(Luận văn thạc sĩ) study on mobile payment adoption in vietnam

VIETNAM NATIONAL UNIVERSITY, HANOI VIETNAM JAPAN UNIVERSITY DAO MANH TAN STUDY ON MOBILE PAYMENT ADOPTION IN VIETNAM MASTER’S THESIS BUSINESS ADMINISTRATION Hanoi, 2019 VIETNAM NATIONAL UNIVERSITY, HANOI VIETNAM JAPAN UNIVERSITY DAO MANH TAN STUDY ON MOBILE PAYMENT ADOPTION IN VIETNAM MAJOR: BUSINESS ADMINISTRATION CODE: 60340102 RESEARCH SUPERVISORS: ASSOC PROF NGUYEN VAN DINH PROF MOTONARI TANABU Hanoi, 2019 DECLARATION OF ACCEPTANCE I declare that this master thesis has been conducted solely by myself This master thesis has not been submitted in any previous articles or application for a degree, in whole or in apart The work contained herein is my own except where stated otherwise by reference or acknowledgment ACKNOWLEDGMENTS I would first thank both advisors Prof Tanabu of Graduate School of International Social Science – Yokohama National University I would like to express my gratitude to professor Tanabu for all the useful comments and engagement through the chain of seminars in YNU Furthermore, I would like to thank Assoc Prof Nguyen Van Dinh of Vietnam National University for wise advised and steered me in the right direction whenever I need in conducting this research I would like to express my sincere thanks for all of the VJU –MBA02 class for their kind support and advised Next, I would like to thank my survey’s participant who shared their time and precious idea Finally, I would like to express my gratitude to my parents to support me unfailing and continuous encouragement throughout my study and writing this thesis This accomplishment would not have been possible without them ABSTRACT In Vietnam “The number of e-payments grew 22% in 2017 from the previous year to $6.14 billion, according to Statista, a local market research firm The figure is projected to double to $12.33 billion in 2022” (TOMIYAMA, 2018) State-owned gas station operator Petro Vietnam Oil introduced a mobile payment system in February, while M-Service, a major fin-tech company, plans to increase the number of subscribers to its MoMo online payment service to 50 million by 2020 from about five million today The research focuses on 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 levels for observation variables: Performance expectancy, social influence, effort expectancy Trust and one dependent variable Behavior Intention The research using a frequency- scale levels for one independent variable: E-commerce Use Behavior and one dependent variable: Use behavior Among hypotheses, were not rejected and 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 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 3.2.1 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 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 Figure 4-1 Revised Research Model 37 CHAPTER 1: INTRODUCTION 1.1 Research motivation 1.1.1 Practical Motivation In the Asia region and ASEAN region: The movement of banking system along with a big leap of personal smartphone devices rate in ASEAN According to Nikkei Asian Review " In Indonesia, Digi bank drew about 600,000 users over the past year "In the next five years, we want to book around 3.5 million customers," said Wawan Salum, managing director of the consumer banking group at PT Bank DBS Indonesia (NAKANO, 2018) “Alibaba's core mobile payment service, Alipay, had more than 520 million users just in China at the end of 2017 The introduction of the service to Alibaba's Taobao.com shopping website the largest e-commerce platform in China -propelled a shift to cashless shopping in the country, including for small eaterie and shops Ant Financial works with CIMB Group Holdings, a bank in Malaysia, as well as Indonesian conglomerate Emtek Alibaba first offered electronic payment to the rising ranks of Chinese tourists to Southeast Asia Building on its experience in China, it seeks to become a major force in mobile payments in the region as well” (MARIMI