ABR Master Doc 251211

Determining Factors for Mobile Commerce Adoption among Postgraduate Part Time Students from Graduate Business Students (GBS) in Universiti Teknologi Mara (UiTM) Shah Alam.Applied Business Research Proposal (ABR)

Prepared by:Mohd Fairuz Nizam Mohd Awal – 2009729397Adzleen Abu Bakar – 2009946039 Azura Osman - 2009968717

Class: EMBA 14A

2011

1/23/2011Classified - Internal use

Determining Factors for Mobile Commerce Adoption among Postgraduate Part Time Students.

Table of Contents

ABSTRACT........................................................................................................................................... 5CHAPTER (A) INTRODUCTION...........................................................................................................71. Problem Statement.......................................................................................................................... 72. Gaps of Knowledge......................................................................................................................... 83. Background of Study....................................................................................................................... 94. Research Issues............................................................................................................................ 115. Research Questions...................................................................................................................... 146. Objective of the Study....................................................................................................................157. Justification of Current Study.........................................................................................................158. Potential Outcome & Practice/Significance of Study......................................................................169. Operational Definition.................................................................................................................... 1710. Scope of Study............................................................................................................................. 1811. Summary of Research Methodology.............................................................................................1812. Chapter Summary......................................................................................................................... 18

CHAPTER (B) LITERATURE REVIEW...............................................................................................201. Overview........................................................................................................................................ 202. Definition of mobile commerce.......................................................................................................203. Mobile Device................................................................................................................................ 234. Mobile Commerce Service Categories..........................................................................................245. Research Theory........................................................................................................................... 25

5.1 The Technology Acceptance Model (TAM) (Davis, 1989).....................................................265.2 The Theory of Planned Behaviour (TPB) (Ajzen, 1991)........................................................275.3 The Diffusion of Innovation Theory (DOI) (Rogers, 1995).....................................................295.4 The Techproved Model (Norzaidi et al, 2007; Norzaidi & Intan Salwani, 2011)....................30

CHAPTER (C) RESEARCH METHODOLOGY...................................................................................331. Introduction.................................................................................................................................... 332. Sampling & Data Collection...........................................................................................................33

2.1 Primary data......................................................................................................................... 332.2 Questionnaires.....................................................................................................................332.3 Sample of Study...................................................................................................................34

3. Secondary Data............................................................................................................................. 354. Theoretical Framework.................................................................................................................. 355. Research Model............................................................................................................................. 356. Hypotheses Development..............................................................................................................37

6.1 Perceived Usefulness...........................................................................................................376.2 Perceived Ease-Of-Use........................................................................................................386.3 Perceived Cost...................................................................................................................... 406.4 Perceived Trust.....................................................................................................................426.5 Intention to Use.....................................................................................................................44

7. Types of Tests and Statistical Software.........................................................................................458. Summary....................................................................................................................................... 46

CHAPTER (D) ANALYSIS AND FINDINGS.......................................................................................471. Introduction.................................................................................................................................... 472. Descriptive Statistics...................................................................................................................... 473. Normality Analysis......................................................................................................................... 504. Scale Reliability & Validity Analysis...............................................................................................54

4.1 Cronbach’s Alpha Reliability Test.........................................................................................554.2 Factor Analysis for Validity....................................................................................................564.3 Kaiser-Meyer-Olkin and Bartletts’s Validity Test...................................................................564.4 Bartlett's Test of Sphericity...................................................................................................574.4 Factor Loading...................................................................................................................... 584.5 Coefficient Correlation Analysis............................................................................................604.6 Multiple Regression Analysis................................................................................................624.7 Analysis Summary................................................................................................................63

2Classified - Internal use


CHAPTER (E) DISCUSSION AND CONCLUSION.............................................................................651. Introduction.................................................................................................................................... 652. Findings of Study........................................................................................................................... 653. Practical Implications.....................................................................................................................674. Limitations of Research.................................................................................................................725. Recommendation for Further Research.........................................................................................736. Conclusion..................................................................................................................................... 74

APPENDIXES...................................................................................................................................... 75REFERENCES.................................................................................................................................... 77Online resources.................................................................................................................................. 77Journals / Research Publication..........................................................................................................77Report / Press Publication................................................................................................................... 83



Abstract

Purpose – The Malaysian Communications & Multimedia Commission, mobile

subscribers has grown a long way from 21.8 mobile phones per 100 inhabitants in

2000 to a whopping 110.6 mobile phones per 100 inhabitants in the 3Q of 2010.

Total number of mobile phone subscribers is 33.9 million or 119.2% by the end of

2010. As such, this paper proposes to provide insight on the factors of mobile

commerce adoption among the part time post graduate students in University

Teknology Mara (UiTM), Malaysia.

Design/methodology/approach – This study will focus on exploratory research

methodology where it relies on secondary research such as reviewing available

literature, report and/or data on mobile commerce activities in Malaysia and other

parts of the world. There were also quantitative approaches in this study where a set

of survey have been distributed among the target population. Various analyses such

as normality test, reliability and validity tests, correlation and multiple regression

analysis have been utilized to test the hypotheses as well as the data obtained from

the survey

Findings – The findings showed that the mobile commerce adoption factors of

Perceived Usefulness (PU), Perceived Ease-Of-Use (PEU) and Perceived Cost (PC)

were found to have a significant relationship with Intention to Use (Actual Usage).

Therefore, the three hypotheses were supported. Meanwhile Perceived Trust (PT)

had no significant relationship with the Intention to Use (Actual Usage). Hence, this

hypothesis are not supported.

Practical implications – The results of this study lead interesting implications to

various groups. In particular, the results could benefit the academics, the



government and policy makers and ultimately, related industry players in that it is

able to provide valuable information and perhaps provide some direction on how the

mobile commerce industry can be shaped into.

Originality/value – The paper investigate the factor of mobile commerce adoption

with different target population from previose studies. The findings represent post

graduate part time student which stand in between full time students and working

adults. The study also extend the Technology Acceptence Model (TAM) introduce by

Davis 1986 with the inclusion of such variables of Perceived Trust and Perceived

Cost.

Keywords – mobile commerce, technology acceptance, post graduate students.

Paper type - Research paper



CHAPTER (A) INTRODUCTION

1. Problem Statement

While the mobile and smart phone adoption in Malaysia flourishes, there is still

very little adoption for mobile-application (Business Times, May 2010). Existing

internet banking users are usually the early adopters for mobile banking. In

principle, mobile commerce applications such as mobile banking and other

transactional services were developed to provide convenience to the customers

who have neither the luxury of time nor accessibility to conduct these

transactions. A postgraduate student is perceived to be a mature, working adult

whom therefore have the means and power to adopt and use mobile commerce

application. Having had to work and study at the same time, mobile commerce

applications is a convenience to the postgraduate student considering

transactional mobile commerce such as mobile-banking and tickets booking

would save time and efforts for the said student. However, the success of mobile

application is basically reliant on various factors; costs, mobile interface and

quality of telecommunications services, amongst other factors.

There had been some studies conducted to find the correlation between mobile

commerce use and adoption and students; (Ng, P.Y et al, 2010), (Fahad, 2009)

and (Hu & Allison, 2010). However, there is minimal research conducted on the

correlation and the perceived attributes that postgraduate students look into

when adopting into mobile application. Thus, this paper will deliberate on the

determining factors for mobile-commerce adoption among postgraduate in the

Graduate Business School in Universiti Teknologi MARA, Shah Alam, Selangor,

Malaysia.



2. Gaps of Knowledge

Mobile communications devices has evolved from becoming just a means for

communicating via telephony and text messages into a fully fledged

communications medium that even allows for transactional operations such as

banking, hotel, flight or cinema bookings. However the rapid development of

technology has brought new meaning to the mobility device (Gered, 2011).

There is now very little distinction between mobile device and desktop Personal

Computer (PC) (Woddrock, 2006). The rapid technology development for

devices, platform and network can render any studies on mobile commerce

irrelevant (O’Donnell, 2007). Thus current study on mobile commerce with the

existence of high technological device is important to justify relevancy of past

studies on mobile commerce.

At the moment, studies on mobile commerce among various group of students

from high school and tertiary students (Ng, P.Y et al, 2010); undergraduate

students (Kini, 2009) and graduate students (Hu & Allison, 2010) have been

done. All of the studies provide different results due to the demographic

characteristic of the test group. On the other hand, there are minimal studies

done on the correlation of postgraduate students and mobile commerce

adoption. Postgraduate students are both a student and working adult; which

makes it an interesting juxtaposition. Thus, this study, which will focus on

different type of population that is the postgraduate students, will definitely return

with dissimilar and fascinating results compared to previous studies on mobile

commerce.



Various studies indicated that dispensable income possessed by students are

the factors that determine the mobile commerce adoption among students (Hu &

Allison, 2010, Harris et al, 2005, Ng, P.Y et al, 2010). They indicated that the

larger the dispensable income, the more likely that the students will use mobile

commerce due to cost issues. Consider the scenario of a postgraduate student

with a higher dispensable income than that of a full time student. As such, it can

be argued that the adoption of mobile commerce among postgraduate student is

high but this is still yet to be proven.

This knowledge of understanding is very crucial to improve the mobile commerce

adoption rate not only for student but for general consumers in Malaysia. These

findings have practical implications for public policymakers, investors, market

researchers and telecommunications and its ancillary services sector in

Malaysia.

3. Background of Study

Telecommunication Industry

According to the telecommunications regulator, the Malaysian Communications

& Multimedia Commission, mobile subscribers has grown a long way from 21.8

mobile phones per 100 inhabitants in 2000 to a whopping 110.6 mobile phones

per 100 inhabitants in the 3Q of 2010 (MCMC, 2010). Total number of mobile

phone subscribers is 33.9 million or 119.2% by the end of 2010 (MCMC, C&M

Statistics 1Q2011). Worldwide sales for mobile devices totaled 440.5 million

units at 3Q2011, 5.6% higher during the same period last year. (Gartner, 2011)

From the total, smartphones sales totaled 115 million units, 42 percent higher

than 3Q2010 and 26 percent of total number of mobile phones purchased. In



Malaysia, wireless broadband subscribers totaled 4.9 million subscribers; 2.1

million mobile wireless broadband and 601,700 3G subscribers (MCMC 2011).

Interest for mobile broadband was fueled by attractive promotions pricing for

high-end mobile phones and other mobile multimedia devices with postpaid

services. Correspondingly, data traffic grew 51% from 68Gbps in 2006 to

234Gbps in 2009 (Frost & Sullivan 2010).

In the recent info-sharing seminar by Gartner, a market research leader

stipulated that by the year 2013, global telecommunications revenue would have

grown at a CAGR of 6.3% to reach USD4.9 trillion while internet subscribers

using dial-ups will be reduced by 75%. Gartner also predicted that by 2015, 50%

of web sales for companies would be via social presence and mobile

applications. This also corresponds with Frost & Sullivan which also predicted

that mobile broadband adoption among Asia Pacific users is on a rise, as shown

below:

Year 2010 2011 2012 2013 2014

No. of Subscribers (Million)

12 15 16 17.5 17.5

Table 1: Number of Mobile Subscribers

The Performance Management & Delivery Unit (PEMANDU), a unit under the

Malaysian Prime Minister’s Department, identified broadband to be key growth

driver for ICT in Malaysia. Ubiquitous broadband is expected to bring a Growth

National Income of RM2.1 billion and create 7,155 jobs by 2020.

Malaysian mobile users might have already hit a 100% penetration rate;

however, the trend seems to still show growth in subscriptions. In tandem to this



growth, the usability for mobile services will also grow, including mobile

commerce applications.

Graduate Business School, UiTM

The Graduate Business School of Universiti Teknologi MARA, Shah Alam was

established in 1984 and is one of the premier public post-graduate schools which

provide business management related courses to students. The Graduate

Business school offers both full and part time courses such as Masters in

Business Administration, Executive Masters in Business Administration, Masters

in Science by research, Doctorate and full PhD. According to the Institute of

Graduate Studies of UiTM, as at July to December 2010, there are 4684 masters

students attending both Masters and DBA. There are no available results for

actual statistics GBS students as yet.

4. Research Issues

Based on a study on mobile value added services carry out by Nokia, the second

largest mobile phone manufacturer after Apple (Andy, 2010), the primary target

markets for mobile commerce consumer services are students from the age

range of 19 to 25 years (Chen Hu et al, 2005). This indicates that students are

the largest mobile users as illustrated in Netherlands where all youths from the

age of 19 to 22 years young adults owns a mobile phone (Wentzel et al, 2005).

This phenomenon is most likely due to their nature that they are typically more

interested in innovation such mobile commerce technology (Morcilio, 2007).

Young adults are also most likely to be early adopters for mobile commerce

(Kini, 2009). Ng, P.Y et al also mentioned that apart from being early adopters to



new technologies, young consumers are technology savvy and always willing to

try new things (Ng, P.Y et al, 2010).

