Factors influencing the customers intention to use mobile commerce services in vietnam an empirical analysis

In order to evaluate those factors, the extended Technology Acceptance Model TAM that integrates Innovation Diffusion Theory IDT is implemented to investigate what determine users’ adopt

UNIVERSITY OF ECONOMICS HO CHI MINH CITY

International School of Business

Tran Duc Thuan

FACTORS INFLUENCING THE CUSTOMERS’ INTENTION TO USE MOBILE COMMERCE

SERVICES IN VIETNAM: AN EMPIRICAL ANALYSIS

Ho Chi Minh City - Year 2015

UNIVERSITY OF ECONOMICS HO CHI MINH CITY

International School of Business

Tran Duc Thuan

FACTORS INFLUENCING THE CUSTOMERS’ INTENTION TO USE MOBILE COMMERCE

SERVICES IN VIETNAM: AN EMPIRICAL ANALYSIS

ID: 22120112

SUPERVISOR: Dr DINH CONG KHAI

ACKNOWLEDGEMENTS

The research described in this Master’s thesis was carried out during the second half of the year 2014 in Ho Chi Minh and Hanoi City I would like to take this honor to acknowledge those who always helped, encouraged, and supported me during writing this thesis, who I will always passionately remember

First of all, I would like to express my gratitude to my supervisor, Dr Dinh Cong Khai, for his guidance, advices and support throughout my thesis It is due to his patient encouragement that I was motivated to accomplish my Master’s thesis Secondly, I would like to thank all the ISB Research Committee, the lecturers, and the staff at International School of Business and University of Economics Ho Chi Minh City for everything you have done for me during the MBA course Thirdly, I would like to express my special thanks to my beloved family and friends who provided support and encouragement throughout this long process Finally, I would like to show my thankfulness to those who participated in this study Your valuable contributions play an important role for the completion and success of the study

Ho Chi Minh City, Vietnam,

December 07th, 2014

Tran Duc Thuan

The purpose of this study is to investigate the factors influencing the customers’ intention to use M-commerce in Vietnam In order to evaluate those factors, the extended Technology Acceptance Model (TAM) that integrates Innovation Diffusion Theory (IDT) is implemented to investigate what determine users’ adoption of M-commerce, in which we explore the relationships among those following factors, namely, Perceived Usefulness & Compatibility, Perceived Ease

of Use, Perceived Cost, Perceived Trust toward Intention to Use M-commerce services Furthermore, Self – Efficacy was analyzed as a moderator

The quantitative research method was used Through the direct and online survey, data collected from 608 users in Vietnam were tested against the research model using the hierarchical multiple regression analysis approach The results strongly support the proposed conceptual model in predicting customer’s intention to use M-commerce services The findings made a contribution in terms of allowing us to understand the factors that can contribute to the adoption of M-commerce in Vietnam This study successfully extends the TAM in the context of M-commerce

by incorporating three additional constructs – Compatibility, Perceived Cost and

technology/information system acceptance research and M-commerce service management practices are discussed

Keywords: Mobile commerce, M-commerce, Perceived Usefulness & Compatibility, Perceived

