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 variabl[r]

VIETNAM NATIONAL UNIVERSITY, HANOI VIETNAM JAPAN UNIVERSITY

-

DAO MANH TAN

STUDY ON

MOBILE PAYMENT ADOPTION IN VIETNAM

MASTER’S THESIS

BUSINESS ADMINISTRATION

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

DECLARATION OF ACCEPTANCE

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

ACKNOWLEDGMENTS

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

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

ABSTRACT

TABLEOFCONTENTS

CHAPTER 1: INTRODUCTION

1.1.1 Practical Motivation

1.1.2 Theoretical Motivation

CHAPTER 2: LITERATURE REVIEW

2.1.1 Theory of Reasoned Action (TRA)

2.1.2 Theory of Planned Behavior (TPB)

2.1.3 Theory of Technology Acceptance Model (TAM)

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

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

3.2.2 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

LISTOFTABLE 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 4.2 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

LISTOFFIGURE Figure 2-1 Theory of reasoned action

Figure 2-2 Theory Of Planned Behavior

Figure 2-3 UTAUT model 10

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

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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)

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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 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 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 1.2 Research Objectives

According to practical issues and theoretical issues, the research focus on objectives:

- To find the factors that affect the customer in selecting a mobile-payment application in Vietnam

- To find the relationship between those factors and customer’s decision in selecting the mobile-payment application

- To find the adoption behavior (uses frequency) of the mobile-payments customer

- Propose suggestions and solutions for mobile-payment application providers to attract more customers as well as improve business efficiencies

1.3 Research Questions

Following the research objectives mention above, research questions were proposed as below:

- What factors affect the customer in selecting a mobile-payment application and its relationship?

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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)

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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.”

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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

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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

2.3 Research Model Proposed

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.”

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 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

2.3.1 Performance Expectancy

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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

I find Mobile Payment useful in my daily life

Likert-scale levels

(Venkate sh &., 2003) PE

2

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

Likert-scale levels

(Venkate sh &., 2003) PE

3

Using Mobile Payment helps me accomplish tasks more quickly

Likert-scale levels

(Venkate sh &., 2003) PE

4

Learning how to use Mobile Payment is easy for me

Likert-scale levels

(Venkate sh &., 2003)

2.3.2 Effort Expectancy

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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

Learning how to use Mobile Payment is easy for me

Likert-scale levels

(Venkate sh &., 2003) EE

2

My interaction with Mobile Payment is clear and understandable

Likert-scale levels

(Venkate sh &., 2003) EE

3

I find Mobile Payment easy to use

Likert-scale levels

(Venkate sh &., 2003) EE

4

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

Likert-scale levels

(Venkate sh &., 2003)

2.3.3 Social Influence

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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

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

Likert-scale levels

(Venkate sh &., 2003)

SI

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

Likert-scale levels

(Venkate sh &., 2003)

SI

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

Likert-scale levels

(Venkate sh &., 2003)

2.3.4 Trust

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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

I believe that Mobile Payment is trustworthy

Likert-scale levels

Geffen et al (2003) TR

2

I trust in Mobile Payment Likert-scale levels

Geffen et al (2003) TR

3

I not doubt the honesty of Mobile Payment

Likert-scale 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 levels

Geffen et al (2003) TR

5

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

Likert-scale levels

Geffen et al (2003) TR

6

Mobile Payment has the ability to fulfill its task

Likert-scale levels

Geffen et al (2003)

2.3.5 Behavioral Intention

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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

I intend to use Mobile Payment in the future

Likert-scale levels

(Venkate sh &., 2003)

BI

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

Likert-scale levels

(Venkate sh &., 2003)

BI

I plan to use Mobile Payment in the future

Likert-scale levels

(Venkate sh &., 2003)

BI

I predict I would use Mobile Payment in the future

Likert-scale levels

(Venkate sh &., 2003)

2.3.6 E-Commerce Behavior Intensive

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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 over 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 levels

