VIETNAM NATIONAL UNIVERSITY, HANOI

VIETNAM JAPAN UNIVERSITY

------------------

DAO MANH TAN

STUDY ON

MOBILE PAYMENT ADOPTION

IN VIETNAM

MASTER’S THESIS
BUSINESS ADMINISTRATION

Hanoi, 2019
 VIETNAM NATIONAL UNIVERSITY, HANOI
VIETNAM JAPAN UNIVERSITY

DAO MANH TAN

STUDY ON

MOBILE PAYMENT ADOPTION

IN VIETNAM

MAJOR: BUSINESS ADMINISTRATION

CODE: 60340102

RESEARCH SUPERVISORS:

ASSOC PROF. NGUYEN VAN DINH

PROF. MOTONARI TANABU

Hanoi, 2019
 DECLARATION OF ACCEPTANCE
I declare that this master thesis has been conducted solely by myself. This
master thesis has not been submitted in any previous articles or application for a degree,
in whole or in apart. The work contained herein is my own except where stated
otherwise by reference or acknowledgment.

ACKNOWLEDGMENTS
I would first thank both advisors Prof. Tanabu of Graduate School of
International Social Science – Yokohama National University. I would like to express
my gratitude to professor Tanabu for all the useful comments and engagement through
the chain of seminars in YNU. Furthermore, I would like to thank Assoc Prof. Nguyen
Van Dinh of Vietnam National University for wise advised and steered me in the right
direction whenever I need in conducting this research.

I would like to express my sincere thanks for all of the VJU –MBA02 class for
their kind support and advised. Next, I would like to thank my survey’s participant who
shared their time and precious idea.

Finally, I would like to express my gratitude to my parents to support me

unfailing and continuous encouragement throughout my study and writing this thesis.
This accomplishment would not have been possible without them.
 ABSTRACT
In Vietnam “The number of e-payments grew 22% in 2017 from the previous year
to $6.14 billion, according to Statista, a local market research firm. The figure is
projected to double to $12.33 billion in 2022” (TOMIYAMA, 2018) State-owned gas
station operator Petro Vietnam Oil introduced a mobile payment system in February,
while M-Service, a major fin-tech company, plans to increase the number of
subscribers to its MoMo online payment service to 50 million by 2020 from about five
million today. The research focuses on 3 objectives: To find the factors that affect the
customer in selecting the mobile-payment application in Vietnam, the relationship
between those factors and propose suggestions and solutions for mobile-payment
application providers to attract more customers as well as improve business
efficiencies. The research constructs and develop on the ground of UTAUT theory with
revised of Facilitating Factor, Trust factor and changes an independent variable. The
research using Likert –scales 5 levels for 4 observation variables: Performance
expectancy, social influence, effort expectancy Trust and one dependent variable
Behavior Intention. The research using a frequency- scale 4 levels for one independent
variable: E-commerce Use Behavior and one dependent variable: Use behavior.
Among 6 hypotheses, 5 were not rejected and 1 was rejected. The research also
provided the multiple linear regression equation and binomial logistic regression
equation of computing variable value. Therefore, predicting the mobile payment usage
behavior of frequency at 75.85% accuracies.
 TABLE OF CONTENTS

CHAPTER 1: INTRODUCTION .................................................................................... 1

1.1.1 Practical Motivation.................................................................................... 1

1.1.2 Theoretical Motivation ............................................................................... 3

CHAPTER 2: LITERATURE REVIEW ..................................................................... 6

2.1.1 Theory of Reasoned Action (TRA) ............................................................ 6

2.1.2 Theory of Planned Behavior (TPB) ............................................................ 6

2.1.3 Theory of Technology Acceptance Model (TAM) ..................................... 8

2.1.4 The Unified Theory Of Acceptance And Use Of Technology (UTAUT) .. 8

2.3.1 Performance Expectancy .......................................................................... 14

2.3.2 Effort Expectancy ..................................................................................... 15

2.3.3 Social Influence ........................................................................................ 16

2.3.4 Trust .......................................................................................................... 17

2.3.5 Behavioral Intention ................................................................................. 18

2.3.6 E-Commerce Behavior Intensive .............................................................. 19

2.3.7 Use Behavior............................................................................................. 20

CHAPTER 3: RESEARCH METHODOLOGY ....................................................... 22

3.2.1 Research Scale .......................................................................................... 23

 3.2.2 Example method and data collection ........................................................ 23

3.2.3 Data Analysis Method .............................................................................. 24

CHAPTER 4: RESEARCH FINDINGS ................................................................... 26

4.4.1 Exploratory Factor Analysis (EFA) .......................................................... 30

4.6.1 Block 0: Beginning Block ........................................................................ 35

4.6.2 Block 1: Method = Enter .......................................................................... 35

CHAPTER 5: CONCLUSIONS AND RECOMMENDATIONS............................. 39

REFERENCES .......................................................................................................... 42

APPENDIX ................................................................................................................ 45

QUESTIONAIRES ................................................................................................ 53

LIST OF TABLE

Table 2.1 Performance expectancy scale ................................................................... 15

Table 2.2 Effort expectancy scale .............................................................................. 16

Table 2.3 Social influence scale ................................................................................ 17

Table 2.4 trust scale ................................................................................................... 18

Table 2.5 behavioral intention scale .......................................................................... 19

Table 2.6 ecommerce behavior scale ......................................................................... 20

Table 3.1 Research process ........................................................................................ 22

Table 4.1 item total statistics of trust variable - original ........................................... 28

 Table 4.2 item total statistics of trust variable after deleted tr6 ................................ 29

Table 4.3 cronbach's alpha ......................................................................................... 29

Table 4.4 Component analysis ................................................................................... 30

Table 5.1 item total statistics of effort expectancy variable ...................................... 45

Table 5.2 item total statistics of social influence variable ......................................... 45

Table 5.3 item total statistics of behavioral variable ................................................. 46

Table 5.4 item statistic of use behavior variable ....................................................... 46

LIST OF FIGURE

Figure 2-1 Theory of reasoned action .......................................................................... 6

Figure 2-2 Theory Of Planned Behavior ..................................................................... 8

Figure 2-3 UTAUT model ......................................................................................... 10

Figure 2-4 Revised UTAUT model with trust and E-commerce Behavior Intensive
........................................................................................................................................ 12

Figure 4-1 Revised Research Model .......................................................................... 37

 CHAPTER 1: INTRODUCTION
1.1. Research motivation

1.1.1 Practical Motivation

In the Asia region and ASEAN region: The movement of banking system along with
a big leap of personal smartphone devices rate in ASEAN. According to Nikkei Asian
Review " In Indonesia, Digi bank drew about 600,000 users over the past year. "In the
next five years, we want to book around 3.5 million customers," said Wawan Salum,
managing director of the consumer banking group at PT Bank DBS Indonesia
(NAKANO, 2018). “Alibaba's core mobile payment service, Alipay, had more than
520 million users just in China at the end of 2017. The introduction of the service to
Alibaba's Taobao.com shopping website -- the largest e-commerce platform in China --
propelled a shift to cashless shopping in the country, including for small eaterie and
shops. Ant Financial works with CIMB Group Holdings, a bank in Malaysia, as well as
Indonesian conglomerate Emtek. Alibaba first offered electronic payment to the rising
ranks of Chinese tourists to Southeast Asia. Building on its experience in China, it
seeks to become a major force in mobile payments in the region as well”. (MARIMI
KISHIMOTO)

World Bank estimates that “the spread of smartphones has granted youth tools to
easily fulfill bank transactions. Only 20% of adult Indonesians held accounts in 2011,
but the share has risen to 49% last year” and “Globally, about 1.7 billion adults have
neither opened an account nor transferred money with a mobile phone, the World Bank
estimates. However, two-thirds of unbanked adults have mobile phones. That shows
digital banking could be ripe for an explosion in places like the Philippines and
Vietnam.” (NAKANO, 2018) .

