factors impact on customers’ intention...

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FACTORS IMPACT ON CUSTOMERS’ INTENTION AND USAGE TOWARDS

MOBILE COMMERCE IN VIETNAM Sang-Lin Han

T. P. Thao Nguyena

V. Anh Nguyen**a

Abstract: Mobile Commerce plays a vital role in business nowadays. In Viet Nam, there is not many researches about

M-commerce. Therefore, based on TAM model, this research investigates factors impact on customers’ intention and

usage towards M-commerce. The results indicate that Perceived usefulness, Perceived ease of use, Perceived

playfulness and Perceived cost impacting on customers’ intention and usage in M-commerce, especially Perceived

usefulness is the most important factor. This research also showed the frequency of M-commerce activities and the

reasons for using it, the top 5 most frequently activities: News, Instant messaging/chatting, Social network (facebook,

Twitter, Cyworld), Ticket purchase and Downloading ringtone; and the top three reasons: For study or work,

Availability of internet access anywhere, Immediate access to internet when needed. In addition, we found that

moderate roles of gender, hedonic and utilitarian tendencies in M-commerce adoption in Viet Nam.

Keywords: Mobile Commerce, Perceived usefulness, playfulness, ease of use, cost, hedonic and utilitarian tendencies.

1. INTRODUCTION

In the 1990s the emergence of e-commerce to businesses brought profound changes to the

competitiveness and structure of industry and business models especially in travel and music

industries. Like E-commerce with the advancement in wireless communication technologies,

mobile commerce (m-commerce) is now seen as the new business model and platform that will

have a similar impact on the business communities and industries. M-commerce offers extra

functionality to existing e-commerce such as location and localization services (Junglas and Watson,

2008). According to ABI Research, the m-commerce will grow into a $119 billion global industry

by 2015, up from $18.3 billion in 2008 (M. Khalifa, Cheng, and Shen, 2012). Also, the increase in

m-commerce is fueled by a unstop development of new mobile smart devices and the increasing

number of people who own mobile phones. Mobile phones have become important personal devices

for listening to music, watching videos, playing games, conducting business transactions, and

connecting to social networking sites. The interactions between consumers and their mobile phones

have presented opportunities for organizations to use m-commerce to personalize services to

customers. Realizing these opportunities, companies have been focused in m-commerce

infrastructure, services and devices investment. Furthermore, since developing countries present a

market which has huge population that make them become the potential markets for many

telecommunication and m-commerce service providers such as China and India. Vietnam a country

with population is over 90 million and for every 100 Vietnamese people, there are 145 mobile

phones is not an exception. Moreover, to date, there is little research which has explicitly addressed

the differences on the adoption of m-commerce between developed and developing countries. Many

scholars strongly support that the criteria for m-commerce adoption in developing countries are

Professor, School of Business, Hanyang University, Seoul 133-791, Republic of Korea PhD student, School of Business, Hanyang University, Seoul 133-791, Republic of Korea aFaculty of Economics and Business Administration, Dalat University, Lam Dong, Viet Nam

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different from that of developed countries, due to cultural, security, social, political, economic, and

technological aspects (Crabbe, Standing, Standing, and Karjaluoto, 2009; Saidi, 2010; Yaseen and

Zayed, 2010). In addition, according to Vaghjiani (2012), there is a perception that innovations

adoption appears to be adopted in different ways in developed and developing countries. Therefore,

this empirical research study aims at achieving several objectives. First, this empirical research is

intended to investigating the main drivers influencing Vietnamese consumers’ behavior toward m-

commerce adoption. The work extends the traditional Technology Acceptance Model (TAM) by

integrating the quality dimensions, personal innovativeness, playfulness and cost factors since the

nature of mobile devices such as screen size that limits access to multimedia contents and slower

speed than conventional PCs. Since, the internet infrastructure of Vietnam is not as good as other

developed countries. There is a need to investigate the other quality factors that would influence the

perception of users towards m-commerce. Moreover, the recent researches have revealed that cost

are able to predict Malaysian and Chinese consumer decisions to adopt m-commerce (Cheong and

Park, 2005; Chong et al., 2012; Wei et al., 2009 ; Zhang et al., 2012) and the cost of service

subscriber is rather high, also Vietnamese customers seems to be sensitive to the price and price

plays a vital role in buying decision making process. Furthermore, Cheong and Park, (2005) also

demonstrated that the exploration of playfulness as an extension of TAM significant influenced the

behavioral intention to use M-internet.

Second, this study also examines the reasons for using m commerce, and types of m-commerce

activities engaged into since the results could have develop a better understanding of their

customers in order to develop specific products or application to meet customers’ need.