Ease of Use, , Perceived Cost, Perceived Trust, Behavioral intention

TABLE OF CONTENTS

ACKNOWLEDGEMENTS i

ABSTRACT ii

TABLE OF CONTENTS iii

LIST OF FIGURES vi

LIST OF TABLES vii

CHAPTER 1: INTRODUCTION 1

1.1 Research Background 1

1.2 Research Motivation 2

1.3 Statement of purposes and research questions 3

1.4 Scope of the study 3

1.5 Significance of the study 3

1.6 Research methodology 4

1.7 Thesis structure 4

CHAPTER 2: LITERATURE REVIEW 6

2.1 M-commerce 6

2.1.1 Mobile Payments 7

2.1.2 Mobile Banking 8

2.1.3 Mobile Advertising 8

2.1.4 Mobile Entertainment 9

2.1.5 Mobile Shopping and Retailing 9

2.1.6 Mobile Ticketing 10

2.1.7 Mobile Learning 10

2.2 M-commerce Technologies 11

2.2.1 General Packet Radio Service (GPRS) 11

2.2.2 Wireless Application Protocol (WAP) 12

2.2.3 Wireless Local Area Network (WLAN) 12

2.2.4 The Third Generation - 3G 13

2.2.5 The Fourth Generation - 4G 13

2.3 Technology Acceptance Model (TAM) 13

2.4 Rationale for hypotheses 15

2.4.1 Perceived usefulness and the behavioral intention to use M-commerce 15

2.4.2 Perceived ease of use and the behavioral intention to use M-commerce 16

2.4.3 Compatibility and the behavioral intention to use M-commerce 16

2.4.4 Perceived cost and the behavioral intention to use M-commerce 18

2.4.5 Perceived trust and the behavioral intention to use M-commerce 18

2.4.6 Moderating variable – Self-efficacy 19

2.5 Research Model 22

CHAPTER 3: RESEARCH METHODOLOGY 23

3.1 The research approach and research procedure 23

3.2 Measurement scale 23

3.2.1 Independent and moderating variables 23

3.2.2 Dependent variable: consumer’s intention to use M-commerce 24

3.3 Sampling design 26

3.3.1 Population 26

3.3.2 Sample size 27

3.4 Questionnaire design 27

3.5 Pilot survey 28

3.6 Data collection 28

3.7 Data analysis method 29

CHAPTER 4: EMPIRICAL RESULTS & DISCUSSIONS 32

4.1 Samples and Demographics statistics 32

4.2 Reliability analysis 35

4.3 Exploratory Factor Analysis 37

4.4 The revised hypotheses and conceptual model 39

4.5 Correlation analysis 43

4.6 Multiple Linear Regression Analysis 44

4.7 Discussions of findings 47

4.7.1 Discussions of findings for H1, H2, H4, H5 47

4.7.2 Discussions of findings for Moderation of Self-efficacy 48

CHAPTER 5: CONCLUSION AND RECOMMENDATIONS 54

5.1 Conclusions 54

5.2 Theoretical implications 56

5.3 Managerial implications 56

5.4 Limitations and recommendations for future researches 59

REFERENCES 60

APPENDIX A: QUESTIONAIRE 67

APPENDIX B: DATA ANALYSIS OUTPUT 74

LIST OF FIGURES

Figure 1: Research Model 22

Figure 2: Research Procedure 24

Figure 3: Revised Conceptual Model 43

Figure 4: The Histogram 84

Figure 5: The Normal P-P Plot of Regression Standardized Residual 85

Figure 6: Scatterplot 85

LIST OF TABLES

Table 3.1: Sources of Measurement 25

Table 3.2: Scale items of factors influencing the intention to use M-commerce 25

Table 4.1: The profile of respondents 33

Table 4.2: The reliability statistics 36

Table 4.3: KMO and Barlett’s test 37

Table 4.4: Total Variance Explained 38

Table 4.5: Rotated Component Matrix 39

Table 4.6: The revised Cronbach’s Alpha summary after EFA 40

Table 4.7: Result of factor analysis 41

Table 4.8: Correlations Matrix 43

Table 4.9: Coefficient of multiple linear regression analysis 46

Table 4.10: Model summary of multiple linear regression analysis 46

Table 4.11: Model summary with moderating variable of Self Efficacy 49

Table 4.12: Hierarchical Regressions result with moderating variable of Self-Efficacy 51

Table 4.13: Hypotheses Summary 52

CHAPTER 1: INTRODUCTION 1.1 Research Background

Mobile Commerce (M-commerce) is a concept that involves different applications, new technologies and services which are accessible from Internet enabled Mobile devices Mobile Commerce is also known as Mobile Electronic Commerce (Zhang

et al., 2012) In other words, M-commerce transactions are basically electronic transactions conducted using a mobile terminal and a wireless network Mobile terminals include all portable devices such as smartphones, laptops, net books, tablet computers and phablets as well as devices mounted in the vehicles that are capable of accessing wireless networks and performing M-commerce transactions (Veijalainen et al., 2006) A simple definition of M-commerce describes it as any transaction with a monetary value that is conducted via a mobile telecommunications network (Müller-Veerse, 1999) M-commerce is a new concept and emerging in a context of an established norms, rules and standards With an increasingly powerful mobile technologies like 3G and the Internet of Things, M-commerce has emerged as a new business phenomenon and has become a market with great potential (Zhang et al., 2012) It will continue to extend the way organizations conduct business, and change the relationships between companies, customers, suppliers and partners

A report by the end of 2012 from BI Intelligence took a close look at the dollar value potential of the mobile commerce market and showed that in the United States, 29 percent of mobile users had made a purchase with their phones Tablets even drove more traffic to online retailers than smartphones, and tablet consumers spent more per transaction than PC-based shoppers According to Bank of America’s forecast, in year 2015, American and European shoppers will spend

$67.1 billion on smartphone and tablet retail purchases An illustration of a topical success of M-commerce is Uber Uber is a ridesharing service headquartered in San Francisco, United States, which operates in multiple international cities The company uses a smartphone application to arrange rides between riders and

drivers By August 29, 2014, Uber has been very successful in 45 countries and more than 200 cities around the world and the estimated value of the company up

to US$18.2 billion Despite having some legal issues, the company received more than US$300 million from the venture capital companies like Google Ventures, Benchmark, and individual investors like CEO of Amazon.com - Jeff Bezos At present, Uber is available in Ho Chi Minh City and Hanoi, Vietnam

In Vietnam, there are a growing number of wireless technology users in mobile devices, especially smartphones and tablets Data from the Vietnamese General Statistics Office (GSO) showed that there were 120 million mobile subscribers in Vietnam by the end of September 2012, and 16 million 3G subscribers in the country by May 2012 Ministry of Information and Communications (MIC) has predicted that there would be 137 million mobile subscribers and 35 million 3G subscribers by the end of 2017 (MIC, Vietnam Telecommunication Report Q 1, 2013) According to Spire, a market research and consulting company, Vietnam is expected to have one smartphone user in every four citizens, increasing the number of smartphones in use in the local market to 20 million units by 2014 Consumers have shifted to using smartphones to connect to social networks, access the Internet and digital entertainment

1.2 Research Motivation

Despite the rapidly growing number of mobile device users and 3G subscribers in Vietnam, M-commerce is a relatively new service compared to other markets The number of people who choose to adopt or use M-commerce technology is still rather low in comparison with other countries such as Europe, The U.S., Japan, South Korea and Singapore, etc The M-commerce service providers and marketers in Vietnam are lacking of a clear direction toward understanding the factors affecting the adoption of M-commerce The current key providers in the market have not satisfied the users with the services well enough Furthermore, mobile devices and wireless technologies are always changing and becoming more and more modern and diverse with a rapid speed, but there are not many

researchers updating studies about this problem in Vietnam to find out the effective way to attract Vietnamese customers' intention toward M-commerce use

To fill those gaps, this research will help find out what factors affect the Vietnam consumers' intention to use the M- commerce services

1.3 Statement of purposes and research questions

This study aims to identify factors that can explain the customers’ intention to use M-commerce in Vietnam To be able to achieve the stated purpose above, following research questions will be investigated:

• What factors affect customers' intention to use M-commerce in Vietnam?

• How does the proposed conceptual research model explain the variances of customer’s M-commerce acceptance intention?

1.4 Scope of the study

The target population of this study includes individuals in Vietnam who are owners of mobile phones or other mobile devices which can be used to connect and access to the Internet through a wireless network Given the limited resources and time, the empirical data for the study was collected from the universities and offices in Ho Chi Minh and Hanoi city, Vietnam Interviewees on this study include students and white - collar workers

1.5 Significance of the study

Nowadays, with a very harsh business competition and intensive growing of the applications of the wireless technologies and mobile devices, this study hopes to explore the insights of the Vietnamese customers about M-commerce services and what key factors which affect them in adoption of the services It will become more and more important how the users perceive the service and the emotional impact and pleasure that the service creates and maintains By explaining usage intention from consumers’ perspectives, we hope that the findings of this study will help M-

commerce practitioners to develop better user accepted M-commerce systems, as well

as provide insights into how to promote advanced information technology to potential customers By knowing all these factors, it may help businesses to build effective and efficient strategies to improve their service quality, to have competitive advantages to others in the market, to maintain their current market share, and even gain more market share

