Study on mobile payment adoption in vietnam

Table 4.2 item total statistics of trust variable after deleted tr6...29Table 4.3 cronbach's alpha...29 Table 4.4 Component analysis...30 Table 5.1 item total statistics of effort expect

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 masterthesis has not been submitted in any previous articles or application for a degree, inwhole or in apart The work contained herein is my own except where stated otherwise

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

Finally, I would like to express my gratitude to my parents to support meunfailing and continuous encouragement throughout my study and writing this thesis.This accomplishment would not have been possible without them

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 stationoperator 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 itsMoMo 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 inselecting the mobile-payment application in Vietnam, the relationship between thosefactors and propose suggestions and solutions for mobile-payment applicationproviders to attract more customers as well as improve business efficiencies Theresearch constructs and develop on the ground of UTAUT theory with revised ofFacilitating Factor, Trust factor and changes an independent variable The researchusing Likert –scales 5 levels for 4 observation variables: Performance expectancy,social influence, effort expectancy Trust and one dependent variable BehaviorIntention The research using a frequency- scale 4 levels for one independent variable:E-commerce Use Behavior and one dependent variable: Use behavior Among 6hypotheses, 5 were not rejected and 1 was rejected The research also provided themultiple linear regression equation and binomial logistic regression equation ofcomputing variable value Therefore, predicting the mobile payment usage behavior offrequency 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: INTRODUCTION1.1 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 AsianReview " In Indonesia, Digi bank drew about 600,000 users over the past year "In thenext 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 520million users just in China at the end of 2017 The introduction of the service toAlibaba'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 andshops Ant Financial works with CIMB Group Holdings, a bank in Malaysia, as well asIndonesian conglomerate Emtek Alibaba first offered electronic payment to the risingranks of Chinese tourists to Southeast Asia Building on its experience in China, itseeks to become a major force in mobile payments in the region as well” (MARIMIKISHIMOTO)

World Bank estimates that “the spread of smartphones has granted youth tools toeasily 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 haveneither opened an account nor transferred money with a mobile phone, the World Bankestimates However, two-thirds of unbanked adults have mobile phones That showsdigital banking could be ripe for an explosion in places like the Philippines andVietnam.” (NAKANO, 2018)

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

this year Users can charge their accounts at 7-Eleven convenience stores, which areoperated by the Charoen Pokphand group in Thailand or link them to a credit card orbank account The vast customer base of the Charoen Pokphand group includingvisitors to the more than 10,000 7-Eleven stores in the country and the 27 millionsubscribers 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 TrueMoney aims to overtake Rabbit Line Pay, the market-leading service from Japanesemessaging app provider Line and elevated train operator BTS Group Holdings About60% of Thailand's population uses the Line chat app, with users of the mobile paymentservice 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 itwould be available to over 1.5 million people traveling between the two countries atmore than 20,000 retail outlets It will then be rolled out progressively to otheraffiliated companies including Advanced Info Service, Bharti Airtel, Telkomsel andGlobe Telecom from the second half of 2018 Mobile payment systems are becomingincreasingly popular with Asian consumers Over 77% of people in the Asia-Pacificregion 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 ashigh as 93%” (LEE, 2018)

Mobile payment application has risen in the last 20 years from PayPal to Alipay andMomo Mobile payment application changed the behavior of people using papercurrency In 3 years, paperless money evolution in China worth 5.5 trillion USD (50times the US market) E-Commerce included 3 angles of iron triangles: e-commerceplatform, logistics and mobile payment application (Alibaba: The House That Jack MaBuilt by Duncan Clark) According to Mr Sean Preston – director of Visa Vietnam

“60% of Vietnamese smartphone users using mobile – e-commerce shopping app”

Therefore, underneath the trend of e-commerce in Vietnam are logistics and mobilepayment.

In Vietnam region: “The number of e-payments grew 22% in 2017 from the previousyear to $6.14 billion, according to Statista, a local market research firm The figure isprojected to double to $12.33 billion in 2022” (TOMIYAMA, 2018) State-owned gasstation operator Petro Vietnam Oil introduced a mobile payment system in February,while M-Service, a major fin-tech company, plans to increase the number ofsubscribers to its MoMo online payment service to 50 million by 2020 from about fivemillion today Zalo Pay terminals will first be available mainly at convenience storesand electronics shops The service allows users to deposit money and pay for onlinetransactions and utility bills It can also be used to transfer money from bank accountsand handle remittances using QR codes Zalo Pay will be VNG's strategic product andplay an important role in Vietnam's e-commerce market, said Pham Thong, businessdevelopment director for the service The potential for Zalo Pay is huge due to thecompany's Zalo messaging app, which already has 70 million users.” The trend ofmobile payment and QR payment transformation for Mobile Banking app is at the peak

of user acquisition Therefore, the key success for expansion and mobile paymentadoption 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 applicationinstead of other dozens The research could provide some answer to how and why theVietnamese customer selects the mobile payment application

1.1.2 Theoretical Motivation

From the theoretical issue, this research will provide an empirical study of newtechnology adoption and re-test the UTAUT framework with a revised model Also, thecoevolution 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 apoint of view for the above statement in finance – technology specifically.Furthermore, this research would examine the newly develop of Use Behaviorfrequency variable and also the state of proving regarding to Ecommerce BehaviorIntensive frequency contribute in predicting Mobile payment behavior frequency.

