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VIETNAM NATIONAL UNIVERSITY, HANOI

<b>VIETNAM JAPAN UNIVERSITY </b>

---

<b>DAO MANH TAN </b>

<b>STUDY ON </b>

<b> MOBILE PAYMENT ADOPTION </b>

<b>IN VIETNAM </b>

<b>MASTER’S THESIS </b>

<b>BUSINESS ADMINISTRATION </b>

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VIETNAM NATIONAL UNIVERSITY, HANOI

<b>VIETNAM JAPAN UNIVERSITY </b>

<b>DAO MANH TAN </b>

<b>STUDY ON </b>

<b>MOBILE PAYMENT ADOPTION </b>

<b>IN VIETNAM </b>

<b>MAJOR: BUSINESS ADMINISTRATION </b>

<b>CODE: 60340102</b>

<b>RESEARCH SUPERVISORS: </b>

<b>ASSOC PROF. NGUYEN VAN DINH </b>

<b>PROF. MOTONARI TANABU </b>

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<b>DECLARATION OF ACCEPTANCE </b>

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.

<b>ACKNOWLEDGMENTS </b>

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.

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<b>ABSTRACT </b>

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<b>TABLEOFCONTENTS </b>

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

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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

<b>LISTOFTABLE </b>
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

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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

<b>LISTOFFIGURE </b>
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

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<b>CHAPTER 1: INTRODUCTION </b>

<b>1.1. Research motivation </b>

<i><b>1.1.1 Practical Motivation </b></i>

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) .

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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)

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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.

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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.
<b>1.2 Research Objectives </b>

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.

<b>1.3 Research Questions </b>

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?

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<b>CHAPTER 2: LITERATURE REVIEW </b>

<b>2.1 Research Model Literature Review </b>

<i><b>2.1.1 Theory of Reasoned Action (TRA) </b></i>

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)

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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.

<i><b>2.1.3 Theory of Technology Acceptance Model (TAM) </b></i>

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.”

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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.”

<b>2.2 Definition of Mobile Payment </b>

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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.

<b>2.3 Research Model Proposed </b>

Figure 0-4 Revised UTAUT Model With Trust And E-Commerce Behavior Intensive
(Author)

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

<b> Trust factor and E-commerce Behavior Intensive: There are a lot of researchers </b>
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”.

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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.

<i><b>2.3.1 Performance Expectancy </b></i>

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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

ms

Question Item Measurem

ent Scales
Source
Performa
nce
Expectan
cy
PE
1

I find Mobile Payment useful
in my daily life

Likert-scale 5 levels

(Venkate
sh .. &.,
2003)
PE

2

Using Mobile Payment
increases my chances of
achieving tasks that are
important to me

Likert-scale 5 levels

(Venkate
sh .. &.,
2003)
PE

3

Using Mobile Payment helps
me accomplish tasks more
quickly

Likert-scale 5 levels

(Venkate
sh .. &.,
2003)
PE

4

Learning how to use Mobile
Payment is easy for me

Likert-scale 5 levels

(Venkate
sh .. &.,
2003)

<i><b>2.3.2 Effort Expectancy </b></i>

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(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

ms

Question Item Measurem

ent scales
Source
Effort
Expectancy
(EE)
EE
1

Learning how to use Mobile
Payment is easy for me

Likert-scale 5 levels

(Venkate
sh .. &.,
2003)
EE

2

My interaction with Mobile
Payment is clear and
understandable

Likert-scale 5 levels

(Venkate
sh .. &.,
2003)
EE

3

I find Mobile Payment easy to
use

Likert-scale 5 levels

(Venkate
sh .. &.,
2003)
EE

4

It is easy for me to become
skillful at using Mobile Payment

Likert-scale 5 levels

(Venkate
sh .. &.,
2003)

<i><b>2.3.3 Social Influence </b></i>

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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

ms

Question Item Measurem

ent scales
Source
Social
Influence
(SI)
SI
1

People who are important to
me think that I should use
Mobile Payment

Likert-scale 5 levels

(Venkate
sh .. &.,
2003)

SI
2

People who influence my
behavior think that I should use
Mobile Payment

Likert-scale 5 levels

(Venkate
sh .. &.,
2003)

SI
3

People whose opinions that I
value prefer that I use Mobile
Payment

Likert-scale 5 levels

(Venkate
sh .. &.,
2003)

<i><b>2.3.4 Trust </b></i>

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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

ms

Question Item Measurem

ent scales
Source
Trust
(TR)
TR
1

I believe that Mobile Payment is

trustworthy

Likert-scale 5 levels

Geffen
et al.
(2003)
TR

2

I trust in Mobile Payment
Likert-scale 5 levels

Geffen
et al.
(2003)
TR

3

I do not doubt the honesty of
Mobile Payment

Likert-scale 5 levels

Geffen
et al.