1.6 Research methodology

To analyze the relationship between one construct with another to test the hypotheses which are proposed in the following parts, the quantitative approach is employed in this study Survey questionnaire with multiple choice answers were designed based on reviewing literatures, previous studies of the same or similar topics Pilot survey was conducted Random sampling was chosen to reduce bias in the sample The data collected from the survey was coded, analyzed and interpreted by IBM SPSS Statistic Descriptive Statistics, Cronbach Alpha, Exploratory Factor Analysis (EFA), Pearson’s Correlation Coefficient and Hierarchical Multiple Regression analyses were employed More details of the research methodology are provided in Chapter 3 and the results of the study are presented in Chapter 4

• Chapter 2 introduces the literature review, rationale for hypotheses as well as the proposed conceptual research model

• Chapter 3 illustrates the detailed research methodology: research process, research design, measurement of the constructs, data collection procedures and

data analysis framework

• Chapter 4 describes empirical results and discussions based on the data collected: characteristics of the sample, analyzing the reliability and validity, testing the assumption of regression and testing hypotheses

• Chapter 5 summarizes the discussions on the research findings, theoretical contributions, managerial implications, limitations of the study, and the

suggestions for future researches

CHAPTER 2: LITERATURE REVIEW

This chapter presents a review of relevant literature associated with Technology Acceptance Model (TAM) and factors affect customers' intention to use M-commerce It consists of definitions related to M-commerce and their subsets, technology for M-commerce This chapter also states the hypotheses and proposes

conceptual model for the study

or “M-commerce”, signifies an "anytime and anywhere access" to business processes managed by computer-mediated networks The access takes place using mobile communication networks, making the availability of these services independent of the geographic location of the users (Stanoevska-Slabeva, 2003; Hohenberg and Rufera, 2004)

M-commerce is a new concept which is hard to define since there are many different definitions for it, and there are constantly new definitions arising with time For instance, M-commerce is the use of mobile (hand-held) devices to communicate and conduct transactions through public and private networks (Balasubramanian et al., 2002) Abu Bakar and Osman (as cited in Wei et al., 2009) defined M-commerce as exchange or buying and selling of commodities and services through wireless handheld devices such as cellular telephones and personal digital assistant (PDAs) M-commerce is seen as an E-commerce over the wireless

devices (Varshney and Vetter, 2002) However, Feng et al (as cited in Wei et al., 2009) suggested that M-commerce is more than E-commerce due to its different interaction style, usage pattern and value chain Feng et al (as cited in Wei et al., 2009) stated that M-commerce is a new and innovative business opportunity with its own unique characteristics and functions, such as mobility and broad reachability According to Tiwari and Buse (2007), M-commerce is defined as any transaction, involving the transfer of ownership or rights to use goods and services, which is initiated and/or completed by using mobiles access to computer-mediated networks with the help of mobile devices

Nowadays, M-commerce is being applied in more and more fields It has many applications such as: mobile payments, mobile banking, mobile advertising, mobile entertainment, mobile shopping and retailing, mobile ticketing and mobile learning, etc It is important to describe and understand different types of M-commerce applications The followings present an essence of predominant M-commerce services:

2.1.1 Mobile Payments

Mobile payment, also referred to as mobile money, mobile money transfer, and mobile wallet, generally refer to payment services operated under financial regulation and performed from or via a mobile device Instead of paying with cash, cheque, or credit cards, a consumer can use a mobile device to pay for a wide range of services or goods Mobile payments are expected to become one of the most important applications in M-commerce (Mallat et al., 2004) Currently, there are large numbers of smartphones and tablets that have set the stage for a new move - the payment transactions can be completely performed through a mobile device In 2011, the first step of Near Field Communication (NFC) technology was developed when it was starting to become a new standard, and many major financial institutions also supported this trend Most smartphone manufacturers are releasing products that support NFC technology Google Wallet has used NFC technology to help customers pay for their items In year 2014, Apple has

announced mobile payment service, Apple Pay The service lets MasterCard cardholders use Apple devices (iPhone 6, iPhone 6 Plus and Apple Watch) to wirelessly communicate with point of sale systems using NFC for everyday shopping

2.1.2 Mobile Banking

Mobile banking is a system that allows customers of a financial institution to conduct a number of financial transactions through a mobile device such as a mobile phone or tablet There are two most common modes used to deliver mobile banking are WAP banking and SMS banking The earliest mobile banking services were SMS banking Banks provide SMS banking services based on creating SMS messages which are sent to customer service centers and getting personal account and related information via customers’ mobile phones (Barnes and Corbitt, 2003) With the introduction of smartphones with WAP support enabling the use of the mobile web, banks started to offer mobile banking on this platform to their customers WAP banking allows customers to interact via a browser (Barnes and Corbitt, 2003) and confirm via a PIN and have transactions authorized via transaction numbers (Laukkanen and Lauronen, 2005) Mobile banking provides various services such as confirmation of direct payments, stock trading, or transactions between accounts Furthermore, they can pay bills and trade equities using a menu-based interface (Mallat et al., 2004)

2.1.3 Mobile Advertising

Mobile advertising is a form of advertising via mobile phones or other kinds of mobile devices It is a subset of mobile marketing Mobile advertising is a very important session of M-commerce; it augments location information with personalization and delivers the obtained history of customers’ purchasing habits Advertising on mobile devices has large potential due to the very personal and intimate nature of the devices and high targeting possibilities (Aalto et al., 2004)

By keeping track of customers’ purchasing habits and current location, a targeted advertising campaign can be performed Messages can be sent to all users who are

currently in a certain area (identified by advertisers or even by customers) or to certain customers in all locations Depending on interests and personality types of individual users, advertisers could decide whether a “push” or “pull” form of advertising is more suitable (Varshney, 2003) Most users do not mind being pushed for mobile location-aware services information, as long as they really needed the information (Aalto et al, 2004) It has been demonstrated in several trials that mobile users are willing to receive advertising messages with incentives (Varshney, 2003)

2.1.4 Mobile Entertainment

Moore and Rutter (2004) have defined mobile entertainment as any leisure activity undertaken via a personal technology which is or has a potential to be, networked and facilitates transfer of data (including voice, sound and images) over geographic distance either on the move or at a variety of discrete location Mobile devices offer the opportunity to play games nearly everywhere Networked games allow individual players to interact with other people and to participate in a larger gaming world, which also provides for new business opportunities (Akkawi et al., 2004) Mobile music is contained in the mobile entertainment services (MacInnes

et al., 2002) Mobile music services are underlined to try to benefit of the unique features of mobile network technology, for example, ubiquity, availability, localization, approachability, and personalization (Baldi and Thaung, 2002) Customers can purchase their favorite songs on online music stores which are then transferred right to their mobile devices