Alibaba's Ant Financial owns about 20% of True Money’s operator, which aims to
expand its network 10-fold from the current level to 100,000 locations by the end of

1
this year. Users can charge their accounts at 7-Eleven convenience stores, which are
operated by the Charoen Pokphand group in Thailand or link them to a credit card or
bank account. The vast customer base of the Charoen Pokphand group -- including
visitors to the more than 10,000 7-Eleven stores in the country and the 27 million
subscribers of telecom company True -- is an asset for True Money. The next frontier
on the radar is cafes and fast-food chains, including Kentucky Fried Chicken. True
Money aims to overtake Rabbit Line Pay, the market-leading service from Japanese
messaging app provider Line and elevated train operator BTS Group Holdings. About
60% of Thailand's population uses the Line chat app, with users of the mobile payment
service now numbering roughly 3 million”. (MARIMI KISHIMOTO)

“The connected service has been approved for use across Singapore and Thailand,
where it is scheduled for launch in mid-2018. SingTel said in a news release that it
would be available to over 1.5 million people traveling between the two countries at
more than 20,000 retail outlets. It will then be rolled out progressively to other
affiliated companies including Advanced Info Service, Bharti Airtel, Telkomsel and
Globe Telecom from the second half of 2018. Mobile payment systems are becoming
increasingly popular with Asian consumers. Over 77% of people in the Asia-Pacific
region with internet access said made their most recent online purchase using a mobile,
in a survey by market research agency Kantar TNS. In Indonesia, the figure was as
high as 93%”. (LEE, 2018)

Mobile payment application has risen in the last 20 years from PayPal to Alipay and
Momo. Mobile payment application changed the behavior of people using paper
currency. In 3 years, paperless money evolution in China worth 5.5 trillion USD (50
times the US market). E-Commerce included 3 angles of iron triangles: e-commerce
platform, logistics and mobile payment application (Alibaba: The House That Jack Ma
Built by Duncan Clark). According to Mr. Sean Preston – director of Visa Vietnam
“60% of Vietnamese smartphone users using mobile – e-commerce shopping app”.

2
Therefore, underneath the trend of e-commerce in Vietnam are logistics and mobile
payment.

In Vietnam region: “The number of e-payments grew 22% in 2017 from the previous
year to $6.14 billion, according to Statista, a local market research firm. The figure is
projected to double to $12.33 billion in 2022” (TOMIYAMA, 2018) State-owned gas
station operator Petro Vietnam Oil introduced a mobile payment system in February,
while M-Service, a major fin-tech company, plans to increase the number of
subscribers to its MoMo online payment service to 50 million by 2020 from about five
million today. Zalo Pay terminals will first be available mainly at convenience stores
and electronics shops. “The service allows users to deposit money and pay for online
transactions and utility bills. It can also be used to transfer money from bank accounts
and handle remittances using QR codes”. Zalo Pay will be VNG's strategic product and
play an important role in Vietnam's e-commerce market, said Pham Thong, business
development director for the service. The potential for Zalo Pay is huge due to the
company's Zalo messaging app, which already has 70 million users.” The trend of
mobile payment and QR payment transformation for Mobile Banking app is at the peak
of user acquisition. Therefore, the key success for expansion and mobile payment
adoption are in need of discovery.

Last year, Alipay signed an agreement with Napas to connect the 2 systems.
Vietnamese market soon follows the trend by entering of dozen player from Asia,
Japan, and investment from domestic as well as an international financial institution.

One important question is why a customer chooses a mobile payment application

instead of other dozens. The research could provide some answer to how and why the
Vietnamese customer selects the mobile payment application.

1.1.2 Theoretical Motivation

3
 From the theoretical issue, this research will provide an empirical study of new
technology adoption and re-test the UTAUT framework with a revised model. Also,
“the coevolution of service and IT is so pronounced that many believe that a service-
centered dominant logic in marketing has now supplanted the traditional goods-
centered premise of marketing theory”(Day et al., 2004). This research also provides a
point of view for the above statement in finance – technology specifically.
Furthermore, this research would examine the newly develop of Use Behavior
frequency variable and also the state of proving regarding to Ecommerce Behavior
Intensive frequency contribute in predicting Mobile payment behavior frequency.

1.2 Research Objectives

According to practical issues and theoretical issues, the research focus on 3

objectives:
- To find the factors that affect the customer in selecting a mobile-payment
application in Vietnam.
- To find the relationship between those factors and customer’s decision in
selecting the mobile-payment application.
- To find the adoption behavior (uses frequency) of the mobile-payments
customer.
- Propose suggestions and solutions for mobile-payment application providers to
attract more customers as well as improve business efficiencies.

1.3 Research Questions

Following the research objectives mention above, research questions were

proposed as below:
- What factors do affect the customer in selecting a mobile-payment application
and its relationship?
- To what extent those factors influence mobile payment adoption in term of
usage frequent?

4
- What factors or solution should mobile-payment application providers apply to
attract more customers as well as improve business efficiencies?

5
 CHAPTER 2: LITERATURE REVIEW
2.1 Research Model Literature Review

2.1.1 Theory of Reasoned Action (TRA)

One of the earliest adoption model used to explain technology acceptance was the
Theory of Reasoned Action. The theory was developed in order to “organize integrated
research in the attitude area within the framework of a systematic theoretical
orientation”. (Fishbein, 1980). Otherwise, the main concern is the relation of these
variables. The TRA framework forms the model of prediction of specific behavior and
intention of use. According to (Fishbein, 1980), the TRA model is appropriate for the
study of determinants behavior of customer as a theoretical foundation framework
cause of it predicts and also explain the user behavior across a variety of domains.

(Fishbein, 1980) state that behavioral intention determined by two factors. The
primary determinant factor is the person’s attitude towards the behavior. In other
words, it explains whether or not a person has a favorable or unfavorable evaluation of
the behavior. “The second factor is the subjective norm, in other words, perceived
social pressure of behavior perform or not. Both two factors are subconscious by sets
of beliefs. The TRA theory looks at behavioral intention rather than an attitude as a key
component of predicting behavior” (Fishbein, 1980).

Figure 0-1 Theory of reasoned action (Fishbein, 1980)

2.1.2 Theory of Planned Behavior (TPB)

6
 In 1985, Ajzen (Ajzen, 1991) proposed a TRA extension which addresses the
problem of volitional control issue. The TRA extended became the Theory of Planned
Behavior. “Theory of Planned Behavior is widely used to predict human behavior and
at the same time explain the roles of individual members in the organization or social
systems in process” (Ajzen, 1991). The theory of planned behaviors was designed to
predict behavior under volitional control by adding measures of perceived behavior
factors. “The perceived behavioral control component where the main point different
from TPB to TRA within a more general framework of interaction factors: beliefs,
behavior, attitude and intentions” (Ajzen, 1991). When the situation and behavior
afford to a person completely control over behavior, “the intentions alone could be a
sufficient factor to predict behavior”. (Ajzen, 1991) argues that the TPB postulates the
behavior is a function of common salient beliefs related to that behavior. The salient
beliefs could be considered as the prevailing determinants of the person’s intensions
and actions.

7
 Figure 0-2 Theory of planned behavior (Ajzen, 1991)
The limitations of the Theory of Planned Behavior is that the model did not
account for the relation of intention and behavior, which could be lead to missing large
amounts of unexplained variance. TPB which is a psychological model that focuses on
internal process, it does not include variables of demographic and assumes that every
people would experience the processes exactly the same. Furthermore, it does not
account for the change in behaviors (Conner, 2001). While TPB was criticized by
(Todd, 1995) for its use of one variable to preventative all non-controllable factors of
the behavior. This aggregation was not identifying specific factors that predict behavior
as criticized but also for the biases it could create.

2.1.3 Theory of Technology Acceptance Model (TAM)

The theory of Technology Acceptance Model or TAM were developed by Davis

(Davis, 1989) is the most applicable and influential theories in the field. “Researchers
have examined mobile banking payment from the technology acceptance model
(TAM). TAM theorizes that an individual's behavioral intention to use technology is
determined by two beliefs: perceived usefulness and perceived ease of use (Davis,
1989). The perceived usefulness is the extent to which a person believes that using the
technology will enhance his or her job performance. The perceived ease of use is the
extent to which a person believes that using the technology will be free of effort.
According to TAM, perceived usefulness is influenced by perceived ease of use
because, other things being equal, the easier the technology is to use the more useful it
can be. Venkatesh and Davis (2000) extend the TAM by including subjective norm as
an additional predictor of intention in the case of mandatory settings. TAM has been
used to identify possible factors affecting mobile banking users' behavioral intention
(Luarn and Lin, 2005). These factors include perceived usefulness, perceived ease of
use, perceived credibility, self-efficacy, and perceived financial cost.”