Author

2.3.7 Use Behavior

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 levels

Author

UB2 I am frequently using the mobile wallet app

Frequency-scale levels

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

Frequency-scale levels

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 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)

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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

3.1 Research Process

Table 0.1 Research Process • Research Problem

• Literature Review

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

• Pre-test Data Collection

• Adjust Questionaire- Official Questionaire • Official survey collection

• Conbach's Alpha analysis • Factor analysis

23 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 levels for observation variables: “Performance expectancy, social influence, effort expectancy Trust and one dependent variable Behavior Intention The research using frequency- scale levels for one independent variable: E-commerce Behavior Intensive and one dependent variable: Use behavior.”

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

Sample size of respondents: prefer 200 (minimum 30*5=150) *Hair, Anderson, Tatham and Black (1998)

3.2.2 Example method and data collection

The questionnaire survey was distributed among Vietnamese citizens in all 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

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

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CHAPTER 4: RESEARCH FINDINGS 4.1 Descriptive Analysis

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

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

- Gender: 58% of respondents relatively 101 persons were women, 39.1% of respondents relatively 68 persons were man, otherwise 2.9% of respondents relatively 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 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 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 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 million vnd while 18 respondents relatively 10.3% have no income

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

4.2 Cronbach’s Alpha Analysis

The reliability test of a measure refers to the degree of the instrument that it free of random error The reliability of a measure relatively related to the consistency and stability of that measurement In this research, there were independent scales and 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)

161 852 High

Reliability Effort Expectancy

(EE)

161 911 Excellent

Reliability Social Influence (

SI)

161 871 High

Reliability

Trust (TR) 161 911 Excellent

Reliability Behavioral

Intention ( BI)

161 870 High

Reliability

Use Behavior (UB) 161 535 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 4.4 Factor Analysis

4.4.1 Exploratory Factor Analysis (EFA)

The factor analysis with independent variables with Varimax rotation proposed Kaiser-Meyer-Olkin Measure of Sampling Adequacy at 0.846 which is between 0.5 and 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 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 the job right

32 Rotated Component Matrix

4.5 Multiple Variables Linear Regression Multiple variable linear regression 1st time

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Coefficients

Model

Unstandardized Coefficients

Standardi zed Coefficients

t Sig

Collinearity Statistics

B

Std

Error Beta

Toler

ance VIF

(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

Model Summary

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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Coefficients

Model

Unstandardized Coefficients

Standardize d Coefficients

t Sig

Collinearity Statistics

B

Std

Error Beta

Toler

ance VIF

(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:

BI = 0.417*PE + 0.274*EE + 0.297*SI 4.6 Binomial Logistic Regression

Recode rules for Use Behavior item: Item

Number

Score Binomial Value

23 [0,3]

24 [0,3]

25 [0,3]

Total [0,9]

Recode [0,3]

35

4.6.1 Block 0: Beginning Block

Classification Table

Observed Predicted

UB Percenta

ge Correct

.00 1.00

Step UB 00 60

1.00 101 100.0

Overall

Percentage 62.7

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

Variables in the Equation

B S.E Wald df Sig Exp(B)

Step Constant 521 163 10.208 001 1.683 Variables not in the Equation

Score df Sig Step Variables BI 20.175 000

EB 21.093 000

Overall Statistics 35.832 000

4.6.2 Block 1: Method = Enter

36

Chi-square df Sig

Step Step 41.436 000

Block 41.436 000

Model 41.436 000

Model Summary

Step

-2 Log likelihood

Cox & Snell R Square

Nagelkerke R Square

1 171.201a 227 310

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

Classification Table

Observed Predicted

UB Percentage

Correct 00 1.00

Step

UB 00 32 28 53.3

1.00 11 90 89.1

Overall

Percentage 75.8

a The cut value is 500 Variables in the Equation

B S.E Wald df Sig Exp(B)

Step 1a BI 1.175 306 14.757 000 3.238

EB 1.068 261 16.762 000 2.911

37

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

The logistic regression equation could be write as: ln(UB) = 1.175*BI + 1.068*EB – 5.458

