Khuong, 2022 (FinRisk)
Khuong, 2022 (FinRisk)
Khuong, 2022 (FinRisk)
Article
Factors Affecting the Intention to Use Financial Technology
among Vietnamese Youth: Research in the Time of COVID-19
and Beyond
Nguyen Vinh Khuong 1,2 , Nguyen Thi Thanh Phuong 2,3 , Nguyen Thanh Liem 2,4, * , Cao Thi Mien Thuy 2,4
and Tran Hung Son 2,5
1 Faculty of Accounting and Auditing, University of Economics and Law, Ho Chi Minh City 700000, Vietnam;
khuongnv@uel.edu.vn
2 Vietnam National University, Ho Chi Minh City 700000, Vietnam; phuongntt194021c@st.uel.edu.vn (N.T.T.P.);
thuyctm17704@sdh.uel.edu.vn (C.T.M.T.); sonth@uel.edu.vn (T.H.S.)
3 Faculty of International Economic Relation, University of Economics and Law,
Ho Chi Minh City 700000, Vietnam
4 Faculty of Finance and Banking, University of Economics and Law, Ho Chi Minh City 700000, Vietnam
5 Institute for Development and Research in Banking Technology, University of Economics and Law,
Ho Chi Minh City 700000, Vietnam
* Correspondence: liemnt@uel.edu.vn
Abstract: This study focuses on understanding the factors that affect the intention of using financial
technology among young Vietnamese in the context of the COVID-19 pandemic. Fintech studies are
abundant in developed countries and mainly focus on consumers’ conditions, awareness, habits, and
capital. These are expected to differ significantly from the situation in developing countries. We have
reviewed factors that can affect the user’s intention, including the Perceived Benefit (PB), Perceived
Risk (PR), Belief (B), and Social Influence (SI), and rely on the Technology Acceptance Model (TAM)
Citation: Khuong, Nguyen Vinh, and the Theory of Reasoned Action (TRA) model in this research. The survey sample comprises
Nguyen Thi Thanh Phuong, Nguyen 161 Z-generation consumers with strong flexibility and knowledge about the use of Fintech. We use
Thanh Liem, Cao Thi Mien Thuy, and the PLS-SEM (partial least squares structural equation modeling) analysis method with the SmartPLS
Tran Hung Son. 2022. Factors
software (SmartPLS GmbH, Oststeinbek, Germany) to evaluate the research model. We find that
Affecting the Intention to Use
the Perceived Benefit (PB) has the most significant impact on the intention to use Fintech, followed
Financial Technology among
by Belief (B). However, in general, the factors are not significant, perhaps due to many reasons that
Vietnamese Youth: Research in the
Time of COVID-19 and Beyond.
are intrinsic in Vietnam. Based on this result, service providers, policymakers, and researchers can
Economies 10: 57. https://doi.org/ calibrate the development and research for the following stages. We offer findings different from the
10.3390/economies10030057 previous research, thus especially extending the literature on young people.
young industry, it is subject to competition from traditional financial services, and the
pressure from gaining the users’ acceptance is enormous. Moreover, consumers can face
some disadvantages in the application of Fintech products, including the risk of financial
losses and privacy concerns (Liébana-Cabanillas et al. 2014).
The intention to use Fintech services is affected by customers’ perception of risk:
financial risks, legal risks, and activity risks have significant influences, while security risk
does not impose any significant impact on the intention to use Fintech (Tang et al. 2020).
Another study explores the use of smartphone applications to manage the financing of
fishers (Carlin et al. 2017). This research shows that men tend to be more able to adopt
new technology than women. Finally, using survey data collected from 244 Fintech users,
Hyun-Sun Ryu (Ryu 2018) finds that legal risks have the most significant negative impact,
while convenience has the most positive influence on the intention to use Fintech.
In Vietnam, there are few Fintech studies, such as the research on the development of
Fintech by Dang Thi Ngoc Lan (Dang) and the study on the factors affecting the choice to
continue using Fintech Payment Services among university students by Nguyen Dang Tue
(Nguyen 2020).
This study seeks to extend extant studies on several fronts. First, the study analyzes
both aspects of benefits and risks in order to offer a more holistic view. Second, the study
was conducted in Vietnam, which shows excellent potential for Fintech’s development.
