factors that drive m-commerce in kenya and south...

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Running Head: DRIVERS OF M-COMMERCE IN AFRICA 1

Factors that Drive M-commerce in Kenya and South Africa

A Research Report

Presented to

The Graduate School of Business

University of Cape Town

In partial fulfillment of the requirement for the degree of

Master of Business Administration

By

Kibe Kamau

December 2010

Supervisor: Dr. Steven Michael Burgess

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DRIVERS OF M-COMMERCE IN AFRICA 2

Acknowledgements

First, I would first like to thank all of the survey participants. Their participation has helped

me learn about the critical concepts required for academic research. In addition, their

opinions have provided the statistics necessary for both this research, as well as future

research in the field of mobile commerce.

I would also like to thank Dr. Steven Michael Burgess for his knowledge, guidance and

inspiration. Without his time, guidance, and expertise, this thesis would not be possible.

I would also like to thank my family for encouragement support and prayer through this

journey. A special vote of thanks goes to my brother, Emmanuel Muhia, for assisting with all

the surveys in Kenya. Without this assistance I would not have been able to gather the

information required.

Finally, I would like to thank Michelle Nderu for her support during the toughest period of

this journey. She provided moral support and encouragement during the longest nights and

toughest exams.

I certify that the thesis is my own work and that all sources referred to are to be found in the

References Section:

Signed:

_______________________________

KIBE KAMAU

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Table of Contents

List of Tables ............................................................................................................................ 5

List of Figures ........................................................................................................................... 5

Abstract ..................................................................................................................................... 6

Development of M-commerce ................................................................................................. 7

Adoption of M-commerce ...................................................................................................... 8

M-commerce in Africa ........................................................................................................... 9

Mobile Development in Kenya and South Africa ................................................................ 12

Kenya ............................................................................................................................... 12

South Africa. .................................................................................................................... 13

The Research Area ................................................................................................................. 14

Research Purpose and Question ........................................................................................... 14

Specific Objectives .............................................................................................................. 15

Research Limitations ........................................................................................................... 15

Research Assumptions ......................................................................................................... 16

Research Ethics .................................................................................................................... 16

Literature Review .................................................................................................................. 17

Defining M-commerce ......................................................................................................... 17

M-commerce Services ......................................................................................................... 20

M-commerce Business Model ............................................................................................. 21

The Technology Behind M-commerce ................................................................................ 22

M-commerce architecture ................................................................................................ 24

M-commerce value chain. ................................................................................................ 27

The Adoption of Innovative High Technology Consumer Products ................................... 28

The Technology Acceptance Model (TAM)........................................................................ 29

Theory of reasoned action (TRA). ................................................................................... 31

Diffusion of Innovations (DOI) ........................................................................................... 33

Mobile Marketing ................................................................................................................ 34

Research Hypotheses ........................................................................................................... 38

Summary of Literature Review ............................................................................................ 41

Research Methodology .......................................................................................................... 42

Research Approach and Strategy ......................................................................................... 42

Research Design................................................................................................................... 43

Data Collection Methods ..................................................................................................... 43

Participants ........................................................................................................................... 44

Research Instrument............................................................................................................. 44

Research Criteria and Validity ............................................................................................. 45

Data Analysis Methods ........................................................................................................ 45

Results ..................................................................................................................................... 46

Sample Characteristics ......................................................................................................... 46

Exposure to M-commerce Services ..................................................................................... 49

Measurement Validation ...................................................................................................... 50

Reliability ......................................................................................................................... 51

Validity: ........................................................................................................................... 52

The Measurement Model ..................................................................................................... 54

Testing the Structural Model ............................................................................................... 55

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Structural Model Results...................................................................................................... 57

Summary of Data Analysis .................................................................................................. 59

Findings and Discussion ........................................................................................................ 59

Summary of the Study ......................................................................................................... 61

Conclusion ........................................................................................................................... 62

Implications for Practice ....................................................................................................... 63

Recommendations for Future Research .............................................................................. 65

Appendices .............................................................................................................................. 66

Appendix 1: Technology Readiness Index of African Countries ........................................ 66

Appendix 2: Model TAM Questionnaire ............................................................................. 67

Bibliography ........................................................................................................................... 72

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List of Tables

Table 1 M-commerce Services ................................................................................................. 21

Table 2 Mobile Technology Generations ................................................................................. 24

Table 3 Customer Interaction Modes ...................................................................................... 27

Table 4 Participants ................................................................................................................. 47

Table 5 Provider Subscription ................................................................................................. 48

Table 6 Mobile Phone Use ....................................................................................................... 49

Table 7 Exposure to m-commerce............................................................................................ 50

Table 8 Composite Reliability .................................................................................................. 51

Table 9 Cronbach's Alpha ....................................................................................................... 52

Table 10 AVE with EU2 & EU3 .............................................................................................. 53

Table 11 AVE without EU2 & EU3 ......................................................................................... 53

Table 12 Measurement Model (Indicators) ............................................................................. 54

Table 13 Measurement Model ................................................................................................. 55

Table 14 Path Coefficients ....................................................................................................... 56

Table 15 Summary of Total Effects .......................................................................................... 58

Table 16 Indirect Effects .......................................................................................................... 58

List of Figures

Figure 1. The m-commerce architecture ................................................................................. 25

Figure 2. The m-commerce value chain .................................................................................. 28

Figure 3. Predicting Consumer Behaviour .............................................................................. 32

Figure 4. Proposed Structural Model....................................................................................... 39

Figure 5: Structural Model ...................................................................................................... 56

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Abstract

M-commerce is the use of mobile phones to conduct commercial transactions. As a business

medium, it continues to grow and provides a unique platform for conducting commercial

transactions in the future. In the last decade, Africa has seen a phenomenal penetration of

mobile phones. With it has come the proliferation of additional services, key of which are m-

commerce related. This research explores how users in Kenya and South Africa are

influenced to adopt m-commerce. A quantitative approach was used to examine attitudes

towards m-commerce using a survey that comprised 234 respondents from Kenya and South

Africa. The Technology Acceptance Model (TAM) was employed to examine factors

affecting consumer attitudes toward this emerging mobile technology and applications.

Empirical data from regression analyses reflect perceived ease of use and perceived

usefulness influence attitude toward using m-commerce. It was also found that trust and

perceived usefulness have a significant effect on adoption behavior. These findings are

important for practitioners who continue to promote m-commerce as a business medium.

Keywords: mobile, m-commerce, mobile commerce, technology acceptance, diffusion of

innovation

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Development of M-commerce

In 1999, Sonera of Finland was the first telecom operator to provide the much needed

wireless technology that enabled customers conduct commerce wirelessly (Raisinghani,

2001). This mode of commerce brought about a new technological buzzword known as

mobile commerce (m-commerce).

The first commercial transaction mediated by a mobile telephone took place in

Helsinki, Finland; whereby Coca-Cola vending machines accepted monetary payments from

cell phones through the use of text messaging technology (Miller, 2009). Since then, mobile

telephones have become widely adopted and technology that enables m-commerce has grown

and improved considerably. Nowadays mobile phones have applications that enable access

to a variety of services, such as text messaging, multimedia services, internet search, gaming,

monetary transactions, and chat services. All these services are offered commercially

through various types of mobile phone applications.

In addition, according to a Gartner (2010) forecast, the rapidly growing worldwide

mobile subscribers will have surpassed 5 billion in 2010, led by growth in China and India.

With improved wireless security, privacy through data encryption and user education, in

addition to wide deployment of 4G systems, it is anticipated that m-commerce will

inescapably, become the most dominant method of conducting business transactions (Grami

& Schell, 2006).

In 2009, there were 81.3 million people worldwide using mobile devices to make

payments to the tune of US$68.7 billion. By the end of 2014, the number of people using

mobile devices to make payments is forecasted to rise to nearly 490 million (8 percent of

mobile subscribers), with a volume of m-payments expected to reach US$633.4 billion. The

Gartner (2010) report, goes ahead to predict that the key drivers for mobile usage are

http://www.ehow.com/phones/

http://mobithinking.com/mobile-marketing-tools/latest-mobile-stats#subscribers

http://mobithinking.com/mobile-marketing-tools/latest-mobile-stats#china-subscribers

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expected to be (in order of importance) money transfer, location-based services, mobile

search, mobile browsing, mobile health monitoring, mobile payment, near-field

communication services, mobile advertising, instant messaging and mobile music.

Adoption of M-commerce

M-commerce was expected to grow very fast in the regions of origin namely North

America and Europe, but growth has been slow. Several reasons such as constrained capital,

consumer fears regarding security, and privacy of information and transactions are cited for

the low adoption rates (Magill, 2009). Concerns about m-commerce are widely held because

consumer payment details must be collected and stored, either on the consumer’s phone or on

a database with the retailer, and there have been several instances where consumer

information was stolen by computer hackers. For example, in New Zealand the database at

Hell's Pizza was cracked revealing passwords, emails, home addresses and phone numbers of

around 230,000 customers (Rawood, 2010). In a more serious case, Wyndham Hotels and

Resorts suffered serious data breach when hackers broke into its customer database stealing

customer's credit card information (Hotchkiss, 2010). Such incidents, among others erode

consumer and service provider confidence mainly due to exposure to fraud.

These concerns are reflected in the products people are willing to buy using m-

commerce transactions. For instance, according to a survey conducted by Harris Interactive

for mobile payment technology provider Billing Revolution, the top consumer rated products

that would be bought via mobile devices were pizza, movie and event tickets, hotel rooms,

fast food, music, travel tickets, games, coffee and videos (Business Week, 2009). This list is

expected to grow as the mobile device becomes more ubiquitous.

Concerns about security and privacy however, are not slowing growth everywhere.

M-commerce transactions in Japan annually top US$400 million. Subscribers can buy sodas

from vending machines, purchase food at fast food restaurants, and shop at Internet retailers

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like Amazon.com, buying all their goods through DoCoMo’s mobile billing system. Other

companies, like Visa, also offer m-commerce services (M-Commerce, 2010). South Korea is

another example that has readily adopted m-commerce and is showing rapid growth. As

mobile payment systems develop, carriers collaborate, and security improves, we expect m-

commerce to develop further.

M-commerce in Africa. Africa is one of the regions where m-commerce growth has

been slowest. In addition to security and privacy concerns, adoption is frustrated by

insufficient telecommunication infrastructure development and high prices of

telecommunications services which are amongst the highest in the world. However, African

growth in mobile telephone penetration is also amongst the highest in the world, and this is

expected to facilitate rapid growth of m-commerce, especially where banking infrastructure is

less developed (Twinomugisha, 2009).

Between 2003 and 2008, Africa saw the number of mobile subscriptions surge from

54 million to almost 350 million - an increase of approximately 550%. In 2008, Gabon,

Seychelles, and South Africa boasted almost 100 subscriptions per 100 inhabitants. In North

Africa, the average penetration stood at almost two thirds of the population, and for Africa as

a whole, it was over one third. Only five African countries - Burundi, Djibouti, Eritrea,

Ethiopia, and Somalia still have a mobile penetration of less than ten per 100 inhabitants.

Mobile penetration in Africa is however expected to reach 75% by 2013 (Twinomugisha,

2009).

In African countries where the mobile phone has seen wide penetration, it has been

used to mitigate poor infrastructure and high telecommunication costs. In such countries the

mobile phone presented an opportunity for developing countries to close the digital divide

between Africa and the more developed countries (Waverman, Meschi, & Fuss, 2005).

According to Atkins (2005), the digital divide between rich and poor nations is narrowing

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fast, which according to the World Bank, is based on the premise that people in the

developing world are getting more access to cell phone communications, far faster than they

got access to new technologies in the past (Atkins, 2005). Computers in general, and with it

the internet, are popular technologies which African countries have not been able to benefit

from in the same levels as developed countries.

Interestingly, in Africa, the mobile phone has overtaken the computer as the most

important information and communication technology (ICT) tool. African countries are

pioneering mobile banking and electronic transaction services. For example, in Kenya, South

Africa, the United Republic of Tanzania and Zambia, cell phones provide companies and

individuals the possibility to make person-to-person payments, transfers and pre-paid

purchases without a bank account (Twinomugisha, 2009).

In addition, mobile phone operators continue to make significant investments in

communication infrastructure, particularly in rural areas. These have transformed the African

countries socially and economically since business and social contacts can be established and

maintained more easily through use of the phones (UNCTAD, 2009). For example, France

Telecom has forecast higher growth for Africa and the Middle East than for other developing

regions, partly due to their phone sectors better resistance to the economic downturn. South-

South investment in Africa, already a major source of funding for developing country mobile

networks, is also likely to continue investing (Ewing, 2007). Zain, MTN Group, and

Orascom, are among the top foreign investors in African telecommunication and continue to

further strengthen their positions in the regional wireless market (Mutula, 2007).

These developments have played an important role for all forms of commerce in

many developing countries, especially countries that lack adequate fixed line infrastructure.

In many cases, this success has been registered in the small to medium sectors of commerce.