4.7 Revised Research Model

Figure 0-1 Revised research model (Author) 4.8 Hypothesis Testing Results

Hypotheses Status

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

Not rejected

38

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

Not rejected

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

Rejected

Hypothesis 5: Behavioral Intention (BI) predicts customer’s frequencies of use of mobile payment services (UB)

Not rejected

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

39

CHAPTER 5: CONCLUSIONS AND RECOMMENDATIONS 5.1 Conclusion

Advance technology, infrastructure development and social movement are open the windows of opportunities to process more convenience and low-cost financial transactions Mobile banking, in order of transformation and adaptation with new trend of technology to provide more and more success for customer Mobile payment app, in another hand, move along with transitional of mega app such as WeChat or Go-Jerk, Zalo, the transitional is 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

40

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

5.2 Recommendation

From finding and conclusion as presents above, there should be some recommendation for mobile payment provider and policy maker which would try to improve cash less environment

From mobile payment provider perspective: There are 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

41

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

42

REFERENCES

Ajzen (1991) Understanding attitudes and predicting social behaviour. Englewood Cliffs,NJ: Prentice-Hall,Inc

Boudreau, M S (2004) Validation guidelines for IS positivist research

communications of the association for information systems, pp 380-427

Conner, A & (2001) The theory of planned behavior: Assessent of predictive validity and perceived control British Journal of Social Psychology, 35-45

Davis, F (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology MIS Quarterly, 319-340

Fishbein, A & (1980) Understanding attitudes and predicting social. Englewood Cliffs: Prentice-Hall,Inc

Gefen, D (2000) Ecommerce: the role of familiarity and trust Omega: The international Journal of Management Science 28, 725-737

Hinton, P & (2004) SPSS explained. East Sussex, England: Routledge,Inc

Kline, R (2005) Principles and practice of structural equation modeling In Principles and practice of structural equation modeling. New York: Guildwood

LEE, J (2018) Singtel expands mobile wallet across Southeast Asia and India Nikkei Asian Review

MARIMI KISHIMOTO, M T (n.d.) Alibaba out to dominate mobile pay in Southeast Asia Nikkei Asian Review, 2018

NAKANO, T (2018) Asian banks tear down brick-and-mortar expansion model

43

Pavlou, P (2003) Consumer acceptance of electronic commerce - integrating trust and risk with the technology acceptance model International Journal of Electronic

Commerce 7, 69-103

Prabhakar, K K (2002) Initial trust and the adoption of B2C e-commerce: the case of Internet banking The ddatabase for Advances in Information systems

Straub, D (1989) Validating instruments in MIS research MIS Quaterly, pp 147 - 166 Straub, D G (2001) Managing User TRust in B2C e-Services e-Services Quarterly Todd, T & (1995) Decomposition and crossover effects in the theory of planned

behavior: A study of consumer adoption intentions International Journal of Research in Marketing, 137-155

TOMIYAMA, A (2018) E-payment soars in Vietnam as a solution to skimpy bank coverage Nikkei Asian Review

Turban, M L (2001) A trust model for consumer Internet shopping International Journal of Electronic Commerce 6, 75-91

Venkatesh, & (2003) User acceptance of information technology: Toward a unified view MIS Quaterly 27, pp 425-478

Venkatesh, V & (2001) A longtitudinal investigation of personal computers in homes: Adoption determinants and emergin challenges MIS quaterly 25, pp 71-102 Venkatesh, V (2000) Deteminants of perceived ease of use: Integrating control

intrinsic motivation, and emotion into the technology acceptance model

44

Venkatesh, V (2000) Why don't men ever stop to ask for directions? Gender, social influence and their role in technology acceptance and useage behavior MIS Quarterly 24, pp 115-139