KISHIMOTO) World Bank estimates that “the spread of smartphones has granted youth tools to easily fulfill bank transactions Only 20% of adult Indonesians held accounts in 2011, but the share has risen to 49% last year” and “Globally, about 1.7 billion adults have neither opened an account nor transferred money with a mobile phone, the World Bank estimates However, two-thirds of unbanked adults have mobile phones That shows digital banking could be ripe for an explosion in places like the Philippines and Vietnam.” (NAKANO, 2018) Alibaba's Ant Financial owns about 20% of True Money’s operator, which aims to expand its network 10-fold from the current level to 100,000 locations by the end of 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) 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 years, paperless money evolution in China worth 5.5 trillion USD (50 times the US market) E-Commerce included 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” 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 1.1.2 Theoretical Motivation 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 Nikkei Asean review 42 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 Information System Research, 342-365 43 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 Venkatesh, V M (2004) Individual reactions to new technologies in the workplace: The role of gender as as psychological construct Journal of applied social psychology, pp 445-467 44 APPENDIX Table 0.1 item total statistics of effort expectancy variable Table 0.2 item total statistics of social influence variable 45 Table 0.3 item total statistics of behavioral variable Table 0.4 item statistic of use behavior variable Linear Regression 1st time BI PE EE SI TR ( nonTR6) Descriptive Statistics Std Mean Deviation 4.3230 69633 4.4441 62623 4.3043 73670 3.7701863 84429130 35403727 7715728 3.486 N 161 161 161 161 8443 161 Correlations Pearson Correlation BI PE BI 1.000 656 PE 656 1.000 46 EE 515 429 SI 525 410 TR ( nonTR6) 358 325 Sig (1-tailed) N EE SI TR ( nonTR6) BI PE EE SI TR ( nonTR6) BI PE EE SI TR ( nonTR6) 515 525 429 410 1.000 208 208 1.000 330 352 358 325 330 352 1.000 000 000 000 000 000 000 000 000 004 000 000 004 000 000 000 000 000 000 000 000 161 161 161 161 161 161 161 161 161 161 161 161 161 161 161 161 161 161 161 161 161 161 161 161 161 Variables Entered/Removeda Mo del Variables Entered Variables Removed TR ( nonTR6), b PE, SI, EE a Dependent Variable: BI b All requested variables entered Meth od Enter Model Summaryb Mo del R 756a R Square 571 Adjusted R Square Std Error of the Estimate 560 46170 a Predictors: (Constant), TR ( nonTR6), PE, SI, EE b Dependent Variable: BI 47 DurbinWatson 1.995 ANOVAa Sum of Mean Model Squares df Square Regression 44.327 11.082 Residual 33.253 156 213 Total 77.580 160 a Dependent Variable: BI b Predictors: (Constant), TR ( nonTR6), PE, SI, EE Coefficientsa Standardiz Unstandardized ed Coefficients Coefficients Model B (Constant) 205 PE 459 EE 252 SI 238 TR 029 ( nonTR6) a Dependent Variable: BI Std Error 293 070 056 049 F 51.987 Beta 048 Sig .000b 412 266 288 t 702 6.577 4.471 4.847 Sig .484 000 000 000 035 597 552 Collinearity Statistics Tolera nce VIF 699 775 778 1.431 1.290 1.285 796 1.256 Collinearity Diagnosticsa Variance Proportions Mo Dimensi del on 1 Eigenva lue 4.908 035 Condition Index 1.000 11.763 (Consta nt) 00 03 PE 00 03 EE 00 03 SI 00 01 TR ( nonTR6) 00 98 033 014 010 a Dependent Variable: BI 12.263 18.533 22.713 02 32 62 00 11 86 14 81 01 83 13 03 01 01 00 Residuals Statisticsa 48 Mini mum 2.158 1.54267 Predicted Value Residual Std Predicted -4.112 Value Std Residual -3.341 a Dependent Variable: BI Maxi mum Std Deviation 1.1650 Mean 4.323 0000 1.455 000 1.000 161 2.523 000 987 161 5.0889 52635 161 45589 161 Linear regression 2nd time Descriptive Statistics Std Mean Deviation 4.3230 69633 4.4441 62623 BI PE E SI 73670 161 3.7701863 35403727 84429130 7715728 161 Pearson Correlation Sig (1-tailed) N N 161 