For example, workers in Uganda send cellular airtime to their families in remote towns, who

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can convert it to cash via village phone operators (Ewing, 2007). Masons working near

Nairobi, report big jumps in income now that builders in need of laborers can reach them by

phone. Poor settlements in Mali, Rwanda, and elsewhere connect to the Web via cell phones,

helping villagers learn better farming methods (Mutula, 2007). In Tanzania, farmers use

mobile phones to communicate with markets and decide when and where to ship their

produce (Dard, 2007).

M-commerce and the internet. M-commerce relies on the internet to deliver

information to mobile phones. This stresses the importance of affordable internet availability

to m-commerce adoption. Internet and computer literacy in Africa are very low compared to

developed countries. For example, African internet penetration (10%) lags far behind more

industrialised regions, such as Europe (58%), North America (77%), and Asia (21%)

(Internet World Stats, 2010). This is due to poor fixed telecommunications infrastructure

technology, and high costs of computers. Broadband use in Africa is highly concentrated,

with five countries accounting for 90% of all broadband subscriptions. These five countries,

namely Algeria, Egypt, Morocco, South Africa, and Tunisia have achieved the greatest

improvements in broadband connectivity since 2003 (UNCTAD, 2009).

Furthermore, there is a huge gap in broadband speed and internet connectivity rates

between different countries. Of the 20 countries with the world´s most expensive access fees,

14 are in sub-Saharan Africa. Even within Africa, the price divide is huge, for example,

monthly access to broadband services cost on average more than $1,300 in Burkina Faso, the

Central African Republic and Swaziland, while subscribers need to pay less than $13 in

Egypt and Tunisia (UNCTAD, 2009).

Critical to improving this situation is the installation fibre-optic cables across the

continent, an area where Africa has been largely excluded in the past. Fortunately, a number

of initiatives are finally coming to fruition, for example, SEACOM, a cable linking the east

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coast of Africa with Europe and India, became operational in July 2009, and the East African

Marine System (TEAMS) cable, linking Kenya to the United Arab Emirates, was completed

in 2009 (Mutula, 2007).

These initiatives will facilitate improved connectivity for Africa to the rest of the

world, increase telecommunication speeds and reduce internet and telecommunication costs.

So far, the few developments that have been completed have started showing signs of what to

expect in the future. In Kenya, South Africa, the United Republic of Tanzania and Zambia,

for example, there has been an increase in cell phone related applications. Cell phones

provide companies and individuals the possibility to make person-to-person payments,

transfers and pre-paid purchases without a bank account (UNCTAD, 2009). Mobile

penetration continues to increase, and along with it mobile based transactions and internet

usage.

Mobile Development in Kenya and South Africa

Kenya. Mobile phone penetration in Kenya reached 51% in the first quarter of 2010,

rising 2.7% to 19.9 million from the previous quarter. This growth can be attributed to

multiple line ownership, and increased number of service providers offering attractive

promotions. Internet users jumped nearly 60% to 6.4 million mainly due to mobile phone

data services through 3G networks. Most of it was activity on social networking sites mainly

by young people (CCK, 2010). According to BMI (2010), mobile phone penetration in

Kenya will exceed the 100% mark by 2013.

There are currently four major companies providing services and support in the

mobile communication as well as internet services. These are Safaricom, Zain, Orange and

YU. Safaricom is the largest mobile provider and boasts 78.3% market share, followed by

Zain (10.6%), Orange (5.6%) and YU (5.4%), (Safaricom, 2010). Safaricom has the widest

coverage and has always offered the cheapest rates and more value added services than all the

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other providers. However since July 2010, Zain has slashed voice tariffs drastically, sparking

a price war with Safaricom. It is such activities that continue to reduce costs of

telecommunications in Africa.

As the competition in this field continues to grow, mobile providers have become

more innovative and started providing other mobile services in addition to voice. In Kenya,

Safaricom and Zain are at the forefront of providing additional value added services (VAS).

Safaricom is ahead of the rest with its most successful M-Pesa (money transfer) service. M-

Pesa has grown phenomenally since 2007, with subscribers increasing from 2 million in 2008

to 10 million in 2010. In order to compete, Zain and YU have recently introduced similar

money transfer services. Recently, Zain has gone a step further by the introducing a

commerce related innovation which provides market information in the form of market prices

of agricultural produce to farmers.

South Africa. South Africa has the most developed mobile market is Africa, and has

been one of the fastest growing markets on the continent in recent years, in terms of net

subscriber additions. The total market has reached 52 million customers, a penetration of

more than 114%, which has been growing at an average of 20% since 2007, (SAinfo, 2009).

This growth however slowed down to 10% per year in 2008 and 2009.

The country has four main cellular network operators, Vodacom, MTN, Cell C and

Virgin mobile. Virgin mobile is a virtual network service provider that operates in

partnership with Cell C. Recently a new operator, 8ta, owned by Telkom South Africa just

entered the market.

Vodacom is the largest mobile provider, controlling 55% of the market share,

followed by MTN (34%), Cell C (9%) and Virgin (2%). Apart from voice services offered by

all mobile providers, Vodacom and MTN also offer mobile banking services. In South

Africa, most users have security concerns and setup issues when doing actual transactions

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over the mobile phone. Many do not go beyond balance inquiries and notification of

transactions (SAinfo, 2009). According to the consumer phase of World Wide Worx's

Mobility 2009 report, backed by First National Bank and BlackBerry-maker Research In

Motion (RIM), only 8% of the banked users in South Africa add beneficiaries via the cell

phone ( (SAinfo, 2009).

Mobile providers in South Africa offer a wide range of services that allow users to

conduct m-commerce transactions. These include purchase of food, clothing, books, tickets

and mobile services. Recently, Vodacom introduced the M-Pesa money transfer service.

These developments show an interesting poise towards further advancement of m-commerce

in Kenya and South Africa.

The Research Area

This research aim of this research is to identify the factors that influence the adoption

of m-commerce in Africa. Until the beginning of this decade, Africa was lagging behind,

when it came to telecommunications and internet use. In the last 5-6 years, investments in

telecommunications have begun changing the landscape and subsequently, Africa has started

taking strides to catch-up with more developed countries. Key in this advancement is the

mobile phone, which has demonstrated impressive penetration in the continent, leapfrogging

the use of computers, and catapulting Africa towards narrowing the digital divide. Interesting

questions arise on what contributes to the adoption of m-commerce that we continue to see in

Africa, especially against a backdrop of traditionally poor telecommunications technology,

low computer literacy and internet use.

Research Purpose and Question

As a first step, an evaluation of the services currently being offered by mobile

providers in Kenya and South Africa was conducted, with keen interest on those that

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facilitate the execution of transactions. The second step entailed an evaluation of user

perceptions in order to understand the acceptance drivers. With this in mind, the research

question to be answered was stated as follows.

―What Factors Contribute to the Adoption of M-commerce in Kenya and South Africa?

The overall objective of this research was to contribute knowledge in regard to user

perceptions that influence the diffusion and adoption of m-commerce in Kenya and South

Africa. This knowledge would contribute to this field in several ways. In practice, the

knowledge of driving factors will enlighten managers when developing high technology

products for use in the African continent. In addition, this study will serve to prove,

disapprove and identify gaps in the existing theory on technology acceptance as applied in the

African context.

Specific Objectives

● To evaluate user perceptions towards m-commerce in Kenya and South Africa.

● To identify the characteristics of products or services that post success when

mediated via m-commerce.

● To evaluate whether computer and internet literacy (i.e. familiarity with

technology) has any impact on the adoption of m-commerce.

● To provide a baseline for further studies on m-commerce in Africa and other

developing countries.

Research Limitations

Due to the limited time available and the number of countries in Africa, it was not

possible to assess the drivers of m-commerce in all African countries. As a result,

this research was focused on the adoption of m-commerce in Kenya and South

Africa.

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In addition, due to geographical differences in the participant locations, surveys

were limited to participants living in urban cities of Nairobi and Cape Town.

Because of this, some bias is expected in the results especially in regard to internet

literacy, because most urban dwellers are expected to have good contact with

computers and the internet.

Since the use of m-commerce is still in development. Insufficient understanding

of m-commerce was expected to lower user intentions to use it.

Due to the shortage of time, the research was conducted at one point in time. As a

result the research does not capture the change of user perceptions with time.

Research Assumptions

The first assumption was that all participants were English literate, providing for

varying levels of understanding. The research also assumed that the participants would

answer the self completion questionnaire honestly. The participants were also assumed to

have at least a basic level exposure to mobile phone usage and own at least one mobile

device.

Research Ethics

● The required ethical clearance was requested and submitted

● The surveys were done anonymously to ensure that the privacy of the individual was

preserved. The information requested by the survey was strictly based on what is

required for the research and all the questions asked were related to the hypotheses.

● The survey was not in any way an invasion of privacy and the participants had the

right to decline.

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In order not to deceive the survey participants, a brief was sent to all participants,

clearly indicating why the research was being performed, and who to contact in case

of the need for clarification.

Literature Review

The literature review encompasses three main areas. The first section explores the

varied definitions of m-commerce that exist and the technology behind m-commerce. The

second section discusses the technology acceptance models. The third section explores the

diffusion of innovations within a society with a key focus on mobile marketing as a

communication channel.

Defining M-commerce

M-commerce is defined in several ways. Most definitions involve a complex set of

products and services facilitated by mobile telecommunications technologies. In addition, a

bewildering array of standard terms, product definitions, pricing and payment schemes add to

this complexity. As a result, the content and nature of the term m-commerce varies across

studies (Okazaki, 2005).

M-commerce has sometimes been defined very broadly. For example, m-commerce

can be defined as ―a set of applications and services that people can access from their Internet

enabled mobile devices.‖ (Sadeh, 2002, p. 22). In the same broad sense, m-commerce has

been defined as the exchange or buying and selling of commodities and services through

wireless handheld devices such as cellular telephones and personal digital assistant (PDAs)

(Abu Bakar & Osman, 2005, p. 34). An even broader perspective extends it to ―mobile

business‖ (m-business), to mean, a business related communication that is conducted among

individuals and companies and that does not necessarily involve any financial transaction

(Moshin, Mudtadir, & Ishaq, 2003). In this sense, m-commerce is seen as a subset of m-

business, where m-business entails both commercial and non-commercial areas. M-business

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is thus seen to have a larger scope since all aspects of m-commerce take place within mobile

business (Tiwari & Buse, 2007).

Other scholars have defined m-commerce much more narrowly. Durlacher, (1999)

defines m-commerce as ―any transaction with a monetary value that is conducted via a

mobile telecommunications network‖. Kao (2009), contends that media used is what defines

m-commerce. In this case, the media has to be wireless, hence restricting m-commerce to the

application of wireless devices and data connection to conduct transactions, which results in

the transfer of value in exchange for information, services, or goods.

Other scholars see m-commerce as a subset of electronic commerce (e-commerce).

When compared to e-commerce, m-commerce highlights the mobility of its transaction

devices, such as PDAs and mobile phone (Kao, 2009). M-commerce has also been seen as an

extension of e-commerce (Moshin, Mudtadir, & Ishaq, 2003). Others argue that the only

difference between m-commerce and e-commerce is the media, wireless devices (Varshney &

Vetter, 2002). Stafford & Gillenson (2003) argue that e-commerce is the buying and selling

of products through computer networks, and is oriented towards supporting and realizing

transactions, while m-commerce only plays the role of delivering information about products

and services. This is exemplified by a typical practice in Japan where phones provide

customers with information about shopping choices, but the actual product orders and

transactions are done via in-store self service computer portals. Leung & Antypas (2001)

argue that m-commerce includes both content delivery and actual transactions done on

mobile devices.

Other scholars argue that, m-commerce is sometimes seen to be more than e-

commerce due to its different interaction style, usage patterns and value chain. This is

because m-commerce is a new and innovative business opportunity with its own unique

characteristics and functions, such as mobility and broad reach ability (Feng, Hoegler, &

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Stucky, 2006). This argument would make m-commerce more than e-commerce, as far as

market reach is concerned. E-commerce is targeted to only those customers who have access

to computers and are internet literate. In contrast, m-commerce services expand the

addressable market size by making the mobile phone the lowest common denominator.

Two primary shortcomings have been identified in these definitions. The first

shortcoming concerns monetary value as a defining element of m-commerce. Including

monetary value as an element of an m-commerce definition ignores the commercial nature of

marketing measures and after-sales services. Consumers often make ―temporal payments‖

for transactions by agreeing to watch commercial messages in exchange for ―free‖ services

(Tiwari & Buse, 2007). Services may also be bundled as part of subscribed products and thus

bear no additional price. In such cases, many consumers may perceive the m-commerce

product to be free of charge.

The second shortcoming, concerns the requirement for m-commerce to be mediated

by wireless telecommunication networks. On this, Tiwari and Buse (2007) argue that this is

misleading because it contends that transactions have to be completed exclusively through

wireless telecommunication networks, hence it limits the scope of m-commerce to only

―immaterial‖ products (e.g. information), thereby ignoring the ―material‖ products like

purchase of clothes where transactions can be completed in other ways (e.g. cash payment).

This thesis pursues the definition which identifies two key characteristics of m-

commerce. First, any business related communication that is initiated and completed with the

help of mobile devices and second, transactions conducted using mobile devices, usually as a

result of business related communication, ultimately resulting in the exchange commodities

and services (Tiwari & Buse, 2007).