2.1.5 Mobile Shopping and Retailing

Mobile devices extend users’ ability to make transactions across time and location and create new transaction opportunities At the current stage of technological development, customers must ideally be faced with a one-button purchase experience for mobile shopping (Müller-Veerse, 1999) Mobile retailing is an interesting M-commerce application It combines with location identification to create a new value, for instance, when ordering a taxi or a pizza, the vendor can

automatically know where the service is to be delivered Uber ridesharing service

is an example Users just need to install Uber application on their smartphones then can order cars With a few simple tasks on the phone, the user will have a luxury car pick-up from and deliver to the desired location with even lower cost than the taxi fares Mobile applications can make shopping easier for consumers Retailers can increase revenue by capturing more mobile consumers by promoting applications across channels in the application itself, through email and online advertising Retailers can also increase loyalty by saving customers’ time, money and energy Moreover, retailers can increase the customers’ awareness and engagement by successfully develop their applications to make their brand more accessible to the customers, and contribute to their lives in a meaningful way that goes far beyond retail sales

2.1.6 Mobile Ticketing

Mobile electronic purchase or reservation of tickets is one of the most compelling proposed services, because ticket reservation/ purchasing are hardly a pleasant experience today Either one has to go in person to a ticket booth, or has to call an agency or the outlet Calling outside opening hour means having to go through a lengthy IVR (Intelligent Voice Response) system It is clearly more convenient to select and book tickets for movies, theatres, opera and concerts, etc directly from

a mobile device, because often the decision to purchase is made while outside or

on the move among friends (Müller-Veerse, 1999) This is one of the first WAP applications being seen in various markets The travel market and especially the frequent business traveler market are likely to be an early WAP growth market Using a WAP handset, train, plane, bus and boat tickets could be booked Tickets

for culture or sport events could also be reserved in a similar manner

2.1.7 Mobile Learning

Mobile learning involves the use of mobile technology, either alone or in combination with other information and communication technology (ICT), to enable learning anytime and anywhere O’Malley et al (2003) defined mobile

learning as any sort of learning that happens when the learner is not at a fixed, predetermined location, or learning that happens when the learner takes advantage

of learning opportunities offered by mobile technologies Traxler (2005) defined mobile learning as any educational provision where the sole or dominant technologies are handheld or palmtop devices In addition, Sharples et al (2007c) defined mobile learning as the processes of coming to know through conversations across multiple contexts amongst people and personal interactive technologies Learning can unfold in a variety of ways: people can use mobile devices to access educational resources, connect with others, or create content, both inside and outside classrooms The smart mobile devices are now capable of playing video files, which paves the way for providing rich sources of multimedia educational materials to learners

2.2 M-commerce Technologies

2.2.1 General Packet Radio Service (GPRS)

GPRS, referring to General Packet Radio Service, is defined by Buckingham (2000) as a non-voice service that permits to transmit data speedily In another word, it is considered as a packet-switch technology allowing data sent to be broken into small packets which are transmitted by the network between different positions based on dispatching data within each packet (Tiwari et al., 2006) GPRS enabled mobile devices, which depend on network coverage of the geographical areas, always access to network Those devices do not require a dial-up connection

to obtain information (Buckingham, 2000) Also, Buckingham (2000) stated that an access to services such as E-mail, Internet-based webpages, music, and office application is also provided by GPRS Besides, the volume of the emitted data is paid only and not the time required in the downloading process when users use GPRS services

2.2.2 Wireless Application Protocol (WAP)

WAP Forum developed WAP by employing a collective set of applications and protocol By using WAP, the interoperability among various wireless networks, devices, and applications is enabled Moreover, WAP employs a micro browser which assists graphics, text, and standard Webpage content Besides, the WAP Gateway which is the most crucial technology applied to WAP, is essentially considered as a border between the Internet and the Network The Gateway plays a role as a proxy which delivers WAP requests to protocols operated by the information server Also, the WAP Gateway performs as an interpreter between a mobile device and a web server which interprets and translates the information to help the server and the mobile device communicate each other (Lei et al., 2004) According to WAP Forum 2001 (as cited in Wang et al., 2006), WAP presents an wide industry specification for improving applications which run on mobile telecommunication network and send Internet contents via mobile devices without using technologies of network carriers

2.2.3 Wireless Local Area Network (WLAN)

The WLAN technology is applied to wireless communication and assists data transmit rates up to 54 megabits per second which is faster than 3G (Kaemarungsi and Krishnamurthy, 2004) Mostly, developing WLAN systems is based on the standards presented by the US Institute of Electrical and Electronics Engineers (IEEE) Besides, Access Points which are known as Hotspots provides the interface between mobile devices and WLAN Moreover, the range in which allows users to connect to WLAN is approximately 100 meters in building blocks and 300 meters

on open ground Furthermore, WLAN enables subscribers to use data-intensive mobile applications outside of their house or offices in compared to the standards

of the 3G technology

2.2.4 The Third Generation - 3G

The aim of the 3G technology is to offer a broad range of services such as speed internet access, video call and interactive multimedia services (Tiwari et al., 2006) Thanks to the high speed of data emitted, the 3G technology has become more suitable for time-critical applications Tiwari et al (2006) also mentioned that the 3G standard is based on a radio access technology called Wideband Code Division Multiple Access Those standards use the Direct Sequence Spread Spectrum (DSSS) in a 5-MHz bandwidth Moreover, mobile devices are required

high-to be compatible with the GSM/GPRS standards because 3G can perform strongly

in municipal areas

2.2.5 The Fourth Generation - 4G

4G, or Fourth G, which stands for fourth-generation wireless communication technology, allowing data transfer with maximum speed in ideal conditions up to 1.5 Gb/ second The name 4G was set out by IEEE to express meaning "3G and beyond" 4G technology is understood as the future standard of wireless devices The Japanese operator NTT DoCoMo defines 4G by introducing the concept of mobile multimedia: anytime, anywhere, anyone; global mobility support; integrated wireless solution; and customized personal service (MAGIC) (Murota,

as cited in Frattasi, 2006) In the world now, there are two standards of 4G network's core technology They are WiMax and Long Term Evolution (LTE) Both WiMax and LTE are using the advanced transmitting technology to enhance the ability of reception and operation of equipment and networks However, each technology uses a different frequency bank A wireless network using 4G technology will have the speed of 4 to 10 times faster than 3G’s, allowing users to download and transfer high quality animation