Third, the impact of gender differences on adoption processes of technologies has played a vital

role in marketing strategy. However to some degree, the importance of it is overlooked in

developing countries. Many studies have reported that gender difference has a significant impact on

consumers’ perception toward adoption of information technology (Venkatesh and Morris, 2000;

Venkatesh et al., 2003; Venkatesh and Bala, 2008; Wang et al., 2009; Deng et al., 2010; Riquelme

and Rios, 2010; Dong and Zhang, 2011). On the other hand, literature has emerged that offers

contradictory findings about the role of gender on the adoption process of various information

technology domains (Bigne et al., 2005; Serenko et al., 2006; Zhou et al., 2007; Lip-Sam and Hock-

Eam, 2011). Therefore, the need for further research to improve the understanding of the impact of

gender on the adoption of m-commerce increases, particularly in Vietnam a developing country.

Consequently, this study contributes toward a greater understanding of how men and women

perceive m-commerce adoption in a developing country context which is fundamental for marketers

to consider for marketing strategies.

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Finally, the study is undertaken to evaluate the moderating role of Utilitarian and Hedonic

Tendencies toward the m-commerce usage for giving the insight for service providers in order to

formulate specific products or applications that match and better satisfy customers’ needs than their

competitors.

2. LITERATURE REVIEW AND HYPOTHESES

2.1 Mobile Commerce

Mobile commerce is any transaction, involving the transfer of ownership or rights to use goods

and services, which is initiated and/or completed by using mobile access to computer-mediated

networks with the help of an electronic device. There has not been an agreed conceptual definition

of the term mobile commerce. Generally, the nature of mobile commerce is that services can be

accessed anywhere, at any time. As viewed by Kannan et al., (2001) and Varshney & Vetter, (2002),

m-commerce is the use of wireless technology, particularly handheld mobile devices and mobile

Internet, to facilitate transaction, information search and user task performance in consumer,

business to business, and intra-enterprise.

Types of Applications

There have been a great number of m-commerce applications since the advent of this new

technology. The most popular of which consist of financial, advertising, and location-based services.

An attempt to identify the several important classes of applications has been made by Varshney &

Vetter, (2002). Their study covered a comprehensive range of m-commerce applications under

different classes as summarized in Table1.

Table 1. M-Commerce Applications by Varshney and Vetter, (2002)

Class of Applications Details Examples

Mobile financial applications (B2C,

B2B)

Applications where mobile

device becomes a powerful

financial medium

Banking, brokerage, and

payments for mobile

users

Mobile advertising (B2C)

Applications turning the wireless

infrastructure and devices into a

powerful marketing medium.

User specific and location

sensitive advertisements.

Mobile inventory management

(B2C, B2B)/

Product locating and shopping

(B2C, B2B)

Applications attempting to

reduce the amount of inventory

needed by managing in-house

and inventory-on-move. /

Applications helping to find the

location of product and services

that are needed.

Location tracking of goods,

boxes, troops, and people. /

Finding the location of a new

used car of certain model, color

and features.

Proactive service management

(B2C, B2B)

Applications attempting to

provide users information on

services they will need in very-

near-future.

Transmission of information

related to aging (automobile)

components to vendors.

Wireless re-engineering (B2C, B2B)

Applications that focus on

improving the quality of business

services using mobile devices

Instant claim-payments by

insurance companies.

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and wireless infrastructure.

Mobile auction or reverse auction

(B2C, B2B)

Applications allowing users to

buy or sell certain items using

multicast support of wireless

infrastructure.

Airlines competing to buy a

landing time slot during runway

congestion (a proposed solution

to air-traffic congestion

problem).

Mobile entertainment services and

games

(B2C)

Applications providing the

entertainment services to users

on per event or subscription

basis.

Video-on-demand, audio-on-

demand, and interactive games.

Mobile office (B2C)

Applications providing the

complete office environment to

mobile users anywhere any time.

Working from traffic jams,

airport, and conferences.

Mobile distance education (B2C)

Applications extending

distance/virtual education

support for mobile users

everywhere.

Taking a class using streaming

audio and video.

Wireless data center (B2C, B2B)

Applications supporting large

amount of stored data to be made

available to mobile users for

making intelligent decisions.

Detailed information on one or

more products can be

downloaded by vendors.

2.2. Technology Acceptance Model

Most m-commerce articles adopted the Technology Acceptance Model (TAM) in establishing a

mobile commerce adoption model (Wu & Wang, 2005; Lu et al., 2003; Yang and Jolly, 2008). In

studying user acceptance and use of technology, the TAM developed by Davis, (1985) to explain

computer-usage behavior, has been one of the cited models. Numerous studies have provided

support to this model in predicting user’s intention to adopt new services and applications ( Davis et

al., 1989); Igbaria and Tan, 1997; Wang et al., 2003; Gefen et al., 2003; and Ikart, 2005). However,

TAM with its original emphasis on the design of system characteristics does not account for social

influence in the adoption and utilization of new information system. And so, an attempt to extend it,

referred to as TAM2, has been undertaken by Viswanath Venkatesh and Davis, 2000 to explain

Perceived Usefulness and usage intentions in terms of social influence and cognitive instrumental

processes. O’Cass and Fenench (2003) argue that TAM is also appropriate for research areas in

electronic commerce applications since electronic commerce is based on computer technology. As

scholars indicate that mobile commerce is an extension of e-commerce, it is thus justifiable to

extend TAM to examine consumer intention to adopt mobile commerce. Thus, the m-commerce

adoption articles extended the TAM with new constructs aside from the original Perceived

Usefulness and Perceived Ease of Use, Attitude, Intention and Actual Use constructs.