This definition was selected because it reflects the nature of m-commerce related

interaction in the African context. In Africa’s small business sector, commerce related

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transactions are initiated with the help of the mobile phone. Monetary transactions may not

always occur through the mobile, for various reasons, but using the mobile phone to mediate

this transaction is enough.

A good example is when small traders in Africa use the mobile phone to gather

market information, and to communicate to their customers. Such exchanges do not always

result in transactions or exchange of goods and services, but, nevertheless, commercial

interaction has taken place. Service providers also send commercial information to

subscribers through mobile devices. For example, marketing offers and after sales service do

not necessarily involve any transactions. Other non commercial information includes free

services, like games, maps and emergency service information which are not commercial in

nature.

It is such exchanges of information that give rise to a large number of transactions

conducted using mobile devices. Because of this, we continue to see an increase in mobile

based services that facilitate the transfer of money, which have led to more transactions being

facilitated via the mobile device. It is for these reasons that we adopt a definition that

incorporates both commercial and non commercial transactions, so long as they are mediated

via the mobile phone. Therefore, for this research, all services that are initiated, terminated

or both on a cellular handheld phone and involve the transfer of a commodity from one

account to another can be termed as mobile commerce services.

M-commerce Services

M-commerce is suited to certain types of purchases based on the simplicity of the

product and the urgency of the demand. Initially limited to the purchase of dematerialized

goods like ring tones and wallpaper, m-commerce now covers a far wider range: online

gaming, coupons and sales offers, loyalty cards, online ticket bookings, leisure services,

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rentals, m-banking, m-shopping, auction sites, access to information and paid services among

many others. The most common m-commerce services are shown in Table 1.

Table 1 M-commerce Services

M-commerce Services

Prepaid transactions Transactions on the go Mobile banking

Voucher-less airtime

distribution

Peer-to-Peer payments Branchless banking

Digital content retailing Retail payments Loan disbursement and repayment

Peer-to-Peer airtime sharing Fund Transfers Banking information - balance

check, statements, etc.

Bill payments Fund transfers

Travel ticketing

Movie/concert ticketing

M-commerce Business Model

There are three types of m-commerce models in use today. The first model is

illustrated when the bank owns the m-commerce service. In this case, the nature of the

service can be envisioned as an extension of the banks internet services to the mobile phone.

Though this model may have evident benefits for the controlling bank and its clients, the

model does not provide for consumers who do not have accounts with the particular bank

(Utiba, 2010).

The second model is when the service is owned by the telecommunications provider.

This model has limitations as far as reach is concerned. Only those consumers who are

subscribers to the network operator have access to the service. The third model is when the

service is owned by a third party, who then provides interfaces for various mobile providers

as well as banks (Dholakia, 2006). In this scenario the consumers are not limited by the

network operator, and also do not need to have a bank account with a conventional banking

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system, since the owner of the m-commerce service creates a fund which acts as an m-

commerce pool from which transactions can be made.

The third party model creates the most value in terms of an all-inclusive commercial

ecosystem. Under such a model the third party can leverage the distribution network of

several network operators thereby creating access points for the larger population.

In Kenya and South Africa, the m-commerce service is predominantly owned by the

network operators, in collaboration with selected banks. This demonstrates that there is room

for m-commerce to evolve further within the African context

The Technology Behind M-commerce

M-commerce is built on mobile phone technology, which earmarked the beginning of

wireless communication. Mobile phones facilitate m-commerce by delivering information

wirelessly (Frolick & Chen, 2004). This technology has developed over the years from slow

and arduous communication services to agile multi platform capabilities. Cellular networks

were originally designed for voice-only communication. To support data based m-commerce

transactions there has been an evolution of these networks from analogue to digital and from

circuit-switched to packet-switched networks (Saidi & Town, 2010).

Unlike fixed-line communication technologies, which have followed gradual

developments, developments in mobile communication technologies have been concurrent

even within the same country (UNCTAD, 2002). Today, a number of these mobile

communication technologies are available and are classified into the so-called first generation

(1G), second generation (2G), third generation (3G) and fourth generation (4G) network

technologies.

1G comprise analogue networks, which use circuit-switched connections for data

transfer and has data transmission rates of up to 2400 bps. These technologies are expensive,

relatively insecure, and limited in bandwidth. 2G are digital communication systems

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supporting both data and voice transmission at transmission rates ranging from 9.6 to 14.4

Kbps. Just like 1G, 2G uses circuit-switched connections but offer increased bandwidth,

increased security and reliable data transfer. 2G network technologies reduce the congestion

problems inherent in 1G technology by having multiple users over a single channel (Boadi &

Shaik, 2006). These technologies offer additional capabilities such as short messaging,

faxing and roaming of mobile end-stations.

With all its advantages, the transmission rate for 2G technologies is still not enough

for the transmission of video and graphic images. Consequently, an intermediate technology,

the so-called second-plus generation (2G+ or 2.5G) has been developed. 2.5G supports

transmission rates of 57.6Kbps or higher and offers parallel voice and data transmission,

including internet access in mobile handsets (UNCTAD, 2002). 2.5G standards in use

include General Packet Radio Service (GPRS), High Speed Circuit Switched Data (HSCSD)

and Enhanced Data rates for GSM Evolution (EDGE). 2G and 2.5G cellular wireless

networks are the most common mobile networks employed by mobile phone operators and

can provide a sufficiently robust platform for m-commerce (Grami & Schell, 2006).

3G networks have much higher network capacity for data and voice transmission.

They provide bandwidths comparable to a wired broadband connection with speeds of up to 2

Mbps. The high transmission rates are suitable for internet access, satellite navigation, video

and audio streaming, video conferencing and access to other multimedia content. Unlike 1G

and 2G technologies, 3G uses packet-switched connections. With some operators just

starting to embrace 3G mobile technologies, 4G networks have already started emerging with

promising possibilities of about 20Mbps bandwidth (though there are possibilities of up to

100Mbps transmission rates) and the ability to roam across different wireless network

standards with one device (Georgiev, 2009).

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Table 2 Mobile Technology Generations

Mobile Technology Generations

Generation Description

1G First generation of wireless - analogue based

2G Second generation - digital. Handles text and basic images

3G Third generation - digital. Supports rich media

4G Fourth generation - digital. More bandwidth, higher security, interactive

sessions

M-commerce delivers information via wireless application protocols (WAP), general

packet radio service (GPRS), and short messaging service (SMS). SMS is the most basic

protocol used, and works by generating sales using push marketing strategy (Rittippant,

Witthayawarakul, Limpiti, & Lertdejdecha, 2009). Wireless push marketing refers to the

business situation in which the vendor initiates the communication and proactively delivers

time sensitive or location-specific promotional messages to consumers.

WAP allows mobile users to exchange data, and access databases instantly over the

Internet. WAP is expensive because all data is transmitted through the telephonic network.

GPRS, in its basic form, can be thought of as wireless broadband. Unlike WAP, GPRS is

cheaper and faster because data is transmitted using the same protocol as the internet (Baldi

& Thaung, 2002).

The most well-known example of this type of service is Japans NTT DoCoMo. This

provider offers many different m-commerce products and services such as hotel reservations,

online auctions, books, airline tickets, and stock quotes. DoCoMo named its service ―i-

mode.‖ i-mode is written in a language known as C-HTML, which is a mobile version of the

language used to write standard internet websites (Krishnamurthy & Rights, 2002).

M-commerce architecture. M-commerce structural architecture consists of three

tiers. Front-end (Client) - this is the piece of application that runs on the mobile device

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(Phones, PDAs, and Communicators), Middle-ware (Server) - this is the software running on

a system that contains the business logic. Back-end (Database) - this is the server that hosts

all the data. In addition, the architecture requires, a connectivity medium, a security authority,

a clearing authority (e.g., a bank), and suppliers of merchandise.

Figure 1. The m-commerce architecture

The front-end or customer interaction layer plays a significant role in determining

how well any mobile technology based innovation is adopted. As shown on table 3, the

mobile channel offers multiple modes of customer interaction. The challenge that most m-

commerce providers encounter is selection of the best technology to use. Interactive Voice

Response (IVR) systems are voice-based applications that rely on aural interactions (Airtel,

2009). They tend to be tedious to use and more so for a service with multiple options and

sub-menus. Short Message Service (SMS), which is a plain text based interface, has the

biggest advantage because of its universal acceptability in terms of user comfort (Georgiev,

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2009). A big drawback however, is the plain text communication lends itself to security

issues. Users also have to learn specific commands to conduct transactions.SIM Tool Kit

(STK) menu is a SIM resident application that provides the user with a menu based service

access. STK uses the SMS channel for communications but provides encryption services that

ensure security. This system is quite costly because the applications have to be embedded

into each SIM card. It is more cost effective to have the applications residing in a central

server, to be accessed by all users.

UnStructured Service Data (USSD) provides the user with a session based interface.

The advantage in using USSD is that the service providers can provide an updated menu

every time the user initiates a request and the communications are also secure as compared to

SMS transactions (Banzal, 2010). However, a session based service often leads to frequent

unfinished transactions especially in places that have weak signal problems.

Java provides subscribers with an applet that resides in the phone memory and uses

SMS or GPRS at the back end to communicate with the service delivery platform. This

provides a highly intuitive interface for the user which can easily be upgraded and

customized (Nysveen, Pedersen, & Thorbjornsen, 2005). The major challenge is the

requirement to have Java compatible, data enabled handsets in a developing economy. This

approach is more suited for mature markets where smart phone penetration is higher as

compared to other regions. Web/GPRS approach enables the user to access a web based

service portal using the mobile phone (J.-H. Wu & Hisa, 2008). The challenge here lies in

network capability and the extent of network usage in a particular market. M-commerce

service providers hence need to go with a combination of channels in order to increase reach

and uptake (Utiba, 2010)

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Table 3 Customer Interaction Modes

Customer Interaction Modes

Mode Description

IVR Voice based

SMS Text based

USSD Session based

STK SIM based

Java Highly intuitive

M-commerce value chain. There are seven links in the m-commerce value chain. At

the bottom, is the infrastructure that provides data communication between mobile users and

application providers. The second link consists of basic enabling services, such as server

hosting, data backup, and systems integration. Transaction support is the third link of the

value chain (Barnett, Hodges, & Wilshire, 2000). Many wireless services will require some

form of payment—usually from the user to the service. Transaction support provides the

mechanisms that assist these transactions, in terms of security, and user billing. The fourth

link is presentation services (Victoria & June, 2002). Providers convert the content of

internet-based applications, which are formatted in a standard known as HTML (HyperText

Markup Language), into a standard such as WML (Wireless Markup Language), an HTML

subset suitable for the small, low resolution screens of wireless devices (J.-H. Wu & Hisa,

2008). Content that isn't already on the Internet can be formatted directly into a wireless

standard. Personalisation support is the fifth link of the chain. One of the main value

propositions of m-commerce is its ability to personalize applications for individual users.

Companies that can provide personalized information will form a valuable link (Mort &

Drennan, 2005). User applications comprise the sixth link of the value chain. Possible

applications range from those currently available on the wired Internet (including banking,

book purchasing, e-mail, news, and travel) to new services designed specifically for mobile

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consumers. For example, information about where to find the nearest shopping mall, hospital

or café.

At the highest point in the chain are the content aggregators: businesses that design

and operate portals that provide information in a category or search facilities to help users

find their way around the internet. This function is particularly important for m-commerce

because mobile telephones have small screens and limited input mechanisms. Users will

want portals that simplify the search, avoid returning too much information, and require

minimum input (Barnett, Hodges, & Wilshire, 2000).

Figure 2. The m-commerce value chain

While cell phone companies continue to upgrade their networks, invent new applications

and provide consumers with content, it is the consumer perceptions and attitude towards m-

commerce that will determine whether this technology will gain traction. This study examines

user perceptions and attitudes towards m-commerce in Kenya and South Africa, with keen

interest on what influences their likeliness to use it.

The Adoption of Innovative High Technology Consumer Products

The research has drawn on the diffusion of innovations literature and multi-attribute

attitude models to evaluate user perceptions and attitude towards m-commerce in Kenya and

South Africa. Consistent with this tradition, the current research relies on the Technology

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Acceptance Model (TAM), which is frequently used to explain the acceptance of new

technologies, and on the Theory of Reasoned Action (TRA), which is perhaps the most

widely employed multi-attribute attitude model.

The Technology Acceptance Model (TAM)

Several theorists have found that, although not every product may share all of the

characteristics, the consumer adoption process for high technology products often exhibits a

set of distinctive characteristics (Parasuraman & Colby, 2001). For any innovation to

succeed it must begin by having an advantage over those that it supersedes. The innovation

should be, consistent with existing values and needs, easy to understand or use, available for

testing, and have results that are easy to see (Rogers, 1995). M-commerce can be seen to

have an advantage over other commerce methods because it allows a wider reach, reduces

transaction costs, and is said to offer competitive pricing. In addition it is ubiquitous, hence

users can access services any time at any place. It is also convenient because the mobile

phone is light and easy to carry. Finally, the ability to personalize services means that end

users can have services tailored to their exact needs. The technology is also easily available

for testing over mobile phones.