2.3 Technology Acceptance Model (TAM)

As M-commerce is a type of technological innovation (Lin and Wang, 2005); existing theories and studies on innovation adoption could be used in the studies of

M-commerce One of the most common models used by researchers in the studies

of individual’s adoption of technology is Technology Acceptance Model (TAM), which was first proposed by Davis in 1989 based on the Theory of Reasonable Action (TRA) (Ajzen, 1991) The TAM has been used to predict users’ intention to accept or adopt varieties of technology and information systems, and has also recently been used to predict the Internet and M-commerce adoption O'cass and Fenench (2003) argued that the TAM is appropriate for research areas in E-commerce applications since E-commerce is based on computer technology As scholars indicate that M-commerce is an extension of E-commerce, the TAM can be applied to M-commerce The TAM proposed that both the perceived usefulness and perceived ease of use can be used to predict the attitude towards using new technology, which in turn affects the behavioral intention to use the actual system directly (Davis, 1989; Venkatesh et al., 2003)

However, many researches state that TAM itself is insufficient to explain users’ decisions to adopt technologies Mathieson et al (2001) argued that the TAM is limited by the lack of barriers that inhibit the individual from using an information system (IS) if he or she chooses to do so One of the approaches uses the TAM as a base model and extends the model by adding additional variables to it depending

on the types of technologies they studied This extended TAM maintains the primary simplicity of the TAM and improves the ability to predict and explain IS usage at the same time (Mathieson et al., 2001) Kamarulzaman (2007) on his study

of internet shopping adoption drew upon the TAM and included personal and cognitive influence Amin (2007) also modified the original TAM by including perceived credibility and the amount of information on mobile credit card to his study of mobile credit card usage intentions Various extensions to the TAM were also conducted in the study of M-commerce such as those conducted by Wu et al (2005) used the TAM as a base and included some factors such as perceived risk, cost and compatibility Wei et al (2009) on his study on mobile commerce adoption in Malaysia added to the TAM three additional constructs: social influence, perceived cost and perceived trust which derived from Ajzen’s Theory of

Planned Behavior (TPB) and Rogers’ Innovations Diffusion Theory (IDT) Schierz

et al (2010) extended the TAM by including four additional constructs: perceived compatibility, security, individual mobility and subjective norm Gil-Lafuente (2011) modified the TAM by adding three factors: incentives, perceived enjoyment and social influence Jeong and Yoon (2013) on his empirical investigation on consumer acceptance of mobile banking services put perceived credibility, self-efficacy and perceived financial cost to the TAM

Based on the theoretical and empirical support from the existing studies, this research will also extend the TAM by including three additional constructs which

we believe are the most important for the studies of M-commerce adoption in Vietnam, they are compatibility (CO), perceived cost (PC) and perceived trust (PT),

as explained below

2.4 Rationale for hypotheses

2.4.1 Perceived usefulness and the behavioral intention to use M-commerce

Perceive usefulness (PU) is defined as the degree of which an individual believes that using a system would improve his or her job performance (Davis, 1989) The effect of the PU on intention to use (IU) has been validated in many existing studies (Luarn and Lin, 2005; Lin and Wang, 2005; Guriting and Ndubisi, 2006) For instance, Wong and Hiew (2005) recommended that the usage of M-commerce is strongly determined by the usefulness of the mobile service, which includes ubiquity, personalization, localization, timeliness and network stability Wei et al (2009) examined mobile commerce adoption in Malaysia In his findings, PU was empirically found to be the most significant determinant to predict consumers’ intention to use M-commerce in Malaysia Thus, based on findings shown above, it is highly supported that the general results studied in the TAM are also applied to M-commerce services Hence, the following hypothesis is presented:

H1: Perceived Usefulness has a positive effect on behavioral intention to use

M-commerce

2.4.2 Perceived ease of use and the behavioral intention to use M-commerce

Another key element in the TAM is the perceived ease of use (PEU) The PEU refers to the degree to which a user perceives that an information system (IS) is easy to understand and use (Davis, 1989) The PEU for a system is defined as the degree to which an individual believes that using a particular technology will be free of effort The results of many of the prior empirical studies have demonstrated that the PEU has a positive correlation with behavioral intention (Davis, 1989; Venkatesh and Davis, 2000; Gefen, 2000; Gefen et al., 2003; Venkatesh et al, 2003) A few empirical studies tested ease of use as predominant determinant of intention to adopt (e.g Agarwal et al., 2000; Karahanna et al., 1999) It is one of the major behavioral beliefs influencing users’ intention to technology acceptance

in both the original and revised TAM and it has been included in the studies to determine this influence the M-commerce intent as well Hong et al (2006) stated that the perceived ease of use has an impact on the behavioral intentions to use in terms of mobile commerce services Therefore, a hypothesis is suggested to present this relationship between those variables:

H2: Perceived ease of use has a positive effect on the behavioral intention to

use M-commerce.

2.4.3 Compatibility and the behavioral intention to use M-commerce

Innovations Diffusion Theory (IDT) is one well-known theory proposed by Rogers (1995) In recent decades, IDT has been widely used for relevant IT and IS researches (Karahanna et al., 1999).The central concept of innovation diffusion is the process in which an innovation is communicated through certain channels, over time, among the members of a social system IDT includes five significant innovation characteristics: relative advantage, compatibility, complexity, trial ability, and observables These characteristics are used to explain the user’s

adoption and decision making process They are also used to predict the implementation of new technological innovations and clarify how these variables interact with one another

Compatibility is one of the important innovation characteristics and refers to the degree to which an innovation is perceived to be consistent with the potential adopters’ existing values, previous experiences, and needs (Rogers, 1995) It is related with how well the innovation fits into the adopters’ existing social structure When an innovation perceived as compatible, it is perceived as consistent with an individual’s life situation High compatibility will lead to preferable adoption An idea that is not compatible with the prevalent values and norms of a social system will not be adopted as rapidly as an innovation that is compatible The adoption of an incompatible innovation often requires the prior adoption of a new value system

According to Rogers (1995), an idea that is more compatible is less uncertain to the potential adopter, and fits more closely with the individual's life situation Such compatibility helps the individual give meaning to the new idea so that it is regarded as familiar An innovation can be compatible or incompatible (1) with sociocultural values and beliefs, (2) with previously introduced ideas, or (3) with client needs for innovations