45 APPENDIX

Table 0.1 item total statistics of effort expectancy variable

46

Table 0.3 item total statistics of behavioral variable

Table 0.4 item statistic of use behavior variable

Linear Regression 1st time

Descriptive Statistics

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

Correlations

BI PE EE SI

TR ( nonTR6) Pearson

Correlation

47

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

Variables Entered/Removeda

Mo del Variables Entered Variables Removed Meth od TR

( nonTR6), PE, SI, EEb

Enter a Dependent Variable: BI

b All requested variables entered

Model Summaryb

Mo

del R

R Square Adjusted R Square Std Error of the Estimate Durbin-Watson 756a 571 560 46170 1.995 a Predictors: (Constant), TR ( nonTR6), PE, SI, EE

48

ANOVAa

Model

Sum of

Squares df

Mean

Square F Sig Regression 44.327 11.082 51.987 000b

Residual 33.253 156 213 Total 77.580 160

a Dependent Variable: BI

b Predictors: (Constant), TR ( nonTR6), PE, SI, EE

Coefficientsa Model Unstandardized Coefficients Standardiz ed Coefficients

t Sig

Collinearity Statistics B Std Error Beta

Tolera

nce VIF (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

Collinearity Diagnosticsa

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

nt) PE EE SI

TR ( nonTR6)

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

49 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

.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

Linear regression 2nd time

Descriptive Statistics

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

Correlations

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

50

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

Variables Entered/Removeda

Mo del Variables Entered Variables Removed Meth od SI, EE,

PEb Enter

a Dependent Variable: BI

b All requested variables entered

Model Summaryb

Mo

del R

R Square Adjusted R Square Std Error of the Estimate Durbin-Watson 755a 570 562 46075 1.998 a Predictors: (Constant), SI, EE, PE

b Dependent Variable: BI

ANOVAa

Model

Sum of

Squares df

Mean

Square F Sig Regressi

on 44.251 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

Coefficientsa Model Unstandardized Coefficients Standardiz ed Coefficients

t Sig

Collinearity Statistics B

Std

Error Beta

Tolera

nce VIF (Constant) 225 290 775 439

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

Coefficient Correlationsa

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

Collinearity Diagnosticsa

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

nt) PE EE SI

1 3.943 1.000 00 00 00 00 033 10.986 02 01 16 85 014 16.568 34 12 82 12 010 20.359 63 87 01 03 a Dependent Variable: BI

Residuals Statisticsa

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

.0000

0 45641 161 Std Predicted

52 Logistic Regression

Correlation Matrix Consta

nt BI EB

Step Constant 1.000 -.979 -.289

BI -.979 1.000 144

53

QUESTIONAIRES Part I:

1 Occupation  Student  Employee  Entrepreneur  Unemployment Your gender

 Male  Female  Other Your ages

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

 Northern part of Vietnam  Middle part of Vietnam  Southern part of Vietnam Monthly income

 None

54 Your highest education

 None

 High school graduate  Bachelor’s Degree

 Master’s Degree or Higher Marriage status

 Single  Married

 Divorce/ Widow Part II:

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

- Mobile Banking app with payment function such as QR scan: BIDV, Techcombank, Vietcombank, TPbank, Agribank,…

- Mobile wallet app: Momo, Viettel Pay, Zalo pay, Payoo, Moca,… - Mobile payment app issued by bank: VCBpay, TPbank Quickpay,… Please choose to what extent you agree with following statements:

(1) Strongly Disagree (2) Disagree (3) Neutral (4) Agree (5) Strongly Agree N

o Statement

1 2 3 4 5

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

55

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

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

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

1

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

2

I believe that Mobile Payment is trustworthy

1

3 I trust in Mobile Payment

4

I not doubt the honesty of Mobile Payment

1

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

1

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

7

Mobile Payment has the ability to fulfil its task

1

I intend to use Mobile Payment in the future

1

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

2

I plan to use Mobile Payment in future

2

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 N

o Statement

0 1 2 3

2

2 I am frequently using mobile e-commerce app

3

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

2

4 I am frequently using the mobile wallet app

5