Vietnam is in a booming period, with nearly 55% of the total population using smartphones
and 52% using the Internet. This makes Vietnam the land of promise for Fintech. In
fact, there has been a marked increase in the number of Fintech companies, from about
40 companies at the end of 2016 to nearly 150 companies by the end of 2019. The recent
COVID-19 pandemic has further encouraged the development of electronic payment.
However, the development of Fintech in Vietnam also carries many security threats as
Fintech’s legal corridor is still in its early stages in this country (Lien et al. 2020). Therefore,
exploring the factors affecting awareness and user intentions is absolutely necessary. This
study will examine the factors that affect the decision to use financial technology products,
especially in the context of the COVID-19 pandemic (Al-Nawayseh 2020). The conditions,
awareness, and habits of people as well as other factors regarding Fintech differ between
developed and developing markets (Lehmann 2020). As a result, studies in developed
countries might not be relevant to Vietnam. The present study will contribute to a broader
view of this topic. Third, we use Model TAM and TRA, with results different from the
previous research. We also focus on the survey subjects: the Z generation of consumers,
who are aged 18–24. They are information consumers and providers who are very good at
device skills (Csobanka 2016).
This research contributes to discovering the effects of factors on the access and usage
of Fintech users in Vietnam, especially for young people in the COVID-19 period. At the
same time, solutions are also provided. Introduced to overcome existing limitations that
users of Fintech encounter, companies operating in Vietnam can refer to results presented
in this study to improve their services. The study also shows differences in Fintech usage
habits and behavior in developing countries when compared to foreign studies.
The article proceeds as follows. Section 2 provides an overview of the Vietnamese
research setting. Section 3 provides a discussion of the literature review and formulates
hypotheses. The models, estimation methods, and data collection are presented in Section 4.
In Section 5, we discuss our findings, followed by conclusions and some recommendations
in Section 6.
2. Background in Vietnam
Context
The Vietnamese economy rebounded in the first quarter of 2021 with a GDP (Gross
Domestic Product) growth of 4.48%. The increase is still lower than that in 2018 (7.45%),
but it has shown signs of recovery compared to 2020. According to the Vietnam Startup
Report in Q1 2021 (Vietnam Fintech Report 2020 2020), Fintech showcases a really impressive
Economies 2022, 10, 57 3 of 17
performance. In recent years, the number of Fintech companies has increased rapidly in the
Vietnamese market and plays a more critical role in the COVID-19 pandemic that hinders
traditional financial activities.
Fintech is a potential industry in digital transformation that reduces costs and increases
utility for users. In 2020, the Fintech sectors thrived in Payments (accounting for 33% of
market share), P2P Lending (15.5%), Blockchain/Crypto (13%), and POS (Point of Sale)
and Wealth Management (7%). The users’ habit of using traditional financial services is
gradually changing as the number of Fintech users is growing, especially with the rise in
the number of young users.
Payment is a high potential field, and this is quite understandable because payment
activity is essential in everyday life. E-commerce or service providers have seized every
opportunity to take part in the trend. Grab bought shares of the startup Moca, a Vietnamese
mobile payment application. VinID also acquired Monpay, a payment application. Lazada
Vietnam integrated Emonkey into their platforms.
According to Fintech and Digital Banking 2025 Asia Pacific 2020 (2020), mobile trans-
actions in Vietnam are expected to increase by 400% in the period of 2020–2025, and the
number of bank accounts is expected to increase further by about 50% for the top eight
leading banks. The rapid increase in accounts and transactions imposes a large pressure on
the current banking system. According to this report, the average life expectancy of the
core banking system among the top 100 banks in the Asia Pacific region is still at 17.5 years,
and this means it would be challenging to respond to the needs of the digital era. Banks in
Vietnam are actively converting to modern digital platforms, but it is expected that only
about 25% of banks in Vietnam will have been converted by 2025.
According to Vietnam Fintech Report statistics for 2020, 69% of Vietnamese people
have savings accounts in banks, 45% of the people have smartphones, and 57% have
internet on their phones. These indicators are expected to increase rapidly in the coming
years, serving as the necessary conditions for customers to access and effectively use Fintech
services quickly.