However, the technology has some drawbacks which cast doubt on its potential to

succeed especially in regard to ease of use. The challenges include; small screens which still

limit types of file and transferable data, limited number of characters and text that can be

transmitted using sms, less functionality for mobile internet in comparison to computer

internet, a user interface that is often difficult to learn how to use, limited bandwidth, high

costs of establishing mobile and wireless broadband infrastructure, technology constraints of

mobile devices (memory, processing power, display capabilities, input methods), and security

of data moved across some mobile and wireless networks. On this premise, it would appear

that m-commerce does not meet the success criteria for a high technology innovation.

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This theory, however, in arguing that the technology has to be easy to use, does not

address the varying levels of techno-readiness that may be inherent in a particular group of

consumers. This can be measured using the technology readiness index (TRI). Based on TRI

score, consumers can be categorised as explorer, pioneer, skeptic, paranoid, or laggard. Each

category can be further refined to include demographics, psychographics, technology belief

patterns, representative opinion about technology, and technology-based product and service

usage (Parasuraman & Colby, 2001).

As such, consumers in different categories will be expected to embrace technology

differently. In Africa, South Africa ranks first in terms of technology readiness, followed by

Tunisia and other North African countries. Kenya is ranked 10th (Appendix 1). On this basis,

we would expect to see the adoption of m-commerce in African countries follow the same

pattern. That is, m-commerce would achieve higher adoption rates in South Africa, than

other African countries, Kenya included.

Having said that, consumers may be techno-ready and thus find a technology easy to

use, however, whether they actually find the technology useful would need to be determined.

Consumers are not expected to adopt an innovation if they do not find it useful. Whether it

addresses existing values and needs and hence perceived useful, is one of the aspects that this

research investigates. This indicates that the socio-economic situation within different

countries and communities also plays a role in adoption of technology. TAM expounds on

two important perceptions. The first perception is perceived usefulness. People tend to use or

not use an application to the extent they believe it will help them perform their job better.

The second perception is the perceived ease of use. Given that an application is useful, they

may, at the same time, believe that the system is too hard to use and that the performance

benefits of usage are outweighed by the effort of using the application.

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Although difficulty of use can discourage adoption of an otherwise useful system, no

amount of ease of use can compensate for a system that is not useful (Davis, 1989, pp 333).

In Africa, m-commerce has mainly been used for money transfer, and to gather market

information. The fact that the number of consumers using these services continues to

increase would be an indicator that the technology has been found to be useful. Once users

find the technology useful, the behavior demonstrated in actual use of the technology is what

demonstrates the successful adoption of any innovation.

Theory of reasoned action (TRA). According to the theory of reasoned action

(TRA), consumer behavior, such as the adoption of m-commerce, is the product of behavioral

intent. Behavioral intent is caused by consumer attitudes and subjective norms (Fishbein &

Ajzen, 1980). Attitudes have two components: evaluation and strength of belief, while

subjective norms are conceptualized as products of normative beliefs (what we think is

expected of us by others), and motivation to comply (level of importance of doing what

others expect us to). Intention is then described as the best predictor of whether or not a

behavior is performed. Intention is determined by our attitude (personal beliefs about the

behavior) and subjective norms (positive or negative value) associated with the behavior.

Ajzen (1991), however, argues that this behaviour is not entirely voluntary, and thus

extended the TRA to include a measure of perceived behavioural control (PBC). The

rationale behind the addition of PBC was that it would allow prediction of behaviours that

were not under complete volitional control. Thus, while the TRA could adequately predict

behaviours that were relatively straightforward (i.e. under volitional control), under

circumstances where there were constraints on action, the mere formation of an intention was

insufficient to predict behaviour. Figure 3 illustrates the extended TRA.

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Figure 3. Predicting Consumer Behaviour

This theory however, does not address experiential perceptions which are very

influential in drawing consumers to behave in certain ways. The impact of perceived

enjoyment, perceived usefulness, and perceived expressiveness on intention to use the

services is significant (Nysveen, Pedersen, & Thorbjornsen, 2005). Enjoyment is particularly

important as a driver for using experiential services, such as contact and gaming services.

TRA’s elements may also be specific to the culture in which it was derived from, and

probably less relevant in others. For example, TRA fails to address the cost, level of comfort

and trust that a consumer may have with the technology, and the impact of social influence,

which may differ from culture to culture. In similar studies, the constructs social influence

(SI), perceived cost (PC) and trust (T) have proved useful to explain consumer behavior (Wei

& Chong, 2008). It is hence important to apply the constructs within context of a socio-

economic and cultural environment (Wei & Chong, 2008). For example, since m-commerce

was still in its infancy in Malaysia, several modifications had to be adapted to the TAM

theory. Intention to use was chosen instead of actual use, in the same case, attitude as a

construct was removed from the model to make it simpler (Luarn & Lin, 2005).

Another weakness with TRA is that it fails address the antecedents of its main

constructs, perceived use and perceived usefulness. A consumer would first need to get

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exposed to the proposed innovation, before they can evaluate its perceived usefulness, and

usability. There several ways in which a consumer would get exposed to the innovation

before they decide to evaluate it. The most common ones are advertising and social

networks. The diffusion of innovations theory elaborates how innovations diffuse through a

society, from exposure to actual usage.

Diffusion of Innovations (DOI)

A technological innovation diffuses among members of a social system through

particular channels over time. The first stage is knowledge or exposure, next is persuasion

(forming an attitude), then decision (commitment to adoption), implementation (using the

idea) and finally confirmation (reaffirmation to continue using) (Rogers, 1995).

Initial knowledge and exposure to a new innovation is usually by way of public

marketing. Subsequently, knowledge of the innovation disseminates via interpersonal

channels. It is these channels that play a significant of persuasion leading to actual usage

(Backer & Rogers, 1997). Inter personal communication diffuses the innovation through

several roles inherent in a society. Opinion leaders, have relatively frequent informal

influence over the behavior of others, and are usually the first to inform others. Thereafter,

change agents influence innovation decisions by mediating between the change agency and

the relevant social system. Change aides complement the change agents by having more

intensive contact with clients. Although they are not usually competent in the technology,

they play an important role of dissemination because they are considered trustworthy

(Rogers, 1995).

This intrinsic dissemination carries emotional appeal which works well for high

involvement products (e.g. cars, computers) where the purchase decision warrants

investigation and information gathering. Rational appeal by foreign endorsers works for low

involvement products (e.g. gum, milk) (Yeh, County, Lin, County, & Road, 2010). Once a

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social system has been exposed to a new technology, adoption takes shape in several stages;

namely, innovators (venturesome), early adopters (respectable), early majority (deliberate),

late majority (skeptical) and finally laggards (traditional) (Rogers, 1995).

For m-commerce, public marketing has been the most common method of initial

exposure. In addition, marketers have adopted advertising via the mobile device which

facilitates target marketing. This is seen to be more efficient than the traditional blanket mass

media marketing, giving mobile marketing an upper hand (Carroll, Barnes, & Fletcher,

2007). Furthermore, due to its personal nature, the mobile device can overcome the major

challenge of getting the time and attention of consumers (Ververidis & Polyzos, n d).

Mobile Marketing

Mobile marketing is viewed as a third screen marketing channel after television and

the internet. Mobile marketing is the use of the mobile medium as a means of marketing

communications (Leppaniemi, Sinisalo, & Karjakuoto, 2006). Today, advertising is

everywhere and as the more cluttered the advertising space gets, the more difficult it becomes

to get customers attention (Godin, 1999). The most significant difference between m-

commerce advertisements and traditional advertisements is that, while traditional

advertisements promote to people in general, mobile marketing advertisements aims at

specific individuals (Yeh, Y. County, Lin, T.-yuan County, & Road, 2010). Although high

mobile phone penetration rates do not translate to high mobile marketing use`, high potential

for communicating marketing messages through mobile phones does exist.

While this medium is good for marketers, mobile marketing also creates perceived

problems of privacy and security risks for consumers (Nysveen, Pedersen, & Thorbjornsen,

2005). Currently, mobile advertising is largely through the use of SMS and consumers have

a general negative attitude towards SMS advertisements as they are considered irritating

(Tsang, Ho, & Liang, 2004). Unsolicited SMS messages raise privacy concerns related to the

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utilisation of personal and location data that is used to personalise mobile marketing

messages. This is the main reason why consumers may be reluctant to respond to the mobile

marketing communication channel because of the perceived risk regarding safety and privacy

of their personal data. Privacy issues are particularly sensitive with respect to mobile

marketing due to the personal nature of mobile devices (Harvey, Deans, & Gray, 2007)

Perceived risk refers to certain financial, product performance, social, psychological,

physical, or time risks when consumers make transactions via their phones (J. Wu & Wang,

2005). Risk is often viewed as an antecedent of involvement and trust especially when the

price is high and the consumer risks losing money. Because of this risk perception,

consumers are more often motivated to avoid mistakes than to maximize utility in purchasing

(Mitchell, 1999).

It is therefore important to reduce the levels of perceived risk so that consumers can

have more trust. Trust also involves uncertainty and risk with no perfect guarantee that

ensures the trustee (mobile vendor) will live up to the trustors (consumer) expectation. The

trustor needs to have faith in the trustee’s honesty and benevolence, and believe that the

trustee will not betray his/her risk-assuming behavior (Li & Huang, 2009). The key to

forming trust is getting customers to start transacting with the mobile vendor through reward

attraction, or by demonstrating features such as convenience, cost efficiency, and personal

necessity. Once they are convinced to buy, customers must also have positive and direct

experiences with the vendor during their transactions for a trust relationship to form. Such

positive direct experiences are considered the strongest trust builders with the highest

potential for reducing perceived risk (Siau & Shen, 2003).

Karvonen, Cardholm, & Karlsson (1999) created the BATE model, which consists of

four dimensions of trust: business trust, administrative trust, technical trust, and experience-

based trust. Cardholm et al. argued that online shoppers dealing with m-commerce

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transactions assign more weights on business trust and experience-based trust than on

administrative trust and technical trust of the model (Kao, 2009, pp 225).

One area that may help in addressing the issue of privacy and security risk is

obtaining consumers’ permission (Kavassalis et al., 2003). Obtaining permission gives

consumers freedom in adopting mobile marketing services (Harvey, Deans, & Gray, 2007).

Permission based advertising would go a long way into reducing the irritation factor for

consumers, which may in-fact change their attitude towards mobile marketing, thereby

creating more avenues for m-commerce to precipitate (Yeh, Y. County, Lin, T.-yuan County,

& Road, 2010). Other ways of reducing irritation include, incentive based, and location-

based mobile advertising (Haghirian & Madlberger, n d).

There are other possible reasons why mobile marketing may not be successful. Firstly,

small screens may prevent consumers from comprehending vendor promotions (Yeh, Y.

County, Lin, T.-yuan County, & Road, 2010). Secondly, when advertising messages

delivered via m-commerce are limited to text characters, there is greater difficulty in

expressing the core value of a product and thirdly, consumers have already built habitual

interaction with other advertising mediums, due to continuous exposure to large mediums

such as large screens, billboards, and banners.

Despite the perceived drawbacks, mobile marketing is becoming an integral part of

multi-channel commerce. Customers can research a product online, and make a quick double

check via their phone to make sure they're getting the best price (El-gayar, 2007). Multiple

channels should satisfy different customer needs and not simply replicate the catalogue or

website (Mohd & Muhammad, 2009)(e.g. use the mobile for promotions and succinct product

updates and use website to give full visual and demonstration of product).

A good retailer always tries to make it as easy as possible for their customers to make

a purchase or find out more about a product (e.g. provide feedback from other consumers

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who have experienced the product). Suggesting related products according to a customer's

favourite or previous browsing history, and predicting where a customer might want to go

next simplifies transactions and makes potentially complicated mobile retail faster and more

user-friendly (Grami & Schell, 2006).

The consistency and flexibility of m-commerce makes it possible for customers to get

what they want, wherever they are, and not rely on proximity to a real life store or their

computer or internet connection. Customers want familiarity with the store or website they

already know, and the option to customise their purchasing experience. Providing stock

details, reservation ability, SMS reports about the delivery status are ways to give the

customer full control and in turn encouraging them to put their full trust in the retailer.

While is important to encourage clients to utilise all variations of the organisations

presence, businesses should be careful not to alienate some divisions of their customer base

by leading them to believe that online customers are getting a better deal. Not all customers

are tech-savvy, however they're likely to know how to use a mobile phone, or at least

understand a special offer via a simple text message (Gatford, 2010)

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Research Hypotheses

M-commerce continues to take hold in Africa despite a backdrop of poor

telecommunications infrastructure and low computer and internet literacy. M-commerce,

which relies on internet availability, lends itself to the inquiry into what to expect of its

growth and adoption in the future. This research is founded on the technology acceptance

model (TAM), which suggests that ease of use and perceived usefulness are two main factors

that predict user intention to adopt a new information system based technology.

Although TAM has been applied for various computer based technology, little has

been done in the context of m-commerce in Africa. Moon & Kim (2001) argue that factors

affecting the acceptance of a new innovation system are likely to vary with the technology,

target users, and context. With this in mind, traditional TAM variables may not fully reflect

the users’ intention to adopt m-commerce, resulting in the need for additional or mediating

factors that better predict the acceptance of m-commerce (Tang, 2004, p 1662).