2.3 M-commerce adoption models

Many researchers based their models on TAM to explore in different contexts, a few of them is

briefly discussed as follow.

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Wu and Wang (2005) integrated TAM and innovation diffusion theory to investigate what

determines user’s mobile commerce acceptance about online banking, shopping, investing, and

online services. The findings indicated that all variables except Perceived Ease of Use significantly

affected user’s behavioral intent. Among them, the compatibility had the most significant influence.

Cheong and Park (2005) measured how different variables affect mobile internet usage and

acceptance among Koreans. They suggested that Perceived Playfulness and Perceived Price Level

should be added to the TAM.

Faqih and Jaradat (2014) proposed a theoretical framework based on TAM3 theory and

concluded that perceived usefulness and perceived ease of use are important factors I explaining the

individual’s intention to adopt mobile commerce in Jordan. The results of these previous studies

confirm that, in the mobile technology context, traditional adoption models such as TAM could be

applied, but need modification and extension in order to increase their prediction and explanation

power. Thus, this paper conforms to these studies and extended the TAM to analyze the usage of

mobile commerce.

Table 2. Summary of Selected M-Commerce Adoption Studies

Authors Situation Independent Variables Mediating Variables

Depende

nt

Variable

Wu and

Wang

(2005)

B2C M-Commerce

Contexts: Online

Transactions, Online

Banking, Shopping,

Investing, and Online

Services

Perceived Risk,

Cost, Compatibility.

Perceived Usefulness, Perceived Ease

of Use

Behavioral Intention to

Use

Actual

Use

Cheong and

Park (2005)

M-Internet Acceptance

in Korea

Perceived System Quality, Content

Quality, Perceived Price Level

Perceived Usefulness,

Perceived Ease of Use,

Perceived Playfulness,

Attitude

Intention

to Use

M-

Internet

Pedersen,

(2005) M-Services

Perceived User Friendliness, Perceived

Usefulness, External Influence,

Interpersonal Influence, Self-Control,

Self-Efficacy, Facilitating Conditions

Attitude towards Use,

Subjective Norm,

Behavioral Control,

Intention to Use

Use

Wang et al.,

(2006) M-Service

Self-efficacy, Perceived Financial

Resource, Perceived Usefulness,

Perceived Ease of Use, Perceived

Credibility

-

Behavior

al

Intention

Kim et al.,

(2007) M-Internet

Usefulness, Enjoyment, Technicality,

Perceived Fee Perceived Value

Adoptio

n

Intention

Bhatti,

(2007)

Mobile Commerce

Services

Subjective Norm, Personal

Innovativeness


Ease of Use, Perceived

Behavioral Control

Intention

to Adopt

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Cho

(2008)

Mobile Commerce in

US and Korea

Information, Price, Service,

Convenience, Technology, Promotional

and Entertainment Factors



Overall Attitudes

toward Mobile Phone

Usage

M-

Satisfacti

on

Wei et al.,

(2009)

Mobile commerce in

Malaysia

Perceived Usefulness, Perceived Ease

of Use, Social influence, Trust and

Perceived Cost

- Intention

to use

Zhang et al.,

(2012)

A meta-analysis of

Mobile Commerce

Perceived behavioral control,

Subjective Norm, Perceived Cost,

Perceived Risk, Trust, Perceived

Enjoyment, Compatibility, and

Innovativeness.



Attitude, Behavioral

Intention

Actual

use

Faqih and

Jaradat

(2014)

Mobile Commerce

Technology (TAM3) in

Jordan

Subject Norm, Output Quality, Result

Demonstrability, Self-efficacy,

Perception of External Control,

Anxiety, and Playfulness.



Behavioral Intention

Use

Behavior

3. RESEARCH MDODEL AND HYPOTHESE

3.1 Conceptual model

The aim of the current study is to develop a success model for mobile commerce adoption in

Viet Nam that would explain how individuals behave in accepting or using m-commerce.

The independent variables include System Quality, Content Quality, Service Quality, Personal

Innovativeness and Perceived Cost factors. For moderating variables, the gender, hedonic and

utilitarian tendency are utilized in the model, as shown in the figure 1.