Prior studies indicated that compatibility had strong direct impact on the variation

in behavioral intention and explained more of it in using group support system (Van et al., 2002b) and in adopting new methodology for software development (Hardgrave et al., 2003) as well as university smart card (Hui et al., 2003) Wu and Wang (2005) investigated what determines the users’ M-commerce acceptance These findings indicated that the most important determinant for behavioral intention to use mobile commerce is compatibility Wu et al (2007) examined what determines mobile healthcare systems (MHS) The results indicated that compatibility significantly affected healthcare professional behavioral intent

The compatibility of an innovation, as perceived by members of a social system, is positively related to its rate of adoption For those reasons, the following hypothesis is proposed:

H3 Compatibility has a positive effect on the behavioral intention to use

M-commerce

2.4.4 Perceived cost and the behavioral intention to use M-commerce

Perceived cost (PC) is the extent to which an individual perceives that using commerce is costly (Carlsson et al as cited in Zhang et al., 2012) Cost in M-commerce, mainly covers initial purchase price (e.g handset fee), ongoing usage cost (subscription fee, service fee and communication fee), maintenance cost and upgrade cost (Luarn and Lin, 2005) The price or cost factor is one of the reasons that could slow down the development of M-commerce Pagani (2004) stated that the price or cost factor was one of the main determinants of 3G services adoption Anil et al (2003) also stated that cost is one of the factors influencing the adoption of M-commerce in Singapore Wei et al (2009) investigated M-commerce adoption

M-in Malaysia and empirically proved the PC has a negative effect on M-commerce adoption Bouwman et al (as cited in Zhang et al., 2012) found out that the impact

of the cost on M-commerce adoption is much stronger than privacy and security issues in Finland Thus, the following hypothesis is recommended:

H4 Perceived cost has a negative effect on the behavioral intention to use

M-commerce

2.4.5 Perceived trust and the behavioral intention to use M-commerce

As M-commerce is thought to be the next major stage in technological involvement following the E-Commerce age, one important factor identified to impact the adoption of E-Commerce, perceived trust (PT), will also be included in this study Trust is the extent to which an individual believes that using M-commerce is secure and has no privacy threats (Zhang et al., 2012) Trust is more crucial and complex in environment such as E-commerce and M-commerce than

general and traditional commerce due to its uncertain environment (Lu et al., 2003; Cho et al., 2007) and information asymmetry (Cho et al., 2007) According to Lu et

al (2003), M-commerce is exposed to greater danger of insecurity than E-commerce and therefore the importance of trust is relatively higher in M-commerce Cho et al (2007) indicated that the issue of trust is much more complicated in M-commerce than in traditional commerce Trust is important because it not only helps consumers overcome perceptions of uncertainty and risk, but also helps build appropriate favorable expectations of performance and other desired benefits (Gefen, 2000; McKnight et al., 2002) According to the study by Wang et al (2003), perceived credibility was measured by two items adapted to reflect specific user beliefs concerning the security and privacy protection of M-commerce Furthermore, for trust to exist, consumers must believe that the sellers have the ability and motivation to reliably deliver goods and services of the quality expected by the consumers (Jarvenpaa and Staples, 2000) Luarn and Lin (2005) found that perceived credibility (security and privacy) has a stronger effect on consumers’ intention to use mobile banking than the PU and PEU Wei et al (2009) and many other researchers have reported significant correlations between trust and behavioral intention, and they all regarded trust as a non-ignorable factor in M-commerce adoption Accordingly, the following hypothesis is developed:

H5 Perceived Trust has a positive effect on the behavioral intention to use

M-commerce

2.4.6 Moderating variable – Self-efficacy

2.4.6.1 Moderating variables - The conceptual framework

A moderating variable is included in the conceptual model to modify the way that the independent variables will affect the dependent variable This might act as a catalyst of these relationships and strengthen or weaken them Baron and Kenny (1986) described moderator as a qualitative or quantitative variable that influences the form or strength of association between an independent and dependent variable They further specified moderating variable with regard to correlation analysis and

stated that it is a third variable that affects the zero-order correlation between two other variables Sharma (2003) stated that a moderating variable is the one that influences the relationship between a predictor variable and a criterion variable by systematically altering its form or strength

2.4.6.2 Self-Efficacy as a moderator

Self-efficacy is the extent to which an individual believes himself or herself is capable of successfully performing a specific behavior (Bandura, as cited in Prussia et al., 1998) These beliefs influence what challenges to undertake, how much effort to expend in the endeavor and how long to persevere in the face of difficulties (Bandura, as cited in Prussia et al., 1998) The higher a person's self-efficacy, the more confident he or she is about success in a particular task domain Self-efficacy is the belief in one's capabilities to organize and execute the sources

of action required to manage prospective situations (Bandura, 2000) The principal source of self-efficacy is enactive mastering which depends on both real and perceived execution of the task Other sources of self-efficacy are the verbal persuasion of others, vicarious learning and emotional activation Perceived self-efficacy plays an important role in affecting motivation and behavior The self-efficacy theory (Bandura, 2000) suggests that there are four source areas of information used by individuals when forming self-efficacy judgments They are performance accomplishments, vicarious experience, verbal persuasion, and physiological state Consumers who consider M-commerce too complex and believe that they will never be able to master the M-commerce technology will prefer to avoid them and are less likely to use them Gist (1989) suggests that self-efficacy is an important motivational variable, influencing effort persistence and motivation In addition, individuals who feel less capable of handling a situation may resist it because of their feelings of inadequacy or discomfort On the other hand, individuals with high self-efficacy will perceive the use of M-commerce to

be user friendly and easy to use due to the effect of self-efficacy on the degree of effort, the persistence and the level of learning (Bandura, 2000), and will be less

resistant to changes Hence, self-efficacy will affect beliefs and behavior of consumers adopting M-commerce services through either direct or indirect usage factors

Rosa et al (2001) found the moderator effect of self-efficacy on occupational stress They examined self-efficacy as a moderator and found that self-efficacy moderates the stress-strain relationship, suggesting that low level of self-efficacy is related to high level of occupational stress on adopting new technology According

to the study by Islam et al (2011), self-efficacy is found to be a moderating factor for the adoption of M-commerce services We, therefore propose that the degree of self-efficacy of a consumer will have a moderating effect on the adoption of M-commerce services The following hypotheses are proposed to test the moderating effects of self-efficacy on the relationships between the independent variables and dependent variable