2.1.4 The Unified Theory Of Acceptance And Use Of Technology (UTAUT)

8
 The Unified Theory of Acceptance and Use of Technology (UTAUT) is newly
adopted and soon became one of the most popular technologies adoption frameworks.
UTAUT aims to explain behavior intentions of the user and therefore explain the usage
behavior. UTAUT is a synthesized model which help comprehend the complete picture
of the user process of accepting new technology. “Technology acceptance research
produced several competing models, each with a set of different determinants. The
work of (Venkatesh .. &., 2003) emerged with the aim of reviewing and discussing the
literature of adoption of new information technology from the main existing models,
comparing them empirically, formulating a unified model and validating it empirically”.
Venkatesh et al. (2003) “formulated and validated the Unified Theory of Acceptance
and Use of Technology (UTAUT) from the integration of elements of eight prominent
models related to the topic after empirical comparisons between them. The eight
models were tested from a sample of four organizations for six months, with three
points of measurement, and explained 53% of the variance in intent to use information
technology. By contrast, the UTAUT formulated from four major constructs of intent
to use and four key relationships moderators explained 70% of variation when applied
to the same database. According to the research, the new model provided an important
managerial tool for the evaluation and construction of strategies for introducing new
technologies”. The eight models revisited by Venkatesh et al. (2003) are the Theory of
Rational Action (TRA), the Technology Acceptance Model (TAM/TAM2), the
Motivational Model (MM), the Theory of Planned Behavior (TPB/DTPB), “a model
agreement between the Technology Acceptance Model and the Theory of Planned
Behavior (C-TAM-TPB), the Model of PC Usage (MPCU), the Innovation Diffusion
Theory (IDT) and the Social Cognitive Theory (SCT). According to the UTAUT, the
intended use of information technology (IT) can be determined by three points:
expected performance, expected effort and social influence. Intent to use has influence
over the actual behavior, with a view to the adoption of technology enabling conditions.

9
The fourth construct, enabling conditions, specifically precedes use behavior”
(Venkatesh et al., 2003).

Figure 0-3 UTAUT Model (Venkatesh et al, 2003)

“Given a large number of citations in scholarly works since the formulation of the
UTAUT model, a systematic review of these was performed by Williams, Rana,
Dwivedi, and Lal (2011) in an attempt to understand its reasons, use, and adaptations
of the theory. In addition, he reviewed variations of use and theoretical advances. A
total of 870 citations of the original article were identified in academic journals, where
we managed to get 450 complete articles.”

2.2 Definition of Mobile Payment

Even though the term mobile payment includes all mobile devices including PCs
and PDAs, the general use of the term often refers to mobile devices with mobile
phone capabilities (Karnouskos and Fokus 2004). For the purpose of this research, we
accept any activity initiation, activation, and confirmation as a form of mobile payment.

10
There are two major categories of mobile payments and the distinction between them is
based on the location of the customer (purchaser), relation to the merchant (seller), and
different use scenarios. Mobile payments also are classified as remote payments or
proximity payments (Zhou 2011): Proximity payments or point of sale payments refer
to payments that take place when the customer is in close proximity to the merchant. In
this type of payment, the credentials are stored on the mobile phone and exchanged
within a small distance using barcode scanning or RFID technology (Chen et al. 2010).
Near field communication (NFC) is seen as the most promising technology in
proximity payments; gaining higher popularity among consumers and merchants as
well. The customers’ base for the technology is getting larger, as it offers them more
convenience and security (Zhou 2011; Ondrus and Pigneur 2009). Research has shown
that Near Field Communication (NFC) presents mobile operators, banks, and
businesses with a faster, and more convenient way for transactions (Beygo and Eraslan
2009). NFC devices provide three different operating modes: Peer-to-peer mode, where
two devices exchange data with one another like in a Bluetooth session; where the
device is used to initiate a connection or to target the tags or smart cards; and the Card
emulation mode: where the device acts as a contactless card. Example: Contactless
payments or ticketing (Gilje 2009; Beygo and Eraslan 2009). The second type of
payment is remote payments. This type of mobile payments is similar to online
shopping scenarios (Chandra et al. 2010), where it covers payments that are conducted
via a mobile web browser or a Smartphone application. Mobile phones produced in the
last few years are supported with capabilities that make them suitable for this payment
method (SMS, secure mobile browsing sessions and mobile apps). This payment
method can be conducted using the already existing infrastructure (The Mobile
Payments 2011). While remote payments seem to be more mature than proximity
payments (as the earlier enjoy a larger more flexible market, and the latter suffer from
time and place restrictions), both types can be integrated to improve the future market

11
of mobile payment technology (Zhou 2011). The later can only use within a close
range of the point of sale (Gilje 2009).”

The definition and boundary of mobile payment are a blur and can be understood
differently according to researchers. In this research, the researcher defined Mobile
Payment regardless of proximity and business model but using a smartphone
application to conduct an economic transaction which includes wireless transaction,
NFC and QR code based transaction.

2.3 Research Model Proposed

Figure 0-4 Revised UTAUT Model With Trust And E-Commerce Behavior Intensive
(Author)
Regarding the moderating effects, age, gender, and experience are not used in this
research for two reasons. First, these moderators seem to have no significant effects
(First-order interaction terms particularly) in the study of Venkatesh, Thong, and Xu
(2012). Second, some authors found that age, gender, and experience have no
significant moderating effects on the behavioral intention and use of Internet banking
(Martins, Oliveira, and Popovǐc, 2014; Riffai, Grant, and Edgar, 2012). The similarities

12
of Internet banking and Mobile payment provided a proof to implement same type of
revised UTAUT model in this research.”

Trust factor and E-commerce Behavior Intensive: There are a lot of researchers
and articles conducted their research which contains trust factor accompany with
Technology acceptant framework such as (Gefen, 2000) “Without trust people would
be confronted with the incomprehensible complexity of considering every possible
eventuality before deciding what to do. The impossibility of controlling the actions of
others or even just fully understanding their motivations makes the complexity of
human interactions so overwhelming that it can actually inhibit intentions to perform
many behaviors. “Many theorists and researchers of trust focus on interpersonal
relationships. However, the analysis of trust in the context of electronic commerce
should consider impersonal forms of trust as well, because in computer-mediated
environments such as electronic markets personal trust is a rather limited mechanism to
reduce uncertainty. The technology itself-mainly the Internet- has to be considered as
an object of trust” (Turban, 2001) . (Gefen, 2000) “developed a model expecting
familiarity with an e-commerce vendor and an individual’s disposition to trust to be
predictors of trust in an e-commerce vendor. Gefen furthermore assumed that
familiarity and trust would affect the consumer’s intention to inquire for a product and
the intention to purchases a product from the e-commerce vendor and that familiarity
would have an additional positive direct effect on inquiry and purchase. Trust in the e-
commerce vendor is conceptualized as a trusting belief, intentions to inquire for a
product from the vendor and to purchase a product represent trusting intentions.
Intended purchase and intended inquiry were also both significantly affected by trust in
the e-commerce vendor”.

Trust influences the customer’s likelihood of accepting a given technology (Gefen,

2000). Surprisingly, trust is an under-investigated variable. In term of mobile payment
is a monetary related technology, our trust in the party that guarantees the value of our

13
money (a central bank or a card payment framework provider) is essential to the
technology acceptance. Trust in mobile payment is the combination of our trust in the
service provider and the technology itself.” In the context of Vietnam, the mobile
payment provider must have a license of money transfer from government and
observation by government agent for anti -money laundry. That context and the
alliance between many mobile payment and ecosystem or strategic partner also lead to
a transfer of credibility among services providers. Some of the mobile payment
services embed on mobile banking application which had a solid root of reputation and
government authorization for a long time. Some of the other mobile payment services
build on top of well-adopted e-commerce ecosystem: Air pay linked with Shopee (both
belong to SEA group ecosystem), VinID/Mon pay linked with Vingroup ecosystem of
real estate, retailing and medical, …. Some of the mobile payment services working
underneath of smartphone producer such as Samsung pay which working on Samsung
smartphone. Other mobile payment was built on top of telephone/internet provider
which also alliance with state own bank, as the case of Viettel pay and MB bank.