Figure 1: Research Model

Personal innovativeness

System quality

Content quality

Service quality

Perceived cost

Perceived ease of use

Perceived usefulness

Perceived playfulness

Intention to use

M-Commerce usage

H2a

H2b

H1c

H3a

H5a

H5b

H6a

H8

H5c H9

H1a

H1b

H3b H4

H7a

H6b

H7b

Gender, hedonic, utilitarian

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3.2 Hypotheses

Personal innovativeness and perceived usefulness, perceived ease of use and intention to use

It has long been recognized that highly innovative individuals are active information seekers

about new ideas in general innovation diffusion research. They are able to cope with high levels of

uncertainty and develop more positive intentions toward acceptance (Rogers, 1995). Drawing upon

Rogers’ theory of the diffusion of innovations, Agarwal and Prasad (1998) argued that individuals

develop beliefs about new technologies by synthesizing information from a variety of media. For

the same exposure to different types of media, individuals with higher personal innovativeness are

expected to develop more positive beliefs about the target technology. Agarwal and Karahanna

(2000) developed a multidimensional construct labeled cognitive absorption and suggested this

construct to be an antecedent of the two commonly recognized behavioral beliefs about technology

use: perceived usefulness and perceived ease of use. In addition, they addressed that the individual

traits of playfulness and personal innovativeness are important determinants of cognitive absorption.

Lewis et al., (2003) found that personal innovativeness in technology significantly affected

perceived usefulness and perceived ease of use. Lu et al., (2003) proposes that personal

innovativeness in technology, along with a number of other factors, all determine user perceived

short-term as well as long-term usefulness, and ease of use, which, in turn, influence user intention

and attitude to adopt wireless Internet services via mobile technology. Since individuals with higher

personal innovativeness in technology tend to be more risk-taking, it is also reasonable to expect

them to develop more positive intentions toward the use of wireless Internet services via mobile

technology. Thus, the innovative disposition may very well serve as the primary and direct

antecedents for adoption decision, without much consideration to perceptions at all. Hence, we

propose:

H1a: Personal innovativeness significantly effects perceived usefulness

H1b: Personal innovativeness significantly effects perceived ease of use

H1c: Personal innovativeness has a direct positive impact on intention to use

System Quality and Perceived Ease of Use, perceived usefulness

Lin and Lu (2000) proposed that in information system context, system quality is especially

important because individuals become reluctant to use the system when they experience frequent

delay in response, lack of access, frequent disconnection and poor security.

According to DeLone and McLean, (1992) the information quality and system quality are found

to be important constructs that bring the success of information system.

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In this study, we also expect that the system quality have positive impact on the perceived

playfulness because the better system can make individuals feel M-Commerce more enjoyable and

playful. Thus, we propose:

H2a: System Quality significantly affects Perceived Ease of Use.

H2b: System Quality significantly affects Perceived Usefulness.

Content Quality and Perceived Usefulness, Perceived Playfulness

The concept of contents quality is similar to the information quality and used in the study of

DeLone and McLean (1992) and Lin and Lu (2000) because information is often regarded as

contents in the context of the Internet. With regards to this study, it is hypothesized that the contents

quality has a positive influence on the perceived playfulness since the better contents can make

individuals feel M-commerce to be more enjoyable and fun. According to Cheong and Park (2005)

the quality of the content and the extent to which that content meets the needs and expectations of

mobile commerce users could affect their perception of its usefulness. Thus, the hypothesis:

H3a: Content quality significantly affects Perceived Usefulness.

H3b: Content quality significantly affects Perceived Playfulness.

Service Quality and Perceived Ease of Use

In this study, service quality is defined as the degree to which m-commerce through the network

and service provider can give customers prompt, promised, and professional service. Cho, (2008)

proposed that the service factor was a predictor of perceived ease of use in Korean context.

Therefore, we argue that service quality has a relationship with perceived ease of use as follows:

H4: Service Quality significantly affects the perceived ease of use.

Perceived Ease of Use and Perceived Usefulness, intention to use and M-Commerce usage

Agarwal and Karahanna (2000) assumed that the relation between Perceived Ease of Use and

Perceived Playfulness lies on the logic that the easier an individual perceives M-Internet, the more

he/she is likely to consider it playful. Cheong and Park (2005) also found that perceive ease of use

has an impact on Perceived Playfulness. Thus, this study proposes that individuals’ perceptions of

mobile device’s ease of use will influence his/her perceived playfulness in using mobile commerce.

Similar to perceived usefulness, perceived ease of use is one of the original variables found in the

TAM model. The perceived ease of use of m-commerce will be different for users with different

educational levels, or age groups. Although one may argue that m-commerce applications should

therefore have a simple interface, sometimes this might be done at the expense of features and

functionalities. M-commerce's advantage also involves personalizing the services to the users. The

perceived ease of use has been studied in past technologies such as mobile gaming, 3G, World

Wide Web and Online Banking. The application features which might affect the perceived ease of

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use, physical features of mobile devices such as its small display screen, or difficulty in keying data,

can also serve as a constraint to the decision to adopt m-commerce. As m-commerce is relatively

new to users in Vietnam, it would be therefore important to determine if they perceive m-commerce

as easy or difficult to use, and whether this perception will lead to their intention to use or not use

m- commerce. Hence we expected that

H5a: Perceived Ease of Use significantly affects Perceived Usefulness.

H5b: Perceived Ease of Use significantly affects Perceived Playfulness.