Based on the foregoing literature review, it is possible to draw several hypotheses

about the relations of trust, perceived ease of use, perceived usefulness, attitude and intention

to use m-commerce. These systematic relations are depicted above in Figure 4. In this

section, I identify each of these hypothesised links and summarise the theoretical rationale

behind each link.

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Figure 4. Proposed Structural Model

H1. Perceived ease of use (PEOU) has a positive influence intention to use (IU).

H2. Perceived ease of use (PEOU) has a positive influence on perceived usefulness (PU).

H3. Perceived ease of use (PEOU) has a positive influence on attitude (A).

These hypotheses were derived because perceived ease of use (PEOU) determines the

degree to which an individual believes that using m-commerce would be free of physical and

mental effort. Based on this, users are expected to find m-commerce useful. The fact that

using m-commerce is perceived not to be tedious and complex due to physical and

technological constraints; is expected to influence the attitude that consumers have towards

m-commerce in a positive way. If consumers find it useful and easy to use, then it is

expected that they will develop intention to use it.

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H4. Perceived usefulness (PU) has a positive impact on the intention to use m-commerce

(IU).

H5. Perceived usefulness (PU) has a positive influence on attitude (A).

Perceived usefulness (PU) is the extent to which an individual believes that the use of

m-commerce will improve his or her job performance in daily activities, as well as address

other personal or commercial related needs. Based on how useful they perceive it to be, users

are expected to have a positive attitude towards m-commerce as well as demonstrate a higher

likelihood of using it.

H6. Attitude (A) has a positive effect on intention to use (IU) m-commerce.

Attitude (A) is a person’s enduring favorable or unfavorable evaluation, emotional

feelings, and action tendencies toward some object or idea. This construct evaluates the

general approach that users have towards m-commerce. A favorable attitude towards m-

commerce is expected to have a positive influence on the intention to use.

H7. Perceived trust (T) has a positive effect on attitude (A) towards m-commerce.

H8. Perceived trust (T) has a positive influence on ease of use (EOU).

H9. Perceived trust (T) has a positive influence on perceived usefulness (PU).

Trust, is the belief that the services are genuine and authentic and that customers do

not stand to lose anything as a result of engaging in m-commerce. Where technological

products are involved, users are concerned about losing their money through fraudulent

transactions. This is because they cannot check the quality of the product before purchase.

Consumers also feel insecure when they send out financial and personal information via the

internet. Online transactions also include numerously inherent uncertainties, such as pricing

injustices, violations of privacy, transmissions information, unauthorized tracks of inaccurate

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transactions, and unauthorized use of credit cards (Li & Huang, 2010). User trust in m-

commerce is thus an important element that affects consumer behavior through the attitude

formed from the perception that m-commerce is trustworthy. If consumers have faith in m-

commerce then we can expect them to have a favourable attitude towards it. Because of this,

users would be expected to perceive m-commerce useful and easy to use.

Summary of Literature Review

There are many views on what m-commerce entails. This research adopts the broad

definition that encompasses any business related communication conducted over mobile

phones as well as actual transactions involving exchange of goods, services and money.

Several theories can be used to understand the factors that influence the adoption of

technological innovations. TAM contends that adoption behavior is driven by perceived

usefulness and perceived ease of use. TRA argues that adoption behavior is as a result of

intent. Intent is influenced by attitude and subjective norms. All the aforementioned theories

address attitude towards a technological innovation but fail to examine the systems that

contribute towards creating awareness of the innovation. Consumers would need to be aware

of the technology for them to form an attitude towards it.

The diffusion of innovations (DOI) theory addresses the issue of awareness and

dissemination of a technological innovation by discussing the various ways in which an

innovation diffuses through a society. Public advertising and marketing are the most

common mediums used as an initial source, thereafter the innovation diffuses through the

community via various adopter roles and in different stages (explorer, pioneer, skeptic,

paranoid, or laggard).

Mobile marketing is seen as a key medium through which awareness of m-commerce

has been generated, however, this method suffers several setbacks. Firstly, marketing via the

mobile creates problems of perceived privacy and risk. Secondly, SMS advertisements are

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generally perceived as irritating by customers. Lastly, there are technological challenges that

exist and limit delivery of the products core values through this medium.

Businesses can reduce the effects of the drawbacks in several ways. To reduce

irritability, vendors can ask the consumers for permission to send them marketing

information via their mobile phones. To build trust, m-commerce service providers should

encourage customers to use their services often by offering incentives, being efficient, cost

effective and convent. In order to promote use, vendors should also strive to ensure that m-

commerce offerings are consistent and flexible to accommodate customer requirements.

Research Methodology

In this chapter, the methodology used to conduct this research is described. The

question to be answered in this research entails inquiry into the factors that influence the

adoption of m-commerce in Kenya and South Africa. The type of method employed was

based on the form and nature of the research issue.

Research Approach and Strategy

A researcher who wants to investigate a phenomenon finds himself thinking about

issues such as, why it is necessary to study the phenomenon, what kind of knowledge is to be

developed, what the best way to gain knowledge is, and who will benefit from the study

(Harnesk, 2004). Since this research aims to identify the factors that influence the adoption

of m-commerce in Kenya and South Africa, a deductive approach was adopted. This

approach was selected for two reasons. Firstly, it is valuable for using hypothesis to test

theory. Secondly, it takes a standpoint in the general principles in the theory to make more

specific conclusions from empirical data (Leedy & Ormrod, 2009). The approach is

illustrated below.

Theory Hypothesis Data Analysis Finding Hypothesis Confirmation/Rejection Revision of

Theory

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A quantitative research strategy was applied by collecting data from Kenya and South

Africa, as well as secondary data sources. This methodology was selected because

quantitative data is more efficient in testing hypothesis (Bryman & Bell, 2007). Because this

method has been used in similar studies, for example Wei & Chong (2008) used the same

strategy to study the drivers of m-commerce in Malaysia; the results of this research could be

comparable to those obtained from previous research studies.

Research Design

This research is exploratory in nature, primarily aiming to gain an understanding of

the driving factors of m-commerce. The research adopted a cross-sectional approach which

entails the collection of quantifiable data on a number of variables from more than one

participant at single point in time (Leedy & Ormrod, 2009).

The cross-sectional approach was suitable for several reasons. Firstly, because of the

short time available for the research, longitudinal and experimental designs were not possible.

Data was collected from a non defined group of people in a relatively short period of time.

Secondly, this method allows us possibility to compare results with findings from previous

studies on m-commerce based on the same approach. Lastly, although this design cannot rule

out the possibility of rival hypotheses, it is good for exploratory research and the data can be

used by other researchers. One disadvantage of this method however, is that it is difficult to

establish time and order; hence we cannot ascertain causal effect. Also, since the data is

collected at a single time point only, we cannot measure the changes that are occurring over

time. However, the results and data of this research may be used by other researchers in the

future, if an evaluation of changes over time is required.

Data Collection Methods

This research required data from both primary and secondary sources. Primary data

was collected from individuals along defined variables. Secondary data was obtained from a

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variety of other sources like technology reports, government websites, statistical agencies,

mobile phone operator websites and a host of telecommunications companies that play a role

in the development of technology within the African continent. Primary data was collected

via self filled questionnaires distributed at random both on hard copy and electronically.

Participants

Financial constraints precluded administration by a professional marketing research

company. Data were collected using snowball sampling techniques. While steps were taken

to develop random sampling points to begin the sampling and to reach participants without

internet access, the samples should be viewed as convenience samples. Data were collected

in Kenya and South Africa via self-completion questionnaires that were administered over the

internet and on paper. Self response surveys have a likelihood of low responses, thus a total

of 340 questionnaires were distributed, 170 in each country.

Research Instrument

This phase involved the use of a cross-sectional self completion questionnaire to test

the intention to use m-commerce. The questionnaire had 32 questions derived from the

constructs of TAM, TRA and DOI (Francis et al., 2004). This method was selected because

it was cheap as well as able to provide a rapid turnaround of data. Surveys are also

advantageous in their ability to make inferences about consumer behaviour for given

populations based on a sample (Babbie, 1990). A pilot survey was conducted with 20

respondents in order to test suitability of the test instrument. Results of the pilot

demonstrated that the original questionnaire contained ambiguous questions, as well as some

that needed rephrasing in order to accurately reflect the themes being tested. The revised

questionnaire was used in a second pilot done with 10 respondents. The result of this pilot

showed that it was easier to understand and as a result the data captured was more accurate.

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Subsequently this revised instrument was made available both online and in hard copies. See

appendix 2 for the survey instrument used.

Research Criteria and Validity

Validity and reliability reflect the degree to which we may have errors in our

measurements (Leedy & Ormrod, 2009). Reliability of the research was dependent on the

design of the questionnaire. In order to achieve this, effort was put to ensure that the

questions in the questionnaire were straight forward, easy to understand and unambiguous.

The data was standardized in order to minimize the chance of collecting erroneous

information and to ensure homogeneity.

Confirmability is the ability of the researcher to remain objective and not allow

personal preferences to interfere with the findings due to bias (Bryman & Bell, 2007). In

order to achieve confirmability, the research was supervised by an experienced senior

researcher.

Data Analysis Methods

The exploratory and deductive nature of this research lends itself to the quantitative

research process (Bryman & Bell, 2007). The analysis method followed was similar to a

grounded theory where a body of data was coded and distilled into concepts (Bryman & Bell,

2007). In this case, the concepts were derived from the theories of TAM and DOI. Based on

these concepts, hypotheses were created around the relationships between constructs of trust

(T), perceived usefulness (PU), perceived ease of use (PEOU), attitude (A) and intention to

use (IU). The data was then coded in order to facilitate quantitative analysis.

Since this research was based on specific hypothetical constructs, the analysis

contained reliability and validity tests followed by factor analysis in the form of correlation

and regression analysis. Basic aggregation was done using Microsoft Excel. All hypotheses

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were tested simultaneously using latent variable partial least squares analysis, which was

implemented using the SmartPLS statistical package.

Results

This chapter presents the research findings from the study. The first section presents

the descriptive results of the qualitative variables in order to show a demographic profile of

the survey respondents. The section also presents results of respondent preferences for

mobile providers as well as an indication of how they use their mobile phones. The second

section presents an empirical analysis of behavioural attributes captured. The final section

compares the results to the research hypothesis.

Sample Characteristics

A total of 340 self-response questionnaires were distributed. 248 questionnaires were

received back, demonstrating a 73% response rate. Of those received back, 25 were either

incomplete or unusable. The analysis was thus done on 223 completed surveys (n=223).

The demographic profile is presented in Table 4. Of the respondents, 49% were male

while 51% were female. 23% were students, 66% were employed, while 11% ran their own

ventures. The age profile demonstrated that, a majority (46%) of the respondents were

between 18 to 30 years old. 44% were between 31 and 40 years, while only 10% were older

than 40 years.

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Table 4 Participants

Participants

Kenya (n=114)

Number Percentage

Gender Male 52 46%

Female 62 54%

Occupation

Student 32 28%

Employed 65 57%

Self Employed 17 15%

Age

18-25 17 15%

25-30 39 34%

31-40 45 39%

41-50 8 7%

Over 50 5 4%

South Africa (n=109)

Gender Male 58 53%

Female 51 47%

Occupation

Student 19 17%

Employed 82 75%

Self Employed 8 7%

Age

18-25 10 9%

25-30 37 34%

31-40 53 49%

41-50 9 8%

Over 50 0 0%

Total (n=223)

Gender Male 110 49%

Female 113 51%

Occupation

Student 51 23%

Employed 147 66%

Self Employed 25 11%

Age

18-25 27 12%

25-30 76 34%

31-40 98 44%

41-50 17 8%

Over 50 5 2%

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All the respondents in this research owned at least one mobile device. Nowadays, it is

common for consumers to subscribe to more than one mobile service provider. This was

evident in the line subscriptions shown in table 5. Vodacom and Safaricom had the most

subscribers 32% and 45% respectively, followed by MTN (21%) and Zain (22%).

Table 5 Provider Subscription

Provider Subscription

Provider Number Percentage

South Africa

Vodacom 72 32%

MTN 47 21%

Cell C 8 4%

Virgin 2 1%

Kenya

Safaricom 100 45%

Zain 50 22%

Orange 17 8%

How respondents are currently using their mobile devices was a point of keen interest.

The results of the survey (Table 6) demonstrate that only 38% used their phones for business

contact. Other mobile services that were utilized included the internet, which was the most

popular service at 85%. 30% used their phones for mobile banking, while 46% used their

phones to transfer money. Interestingly, only 11% had actually used their phones to purchase

items like food, clothes and movie tickets. 52% had used their phones to purchase mobile

based services like ring tones, music and games. These results demonstrate that at least 40%

of the respondents have used services that fall in the category of m-commerce (banking,

money transfer and purchasing items).

