1. technology and computing
2. consumer electronics
3. telephones
4. mobile phones

FACTORS INFLUENCING THE ADOPTION OF MOBILE
CREDIT SYSTEM IN KENYA
BY
KISILA ERIC JOSEPH
UNITED STATES INTERNATIONAL UNIVERSITY
SUMMER 2014
FACTORS INFLUENCING THE ADOPTION OF MOBILE
CREDIT SYSTEM IN KENYA
BY
KISILA ERIC JOSEPH
A Project Report Submitted to the Chandaria School of Business
in Partial Fulfillment of the Requirement for the Degree of
Executive Masters of Organizational Development (EMOD)
UNITED STATES INTERNATIONAL UNIVERSITY
SUMMER 2014
STUDENT’S DECLARATION
I, the undersigned, declare that this project is my original work and has not been submitted to
any other college, institution, or university for academic credit other than United States
International University – Nairobi, Kenya.
Signed:
Date:
Kisila Eric Joseph (ID NO. 623797)
This project has been presented for examination with my approval as the appointed
supervisor
Signed:
Date:
Dr. Gerald Chege
Signed:
Date:
Dean, Chandaria School of Business
ii
COPYRIGHT
No part of this project may be produced or transmitted in any form or by any means
electronic, magnetic tape or mechanical including photocopy, recording on any information
storage and retrieval system without prior written permission from the author.
Copyright by Kisila Eric Joseph, 2014
iii
ABSTRACT
The purpose of this study was to identify factors influencing the adoption of mobile credit
system in Kenya and the specific research questions of the study were: to establish the
influence of acceptability factors on the adoption of mobile credit system in Kenya; to
establish the influence of affordability factors on the adoption of mobile credit system in
Kenya; and to establish the influence of accessibility factors on the adoption of mobile credit
system in Kenya.
The research design was descriptive in nature. The research was conducted among Airtel
Kenya employees – Mombasa Road Headquarters’ Office. The sampling frame from this
study was selected from a population of 350 employees as provided by the Human Resources
office electronic mails. A sample of 78 employees was targeted to represent the population of
interest. The data gathered was edited and transformed into a quantitative form through
coding. It was then entered into a computer. Univariate analysis like frequency distribution
was adopted in the study. The analyzed data was presented inform of tables. Statistical
Package for the Social Science (SPPS) was used to aid in data analysis.
In regards to acceptability factors and adoption of mobile credit system in Kenya, the first
major findings of the study was that the questions which were asked included amongst others
on marketing exposure, demographic characteristics, and security concerns. On marketing
exposure, respondents agreed that promotional efforts can create awareness of mobile credit
system in establishing the influence of acceptability factors; on demographic characteristics,
respondents strongly agreed that age, education and wealth characteristics are predictor of
adoption decision in establishing the influence of acceptability factors; and on security
concerns, out of respondents agreed that mobile provider should generate trust among
uneducated rural subscribers in establishing the influence of acceptability factors. Thus, the
researcher concluded that perceived security concerns has the higher ability to predict and
explain the intention of users to adopt mobile credit system in Kenya.
With respect to affordability factors and adoption of mobile credit system in Kenya the
second major findings of this study indicated that the questions which were asked included
amongst others on online transaction, credit availability, and transaction cost. On online
iv
transaction, respondents agreed that credibility was everything if it comes to mobile credit
system in establishing the affordability; on credit availability, respondents agreed that out of
stock lead the subscribers not to adopt the credit system in establishing the affordability
factors; and on transaction cost, respondents strongly agreed that the cost of transaction to be
lowered to attract rural population in establishing affordability factors. Thus, the researcher
concluded that transaction cost has the higher ability to predict and explain the intention of
users to adopt mobile credit system in Kenya.
In relation to accessibility factors and adoption of mobile credit system in Kenya, the third
and last major findings of this study indicated that the questions which were asked included
amongst others on service convenience, technical issues, and network efficiency. On service
convenience, respondents strongly agreed that credit system should be reasonably priced in
establishing accessibility factors; on technical issues, respondents strongly agreed that
limitation increases complexities thus reduces the possibility of adoption in establishing
accessibility factors; and on network efficiency, respondents were neutral on the speed of
mobile web connections in establishing accessibility factors. Thus, the researcher concluded
that service convenience has the higher ability to predict and explain the intention of users to
adopt mobile credit system in Kenya.
The study made several conclusions among them that firstly, with respect to the influence of
acceptability factors on mobile credit system adoption, this study concluded that perceived
security risk was all about the extent to which technology-enabled services were perceived to
be secure, sufficiently safe and reliable to use. Secondly, in regards to the influence of
affordability factors on mobile credit system adoption, this study concluded that, users would
agree to pay a reasonable fee to use a service depending on the credit cost and service
provider. Thus, value for money barrier may be another factor influencing the adoption of
mobile credit system. Thirdly and final, in relations to accessibility factors on mobile credit
system adoption, this study concluded that, price of a technology was an important factor that
influenced the adoption of the technology. This was because in times of increased
competition, a distribution channel organized business processes efficiently so as to reduce
distribution costs.
v
The study also made a recommendation that the location of this study is only confined to
Airtel Kenya employees at Kenya Headquarter in Nairobi. Thus, the sample and its responses
may not be a representation of the beliefs and intention of Kenya towards using mobile credit
system. Future research can improve on this limitation by increasing the sample size and
performing future research across different respondents.