H5c: Perceived ease of use significantly affects M-commerce Usage

Perceived usefulness and intention to use and m commerce usage

Perceived usefulness is one of the most widely studied variables in technology adoption.

Perceived usefulness is defined as the extent to which individuals believe that using the new

technology will enhance their task performance. The usefulness construct has been used extensively

in information systems and technology research, and has strong empirical support as an important

predictor of technology adoption (Mathieson, 1991). Other studies providing evidence of the

significant effect of perceived usefulness on intention are from Davis et al., (1989); Venkatesh and

Morris (2000). The ultimate reason for people to utilize m-commerce is that they find it useful to

their tasks, transactions or everyday living. An individual evaluates the consequences of their

behavior in terms of perceived usefulness and base their choice of behavior on the desirability of the

perceived usefulness. Hence, we posit that

H6a: Perceived Usefulness significantly affects Intention to Use.

H6b: Perceived Usefulness significantly affects m commerce usage

Perceived Playfulness to Intention to Use and m-commerce usage

Moreover, individuals who experience immediate pleasure or joy from using a technology and

perceive any activity involving the technology to be personally enjoyable in its own right aside

from the instrumental value of the technology, are more likely to adopt the technology and use it

more extensively than others (Davis, 1986). Agarwal and Karahanna, (2000), Moon and Kim, (2001)

and Teo et al., (1999) insisted that Perceived Playfulness plays a significant role in developing the

intention to use. Thus, the hypothesis

H7a: Perceived Playfulness significantly affects Intention to Use.

H7b: Perceived Playfulness significantly affects Intention to Use.

Perceived cost and M-commerce Usage

In the development of behavioral intention, customers compare the benefit from the service to

the cost of using the service. If the cost exceeds the benefit, they do not subscribe the service. Also,

Wei et al stated that cost is one factor that can slow the development of m-commerce. It should also

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be noted that most of the users of mobile phones include younger students, such as university and

high school students. Furthermore, the key question here is whether the users view that m-

commerce is worth its value therefore the price of 3G services, and m-commerce may affect their

mobile commerce usage. Therefore this study hypothesizes that:

H8: Perceived cost significantly affects the M-commerce Usage.

Intention to Use and M-Commerce Usage

Individual’s intention to use m-commerce affects positively the usage of m-commerce. This was

supported in previous studies that focused on the acceptance and use of new technology (Compeau

and Higgins, 1995; Venkatesh and Davis, 2000; Hung et al., 2003; Yaseen and Zayed, 2010).

H9: Intention to Use significantly affects the M-commerce usage.

4. METHOD

4.1. Population and sample

The data was collected using a paper-based survey questionnaire. Respondents are from Law

University of Ho chi Minh City, the principal business center of Vietnam, and Da Lat City. The

sample design would comprise from big city-dweller students and small city students because the

students from different regions have different habits, views, cultures and norms. Consequently, big

city students are expected to have different behavior intentions to adopt innovative technology such

as m- commerce. In addition, in this study students were selected since they tend to have higher

technology readiness than the others. Also, across gender type university students are heavy users of

mobile devices to get ubiquitous access to social media. In fact, according to Jurisic & Azevedo,

(2011)university students are one of the most important target markets.

After gathering the answered questionnaires, they were checked thoroughly to assess the validity

whether to be included in the study. Those with NO answers on the query Usage of Mobile

Commerce were excluded right away. Then, those with YES were given numbers for the data input.

Among the responded cases, 19 cases were discarded because of insincerity as evidenced by same

answers all throughout. Final sample size is 532 which are used for analysis.

4.2 Measuring the constructs

A questionnaire was developed to achieve closely the objectives of this study. The measurement

items of the questionnaire are adapted from scale items that were validated and used in previous

research studies (Wu and Wang, 2005; Pedersen 2005; Wang et al., 2006; Kim et al., 2007; Davis,

1989, Cheong and Park, 2005; Yang and Folly, 2008; Delone and McLean, 2003; Faqih and Jaradat,

2014). The items were translated into Vietnamese, then were modified based on group discussion.

The final questionnaire consists of 36 items measuring 12 constructs, using 7- point Likert scales

ranging from (1) strongly disagree to (7) strongly agree.

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5. RESULTS

5.1 Demographic Results

The respondent’s demographics are summarized in Table 3. Clearly, almost 57.1% of the

respondents are female. The majority of the respondents ages (64.1%) were students in the 20–Less

than 25 years old range.

Table 3. Demographics

Frequency Percentage (%)

Gender

Male 228 42.9

Female 304 57.1

Total 532 100

Age Group

<20 89 16.7

20-25s 341 64.1

25-30s 75 14.1

30-35s 27 5.1

Total 532 100

5.2 Frequencies

Multiple response analysis was also done to evaluate the respondent’s answers in the m-

commerce activities they frequently use.

As shown in the table 4, the M-commerce activity that are the top 5 most frequently use by the

respondents is the reading news (69.7% of all cases), followed by Instant Messaging/Chatting (64.8%

of all cases), Social Network (Facebook, Twitter, Cyworld) (62.6 % of all cases), Ticket Purchase

(55.8 %) and Downloading ringtone (55.8%), respectively.