(2003)
TR

4

I feel assured that legal and
technological structures adequately
protect me from problems on Mobile
Payment

Likert-scale 5 levels

Geffen
et al.
(2003)
TR

5

Even if not monitored, I would
trust Mobile Payment to do the job
right

Likert-scale 5 levels

Geffen
et al.
(2003)

TR

6

Mobile Payment has the ability to
fulfill its task

Likert-scale 5 levels

Geffen
et al.
(2003)

<i><b>2.3.5 Behavioral Intention </b></i>

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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

ms

Question Item Measurem

ent scales

Source
Behavior

al Intention
(BI)

BI
1

I intend to use Mobile
Payment in the future

Likert-scale 5 levels

(Venkate
sh .. &.,
2003)

BI
2

I will always try to use
Mobile Payment in my daily
life.

Likert-scale 5 levels

(Venkate
sh .. &.,
2003)

BI
3

I plan to use Mobile Payment
in the future.

Likert-scale 5 levels

(Venkate
sh .. &.,
2003)

BI
4

I predict I would use Mobile
Payment in the future

Likert-scale 5 levels

(Venkate
sh .. &.,

2003)

<i><b>2.3.6 E-Commerce Behavior Intensive </b></i>

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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

scales

Source

E-commerce

Behavior
Intensive

EB1 I am frequently using
mobile e-commerce app

Frequency-scale 4 levels

Author

<i><b>2.3.7 Use Behavior </b></i>

Factor Items Question Item Measurement

scales

Source

Use
Behavior

UB1 I am frequently using the
mobile payment function on
the mobile banking app

Frequency-scale 4 levels

Author

UB2 I am frequently using the
mobile wallet app

Frequency-scale 4 levels

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UB3 I am frequently using the
mobile payment app issued
by the bank

Frequency-scale 4 levels

Author

<b>2.5 Research Hypothesis </b>

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.

<i>Hypothesis 1: Performance expectancy (PE) has a positive influence on </i>
<i>customers’ intentions (BI) to use mobile payment </i>

<i>Hypothesis 2: Effort Expectancy (EE) has a positive influence on customers’ </i>
<i>intentions (BI) to use mobile payment. </i>

<i>Hypothesis 3: Social Influence (SI) has a positive influence on customers’ </i>
<i>intentions (BI) to use mobile payment. </i>

<i>Hypothesis 4: Trust (TR) has a positive influence on customers’ intentions (BI) </i>
<i>to use mobile payment. </i>

<i>Hypothesis 5: Behavioral Intention (BI) has a positive influence on customer’s </i>
<i>frequencies of use of mobile payment services (UB). </i>

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<b>CHAPTER 3: RESEARCH METHODOLOGY </b>

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.

<b>3.1 Research Process </b>

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

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<b>3.2 Research Design </b>

<i><b>3.2.1 Research Scale </b></i>

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).

<i><b>3.2.2 Example method and data collection </b></i>

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.

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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.

<i><b>3.2.3 Data Analysis Method </b></i>

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.

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<b>CHAPTER 4: RESEARCH FINDINGS </b>

<b>4.1 Descriptive Analysis </b>

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%.

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- 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.

<b>4.2 Cronbach’s Alpha Analysis </b>

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.

