite different.
Therefore, comparing the comments of Chinese users and local
Korean users may lead to a better understanding of usage
intention. Based on this manuscript, PE, EE, SI, FC, and ITM are
the elements directly determining user willingness, so we studied
them further. The different roles of WeChat Pay’s and Kakao Pay’s
Korean consumer groups should mitigate the influence of these
elements on usage willingness.
The empirical results of hypothesis regulation (H4–H13) are
shown in Table 7. First of all, judged by the p-values of SI and
EE on usage intention, the moderating effects of WeChat Pay
and Kakao Pay groups are not significant. Second, the other
seven p-values show differences in the regulatory effect between
the WeChat Pay and Kakao Pay groups. In the WeChat Pay’s
Chinese user groups, US (b = 0.351, p < 0.01), PE (b = 0.291,
p < 0.01), HM (b = 0.269, p < 0.01), and HA (b = 0.285,
p < 0.01) positively influence usage intention at a 5% basic level,
unlike the Kakao Pay’s Korean groups. In contrast, IT (b = 0.509,
p < 0.01), FC (b = 0.278, p < 0.01), and PV (b = 0.330, p < 0.01)
positively affected the basic level of usage intention at 1% in
Kakao Pay’s Korean consumer group, unlike in the WeChat Pay’s
Chinese users’ group.
DISCUSSION
This study took WeChat Pay and Kakao Pay as the research
objects and completely analyzed most elements influencing
the sustainable growth of Korean m-payments. Thus far,
there has been no theoretical framework comprehensively
examining the common influence of various qualities, IT, and
technology elements on customer acceptance of m-payment
platforms. Additionally, most previous studies (Afshan and
Sharif, 2016; Baabdullah et al., 2019) treated customers as
an indivisible whole sample group, and few studies examined
the regulatory role of different cultural platform features in
the trust construction process of the m-payment market.
Embracing the concept of integration models will help refine
the present literature, since there is no recent research that
has been able to combine the successful ITM and D&M
models into UTAUT2 to form a combined model to examine
the elements affecting the mpayment’s usage intention. After
the empirical analysis of the path results proposed in the
model, through the multi-group comparison between China
and South Korea, we conduct a multi-group test on the
cultural effect of the model. In other words, the cultural
impact on the path coefficient is tested according to the
pairwise parameter comparisons test between the structural loads
of two countries.
Hypothesis 14 maintained the influence of IT on usage
intention was less for WeChat Pay than for Kakao Pay. The results
show initial trust has little positive impact on the intention to use
WeChat Pay (b = 0.057, p = 0177) than Kakao Pay (b = 0.509,
p < 0.001). Thus, H14 was supported.
In Hypothesis 15, for Kakao Pay, US on usage intention
(b = 0.061, p = 0.241) does not significantly affect the intention
to use, and in WeChat Pay, satisfaction (b = 0.351, p < 0.001)
significantly affects the intention to use. Therefore, it is verified
that satisfaction in WeChat Pay positively influences usage
willingness. Still, for Kakao Pay, US does not affect usage
intention. Thus, H15 was supported.
Hypothesis 16 contended that the influence of PE on usage
intention would be greater for WeChat Pay users than for the
Kakao Pay users. To test H16, the path coefficient between PE and
trust was first checked and proved to be statistically significant in
the two m-payment systems (shown in Table 7). As stated in H16,
the impact of PE on usage intention for WeChat Pay (b = 0.291,
p < 0.001) was stronger than for Kakao Pay (b = 0.050, p = 0.393).
This result means the perceived PEs of the technology may affect
usage intention. Thus, H16 was supported.
Hypothesis 17 also assumed that there are differences in the
relationship between EE and usage intention. Both countries
used the same analytics program: effort expectations had a small
positive impact on users’ trust for WeChat Pay (b = 0.020,
p = 0.593) than for Kakao Pay (b = 0.040, p = 0.292). As
predicted in H17, although the EE of technology’s adoption
becomes an important component of trust formation, the
relationship between them can be influenced by culture. Thus,
we failed to demonstrate the influence of culture on effort
expectations and usage intention. Even if the effect of EE on
usage intention might vary across cultures, the extent of the
difference is too weak to support the expected cultural effect. To
some extent, these findings are consistent with previous studies
(Bandyopadhyay and Fraccastoro, 2007; Im et al., 2011). Thus,
H17 was rejected.
Frontiers in Psychology | www.frontiersin.org 12 January 2022 | Volume 12 | Article 634911
Lin et al. Usage Intention of Mobile Payment
TABLE 7 | The difference of path coefficients between WeChat Pay’s and Kakao Pay’s different consumers.