According to the study made among the high school and tertiary students in

Malaysia, it is found out that students rely on mobile phones to connect with

friends and family. (Ng, P.Y et al, 2010). This also were proven by a study made

by Cyril de Run where it indicate that internet is becoming the preferred pass

time and leading form of communication for students and educated youth (Cyril

de Run et al, 2010).

Despite the obvious correlation and benefits between students and mobile

communications for education, both mobile and internet are rarely used for

educational purposes (Wentzel et al, 2005). A study found out that transaction

based, location based and content delivery in mobile applications were less

significant among the high school and tertiary students. This is perhaps due to

the fact that income source for these groups are limited and mostly dependent

on their parents. (Ng, P.Y et al, 2010)

Postgraduate students, on the other hands, paint a different picture altogether:

seeing that this group has larger dispensable income which enables them to

have higher degree of commitment on any mobile commerce activities and

applications. A study has indicated that students with higher mobile services

expenditure recognize the importance of other more extensive web-related

services such as web browsing. (Ng, P.Y et al, 2010). Again, it must be noted

that this study was made among high school and tertiary students where the

income of the subjects are limited.



From a university student perspective, respondents agreed that mobile

technologies are perceived as an effective tool in improving communication as

well as learning. They supported the idea that the wireless networks augment

the flexibility of access to learning resources (Fahad, 2009, Kim et al, 2006). The

study, however were focused on full time university students where they do not

have fixed income to cope with better mobile application and facilities (Harris et

al, 2005).

Many universities have mitigated the adoption rate for wireless technology

among the university communities by providing wireless infrastructure around

campus plus the communication and transaction of academic information are

done through Personal Digital Assistance (PDA) and mobile phones (via Short

Messaging System & Multimedia Messaging Services) as part of the learning

processes from students to lecturers (Kim et al 2006). The study by Kim et al

focused on the implementation of the technologies and not the adoption of the

technology by the student itself. On the other hand, the study of academic

communities’ behavior on mobile device ownership, preferences, and activities

indicate that ownership and use of mobile device has not quite achieved a

tipping point of mass adoption. (Hu & Allison, 2010)

The other factor that is looked into is the perceived usefulness of mobile

commerce applications where it promotes convenience via its transactional

mobile commerce applications. Mobile commerce allows devices to conduct

electronic business transactions such as purchasing or ordering of products,

fund transfers and stock trading (Kalakota and Robinson, 2002). Transactional

mobile commerce includes mobile-banking or booking applications for

transportations, hotels and others. These are useful for postgraduate students



who might not have the time to conduct daily errands as above. However, Tao

Zhou (2010) noted that for this transactional mobile commerce to work

successfully, system quality and information quality are the main qualifying

factors for the success of mobile website adoption.

5. Research Questions

General

1. What are the factors that influence the adoption of mobile commerce

among part time Masters in Business Administration (MBA) student in

Universiti Teknologi Mara (UiTM) Shah Alam, Malaysia?

Specific

1. What is the relationship between the perceived usefulness and mobile

commerce adoption among part time MBA students in UiTM Shah Alam,

Malaysia?

2. What is the correlation between perceived usefulness and mobile

commerce adoption among MBA part time students in UiTM Shah Alam,

Malaysia?

3. What is the association between perceived cost and mobile commerce

offerings among MBA part time students in UiTM Shah Alam, Malaysia?

4. What is connection between perceived time effectiveness and mobile

commerce offering among MBA part time students in UiTM Shah Alam,

Malaysia?



6. Objective of the Study

General

5. To identify the factor that influences the adoption of mobile commerce

among part-time post-graduate students from the Graduate Business

School of Universiti Teknologi Mara (UiTM) Shah Alam, Malaysia

Specific

1. To identify the relationship between perceived usefulness and mobile

commerce adoption among part time MBA students in UiTM Shah Alam,

Malaysia.

2. To define the relationship between perceived usefulness and mobile

commerce adoption among MBA part time students in UiTM Shah Alam,

Malaysia.

3. To determine the association between perceived cost and mobile

commerce offerings among MBA part time students in UiTM Shah Alam,

Malaysia.

4. To ascertain connection between perceived time effectiveness and mobile

commerce offering among MBA part time students in UiTM Shah Alam,

Malaysia.

7. Justification of Current Study

Malaysia mobile penetration has reached above 100%. Ownership for

smartphone products such as Apple, HTC and Blackberry is set to grow higher.

According to International Data Corporation (IDC) Worldwide Quarterly Mobile

Phone Tracker, globally, vendors shipped a total of 100.9 million smartphones

during the fourth quarter of 2010, an 87.2% increase from 4Q09. In Malaysia,



IDC predicted growth of 1.8 million units of smartphones sales in Malaysia,

19.8% of total mobile telephones sold in 2010. Channelnewsasia.com reported

in June 2010 that the sales of smartphones in Malaysia creates path for mobile

commerce adoption as it was observed that there is a huge jump in mobile

commerce due to the growing popularity of smartphones featuring multimedia

capabilities, including speedy Internet access. This justifies the need to study

mobile commerce adoption among mobile users in Malaysia.

Post-graduate students from the Graduate Business School of Universiti

Teknologi Mara (UiTM) Shah Alam fall into the age group of the highest numbers

of mobile users in Malaysia. Post-graduate students are assumed to have their

own dispensable income and as such will be able to make purchases through

mobile commerce application. Additionally, the mobile commerce applications

are being developed in view of the demand for faster pace of lifestyle nowadays.

For part-time students, this would be a compelling product due to their busy

schedule of having to balance work and studies commitments. Therefore, this

study aims to investigate the compelling factors that would allow for mobile

commerce adoption among the part-time students.

8. Potential Outcome & Practice/Significance of Study

To the society

The business communities would be able to identify the requirements and

expectations of post-graduate students from the Graduate Business School of

Universiti Teknologi Mara (UiTM) Shah Alam, Malaysia for mobile commerce

applications. This study will allow them to provide the best product and services

to cater to their needs. This study is also hoped to trigger the awareness of m-



Commerce among these students thus increasing the take-up rate for mobile

commerce.

To the academician

The academician would be able to use this paper as a reference for future

researches related to mobile commerce which includes both the criteria and

findings of this research.

To the policy maker

This study can provide an impact to the policy makers in drafting rules and

regulations of mobile commerce practices such as contractual arrangements,

term and condition; and monitoring of mobile commerce transactions.

9. Operational Definition

Mobile commerce transactional services between the owner of smart telephones

or personal digital assistants (PDAs) and the mobile commerce vendors or

suppliers. An example of this is booking of hotel rooms, flight tickets and/or

mobile banking.

Post-graduate student is an individual who is a full-time employee and attends

classes after working hours and/or during the weekends.

Graduate Business School is an academic school which offers post-graduate

business management-related courses; in full time or part time basis.

Universiti Teknologi MARA is a public educational institution based in Shah

Alam, Selangor.



10. Scope of Study

The stud y to determine the factor of mobile commerce adoption is limited to

post-graduate students from the Graduate Business School of Universiti

Teknologi Mara (UiTM) Shah Alam, Malaysia only.

11. Summary of Research Methodology

First, a questionnaire will be adopted from existing past studies. This is to ensure

the questionnaire is justified and will be able to capture the essence of this study.

The questionnaires will then be distributed among eMBA and e-Track students

from the Graduate Business School of Universiti Teknologi Mara (UiTM) Shah

Alam. This would mean that the questionnaires will need to be distributed during

the evening (for e-Track students) and weekend classes.

The data collected will then be tabulated and analyzed accordingly.

Subsequently, the study will be able to determine the compelling factors that

allows for technology adoption of mobile commerce applications among the

students.

12. Chapter Summary

This chapter delineates the justification and raison d’être behind this study. It

provides an overview of the mobile communications industry and its correlation

with postgraduate students. Postgraduate students are perceived to have the

buying power but not the convenience of time; therefore is perceived to want to

adopt to mobile communications services to enable to carry out transactional

applications such as mobile-banking or mobile purchasing such as airline ticket

bookings, movie cinema purchasing and hotel bookings. There is a gap between

the availability of mobile commerce applications and the perceived usefulness of



the applications. From the problem and the gap of knowledge, we explored other

previous studies that were conducted on mobile commerce and adoption of the

same among students. A detailed literature review pertaining to mobile

commerce adoption is extensively discussed in the next chapter.



CHAPTER (B) LITERATURE REVIEW

1. Overview

The success and relevance of today’s business is heavily dependent on

technology; its platform, networks and applications used by the company. An

efficient technology platform will be able to cut a lot of cost and time for office

operations and allow for the selling, purchasing and reselling via virtual world via

a connected laptop. A study revealed that 99% of the top Management in the

companies; Chief Executive Officers (CEOs) and 86% of Chief Information

Officers (CIOs) believe technology is essential for the companies’ development.

At the same time, 88% of CEOs and 90% of CIOs says they have the same

insights for how technology can help achieved desirable business outputs at

their own company (Penn, Schoen and Berland, 2007). This indicates that

business operating without technology adaptation will likely to be left behind and

face the consequences of ferocious competition thus in the end make the

company or business insolvent.

Similarly, the adoption of mobile commerce is an almost natural progression for

the ownership of mobile phones. This section looks into the related literature

reviews on the determining factors for mobile commerce adoption.

2. Definition of mobile commerce

There are many types of business technologies available on the information

communications technology platform for the world market today such as social

media, cloud computing, business intelligence and others. Current development

of social media networks, e-commerce and most recently, mobile commerce is

providing to be a strong contender against traditional physical shops.



Before going further into mobile commerce, it is better to understand what mobile

commerce really is in terms of definition and its features. Both e-commerce and

mobile commerce have similarities in term of the commercialisation aspect (that

is it an electronic business transaction process) but differs in terms of mobility

capability platform. Mobile commerce is really, the extension of and the evolution

of e-commerce. Thus, some definition may define both e-commerce and mobile

commerce; especially on its features.

There are various definitions of mobile commerce. A simple definition would be

the conduct of business with the support of wireless technology (Sugianto,

2008). More detailed definition is stated in the research paper on mobile

commerce from the Department of Justice, Australia, which translated mobile

commerce to literally mean using handheld wireless devices to communicate,

interact, and transact via high-speed connection to the Internet (Consumer

Affairs Victoria, Department of Justice, 2002). Both definitions indicates that

mobile commerce is about the conduct of doing business (communicates,

interact and transact) via a mobile wireless device.

However, the Organisation de Coopération et de Développement Économiques

(ODEC) in 2008 defined mobile commerce as a commercial transaction and

communication activities conducted through wireless communication services

and networks by means of short message services (“SMS”), multimedia

messaging service (“MMS”), or the Internet, using small, handheld mobile

devices that typically have been used for telephonic communications.

The definition above detailed out the mobile type transactions available, that is,

SMS or MMS. However, technology has evolved in leaps and bounds since then



and mobile commerce transactions are not just limited to SMS and MMS but also

via other interactive applications such as mobile games; mobile banking

operations such as paying bills and transferring of funds; and bookings of flights,

hotels or movie tickets.

To add, current mobile devices are no longer restricted to only telephonic

services. There are also mobile devices that are able to connect using only on

wireless internet services such as iPad or tablet PCs such as HTC sense (Gered

2011).

As defined by Grosche and Knospe (2007), mobile commerce is an activity using

mobile device for business transactions performed over a mobile

telecommunication network, possibly involving the transfer of monitory value.

Tiwari, Buse and Herstatt (2008) argued that mobile commerce definitions that

emphasis on monetary value are inappropriate because its neglects the

commercial nature of marketing measures and after sales service. They also

argued that the definition also lacks the physical outcome of a product as

wireless technology in the definition solely focused on electronic transaction.

The argument continued by stipulating that some electronic business

transactions does not incur any costs; a clear example would be the free

downloadable applications such as certain downloading free games or e-books.

Another example would be the free downloadable applications from the smart

phones like a Samsung Galaxy tablet or the HTC smart telephones. This is part

of a marketing strategy to attract more people to buy and use the devices where

hundreds of applications are made available to be used at will.



Thus by combining all of the important aspect of mobile commerce definition,

Tiwari et al derived a new definition of mobile commerce as any transactions

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. This research paper will follow the

latter definition as a foundation of our paper.

3. Mobile Device

The definition above did not provide a clear definition of a mobile device. The

emerging trends available on new technologies allows for the difference between

mobile devices and a personal computer (PC) almost negligent. Both mobile and

PC capabilities are becoming similar. This can be clearly seen by the intensive

innovation of smartphones and tablets. For example Samsung Galaxy SII and

iphone4S are fabricated with dual core processing chip which are available in

any PC or laptops nowadays.

Based on a paper by Woddrock (2006), the main difference between both mobile

device and PC is that user interaction with a desktop PC is done by using the

mouse and the keyboard where both of which are non-existence on a mobile

device.

Zhang et al (2002) indicated that mobile devices can be in the form of pagers,

cell phones, palmtops, pocket PCs and PDAs (Personal Digital Assistants). The

devices identified can all support Wireless Application Protocol (WAP)

technology but with the drawback of limited bandwidth capability which makes

high graphical contents of certain applications rendered unsuitable.