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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

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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

Samples

No. of
Items

Cronbach’s
Alpha

Comments
Performance

Expectancy (PE)

161 4 .852 High

Reliability
Effort Expectancy

(EE)

161 4 .911 Excellent

Reliability
Social Influence (

SI)

161 3 .871 High

Reliability

Trust (TR) 161 5 .911 Excellent

Reliability
Behavioral

Intention ( BI)

161 4 .870 High

Reliability

Use Behavior (UB) 161 3 .535 High Moderate

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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.
<b>4.4 Factor Analysis </b>

<i><b>4.4.1 Exploratory Factor Analysis (EFA) </b></i>

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

ing

Observation variable Extract

ion
Performa

nce

Expectan
cy

PE1 I find Mobile Payment useful in my daily life 0.697
PE2 Using Mobile Payment increases my chances

of achieving tasks that are important to me

0.722
PE3 Using Mobile Payment helps me accomplish

tasks more quickly

0.768
PE4 Learning how to use Mobile Payment is easy

for me

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Expectancy for me

EE2 My interaction with Mobile Payment is clear
and understandable

0.804

EE3 I find Mobile Payment easy to use 0.829
EE4 It is easy for me to become skillful at using

Mobile Payment

0.775
Social

Influence

SI1 People who are important to me think that I
should use Mobile Payment

0.773
SI2 People who influence my behavior think that

I should use Mobile Payment

0.861
SI3 People whose opinions that I value prefer that

I use Mobile Payment

0.771
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
Payment

0.806
TR4 I feel assured that legal and technological

structures adequately protect me from problems
on Mobile Payment

0.728

TR5 Even if not monitored, I would trust Mobile
Payment to do the job right

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Rotated Component Matrix

<b>4.5 Multiple Variables Linear Regression </b>
Multiple variable linear regression 1st time

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<b>Coefficients</b>

Model

Unstandardized
Coefficients

Standardi
zed
Coefficients

t Sig.

Collinearity
Statistics

B

Std.

Error Beta

Toler

ance 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

( nonTR6) .029 .048 .035 .597 .552 .796 1.256
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.

<b>Model Summary </b>

Model R

R
Square

Adjusted R
Square

Std. Error of

the Estimate Durbin-Watson

1 .755a .570 .562 .46075 1.998

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<b>Coefficients</b>

Model

Unstandardized
Coefficients

Standardize
d Coefficients

t Sig.

Collinearity
Statistics

B

Std.

Error Beta

Toler

ance VIF
1

(Constant) .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:

<b>BI = 0.417*PE + 0.274*EE + 0.297*SI </b>
<b>4.6 Binomial Logistic Regression </b>

Recode rules for Use Behavior item:
<b>Item </b>

<b>Number </b>

<b>Score </b> <b>Binomial Value </b>

23 [0,3]

24 [0,3]

25 [0,3]

Total [0,9]

Recode [0,3] 0

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<i><b>4.6.1 Block 0: Beginning Block </b></i>

<b>Classification Table </b>

Observed Predicted

UB Percenta

ge Correct

.00 1.00

Step 0 UB .00 0 60 .0

1.00 0 101 100.0

Overall

Percentage 62.7

a. Constant is included in the model.
b. The cut value is .500

<b>Variables in the Equation </b>

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

Step 0 Constant .521 .163 10.208 1 .001 1.683
<b>Variables not in the Equation </b>

Score df Sig.
Step 0 Variables BI 20.175 1 .000

EB 21.093 1 .000

Overall Statistics 35.832 2 .000

<i><b>4.6.2 Block 1: Method = Enter </b></i>

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Chi-square df Sig.

Step 1 Step 41.436 2 .000

Block 41.436 2 .000

Model 41.436 2 .000

<b>Model Summary </b>

Step

-2 Log

likelihood

Cox & Snell R
Square

Nagelkerke
R Square

1 171.201a .227 .310

a. Estimation terminated at iteration number 5
because parameter estimates changed by less than .001.

<b>Classification Table </b>

Observed Predicted

UB Percentage

Correct
.00 1.00

Step
1

UB .00 32 28 53.3

1.00 11 90 89.1

Overall

Percentage 75.8

a. The cut value is .500
<b>Variables in the Equation </b>

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

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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:
<b> ln(UB) = 1.175*BI + 1.068*EB – 5.458 </b>

<b>4.7 Revised Research Model </b>

Figure 0-1 Revised research model (Author)
<b>4.8 Hypothesis Testing Results </b>

Hypotheses Status

<i>Hypothesis 1: Performance expectancy (PE) has a positive </i>
<i>influence on customers’ intentions (BI) to use mobile payment</i>