161 4.3043 E BI PE EE SI BI PE EE SI BI Correlations BI PE 1.000 656 656 1.000 515 429 525 000 000 000 161 410 000 000 000 161 49 N EE 515 429 1.000 SI 525 410 208 208 000 000 004 161 1.000 000 000 004 161 PE EE SI 161 161 161 161 161 161 161 161 161 161 161 161 Variables Entered/Removeda Mo Variables Variables Meth del Entered Removed od SI, EE, Enter b PE a Dependent Variable: BI b All requested variables entered Model Summaryb Std Error of the Estimate 46075 Mo R Adjusted Durbindel R Square R Square Watson a 755 570 562 1.998 a Predictors: (Constant), SI, EE, PE b Dependent Variable: BI ANOVAa Sum of Mean Model Squares df Square F Sig Regressi 69.48 44.251 14.750 000b on Residual 33.329 157 212 Total 77.580 160 a Dependent Variable: BI b Predictors: (Constant), SI, EE, PE Coefficientsa Standardiz Unstandardized ed Collinearity Coefficients Coefficients Statistics Std Tolera Model B Error Beta t Sig nce VIF (Constant) 225 290 775 439 PE 463 069 417 6.706 000 708 1.412 50 EE 259 SI 245 a Dependent Variable: BI 055 047 274 297 Coefficient Correlationsa Model SI EE Correlati SI 1.000 -.039 ons EE -.039 1.000 PE -.363 -.386 Covarian SI 002 000 ces EE 000 003 PE a Dependent Variable: BI -.001 4.728 5.175 000 000 815 831 1.228 1.204 PE -.363 -.386 1.000 -.001 -.001 -.001 005 Collinearity Diagnosticsa Variance Proportions Mo Dimens del ion 1 EBgenv alue 3.943 033 Condition Index 1.000 10.986 (Consta nt) 00 02 PE 00 01 EE 00 16 SI 00 85 014 010 a Dependent Variable: BI 16.568 20.359 34 63 12 87 82 01 12 03 Predicted Value Residual Residuals Statisticsa Mini Maxi mum mum Mean 2.159 4.323 5.0620 1.1452 0000 1.56204 Std Predicted -4.113 Value Std Residual -3.390 a Dependent Variable: BI Std Deviation N 52590 161 45641 161 1.405 000 1.000 161 2.486 000 991 161 51 Logistic Regression Correlation Matrix Consta nt BI EB 1.000 -.979 -.289 BI -.979 1.000 144 EB -.289 144 1.000 Step Constant 52 QUESTIONAIRES Part I: 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 Under million vnd -12 million vnd 13 -20 million vnd Over 20 million vnd 53 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 I find Mobile Payment useful in my daily life Using Mobile Payment increases my chances of achieving tasks that are important to me Using Mobile Payment helps me accomplish tasks more quickly 54 Using Mobile Payment increases my productivity Learning how to use Mobile Payment is easy for me My interaction with Mobile Payment is clear and understandable I find Mobile Payment easy to use It is easy for me to become skilful at using Mobile Payment People who are important to me think that I should use Mobile Payment People who influence my behaviour think that I should use Mobile Payment People whose opinions that I value prefer that I use Mobile Payment I believe that Mobile Payment is trustworthy I trust in Mobile Payment I not doubt the honesty of Mobile Payment I feel assured that legal and technological structures adequately protect me from problems on Mobile Payment Even if not monitored, I would trust Mobile Payment to the job right Mobile Payment has the ability to fulfil its task I intend to use Mobile Payment in the future I will always try to use Mobile Payment in my daily life I plan to use Mobile Payment in future I predict I would use Mobile Payment in the future 55 Part III: Please choose to the appropriate frequency of use as statement below: (0) Less than once a month N o 2 (1) Monthly (2) Weekly Statement I am frequently using mobile e-commerce app I am frequently using the mobile payment function on the mobile banking app I am frequently using the mobile wallet app I am frequently using the mobile payment app issued by the bank 56 (3) Daily ... 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. .. 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.. .VIETNAM NATIONAL UNIVERSITY, HANOI VIETNAM JAPAN UNIVERSITY DAO MANH TAN STUDY ON MOBILE PAYMENT ADOPTION IN VIETNAM MAJOR: BUSINESS ADMINISTRATION CODE: 60340102 RESEARCH