In addition to the advantages of the demographic characteristics, Fintech companies
also have advantages from government support and foreign investment. The State Bank
has planned to allow banks and Fintech companies to participate in Sandbox starting in
2022, for a period of 1–2 years. The areas include: payment, credit, P2P lending, customer
identification, application programming interface, tech-based solution, and other banking
support services. In the future, foreign investment capital pouring into this industry is
expected to increase significantly. At the same time, the relationship between commercial
banks and Fintech companies will become increasingly tighter to suit the diverse needs
of customers.
The competition between the banking system and Fintech companies has been robust,
requiring companies to evolve constantly in order to capture customers’ needs and improve
service quality to retain customers, attract new customers, and expand market share.
Therefore, studies on factors that may affect the intention to use Fintech in Vietnam could
have practical implications for Fintech firms.
on perceived risk. In addition, there is a study on the adoption of Fintech services through
the generation group of (Carlin et al. 2017). This research exploits the advent of smartphone
apps for personal financial management as an exogenous source of transformation. In
this study, in addition to the benefits that Fintech brings, people also point out some
risks when using financial technology products. The research also shows that a higher
proportion of men tend to adopt new technologies and access information, and the impact
of their access on the economy is greater than that of women. Research by (Hyun-Sun Ryu
2018) on the framework of benefits and risks of Fintech adoption includes a comparison
between adopters and early adopters (Ryu 2018). The research, with the aim of answering
the question: “Why are users willing or hesitant to apply Fintech?”, was performed by
collecting data from 244 Fintech users. The study investigates the perceived benefits
and risks that have a significant impact on Fintech adoption. In addition, the study also
examines the effects of perceived benefits and risks when applying Fintech to each type of
person. The results show that: Legal risk has the biggest negative impact, while convenience
has the most positive influence on the intention to use Fintech.
In Vietnam, there is some research on Fintech, such as the research on the development
of Fintech and movements in the field of Finance/Banking by Dr. Dang Thi Ngoc Lan, or
research on factors affecting the continuous usage of Fintech payment services—a study on
university students in Vietnam by Nguyen Dang Tue. In general, there is still no research
on the factors affecting the intention to use Fintech. Therefore, this study may respond to
and complement previous studies.
Hypothesis Development
This study proposes a benefit and risk framework by integrating positive and nega-
tive factors related to the intention to use Fintech. Previous studies have applied multi-
behavioral belief structures to determine the overall benefits and risks. Three key elements
of perception have been discussed: economic benefits, seamless transactions, and conve-
nience. Additionally, there are four main factors of risks: financial risks, legal risks, security
risks, and operational risks. Therefore, this study assumes that positive and negative factors
affect the perceived benefits and risk, significantly affecting the intention to continue using
Fintech. The proposed model is summarized in Figure 1.
In this research model, the perceived benefits are defined as “user perception about
the benefits of using Fintech”. Perceived benefit is how users believe that using technology
will improve efficiency (David 1989). Perceived benefit has a positive impact on the use
of products and services (Peter and Tarpey 1975), the use of mobile payment (Liu et al.
Economies 2021, 9, x FOR PEER REVIEW 5 of 17
of perception have been discussed: economic benefits, seamless transactions, and conven-
ience. Additionally, there are four main factors of risks: financial risks, legal risks, security
2012),and
risks, the use of Bitcoin
operational (Abramova
risks. andthis
Therefore, Böhme
study2016), online
assumes shopping
that positive(Batara et al. 2018),
and negative fac-
and the intention to use Internet Banking. Scientists have not agreed on the definition
tors affect the perceived benefits and risk, significantly affecting the intention to continue of
perceived risk. Perceived risk has a negative impact
using Fintech. The proposed model is summarized in Figure 1.on the intention to use Fintech (Ryu
2018). Rich defines perceived risk as akin to the uncertainty of whether a person will win
or lose the bet amount (Rich and Cox 2014).
Figure 1. Research framework. H1-H10 are the hypotheses presented below. Source: Proposal from
the author.
Figure 1. Research framework. H1-H10 are the hypotheses presented below. Source: Proposal from
the author.
Therefore, perceived risk is defined as bad impressions when using Fintech. We
suggest the following hypotheses:
In this research model, the perceived benefits are defined as “user perception about
the benefits of using Fintech”. Perceived benefit is how users believe that using technology
Hypothesis 1: Perceived benefit positively impacts intention to continue using Fintech.
will improve efficiency (David 1989). Perceived benefit has a positive impact on the use
of products and services (Peter and Tarpey 1975), the use of mobile payment (Liu et al.