Not rejected

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<i>on customers’ intentions (BI) to use mobile payment. </i>

<i>Hypothesis 3: Social Influence (SI) has a positive influence on </i>
<i>customers’ intentions (BI) to use mobile payment. </i>

Not rejected

<i>Hypothesis 4: Trust (TR) has a positive influence on </i>
<i>customers’ intentions (BI) to use mobile payment. </i>

Rejected

<i>Hypothesis 5: Behavioral Intention (BI) predicts customer’s </i>
<i>frequencies of use of mobile payment services (UB). </i>

Not rejected

<i>Hypothesis 6: E-commerce Behavior Intensive (EB) predicts </i>
<i>customer’s frequencies of use of mobile payment services (UB). </i>

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<b>CHAPTER 5: CONCLUSIONS AND RECOMMENDATIONS </b>

<b>5.1 Conclusion </b>

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.

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

<b>5.2 Recommendation </b>

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.

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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.

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Fishbein, A. &. (1980). <i>Understanding attitudes and predicting social.</i> Englewood
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Hinton, P. &. (2004). <i>SPSS explained.</i> East Sussex, England: Routledge,Inc.

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MARIMI KISHIMOTO, M. T. (n.d.). Alibaba out to dominate mobile pay in Southeast
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Pavlou, P. (2003). Consumer acceptance of electronic commerce - integrating trust and
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Straub, D. (1989). Validating instruments in MIS research. <i>MIS Quaterly</i>, pp. 147 - 166.
Straub, D. G. (2001). Managing User TRust in B2C e-Services. <i>e-Services Quarterly</i>.
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TOMIYAMA, A. (2018). E-payment soars in Vietnam as a solution to skimpy bank
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Venkatesh, .. &. (2003). User acceptance of information technology: Toward a unified
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Venkatesh, V. &. (2001). A longtitudinal investigation of personal computers in homes:
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Venkatesh, V. (2000). Deteminants of perceived ease of use: Integrating control.

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<b>APPENDIX </b>

Table 0.1 item total statistics of effort expectancy variable

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Table 0.3 item total statistics of behavioral variable

Table 0.4 item statistic of use behavior variable

Linear Regression 1st time

<b>Descriptive Statistics</b>

Mean

Std.

Deviation N
BI 4.3230 .69633 161
PE 4.4441 .62623 161
EE 4.3043 .73670 161
SI 3.7701863

35403727

.84429130

7715728 161
TR

( nonTR6) 3.486 .8443 161

<b>Correlations</b>

BI PE EE SI

TR

( nonTR6)
Pearson

Correlation

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EE .515 .429 1.000 .208 .330
SI .525 .410 .208 1.000 .352
TR

( nonTR6) .358 .325 .330 .352 1.000
Sig. (1-tailed) BI . .000 .000 .000 .000
PE .000 . .000 .000 .000
EE .000 .000 . .004 .000
SI .000 .000 .004 . .000
TR

( nonTR6) .000 .000 .000 .000 .

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

( nonTR6) 161 161 161 161 161

<b>Variables Entered/Removeda</b>

Mo
del
Variables
Entered
Variables
Removed
Meth
od
1 TR

( nonTR6),
PE, SI, EEb

. Enter
a. Dependent Variable: BI

b. All requested variables entered.

<b>Model Summaryb</b>

Mo

del R

R
Square

Adjusted
R Square
Std. Error
of the
Estimate

Durbin-Watson
1 .756a .571 .560 .46170 1.995
a. Predictors: (Constant), TR ( nonTR6), PE, SI, EE

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<b>ANOVAa</b>

Model

Sum of

Squares df

Mean

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

<b>Coefficientsa</b>
Model
Unstandardized
Coefficients
Standardiz
ed
Coefficients

t Sig.

Collinearity
Statistics
B Std. Error Beta

Tolera

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

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

<b>Collinearity Diagnosticsa</b>

Mo
del
Dimensi
on
Eigenva
lue
Condition
Index
Variance Proportions
(Consta

nt) PE EE SI

TR
( 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

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Mini

mum

Maxi

mum Mean

Std.