However, mobile devices have since improved for the past five or six years. The

smartphones and tablets are now equipped with high processing speed that is

equal to a desktop PC, with larger screen plus high definition quality enabling a

lofty graphic content to be uploaded and play within minutes.

Gered (2011) identified some new devices (tablets or ipad) that do not have the

capability of telephony function but which main function to access the internet

wirelessly from anywhere and anytime the users wanted. This is done either by

smartcard or through mobile communications networks (sim card), or by a

wireless local area network (“WLAN”) access point. This class of devices

currently has no common name, apart from the generic term “mobile device”.

It can be estimated that within several more years the mobile device will be a

strong contender in making the desktop PCs obsolete. As to date, record shows

that the smartphones market grew by 50% yearly (Weintraub, 2010). The

numbers of users increases every year due to the availability of increased

capacity of the mobile device. This also indicates that there is a golden

opportunity for mobile commerce to be flourishing.

For the purpose of this paper, mobile device will cover the following: mobile

phones, smartphones, laptops, tablets and smart phones (excluding only for

desktop PC) in making mobile commerce transaction.

4. Mobile Commerce Service Categories

As cited by Roshanak mobile commerce is an extension of e-commerce

(Roshanak, 2009). The e-commerce services comprise of several business

activities such as Business to Consumer (B2C), Business to Business (B2B),

Business to Government (B2G) and Government to Consumer (B2C). Within the



context of mobile commerce, the most refer to services for mobile commerce is

on B2C and B2B (Jahanshahi, 2001, Grosche and Knospe, 2007, Hsieh, 2007,

O’Donnell et al, 2007).

To add, mobile commerce services may also be categorized into four main

categories. Firstly, Entertainment category which comprises of music, games,

graphic, video and TV streaming. Second is a Communications tool which is

Short messaging, unified messaging, e-mail, chat rooms and video conferencing.

The third is Transaction which includes Banking, broking, shopping, auctions,

betting, booking and reservations, mobile wallet, voting, and

competition/contests and lastly Information which includes News, city guide,

directory service, maps, traffic and weather, corporate information, market data

and mobile advertising. (Islam et al, 2010).

5. Research Theory

There are various research theories available that can be used to measure users

adoption on technology product. Oliveira and Martins (2011) in its literature have

indentified 5 popular theories for measuring user’s adoption and they are the

Technology Acceptance Model (TAM) (Davis 1986, Davis 1989, Davis et al.

1989), Theory Of Planned Behaviour (TPB) (Ajzen 1985, Ajzen 1991), Unified

Theory Of Acceptance And Use Of Technology (UTAUT) (Venkatesh et al.

2003), Diffusion Of Innovation (DOI) (Rogers 1995), and the Technology

Organization and Environment (TOE) framework (Tornatzky and Fleischer

1990).



Although there are several framework and models to clarify the factors or

determinants influencing the acceptance of technology in consumer context,

most of them are based on theory such as TAM, TPB and DOI (Wei et al, 2009).

5.1 The Technology Acceptance Model (TAM) (Davis, 1989)

Wei et al (2009) and Chuttur (2009) mentioned in their literature that TAM is

widely used in adoption studies as it is information system (IS) specific and is

based on a theory of social psychology. TAM was founded on the Theory of

Reasoned Action, In 1989 Davis had developed the TAM with the objective to

identify the factors that caused people to accept or reject an information

technology. During the time it was proposed that perceived usefulness and

perceived ease of use are the most important individual beliefs about using an

information technology tool.

According to Davis, perceived usefulness is defined as “the degree to which a

person believes that using a particular system would enhance his or her job

performance” where as perceived ease of use is defined as “the degree to which

a person believes that using a particular system would be free of effort”. These

then lead to individual behaviour intention and actual behaviour. It is noted that

perceived usefulness possess the strongest predictor of an individual’s intention

to use an information technology (Li, 2007). Refer to the diagram 1 below for the

TAM model frame work.


Actual UsageAttitude toward

Usage

Perceived Ease of Use

Perceived Usefulness

Intention to Use


Diagram 1: The Technology Acceptance Model (TAM) Davis 1989

There are many issues associated with the TAM model and as pointed out by

Bogazzi (2007) the model poses a lack theoretical relationship where there were

weak relationship between the intention to use and actual usage. Furthermore,

behaviour could not be treated as terminal goal and it should be treated as

fundamental goal. In addition, intention may not characterized enough the actual

use due to the period of time taken from intention to adoption could be full of

uncertainties. To add further, he also thinks that there would be other factors

then perceived usefulness and perceived ease of use and TAM was a

deterministic model (Chuttur, 2009).

5.2 The Theory of Planned Behaviour (TPB) (Ajzen, 1991)

The Theory of Planned Behaviour (TPB) was developed by Ajzen in 1991 and it

was an extension to Theory of Reasoned Action (TRA) so as to include the

mandatory situation. The TRA is used to envisage an individual’s behaviour in a

real voluntary situation which is contradicting to the TAM where the situation is

more towards a mandatory context (Li, 2007).

TPB identifies that behavioural intention to perform an activity is determined by

attitude, perceived behavioural control, and subjective norm where attitude is

defined as a person’s feelings about performing behaviour (Wei et al 2009). Li


Actual Behaviour

Subjective NormNormative beliefs and motivation to

comply

Belief and Evaluations

Behaviour Intention

Control Beliefs & Perceived

Facilitation

Attitude toward Behaviour

Perceived Behaviour

Control


(2007) cited that perceived behavioural control is defined as “the perceived ease

or difficulty of performing the behaviour” (Ajzen 1991, p. 188) or “perceptions of

internal and external constraints on behaviour” in IS research (Taylor and Todd

1995, p. 149). Whereas We et al (2007) cited that subjective norm is defined as

“one’s beliefs about whether significant others think that one should engage in

the activity” (Fusilier and Durlabhji, 2005, p. 234).

Diagram 2: The Theory of Planned Behaviour (TPB) Ajzen 1991

However, the problem of TPB is the predictions concerning the effects of

perceived behavioural control (PBC) on behaviour where it is clouded by the

explicit assumption that PBC is an accurate representation of actual control

(personal constraints on behaviour). For example, consider cigarette smoking:

environmental barrier might be that everyone at work smokes; a personal barrier

might be the level of craving for cigarettes. Thus, where PBC and actual control

are discrepant, the effect of PBC on behaviour is more problematic. It seems

likely that PBC will rarely reflect actual control in a very accurate way. (Armitage

et al, 2001)



5.3 The Diffusion of Innovation Theory (DOI) (Rogers, 1995)

DOI is a theory to answer a questions of how, why, and at what rate new ideas

and technology stretch through cultures, operating at the individual and firm level

(Oliveira & Martins, 2011). DOI theory identifies five perceived elements of an

innovation that can verify the rate of adoption (Rogers, 1995; Oliveira & Martins,

2011) and they are relative advantage, compatibility, complexity, trialability and

observability (Rogers, 1995). Adopters are grouped according to different

degrees of willingness to adopt the innovations and they are innovators, early

adopters, early majority, late majority and laggards (Rogers 1995).

Wei et al (2009) elucidated that relative advantage is basically the degree to

which an innovation is perceived as being better than the idea it supersedes

whereas compatibility is the degree of an innovation seems as consistent with

past values, past experience, and the needs of the potential adopters. The

complexity prescribes whether the innovation is perceived as relatively difficult to

use and understand and trialability refers to whether an innovation may be

experimented with on a limited basis. Finally, observability is defined as whether

the results of an innovation are visible to others (Rogers, 1995).

The DOI found that individual characteristics, internal characteristics of

organizational structure, and external characteristics of the organization are

important antecedents to organizational innovativeness. The TOE framework

identifies three aspects of an enterprise's context that influence the process by

which it adopts and implements a technological innovation: technological

context, organizational context, and environmental context.



However, as with other theories discuss previously, DOI was also subject to

debate. Lyytinen & Damsgaard (2001) argued that technologies are not discrete

packages for example it is not clear whether the DOI list is complete and covers

all features that affect adopters behaviour and not all technological innovations

have a same set characterized attributes. In addition different characteristics

may imply different things among different stages of diffusion for example

compatibility may mean different things for the late and early adopters (Lyytinen

& Damsgaard, 2001).

5.4 The Techproved Model (Norzaidi et al, 2007; Norzaidi & Intan Salwani,

2011)

The techproved model represents a few variables, for example, technology

characteristics, task characteristics that have effects on task technology fit. Task

technology fit, on the other hand would operate as predictor on manager’s

performance, perceived usefulness, usage and user resistance. To determine

which variable that mainly predicts performance, user satisfaction (beside usage

and resistance) is included in the model since user satisfaction and usage have

rarely been included in the same study or measured simultaneously within a

single sample (Al-Ghatani, 2004).

Despite the fact that no single model can meet all needs, the present model

should be considered as an attractive option for researchers and practitioners

seeking to measure the effectiveness and efficiency of Intranet usage in the port

industry in Malaysia. The techproved model is a combination of four models (i.e.

task-technology fit, technology acceptance model, DeLone and McLane IS

model and three theories of resistance) in determining the critical success



factors of Intranet usage, which impact on individual and organisational

performance. A few variables from those four models are used to form a

comprehensive techproved model (Figure 3). Details on each construct are

explained as follow;

1) Task characteristics, technology characteristics, task-technology fit, and

performance impact, are extracted from the TTF model (Goodhue and

Thompson, 1995).

2) Perceived usefulness is extracted from the TAM (Davis, 1993).

3) Usage and user satisfaction, are extracted from the DMISM (DeLone

and McLane, 1992).

4) User resistance is extracted from the three theories of resistance

(Markus, 1983).

The combination of existing IS models is not new since, a few prior models (i.e.

TTF, TAM, TRA) were merged to present a holistic and comprehensive model to

test the success of IS. For example, Klopping and McKinney (2004) combined

TTF and TAM to determine merits of workplace technology adoption models in

modelling consumer e-consumer. Klaus et al. (2003) merged TTF and TAM

models and tests whether they are applicable to the Web. Dishaw and Strong

(1999) combined TTF, and TAM to determine the relationship between TTF and

perceived ease of use as well as perceived usefulness; and Dishaw et al. (2002)

combined TTF, TAM and Computer Self-efficacy (CSE) to determine whether the

addition of CSE to the integrated TAM/TTF, in place of or in addition to

experience, increases its predictive or explanatory power.


Performance Impact

Perceived Usefulness

Technology Characteristics

Task Characteristic

Usage

Task Technology Fit

User Resistance

User Satisfaction


Diagram 3: Techproved Model Norzaidi et al, 2007; Norzaidi & Intan Salwani, 2011



CHAPTER (C) RESEARCH METHODOLOGY

1. Introduction

This chapter deliberates the research methodology of the study, including

providing the sources for data, theoretical framework and hypotheses

development.

2. Sampling & Data Collection

2.1 Primary data

Primary data is collected at the final stage of the data processing. The data

gathered would be from our questionnaires that will be distributed to the

respondents.

2.2 Questionnaires

The data will be collated via questionnaires. This questionnaire is designed

based on five-point Likert scales where 1 indicates a strong disagreement to the

statement and 5 indicates a strong agreement to each statement. The

questionnaire is divided into separate sections such as capturing the

respondents’ demographic details. The remaining sections will be per details

below.

In order to test the hypothesis construct above, a survey technique were

developed using the construct from previous research. As cited by Wei et al

(2009), this is to ensure the content validity of the scale used. Hence, in this

research, 26 survey items for five constructs in the questionnaire actually come

from the prior empirical studies, and are modified to fit the context of mobile

commerce. Refer to table below where its shows the sources of where the

questions were adapted from:



Constructs No of Item SourcesPerceived usefulness 1-8 Wei et al (2009)Perceived ease-of-use 9-12 Wei et al (2009), Park (2009)Perceived trust 13-17 Wei et al (2009), Lewis et al (2010)

Perceived cost 18-21Wei et al (2009), Faziharudean & Li-Ly, (2011),

Intention to use 22-26Wei et al (2009), Lewis et al (2010), Faziharudean & Li-Ly, 2011

Table 2: Sample Questionnaires Constructs and its Sources

For Perceived Cost, the questions were adapted from Wei et al (2009) and

Faziharudean & Li-Ly (2011). However, following Field (2005), statements were

positively worded as negative worded items are important in reducing response

bias since respondents needs time to digest the questions in case they are

phrased negatively.

As the questions were all adopted from previous studies as listed above, no

sampling study weas done to ascertain that the hypotheses constructed is

correctly identifying the right variables and the right concepts.

2.3 Sample of Study

The target population of this study are individual postgraduate students in

Graduate Business School of Universiti MARA Shah Alam, Selangor, Malaysia;

who are also mobile device users. The reason why these users were considered

was due to the fact that they are perceived to have the means to pay for the

mobile devices and its related costs but also perceived as a natural adopter to

mobile commerce due to the perceived convenience offered in terms of time

saving and low costs. This survey aims to check on the viability of mobile

commerce among said postgraduate students.

