Study on mobile payment adoption in Vietnam

The research using Likert –scales 5 levels for 4 observation variables: Performance expectancy, social influence, effort expectancy Trust and one dependent variable Behavior Intention..

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

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

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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

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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)

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”

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

5

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

6

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)

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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

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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

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)

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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

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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.”

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

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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

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

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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

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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

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

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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

Likert-(Venkate

sh &., 2003) PE

2

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

scale 5 levels

Likert-(Venkate

sh &., 2003)

PE

3

Using Mobile Payment helps

me accomplish tasks more quickly

scale 5 levels

Likert-(Venkate

sh &., 2003) PE

4

Learning how to use Mobile Payment is easy for me

scale 5 levels

Likert-(Venkate

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

sh &., 2003)

EE

2

My interaction with Mobile Payment is clear and understandable

scale 5 levels

Likert-(Venkate

sh &., 2003)

Likert-(Venkate

sh &., 2003)

Likert-(Venkate

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

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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

People who are important to

me think that I should use Mobile Payment

scale 5 levels

Likert-(Venkate

sh &., 2003)

SI

2

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

scale 5 levels

Likert-(Venkate

sh &., 2003)

SI

3

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

scale 5 levels

Likert-(Venkate

sh &., 2003)

Table 0.4 Trust Scale Factor Ite

Likert-Geffen

et al (2003) TR

3

I do not doubt the honesty of Mobile Payment

scale 5 levels

Likert-Geffen

et al (2003) TR

4

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

scale 5 levels

Likert-Geffen

et al (2003)

TR

5

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

scale 5 levels

Likert-Geffen

et al (2003) TR

6

Mobile Payment has the ability to fulfill its task

scale 5 levels

Likert-Geffen

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

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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

Likert-(Venkate

sh &., 2003)

BI

2

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

scale 5 levels

Likert-(Venkate

sh &., 2003)

Likert-(Venkate

sh &., 2003)

Likert-(Venkate

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,

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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

EB1 I am frequently using

mobile e-commerce app

scale 4 levels

UB1 I am frequently using the

mobile payment function on the mobile banking app

scale 4 levels

Frequency-Author

UB2 I am frequently using the

mobile wallet app

scale 4 levels

Frequency-Author

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UB3 I am frequently using the

mobile payment app issued

by the bank

scale 4 levels

Frequency-Author

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)

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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

• Pre-test Data Collection

• Adjust Questionaire- Official Questionaire

• Official survey collection

• Conbach's Alpha analysis

• Factor analysis

• Linear Regression Analysis

• Conclusion

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

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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

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