In any cases, the e-commerce apps usage behavior would lead to the need for
internet/ mobile payment. E-commerce and buying online is widely spread in Vietnam
in the last few years and that e-commerce usage behavior intensive are influential in
the domain of other activities such as logistics and online payment.

According to the UTAUT framework and the other research of mobile payment
domain combine with the research territory – Vietnam, the proposed research model
could be described as the figure 2.4.

2.3.1 Performance Expectancy

Performance expectancy: this factor encompasses other factors in technology

acceptance including perceived usefulness, relative advantage and outcome expectation.
Venkatesh et al. (2003) defined the term as the degree to which the user thinks using a

14
particular technology will improve the overall performance. Previous research stressed
this construct as one of the strongest predictors of technology acceptance (Louho et al.
2006; Al-Shafi and Weerakkody 2009; Abu-Shanab et al. 2010; Zhou 2013b).”
Table 0.1 Performance Expectancy Scale
Factor Ite Question Item Measurem Source
ms ent Scales
Performa PE I find Mobile Payment useful Likert- (Venkate
nce 1 in my daily life scale 5 levels sh .. &.,
2003)
Expectan
cy PE Using Mobile Payment Likert- (Venkate
2 increases my chances of scale 5 levels sh .. &.,
achieving tasks that are 2003)
important to me
PE Using Mobile Payment helps Likert- (Venkate
3 me accomplish tasks more scale 5 levels sh .. &.,
quickly 2003)
PE Learning how to use Mobile Likert- (Venkate
4 Payment is easy for me scale 5 levels sh .. &.,
2003)
2.3.2 Effort Expectancy

“Effort expectancy: the term effort expectancy refers to how comfortable, and easy
to adopt customers feel the technology will be. This factor is an important predictor of
technology acceptance (Abu-Shanab and Pearson 2007). Effort expectancy usually
turns out to be of higher significance in early adoption. Effort expectancy captures the
meaning of both ease of use and complexity (Baron et al. 2006). Effort expectancy
indirectly impacts behavioral intentions through performance expectancy, This means
that if a customer thinks that using a particular technology will need huge effort, their
perception of that technology will be decreased (Zhou 2011). This construct is believed
to have a significant influence on behavioral intentions towards technology acceptance
in early stages, but its impact diminishes over long periods of continues usage

15
(Venkatesh et al. 2003), and some research failed to support its influence when testing
for e-recruitment systems (Laumer et al. 2010).”
Table 0.2 Effort Expectancy Scale
Factor Ite Question Item Measurem Source
ms ent scales
Effort EE Learning how to use Mobile Likert- (Venkate
Expectancy 1 Payment is easy for me scale 5 levels sh .. &.,
2003)
(EE)
EE My interaction with Mobile Likert- (Venkate
2 Payment is clear and scale 5 levels sh .. &.,
understandable 2003)

EE I find Mobile Payment easy to Likert- (Venkate

3 use scale 5 levels sh .. &.,
2003)

EE It is easy for me to become Likert- (Venkate

4 skillful at using Mobile Payment scale 5 levels sh .. &.,
2003)
2.3.3 Social Influence

Social influences: referred to as external influences. Social influence is the pressure

exerted by members of the social surroundings of an individual to perform or not
perform the behavior in question (Taylor and Todd 1995). Social influence was
reported by research to significantly impact behavioral intentions. Social factors
influence customers’ behavior in three ways: identification, internalization, and
compliance. While the earlier two factors refer to alterations in an individual believe
structure in hope of a potential status gain, compliance refers to change in the belief
structure of an individual caused by social pressure (Venkatesh et al. 2003). It’s
believed that the significance of social influence as a driver of technology acceptance
arises from the presumption that individuals tend to consult with important people in

16
their environment to reduce the anxiety attached with the use of new innovation (Slade
et al. 2014). In addition to such conclusion, researchers proclaimed that external
influences and social image have a great significant prediction of customers’ behavior
(Liébana-Cabanillas et al. 2014; Chung et al. 2010; Suntornpithug and Khamalah
2010).”
Table 0.3 Social Influence Scale
Factor Ite Question Item Measurem Source
ms ent scales
Social SI People who are important to Likert- (Venkate
Influence 1 me think that I should use scale 5 levels sh .. &.,
(SI) Mobile Payment 2003)
SI People who influence my Likert- (Venkate
2 behavior think that I should use scale 5 levels sh .. &.,
Mobile Payment 2003)
SI People whose opinions that I Likert- (Venkate
3 value prefer that I use Mobile scale 5 levels sh .. &.,
Payment 2003)
2.3.4 Trust

In the line with Gefen, Karahanna, and Straub (2003) definition of trust, customer
trust in Mobile banking can be operationalized as the accumulation of customer beliefs
of integrity, benevolence, and ability that could enhance customer willingness to
depend on Mobile banking to attain the financial transactions. Trust has been widely
examined and proven to be a crucial factor predicting customer’s perception and
intention toward Mobile banking (Hanafizadeh et al., 2014; Luo et al., 2010; Zhou,
2012). For instance, trust was empirically supported by Luo et al. (2010) to have
significant influence not only on the customer’s intention but also on performance
expectancy. In his study to examine the factor predicting customers’ initial trust in
Mobile banking, Zhou (2011) also confirmed trust as a key factor determining the
likelihood of customers using Mobile Banking. The role of trust and perceived

17
credibility have been sustained by Hanafizadeh et al. (2014) as key drivers for the
adoption of Mobile banking by Iranian bank customers as well. In the current study and
as proposed by Gefen et al. (2003), trust is supposed to have a direct effect on the
customers’ intention to adopt Mobile banking or it could indirectly influence BI via
facilitating the role of performance expectancy.”

Table 0.4 Trust Scale

Factor Ite Question Item Measurem Source
ms ent scales
Trust TR I believe that Mobile Payment is Likert- Geffen
(TR) 1 trustworthy scale 5 levels et al.
(2003)
TR I trust in Mobile Payment Likert- Geffen
2 scale 5 levels et al.
(2003)
TR I do not doubt the honesty of Likert- Geffen
3 Mobile Payment scale 5 levels et al.
(2003)
TR I feel assured that legal and Likert- Geffen
4 technological structures adequately scale 5 levels et al.
protect me from problems on Mobile (2003)
Payment
TR Even if not monitored, I would Likert- Geffen
5 trust Mobile Payment to do the job scale 5 levels et al.
right (2003)
TR Mobile Payment has the ability to Likert- Geffen
6 fulfill its task scale 5 levels et al.
(2003)
2.3.5 Behavioral Intention

Over the prior literature of IS/IT, the behavioural intention has been largely and
repetitively reported to have a strong role in shaping the actual usage and adoption of
new systems (Ajzen, 1991; Venkatesh et al., 2003, 2012). Accordingly, the current

18
study supposes that the actual adoption of Mobile banking could be largely predicted
by the customers’ willingness to adopt such a system. This relationship has also been
largely proven by many online banking studies such as in the studies of
Jaruwachirathanakul and Fink (2005), Martins et al. (2014), and many others.”
Table 0.5 Behavioral Intention Scale
Factor Ite Question Item Measurem Source
ms ent scales
Behavior BI I intend to use Mobile Likert- (Venkate
al Intention 1 Payment in the future scale 5 levels sh .. &.,
(BI) 2003)
BI I will always try to use Likert- (Venkate
2 Mobile Payment in my daily scale 5 levels sh .. &.,
life. 2003)
BI I plan to use Mobile Payment Likert- (Venkate
3 in the future. scale 5 levels sh .. &.,
2003)
BI I predict I would use Mobile Likert- (Venkate
4 Payment in the future scale 5 levels sh .. &.,
2003)
2.3.6 E-Commerce Behavior Intensive

In the original UTAUT framework, there is facilitating conditions but as described

above of strong connected side by side of Trust factor and as in the context of Vietnam
– the e-commerce behavior intensive. While Facilitating conditions: the term
facilitating conditions is used to refer to the degree to which technical and
organizational infrastructure that facilitates the use of a particular technology is already
in place (Attuquayefio and Add 2014). It yielded a significant influence for some
research in declining the adoption process jointly with compatibility (Zhang et al.
2011). It comprises three main constructs: 1) perceived behavioral control including
internal and external behavioral constraints, 2) facilitating conditions: which refers to
objective factors within the environment that make using a particular technology easy,

19
and finally, 3) compatibility: how compatible is this new technology with the values
and needs of its expected users (Venkatesh et al. 2003). As technology adoption is a
technology-specific domain, the abundance and ubiquity of mobile technology would
be considered important for the adoption process, which emphasizes the role of
facilitating condition as a predictor of behavioral intention ( Peng et al. 2011).