Table 4. Frequency of M-Commerce Activities (Multiple Responses)

ACTIVITIES RESPONSE

PERCENT OF CASES (%) N %

1. News 371 6.6 69.7

2. Instant Messaging/Chatting 345 6.2 64.8

3. Social Network (Facebook, Twitter, Cyworld) 333 6.0 62.6

4. Ticket Purchase 297 5.3 55.8

5. Downloading ringtone 297 5.3 55.8

6. Playing or downloading game 296 5.3 55.6

7. Weather Forecast 294 5.3 55.3

8. Downloading Wallpaper/ Screensaver 294 5.3 55.3

9. Mobile Banking 291 5.2 54.7

10. Mobile Coupon 291 5.2 54.7

11. Information search and general web surfing 290 5.2 54.5

12. Location/Travel Services 288 5.1 54.1

13. Mobile Shopping 286 5.1 53.8

14. Downloading or streaming video 286 5.1 53.8

15. Navigation 279 5.0 52.4

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16. Financial Information 276 4.9 51.9

17. Entertainment or Sports Information 272 4.9 51.1

18. Downloading or streaming music 270 4.8 50.8

It can be seen from Table 5, the top three reasons for using M-Commerce were the “For study or

work”, “Availability of Internet access anywhere”, “Immediate access to Internet when needed”. It

was noted that “Availability of Internet access anywhere”, “Immediate access to Internet when

needed” are the reasons show interestingly difference between E-Commerce and M-Commerce.

Table 5. Top Reasons for Using M-Commerce

REASONS FOR USING RESPONSE

PERCENT OF CASES (%) N PERCENT

1. For study or work 286 17.9 53.8

2. Availability of Internet access anywhere 260 16.3 48.9

3. Immediate access to Internet when needed 234 14.7 44.0

4. Relieves boredom 187 11.7 35.2

5. Friends strongly recommend it 163 10.2 30.6

6. Friends strongly recommend it 162 10.2 30.5

7. Unavailability of the wired Internet 160 10.0 30.1

8. Out of curiosity about new service or technology 143 9.0 26.9

5.3 Measurement Assessment

Reliability Analysis

Reliability was done to test the degree to which the set of latent construct indicators are

consistent in their measurements. The reliability of the variables was assessed by the Cronbach’s

Alpha and Item-total Correlation. The acceptable threshold for Cronbach’s Alpha is 0.70, while

constructs which are highly inter-correlated indicates that they are all measuring the same latent

constructs. Table 6 shows that the resulting alpha values ranged from 0.787 to 0.875, which is

above the acceptable threshold of 0.70. Also, the Item-total correlation test results are satisfactory.

Table 6. Reliability with Cronbach’s alpha

Constructs Items Reliability

Cronbach’s alpha

Personal innovativeness 3 0.787

System quality 4 0.839

Content quality 3 0.860

Service quality 3 0.870

Perceived usefulness 3 0.865

Perceived ease of use 3 0.863

Perceived playfulness 4 0.867

Intention to use 3 0.875

M-Commerce usage 3 0.845

Perceived cost 3 0.875

Construct Validity Analysis

A confirmatory factor analysis was conducted to test the measurement model. All the model-fit

indices exceeded their respective common acceptance levels suggested by previous research, thus

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demonstrating that the measurement model exhibited a fairly good fit with the data collected (χ2

(419) = 799.514, CMIN/df= 1.908, p = .000; GFI = .915; CFI = .965; RMSEA = .041).

This assesses what the construct (concept) or scale is, in fact, measuring. To construct validity,

two checks have to be performed: the convergent validity and discriminant validity. Convergent

validity was evaluated by examining composite reliability and average variance extracted (AVE)

from the measures. Values for composite reliability are recommended to exceed 0.70 (Chin,

Marcolin, & Newsted, 2003) and AVE values should be greater than the generally recognized cut-

off value of 0.50 (Fornell & Larcker, 1981). All composite reliability and AVE values meet the

recommended threshold values. Table 7 summarizes the results. The AVE for each variable was

obtained to check discriminant validity. As shown in Table 7, the square root of AVE for each

construct is greater than the correlations between the constructs and all other constructs, indicating

that these constructs have discriminant validity (Fornell & Larcker, 1981).

Table 7. Composite reliability, AVE and correlation of constructs values

CR AVE 1 2 3 4 5 6 7 8 9 10

M-commerce usage 0.847 0.649 0.806

Usefulness 0.868 0.687 0.728 0.829

System quality 0.841 0.570 0.628 0.695 0.755

Service quality 0.873 0.696 0.447 0.556 0.677 0.834

Ease of use 0.867 0.686 0.635 0.638 0.713 0.602 0.828

Playfulness 0.869 0.625 0.625 0.651 0.751 0.549 0.717 0.790

Content quality 0.861 0.675 0.597 0.607 0.734 0.604 0.653 0.670 0.821

Perceived cost 0.874 0.699 0.465 0.443 0.573 0.609 0.469 0.449 0.662 0.836

Intention to use 0.876 0.702 0.786 0.774 0.688 0.557 0.708 0.697 0.667 0.458 0.838

P Innovativeness 0.795 0.566 0.520 0.517 0.566 0.562 0.516 0.505 0.529 0.499 0.519 0.752

Note: Diagonal elements are the square root of AVE. Off-diagonal elements are the correlations among

constructs.