- Propose suggestions and solutions for mobile-payment application providers

to attract more customers as well as improve business efficiencies

- What factors or solution should mobile-payment application providers apply

to attract more customers as well as improve business efficiencies?

CHAPTER 2: LITERATURE REVIEW2.1 Research Model Literature Review

2.1.1 Theory of Reasoned Action (TRA)

One of the earliest adoption model used to explain technology acceptance was theTheory of Reasoned Action The theory was developed in order to “organize integratedresearch in the attitude area within the framework of a systematic theoreticalorientation” (Fishbein, 1980) Otherwise, the main concern is the relation of thesevariables The TRA framework forms the model of prediction of specific behavior andintention of use According to (Fishbein, 1980), the TRA model is appropriate for thestudy of determinants behavior of customer as a theoretical foundation frameworkcause of it predicts and also explain the user behavior across a variety of domains.(Fishbein, 1980) state that behavioral intention determined by two factors Theprimary determinant factor is the person’s attitude towards the behavior In otherwords, it explains whether or not a person has a favorable or unfavorable evaluation ofthe behavior The second factor is the subjective norm, in other words, perceived socialpressure of behavior perform or not Both two factors are subconscious by sets ofbeliefs The TRA theory looks at behavioral intention rather than an attitude as a keycomponent of predicting behavior (Fishbein, 1980)

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

2.1.2 Theory of Planned Behavior (TPB)

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

Figure 0-2 Theory of planned behavior (Ajzen, 1991)The limitations of the Theory of Planned Behavior is that the model did notaccount for the relation of intention and behavior, which could be lead to missing largeamounts of unexplained variance TPB which is a psychological model that focuses oninternal process, it does not include variables of demographic and assumes that everypeople would experience the processes exactly the same Furthermore, it does notaccount 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 ofthe 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 Researchershave examined mobile banking payment from the technology acceptance model(TAM) TAM theorizes that an individual's behavioral intention to use technology isdetermined 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 thetechnology will enhance his or her job performance The perceived ease of use is theextent 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 usebecause, other things being equal, the easier the technology is to use the more useful itcan 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 beenused to identify possible factors affecting mobile banking users' behavioral intention(Luarn and Lin, 2005) These factors include perceived usefulness, perceived ease ofuse, perceived credibility, self-efficacy, and perceived financial cost

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

The Unified Theory of Acceptance and Use of Technology (UTAUT) is newlyadopted and soon became one of the most popular technologies adoption frameworks.UTAUT aims to explain behavior intentions of the user and therefore explain the usagebehavior UTAUT is a synthesized model which help comprehend the complete picture

of the user process of accepting new technology “Technology acceptance researchproduced several competing models, each with a set of different determinants Thework of (Venkatesh &., 2003) emerged with the aim of reviewing and discussing theliterature 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 Acceptanceand Use of Technology (UTAUT) from the integration of elements of eight prominentmodels related to the topic after empirical comparisons between them The eight modelswere tested from a sample of four organizations for six months, with three points ofmeasurement, and explained 53% of the variance in intent to use informationtechnology By contrast, the UTAUT formulated from four major constructs of intent touse and four key relationships moderators explained 70% of variation when applied tothe same database According to the research, the new model provided an importantmanagerial tool for the evaluation and construction of strategies for introducing newtechnologies” The eight models revisited by Venkatesh et al (2003) are the Theory ofRational Action (TRA), the Technology Acceptance Model (TAM/TAM2), theMotivational Model (MM), the Theory of Planned Behavior (TPB/DTPB), “a modelagreement between the Technology Acceptance Model and the Theory of PlannedBehavior (C-TAM-TPB), the Model of PC Usage (MPCU), the Innovation DiffusionTheory (IDT) and the Social Cognitive Theory (SCT) According to the UTAUT, theintended use of information technology (IT) can be determined by three points:expected performance, expected effort and social influence Intent to use has influenceover the actual behavior, with a view to the adoption of technology enabling conditions