H6 Self-Efficacy has a significant moderating effect on the relationship

between perceived usefulness and the intention to use M-commerce services

H7 Self-Efficacy has a significant moderating effect on the relationship

between perceived ease of use and the intention to use M-commerce services

H8 Self-Efficacy has a significant moderating effect on the relationship

between compatibility and the intention to use M-commerce services

H9 Self-Efficacy has a significant moderating effect on the relationship

between perceived cost and the intention to use M-commerce services

H10 Self-Efficacy has a significant moderating effect on the relationship

between perceived trust and the intention to use M-commerce services

In summary, based on the prior studies and the theoretical concepts, the conceptual frameworks was constructed with five independent factors including perceived usefulness, perceived ease of use, compatibility, perceived cost and perceived trust

impact on the dependent factor of intention to use M-commerce Self – efficacy was

built as a moderator

2.5 Research Model

Figure 1: Research Model

CHAPTER 3: RESEARCH METHODOLOGY

The purpose of this chapter is to introduce the research methods and approaches used to examine factors that can explain the customers’ intention to use M-commerce

in Vietnam Specifically, this chapter will provide the outline of what we conducted the research including the research approach, measurement scale, sampling design, questionnaire design, pilot survey, data collection, and data analysis technique

3.1 The research approach and research procedure

The survey research methodology (which is a positivistic methodology), was considered to be the most appropriate for this research In particular, this study was classified as an analytical survey where the main intention was to determine whether there was any relationship between different variables Quantitative research is associated with exploring connections between variables (Bryman & Bell, 2011, p.426) In order to test the model and hypotheses stated in the previous chapter, this research adopted the quantitative approach in which the data were collected by means of a questionnaire survey The research followed the process illustrated in Figure 2

3.2 Measurement scale

3.2.1 Independent and moderating variables

The independent and moderating variables were based on factors derived from

existing literatures The questions were modified to fit the context of M-commerce For example, the scales for the PEU, PU of M-commerce were measured using the items adapted from the original TAM (Davis, 1989)

A total of 26 questions were developed to capture the five adoption factors under the investigation Each question was measured by five-point Likert scale where "1" indicated as strongly disagree, "2" meant disagree, "3" denoted as neutral, "4" designated as agree and "5" denoted as strongly agree

ro

Figure 2: Research Procedure

3.2.2 Dependent variable: consumer’s intention to use M-commerce

The measurements for consumer’s intention to use M-commerce were measured using items adapted from the original TAM (Davis, 1989) The IU items were measured using five-point Likert scale where 1 - Strongly disagree, 2 - Disagree, 3

- Neutral, 4 - Agree and 5 - Strongly agree Four questions were used to capture the intention to use M-commerce by the users of smart mobile devices Table 3.1 shows the

Main Survey & Data Collection

Define Research Problem Literature Review Conceptual Research Model Research Design

Data Needs & Source Measurement Scales

Sampling Design Framework for Data Analysis

sources of measurement where the questions are adapted from

Table 3.1: Sources of Measurement

T

The measurement scales of this study are represented in Table 3.2

Table 3.2: Scale items of factors influencing the intention to use M-commerce

No Factor Code Variables/Measurement Scale

1 Perceived

Ease-of-Use (PEU)

PEU1 Learning how to use mobile commerce services is easy to me PEU2 It is easy to make the mobile commerce services do what I want them to PEU3 Using M-commerce services does not require many skills or knowledge PEU4 I feel that the user interfaces of the mobile commerce services are easy to understand and manipulate PEU5 I find it easy to use mobile commerce services

2 Perceived

Usefulness (PU)

PU1 M-commerce services support my daily work PU2 Using M-commerce services is a way to save time PU3 M-commerce services is convenient

PU4 Using M-commerce services helps me complete my tasks better PU5 M-commerce is useful to me

Construct Characteristic Authors

Perceived Usefulness (PU) 5 items on 5-point Likert scale Davis (1989)

Perceived Ease of Use (PEU) 5 items on 5-point Likert scale Davis (1989)

Compatibility (CO) 3 items on 5-point Likert scale Rogers (1995)

Perceived Cost (PC) 3 items on 5-point Likert scale Wei et al (2009)

Perceived Trust (PT) 6 items on 5-point Likert scale Koufaris and Hampton-Sosa (2004) Self-Efficacy (SE) 4 items on 5-point Likert scale Schwarzer (1999)

Intention to Use (IU) 4 items on 5-point Likert scale Davis (1989)

5 Perceived Trust

(PT)

PT1 I believe in the information which M-commerce services provide PT2 I believe that the errors while using M-commerce services rarely occur PT3 I believe payments made through M-commerce channel will be processed securely PT4 I believe transactions conducted through M-commerce will be secure PT5 I believe my personal information will be kept confidential while using M-commerce technology PT6 In my opinion, M-commerce services are trust-worthy

6 Perceived Self -

Efficacy (SE)

SE1 I am able to use mobile commerce services without the help of others SE2 I have the necessary time to make mobile commerce services useful to me SE3 I have the knowledge and skills required to use mobile commerce services SE4 I am able to use mobile commerce services reasonably well on my own

7 Intention to Use

(IU)

IU1 Assume that I have access to M-commerce systems, I intent to use them IU2 I intend to use M-commerce if the cost is reasonable for me IU3 I believe my interest towards M-commerce will increase in the future IU4 I believe I will use mobile commerce in the future

3.3 Sampling design

3.3.1 Population

The target population of this study was individuals who access the internet on their

mobile phones or other mobile devices The target subjects of the study should have been diverse in terms of age, income, location and especially occupation However, because of the limitation of time as well as budget, in this study, the questionnaires were delivered directly and via email to students and office staff

who studying and working in Ho Chi Minh and Hanoi City, Vietnam

3.3.2 Sample size

According to Hair et al (1998), minimum sample size used in statistics analysis should be equal to or greater than five times of the number of independent variables, but not less than 100 to generate reliable results: n ≥ 100 and n ≥ 5k (where k is the number of items) This research has 30 items, as a result, the minimum sample size required to run EFA in this research is: n = 5 x 30 = 150 Otherwise, Tabachnick and Fidell (2001) stated that the minimum sample size in case of multiple regressions should be: n = 50+8m (where m is the number of independent variables) Apply this formula for 6 independent variables of this research, we have the minimum sample size for multiple regression: n = 50 + 8 x 6