5.4 Structural Results: Hypothesis Testing

SEM was used to test the hypotheses. The structural model had 437 degrees of freedom. It is

noted that the final measurement model and the structural model had the same degrees of freedom.

The SEM results indicated that the model had an acceptable fit, χ2 (437) = 875.069,

CMIN/df=2.002, p = .000; GFI = .909; CFI = .960; RMSEA = .043.

Table 8 presents the unstandardized structural paths; and Figure 2 presents the significant

structural relationship among the research variables and the standardized path coefficients with their

respective significance levels. Only 2 of 17 hypotheses proposed are found insignificant (H1c, H7b).

In addition, the figure 2 shows the model explained substantial variance in both perceived

usefulness (R2=0.554) and intention to use (R

2 = 0.70, perceived ease of use (R

2=0.583), perceived

of playfulness (R2= 0.62) and m commerce usage (R

2 =0.66).

14

Table 8: Unstandardized structural paths

Hypothesis Construct Regression

estimate S.E C.R P-value

Accept/r

eject

H1a Personal Innovativeness

Perceived Usefulness .129 .056 2.315 .021 Accept

*

H1b Personal Innovativeness

Perceived Ease of Use .120 .055 2.179 .029 Accept

*

H1c Personal Innovativeness

Intention to Use .079 .053 1.485 .138 Reject

H2a System Quality Perceived

Usefulness .412 .107 3.866 .000 Accept

**

H2b System Quality Perceived Ease

of Use .671 .079 8.530 .000 Accept

**

H3a Content Quality Perceived


*

H3b Content Quality Perceived

Playfulness .379 .051 7.457 .000 Accept

**

H4 Service Quality Perceived Ease

of Use .139 .055 2.551 .011 Accept

*

H5a Perceived Ease of UsePerceived


**

H5b Perceived Ease of Use

Perceived Playfulness .571 .059 9.718 .000 Accept

**

H5c Perceived Ease of Use Intention

to Use .266 .071 3.739 .000 Accept

**

H6a Perceived UsefulnessIntention

to Use .527 .060 8.781 .000 Accept

**

H6b Perceived Usefulness

M-Commerce Usage .308 .077 3.994 .000 Accept

**

H7a Perceived PlayfulnessIntention

to Use .212 .056 3.804 .000 Accept

**

H7b Perceived Playfulness

M-Commerce Usage .084 .057 1.475 .140 Reject

H8 Perceived Cost


*

H9 Intention to Use


**

Note: *: significant at P <.05; **: significant at P <.000

15

Figure 2: Results of testing hypotheses

5.5. MODERATOR ANALYSIS

We used multi-group analysis to test moderator role of gender, hedonic and utilitarian tendencies in

intention to use and m- commerce adoption.

The chi-squared differences were compared between the two groups (models). In one model, the

path co-efficient was constrained to be equal across both groups and in the other, the path co-

efficient was left to be unconstrained (unconstrained model). The difference between the two

models is then tested again by Z-score to find exactly the differences among path coefficients.

Gender

Table 9 showed that for male, the perceived ease of use more affect intention to use than female

whereas for female, the perceived of usefulness more affect intention to use than male.

χ2 (437) = 875.069, CMIN/df=2.002, p = .000;

GFI = .909; CFI = .960; RMSEA = .043.

Personal innovativeness

System quality

Content quality

Service quality

Perceived cost

Perceived ease of use

Perceived usefulness

Perceived playfulness

Intention to use

M-Commerce usage

0.35**

0.58**

0.14*

0.24**

0.51**

0.47**

0.09*

0.15*

0.23**

0.49**

0.12*

0.11*

0.37**

0.21**

0.26**

R2=58.3%

R2=62%

R2=55.4%

R2=70% R

2=66%

16

Table9. Result of multi-group analysis for gender

Path coefficient male female

Estimate P Estimate P z-score

Intention to use <--- Perceived

playfulness 0.257 0.000 0.242 0.007 -0.137

Intention to use <--- Perceived ease of

use 0.423 0.000 0.209 0.007 -1.852*

Intention to use <--- Perceived

usefulness 0.382 0.000 0.577 0.000 2.131**

M-commerce

usage <--- Intention to use 0.571 0.000 0.555 0.000 -0.109

M-commerce

usage <--- Perceived cost 0.132 0.025 0.048 0.409 -1.003

M-commerce

usage <--- Perceived usefulness 0.279 0.002 0.295 0.002 0.117

Notes: *** p-value < 0.01; ** p-value < 0.05; * p-value < 0.10

Hedonic tendency

As shown in the table 10, for high hedonic group, perceived playfulness has significantly affect

intention to use by contrast the effect of perceived playfulness for the low hedonic group to

intention to use is insignificant. In addition, for high hedonic group, the perceived cost has

significantly affect m-commerce usage by contrast the effect of perceived cost for the low hedonic

group to m-commerce usage is insignificant.