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 theUTAUT 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 Atotal 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 andPDAs, the general use of the term often refers to mobile devices with mobile phonecapabilities (Karnouskos and Fokus 2004) For the purpose of this research, we acceptany activity initiation, activation, and confirmation as a form of mobile payment

There are two major categories of mobile payments and the distinction between them isbased on the location of the customer (purchaser), relation to the merchant (seller), anddifferent use scenarios Mobile payments also are classified as remote payments orproximity 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 Inthis type of payment, the credentials are stored on the mobile phone and exchangedwithin a small distance using barcode scanning or RFID technology (Chen et al 2010).Near field communication (NFC) is seen as the most promising technology inproximity payments; gaining higher popularity among consumers and merchants aswell The customers’ base for the technology is getting larger, as it offers them moreconvenience and security (Zhou 2011; Ondrus and Pigneur 2009) Research has shownthat Near Field Communication (NFC) presents mobile operators, banks, andbusinesses with a faster, and more convenient way for transactions (Beygo and Eraslan2009) NFC devices provide three different operating modes: Peer-to-peer mode, wheretwo devices exchange data with one another like in a Bluetooth session; where thedevice is used to initiate a connection or to target the tags or smart cards; and the Cardemulation mode: where the device acts as a contactless card Example: Contactlesspayments or ticketing (Gilje 2009; Beygo and Eraslan 2009) The second type of

payment is remote payments This type of mobile payments is similar to onlineshopping scenarios (Chandra et al 2010), where it covers payments that are conductedvia a mobile web browser or a Smartphone application Mobile phones produced in thelast few years are supported with capabilities that make them suitable for this paymentmethod (SMS, secure mobile browsing sessions and mobile apps) This paymentmethod can be conducted using the already existing infrastructure (The MobilePayments 2011) While remote payments seem to be more mature than proximitypayments (as the earlier enjoy a larger more flexible market, and the latter suffer fromtime and place restrictions), both types can be integrated to improve the future market

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 understooddifferently according to researchers In this research, the researcher defined MobilePayment regardless of proximity and business model but using a smartphoneapplication 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 thisresearch 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 nosignificant moderating effects on the behavioral intention and use of Internet banking(Martins, Oliveira, and Popovǐc, 2014; Riffai, Grant, and Edgar, 2012) The similarities

of Internet banking and Mobile payment provided a proof to implement same type ofrevised 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 withTechnology acceptant framework such as (Gefen, 2000) “Without trust people would

be confronted with the incomprehensible complexity of considering every possibleeventuality before deciding what to do The impossibility of controlling the actions ofothers or even just fully understanding their motivations makes the complexity ofhuman interactions so overwhelming that it can actually inhibit intentions to performmany behaviors Many theorists and researchers of trust focus on interpersonalrelationships However, the analysis of trust in the context of electronic commerceshould consider impersonal forms of trust as well, because in computer-mediatedenvironments such as electronic markets personal trust is a rather limited mechanism toreduce uncertainty The technology itself-mainly the Internet- has to be considered as

an object of trust” (Turban, 2001) (Gefen, 2000) “developed a model expectingfamiliarity with an e-commerce vendor and an individual’s disposition to trust to bepredictors of trust in an e-commerce vendor Gefen furthermore assumed thatfamiliarity and trust would affect the consumer’s intention to inquire for a product andthe intention to purchases a product from the e-commerce vendor and that familiaritywould 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 aproduct from the vendor and to purchase a product represent trusting intentions.Intended purchase and intended inquiry were also both significantly affected by trust inthe 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

money (a central bank or a card payment framework provider) is essential to thetechnology acceptance Trust in mobile payment is the combination of our trust in theservice provider and the technology itself In the context of Vietnam, the mobilepayment provider must have a license of money transfer from government andobservation by government agent for anti -money laundry That context and thealliance between many mobile payment and ecosystem or strategic partner also lead to

a transfer of credibility among services providers Some of the mobile paymentservices embed on mobile banking application which had a solid root of reputation andgovernment authorization for a long time Some of the other mobile payment servicesbuild on top of well-adopted e-commerce ecosystem: Air pay linked with Shopee (bothbelong to SEA group ecosystem), VinID/Mon pay linked with Vingroup ecosystem ofreal estate, retailing and medical, … Some of the mobile payment services workingunderneath of smartphone producer such as Samsung pay which working on Samsungsmartphone Other mobile payment was built on top of telephone/internet providerwhich 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 forinternet/ 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 inthe domain of other activities such as logistics and online payment

According to the UTAUT framework and the other research of mobile paymentdomain combine with the research territory – Vietnam, the proposed research modelcould 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

particular technology will improve the overall performance Previous research stressedthis 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 ScaleFactor

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 oftechnology acceptance (Abu-Shanab and Pearson 2007) Effort expectancy usuallyturns out to be of higher significance in early adoption Effort expectancy captures themeaning of both ease of use and complexity (Baron et al 2006) Effort expectancyindirectly impacts behavioral intentions through performance expectancy, This meansthat if a customer thinks that using a particular technology will need huge effort, theirperception 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

(Venkatesh et al 2003), and some research failed to support its influence when testingfor e-recruitment systems (Laumer et al 2010).