Currently there are approximately 300 students from the Graduate Business

School during the time this study was conducted. Questionnaires were randomly



distributed to both eMBA and part-time evening track postgraduate students from

the Graduate Business School. A total of 240 questionnaires were distributed to

these students and 181 responses were received. Sekaran (2003) stipulated that

responses to any analysis should be 30 percent or higher as Roscoe (1975) too

stipulated that a response rate of higher than 10 percent must be obtained to

avoid sample biasness. Thus, 181 responses or 75 percent responses is

considered as highly representative of the population studied.

3. Secondary Data

The secondary data used are from various sources namely journals articles,

press releases, magazine articles, books and newsletters which were obtained

both offline and online. For online resources, the following websites were

obtained to extract the articles: www.emeraldinsight.com and www.google.com.

Additionally resources in forms of newsletters and presentation slides from the

2011 CommunicAsia event in June 2011 in Singapore were also obtained.

4. Theoretical Framework

The hypotheses model constructed for the purpose of this study is derived from

the literature reviews.

5. Research Model

The approach for the research model is constructed by extending the TAM

theory; adding appropriate constructs from TPB and DOI. Wei et al (2009)

described that the extended TAM model retains the underlying simplicity of the

TAM and improve the ability to predict and explain information system usage at

the same time (Mathieson et al., 2001). The same is applied here using the

usage for mobile commerce instead. Prior empirical studies have validated the



better prediction capabilities of extended TAM (Riquelme & Rios, 2010; Cheah et

al, 2011; Faziharudean and Li-Ly, 2011; Jayasingh & Eze, 2009; Wessels and

Drennan, 2010, etc).

As such, in order to augment the forecast of usage intention for mobile

commerce among post graduate students in UiTM Shah Alam, while still

maintaining its model simplicity, this study decides on the concept of TAM but to

also expand it to include two additional building blocks. They are perceived cost

and perceived trust. Wei et al, (2009) explained perceived cost is the construct

derived from TPB and DOI theories. He also suggested that the study should

eliminate the attitude construct from TAM for simplicity purposes. The new

extended TAM for this study is as depicted in Diagram 3 below:

Diagram 3: Proposed Research Hypothesis


H3

H4

Perceived usefulness

Perceived ease of use

Intention to use mobile commerce

Perceived Trust

Perceived Cost

H1

H2

Actual UsageH5


6. Hypotheses Development

6.1 Perceived Usefulness

As discuss earlier, perceived 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). This definition has been proven by many studies in

relation to intention to use (IU) (Jayasingh & Eze, 2009; Lewis et al, 2010;

Wessels and Drennan, 2010). A study by Jayasingh & Eze (2009) have found

out perceived usefulness has a determinant contribution factor towards the

behaviour factors in using mobile commerce related services which is m-coupon.

Lewis et al (2010) also found out that perceived usefulness and compatibility

factors to have a significant effect on behavioural intention to use m-banking

which later support by a study made by Wessels and Drennan in 2010.

A contradicting finding on PU was indicated in a study done by Islam et al

(2010). In his study on adoption of mobile commerce services in Bangladesh

where it showed that PU was not a significant factor in influencing people to

adopt mobile commerce services. However, this could possibly be due to the

very early stages of mobile commerce in Bangladesh. The mobile commerce

services will eventually be attractive enough for consumer adoption as the belief

that mobile commerce will enhance their working lifestyle grows.

As such, the study will look into the definition of PU in a different context as the

subject of this study comprises of post graduate students. Wei at al (2009) cited

that PU in mobile commerce is able to help the users to achieve task-related

goals, such as effectiveness and efficiency. Thus it is proposed that the effect


Perceived usefulness



should be, in many ways, extended to postgraduate students. Hence, the

hypothesis is proposed as:

H1. PU has a positive effect on postgraduates students IU mobile commerce in

UiTM Shah Alam.

Diagram 4: Perceived Usefulness

6.2 Perceived Ease-Of-Use

According to Davis (1989), even if a technology is perceived to be useful for the

end users, there were also instances where the technology is not user friendly

and difficult to use. Ease of use refers to the degree to which an individual

believes that using mobile commerce would be free of physical and mental

efforts. Perceived ease of use (PEOU) has been considered as a prime

determinant in adoption of previous information technologies studies (Wei et al

2009). Many studies on mobile commerce adaptation have used and tested

PEOU (Faziharudean & Li-Ly, 2011; Sadi & Noordin, 2011; Saneifard 2009). In a

study on consumers’ behavioural intentions to use mobile data services in

Malaysia, Faziharudean & Li-Ly (2011) found out that mobile data services

adoption will increased when users believed that they have the skills to use such

services and that the services are easy and user-friendly .

Sadi & Noordin (2011) tested the PEOU within Malaysian environment and found

that PEOU was an important factor for the consumer’s intention to use mobile


H1

Perceived ease of use


H2


commerce. They concluded that the mobile commerce providers should focus on

the development of usefulness of the system. Similarly, Saneifard (2009) in his

study concluded that PEOU was a significant factor in Iran for mobile commerce

adoption. In line with the Diffusion of Innovation theory, if the mobile commerce

system is too complex, it will then be a factor that which will discourage the

adoption of innovation (Rogers, 1995).

FOXNews.com reported that Princeton University, a private research university

in New Jersey, United States launched a Kindle e-reader program and expected

the e-readers to be both sustainable and a valuable academic tool. However,

after two weeks of implementation, students felt dissatisfied and uncomfortable

to using the e-reader compared to physical books (Lee, 2009). This report

indicates that the technology is not free from mental and physical implications.

This implicates the same as other studies; that mobile commerce must be easy-

to-use thus this study proposes the following hypothesis:

H2. PEOU has a positive effect on postgraduates students IU mobile commerce

in UiTM Shah Alam.

Diagram 5: Perceived Ease-of-Use



6.3 Perceived Cost

Wei et al (2009) stipulated that the price or cost factor is one of the reasons that

impede the development for mobile commerce. The cost factor is into four

categories which are initial purchase price, on-going costs, maintenance costs

and upgrading costs.

The initial purchase price is the cost paid for the purchase of the mobile devices

such as smart phones, tablet or a laptop. The on-going costs are the incidentals

costs that users have to bear such as subscription fees, services fees and

communication fees like packaging fees or roaming services provided by the

subscribed telecommunication company. The third associated costs is

maintenance cost such as repairing and servicing of the mobile device

possessed where such device. The fourth cost, as cited by Wei et al (2009) is

associated with the upgrading costs concerning to software or hardware

upgrades on the mobile devices to enable new features available to be used.

Some studies indicated that associated cost of using mobile commerce is high

(Wentzel et al, 2005; Moqbel et al, 2010; Ng, P.Y et al, 2010; Harris et al, 2005)

and others believed that they are quite affordable (Lewis et al, 2010; Riquelme &

Rios, 2010, Kim et al, 2006; Jahanshahi et al, 2011). However, it must be noted

that costs associated with the use of mobile commerce has depreciated over the

years therefore, it is interesting to note of the studies above arrived at differing

conclusions as they were conducted at different times.

Lewis et al (2010) stated that internet allowed consumers to have access to

many bank facilities throughout the day; thus allowing banks to significantly cut

their costs and allow them to charge cheaper services. This is supported by



Riquelme & Rios (2010) where it was concluded that the cost of a financial

transaction becomes lower when using an electronic device compared to manual

transactions done at a bank branch. Kim et al (2006) explained that the price of

wireless computers devices becomes similar with wired computers devices

simply because of decreasing prices of hardware and software. Jahanshahi et al

(2011) indicated that the cost for healthcare services will become lower when

doctor and nurses can interchange patient profiles using mobile commerce

technology.

Despite the above, a study titled Using Mobile Technology to Enhance Student’s

Educational Experience by Wentzel et al (2005) for the Educause Centre for

Applied Research (ECAR) identified that majority of the students agreed that low

costs for mobile services adds value to mobile phones. Moqbel et al 2010

indicated that Western Europe and United States have been slow to embrace

the mobile commerce phenomenon due to the slow response to the technical

infrastructure, low speed wireless access links, as well as high costs associated

with mobile commerce implementation. As indicated earlier in literature review,

both high school and university students are cost cautious consumer of

technology because they don’t possess a fixed income and are mostly

dependent on their parents or part time jobs with limited source of money (Ng,

P.Y et al, 2010; Harris et al, 2005).

Naqvi and Al-Shihi (2009) stated that the nonexistence of 3G network in Oman

and the existing sky-scraping costs of MMS and WAP limit adopters and

developers to certain types of mobile applications. Islam et al (2010) also stated

that cost is one of the aspects in influencing the adoption for mobile commerce in

Bangladesh. Tiwari et al (2008) indicated that the transaction costs seem to be


H3Intention to use mobile commerce

Perceived Cost


an important criterion for mobile payment acceptance. Hu & Allison (2010), found

that the data plan costs in mobile services have become a primary deterrent for

customers to use internet via mobile. However, a study done by Saneifard,

(2009) has found that price or cost factors were not one of the main

determinants of mobile commerce services adoption in Iran because the mobile

commerce is still at its infancy stage where the research subjects were not able

to afford such services or data transaction fees. This later were supported by

Lewis et al (2010) who also concluded that previous studies on the effect of

costs produced mixed results due to the hazy distinction between actual costs of

purchase and use; and hidden transaction costs.

Thus, considering that the postgraduate students is a different set of study group

altogether; the cost factor will be tested as perceived cost constructed which is

defined as the degree of a post graduate student perceived the costliness of a

mobile commerce service. As such, the following hypothesis is projected:

H3. Perceived cost has a negative effect on postgraduates students IU mobile

commerce in UiTM Shah Alam.

Diagram 6: Perceived Costs

6.4 Perceived Trust

Many studies indicated that the element of trust is an important factor that affects

the success of technologies adoption (Islam et al, 2010; Lewis et al, 2010;



Rehman et al, 2011; Yeh & Li ,2009). Jayasingh & Eze, (2009); investigated the

acceptance and use of mobile commerce using Unified Theory of Acceptance

and Use of Technology (UTAUT) models in Bedford, United Kingdom where he

inserted trust as one of the factor of behavioural intent. The results augmented

total variance from the original model by 2%. In a case study done by O’Donnell

(2007) titled Australian Case Studies in Mobile Commerce, privacy, security and

confidentiality are concluded to be the key success factors for Trust.

Lewis et al (2010) indicated that perceived risk and trust are intertwined notions

and were repeatedly recognized as prime obstructions to adopting online and

mobile services. He also state that, m-banking is reliant to the literate level of

mobile phone consumers where the higher the understanding of mobile

commerce and its technology, the more likely the users place greater trust in the

channel/competence.

Rehman et al (2011) in his study indicated that the mobile users around the

world are slow to adapt and trust mobile commerce. This is mainly due to slow

variation and lack of trust in mobile payment systems. He added that trust on

mobile commerce was built by two factors; usability of interface design and

security of mobile payment system. He also added that skilful and attractive

design aesthetics consequently results in the ease of use, usefulness and

customization. This, in turn, builds mobile trust (m-trust). The m-trust is an issue

in mobile commerce culture because a mobile device is a personal device

almost like a wallet (Taipaleenmäki, 2006).

Yeh & Li (2009) explained that building trust in e-commerce is complicated due

to the security apprehension. The same can also be applied to mobile commerce


H4


Perceived Trust


devices. According to Yeh and Li, customers feel insecure towards conducting

business activities without the observation of others, personal interaction and the

ability to feel, touch and inspect products. To add, building customer trust on a

mobile platform which is bounded by interface design, speed of communication

and physical capability, is very much essential for vendors in order to gain long-

term profitability. In his study, a modified SERVQUAL models that are related to

mobile commerce were used to test the concept of service quality in relation to

trust.

As such it is difficult to disregard the element of trust on IU of online technology.

Thus the following hypothesis is developed:

H4. Trust has a positive effect on the postgraduates students IU mobile commerce

in UiTM Shah Alam.

Diagram 7: Perceived Trust

6.5 Intention to Use

Park (2009) cited that according to the TAM approach that was developed by

Davis in 1989, a person’s actual use of a technology system is influenced

directly or indirectly by the user’s behavioural intentions, which comprises of

perceived usefulness of the system, and perceived ease of the system. In other


H5Intention to use mobile commerce Actual Usage


words, the user’s behaviour of intention to use the information technology is

directly correlated to actual use. The link of intention to use and actual usage

has been established by various studies (Schaper & Pervan, 2004). Teo (2011),

Davis et al and Taylor & Todd in 1995 explained that behavioural intentions were

found to be a strong predictor of actual use.

Thus this study will follow the original TAM within the context of post graduate

students in proposing the last hypothesis as follow:

H5: Behavioural intention will have a significant positive influence on post graduate

students actual use behaviour.