In the context of Vietnam, the E-commerce Behavior Intensive could account for 2
over 3 main constructs of facilitating condition: By purchasing on e-commerce
application – environment which is interconnected with payment system ( in case of
Zalo chat –Zalo pay, VinID and Shopee- Airpay) then make using mobile payment
technology easy. Secondly, by purchasing good or services on e-commerce application,
customer need of compatible online payment approach which mobile payment
sacrificed the need of expected users (Venkatesh V. , 2000).

Therefore, E-commerce Behavior Intensive is not only account for a part of

facilitating condition in the UTAUT model, but rather than new influence factor.
Table 0.6 Ecommerce Behavior Scale
Factor Items Question Item Measurement Source
scales
E-commerce EB1 I am frequently using Frequency- Author
Behavior mobile e-commerce app scale 4 levels
Intensive
2.3.7 Use Behavior

Factor Items Question Item Measurement Source

scales
UB1 I am frequently using the Frequency- Author
mobile payment function on scale 4 levels
Use the mobile banking app
Behavior
UB2 I am frequently using the Frequency- Author
mobile wallet app scale 4 levels

20
 UB3 I am frequently using the Frequency- Author
mobile payment app issued scale 4 levels
by the bank
2.5 Research Hypothesis

In the research model proposed, there are two dependent variables which are Mobile
Payment Use Behavior and Mobile Payment Behavioral Intention. There are 6
hypotheses in proposed theory which are described below. All the hypotheses have
support relationship to Use Behavior variable while Behavior Intention is mediator.

Hypothesis 1: Performance expectancy (PE) has a positive influence on

customers’ intentions (BI) to use mobile payment

Hypothesis 2: Effort Expectancy (EE) has a positive influence on customers’

intentions (BI) to use mobile payment.

Hypothesis 3: Social Influence (SI) has a positive influence on customers’

intentions (BI) to use mobile payment.

Hypothesis 4: Trust (TR) has a positive influence on customers’ intentions (BI)

to use mobile payment.

Hypothesis 5: Behavioral Intention (BI) has a positive influence on customer’s

frequencies of use of mobile payment services (UB).

Hypothesis 6: E-commerce Behavior Intensive (EB) has a positive influence on

customer’s frequencies of use of mobile payment services (UB).

21
 CHAPTER 3: RESEARCH METHODOLOGY
This chapter covers the content of research methodology which including
research background, research process and design, build up scales metrics and
questionnaire survey, data collection plan, sample size and data analysis method.
Otherwise, this chapter also proposed data analysis process of the study.

3.1 Research Process

Table 0.1 Research Process

• Research Problem

• Literature Review

• Pilot Research - Translate Question to Vietnamese - Pre-test

Questionaire

• Pre-test Data Collection

• Adjust Questionaire- Official Questionaire

• Official survey collection

• Conbach's Alpha analysis

• Factor analysis

• Linear Regression Analysis

• Conclusion

22
3.2 Research Design

3.2.1 Research Scale

This part of study provides the detail of research questionnaire items and parameter.
The research using Likert –scales 5 levels for 4 observation variables: “Performance
expectancy, social influence, effort expectancy Trust and one dependent variable
Behavior Intention. The research using frequency- scale 4 levels for one independent
variable: E-commerce Behavior Intensive and one dependent variable: Use behavior.”

The research constructs and develop on ground of UTAUT theory, therefore, the
research scale was translated into Vietnamese from original research scale which was
used in publish article and research paper. Before officially distributed survey, there
were pre-test translated questionnaire and qualitative interview with sample respondent
to make sure the translation is in fully understandable.

Sample size of respondents: prefer 200 (minimum 30*5=150) *Hair, Anderson,

Tatham and Black (1998).

3.2.2 Example method and data collection

The questionnaire survey was distributed among Vietnamese citizens in all 3 major
population center of Vietnam by Google Form. The questionnaire survey was
conducted from April 7th to April 24th, 2019. The distribution channels were electronic
solely.

The questionnaire started with a cover letter explaining the purpose of this study, the
nature of questions and the ethical considerations of research. The questionnaire
consists of two parts. Part one includes multiple choice questions designed to collects
responses of UTAUT model statements. All UTAUT model statements measured by
Likert-type scale of five. Responses were ordered as 5: Strongly Agree, 4: Agree, 3:
Neural, 2: Disagree, 1: Strongly Disagree. The second part consists of question about

23
E-commerce Behavior Intensive of respondent. Responses were ordered as 0: Never
Use, 1: At least once a month, 2: At least once a week, 3: At least once a day. The third
part consists of question about Mobile Payment Use Behavior of respondent.
Responses were ordered as 0: Never Use, 1: At least once a month, 2: At least once a
week, 3: At least once a day. The forth part collected demographic information of
respondents.

3.2.3 Data Analysis Method

Data collected has clean and analyzed with SPSS 23 which include:

- Descriptive statistics analysis: using descriptive statistics analysis to categorical

analyze in gender, age, marriage status, education level, monthly income…

- Reliability analysis of research scale using Cronbach’s alpha and exponential

factor analysis.

- Confirmatory factor analysis

- Factor loading analysis.

- Multiple Linear Regression for relation between independent variable:

Performance Expectancy, Effort Expectancy, Social Influence and Trust toward
Behavior Intention.

- Binomial Logistic Regression for relation between Behavior Intention and

independent variable E-commerce Behavior Intensive Toward Mobile Payment Use
Behavior. There are two reasons to apply binomial logistic regression in this research,
first of all, author would like to predict the binary dependent variable. The logistic
regression analysis applicable to analyze and estimate the likelihood of frequency of
mobile payment adopted in order to describe the research data collected. One source of
collected data were from another dependent variable which measured and estimated by

24
multiple variables linear regression, otherwise, another independent variable proposed
E-commerce Behavior Intensive which has similar frequency scale. One disadvantage
of binomial logistic approach is that logistic regression requires large volume of
observation in order to have a consistent result.

25
 CHAPTER 4: RESEARCH FINDINGS
4.1 Descriptive Analysis

This part presents the analysis and related findings of all data collected from the
survey. Descriptive data analysis is an appropriate method to analyze descriptive
questionnaire survey.

The research questionnaires were distributed via Google Form link to more than 400
participants chosen from all major part geographic regions: Northern region, Middle
region, Southern region. However, regardless of distributed link, the 174 responses and
there are 161 qualify responses.

- Gender: 58% of respondents relatively 101 persons were women, 39.1% of

respondents relatively 68 persons were man, otherwise 2.9% of respondents relatively
5 persons were gender undisclosed.

- Age: 148 respondents relatively 85.1% are at the age of 23 to 35 while 21

respondents relatively 12.1% are at the age of 18 to 22, otherwise 5 respondents
relatively 2.9% are at the age of 35 to 52. None of respondents under 18 or above 52
years old.

- Marriage status: 61 respondents relatively 35.1% are married while 112

respondents relatively 64.4% are single, otherwise 1 respondent are divorced relatively
0.6%.

- Education level: The question collected data of the highest education degree of
respondents. 138 respondents relatively 79.3% had undergraduate degree, 31
respondents relatively 17.8% had graduate/doctoral degree, 4 respondents relatively
2.35 had high school degree, 1 respondent relatively 0.6% hadn’t had any education
degree.