= 98 In summary, this research needs 150 samples at least to take EFA and multiple regression analysis With quantitative approach, in order to increase the reliability and validity, the initial target sample size for this research was about three hundreds of users of smart mobile devices The survey was conducted from the middle of August to the middle of September, 2014 with potential respondents

to get the actual sample size of more than 600 respondents

3.4 Questionnaire design

Givern (2006) stated that questionnaire is at the front line of the research – it is what the general public understanding research, particularly social research, to be about In this study, the form of questionnaire consists of three sections The first section was the filter question about respondents’ experience with M-commerce Respondents who already heard of or used M-commerce services will continue to answer the questions in the later sections; the questions in the second section focused on respondents’ opinions on M-commerce services and their adoption of

the services; and the last section contained demographic questions Basically, the questionnaire contained statements that could be answered through ranking and open-ended questions as well In the second section, a five-point Likert scale ranging from “Strongly disagree” (1) to “Strongly agree” (5) was used to measure respondents’ perceptions about M-commerce services and intention to use the services The questionnaire was initially prepared in English and then translated into Vietnamese because the respondents were all Vietnamese

3.5 Pilot survey

Based on literatures of factors affect customers' intention to use M-commerce, the pilot test research of the conceptual model have been developed with five main factor which are supposed to be effected to the customers' intention to use M-commerce in Vietnam The draft questionnaire was built from the consolidation and citing from previous studies of the same topic Both qualitative and quantitative methods are conducted in order to consolidate for the quality of this research There are two phases in research design step of this study: Pilot test survey and main survey Afterward, the measurement scales and pilot questionnaire on factors affecting customers' intention to use M-commerce were prepared in qualitative research and the pilot survey is included two steps At the first step, a qualitative research with preliminary questionnaire was conducted with pilot interview with four technical managers from Telecommunication companies and five marketing managers from M-commerce Service Providers in Vietnam by in-depth interview This survey is constructed from literature review to find out the mistakes or ambiguities of the draft questionnaires At the second step, a quantitative research was conducted randomly by using a survey questionnaire which was completed by users of smart mobile devices and knowing about M-commerce 50 samples were used for the final measurement scale testing before launching the main survey

3.6 Data collection

Data collection is the process of collecting data associated with variables in the

hypotheses in order to test the hypotheses that would be generated in the study Primary data was collected using a structured questionnaire which is a self- administered survey The questionnaire employing the non-probability sampling was implemented with the application of the convenience sampling The purpose

of convenience sampling is to attempt to collect a sample of convenient factors

3.7 Data analysis method

Following data collection, the next step is the process of data analysis in order to reveal the research questions All accepted questionnaires were reviewed for validity and completion The procedure of converting raw data into information consists of several processes including editing, coding, data entry and data analysis (Zikmund, 2000)

Reverse-scoring negatively-keyed items were implemented before computing individuals’ total scores and before conducting data analyses We did this so that high scores on the questionnaire reflect relatively high levels of the attribute being measured by the questionnaire Reverse-scoring the negatively-keyed item ensures that all of the items - those that are originally negatively-keyed and those that are positively-keyed - are consistent with each other, in terms of what an “agree” or

“disagree” imply

In this study, IBM SPSS Statistic version 20 was chosen to analyze the data Data processing procedures and analytic techniques used in this study are discussed as followings:

Firstly, Descriptive Statistics was employed for demographics statistics Then Cronbach Alpha was used to examine the reliability of measuring scale Cronbach Alpha is a statistical test if the correlation of the items in the scale is relative to each other (Hair et al., 1998) Consequently, this method of analysis can remove inappropriate variables and limit junk variables in the study process and evaluate the reliability of the scale through the Cronbach’s Alpha coefficient (Hair et al.,

1998) The scales are reliable when Cronbach’s Alpha coefficient of each scale is equal to or higher than 0.7 (Pallant, 2007)

Secondly, Exploratory Factor Analysis (EFA) was conducted to test the number of factors extracted Factor analysis is a multivariate statistical technique which defines the underlying structure among a large number of variables (Hair et al., 1998) The two fundamental purposes of factor analysis are to summarize the information contained in a large number of variables and condense the data into smaller number of factors (Hair et al., 1998) In the study, there were five factors that were assumed to have influence on customers’ intention to use M-commerce services Factor analysis was used to create factors for each of the five measuring scales related In addition, factor analysis assisted the researcher to determine which factors were highly correlated to customers’ adoption of the M-commerce services The result is considered to be accepted when following conditions are met (Pallant, 2007):

• The sample size should be equal to or greater than five cases for each variable

• Factor analysis is appropriate for data if:

The Kaiser-Meyer-Olkin value (KMO) is 0.6 or greater

The Bartlett’s test of sphericity is statistically significant: p < 0.05

• The number of factors is determined when:

The total variance explained by these components should be above 50%

Factor loading criteria should be 0.5 or greater to ensure a practical significance

Thirdly, Pearson’s correlation coefficient was used to examine the relationships between two or more research variables (Veal, 2005) According to Veal (2005), the relationship between the variables can take different forms If the value of correlation coefficient is 1.0, then there is a perfect positive correlation between two variables In contrast, if the value of correlation coefficient is -1.0, it can be

concluded that there is a perfect negative correlation between two variables In addition, there is no relationship between two variables if the value of correlation coefficient is zero

Finally, multiple regression analysis was conducted to analyze the relationship among variables Multiple regression analysis is a statistical technique that is used

to analyze the relationship between several independent variables and a single dependent variable (Hair et al 1998) It was used to examine the simultaneous effects of several independent variables on a dependent variable In this study, Multiple Linear Regression method was used to test the research model and hypotheses Moderated regression analysis as the recommended method for testing interaction effects, were used (Zedeck, 1971; Cohen et al., 2013) Three-block hierarchical multiple regression analyses were performed to detect main effects and interaction effects of independent variables and the moderator Pallant (2007) explains the conditions to accept the result are:

• The sample size is: n > 50 + 8m (where m is the number of independent variables)

• No multicollinearity is found

• Normality and linearity should exist

• We also use R-square value to express how much of the variance in the dependent variable was explained by the model

In conclusion, this chapter introduces the research methods and general information about the way we conducted the research including the research approach, measurement, sampling, data collection, and data analysis technique More detailed discussions of the analytic methods used and results are provided in the Chapter 4