Diagram 7: Intention to Use

7. Types of Tests and Statistical Software

Upon data collection, data is then analysed using the SPSS Statistical software

and Microsoft Office Excel 2007 software. Common research assumptions are

used for statistical techniques. As the questionnaire followed previous studies on

various hypotheses, no pilot study was carried out. However, in order to

ascertain the reliability of the questionnaire, three reliability tests were used; that

is the Cronbach’s Alpha, the Kaiser-Meyer-Olkin (KMO) and the Bartlett’s Tests.

The three reliability tests are further explained in the following chapter.



8. Summary

About 240 questionnaires were distributed to the targeted population which are

the eMBA and evening track Graduate Business School postgraduate students

of which received 181 responses. The questionnaire were adopted from previous

accepted studies based on five hypotheses which are based on independent

variables that is perceived usefulness, perceived ease of use, perceived cost,

perceived trust and intention to use. The dependent variable is based on the

hypothesis of actual intention to use. The data are then collected and analysed

using two software, Microsoft Office Excel and SPSS statistical software. The

tests conducted and its finding will be deliberated in the next chapter.



CHAPTER (D) ANALYSIS AND FINDINGS

1. Introduction

This chapter deliberates on the findings from the analysis conducted. From the

data collected, a series of analyses were conducted normality analysis via

Skewness and Kurtosis and Shapiro-Wilk Tests; scale reliability and validity

analyses utilising Cronbach’s Alpha reliability test and Kaiser-Meyer-Olkin and

Bartlett’s Validity tests; coefficient correlation analysis and lastly, multiple

regression analysis. Each of the analysis will be discussed separately below.

Additionally descriptive statistics of the respondents are also tabulated below.

2. Descriptive Statistics

The questionnaires were randomly distributed to eMBA and evening track

Graduate Business School students; of which 75% of the 240 questionnaires

were received. The demographics of the respondents are deliberated below. The

demographics are based on gender, age, marital status, monthly income, types

of mobile phone, types of subscription (post-paid versus prepaid mobile users)

and the telecommunications provider; types of mobile phone experience and

frequency of usage.

Frequency Percentage Cumulative Percentage

Valid Male 57 31.49 31.49

Female 124 68.51 100.0

Total 181 100

Table 3: Gender

The table above shows that more than two thirds of the respondents are female

(68.51%) and the balance 31.49% of the respondents are male.




Valid < 20 years 0 0 0

21-25 years 7 3.87 3.87

26-30 years 76 41.99 45.86

31-35 years 53 29.28 75.14

36-40 years 28 15.47 90.61

41 and above 18 9.94 100

Total 181 100

Table 4: Age

According to the table above, the majority of the respondent belongs to the 26-

30 years of 41.99%, followed closely by the 31-35 age group (29.28%), 36-40

age group (15.47%), 41 years and above (9.94%) and 21-25 years (3.87%).

There are no respondents that are below 20 years of age. This resonates with

literature review of Suruhanjaya Komunikasi dan Multimedia Malaysia

handphone users’ survey in 2009 that a significant number of mobile users fall

between the 25-40 age group.


Valid Single 67 37.02 37.02

Married 112 61.88 100.0

Total 181 100

Table 5: Marriage Status

The study shows that 61.88% of the respondents are married.




Valid < RM2,000 4 2.21 2.21

RM2,001-RM4,000 78 43.09 45.3

RM4,001-RM6,000 57 31.49 76.79

RM6,001-RM8,000 14 7.73 84.52

RM8,001-RM10,000 13 7.18 91.7

> RM10,001 15 8.29 100.0

Total 181

Table 6: Personal Income

The monthly personal income of the respondents are below RM2,000 (2.21%),

between RM2,001 to RM4,000 (43.09%), between 4,0001 to RM6,000 (31.49%),

between RM6,001 to RM8,000 (7.73%), between RM8,001 to RM10,000 (7.18%)

and above RM10,0001 (8.29%).


Valid Nokia 55 27.36 27.36

HTC 10 4.98 32.34

Apple 32 15.92 48.92

Samsung 27 13.43 61.69

Blackberry 60 29.85 91.54

Others 17 8.46 100

Total 201 100

Table 7: Types of Mobile Phone Used

Most of the respondents (29.85%) use Blackberry, followed Nokia (27.36%),

Apple (15.92%), Samsung (13.43%) and HTC (4.98%). About 8.5% of

respondents use other brands. It is note that some of the respondent owns more

than two mobile phones in view of the total frequency for this survey is 201,



twenty more than the number of the respondents. This is aligned with the

national mobile telephone penetration rate of more than 100% where 23.8

percent of mobile telephone users own more than one mobile telephone (SKMM

Handphone Users Survey 2009).

SMS/MMS30%

Downloads13%

Email/News/Information24%

Banking14%

Booking (Hotels/Movies)12%

Online Purchase8%

Use of Mobile Phone

Chart 1: Uses of Mobile Phone.

From the responses, the majority of the responses use the mobile phone for

Short Messaging System (SMS) or Multimedia Messaging System (MMS) at

30% and to check email, news and information (24%). The least use for mobile

phone is online purchasing (7%).

3. Normality Analysis

Park (2008) cited that statistical analysis is based on various underlying

assumptions and one common assumption is that a random variable is normally

distributed. These assumptions are without empirical evidence or test. As such,

any interpretation and conclusion from improper assumptions may not be

accurate. Therefore, it is very important to ensure that a normality test is



conducted to assure that the variable is randomly distributed. According to Park

(2008) the normality test can be conducted in two ways; which are the Graphical

method or the Numerical Method. These are then divided into two more

categories; that is descriptive or theory driven. For clarity, the explanation on the

above is illustrated below:

Graphical Methods Numerical Methods

Descriptive Stem-and-leaf plot, (skeletal) box plot, dot plot, histogram

Skewness, Kurtosis

Theory-driven

P-P plot, Q-Q plot

Shapiro-Wilk, Shapiro- Francia test, Kolmogorov-Smirnov test (Lillefors test), Anderson-Darling/Cramer-von Mises tests, Jarque-Bera test, Skewness-Kurtosis test

Table 8: The different approaches of Normality Analyses.

This study has chosen the numerical methods specifically; Skewness, Kurtosis

and Shapiro-Wilk to test the normality of the data. Graphical Methods can

provide interactive diagrams but the bell shape diagram would not accurately

show the results with a lower sample size. The study, however, shows that both

graphical and numerical methods can complement each other.

Tests of Normality

Shapiro-Wilk

Statistic df Sig.Perceived Usefulness .905 181 .181Perceived Ease-Of-Use .927 181 .103Perceived Trust .960 181 .051Perceived Cost .961 181 .121Intention To Use .916 181 .153

Table 9: Normality Tests results using Shapiro-Wilk tests.

The above table was derived from SPSS Descriptive Statistic on Explore

function using the Shapiro-Wilk analysis. As a comparison, the Kolmogorov-

Smirnov test can be used if the sample size available is larger than 50. In



contrast, if the sample sizes are 50 or less, the study should use the Shapiro-

Wilk test instead. However, for the this study, the Shapiro-Wilk is used instead.

The table above showed that the entire 181 samples were used as there had

been no missing value.

Even so, the test of normality for this study indicate that all the variable were

more than to the level of significance (0.01), thus the study accepts the null

hypothesis and conclude that all the variables were normally distributed. The null

hypothesis for the test of normality states that the actual distribution of the

variable is equal to the expected distribution, for example the variable is normally

distributed.

Perceived Useful-

ness

Perceived Ease-Of-Use

Percei-ved Trust

Percei-ved Cost

Inten-tion To

UseN Statistic 181 181 181 181 181

Minimum Statistic 8 4 5 4 5Maximum Statistic 40 20 25 20 25

Mean Statistic 30.8674 14.8122 15.9227 12.9613 19.5414Std

DeviationStatistic 5.51097 2.74632 3.86646 2.94838 3.5221

SkewnessStatistic -.891 -.667 -.587 -.384 -.588

Std. Error

.181 .181 .181 .181 .181

KurtosisStatistic 3.613 1.919 .392 .269 1.207

Std. Error

.359 .359 .359 .359 .359

Table 10: Normality Analysis via Skewness & Kurtosis tests.

The normality test was further interpreted through looking at the Skewness and

Kurtosis tests. As cited by Hani et al (2009), Haris et al in 1998, a value greater

than one in the Skewness and Kurtosis tests indicates a distribution that differs

significantly from normal symmetric distribution. In addition, Skewness and

Kurtosis values within the range of -1 to +1 indicate an acceptable range, while



values outside that range indicate a substantial departure from normal

distribution.

Skewness is a measure of symmetry, or more precisely, the lack of symmetry. A

distribution, or data set, is symmetric if it shows the same curve to the left and

right of the center point. Skewness of more than 0 indicate the curve were right

skewed distribution. This explained that most values are concentrated on left of

the mean, with extreme values to the right. Skewness of less than 0 indicates

that the curve was left skewed distribution. This explained that most values are

concentrated on the right of the mean, with extreme values to the left. The

Skewness of equals to 0 or in other words mean equals to median shows the

distribution is symmetrical around the mean.

As such, from Skewness perspective, value obtained from SPSS Frequency

analysis shows that the value which is less than zero indicates that the curve

was left skewed distributed; with most values concentrated on the right of the

mean; and extreme values to the left. The values were also less than one which

indicates a distribution that did not differ significantly from normal symmetric

distribution.

Kurtosis in distribution analysis is used to indicate a sign of flattening or

"peakedness" of a distribution. Data sets with high kurtosis value tend to have a

distinct peak near the mean and the curve will decline rather rapidly showing

heavy tails to the left and right. On the other hand, data sets with low kurtosis

tend to have a flat top curve near the mean rather than a sharp peak curve. A

uniform or linear distribution would be an extreme case.



As an indicator, Kurtosis value of more than 3 indicates a Leptokurtic distribution

or sharper than a normal distribution, with values concentrated around the mean

and thicker tails. This means high probability for extreme values. Kurtosis value

of less than 3 indicates a Platykurtic distribution or flatter than a normal

distribution with a wider peak. The probability for extreme values is less than for

a normal distribution, and the values are wider spread around the mean. Kurtosis

value of equivalence to 3 indicates a Mesokurtic distribution or normal

distribution.

Looking at the same results, this study have found out that the results for all the

variable were less than 3 which 3 thus explained the peak of the curve are wider

where probability for extreme values is less than for a normal distribution. Hair

et al., (1998) stipulates that a Kurtosis level that is higher than three indicates a

non normality. Thus the Kurtosis result shows a normal distribution. Many

researchers however known that in reality there were rarely analyzed results of

perfectly normal distribution unless the analysis were base on the larger sample

size.

4. Scale Reliability & Validity Analysis

This study will look at both the reliability and validity of the data obtained from

the survey. Golafshani et al (2003) explained that in Quantitative research;

reliability is where the result is replicable, consistent over time and an accurate

representation of the total population under study. He also explained that validity

refers to whether the means of measurement are accurate and whether they are

actually measuring what they are intended to measure and to approximate the

truthfulness of the results. According to Tavakol et al (2011) reliability of an



instrument is closely associated with its validity. Thus, it can be concluded that

an instrument can be valid if it is reliable. As such this study will look upon both

reliability and validity analysis and the findings were explained as follows:

4.1 Cronbach’s Alpha Reliability Test

Cronbach’s Alpha is most widely used objective measure of reliability (Tavakol et

al, 2011). It is used to measure the internal consistency (the extent to which all

the items in a test measure the same concept or construct) of a test or scale.

The measurement is expressed as a number between 0 and 1. As cited by

Gliem et al (2003), the nearer Cronbach’s Alpha coefficient is to 1, the better the

internal consistency of the items in the scale. They also cited that the following

rules of thumb for Cronbach’s Alpha reliability scale were proposed by George

and Mallery in 2003:

Scale Interpretation> 0.9 Excellent> 0.8 Good> 0.7 Acceptable> 0.6 Questionable> 0.5 Poor< 0.5 Unacceptable

Table 11: Interpretation Scale of Cronbach’s Alpha tests results

Fahad (2009) cited Hair et al in 1998 stipulated that the Cronbach’s Alpha value

of 0.60 were acceptable at 0.60 in exploratory research. As such, by running the

survey data on the SPSS software for each of the variables the following results

were obtained.



Variables Cronbach’s Alpha ResultsPerceived Usefulness 0.942Perceived Ease-Of-Use 0.878Perceived Trust 0.907Perceived Cost 0.842Intention To Use 0.939

Table 12: Results via Cronbach’s Alpha Test

By looking at the results above it show that all the variables measures achieved

results between the ranges of 0.842 to 0.939. Thus according to rule of thumb,

the variables were between the range of Good and Excellent. In other word the

variables have very good internal consistency reliability (Faziharudean & Li-ly,

2011).