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Table 6 Mobile Phone Use

Mobile Phone Use

Frequency Percent

Communication

Casual 209 94%

Business 94 38%

Other uses

Mobile banking 68 30%

Money transfer 102 46%

Mobile internet 189 85%

Purchase items e.g. food 24 11%

Purchase mobile services 115 52%

Exposure to M-commerce Services

Exposure to m-commerce services through communication ensures that consumers

are aware of the services as well as their capabilities. According to the DOI theory,

communication channels are one of the key elements that influence the adoption of an

innovation. Other elements entail the innovation itself, time and the social system within

which the innovation is made (Rogers, 1995). This survey posed questions about the

communication channels through which the respondents had come to learn about m-

commerce services. The majority, 47%, first heard about m-commerce from public

advertising (TV and radio), while 25% got to know about it from their friends, family and

social networks (Table 7).

DOI contends that mass media channels are more effective in creating knowledge of

innovations, whereas interpersonal channels are more effective in forming and changing

attitudes toward a new idea, and thus in influencing the decision to adopt or reject new

innovative ideas (Rogers, 1995). Most individuals evaluate an innovation, not on the basis of

scientific research by experts, but through the subjective evaluations of near-peers who have

adopted the innovation (Backer & Rogers, 1997). These results demonstrate that while there

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is considerable exposure through media channels in Kenya and South Africa, most

respondents appear not to have been influenced enough to actually adopt the technology.

According to Table 7, while 98% of respondents had heard about m-commerce, only 11%

had purchased items like food, 30% had used m-commerce for banking and 46% mainly from

Kenya, for money transfer (Table 6).

Table 7 Exposure to m-commerce

Exposure to m-commerce

Source Percentage

TV/Radio 47%

Friends/Family 25%

Other 26%

Never 2%

According to TAM, exposure is not enough. Attitude formed by consumers about the

technology plays a significant role in adoption behavior. An empirical analysis was used to

evaluate other perceptual aspects that influence the intention to use m-commerce.

Measurement Validation

An empirical study was conducted in the second part of the analysis. From the

questionnaire, the latent variables analysed were perceived trust (T), perceived usefulness

(PU), perceived ease of use (PEOU), behavioral intention (IU), and consumer attitude (A).

The questionnaire measured the variables on a five-point Likert-style scale, with responses

ranging from 1 = ―strongly disagree,‖ 2 = ―disagree‖, 3 = ―neutral‖, 4 = ―agree‖ and 5 =

―strongly agree‖.

Testing the research model included two stages, estimation of the measurement model

and the structural model. In the estimation of the measurement model, the psychometric

properties of the measures were evaluated in terms of reliability and validity. In the structural

model, path coefficients were examined to determine the precision of the PLS estimates.

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Reliability: Reliability is the extent to which a technique, when applied repeatedly to

the same object, yields the same result each time (Babbie, 1992). Reliability was assessed in

using two popular measures, composite reliability and Cronbach’s Alpha. These two

measures differ in that composite reliability does not assume that all items are equally

weighted (Chin, 1998). Both measures indicate that all scales have acceptable internal

reliability.

Composite reliability: Composite reliability scores are traditionally considered

acceptable when greater than 0.70. In this research, all composite reliability scores exceed

0.70, indicating excellent composite reliability (see Table 8).

Table 8 Composite Reliability

Composite Reliability

Attitude 0.87

Ease of Use 0.77

Intention to Use 0.93

Perceived Usefulness 0.83

Trust 0.85

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Cronbach’s alpha: Cronbach’s alpha provides an estimate for the reliability based on

the indicator inter-correlations. The results of this test indicated that items EU2 and EU3

were problematic. This was evident because, when EU2 and EU3 were included in the

model, the latent variable EU demonstrated inter-item correlation of 0.45, which is below the

accepted benchmark of 0.60. After examining the results and speaking to several

respondents, I concluded that the meaning of these items was not consistent across

respondents. Dropping these items did not appear to compromise the meaning of the

underlying latent variable. This resulted in an improvement of Cronbach’s alpha to 0.67 for

EU. Results for all other scales exceeded 0.70, indicating good internal consistency (Bagozzi

& Yi, 1988).

Table 9 Cronbach's Alpha

Cronbach's Alpha

Attitude 0.71

Ease of Use 0.61



Trust 0.76

Validity: Validity is the extent to which measures indicate what they are intended to

measure (Bohrnstedt, 1970). Construct validity can be measured in terms of convergent

validity and discriminant validity. Convergent validity is the extent to which multiple

attempts to measure the same construct are in agreement (Campbell & Fiske, 1959).

Discriminant validity measures the extent to which indicators complement each other,

even though they may not be uni-dimensional.

The indicators in this research are reflective; hence convergent validity was deemed

the more appropriate measure. Fornell & Larcker (1981) suggest using the average variance

extracted (AVE) as a criterion of convergent validity. An AVE value of at least 0.5 indicates

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sufficient convergent validity, meaning that a latent variable is able to explain more than half

of the variance of its indicators on average. With the indicators EU2 and EU3, the analysis

resulted in an AVE value of 0.36 for the latent variable EU (Table 10). Removal of these

indicators resulted in a value of 0.56, which is considered acceptable. The Indicators EU2

and EU3 were subsequently removed from further analysis. As shown in table 11, all other

variables demonstrated AVE values greater than 0.5. Since our model uses reflective

indicators, all loadings are statistically significant at the .05 level.

Table 10 AVE with EU2 & EU3

AVE with EU2 & EU3

Attitude 0.77

Ease of Use 0.36



Trust 0.59

Table 11 AVE without EU2 & EU3

AVE without EU2 & EU3

Attitude 0.77

Ease of Use 0.56



Trust 0.59

The hypothesised relations were assessed simultaneously in a latent variable partial

least squared model. The structural model depicts the relationships among the constructs

which were measured using multiple indicators (see Figure 5). The model was implemented

using smart PLS 2.0 M3, options: Path Weighting, data metric (mean0, var1), maximum

iterations 300. In order to facilitate statistical significance tests of effect size estimates for the

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structural and measurement model, 1500 bootstrap estimates were run (Davison & Hinkley,

2003). For each bootstrap estimate, 200 cases were selected randomly from the total of 234

cases.

The Measurement Model

The results for the measurement model are reported in Table 12. The first column

reports the mean effect size estimate for each scale item to its intended latent variable (i.e.,

the outer loading for that item). All measurement items have a statistically-significant

loading on their latent variable (t > 2.58 indicates p < .01), which is an indication of

convergent validity.

Table 12 Measurement Model (Indicators)

Measurement Model (indicators)

Mean

effect

Standard

deviation

Standard

error t-statistic

AT1_21 <- Attitude 0.90 0.01 0.01 72.08

AT2_27 <- Attitude 0.88 0.02 0.02 42.71

EU1_10 <- Ease of Use 0.71 0.06 0.06 11.76

EU4_24 <- Ease of Use 0.69 0.05 0.05 13.88

EU5_31 <- Ease of Use 0.84 0.03 0.03 31.17

EU6_32 <- Ease of Use 0.60 0.07 0.07 9.20

IU1_18 <- Intention to Use 0.92 0.01 0.01 76.64

IU2_22 <- Intention to Use 0.93 0.01 0.01 92.78

PU1_13 <- Perceived Usefulness 0.76 0.04 0.04 19.49




T1_9 <- Trust 0.78 0.03 0.03 23.22

T2_25 <- Trust 0.69 0.05 0.05 13.38

T3_29 <- Trust 0.80 0.03 0.03 29.35

T4_12 <- Trust 0.81 0.03 0.03 24.53

Note: Reported are partial least squares regression coefficients

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Testing the Structural Model

Since SmartPLS does not calculate any goodness-of-fit values, R2 values were

evaluated to assess the ability of various proposed relationships to predict a significant degree

of explanatory power in each construct, while t-values were assessed to determine the

strength of the various paths.

R2 for intended use (IU) is 0.73, indicating that the total effects of perceived

usefulness (PU), perceived ease of use (EU), attitude (A) and trust (T) explain 73% of the

variance in intention to use (IU). R2 for attitude is 0.61, indicating that perceived usefulness;

trust and perceived ease of use explain approximately 61% of the variance in attitude. These

results indicate that the model explains the variance for the endogenous variables accurately

and is thus effective in explaining the variance of intended use.

Table 13 Measurement Model

Measurement Model

R Square

Attitude 0.61

Ease of Use 0.31



Trust

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Figure 5: Structural Model

Figure 5 depicts the structural model showing path coefficients and R2 for dependent

variables. The R2 values for each dependent variable indicate that the model explains

variances for attitude (61%) and intention to use (73%).

Table 14 Path Coefficients

Path Coefficients

Sample

Mean

(M)

Standard

Deviation

(STDEV)

Standard

Error

(STERR)

T Statistics

(|O/STERR

|)

Attitude -> Intention to Use 0.45 0.03 0.03 14.56

Ease of Use -> Attitude 0.33 0.03 0.03 11.06

Ease of Use -> Intention to Use 0.07 0.02 0.02 3.08

Ease of Use -> Perceived Usefulness 0.48 0.03 0.03 19.10

Perceived Usefulness -> Attitude 0.36 0.02 0.02 14.60

Perceived Usefulness -> Intention to Use 0.43 0.02 0.02 21.60

Trust -> Attitude 0.23 0.02 0.02 9.54

Trust -> Ease of Use 0.56 0.02 0.02 30.70

Trust -> Perceived Usefulness 0.25 0.02 0.02 11.36

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Structural Model Results

The total and indirect effects are reported in tables 15 and 16 respectively. In

assessing the effects that perceived ease of use has on other factors, the path coefficient for

perceived ease of use to intention to use is .07 (Table 14), which is not statistically significant

at the .05 level. This suggests that perceived ease of use does not have a direct effect on

intended use of m-commerce and hence hypothesis 1 is not supported. Perceived ease of use

was however found to have a positive indirect influence on intention to use through its

positive effect on attitude (Table16).

The path coefficient for perceived ease of use to perceived usefulness is 0.48 which is

significant at the 0.05 level. This supports hypothesis 2 and implies that an increase in

perceived ease of use would influence perceived usefulness in a positive way. Hypothesis 3

states that an increase in perceived ease of use is expected to result in a positive impact on the

attitude towards m-commerce. This is supported by the results, as shown by path coefficient

for perceived ease of use to attitude (0.33), which is significant at the 0.5 level. Perceived

ease of use is also found to have an indirect influence on intention to use through its positive

relationship with perceived usefulness.

Hypothesis 4 is also supported by the results. The path coefficient for perceived

usefulness to intention to use is 0.43, which is significant at the 0.05 level. This is the second

most significant relationship demonstrated by the results. This implies that second to

attitude, perceived usefulness has the most influence on intention to use m-commerce.

Another mediating positive relationship is found between perceived usefulness and intention

to use through the positive influence that perceived usefulness has on attitude. This follows

through to support hypothesis 5 which states that perceived usefulness has a positive

influence on attitude. This is demonstrated by a path the coefficient of 0.36, which is

significant at 0.05.

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Table 15 Summary of Total Effects

Summary of Total Effects

Sample

Mean

(M)

Standard

Deviation

(STDEV)

Standard

Error

(STERR) T Statistics

(|O/STERR|)

Attitude -> Intention to Use 0.44 0.03 0.03 16.53

Ease of Use -> Attitude 0.51 0.02 0.02 21.13

Ease of Use -> Intention to Use 0.50 0.02 0.02 23.59

Ease of Use -> Perceived Usefulness 0.48 0.02 0.02 20.56

Perceived Usefulness -> Attitude 0.36 0.03 0.03 13.98

Perceived Usefulness -> Intention to Use 0.59 0.02 0.02 30.46

Trust -> Attitude 0.60 0.02 0.02 36.78

Trust -> Ease of Use 0.56 0.02 0.02 31.71

Trust -> Intention to Use 0.53 0.02 0.02 34.12

Trust -> Perceived Usefulness 0.52 0.02 0.02 27.47

Note. Reported are the sample means, standard deviations, standard errors and t-statistics

Table 16 Indirect Effects

Indirect Effects

Mediator Attitude Intention to use

Perceived ease of use -> Intention to use Attitude 0.23

Perceived usefulness -> Intention to use Attitude 0.16

Perceived ease of use -> Attitude Usefulness 0.17

Trust -> Intention to use Attitude 0.27

Trust -> Intention to use Usefulness 0.31

Trust -> Intention to use Ease of use 0.28

Trust -> Attitude Ease of use 0.28

Trust -> Attitude Usefulness 0.19

Table 14 shows that the coefficient for attitude to intention to use is 0.45. This is the

most significant effect on intention to use at the 0.05 level. This supports hypothesis 6 and

also appears to be the most significant determinant for intention to use m-commerce. Attitude

also plays an important mediating role for perceived ease of use, trust and perceived

usefulness.

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The results also support hypothesis 7 which suggests that trust has a positive impact

on attitude. This implies that trust has a significant influence on consumer attitude towards

m-commerce. As seen in hypothesis 3, attitude is the most significant determinant for

intention to use. Through its influence on attitude, trust is seen to have an implicit positive

influence on intention to use. Trust was also expected to influence perceived ease of use.

This is supported by the results (coefficient 0.56), which implies that an increase in perceived

trust would influence the perceived ease of use positively. This result supports hypothesis 8.

In addition, through this influence, trust demonstrates a positive effect on the intention to use.