Hypothesis 2: Perceived risk negatively impacts intention to continue using Fintech.
2012), the use of Bitcoin (Abramova and Böhme 2016), online shopping (Batara et al. 2018),
and the
Theintention
economictobenefit
use Internet
includes Banking. Scientists
cost reduction have
and not agreed
increased profiton the Fintech
from definition of
trans-
perceived risk. Perceived risk has a negative impact on the intention to use
actions, which are the motivation behind the consumers’ intention to continue. According Fintech (Ryu
2018). Rich defines
to Mackenzie perceived
(Mackenzie riskFintech’s
2015), as akin to the uncertainty
mobile transfer or of
P2Pwhether
loan maya person
lower will
costswin
for
or lose the bet amount (Rich 2014).
users compared to traditional financial service providers. The online shopping research
modelTherefore, perceivedetrisk
of Liu (Forsythe is defined
al. 2006) as badfour
proposed impressions
perceivedwhen using
benefits Fintech. We sug-
(convenience, ease,
gest the following hypotheses:
enjoyment, product selection) and three perceived risks (talent risk main, product risk,
time risk). According to Zavolokina et al. (2016), seamless transactions help customers to
Hypothesis 1: Perceived
obtain immediate benefit
benefits with positively
easy to impacts
relate tointention to continue usingfinancial
and customer-friendly Fintech. services
platforms.
Hypothesis 2: Perceived risk negatively impacts intention to continue using Fintech.
Hypothesis 3: Economic benefit positively impacts the intention to continue using Fintech.
The economic benefit includes cost reduction and increased profit from Fintech trans-
actions, which
Hypothesis 4: are the motivation
Seamless transactionbehind theimpacts
positively consumers’ intention
the intention to continue.
to continue According
using Fintech.
to Mackenzie (Mackenzie 2015), Fintech’s mobile transfer or P2P loan may lower costs for
users compared
Hypothesis to traditional
5: Convenience financial
positively service
impacts providers.
the intention The online
to continue shopping
using Fintech. research
model of Liu (Forsythe et al. 2006) proposed four perceived benefits (convenience, ease,
enjoyment, product
This study showsselection)
four risksand three
in the perceived
context risks (talent
of Fintech: financialrisk main,
risk, legalproduct risk,
risk, security
risk, risk).
time and operational
According to risk. Derbaix et
Zavolokina defined perceived
al. (2016), seamless risk as the “potential
transactions net loss to
help customers of
money”
obtain (Derbaix benefits
immediate 1983). Financial
with easy risk is thetopotential
to relate loss due to trading
and customer-friendly errors
financial and
services
misused bank accounts (Lee 2009). According to Kuisma et al. (2007), many customers are
platforms.
afraid of losing money when making transactions and transferring money over the Internet.
The research3:by
Hypothesis Maignan
Economic and positively
benefit Lukas indicates
impactsthat legal risktoiscontinue
the intention a perception of insecurity
using Fintech.
related to online credit cards (Maignan and Lukas 1997). Consumers perceive the security
Hypothesis 4: Seamless
risk inherently transaction
when using Fintechpositively
(Schierz impacts the intention
et al. 2010). Fraud and to continue
hackers’using Fintech.
intrusion lead
to users’ loss and violate user privacy. Consumers said that their information could be
easily stolen using online banking services (Littler and Melanthiou 2006). Operational risk
Economies 2022, 10, 57 6 of 17
Hypothesis 6: Financial risk negatively affects the intention to continue using Fintech.
Hypothesis 7: Legal risk negatively affects the intention to continue using Fintech.
Hypothesis 8: Security risk negatively affects the intention to continue using Fintech.
Hypothesis 9: Operational risk negatively affects the intention to continue using Fintech.
Social influence is the effect of others within a personal invitation system (Chuang
et al. 2016) and the perception of the subjective culture of the reference group (Oliveira et al.
2016), showing that social influence has a significant effect on behavioral intentions to use
mobile payment technology in Portugal. Their research asserts that social influence will
affect the intention to utilize mobile payment because individuals are easily affected by
other people (Oliveira et al. 2016).