4.2 Factor Analysis for Validity

According to Golafshani et al (2003 cited in Wainer and Braun 1998),

quantitative research validity is referred to construct validity. In this study, the

construct validity is determined by factor analysis. Before factor analysis can be

conducted, it must first pass the Kaiser-Meyer-Olkin Measure of Sampling

Adequacy test and Bartlett's Test of Sphericity. Both tests are as below:

4.3 Kaiser-Meyer-Olkin and Bartletts’s Validity Test

The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy is an index used

to examine the appropriateness of factor analysis. High values (between 0.5 and

1.0) indicate factor analysis is appropriate. Values below 0.5 imply that factor

analysis may not be appropriate. Kaiser in 1974 had refined the index further

and suggested the following:



Value Interpretation0.00 to 0.49 Unacceptable0.50 to 0.59 Miserable0.60 to 0.69 Mediocre0.70 to 0.79 Middling0.80 to 0.89 Meritorious0.90 to 1.00 Marvellous

Table 13: Interpretation Scale using KMO Test

Looking at the table generated through SPSS below, the KMO measure is 0.878

(refer to table 14 below) and by matching this value with the extended index by

Kaiser, this fall under Meritorious which is commendable. This indicates that the

sampling identify in this study is satisfactory for factor analysis to proceed.

KMO Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.

.879

Table 14: KMO Test Results

4.4 Bartlett's Test of Sphericity

The Bartlett’s test of sphericity is to test the null hypothesis that the variables are

uncorrelated in the population. In other words, the population correlation matrix

is an identity matrix. An identity matrix is the matrix in which all of the diagonal

elements are 1 and all off diagonal elements are 0. where this study needs to

reject this null hypothesis. The study indicates strong relationship between all

variables where in the correlation table all the diagonal elements are 1. In

Bartlett’s test, this study needs to reject the null hypothesis of uncorrelated

variable or non-identity matrix. The study however indicates strong relationship

between all variables where in the correlation table all the diagonal elements are



1. This is proven further on the observed significance level is .000 (refer to table

15). This result is small enough to reject the hypothesis thus it is a good idea to

proceed with a factor analysis for the data.

Bartlett's Test

Bartlett's Test of Sphericity

Sig. .000

Table 15: Bartlett’s Test Results

4.4 Factor Loading

Both KMO and Bartlett tests indicated the data are ready for Factor analysis for

measuring validity. DeCoster, J. (1998) explained that Factor analysis is a

collection of methods used to examine how underlying constructs influence the

responses on a number of measured variables. Basically there are two types of

factor analysis which is exploratory type analysis and confirmatory type analysis.

Exploratory Factor Analysis (EFA) attempts to discover the nature of the

constructs influencing a set of responses whereas Confirmatory Factor Analysis

(CFA) tests whether a specified set of constructs is influencing responses in a

predicted way. As such this study will look at the confirmatory type of factor

analysis where it measure the most influencing predicted item in the m-

commerce adoption among post graduate part time student in UiTM.

There are five elements examined in the study and they are Perceived

Usefulness, Perceived Ease-Of-Use, Perceived Trust, Perceived Cost and

Intention to Use. Each element was tested on SPSS and the findings are

depicted as individual table below. As cited by Wei et al (2009, cited on Liao et al

,2007) items that do not meet factor loading of greater than 0.5 value and loads

on one and only one factor will be moved out from the study.



Table 16: Perceived Usefulness

Item LoadingUsing mobile commerce would improve my efficiency in my studies. .875Using mobile commerce would enhance my effectiveness in my studies. .852Using mobile commerce enables me to do my studies conveniently. .844Using mobile commerce enables me to do my work conveniently. .786Using mobile commerce saves my time so that I am able to juggle my work and studies better

.778

Using mobile commerce would enhance my effectiveness in my daily work.

.748

Using mobile commerce would improve my efficiency in my daily work. .712In general, I believe mobile commerce will be useful. .645

Table 17: Perceived Ease-Of-UseItem LoadingI intend to use mobile commerce if the cost is reasonable for me .836I believe I will use mobile commerce in the future .819Assume that I have access to mobile commerce systems, I intent to use them

.815

I expect to use mobile commerce frequently in the future. .815I believe my interest towards mobile commerce will increase in the future .797

Table 18: Perceived TrustItem LoadingI believe transaction conducted through mobile commerce will be secure .867I believe my personal information will be kept confidential while using mobile commerce technology

.846

I believe payments made through mobile commerce channel will be processed securely

.839

I would trust my telecommunication operator to provide secure data connections to conduct mobile commerce

.815

I would trust my mobile phone manufacturer to provide a mobile phone which is appropriate for conducting mobile commerce.

.814

Table 19: Perceived Cost Item LoadingThe cost of mobile phone is affordable for me .796I believe that at the current price, “mobile data services” would provide a good value.

.782

The access or subscription fee is acceptable for me .775The mobile data services package that I subscribed to is affordable for me .767

Table 20: Intention to UseItem LoadingIt is/might be easy to learn to use mobile commerce .823Mobile commerce is/might be easy-to-use .774It would be easy for me to become skilful at mobile commerce. .749Mobile commerce is understandable and clear .704



The above table was form from extraction method using Principal Component

Analysis; with a rotation method of Varimax with Kaiser Normalization with

rotation converged in 5 iterations. This factor analysis has produced 5 factors

with 26 scales loading. All the 26 items are sorted and clustered into the five

factors namely Factor 1 (Perceived Usefulness), Factor 2 (Perceived Ease-Of-

Use), Factor 3 (Perceived Trust), Factor 4 (Perceived Cost) and Factor 5

(Intention to Use). All the 26 items obtained factors loading between 0.6 to 0.8.

This represents that the item used in the questionnaire have satisfactory validity.

4.5 Coefficient Correlation Analysis

Cheah et al (2011) and Wei et al (2009) explained that the purpose of Pearson

correlation analysis is to examine the strength of relationships among variables.

Perera et al (et al 2011 cited on Cohen 1998) identify a set of matrix value of

correlation coefficient range as a guide for interpretation in physiological

research as follows:

Negative Value Positive ValueSmall correlation -0.10 to-0.29 +0.10 to +0.29

Medium correlation -0.30 to-0.49 +0.30 to +0.49Large correlation -0.50 to-1.00 +0.50 to +1.00

Table 21: Interpretation Scale of Coefficient Correlation Analysis

As cited by Cheah et al (2011, cited on Field, 2005), correlation coefficient

should not exceed the value of 0.8; where value closer to 1.0 is more favourable.

This is to avoid two or more variables in the model to be correlated and thus

provide redundant information which can be confusing and lead to misleading

results. This is also known as multicollinearity. As indicated in table 22 below,



there are no values that is higher than 0.8. As such, it can be concluded that

multicollinearity problem did not exist in this study.

Table 22: Results of Coefficient Correlation Analysis

As per table above, the relationships among variable were medium to strong

correlation as the respective correlation value is between 0.323 to 0.594 but not

for the perceived trust variable where it indicates weak or small correlation

among other variables. This is also the same as for the sig (2-tailed), where the

values for all other variables are less than 0.01 which indicate that there are

statistically significant correlations between variables except for one variable

which is perceived trust. Therefore, if any of the variable values increases or

decreases, the other variables also increases or decreases significantly accept

for the perceived trust variable.


Correlations

PU PEU PT PC ITU

PU Pearson Correlation 1 .540** .222** .323** .594**

Sig. (2-tailed) .000 .003 .000 .000

PEU Pearson Correlation .540** 1 .304** .411** .527**

Sig. (2-tailed) .000 .000 .000 .000

PT Pearson Correlation .222** .304** 1 .271** .229**

Sig. (2-tailed) .003 .000 .000 .002

PC Pearson Correlation .323** .411** .271** 1 .390**

Sig. (2-tailed) .000 .000 .000 .000

ITU Pearson Correlation .594** .527** .229** .390** 1

Sig. (2-tailed) .000 .000 .002 .000

**. Correlation is significant at the 0.01 level (2-tailed).


4.6 Multiple Regression Analysis

Regression analysis is a constructive statistical technique that can be used to

analyze the associations between a set of independent variables and a single

dependent variable (Hair et al., 2005). Multiple regressions are used to examine

the relationship between intention to use, perceived cost, perceived time,

perceived usefulness, and perceived ease of use.

Model Summary

Model R R Square Adjusted R Square Std. Error of the Estimate

1 .658a .434 .421 2.68082

a. Predictors: (Constant), PC, PT, PU, PEUTable 23: Regression Summary

ANOVAb

Model Sum of Squares Df Mean Square F Sig.

1 Regression 968.059 4 242.015 33.675 .000a

Residual 1264.880 176 7.187

Total 2232.939 180

a. Predictors: (Constant), PC, PT, PU, PEU

b. Dependent Variable: ITUTable 24: ANOVA Table

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

T Sig.

Correlations

B Std. Error Beta Zero-order Partial Part

1 (Constant) 4.242 1.392 3.048 .003

PU .263 .043 .412 6.054 .000 .594 .415 .343

PEU .299 .092 .233 3.255 .001 .527 .238 .185

PT .022 .055 .025 .405 .686 .229 .031 .023

PC .185 .076 .155 2.429 .016 .390 .180 .138

a. Dependent Variable: ITU

Table 25: Coefficients Table Analysis Results



Based on table 25 above, it can be observed that the R Square was 0.658,

representing that 65.8 percent of the actual usage of mobile commerce can be

explained by Perceived Usefulness, Perceived Ease-Of-Use, Perceived Trust,

Perceived Cost and Intention to Use. We can conclude the following equation:

Intention to Use (Actual Usage) = β1 PU+ β2 PEU + β3 PT + β4 PC privacy + ε

Intention to Use (Actual Usage) = 0.263 *PU + 0.299 *PEU + 0.022 *PT + 0.185 *PC + 4.242

A big absolute t value and small p (Sig) value suggests that a predictor variable

is having a large impact on the criterion variable. Looking at the individual

variables reveals that Perceived Usefulness (PU) (t-value = 6.054 and p<0.05),

Perceived Ease-Of-Use (PEU) (t-value = 3.255 and p<0.05) and Perceived Cost

(PC) (t-value = 2.429 and p<0.05) were found to have a significant relationship

with Intention to Use (Actual Usage). Therefore, the hypotheses H1, H2 and H4

were supported. Meanwhile Perceived Trust (PT) (t-value = 0.405and p>0.05)

had no significant relationship with the Intention to Use (Actual Usage). Hence,

H3 are not supported.

4.7 Analysis Summary

The findings were first analysed using descriptive statistics from demographics

of the respondents. Most of the respondents have experienced with internet

banking and this make our research more reliable. Normality analysis was done

to ensure the population of the sample comes from a normal distribution.

However, the actual results shown a normal distribution as the value of Shapiro-

Wilk are more than 0.05 (Shapiro and Wilk, 1965).



This finding is discussed further in later topic of discussion and findings.

Reliability test was deployed and the Cronbach’s Alpha valued at the ranges of

0.842 to 0.939 as compared to the acceptance level by Hair et. al, (1998) that

value should exceed 0.7.

Next, the correlation analysis was conducted and all of the variables show strong

relationships accept one which is Perceive Trust. The same results were also

revealed in regression analysis where Perceive Trust did not give significant

relationships with Intention to Use (Actual Usage). From the result, we conclude

our study with recommendation and practical implications which discuss in

chapter five.



CHAPTER (E) DISCUSSION AND CONCLUSION

1. Introduction

This chapter will discourse some of the pertinent findings of the study based on

the analysis of the previous chapter. Specifically, the findings of the study and

the practical implications will be discussed followed by limitation of the study.

Finally, there will be a recommendation for future studies prior to the conclusion

portion of this report.

2. Findings of Study

It can be surmised at this point that the proposed hypothesis and theoretical

framework has been validated by the empirical studies and analysis above.

Various analyses such as normality test, reliability and validity tests, correlation

and multiple regression analysis have been utilized to test the five hypotheses as

well as the data obtained from the survey. As such, we are able to observe if

there is a significant effect of influencing postgraduate students’ perception

construct towards mobile commerce adoption from usefulness, ease of use,

trust, cost, intention to use and actual use perspective.

The normality test is conducted to assure that the data received from the survey

conducted is normal to assure accuracy. For this purpose, these tests were

conducted, specifically, Skewness, Kurtosis and Shapiro-Wilk tests. These are

all under Numerical Approaches due to lack of sample size and experience in

interpreting graphical methods. Using Shapiro-Wilk tests, it concluded that the

variables were greater than 0.01; indicative that the variables are normally

distributed. The data is further tested against its skewness and kurtosis tests. In

Skewness and Kurtosis tests, values within the range of -1 to +1 indicate an



acceptable range, while values outside that range indicate a substantial

departure from normal distribution. Both the Skewness and Kurtosis indicated

that the distribution did not differ significantly from normal symmetric distribution.