Finally, the coefficient for trust to perceived usefulness is 0.25, which is significant at

the 0.5 level and supports hypothesis 9. In addition, indirectly, trust is also found to have a

positive influence on intention to use through its positive relationship with perceived

usefulness.

Summary of Data Analysis

The previous section presented the results of the data analysis. Descriptive statistics

were provided to illustrate the demographic profile of the respondents. The results of the

estimation of the measurement and structural models were presented. In the estimation of the

measurement model, the psychometric properties of the measures were assessed in terms of

reliability and validity. In the estimation of the structural model, the validity of the research

model was evaluated. The results of hypotheses testing were also presented. The next

section will present a summary of the study, conclusion, implications for practice, as well as

recommendations for future research.

Findings and Discussion

This section discusses the findings in connection with the research question, which

was stated as; what drives M-commerce in Kenya and South Africa? In order to address the

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question, relationships between trust, perceived ease of use, perceived usefulness, attitude

and intention to use were examined.

TAM implies that both perceived usefulness and perceived ease of use are expected to

have significant direct effects on intended use. Consistent with the findings of prior research,

our results demonstrate that the effect of perceived usefulness on intended use is the most

significant. However, perceived ease of use was found not to demonstrate a significant direct

effect on the intention to use m-commerce. An indirect effect was however traced through

the positive influence that perceived ease of use has on attitude.

Although ease of use helps to engender a positive attitude, users are more likely to

adopt m-commerce if they perceive it to be useful, irrespective of the ease of use. These

results suggest that the user’s positive beliefs about usefulness influence their acceptance of a

technological innovation system. These results support the assertion that ease of use cannot

compensate for a system that lacks functionality (Davis, Bagozzi, & Warshaw, 1989). This is

also confirmed by previous studies which have shown that usefulness has a stronger effect on

user acceptance of an information system than ease of use (Agarwal & Karahanna, 1992).

The second set of relationships that were examined were those between perceived

trust, perceived usefulness, perceived ease of use and attitude towards m-commerce.

Together, trust, ease of use and perceived usefulness explained 61% of the variance in

attitude

Among these antecedents, trust has positive influence on all of them, with the

strongest one shown towards perceived ease of use. This suggests that when users perceive

m-commerce as trustworthy, they are also likely to perceive it as easy to use. This perception

in turn influences the attitude towards m-commerce. Trust and perceived ease of use together

have a positive influence on the perception that m-commerce is useful, explaining 43% of the

variance in perceived usefulness. According to the results, perceived ease of use has a

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significant effect on perceived usefulness which according to TAM, demonstrates that users

are likely to accept m-commerce, if they find that it can help improve performance by

reducing effort needed to do the same task.

Trust appears to be a very important factor that influences ease of use, usefulness and

most importantly attitude. Attitude has been found to have the most significant influence on

intention to use. It is crucial that practitioners take time to build trust of m-commerce in

users. Trust not only takes time to engender, but is also fragile and easily destroyed, hence

the process of continuous trust development deserves special attention. Many successful

methods adopted by e-commerce companies to overcome trust barriers are also applicable to

mobile commerce. For example, Amazon provides an unconditional guarantee of security

and is willing to cover any losses due to credit card fraud. Travelocity posts a detailed

explanation of its privacy policies on its Web site. eBay provides a forum for buyers and

sellers to rate each other after transactions. This has to be continuously developed (Siau &

Shen, 2003).

Summary of the Study

The purpose of this study was to determine the factors that influence the use of m-

commerce in Kenya and South Africa. Current trends indicate that mobile usage has grown

phenomenally in the African continent. With it has come a myriad of value added services,

some of which encompass aspects of mobile commerce. Plagued by expensive and

inadequate telecommunications infrastructure, the African continent has been slow to adopt

highly technical products and services. However, m-commerce has shown interesting trends

in terms of continuous adoption in the African continent. Thus, this study aimed to provide a

better understanding of the determinants of user acceptance of m-commerce. How user

perceptions relate to intention to use m-commerce was examined.

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TAM, one of the most prominent models, provided the theoretical perspective for this

study and was used to explain the effects of user perceptions and attitudes on their system

usage behavior. TAM has received extensive theoretical and empirical support by a number

of studies. The study adds to the realm and will provide practitioners with insights into the

strategies for facilitating user acceptance and intention to use m-commerce services.

This study employed a cross-sectional field study using a self completion

questionnaire method which was disseminated both as hard copies and via the web. Random

respondents from Kenya and South Africa were targeted. The instrument used to measure the

constructs was developed by adopting existing scales from previous studies and constructing

new scales where necessary. A pilot test was conducted to verify that the instrument was

able to collect the intended information effectively. Subsequently, several changes were

made to make it more effective and easier to understand.

A final sample of 234 responses was analysed. The measurement model was tested to

assess reliability and validity of the scales. The structural model was tested to examine the

relationships between the constructs in the research model using PLS software. The

responses from this study were examined at the p<.05 level of significance. Overall, the

research model was found effective in explaining 73% of the variance in intended use and

61% of the variance in attitude towards m-commerce.

Conclusion

The study examined the effects of user perceptions on m-commerce on their intention

to use it. TAM provides a theoretical framework to explain user acceptance of an information

system based on user perceptions. The results in this study demonstrate that user perceptions

of usefulness, trust and ease of use are fundamental determinants of user acceptance of an

information system. The following conclusions are thereby drawn.

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1. According to the research model developed in this research, trust, perceived usefulness,

perceived ease of use and attitude explain 73% of the variance that is seen in user

intention to use m-commerce. Influencing consumer attitude will have the most

significant direct effect towards intention to use. Attitude also acts as a mediating

factor for trust, perceived usefulness and perceived ease of use.

2. Both perceived ease of use and perceived usefulness have positive effect on intended

use of m-commerce. However, perceived usefulness has a stronger direct effect on

user acceptance than ease of use. In fact, perceived ease of use only demonstrates an

indirect effect to intention to use, through attitude. The finding here is that user

acceptance of m-commerce depends primarily on how useful the users perceive it to

be, irrespective of how easy to use it is.

3. Perceived trust has a significant influence on perceived usefulness, perceived ease of

use and attitude. These results imply that if users perceive m-commerce as

trustworthy then they are likely to have a positive attitude towards it. Trust is thus

seen as a very significant factor that influences attitude. Attitude has been shown to

have the most significant effect on intention to use.

This conclusion may be useful for management to focus on the key drivers for m-commerce

as a medium for business.

Implications for Practice

The findings from this study have meaningful managerial implications and raise

challenging questions for future research. The model provides a useful framework for

managers who need to understand the drivers of m-commerce. The results illustrate the

importance of increasing perceived usefulness in order to encourage adoption. Also

important to note is the fact that adoption attitude and behavior are largely influenced by the

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endorsement of family and friends. Marketing practitioners need to take these factors into

consideration when promoting m-commerce.

The results indicate that trust and privacy issues are important concerns for

consumers. M-commerce providers should be patient with potential customers and provide

as much information as possible in order to build trust. They should also demonstrate the

willingness to take responsibility for any losses incurred by customers while conducting m-

commerce transactions.

To reduce irritability, mobile marketing practitioners need to obtain permission from

customers before they send m-commerce related material via mobile phones.

The ease of using m-commerce plays an indirect role in the adoption of this

innovation. Practitioners should strive to ensure that m-commerce can be conducted with

standard phones using the same key strokes, menus and terminology used in the day to day

operation of a mobile phone.

By doing all the above, practitioners are likely to encourage a positive attitude

towards m-commerce. Attitude has been shown to have the most significant impact on

intention to use.

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Recommendations for Future Research

The results demonstrate that ease of use does not have a significant direct effect on

intention to use m-commerce. However, ease of use has a significant effect on

attitude. Additional research is needed to determine other factors that mediate the

relationship between ease of use and intention to use.

The results indicated that perceived usefulness has significant effect on intended use.

Ease of use was reported to have significant influence on perceived usefulness.

Further research can be pursued to investigate the factors that determine ease of use.

Perceived Trust was found to have significant effect on attitude. Further research can

be pursued to identify the antecedents of trust.

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Appendices

Appendix 1: Technology Readiness Index of African Countries

Country Technology Readiness

Africa Rank Technology Readiness World

Rank

South Africa 1 42

Tunisia 2 52

Mauritius 3 55

Morocco 4 78

Senegal 5 81

Egypt 6 84

Namibia 7 85

Botswana 8 89

Gambia 9 91

Kenya 10 93

Nigeria 11 94

Libya 12 98

Cote d’Ivoire 13 99

Mauritania 14 102

Mali 15 105

Zambia 16 106

Cameroon 17 110

Madagascar 18 111

Benin 19 113

Algeria 20 114

Ghana 21 115

Mozambique 22 116

Tanzania 23 117

Burkina Faso 24 120

Uganda 25 121

Lesotho 26 125

Malawi 27 127

Zimbabwe 28 129

Burundi 29 131

Ethiopia 30 132

Chad 31 134

http://www.ezega.com/news/NewsDetails.aspx?Page=news&NewsID=1585

http://www.ezega.com/news/NewsDetails.aspx?Page=news&NewsID=1585

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Appendix 2: Model TAM Questionnaire

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Copyright UCT


Bibliography

ABI Research. (2010, February 16). Retrieved August 5, 2010, from Shopping by Mobile

Will Grow to $119 Billion in 2015: http://www.abiresearch.com/press/1605

Abu Bakar, F., & Osman. (2005). Towards the future of mobile commerce (m-commerce) in

Malaysia. IADIS International Conference. Algrave, Portugal.

Agarwal, R., & Karahanna, E. (1992, April). Perceived usefulness, ease of use and usage of

information technology: A replication. MIS Quarterly , pp. 227-247.

Airtel, B. (2009, August 26). m-commerce on voice. Retrieved November 02, 2010, from

Teck In: http://teck.in/mchek-on-airtel-airtels-m-commerce-on-voice.html

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human

Decision Processes , 179–211.

Ankeny, J. (2008, January 14). Forecast: Mobile Marketing Growth. Retrieved July 16, 2010,

from http://www.fiercemobilecontent.com:

http://www.fiercemobilecontent.com/story/forecast-mobile-marketing-growth-24b-

2013/2008-01-14

Atkins, T. (2005, February 26). 'Digital Divide' Narrowing Fast, World Bank Says. Retrieved

June 16, 2010, from Technology For Humanity:

http://www.technologyforhumanity.org/articles/dividecloses.html

Babbie, E. (1990). Survey Research Methods (2nd

ed). Belmont,CA: Wadsworth.

Babbie, E. (1992). The practice of social research (6th ed.). Belmont, CA: Wadsworth.

Babbie, E. (1991). Social worlds, individual differences, and implementation: predicting

attitudes toward a medical information system. Information Management , 119-136.

Backer, T. E., & Rogers, E. M. (1997). Diffusion of innovations theory and work-site AIDS

programs. Journal of health communication, 3(1), 17-28. Retrieved from

http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2679503&tool=pmcentrez&

rendertype=abstract.

Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural evaluation models. Journal of

the Academy of Marketing Science , 74-94.

Baldi, S., & Thaung, H. P.-P. (2002). The Entertaining Way to M-Commerce: Japan’s

Approach to the Mobile Internet - A Model for Europe?. Electronic Markets, 12(1), 6-

13. doi: 10.1080/101967802753433218.

Banzal, S. (2010). Table of contents. Journal of deaf studies and deaf education, 15(4), NP.

doi: 10.1093/deafed/enq042.

Copyright UCT


Barnett, N., Hodges, S., & Wilshire, M. J. (2000). m-commerce: An Operator’s Manual.

McKinsey Quarterly, (3), 163-173.

BMI. (2010, July 30). Kenya Business Forecast Report. Retrieved September 13, 2010, from

Business Monitor International:

http://store.businessmonitor.com/bf/kenya_business_forecast_report/

Boadi, R. A., & Shaik, A. G. (2006). MASTER’S THESIS M-commerce Breakthrough.

Systems Science.

Bohrnstedt, G. W. (1970). Reliability and Valitidity in attitude measurement. Chicago: Rand

McNally.

Bryman, A., & Bell, E. (2007). Business Research Methods. Oxford: Oxford University

Press.

Business Week. (2009, October 15). Retrieved August 12, 2010, from M-Commerce's Big

Moment:

http://www.businessweek.com/technology/content/oct2009/tc20091011_278825.htm

Campbell, D. T., & Fiske, D. F. (1959, 56). Convergent and discriminant validation by the

multitrait multimethod matrix. Psychological Bulletin , pp. 81-105.

Carroll, A., Barnes, S. J., & Fletcher, K. (2007). Consumer perceptions and attitudes towards

SMS advertising : recent evidence from New Zealand. International Journal, 26(1), 79-

98.

CCK. (2010, March 16). Sector Statistical Reports. Retrieved September 13, 2010, from

Communications Commission of Kenya: http://www.cck.go.ke/resc/statcs.html

Chin, W. (1998). The partial least squares approach to structural equation modeling. In G.

A. Marcoulides (Ed.), Modern methods for business research. Mawah, NJ: Lawrence

Erlbaum.

Dard, R. (2007). Mobile Technology Helps African Exporters. International Trade Forum ,

32.

Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of

Information Technology. MIS Quarterly, 13(3), 319. doi: 10.2307/249008.

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer

technology: A comparison of two theoretical models. Managment Science , 982-1003.

Davison, A. C., & Hinkley, D. V. (2003). Bootstrap methodsandtheirapplication (2nd ed.).

New York: CambridgeUniversityPress.

Dholakia, R. R. (2006). M-commerce: global experiences and perspectives. Heryshey: Idea

Group Inc (IGI).

Copyright UCT


Durlacher. (1999). Mobile Commerce Report. Retrieved 06 12, 2010, from Durlacher

Research: www.durlacher.com

El-gayar, O. (2007). Examining Students ’ Acceptance Of Tablet Pc Using TAM.

Information Systems, VIII(1).

Ewing, J. (2007). Afric Upwardly Mobile. Business Week , 66.

Feng, H., Hoegler, T., & Stucky, W. (2006). Exploring the critical success factors for mobile

commerce . International Conference on Mobile Business (ICMB’06). Copenhagen,

Denmark.

Fishbein, M., & Ajzen, I. (1980). Massachusetts and Illinois: Prentice-Hall, Inc.

Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention and Behavior: An Introduction

to Theory and Research. Reading, MA: Addison-Wesley.

Fornell, C., & Larcker, D. F. (1981). pp. 39-50.

Fornell, C., & Lercker, D. F. (1981). Structural Equation Models with Unobservable

Variables and Measurement Error -Algebra and Statistics. Journal of Marketing

Research , 382-388.

Francis, A. J. J., Eccles, M. P., Johnston, M., Walker, A., Grimshaw, J., Foy, R., et al. (2004).

CONSTRUCTING QUESTIONNAIRES BASED ON THE THEORY OF PLANNED

BEHAVIOUR A MANUAL for HEALTH SERVICES RESEARCHERS. Direct,

(May).

Frolick, M., & Chen, L.-D. (2004). Assessing M-Commerce Opportunities. Information

Systems Management, 21(2), 53-61. doi: 10.1201/1078/44118.21.2.20040301/80422.8.

Gartner. (2010, April). Retrieved July 15, 2010, from Gartner Outlines 10 Mobile

Technologies to Watch in 2010 and 2011:

http://www.gartner.com/it/page.jsp?id=1328113

Gatford, J. M. (2010, July 23). M-commerce - Welcome to the Future. Retrieved August 15,

2010, from Ezine Articles: http://ezinearticles.com/?M-Commerce---Welcome-to-the-

Future&id=4694273

Georgiev, G. (2009). Driving Adoption of M-Commerce with Innovative Solutions.

Networks, 1-13.

Grami, A., & Schell, B. H. (2006). Future Trends in Mobile Commerce : Service Offerings ,

Technological Advances and Security Challenges, 1-14.

Godin, S. (1999). Permission Marketing: turning strangers into friends, and friends into

customers. . New York: Simon & Schuster.

Copyright UCT


Haghirian, P., & Madlberger, M. (n.d.). CONSUMER ATTITUDE TOWARD

ADVERTISING VIA MOBILE DEVICES – AN EMPIRICAL INVESTIGATION.

Business.

Harnesk, D. (2004). A framework for Strategic Alignment of Business and Information

Technology in Small and Medium sized Firms. Licentiate thesis, Luleå University of

Technology; Department of Business Administration and Social Science.

Harvey, G., Deans, K. R., & Gray, B. J. (2007). Third Screen Communication and the

Adoption of Mobile Marketing : A Malaysia Perspective. Journal of Marketing, 36-47.

Hotchkiss, K. (2010, March 2nd). 4Hoteliers. Retrieved November 15, 2010, from

4Hoteliers: http://www.4hoteliers.com/4hots_nshw.php?mwi=7037

Internet World Stats. (2010, June 30). Retrieved July 13, 2010, from Internet World Stats:

http://www.internetworldstats.com/stats.htm

Kao, D. T. (2009). The impact of transaction trust on consumers’ intentions to adopt m-

commerce: a cross-cultural investigation. Cyberpsychology & behavior : the impact of

the Internet, multimedia and virtual reality on behavior and society, 12(2), 225-9. doi:

10.1089/cpb.2008.0212.

Karvonen, K., Cardholm, L., & Karlsson, S. (1999). Cultures of Trust : A Cross-Cultural

Study on the Formation of Trust in an Electronic Environment. Technology.

Kavassalis, P., Spyropoulou, N., Drossos, D., Mitrokostas, E., Gikas, G., & Hatzistamatiou,

A. (2003). Mobile Permission Marketing : Framing the Market Inquiry. International

Journal of Electronic Commerce, 8(1), 55-79.

Krishnamurthy, S., & Rights, A. (2002). Case # 7 1 NTT DoCoMo’s I-Mode Phone: A Case

Study 2, 1-41.

Leedy, P. D., & Ormrod, J. E. (2009). Practical Research:Planning and Design. New

Jersey: Pearson Education.

Leppaniemi, M., Sinisalo, J., & Karjakuoto, H. (2006). A review of mobile marketing

research. Journal of Mobile Marketing , 2-12.

Leung, K., & Antypas, J. (2001). Improving Returns on M-commerce Investments. Journal

of Business Strategy , 12-16.

Li, Y.-hui, & Huang, J.-wen. (2009). Applying Theory of Perceived Risk and Technology

Acceptance Model in the Online Shopping Channel. Engineering and Technology, 919-

925.

Li, Y.-hui, & Huang, J.-wen. (2010). Applying Theory of Perceived Risk and Technology

Acceptance Model in the Online Shopping Channel. Journal of Business, 132-138.

Luarn, P., & Lin, H. H. (2005, June). Toward an understanding of the behavioral intention to

use mobile banking. Computer in Human Behaviour , pp. 873-891.

Copyright UCT


Magill, K. (2009). THE MOBILE CHANNEL IS FINALLY GETTING SOME UPWARD

MOMENTUM. October, 30-33.

M-Commerce. (2010, May). Retrieved August 12, 2010, from Fierce Wireless:

http://www.fiercewireless.com/topics/mcommerce.asp

Miller, S. (2009, October 23). Definition of Mobile Commerce. Retrieved July 15, 2010,

from How to just about everything: http://www.ehow.com/facts_6069559_definition-

mobile-commerce.html

Mitchell, V.-W. (1999). Consumer perceived risk: conceptualisations and models. European

Journal of Marketing, 33(1/2), 163-195. doi: 10.1108/03090569910249229.

Mohd, Y. Y., & Muhammad, Z. (2009). Individual Differences , Perceived Ease of Use , and

Perceived Usefulness in the E-Library Usage. Knowledge Creation Diffusion Utilization,

2(1), 2-9.

Moon, J. W., & Kim, Y. G. (2001). Extending the TAM for a world-wide-web context.

Information & Management , 217-230.

Mort, G. S., & Drennan, J. (2005). Marketing m-services: Establishing a usage benefit

typology related to mobile user characteristics. Journal of Database Marketing &

Customer Strategy Management, 12(4), 327-341. doi: 10.1057/palgrave.dbm.3240269.

Moshin, M., Mudtadir, R., & Ishaq, A. (2003). Mobile commerce – the emerging frontier:

exploring the prospects, application and barriers to adoption in Pakistan. Islamabad.

Mutula, S. M. (2007). Digital Divide and Economic Development:Case Study of Sub

Saharan Africa. Emerald Insight , 481.

Nysveen, H., Pedersen, P., & Thorbjonsen, H. (2005). Intentions to Use Mobile Services:

Antecedents and Cross- Service Comparison. Journal of the Academy of Marketing

Science , 330-346.

Okazaki, S. (2005). NEW PERSPECTIVES ON M-COMMERCE RESEARCH. Current,

160-164.

Parasuraman, A., & Colby, C. L. (2001). Techno-ready Marketing. Innovation.

Portio Research. (2010, March). Retrieved August 5, 2010, from Mobile Payments 2010-

2014: http://www.portioresearch.com/Mob_payments10-14.html

Raisinghani, M. S. (2001). WAP: Transactional Technology for M-Commerce. Information

Systems Management , 8–16.

Rawood, D. (2010, July 26). Web Threats. Retrieved November 15, 2010, from Secure

Business Intelligence: http://www.scmagazineuk.com/database-hacked-at-new-zealand-

pizza-chain-to-reveal-personal-details-of-230000-customers/article/175358/

Copyright UCT


Rittippant, N., Witthayawarakul, J., Limpiti, P., & Lertdejdecha, N. (2009). Consumers ’

Perception of the Effectiveness of Short Message Service ( SMS ) and Multimedia

Message Service ( MMS ) as Marketing Tools. Media, 815-821.

Rogers, E. M. (1995). Diffusion of Innovations. New York: Free Press.

Sadeh, N. (2002). M-Commerce: Technologies, Services, and Business Models. New York:

Wiley Computer Publishing.

Safaricom. (2010, May 26). Safaricom Annual Report 2010. Retrieved August 16, 2010,

from Safaricom Reports & Presentations: http://www.safaricom.co.ke/index.php?id=335

Saidi, E., & Town, O. (2010). Journal of Internet Banking and Commerce. Journal of Internet

Banking and Commerce, 15(1), 1-13.

SAinfo. (2009, September 17). Mobile Banking on the Increase. Retrieved October 5, 2010,

from South Africa Info: http://www.southafrica.info/business/trends/newbusiness/cell-

171109.htm

SAInfo. (2009, November 17). South Africa's telecommunications. Retrieved September 25,

2010, from South Africa Info:

http://www.southafrica.info/business/economy/infrastructure/telecoms.htm

Science, I. M. (2001). Invited chapter under review for inclusion in the forthcoming book.

Provider.

Schutt, R. K. (1999). Investigating the social world: The process and practice of research

(2nd ed.). Thousand Oaks,CA: Pine Forge.

Siau, K., & Shen, Z. (2003). Building customer trust in mobile commerce. Communications

of the ACM, 46(4), 91-94. doi: 10.1145/641205.641211.

Stafford, T. F., & Gillenson, M. L. (2003). Mobile commerce. Communications of the ACM,

46(12), 33. doi: 10.1145/953460.953483.

Tang, T.-i. (2004). Toward An Understanding of the Behavioral Intention to Use Mobile

Banking Services. differences.

Tiwari, R., & Buse, S. (2007). The Mobile Commerce Prospects: A Strategic Analysis of

Opportunities in Banking Sector. Hamburg: Hamburg University Press.

Tsang, M. M., Ho, S.-chun, & Liang, T.-peng. (2004). Consumer Attitudes Toward Mobile

Advertising : An Empirical Study. Spring, 8(3), 65-78.

Twinomugisha, A. (2009, 06 16). m-commerce and impact on Africa. Retrieved 05 12, 2010,

from Africa Business Source:

http://www.africabusinesssource.com/articles/innovation/m-commerce-and-impact-on-

africa/

UNCTAD. (2002). E-COMMERCE AND DEVELOPMENT REPORT 2002. Development.

Copyright UCT


UNCTAD. (2009, 6 13). Retrieved 06 02, 2010, from Mobile penetration in Africa:

http://www.unctad.org/Templates/webflyer.asp?docid=12273&intItemID=1528&lang=1

Utiba. (2010, June). m-commerce tutorial. Retrieved October 15, 2010, from Utiba Mobility:

http://www.utiba.com/m-commerce-tutorial.html

Varshney, U., & Vetter, R. (2002). Mobile commerce: framework, applications and

networking support. Mobil Mobile Networks and Applications , 185-98.

Ververidis, C., & Polyzos, G. C. (n.d.). MOBILE MARKETING USING A LOCATION

BASED SERVICE. Communication.

Victoria, C. A., & June, J. (2002). WHAT IS IT ? WHAT WILL IT MEAN FOR

CONSUMERS ?, (June).

Waverman, L., Meschi, M., & Fuss, M. (2005). The Impact of Telecoms on Economic

Growth in Developing Countries, (1980), 1-23.

Wei, T. T., & Chong, A. Y.-loong. (2008). What drives Malaysian m-commerce adoption ?

An empirical analysis. Industrial Management & Data Systems, 370-388. doi:

10.1108/02635570910939399.

Wisegeek. (2009, 01 16). Retrieved 04 16, 2010, from www.wisegeek.com

Wu, J., & Wang, S. (2005). What drives mobile commerce?An empirical evaluation of the

revised technology acceptance model. Information & Management, 42(5), 719-729. doi:

10.1016/j.im.2004.07.001.

Wu, J.-H., & Hisa, T.-L. (2008). Developing E-Business Dynamic Capabilities: An Analysis

of E-Commerce Innovation from I-, M-, to U-Commerce. Journal of Organizational

Computing and Electronic Commerce, 18(2), 95-111. doi:

10.1080/10919390701807525.

Yeh, J.-tsann, County, Y., Lin, C.-ling, County, T.-yuan, & Road, C. P. (2010). Measuring

the effectiveness of advertiseMents sent via Mobile phone : iMplications of the appeal ,

endorser , and involveMent Model and purchasing behavior. Journal of Database

Marketing, 38(200), 249-256. doi: 10.2224/sbp.2010.38.2.249.

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