Hypothesis 10: Social influence positively impacts the intention to continue using Fintech.
According to the research by Ali, beliefs strongly affect the intention to use online
banking (Omar Ali et al. 2020). Beliefs are a significant component contributing to online
banking applications and the integrity of the information technology group that they
manage (Chandio et al. 2013).
According to Liébana-Cabanillas et al. (2018) and Oliveira et al. (2016), the critical
factor in information technology deployment is the innovation of individuals. Líebana-
Cabanillas found a positive and significant relationship between the renewal of the individ-
ual and the intention to apply the user’s new technology (Liébana-Cabanillas et al. 2018).
The innovation of individuals is defined as an individual’s readiness to accept new things
and use new information technology beyond traditional methods (Agarwal et al. 1998).
The innovation of individuals helps reduce their anxiety, producing a positive impact on
the acceptance of technology. In contrast to early accepting consumers, late adopters are
supposed to accept products in the mature stage and the decline in the life cycle of the
product. Rogers and Everett soon accepted innovative technologies even without certainty
with its benefits (Rogers 1995). Therefore, this study proposed the following hypothesis.
Hypothesis 11: Beliefs positively impact the intention to continue using Fintech.
4. Research Design
4.1. Scale and Structure of the Questionnaire
We divide the questionnaire content into 12 parts, which include: Demographics,
Perceived Benefit (PB), Economic Benefit (EB), Seamless Transaction (ST), Convenience (CV),
Perceived Risk (PR), Financial Risk (FR), Legal Risk (LR), Security Risk (SR), Operational
Risk (OR), Social Influence (SI), Beliefs (B), and Continuance Intention (CI). The above
concepts were selected from related studies. In particular, Perceived Risk is from Benlian
and Hess (2011), Kim et al. (2008); the Economic Benefit concept and Financial Risk are
from Featherman and Pavlou (2003), Lee (2009); Chishti’s Seamless Transaction concept
is from Chishti (2016); the Convenience concept is from Okazaki and Mendez (2013); the
concept of Operational Risk is from Barakat and Hussainey (2013); the Social Influence
concept is from Ajzen (1991), Chatterjee (2008), Venkatesh et al. (2003); the Beliefs concept
is from Chatterjee (2008), Venkatesh et al. (2003); and the concept of Continuance Intention
is from Cheng et al. (2006), Lee (2009).
Economies 2022, 10, 57 7 of 17
4.2. Methodology
Quantitative research methods were used in the deductible process (Nguyễn 2012).
We used a quantitative survey method to achieve the main research objectives.
A system of concept/scale and observed variables were synthesized and selected from
previous studies to match the research objectives, but there was no change in the scale
of original concepts. Variables were measured using a Likert scale with five levels: (1)
completely disagree; (2) disagree; (3) neutral; (4) agree; (5) completely agree. We used
an SPSS 20.0 (International Business Machines Corporation, New York, NY, USA) and
Smart Pls software (GmbH, Oststeinbek, Germany) to conduct reliability, correlation, factor,
regression, and hypothetical testing.
The PLS_SEM model (Structural Equation Modeling) is one of the complex techniques
used to analyze complex relationships in the causal model. This is a widely used model in
research areas, especially in customer satisfaction model research. The SEM model coordi-
nates the techniques for multivariate regression, factor analysis, and mutual relationship
between elements in the diagram to check the complex relationship in the model.
This study adopted two models: the measurement model and the structural model.
Measurement models (also called factor models) deal with potential variables through
indicators such as the reliability of observation variables, determined via Cronbach’s Alpha.
The structural model is a model that identifies links between potential variables. These
relationships can describe the theoretical forecasts that researchers are interested in. The
model used an estimated method, with the multiple regression of the observed variables.
In addition, to ensure the highest robustness and accuracy for the model, we estimated
the model with the Bootstrap method. Bootstrap is a quantitative research method per-
formed by sampling. The study sample was divided into two sub-samples: one sample was
used to estimate the parameters and the other was used to reevaluate the estimated results.
The estimated results, after implementing the bootstrap with the number of N times of
repetition, was subsequently averaged. If this value was close to the overall estimate, the
model estimates could then be trusted.