The data was also tested against reliability analyses. This is essential to assure

that all the variables tested are reliable. Firstly, the data was tested through the

Cronbach’s Alpha test for its reliability. Through all the variables tested, the

results showed the range between 0.842 (Perceived Cost) to 0.942 (Perceived

Usefulness) which falls within the good to excellent range per George and

Mallery (2003). Similarly, when the same data is run through the Kaiser-Meyer-

Olkin measure of Sampling Adequacy, result showed that it falls under

meritorious. Bartlett’s test of Spherecity was also conducted on the data which

showed 0.000 significance which means that null hypothesis of the variables are

uncorrelated in the population were rejected. From this, factor loading analysis

were conducted. All 26 items were analysed and the results showed factors

loading in between 0.6 to 0.8 which resultants the items used in the

questionnaire is valid.

Next, coefficient correlation analysis is conducted to see if multicollinerity

existed. Multicollinerity, or the avoidance of two or more variables to be

correlated and hence, confusing or lead to redundancy and misleading results.

Cheah et al (2011) and Field (2005) enumerated that correlation coefficient

should not exceed 0.8; where the closer to 1.0 is better. The results all showed a

less than 0.8, therefore it can be concluded that multicollinearity problem did not

exist in this study.



Lastly, the multiple regression analysis was applied to see the respondents, the

postgraduate students of Graduate Business School in UiTM Shah Alam’s

perception towards mobile commerce adoption. Using the multiple regression

analysis, it is observed that the 65.8% of actual usage or adoption for mobile

commerce can be explained through Perceived Usefulness, Perceived Ease-Of-

Use, Perceived Trust, Perceived Cost and Intention to Use. Our study concludes

where the hypotheses H1, H2 and H4 (Perceived Usefulness, Perceived Ease-

of-Use and Perceived Cost) are supported.

However, the hypothesis H3 (Perceived Trust) has no significant relationship to

Intention to Use which directed to user Actual Usage of mobile commerce.

Therefore, perceived usefulness, ease-of-use and perceived costs are found to

create positive effect for mobile commerce adoption. Trust, however, is of lesser

important variable. In this manner, it can be deduced that the respondents don’t

have the trust in the telecommunications companies in ensuring the security of

the mobile commerce platform. It must be noted that the sample size is

somewhat restricted; the Trust variable could likely increase its significance at a

larger sample size.

3. Practical Implications

The results of this study lead interesting implications to various groups. In

particular, the results could benefit the academics, the government and policy

makers and ultimately, related industry players in that it is able to provide

valuable information and perhaps provide some direction on how the mobile

commerce industry can be shaped into. This is important, considering the

penetration of mobile telephone has reached 100% in Malaysia. This alone can



surely provide enough incentive for the mobile commerce industry players

considering the wealth of opportunity to tap into the mobile commerce market.

From the academics point of view, this study is able to provide another

perspective of mobile commerce adoption. As mentioned in the Literature

Review chapter of this report, there had not been any specific research done on

postgraduate’s perception on mobile commerce adoption thusfar. Previous

researches generally and mostly looked into a different category of sample size

altogether, i.e. full time students that either in primary, secondary, college or

university. This study, however, provides a different perspective as it is thought

that part-time postgraduate student would have the extra “push” factor for mobile

commerce adoption seeing that they have the dispensable income but not the

time to conduct daily errands.

This study could likely provide an insight to the government agencies as to how

these governmental agencies could shape future policies in regard to mobile

commerce adoption policy landscape. This is to allow the government agencies

to provide greater regulatory monitoring and performance audit on the mobile

commerce industry players. Thus this would curb the feeling of distrust among

mobile commerce users as indicated in this study. For example, the

telecommunications industry had been inundated with unscrupulous

telecommunications application players selling illegal services and even sending

unsolicited messages to mobile telephone users. This study is hoped to provide

a catalyst reaction to the government agencies in that they are able to provide

greater policy to not only proliferate the mobile commerce adoption but also, the

right control mechanisms for the industry to remedies any illegal or unsavoury

business mobile commerce transactions.



The study also provides an impact to the society at large. Mobile commerce can

be seen as an important tool to conduct errands or even business transaction in

view of the greater ease it provide to the customers. Customers can access and

conduct mobile commerce transactions at any time of the day without having to

wait for a physical shop to be open, for example. It would largely convenience

customers who are always on the run, as it saves time and it allows for the

flexibility of the customer to conduct its businesses and transactions at a time

most convenience to them. However this study has found out that the

respondent have little perception on trust of the mobile commerce use. Thus this

could be the hindering factor for consumer to use the mobile commerce as at

their disposal.

Ultimately, this study effect the related industry players – the platform providers

such that are the telecommunications companies, the service providers such as

the hotels, airline companies and also the payment channel getaway provides

such as the banks and other financial institutions. Ancillary industries such as the

manufacturers of mobile telephones, vendors and retailers of the same can also

be affected by the results of the study. This study provides an insight to the

industry players on the importance of use, ease of use and costs as primary

factors for mobile commerce adoption. However the industry players have to

take note that based on this study the factor of trust is deem important by the

respondents and may be by the overall customers as well. Thus they must

ensure that the security and protection of any transaction from unsolicited users.

They also need to work closer with the government agencies to apprehend such

act that jeopardize the perception of trust among customers towards mobile

commerce.



A consumer is likely to adopt mobile commerce if the platform provided is

pervasive and deem convenient. Early adopters, which age group fall into the

post-graduate students of the Graduate Business School of UiTM Shah Alam,

might be the first to jump into the bandwagon for any new available technology,

however, there is a greater need for “staying power” elements to create a longer

lasting mobile commerce adoption as well as to garner greater interests from

other groups of consumers.

The banking industry landscape changed in the advent of e-banking where the

services provided and created greater convenience for the consumers by

eliminating the need for consumers to physically be at the bank for any

transactions. Other e-commerce services such as flight ticket bookings and

online purchasing also sold the idea of convenience to the masses to garner

greater adoption for e-commerce. Similarly, mobile commerce services and

applications must be seen to provide greater convenience to the users in order

to create greater awareness and adoption for mobile commerce. Thus, the

service providers such as telecommunications companies, banks, tour and flight

operators and others must consider selling the idea of usefulness of the said

mobile commerce application to garner higher consumer base.

Perhaps, mobile commerce application providers should consider taking cue

from Apple Inc’s product strategy where each product is bundled together with a

pervasive application (for example, iPad and iPhone with App Store) to ensure

customer’s loyalty and prolonged use of the products. This can surely garner

higher adoption for mobile commerce as the application transcend from a “nice

to have” to a “must-have” ticket item. Mobile commerce must be strategized to

provide ultimate convenience to the consumers where they are able to access



any products or services at any time of the day and monitor their transaction

from the comforts of their mobile telephone.

Additionally, mobile commerce application must be deemed easy to use by

customers to create a larger pool of users. By ease of use, the application must

be seen uncomplicated, easy and not wrought with complicated processes and

applications. This is where there is a clear difference between e-commerce and

mobile commerce. An mobile commerce application must provide a simpler

platform with lesser graphics and more text based. This is to consider the slower

technology adopted in mobile telephones as compared to a personal computer,

notebook or netbook platform. Also, mobile commerce industry players must also

consider the look and feel of the products or services that is viewed via a smaller

screen of a mobile telephone, thus, the services provided must be simple and

adopt less but impactful text and instructions.

Lastly, the mobile commerce service providers must also consider the cost

factor. It is generally agreed that consumers have greater discerning tastes and

are more penny-wise nowadays in view of escalating lifestyle and living costs.

Thus, cost factor is one of the pertinent aspects that must be looked into. This is

especially important considering there are already other outlet or method (e-

commerce) that is already seem to be convenient and easy to use. However,

based on the result of this study it can be concluded that the respondent feel the

cost for conducting a mobile commerce transaction are still acceptable and

affordable. This is mainly because that the respondents are students that

working and have disposable income to spend on mobile commerce services.

Thus they are less cost sensitive compared to fulltime students that did not

poses controllable income.



All in all, the factor of trust is the most important factor that everybody needs to

take really good consideration. No matter how good the design of the product or

mobile commerce services, the feeling of distrust in conducting such transaction

using mobile commerce is still prevail in the mind of the customers. It is said that

most of Asian people are difficult to trust on new or alien offering. Thus in order

for mobile commerce to be successful not only in Malaysian market but as well

as Asian market which constitute the most largest market population in the whole

world, the industry players must tackle the element of trust.

4. Limitations of Research

The results of this study have its limitations. Firstly, the research focuses only on

the postgraduate students of Graduate Business School in UiTM Shah Alam,

Selangor. This might limit the utility of the overall findings. A replication of similar

study could be extended to other groups or educational institutions. This can

create a greater understanding of the subject by expanding the correlation of the

adoption of mobile commerce and respondents in other groups. For example, it

must be noted that the post-graduate students of the Graduate Business School

of UiTM are mostly Bumiputra, therefore it does not truly reflect the entire mobile

phone user portfolio in the country.

Secondly, the study was done within a limited period of time. There are also

other hypotheses that can also be conducted, for example, the perceived privacy

and customer’s perceived perception on mobile commerce services were not

looked into. These additional hypotheses could help give a stronger case study

results.



5. Recommendation for Further Research

In view of the above, there are a couple of recommendation that we felt could be

done to improve the study further. Firstly, a further research involving a larger

pool of respondents that are not limited to postgraduate students of Graduate

Business School of UiTM Shah Alam only. This is to say that the study could be

stretched further to also include postgraduate students across of Schools in all

universities in the country. This will allow the result to be more realistic and even

more reliable from the viewpoint of mobile commerce industrial players as there

are respondents from other Schools or educational institutions as it captures the

micro details of the mobile commerce use.

Secondly, the study could be expanded to be specific in terms of mobile

commerce services; i.e. specifically tailored for m-banking or m-purchasing only,

for example. This could reveal some interesting insights as it can likely provide

differing results from the results above. Additionally, an extension of the study in

terms of comparative study between differing mobile commerce services could

also allude to differing, yet interesting results.

Thirdly, comparative studies in terms of postgraduate students in other countries

can also be extended considering the different mobile telephone landscape,

perspective and likelihood of difference of customer behaviours and purchasing

power.



6. Conclusion

The country is currently facing more than 100% adoption for mobile commerce.

Industry analysts stipulated that there is higher percentage of users for mobile

internet comparing to traditional internet browsing via computers. This trend is

said to grow bigger in view of the strong growth and demand for smart phones in

the country. Further, as telecommunications charges and fees plateau due to the

eventual product life cycle norm, telecommunication players should look into

other pockets of opportunities to expand its product line. Hence, mobile

commerce provides a plethora of opportunity for the industry players.

The objective of this study is to find the pervasive reasons for mobile commerce

adoptions among post-graduate students from Graduate Business School in

UiTM Shah Alam, Selangor, Malaysia. This study explored various hypotheses,

particularly, perceived use, perceived ease-of-use, perceived costs, trusts and

actual intention to use of adopting mobile commerce. Respondents to the studies

generally agree to the hypothesis that the usage, ease of use, costs are

important factors for mobile commerce adoption.

That said, trust is not one of the more pertinent factors in mobile commerce

adoption as perhaps the respondents already placed trusts in the

telecommunications companies to provide a secure and safe platform for mobile

commerce or they merely did not have much trust to place at such services.

Finally, the study will enable the mobile commerce industry players to strategise

their products and services for greater adoption for mobile commerce.



APPENDIXES

1. TIA’s 2010 ICT Market Review & Forecast

2. TIA’s 2010 ICT Market Review & Forecast



3. PEMANDU, 2010

4. MCMC launched a Handphone User Survey in 2009 (latest available data)

and the following were found:

Age Percentage

2006 2007 2008

Below 15 2.4 4.5 2.3

15-19 12.1 19.9 12.4

20-24 19.9 16.0 20.0

25-29 17.1 13.4 15.9

30-34 14.4 11.3 14.2

35-39 9.1 8.0 9.3

40-44 8.7 8.4 8.1

45-49 5.7 5.4 5.9

Above 50 10.5 13.1 11.8



REFERENCES

Online resources

1. DeCoster, J. (1998). Overview of Factor Analysis. August 1, 1998, Retrieved

<December 24, 2011> from http://www.stat-help.com/notes.html

Journals / Research Publication

2. Asghar Afshar Jahanshahi, Alireza Mirzaie, Amin Asadollahi, Mobile Commerce

Beyond Electronic Commerce: Issue And Challenges, Asian Journal of Business

and Management Sciences, ISSN: 2047-2528 Vol. 1 No. 2 [119-129], 2011

3. Christopher J. Armitage & Mark Conner, Efficacy of the Theory of Planned

Behaviour: A meta-analytic review, British Journal of Social Psychology (2001),

4. Jehad S. Bani-Hani & Faleh, Abdelgader AlHawar(2009), The Impact of Core

Competencies on Competitive Advantage: Strategic Challenge, International

Bulletin of Business Administration ISSN: 1451-243X Issue 6 (2009) ©

EuroJournals, Inc. 2009

5. Ching Mun Cheah, Aik Chuan Teo, Jia Jia Sim, Kam Hoe Oon and Boon In Tan,

Factors Affecting Malaysian Mobile Banking Adoption: An Empirical Analysis,

International Journal of Network and Mobile Technologies, ISSN 2229-9114

Electronic Version, Vol 2 / Issue 3 / September 2011

6. Mohamed Saber Chtourou & Nizar Souiden, Rethinking the TAM Model: Time to

Consider Fun, Journal of Consumer Marketing, 27/4 (2010) 336–344, Emerald

Group Publishing Limited

7. Chuttur M.Y. (2009). "Overview of the Technology Acceptance Model: Origins,

Developments and Future Directions," Indiana University, USA. Sprouts Working

Papers on Information Systems, Article - Volume 9 Article 37 (2009).


http://www.stat-help.com/notes.html


8. Ernest Cyril de Run, Mohsin Butt, Chung Yen Nee, The influence of role models

on young adults purchase, Jurnal Kemanusiaan bil.15 Jun 2010.