26
 - Region: 143 respondents relatively 82,2% are living in Northern part of Vietnam,
16 respondents relatively 9,2% are living in Middle part of Vietnam, 15 respondents
relatively 8.6% are living in Southern part of Vietnam. Regarding imbalance of living
location of respondents, the region should be a limitation/ bias of the study.

- Monthly Income: 53 respondents relatively 30.5% have monthly income from 7 to

12 million vnd, 41 respondents relatively 23.6% have monthly income from 13 to 20
million vnd. 34 respondents relatively 19.5% have monthly income above 20 million
vnd. 28 respondents relatively 16.1% have monthly income under 7 million vnd while
18 respondents relatively 10.3% have no income.

- Working status: 110 respondents relatively 63.2% are working for companies. 16
respondents relatively 9.2% are business owner. 36 respondents relatively 20.7% are
student. 12 respondents relatively 6.9% are unemployment.

4.2 Cronbach’s Alpha Analysis

The reliability test of a measure refers to the degree of the instrument that it free of
random error. The reliability of a measure relatively related to the consistency and
stability of that measurement. In this research, there were 5 independent scales and 2
dependent scales which used to measure the constructs of UTAUT revised model. The
independent scales are Performance Expectancy (PE), Effort Expectancy (EE), Trust
(TR), Social Influence (SI) and E-commerce Behavior Intensive (EB). The dependent
scales are Behavioral Intention (BI) and Use Behavior (UB) to use mobile payment
services. In order to prove that the set of scales appropriately captures the meaning of
proposed model consistently and accurately, the reliability test of measurement was
performed to assess the internal and item-total correlations.

Internal consistency reliability refers to the degree of which responses are consistent
among the variables within a single measurement scale (Kline, 2005). Cronbach’s
alphas were used to measure the internal consistency in this research. According to

27
Straub, “high correlations between alternative measures of large Cronbach’s alphas are
usually signs that the measures are reliable” (Straub D. , 1989). Cronbach’s coefficient
alpha value was assessed to examine the internal research consistency of measuring
(Boudreau, 2004). According to current related study and stated model of UTAUT
(Venkatesh .. &., 2003) should have a good internal consistency which a high value of
Cronbach’s Alphas of 0.7. (Hinton, 2004) propose four levels of reliability scale: low
(0.50 and lower), high moderate (0.50 to 0.70), high (0.70 to 0.90) and excellent (0.90
and above). The measurement of reliability of Use Behavior in this study focus on
frequency of mobile payment use behavior which is new scale measurement (Once a
day, once a week, once a month and never use), therefore, are newly adapted compare
to the original model.

A reliability coefficient – Cronbach’s alpha was run using SPSS software for set of
constructs.
Table 0.1 Item Total Statistics Of Trust Variable - Original

In order of analyses Trust (TR) independent variable, the Item-total statistics shows
that the Cronbach’s Alpha if item TR6 deleted is 0.911 higher than overall Cronbach’s
Alpha of TR, therefore, TR variables must re-run without TR6, otherwise, TR6 was
deleted from research question item.

28
 Table 0.2 Item Total Statistics Of Trust Variable After Deleted Tr6

Table 4.3 item statistic of use behavior variable

The result of revised item analyses shows as the table below

Table 0.4 Cronbach's Alpha
Variable No. of No. of Cronbach’s Comments
Samples Items Alpha
Performance 161 4 .852 High
Expectancy (PE) Reliability
Effort Expectancy 161 4 .911 Excellent
(EE) Reliability
Social Influence ( 161 3 .871 High
SI) Reliability
Trust (TR) 161 5 .911 Excellent
Reliability
Behavioral 161 4 .870 High
Intention ( BI) Reliability
Use Behavior (UB) 161 3 .535 High Moderate
Reliability
29
 The Cronbach’s Alpha shows that all variables Performance expectancy, social
influence, effort expectancy Trust, Behavioral Intention had Alpha ratio at high
reliability or excellent reliability. The Use Behavior adapted with frequencies scale of
customer behavior use had high moderate reliability which is affordable for analysis.

4.4 Factor Analysis

4.4.1 Exploratory Factor Analysis (EFA)

The factor analysis with 4 independent variables with Varimax rotation proposed
Kaiser-Meyer-Olkin Measure of Sampling Adequacy at 0.846 which is between 0.5
and 1. All the observed variables had factor loading greater than 0.5. The null
hypothesis rejected with statistic significant level of approximately 0% (Sig. =0.000).
Therefore, the exploratory factor analysis is congruous.

Table 0.3 Component analysis

Variable Cod Observation variable Extract
ing ion
Performa PE1 I find Mobile Payment useful in my daily life 0.697
nce
PE2 Using Mobile Payment increases my chances 0.722
Expectan of achieving tasks that are important to me
cy
PE3 Using Mobile Payment helps me accomplish 0.768
tasks more quickly
PE4 Learning how to use Mobile Payment is easy 0.691
for me
Effort EE1 Learning how to use Mobile Payment is easy 0.780

30
Expectancy for me

EE2 My interaction with Mobile Payment is clear 0.804

and understandable

EE3 I find Mobile Payment easy to use 0.829

EE4 It is easy for me to become skillful at using 0.775
Mobile Payment
Social SI1 People who are important to me think that I 0.773
Influence should use Mobile Payment
SI2 People who influence my behavior think that 0.861
I should use Mobile Payment
SI3 People whose opinions that I value prefer that 0.771
I use Mobile Payment
Trust TR1 I believe that Mobile Payment is trustworthy 0.727
TR2 I trust in Mobile Payment 0.849
TR3 I do not doubt the honesty of Mobile 0.806
Payment
TR4 I feel assured that legal and technological 0.728
structures adequately protect me from problems
on Mobile Payment
TR5 Even if not monitored, I would trust Mobile 0.650
Payment to do the job right

31
 Rotated Component Matrix

4.5 Multiple Variables Linear Regression

Multiple variable linear regression 1st time

As can see in the table below, Trust variable had sig of 0.552 greater than significant
level of 5%, therefore, TR hasn’t had significant statistic meaning in the regression
function.

32
 Coefficients

Standardi
Unstandardized zed Collinearity
Coefficients Coefficients Statistics

Std. Toler
Model B Error Beta t Sig. ance VIF
(Constant)
1
.205 .293 .702 .484

PE
.459 .070 .412 6.577 .000 .699 1.431

EE
.252 .056 .266 4.471 .000 .775 1.290

SI
.238 .049 .288 4.847 .000 .778 1.285

TR
.029 .048 .035 .597 .552 .796 1.256
( nonTR6)
a. Dependent Variable: BI

After eliminated TR variable from the model, re-run Multiple Variables linear
regression by SPSS, the result shows as below.

Multiple variables linear regression 2nd time.

Model Summary

R Adjusted R Std. Error of

Model R Square Square the Estimate Durbin-Watson

1 .755a .570 .562 .46075 1.998

a. Predictors: (Constant), SI, EE, PE

b. Dependent Variable: BI

33
 Coefficients

Unstandardized Standardize Collinearity

Coefficients d Coefficients Statistics

Std. Toler
Model B Error Beta t Sig. ance VIF

(Constant)
1 .225 .290 .775 .439

PE .463 .069 .417 6.706 .000 .708 1.412

EE .259 .055 .274 4.728 .000 .815 1.228

SI .245 .047 .297 5.175 .000 .831 1.204

a. Dependent Variable: BI

All three independent variables have sig < 0.05% and VIF less than 10, therefore,
the multiple variable linear regression in standardized form can be written as:

BI = 0.417*PE + 0.274*EE + 0.297*SI

4.6 Binomial Logistic Regression

Recode rules for Use Behavior item:

Item Score Binomial Value

Number

23 [0,3]

24 [0,3]

25 [0,3]

Total [0,9]

Recode [0,3] 0

Recode [4,9] 1

34
4.6.1 Block 0: Beginning Block

Classification Table
Observed Predicted
UB Percenta
.00 1.00 ge Correct
Step 0 UB .00 0 60 .0
1.00 0 101 100.0
Overall
62.7
Percentage
a. Constant is included in the model.
b. The cut value is .500

Variables in the Equation

B S.E. Wald df Sig. Exp(B)

Step 0 Constant .521 .163 10.208 1 .001 1.683

Variables not in the Equation

Score df Sig.