Living Area
Quantity Percentage
Ho Chi Minh City 149 92.5%
Others 12 7.5%
Total 161 100%
Age
18–24 158 98.1%
25–34 2 1.2%
35–39 0 0.0%
Over 39 1 0.6%
Total 161 100%
Conclusion
Results of the measurement model inspection, internal stability tests, and the conver-
gence values of factors in the measurement model are presented in Table 2 below.
Discriminant Validity
The results of the HTMT (Heterotrait-monotrait Ratio of Correlations) index for the
model are presented in Table 3.
B CI CV EB FR LR OR PR PR SI SR ST
B
CI 0.534
CV 0.519 0.684
EB 0.359 0.512 0.544
FR 0.245 0.090 0.209 0.090
LR 0.161 0.142 0.079 0.074 0.584
OR 0.088 0.166 0.209 0.101 0.403 0.456
PB 0.430 0.777 0.766 0.549 0.175 0.164 0.178
PR 0.200 0.084 0.131 0.116 0.700 0.406 0.377 0.135
SI 0.656 0.486 0.379 0.405 0.125 0.118 0.116 0.363 0.064
SR 0.169 0.071 0.057 0.100 0.297 0.382 0.631 0.094 0.520 0.067
ST 0.507 0.564 0.715 0.586 0.270 0.238 0.069 0.691 0.285 0.346 0.056
Table 3 shows that all variables have an HTMT index of less than 0.9. We also checked
the index on the diagonal of the Fornell–Larcker, which is greater than that of other factors.
All observed variables have a factor higher than the factor it measured other factors with.
Thus, based on the three criteria for measuring the distinction value of the research factors,
all the factors in the model achieved differential values.
Table 2 has synthesized specific measurement model inspection results. After we used
the three evaluation criteria of the scale: (1) Internal Stability, (2) Convergent Validity, and
(3) Discriminant Validity, we found that most research factors were accepted (except the B6,
LR1, FR2, and PR3 variables). The research model had no changes compared to the original
and was used for analyzing the structural model in the next step.
Constructs CI
B. Beliefs 1.927
CI Continuance intention
CV Convenience 2.341
EB Economic benefit 1.514
FR Financial risk 1.621
LR Legal risk 1.385
OR Operational risk 1.571
PB Perceived benefit 1.996
PR Perceived risk 1.587
SI Social influence 1.661
SR Security risk 1.583
ST Seamless transaction 1.860
Source: Author synthesized the information from research data.
Table 5. Results for p-values, T Statistics, and Standard Deviation of each factor.
Standard
Original Sample T Statistic
Deviation p Value
Sample (O) Mean (M) (|O/STDEV|)
(STDEV)
B → CI 0.169 0.176 0.075 2.242 0.025
CV → CI 0.167 0.149 0.100 1.669 0.096
EB → CI 0.067 0.067 0.074 0.901 0.368
FR → CI 0.115 0.070 0.077 1.493 0.136
LR → CI −0.081 −0.072 0.071 1.150 0.251
OR → CI 0.031 0.042 0.066 0.466 0.641
PB → CI 0.408 0.403 0.080 5.120 0.000
PR → CI −0.047 −0.024 0.090 0.521 0.603
SI → CI 0.111 0.112 0.070 1.572 0.117
SR → CI 0.045 0.042 0.079 0.562 0.574
ST → CI 0.025 0.034 0.083 0.300 0.764
Source: Author synthesized the information from research data.
Considering a p-value index of 0.01, it can be seen that only two factors are of statistical
significance, which are beliefs and perceived benefit. All this demonstrates that other factors
generally do not affect the Continuance Intention to use Fintech, as originally expected.
The cause of this outcome will be explained more clearly in the results.
Figure 2 below shows the results of the model inspection on the Smart Pls software.
Economies2022,
Economies 2021,10,
9, x57FOR PEER REVIEW 12 of
12 of 17
17
Figure 2.
Figure 2. Model results.
results. Source: Author synthesized the information from
from research
research data.
data.