Hypothesis Route WeChat Pay Kakao Pay Pairwise parameter comparisons
b P b P T value p-Value
H6b IT ! UI 0.057 0.177 0.509 *** 7:854 0.000
H4b US ! UI 0.351 *** 0.061 0.241 –2:879 0.004
H7 PE ! UI 0.291 *** 0.050 0.393 –2:704 0.007
H8 EE ! UI 0.020 0.593 0.040 0.292 0:564 0.573
H9 SI ! UI 0.189 *** 0.213 *** 1:470 0.142
H10 FC ! UI 0.107 0.008* 0.278 *** 3:106 0.002
H11 HM ! UI 0.269 *** 0.099 0.010* –2:536 0.012
H12 PV ! UI 0.079 0.032* 0.330 *** 5:193 0.000
H13 HA ! UI 0.285 *** 0.097 0.010* –2:951 0.003
*p-value < 0.05 and ***p-value < 0.001.
In Hypothesis 18, the influence of SI on use intention is less
significant in WeChat Pay than in Kakao Pay. However, as shown
in the analytical results in Table 7, the SI on usage intention is
significant for WeChat Pay (b = 0.189, p < 0.01), whereas it is also
significant for Kakao Pay (b = 0.213, p < 0.01). That is, for Kakao
Pay Korean consumers who are more sensitive to social pressure,
SI appears to be an important factor in developing behavioral
intentions, whereas, for WeChat Pay Chinese consumers who
are more focused on personal goals, the opinions or pressures
of others may not be important in their decision-making. Thus,
H18 was supported.
In Hypothesis 19, for Kakao Pay, the facilitating condition
on the usage intention (b = 0.278, p < 0.001) significantly
affects the usage intention, and in WeChat Pay, the facilitating
condition (b = 0.107, p = 0.008) does not significantly affect
the usage intention. Therefore, Kakao Pay’s FCs are considered
to significantly affect the usage intention, and WeChat Pay’s
FCs are verified not to affect the usage intention. Thus,
H19 was supported.
In Hypothesis 20, for Kakao Pay, the hedonic motivation on
usage intention (b = 0.099, p = 0.010) does not significantly affect
the intention to use, and in WeChat Pay, hedonic motivation
(b = 0.269, p < 0.001) significantly affects the intention to use.
Therefore, Kakao Pay’s hedonic motivation was considered to
affect usage intention significantly, and WeChat Pay’s hedonic
motivation was verified to not significantly affect the usage
intention. Thus, H20 was supported.
In Hypothesis 21, for Kakao Pay, the PV on usage intention
(b = 0.330, p < 0.001) significantly affects the intention to use, and
in WeChat Pay, the value of the price (b = 0.079, p = 0.032) does
not significantly affect the intention to use. Therefore, the PV of
Kakao Pay is considered to affect the intention to use significantly,
and the PV of WeChat Pay is verified to not significantly affect
usage intention. Thus, H21 was supported.
In Hypothesis 22, for Kakao Pay, habit on usage intention
(b = 0.097, p = 0.010) cannot positively affect the usage intention,
and in WeChat Pay, habit (b = 0.285, p < 0.001) positively
affects the usage intention. Therefore, Kakao Pay’s habit is
considered to affect usage intent significantly, and WeChat Pay’s
habit is not verified to significantly affect usage intention. Thus,
H22 was supported.
CONCLUSION
Our research conclusions are valuable to researchers and
practitioners in the m-payment industry. For the former,
this research provides a basis in systematically improving
the theoretical model of acceptance, and is a new basis for
the theoretical research of new technology acceptance in the
future. For practitioners, focusing on the key aspects of the
research model is very important for designing, upgrading,
and implementing m-payment technologies that can produce
high acceptance.
Theoretical Contribution
Integrating several models and different theories into a
comprehensive model, used as an operational framework, it may
be helpful to identify potentially important variables between
behavior and intentions. We think D&M ISS and ITM are
critical elements of UTAUT2, and different researchers have
looked for factors that determine usage intention. However,
these models are seldom integrated into an integrated model.
To solve the shortage of research in related fields, a successful
information system model (D&M ISS and ITM) is combined with
UTAUT2, and the integration model is used as the conceptual
model in this manuscript. The integrated model makes up for
the shortcomings of the three separated construers, as well as
fully considers the subjective and objective elements of usage
intention for two different m-payment apps. Therefore, the
contribution is trifold.
First, the above results reveal the integrated model provides
a stronger interpretation of usage intention than the ISS model,
ITM, or UTAUT2, separately. In other words, we believe that the
integrated model offers more demonstrative insights than using
a single model view. Consequently, future SEM studies should
take a comprehensive viewpoint to test the usage intention of
any m-payment platform. This manuscript combined D&M ISS
and ITM with UTAUT2 to confirm the influencing factors of the
usage intention for m-payments. We found ISS and ITM not only
directly affect usage intention, but also through US and PEs alone.
The PE’s influence can be the basis and important starting point
of future research.
Frontiers in Psychology | www.frontiersin.org 13 January 2022 | Volume 12 | Article 634911
Lin et al. Usage Intention of Mobile Payment
Second, few academics have concentrated on the willingness
of potential Chinese users to choose either of the WeChat
Pay and Kakao Pay. This is an m-payment research
gap which has not been studied at all. This comparison
technique increases the effectiveness of several scenarios
testing between China and Korea, and also fulfills new
specific blanks in m-payment research. We also considered
the moderating variables and multi-group analysis of the
Chinese and Korean m-payment platforms’ differences to
improve the multi-model integration method. The limitation
of m-payment knowledge is increased by checking the
adjustment variables with the Chinese and Korean payment
difference regulators.