Step 0 Variables BI 20.175 1 .000

EB 21.093 1 .000

Overall Statistics 35.832 2 .000

4.6.2 Block 1: Method = Enter

Omnibus Tests of Model Coefficients

35
 Chi-
square df Sig.
Step 1 Step 41.436 2 .000
Block 41.436 2 .000
Model 41.436 2 .000
Model Summary
-2 Log Cox & Snell R Nagelkerke
Step likelihood Square R Square
1 171.201a .227 .310
a. Estimation terminated at iteration number 5
because parameter estimates changed by less than .001.
Classification Table
Observed Predicted
UB Percentage
.00 1.00 Correct
Step UB .00 32 28 53.3
1 1.00 11 90 89.1
Overall
75.8
Percentage
a. The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 1a BI 1.175 .306 14.757 1 .000 3.238
EB 1.068 .261 16.762 1 .000 2.911
Constant -5.458 1.368 15.912 1 .000 .004
a. Variable(s) entered on step 1: BI, EB.

36
 As shows in the prediction table that 2 variables Behavior Intention and E-
commerce Behavior Intensive have predicted the weekly mobile payment use behavior
with accuracy of 75.8%.

The logistic regression equation could be write as:

ln(UB) = 1.175*BI + 1.068*EB – 5.458

4.7 Revised Research Model

Figure 0-1 Revised research model (Author)

4.8 Hypothesis Testing Results

Hypotheses Status
Hypothesis 1: Performance expectancy (PE) has a positive Not rejected
influence on customers’ intentions (BI) to use mobile payment
Hypothesis 2: Effort Expectancy (EE) has a positive influence Not rejected

37
on customers’ intentions (BI) to use mobile payment.
Hypothesis 3: Social Influence (SI) has a positive influence on Not rejected
customers’ intentions (BI) to use mobile payment.
Hypothesis 4: Trust (TR) has a positive influence on Rejected
customers’ intentions (BI) to use mobile payment.
Hypothesis 5: Behavioral Intention (BI) predicts customer’s Not rejected
frequencies of use of mobile payment services (UB).
Hypothesis 6: E-commerce Behavior Intensive (EB) predicts Not rejected
customer’s frequencies of use of mobile payment services (UB).
The main contribution of this study was extend the UTAUT model to examine with
empirical study in Vietnam mobile payment market which also revised UTAUT
framework with newly factors. The study investigates the UTAUT framework well-
predicted user behavior of frequency used. From the analysis, hypothesis 5 has minor
effect on function and therefore, UTAUT is at good fitting theoretical framework in
order to explain the use behavior regardless frequency scale of it.

38
 CHAPTER 5: CONCLUSIONS AND RECOMMENDATIONS
5.1 Conclusion

Advance technology, infrastructure development and social movement are open the
windows of opportunities to process more convenience and low-cost financial
transactions. Mobile banking, in order of transformation and adaptation with new trend
of technology to provide more and more success for customer. Mobile payment app, in
another hand, move along with transitional of mega app such as WeChat or Go-Jerk,
Zalo, the transitional is 2 ways, mobile payment is not only integrated with mega app
but also migrated utility payment connection to other services providers. The
possibilities are endless and promise a great disruptive innovation and adoption in the
near future, in Vietnam. In order to conclude the research, three questions which are
rose from start of study. Factors affect customer in selecting mobile-payment
application are Performance Expectancy, Effort Expectancy, Social Influence and E-
commerce Behavior Intensive. The factors or solution should mobile-payment
application providers improved to attract more customers as well as improve business
efficiencies: As mention in Recommendation section.

“Our major theoretical contribution is in modifying UTAUT for the consumer

technology acceptance and use context. By doing so, we extend the generalizability of
UTAUT from an organizational to a consumer context. Prior technology acceptance
and use research has investigated the phenomenon in organizational contexts while in
the case of consumers’ acceptance and use of technology, other drivers come to the
fore. Another important aspect of the extension of UTAUT to the consumer context
involves the influence of facilitating conditions – E-commerce Behavior Intensive.
While the original UTAUT only proposed a path from facilitating conditions to actual
behavior, in a consumer context, we theorized new facilitating conditions adopted by
research context - influence behavioral intention. The study also provides equation to
calculate the behavior intention of using mobile payment services in Vietnam as well

39
as provide the equation to predict the frequency of mobile payment use behavior with
accuracy at 75.8%.”

5.2 Recommendation

From finding and conclusion as presents above, there should be some

recommendation for mobile payment provider and policy maker which would try to
improve cash less environment.

From mobile payment provider perspective: There are 4 independents variables

which are shown in the research: Performance Expectancy, Effort Expectancy, Social
Influence and E-commerce Behavior Intensive. In order to improve mobile payment
adoption or specifically the frequency of mobile payment usage, the services provider
can take effect on:

- Performance Expectancy: improve the speed and ease of use of mobile

payment which can help customer accomplish task more quickly. Improve the numbers
of integrated utilities payable such as insurance, household utilities and merchandize
acceptant which increase the usefulness and productivities for customer.

- Effort Expectancy: improve the UI and UX of mobile payment app, speed up

the charging and withdrawing money from payment app.

- Social Influence: advertise by influencer person who influences other people

behavior (SI2, SI3) to acquire more mobile payment follower.

- E-commerce Behavior Intensive: integrated system of inter connected system

between mobile e-commerce app and mobile payment app. Strategic support for
ecommerce company to promote E-commerce Behavior Intensive could lead to higher
rate of mobile payment usage.

5.3 Limitation and future research

40
 The study did not well balance between geographically of respondents, therefore, it
could be biased in responses. Regarding the differences of culture, payment system
adoption, mobile usage rate and other factors between Northern part and Middle or
Southern part of Vietnam, the research more likely represented for Northern mobile
payment adoption than other regions.

The second limitation is that the translation between English and Vietnamese could
mislead the precisely of meaning as original. In addition, there is also limitation of
impossible to assess whether every participant was fully honest in responses to the
questionnaire.

The third limitation as the main concern is that the newly developed question Item
of E-commerce Behavior Intensive and Mobile Payment Use Behavior have different
measurement scale, furthermore E-commerce Behavior Intensive frequency variable
had only one question item that in some circumstance lowering the solid concrete of
research outcome.

Base on that limitation as listed above, the future research could conduct more focus
on balance of respondents in geographical dimension. Also, the future research could
address more deeply of deprecated trust factor which commonly contributes big
concern to user regarding money transfer, however in this study the responses had
opposite idea.

41
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 APPENDIX

Table 0.1 item total statistics of effort expectancy variable

Table 0.2 item total statistics of social influence variable

45
 Table 0.3 item total statistics of behavioral variable

Table 0.4 item statistic of use behavior variable

Linear Regression 1st time

Descriptive Statistics
Std.
Mean Deviation N
BI 4.3230 .69633 161
PE 4.4441 .62623 161
EE 4.3043 .73670 161
SI 3.7701863 .84429130
161
35403727 7715728
TR
3.486 .8443 161
( nonTR6)
Correlations
TR
BI PE EE SI ( nonTR6)
Pearson BI 1.000 .656 .515 .525 .358
Correlation PE .656 1.000 .429 .410 .325

46
 EE .515 .429 1.000 .208 .330
SI .525 .410 .208 1.000 .352
TR
.358 .325 .330 .352 1.000
( nonTR6)
Sig. (1-tailed) BI . .000 .000 .000 .000
PE .000 . .000 .000 .000
EE .000 .000 . .004 .000
SI .000 .000 .004 . .000
TR
.000 .000 .000 .000 .
( nonTR6)
N BI 161 161 161 161 161
PE 161 161 161 161 161
EE 161 161 161 161 161
SI 161 161 161 161 161
TR
161 161 161 161 161
( nonTR6)