The
The model
modeltest
testresults
resultsshow
show that benefits
that of feelings
benefits from
of feelings Fintech,
from beliefs,
Fintech, convenience,
beliefs, conven-
and financial risks have a negative impact, decreasing the intention to
ience, and financial risks have a negative impact, decreasing the intention to continue continue using
us-
Fintech (the path factors were respectively 0.408; 0.169; 0.167; 0.115). Furthermore,
ing Fintech (the path factors were respectively 0.408; 0.169; 0.167; 0.115). Furthermore, economic
benefits,
economicsecurity risks,
benefits, and operating
security risks, andrisks have little
operating risksimpact on the
have little intention
impact to intention
on the use Fintechto
(with a path (with
use Fintech coefficient
a pathof 0.067; 0.045;of0.031).
coefficient 0.067;Legal
0.045;risk variables
0.031). Legaland
riskperceived
variables risk
andhave
per-
an opposite
ceived effectan
risk have (with a patheffect
opposite coefficient −0.047;
(with aofpath −0.081).
coefficient of −0.047; −0.081).
6. Discussion
6. Discussion
Generally, there are only a few studies in developing countries, and in Vietnam in
Generally, there are only a few studies in developing countries, and in Vietnam in
particular, which is what motivated us to conduct this study, focusing on benefits and risks
particular, which is what motivated us to conduct this study, focusing on benefits and
that affect the decision to continue to use Fintech in the future. In addition, along with
risks that affect the decision to continue to use Fintech in the future. In addition, along
Fintech’s rapid development in the context of the COVID-19 pandemic, it is an important
with Fintech’s rapid development in the context of the COVID-19 pandemic, it is an im-
new point to understand what affects the intention to use as well as the awareness of
portant new point to understand what affects the intention to use as well as the awareness
Fintech services.
of Fintech services.
Firstly, Continuance Intention was greatly influenced by Perceived Benefit, including
threeFirstly,
types of Continuance Intention was
benefits: Convenience, greatly influenced
Economic Benefits, and by Perceived
Seamless Benefit, including
Transaction. This
three types of benefits: Convenience, Economic Benefits, and Seamless
model was also applied in Forsythe’s study (Forsythe et al. 2006). Convenience is obtained Transaction. This
model was also applied in Forsythe’s study (Forsythe et al. 2006). Convenience
when Fintech changes the user’s habit from mainly in-cash to non-touch transactions. In is obtained
when Fintech
addition, userschanges the smart
can utilize user’sdevices
habit from mainly many
to perform in-cashremote
to non-touch
financialtransactions.
services. ThisIn
addition, users can utilize smart devices to perform many remote financial
promotes the participation of a young user group in the use of Fintech services; this group services. This
promotes
adapts fastthe participation
to technology of aopts
and young user group ininthe
for convenience uselife.
daily of Fintech
Seamless services; this group
Transaction also
adapts fast to technology and opts for convenience in daily life. Seamless
affects the benefits of feeling. Seamless transactions help clients to get immediate benefits,Transaction also
affects the benefits of feeling. Seamless transactions help clients to get
providing easy contact with customer-friendly financial services platforms (Zavolokina immediate benefits,
providing
et al. 2016).easy contact with of
A characteristic customer-friendly
traditional financefinancial
is that services
users cannotplatforms
trade(Zavolokina
when trading et
al. 2016). Ahave
institutions characteristic of traditional
offended their finance
sessions, while is thatallows
Fintech users users
cannot to trade when trading
trade quickly at any
institutions
time. haveeconomic
Similarly, offendedbenefit
their sessions, whileconsidering
is also worth Fintech allows usersthe
because to fee
trade quickly at
is exempted
from fixed costs such as those imposed by the trading system, and the personnel isfee
any time. Similarly, economic benefit is also worth considering because the is ex-
replaced
empted from fixed costs such as those imposed by the trading system,
with technology. This more or less affects trading costs on fintech platforms. According and the personnel
is replaced
to Mackenzie with technology.
(2015), FintechThis more
mobile or less affects
transfers or P2Ptrading
loans can costs on fintech
reduce costs platforms.
for users,
According totoMackenzie
comparable traditional(2015), Fintech
financial mobile
service transfers The
providers. or P2P loansof
benefit canthe
reduce costs for
Continuance
Economies 2022, 10, 57 13 of 17
Intention suggests that users have gradually become aware of the valuable benefits that
Fintech brings. They use it more in everyday life, especially for small transactions.
Secondly, Perceived Risk has a negative impact on Continuance Intention to use
Fintech, which means that users hesitate when faced with the risks they may encounter,
including Operational Risk, Security Risk, Legal Risk, and Financial Risk. Indeed, the
fact that technology platforms often have errors will prevent the intention to continue
transactions, generating psychological insecurity if important transactions must be made.