Third, attributable to the limited research on the UTAUT2
model for m-payment technology, this study is one attempt to
improve UTAUT2 in a multi-model and multi-group integration
perspective. In particular, it should be noted that UTAUT2 was
originally improved only to examine the use intention of new
technology and extended the application of the core variables in
the UTAUT2 model to other theoretical fields, such as the crosscultural background research of consumers, which is composed
of many technology platforms and application apps.
Managerial Implications
Judging by a large amount of m-payment literature, it is
clear in different business environments, the factors influencing
m-payment adoption intentions vary, so it is necessary to study
m-commerce adoption intentions and treat them differently,
judged by different countries (Zhang et al., 2012). Under
the background of global economic integration, different
m-payment providers tend to operate in multiple countries,
so it is very important to adopt appropriate strategies
to promote m-payment solutions. In general, given the
extensive cooperation between WeChat Pay and Kakao Pay,
which originally began in early 2017, the two sides have
a very broad space for cooperation. By comparing the two
m-payment users’ usage intentions, this manuscript aims
to focus on vital elements that significantly influence the
elements of the two countries’ respective consumers and also
provides a necessary theoretical basis and practice for largescale sustainable cooperation in the m-payment markets of
China and Korea.
According to the results of UTAUT2, we offer some
practical guidelines for m-payment platform operators and
developers. On the one hand, Kakao Pay’s Korean consumers’
IT (H6b), convenience (H10), and PV (H12) are more sensitive,
which Chinese mobile operators should focus on, such as
establishing transaction privacy protection, business reputation
promotion (IT), providing free Wi-Fi (facilitating conditions),
and professional Korean after-sales consulting (PV). On the
other hand, when Korean m-payment providers enhance China’s
consumer usage intention, the focus should be placed on
the sensitive factors of Chinese users. For example, Chinese
customers are more sensitive to customer satisfaction (H4b),
expected performance (H7), hedonic motives (H11), and habits
(H13). Therefore, if Korean m-payment providers employ
red envelope incentives (US), developing some entertainment
functions, such as QR code, radar, and radio to friends
(hedonic motivation), and daily special purchase rights (habit)
to stimulate the sensitive elements affecting Chinese users, the
usage intention of Kakao Pay will be immediately increased.
Chinese students in Korea only pay tuition in the form
of currency exchange and international remittance. Problems
such as money exchange and poor language communication
also affect the payment process and efficiency. By stimulating
the corresponding sensitive factors affecting use intention,
Chinese and Korean m-payment platforms will be able to
efficiently improve the usage intention of their respective
users and even benefit cross-border m-payments and economic
cooperation in APEC.
Limitations and Future Work
Although this research has theoretical and managerial
contributions, it also has other limitations, which are worthy
of further study. Future study directions to be discussed are as
follows. First, the questionnaire of this manuscript is collected
in Korea, and all the answers are in Korea. Future research
could be conducted in some more countries to further test
the universality of this proposed integrated model. Future
research could apply innovative data science and marketing
algorithms (Saura, 2021) to evaluate the age, experience, and
gender factors of customers into the theoretical model as
moderators to survey whether some differences among different
consumer samples can be classified according to these features.
Therefore, follow-up studies are needed. Second, we focused
on only one Korean m-payment provider in Korea. While
Kakao Pay is a representative provider of m-payments in Korea,
it does not include every area of worldwide m-payments.
To enhance the systematic nature of this study, we aim to
compare the results from different countries. Last, we combined
D&M ISS and ITM with UTAUT2 to determine the elements
affecting the m-payment’s usage intention. Future research
can use other theories. Research should test the impact of
other elements (perceived value, ease of use, and behavior
willingness, etc.) on stimulating users’ continued usage intention
of m-payment platforms.
DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will be
made available by the authors, without undue reservation.
ETHICS STATEMENT
Ethical review and approval were not required for the
study on human participants in accordance with the local
legislation and institutional requirements. Written informed
consent from the patients/participants or patients/participants
legal guardian/next of kin was not required to participate in
this study in accordance with the national legislation and the
institutional requirements.
Frontiers in Psychology | www.frontiersin.org 14 January 2022 | Volume 12 | Article 634911
Lin et al. Usage Intention of Mobile Payment
AUTHOR CONTRIBUTIONS
XL, Y-TL, and S-CC: conceptualization. XL, KS, AR, and S-CC:
methodology and writing—review and editing. XL and S-CC:
formal analysis. XL: investigation. XL, AR, Y-TL, and S-CC:
writing—original draft preparation. KS and AR: visualization and
supervision. All authors have read and agreed to the published
version of the manuscript.
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Conflict of Interest: The authors declare that the research was conducted in the
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