9. Dr. Fahad N. Al-FAHAD, Students' Attitudes And Perceptions Towards The

Effectiveness Of Mobile Learning In King Saud University, Saudi Arabia, The

Turkish Online Journal of Educational Technology – TOJET April 2009 ISSN:

1303-6521 volume 8 Issue 2 Article 10.

10.TM Faziharudean and Tan Li-Ly, Consumers’ Behavioral Intentions to Use

Mobile Data Services in Malaysia, African Journal of Business Management Vol.5

(5), pp. 1811-1821, 4 March, 2011

11.Arpad Gered (2011), "A" is for Apple, "M" is for Commerce - On What Mobile

commerce Is and Why It Is Special, Paper accepted at the UNCITRAL

Colloquium on Electronic Commerce, 14-16 February 2011, New York, US. Pg 3.

12.Clint Gilham, Jean-Paul Van Belle, Factors Affecting the Adoption of Mobile

Content Services amongst Youth in the Western Cape, South Africa, Conference,

(IBIMA), 5-7th July 2005

13.Joseph A. Gliem & Rosemary R. Gliem (2003) Calculating, Interpreting, and

Reporting Cronbach’s Alpha Reliability Coefficient for Likert-Type Scales, 2003

Midwest Research to Practice Conference in Adult, Continuing, and Community

Education.

14.Nahid Golafshani, Understanding Reliability and Validity in Qualitative Research,

The Qualitative Report Volume 8 Number 4 December 2003 597-607

15.Patricia Harris, Ruth Rettie, Cheung Chak Kwan, Adoption And Usage Of Mobile

commerce: A Cross-Cultural Comparison Of Hong Kong And The United

Kingdom, Journal of Electronic Commerce Research, VOL. 6, NO.3, 2005



16.Chang-tseh Hsieh (2007), Mobile Commerce: Assessing New Business

Opportunities. Communications of the IIMA. 87. 2007 Volume 7 Issue 1. Page 93

17.Wen-Chen Hu, Jyh-haw Yeh, Hung-Ju Chu, Chung-wei Lee (2005), Internet-

Enabled Mobile Handheld Devices for Mobile Commerce, Contemporary

Management Research Vol. 01, No. 01, September, 2005, Page 13-34

18.Md. Aminul Islam, Tunku Salha Binti Ahmad, Mohammad Aktaruzzaman Khan &

Mohammad Hasmat Ali,(2010) Adoption Of Mobile commerce Services: The

Case Of Bangladesh, World Journal of Management Vol.2 No.1 March 2010, Pp.

37‐54

19.Sudarsan Jayasingh & Uchenna Cyril Eze, An Empirical Analysis of Consumer

Behavioral Intention Toward Mobile Coupons in Malaysia, International Journal of

Business and Information, Volume 4, Number 2, December 2009

20.Charalampos Karagiannidis, Adamantios Koumpis, George Lekakos, (2009) m-

Learning and mobile commerce in Pervasive Environments, Communications

(2009), Volume: 3, Issue: 1, Pages: 1-17

21.Kim, S.H., Mims, C., & Holmes, K.P. (2006). An Introduction To Current Trends

And Benefits Of Mobile Wireless Technology Use In Higher Education, AACE

Journal, 14(1), 77-100.

22.Ranjan B. Kini, Adoption and Evaluation of Mobile Commerce in Chile, Jan

2009 Volume 12 Issue 1, ECIME 2008, pp75-88,

23.Nicole Koenig-Lewis, Adrian Palmer, Alexander Moll, Predicting Young

Consumers’ Take Up Of Mobile Banking Services, International Journal of Bank

Marketing, Vol. 28 No. 5, 2010 pp. 410-432

24.Long Li, A Critical Review of Technology Acceptance Literature, Chapter VI. A

Proposed Framework for Mobile Services Adoption: A Review of Existing



Theories, Extensions, and Future Research Directions, Mobile Multimedia

Communications: Concepts, Applications, and Challenges, ,By: Gour Karmakar;

Laurence S. Dooley, Publisher: IGI Global, Pub. Date: November 30, 2007

25.Kalle Lyytinen & Jan Damsgaard, What’s Wrong With the Diffusion of Innovation

Theory? The Case of a Complex and Networked Technology, Proceedings of the

IFIP TC8 WG8.1 Fourth Working Conference on Diffusing Software Products and

Process Innovations, Kluwer, B.V. Deventer, The Netherlands, The Netherlands,

2001

26.Asem Moqbel, Mirella Yani-Di-Soriano, Shumaila Yousafzai, Mobile Commerce

Use among UK Mobile Users Based on a Proposed Mobile Network Utilization

Framework: An Experimental Approach – Part 2, International Journal of

Technology Diffusion, 1(3), 1-34, July-September 2010

27.Ng, P.Y., Voges, K. and Goi, C.L. (2010), The Importance of Mobile Phone

Applications to Young Consumers: An exploratory study in Malaysia, Australian &

New Zealand Marketing Academy (ANZMAC) 2010, Christchurch, New Zealand,

29 November - 1 December.

28.Jonathan O'Donnell, Margaret Jackson, Marita Shelly and Julian Ligertwood,

Australian Case Studies in Mobile Commerce, Journal of Theoretical and Applied

Electronic Commerce Research, ISSN 0718–1876 Electronic Version, VOL 2 /

Issue 2 / August 2007 / 1 – 18

29.Tiago Oliveira and Maria Fraga Martins, Literature Review of Information

Technology Adoption Models at Firm Level, Information Systems Journal (2011)

Volume: 14, Issue: 1, Pages: 110- 121.



30.Hun Myoung Park, Univariate Analysis and Normality Test Using SAS, Stata, and

SPSS, Working Paper. The University Information Technology Services (UITS)

Center for Statistical and Mathematical Computing, Indiana University.

31.Sung Youl Park, An Analysis of the Technology Acceptance Model in

Understanding University Students’ Behavioral Intention to Use e-Learning,

Educational Technology & Society, 2009, 12 (3), 150–162.

32.Rafael Perera, Carl Heneghan, Douglas Badenoch, Statistics Toolkit Book,

Publish by John Wiley & Sons, Sep 23, 2011

33.Hernan E. Riquelme & Rosa E. Rios, The Moderating Effect Of Gender In The

Adoption Of Mobile Banking, International Journal of Bank Marketing, Vol. 28 No.

5, 2010, pp. 328-341

34.A.H.M. Saifullah Sadi & Mohamad Fauzan Noordin, Factors influencing the

adoption of Mobile commerce: An exploratory Analysis, Proceedings of the 2011

International Conference on Industrial Engineering and Operations Management

Kuala Lumpur, Malaysia, January 22 – 24, 2011. Pg 492-499

35.Roshanak Saneifard, Exploring Factors Affecting Mobile Commerce B2C

Adoption in Iran, Journal of Computers, Vol.20, No.2, July 2009

36.Louise Schaper & Graham Pervan, A Model of Information and Communication

Technology Acceptance and Utilisation by Occupational Therapists, Decision

Support in an Uncertain and Complex World: The IFIP TC8/WG8.3 International

Conference 2004, Pg 734-744

37.Scarlet Schwiderski-Grosche, Heiko Knospe, Secure Mobile commerce,

Electronics & Communication Engineering Journal, 2002 Volume: 14, Issue:5,

Pg. 228 – 238



38.Sugianto, L, Zhigang, L., and Tojib, D.R. (2008). Unraveling the Inhibiting Factors

of Mobile commerce Adoption. Paper accepted at the International Conference

on Business and Information(BAI), 7-9 July, Seoul, South Korea. Pg

39.Syed Jafar Naqvi and Hafedh Al-Shihi (2009), M-Government Services Initiatives

in Oman, Issue s in Informing Science and Information Technology, Volume 6,

2009.

40.Merja Taipaleenmäki, An Analysis of M-Commerce Services in Digital Content

Markets: Six Societal Environments Effecting M-Commerce, Frontiers of e-

Business Research 2006, ICEB + eBRF 2006 – Conference Proceedings

41.Mohsen Tavakol & Reg Dennick (2011) Making sense of Cronbach’s alpha,

International Journal of Medical Education. 2011; 2:53-55

42.Timothy Teo, Technology Acceptance in Education Research and Issues,

Published by: Sense Publishers, All Rights Reserved © 2011 Sense Publishers,

Pg 43-63.

43.Rajnish Tiwari, Stephan Buse, and Cornelius Herstatt (2008), From Electronic to

Mobile Commerce Opportunities through technology convergence for business

services. CACCI Journal, Vol. 1, 2008, pg 3-4.

44.Shafiq Ur Rehman and Jane-Lisa Coughlan, Building Trust of Mobile Users and

their Adoption of M-Commerce, World Academy of Science, Engineering and

Technology 75 2011, Communications of the ACM (2003) Volume: 46, Issue: 4,

Publisher: ACM, Pages: 91-94

45.Toh Tsu Wei, Govindan Marthandan, Alain Yee-Loong Chong, Keng-Boon Ooi,

Seetharam Arumugam (2009), What drives Malaysian mobile commerce

adoption? An empirical analysis, Industrial Management & Data Systems, Vol.

109 No. 3, 2009, pg. 370-388,



46.Lisa Wessels and Judy Drennan, an Investigation of Consumer Acceptance of M-

Banking, International Journal of Bank, Marketing, Vol. 28 No. 7, 2010, pp. 547-

568.

47.Peter Wentzel, Ron van Lammeran, Mathilde Molendijik, Sytze de Bruin, Alferd

Wagtendook, Using Mobile Technology to Enhance Students' Educational

Experience, Educare Center for Applied Research (ECAR), Case Study 2, 2005

48.Jacob O. Wobbrock, The Future of Mobile Device Research in HCI, CHI 2006,

Paper accepted at the “What is the Next Generation of Human-Computer

Interaction?” April 22-27, 2006, Montréal, Québec, Canada.

49.Yung Shao Yeh & Yung-Ming Li, Building Trust In M-Commerce: Contributions

From Quality And Satisfaction, Online Information Review, Vol. 33 No. 6, 2009,

pp. 1066-1086, Emerald Group Publishing Limited

50.Jason J. Zhang, Yufei Yuan, and Norm Archer, Driving Forces for Mobile

commerce Success, Journal of Internet Commerce, Volume 1, Issue 3, 2002, Pg

81-104

51.Tao Zhou, Examining the Critical Success Factors of Mobile Website Adoption,

Online Information Review, Vol. 35, No. 4, 2011, pages 636-652

Report / Press Publication

52.Consumer Affairs Victoria, Department of Justice (2002), Mobile commerce What

Is It? What Will It Mean For Consumers?,e 2002, pg 2.

53.Clint Gilham, Jean-Paul Van Belle, Factors Affecting the Adoption of Mobile

Content Services amongst Youth in the Western Cape, South Africa,Conference,

(IBIMA), 5-7th July 2005

54.Rachael Hu & Alison Meier, 2010, Mobile Strategy Report, Mobile Device User

Research, California Digital Library



55.Hyung Lee, 2009, Princeton Students: Kindle 'Disappointing, Difficult to Use',

FOXNews.com, Published September 28, 2009

56.Mobile-Financial.com, PayPal collaborates with government agency in Asia to

spur the adoption of m-commerce, 16th June 2010.

57.Organisation de Coopération et de Développement Économiques – OCDE

(2008), Policy Guidance for Addressing Emerging Consumer Protection and

Empowerment Issues in Mobile Commerce, DSTI/CP(2007)5/FINAL, 2008 Pg. 4

58.Penn, Schoen and Berland (2007), HP Business Technology Study, PSB

Associates, LLC, 2007, pg 3.

59.Seth Weintraub, The numbers don't lie: Mobile devices overtaking PCs,

CNNMoney, August 11, 2010,

60.Suruhanjaya Komunikasi & Multimedia Malaysia, Global Broadband Staistics, My

Broadband Quarterly Newsletter, Vol. 3, no. 3, 3rd Quarter 2010,

61.Peter Wentzel, Ron van Lammeran, Mathilde Molendijik, Sytze de Bruin, Alferd

Wagtendook, Using Mobile Technology to Enhance Students' Educational

Experience, Educare Center for Applied Research (ECAR), Case Study 2, 2005.


Documents

ABR Master Doc 251211