Deviation N
Predicted

Value

2.158

7 5.0889

4.323

0 .52635 161

Residual

-1.54267

1.1650
1

.0000

0 .45589 161
Std. Predicted

Value -4.112 1.455 .000 1.000 161
Std. Residual -3.341 2.523 .000 .987 161
a. Dependent Variable: BI

<b>Linear regression 2nd time </b>

<b>Descriptive Statistics</b>

Mean

Std.

Deviation N
BI 4.3230 .69633 161
PE 4.4441 .62623 161
E

E 4.3043 .73670 161
SI 3.7701863

35403727

.84429130

7715728 161

<b>Correlations</b>

BI PE EE SI
Pearson

Correlation

BI 1.000 .656 .515 .525
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 .

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

<b>Variables Entered/Removeda</b>

Mo
del
Variables
Entered
Variables

Removed
Meth
od
1 SI, EE,

PEb . Enter

a. Dependent Variable: BI

b. All requested variables entered.

<b>Model Summaryb</b>

Mo

del R

R
Square
Adjusted
R Square
Std. Error
of the
Estimate

Durbin-Watson
1 .755a .570 .562 .46075 1.998
a. Predictors: (Constant), SI, EE, PE

b. Dependent Variable: BI

<b>ANOVAa</b>

Model

Sum of

Squares df

Mean

Square F Sig.
1 Regressi

on 44.251 3 14.750

69.48

2 .000

b

Residual 33.329 157 .212
Total 77.580 160

a. Dependent Variable: BI

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

<b>Coefficientsa</b>

Model
Unstandardized
Coefficients
Standardiz
ed
Coefficients

t Sig.

Collinearity
Statistics
B

Std.

Error Beta

Tolera

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

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51

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

<b>Coefficient Correlationsa</b>

Model SI EE PE

1 Correlati
ons

SI 1.000 -.039 -.363
EE -.039 1.000 -.386
PE -.363 -.386 1.000
Covarian

ces

SI .002 .000 -.001
EE .000 .003 -.001
PE -.001 -.001 .005
a. Dependent Variable: BI

<b>Collinearity Diagnosticsa</b>

Mo
del
Dimens
ion
EBgenv
alue
Condition
Index
Variance Proportions
(Consta

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

<b>Residuals Statisticsa</b>

Mini
mum

Maxi

mum Mean

Std.

Deviation N
Predicted

Value

2.159

8 5.0620

4.323

0 .52590 161

Residual

-1.56204

1.1452
8

.0000

0 .45641 161
Std. Predicted

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52
Logistic Regression

<b>Correlation Matrix </b>
Consta

nt BI EB

Step 1 Constant 1.000 -.979 -.289

BI -.979 1.000 .144

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53

<b>QUESTIONAIRES </b>
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

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54
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
<b>N</b>

<b>o. </b> <b>Statement </b>

<b>1 </b> <b>2 </b> <b>3 </b> <b>4 </b> <b>5 </b>

1

I find Mobile Payment useful in my
daily life.

2

Using Mobile Payment increases my
chances of achieving tasks that are
important to me

3

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55
4

Using Mobile Payment increases my
productivity

5

Learning how to use Mobile Payment
is easy for me

6

My interaction with Mobile Payment
is clear and understandable

7 I find Mobile Payment easy to use
8

It is easy for me to become skilful at
using Mobile Payment

9

People who are important to me think
that I should use Mobile Payment

1
0

People who influence my behaviour
think that I should use Mobile Payment
1

1

People whose opinions that I value
prefer that I use Mobile Payment
1

2

I believe that Mobile Payment is
trustworthy

1

3 I trust in Mobile Payment
1

4

I do not doubt the honesty of Mobile
Payment

1
5

I feel assured that legal and
technological structures adequately
protect me from problems on Mobile
Payment

1
6

Even if not monitored, I would trust
Mobile Payment to do the job right
1

7

Mobile Payment has the ability to
fulfil its task

1

8

I intend to use Mobile Payment in the
future

1
9

I will always try to use Mobile
Payment in my daily life.

2
0

I plan to use Mobile Payment in
future.

2
1

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56
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
<b>N</b>

<b>o </b> <b>Statement </b>

<b>0 </b> <b>1 </b> <b>2 </b> <b>3 </b>

2

2 I am frequently using mobile e-commerce app
2

3

I am frequently using the mobile payment
function on the mobile banking app

2

4 I am frequently using the mobile wallet app
2

5

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