Table 10. Result of multi-group analysis for hedonic tendency

Path coefficient Low hedonic High hedonic


Intention to use <--- Perceived playfulness 0.018 0.865 0.381 0.000 2.707***

Intention to use <--- Perceived ease of use 0.236 0.028 0.225 0.002 -0.078

Intention to use <--- Perceived usefulness 0.502 0.000 0.491 0.000 -0.089

M-commerce usage <--- Intention to use 0.604 0.000 0.515 0.000 -0.488

M-commerce usage <--- Perceived cost -0.096 0.245 0.219 0.000 3.181***

M-commerce usage <--- Perceived usefulness 0.443 0.003 0.244 0.004 -1.156


Utilitarian tendency

As illustrated in table 11, the perceived of use and perceived cost of high utilitarian group

significantly affect intention to use and m-commerce usage whereas the low utilitarian group has no

significant effect on high utilitarian group. In addition, perceived ease of use has an effect on

intention to use in the high utilitarian group while has no effect in the low utilitarian group.

17

Table 11: Result of multi-group analysis for utilitarian tendency

Path coefficient Low utilitarian High utilitarian


Intention to use <--- Perceived playfulness 0.207 0.016 0.296 0.000 0.725

Intention to use <--- Perceived ease of use 0.100 0.200 0.342 0.000 1.974**

Intention to use <--- Perceived usefulness 0.433 0.000 0.526 0.000 0.851

M-commerce usage <--- Intention to use 0.433 0.000 0.771 0.000 1.972**

M-commerce usage <--- Perceived cost -0.017 0.776 0.251 0.000 2.802***

M-commerce usage <--- Perceived usefulness 0.563 0.000 0.035 0.763 -3.317***


6. CONCLUSION

This research aims to discover the general drivers that can influence the m-commerce adoption

in Vietnam consumers. The results draw the following conclusions.

First, the current study demonstrates perceived usefulness is the most important factor that

affects individual’s intention to use m-commerce in comparison with perceived ease of use and

perceived playfulness, personal innovativeness and perceived cost. The perceived usefulness is

determined by personal innovativeness, system quality and content quality, particularly system

quality such as reliable service, fast response and instant transaction processing has strongest impact

on perceived of usefulness. In contrast, perceived content quality, presenting the various

information and services in M-Commerce such as services needed and the availability of the

services and contents, plays the most important role in perceived playfulness.

Second, in the light of the context of this study, the findings show that gender does significantly

have moderating effects in the current model. Apparently, this indicates that gender leads to

variation in consumers’ behavior toward adoption of a new technology such as m-commerce. The

current findings are in line with some of prior findings for example (Jayawardhena et al., 2009). As

a result, the gender gap in various mobile computing applications appears to rather wide in Vietnam.

Furthermore, hedonic and utilitarian tendencies have also moderating role to play in the

relationships between the perceived ease of use, perceived playfulness, perceived usefulness,

perceived cost and intention to use, m-commerce usage.

MANAGERIAL IMPLICATIONS

The research has also brought some implications for mobile commerce providers and operators

whose purpose is promoting m-commerce adoption of consumers.

First of all, managers should emphasize the usefulness and ease of use features offered by their

applications more heavily than playfulness function. Interestingly, the result implies that with the

female customers, service provider should focus on the usefulness feature whereas for the male

customers, they should emphasis the ease of use factor. Moreover, they should develop the friendly

application that can attract more users. M-commerce applications related to financial transactions

18

for example mobile banking, mobile purchasing of products should pass the message to consumer

that it is not only useful but also easy to use and safety.

Secondly, the results also show that across hedonic and utilitarian tendencies, Vietnam

consumers are conscious with the price. Therefore, managers should consider price strategy

carefully as well as develop creative promotion campaigns to attract more and more price-

conscious customers. The result is quite reasonable because the cost 3G Service is rather high in

comparison with other countries. Hence, there is likely that Vietnam consumers not willing to pay

for m-commerce even the service is easy to use and usefulness. This result is consistent with the

finding of previous studies in China and Malaysia which have the same developing country context

with Vietnam.

LIMITATIONS AND FUTURE RESEARCHS

There are several limitations in this study. Firstly, the study is restricted by investigating the

specific user group in developing country context, in Vietnam. Thus, caution must be taken when

generalizing our findings. A further study comparing between various developing and developed

countries could improve the generality of the model. Secondly, studies can consider the measuring

the diffusion of m-commerce across the time and identify the whether the factors that drives the m-

commerce adoption change over the time. Finally, there are some other factors that may be included

in the model for example self-efficacy that should be included in the future research models.

19

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