Table 0.2 Effort Expectancy ScaleFactor

Effort

Expectancy

(EE)

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 notperform the behavior in question (Taylor and Todd 1995) Social influence wasreported 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 believestructure 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 acceptancearises from the presumption that individuals tend to consult with important people in

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 Khamalah2010)

Table 0.3 Social Influence ScaleFactor

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

TR3

TR4

TR5

TR6

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

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 beenlargely proven by many online banking studies such as in the studies ofJaruwachirathanakul and Fink (2005), Martins et al (2014), and many others

Table 0.5 Behavioral Intention ScaleFactor

Behavior

al Intention

(BI)

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 termfacilitating conditions is used to refer to the degree to which technical andorganizational infrastructure that facilitates the use of a particular technology is already

in place (Attuquayefio and Add 2014) It yielded a significant influence for someresearch in declining the adoption process jointly with compatibility (Zhang et al.2011) It comprises three main constructs: 1) perceived behavioral control includinginternal and external behavioral constraints, 2) facilitating conditions: which refers toobjective factors within the environment that make using a particular technology easy,

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

E-commerce

BehaviorIntensive

2.3.7 Use Behavior

Factor

UseBehavior

2.5 Research Hypothesis

In the research model proposed, there are two dependent variables which are MobilePayment Use Behavior and Mobile Payment Behavioral Intention There are 6hypotheses in proposed theory which are described below All the hypotheses havesupport 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).

CHAPTER 3: RESEARCH METHODOLOGY

This chapter covers the content of research methodology which includingresearch background, research process and design, build up scales metrics andquestionnaire 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

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: Performanceexpectancy, social influence, effort expectancy Trust and one dependent variableBehavior Intention The research using frequency- scale 4 levels for one independentvariable: E-commerce Behavior Intensive and one dependent variable: Use behavior.The research constructs and develop on ground of UTAUT theory, therefore, theresearch scale was translated into Vietnamese from original research scale which wasused in publish article and research paper Before officially distributed survey, therewere 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 majorpopulation center of Vietnam by Google Form The questionnaire survey wasconducted from April 7th to April 24th, 2019 The distribution channels were electronicsolely

The questionnaire started with a cover letter explaining the purpose of this study, thenature of questions and the ethical considerations of research The questionnaireconsists of two parts Part one includes multiple choice questions designed to collectsresponses of UTAUT model statements All UTAUT model statements measured byLikert-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

E-commerce Behavior Intensive of respondent Responses were ordered as 0: NeverUse, 1: At least once a month, 2: At least once a week, 3: At least once a day The thirdpart 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 aweek, 3: At least once a day The forth part collected demographic information ofrespondents.

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

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

- Confirmatory factor analysis

- Factor loading analysis

- Multiple Linear Regression for relation between independent variable:Performance Expectancy, Effort Expectancy, Social Influence and Trust towardBehavior Intention

- Binomial Logistic Regression for relation between Behavior Intention andindependent variable E-commerce Behavior Intensive Toward Mobile Payment UseBehavior 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 logisticregression analysis applicable to analyze and estimate the likelihood of frequency ofmobile payment adopted in order to describe the research data collected One source ofcollected data were from another dependent variable which measured and estimated by

multiple variables linear regression, otherwise, another independent variable proposedE-commerce Behavior Intensive which has similar frequency scale One disadvantage

of binomial logistic approach is that logistic regression requires large volume ofobservation in order to have a consistent result

CHAPTER 4: RESEARCH FINDINGS4.1 Descriptive Analysis

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

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

- Gender: 58% of respondents relatively 101 persons were women, 39.1% ofrespondents 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 21respondents relatively 12.1% are at the age of 18 to 22, otherwise 5 respondentsrelatively 2.9% are at the age of 35 to 52 None of respondents under 18 or above 52years old

- Marriage status: 61 respondents relatively 35.1% are married while 112respondents relatively 64.4% are single, otherwise 1 respondent are divorced relatively0.6%

- Education level: The question collected data of the highest education degree ofrespondents 138 respondents relatively 79.3% had undergraduate degree, 31respondents relatively 17.8% had graduate/doctoral degree, 4 respondents relatively2.35 had high school degree, 1 respondent relatively 0.6% hadn’t had any educationdegree