Variables Entered/Removeda

Mo Variables Variables Meth

del Entered Removed od

1 TR
( nonTR6), . Enter
b
PE, SI, EE
a. Dependent Variable: BI
b. All requested variables entered.

Model Summaryb

Std. Error
Mo R Adjusted of the Durbin-
del R Square R Square Estimate Watson

1 .756a .571 .560 .46170 1.995

a. Predictors: (Constant), TR ( nonTR6), PE, SI, EE
b. Dependent Variable: BI

47
 ANOVAa
Sum of Mean
Model Squares df Square F Sig.
1 Regression 44.327 4 11.082 51.987 .000b
Residual 33.253 156 .213
Total 77.580 160
a. Dependent Variable: BI
b. Predictors: (Constant), TR ( nonTR6), PE, SI, EE
Coefficientsa
Standardiz
Unstandardized ed Collinearity
Coefficients Coefficients Statistics
Tolera
Model B Std. Error Beta t Sig. nce VIF
1 (Constant) .205 .293 .702 .484
PE .459 .070 .412 6.577 .000 .699 1.431
EE .252 .056 .266 4.471 .000 .775 1.290
SI .238 .049 .288 4.847 .000 .778 1.285
TR
.029 .048 .035 .597 .552 .796 1.256
( nonTR6)
a. Dependent Variable: BI

Collinearity Diagnosticsa
Variance Proportions
Mo Dimensi Eigenva Condition (Consta TR
del on lue Index nt) PE EE SI ( nonTR6)
1 1 4.908 1.000 .00 .00 .00 .00 .00
2 .035 11.763 .03 .03 .03 .01 .98
3 .033 12.263 .02 .00 .14 .83 .01
4 .014 18.533 .32 .11 .81 .13 .01
5 .010 22.713 .62 .86 .01 .03 .00
a. Dependent Variable: BI

Residuals Statisticsa

48
 Mini Maxi Std.
mum mum Mean Deviation N
Predicted 2.158 4.323
5.0889 .52635 161
Value 7 0
Residual - 1.1650 .0000
.45589 161
1.54267 1 0
Std. Predicted
-4.112 1.455 .000 1.000 161
Value
Std. Residual -3.341 2.523 .000 .987 161
a. Dependent Variable: BI

Linear regression 2nd time

Descriptive Statistics
Std.
Mean Deviation N
BI 4.3230 .69633 161
PE 4.4441 .62623 161
E
4.3043 .73670 161
E
SI 3.7701863 .84429130
161
35403727 7715728

Correlations
BI PE EE SI
Pearson BI 1.000 .656 .515 .525
Correlation PE .656 1.000 .429 .410
EE .515 .429 1.000 .208
SI .525 .410 .208 1.000
Sig. (1-tailed) BI . .000 .000 .000
PE .000 . .000 .000
EE .000 .000 . .004
SI .000 .000 .004 .
N BI 161 161 161 161

49
 PE 161 161 161 161
EE 161 161 161 161
SI 161 161 161 161

Variables Entered/Removeda
Mo Variables Variables Meth
del Entered Removed od
1 SI, EE,
b . Enter
PE
a. Dependent Variable: BI
b. All requested variables entered.

Model Summaryb
Std. Error
Mo R Adjusted of the Durbin-
del R Square R Square Estimate Watson
a
1 .755 .570 .562 .46075 1.998
a. Predictors: (Constant), SI, EE, PE
b. Dependent Variable: BI
ANOVAa
Sum of Mean
Model Squares df Square F Sig.
1 Regressi 69.48
44.251 3 14.750 .000b
on 2
Residual 33.329 157 .212
Total 77.580 160
a. Dependent Variable: BI
b. Predictors: (Constant), SI, EE, PE
Coefficientsa
Standardiz
Unstandardized ed Collinearity
Coefficients Coefficients Statistics
Std. Tolera
Model B Error Beta t Sig. nce VIF
1 (Constant) .225 .290 .775 .439
PE .463 .069 .417 6.706 .000 .708 1.412

50
 EE .259 .055 .274 4.728 .000 .815 1.228
SI .245 .047 .297 5.175 .000 .831 1.204
a. Dependent Variable: BI

Coefficient Correlationsa
Model SI EE PE
1 Correlati SI 1.000 -.039 -.363
ons EE -.039 1.000 -.386
PE -.363 -.386 1.000
Covarian SI .002 .000 -.001
ces EE .000 .003 -.001
PE -.001 -.001 .005
a. Dependent Variable: BI

Collinearity Diagnosticsa
Variance Proportions
Mo Dimens EBgenv Condition (Consta
del ion alue Index nt) PE EE SI
1 1 3.943 1.000 .00 .00 .00 .00
2 .033 10.986 .02 .01 .16 .85
3 .014 16.568 .34 .12 .82 .12
4 .010 20.359 .63 .87 .01 .03
a. Dependent Variable: BI

Residuals Statisticsa
Mini Maxi Std.
mum mum Mean Deviation N
Predicted 2.159 4.323
5.0620 .52590 161
Value 8 0
Residual - 1.1452 .0000
.45641 161
1.56204 8 0
Std. Predicted
-4.113 1.405 .000 1.000 161
Value
Std. Residual -3.390 2.486 .000 .991 161
a. Dependent Variable: BI

51
Logistic Regression

Correlation Matrix

Consta
nt BI EB

Step 1 Constant 1.000 -.979 -.289

BI -.979 1.000 .144

EB -.289 .144 1.000

52
 QUESTIONAIRES

Part I:

1. Occupation

 Student
 Employee
 Entrepreneur
 Unemployment

2. Your gender

 Male
 Female
 Other

3. Your ages

 Under 18
 18 – 22
 23 – 35
 35 – 52
 Over 52

4. Living location

 Northern part of Vietnam

 Middle part of Vietnam
 Southern part of Vietnam

5. Monthly income

 None
 Under 7 million vnd
 7 -12 million vnd
 13 -20 million vnd
 Over 20 million vnd

53
 6. Your highest education

 None
 High school graduate
 Bachelor’s Degree
 Master’s Degree or Higher

7. Marriage status

 Single
 Married
 Divorce/ Widow

Part II:

The survey focus on persons who using mobile payment which could be one among
kinds:

- Mobile Banking app with payment function such as QR scan: BIDV,

Techcombank, Vietcombank, TPbank, Agribank,…

- Mobile wallet app: Momo, Viettel Pay, Zalo pay, Payoo, Moca,…

- Mobile payment app issued by bank: VCBpay, TPbank Quickpay,…

Please choose to what extent you agree with following statements:

(1). Strongly Disagree (2). Disagree (3). Neutral (4). Agree (5). Strongly Agree
N 1 2 3 4 5
Statement
o.
I find Mobile Payment useful in my
1 daily life.
Using Mobile Payment increases my
chances of achieving tasks that are
2 important to me
Using Mobile Payment helps me
3 accomplish tasks more quickly

54
 Using Mobile Payment increases my
4 productivity
Learning how to use Mobile Payment
5 is easy for me
My interaction with Mobile Payment
6 is clear and understandable
7 I find Mobile Payment easy to use
It is easy for me to become skilful at
8 using Mobile Payment
People who are important to me think
9 that I should use Mobile Payment
1 People who influence my behaviour
0 think that I should use Mobile Payment
1 People whose opinions that I value
1 prefer that I use Mobile Payment
1 I believe that Mobile Payment is
2 trustworthy
1
I trust in Mobile Payment
3
1 I do not doubt the honesty of Mobile
4 Payment
I feel assured that legal and
technological structures adequately
1 protect me from problems on Mobile
5 Payment
1 Even if not monitored, I would trust
6 Mobile Payment to do the job right
1 Mobile Payment has the ability to
7 fulfil its task
1 I intend to use Mobile Payment in the
8 future
1 I will always try to use Mobile
9 Payment in my daily life.
2 I plan to use Mobile Payment in
0 future.
2 I predict I would use Mobile Payment
1 in the future

55
 Part III:

Please choose to the appropriate frequency of use as statement below:

(0) Less than once a month (1) Monthly (2) Weekly (3) Daily

N 0 1 2 3
Statement
o

2
I am frequently using mobile e-commerce app
2

2 I am frequently using the mobile payment

3 function on the mobile banking app

2
I am frequently using the mobile wallet app
4

2 I am frequently using the mobile payment app

5 issued by the bank

56