This is consistent with the views of many studies, such as those by Kuisma et al. (2007);
Benlian and Hess (2011); Kim et al. (2008). Moreover, the psychological fear of leaking
personal information, especially finance-related information, is a big concern because there
have been many information leaks that have caused significant damage in the financial
industry. This result is also consistent with the results of Schierz et al. (2010) and Littler
and Melanthiou (2006). The financial risk handling policy of most Fintech companies has
not guaranteed users’ interests. Risk handling in cases related to financing is quite delayed
and often takes a long time, sometimes not resolved, which is also the main reason why
Fintech transactions often do not have a high value. In the case of a dispute, although the
government has relevant laws, it is also a new field, the code is not really complete, and
has not been accessed by many people.
Thirdly, Social influence positively affects users; they use Fintech due to the introduc-
tion by other people or from seeing people around them. This will help expand the user
network and promote the range of other utilities. Oliveira’s research confirms that Social
Influence will influence the intention of mobile payment behavior because individuals
are easily influenced by others (Oliveira et al. 2016). An example is in the field of money
transfer; if more people use Fintech, they can transfer money to each other in the same
system or outside the system. However, if the recipients do not use it, they will have to
transfer money in another form, such as cash.
Fourthly, users put much belief in the service providers and Fintech platforms. In
doing so, they believe that the security of their personal information and their transactions
occur when they do not dare to question. A similar opinion was also concluded by
(Omar Ali et al. 2020) and (Chandio et al. 2013), who hold the view that trust strongly
influences the intention to use online banking and is an important component contributing
to online banking and the integrity of the information technology management team. This
can explain the company that provides services to improve the security system to meet
international certification requirements on their platforms and extensive information on
communication channels. In addition, the regulations of the state are not adequate but
have been and are being amended gradually to ensure that the benefits of users are also the
reason for more trust from users. Notifications of transparent transactions that are clear
and sent to them immediately allow users to check for themselves and directly report to
the system in case problems arise. Therefore, the factor of confidence positively influences
the intention to use Fintech.
In general, the research results presented are mostly without statistical significance.
This could be because the Fintech market has only developed in recent years, so the number
of people who know and use it is very small compared to the population size. In the era of
the COVID-19 pandemic, new consumers have a more precise awareness of the benefits
that Fintech brings, but because the study was deployed in the early stages of the epidemic,
they are still unable to evaluate all the sets. Despite the benefits, the risks still exist, even
in epidemics. The legal corridor is unclear with most units of the release of unexpected
applications, which do not help users to understand the rights and risks, leading to a lack of
motivation to use the Fintech service. Therefore, further research is needed in this context
during and after the COVID-19 pandemic in order to make a more accurate assessment of
this issue.
Economies 2022, 10, 57 14 of 17
services is still weak, but not deeply, leading to delays in risk processing and damage to
users. Therefore, companies need to focus on this particular team, improve the relevant
professional capacity, and commit to compensation and transparent policies for users to
trust the products derived from technology. Fourthly, financial technology products show
the capacity to be superior to traditional financial services in terms of convenience. This is
also why users are gradually using these products more in everyday life. Therefore, busi-
nesses and service companies need to invest in improved research to enhance convenience
and integration with many new features close to users, thereby creating the riddle effect
and expanding the user network. Fifthly, state regulations can affect the intention to use
financial technology products. Regulations are still loose and do not solve core problems.
With the goal of developing non-cash payments, the improvement of the legal system will
inevitably help financial technology services in particular, and the Vietnamese financial
landscape in general, facilitating the transfer of users and catching up with the trends of
the world.
Although the study was completed and achieved significant results, there are some
limitations. Firstly, because the study uses a small number of observations, generalizability
may suffer. Secondly, this study focuses on young people in Vietnam, so it is not representa-
tive of all behaviors across age groups. Research subjects were mainly aged 18–24, limiting
the types of subjects and not considering more age categories. In the future, we expect
to expand the scope of the research, especially in the age group from 25 to 39, to have a
more comprehensive assessment to supplement the existing results, thereby improving the
applicability of the research topic. Finally, though the conclusions are meant to encourage
users to use Fintech, due to the restriction of sampling, the general ability of the study
is limited.
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