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ACKNOWLEDGEMENT
I wish to extend my deep felt gratitude to all the people who offered their support and
assistance. In particular, I thank my supervisor, Dr. Chege, for offering a lot guidance and
assistance in coming up with this research report. Gratitude also to my family for their
understanding and support during the many hours I was doing the project. I cannot forget
also to acknowledge the reference of other writers for their work which assisted me a lot in
coming up with the project. Lastly, I would like to thank the Almighty God for providing the
resources and energy to make this research project become a reality.
vii
DEDICATION
This work is dedicated to my family whose encouragement and support gave me the drive to
carry on and my friends who are my inspiration and mentors.
viii
TABLE OF CONTENTS
STUDENT’S DECLARATION........................................................................................................... ii
COPYRIGHT ...................................................................................................................................... iii
ABSTRACT ......................................................................................................................................... iv
ACKNOWLEDGEMENT ................................................................................................................. vii
DEDICATION ................................................................................................................................... viii
LIST OF TABLES............................................................................................................................... xi
LIST OF FIGURES............................................................................................................................xiii
CHAPTER ONE ................................................................................................................................... 1
1.0 INTRODUCTION .......................................................................................................................... 1
1.1 Background of the Study ..................................................................................................................1
1.2 Statement of the Problem .................................................................................................................4
1.3 Purpose of the Study .........................................................................................................................6
1.4 Research Questions ...........................................................................................................................6
1.5 Importance of the Study ...................................................................................................................6
1.6 Scope of the Study .............................................................................................................................7
1.7 Limitations of the Study....................................................................................................................7
1.8 Definition of Terms............................................................................................................................7
1.9 Chapter Summary .............................................................................................................................8
CHAPTER TWO .................................................................................................................................. 9
2.0 LITERETURE REVIEW .............................................................................................................. 9
2.1 Introduction .......................................................................................................................................9
2.2 Influence of Acceptability Factors on Mobile Credit System Adoption .......................................9
2.3 Influence of Affordability Factors on Mobile Credit System Adoption .....................................14
2.4 Influence of Accessibility Factors on Mobile Credit System Adoption ......................................19
2.5 Chapter Summary ...........................................................................................................................23
CHAPTER THREE ........................................................................................................................... 24
3.0 RESEARCH METHODOLOGY ................................................................................................ 24
3.1 Introduction .....................................................................................................................................24
3.2 Research Design ...............................................................................................................................24
3.3 Population and Sampling Design ...................................................................................................25
3.4 Data Collection Methods .................................................................................................................27
3.5 Research Procedures .......................................................................................................................28
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3.6 Data Analysis Methods....................................................................................................................28
3.7 Chapter Summary ...........................................................................................................................29
CHAPTER FOUR .............................................................................................................................. 29
4.0 RESULTS AND FINDINGS ........................................................................................................ 29
4.1 Introduction .....................................................................................................................................29
4.2 General Information .......................................................................................................................29
4.3 Influence of Acceptability Factors on Adoption of Mobile Credit System .................................32
4.4 Influence of Affordability Factors on Adoption of Mobile Credit System .................................39
4.5 Influence of Accessibility Factors on Adoption of Mobile Credit System ..................................45
4.6 Chapter Summary ...........................................................................................................................51
CHAPTER FIVE ................................................................................................................................ 53
5.0 DISCUSSION, CONCLUSION, AND RECOMMENDATIONS ............................................ 53
5.1 Introduction .....................................................................................................................................53
5.2 Summary ..........................................................................................................................................53
5.3 Discussion .........................................................................................................................................55
5.4 Conclusion ........................................................................................................................................62
5.5 Recommendation .............................................................................................................................64
REFERENCES ................................................................................................................................... 66
APPENDICES .................................................................................................................................... 69
APPENDIX I: COVER LETTER ........................................................................................................69
APPENDIX II: QUESTIONNAIRE ....................................................................................................70
SECTION A: GENERAL INFORMATION ......................................................................................70
SECTION B: RESEARCH TOPIC .....................................................................................................71
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LIST OF TABLES
Table 3.1 Sample Size Distribution………………………………………………………….26
Table 3.2 Secondary Data……………………………………………………………………27
Table 4.1 Respondents Gender………………………………………………………………28
Table 4.2 Respondents Age……………………………………………………………….....29
Table 4.3 Cross Tabulation of Gender against Age………………………………………….29
Table 4.4 Level of Education………………………………………………………………...30
Table 4.5 Average Annual Income………………………………………………………......30
Table 4.6 Cross Tabulation of Gender against Level Education………………………….....31
Table 4.7 Create Awareness…………………………………………………………………31
Table 4.8 Create Awareness through Training Session……………………………………...32
Table 4.9 Understand Subscriber behavior Patterns…………………………………………32
Table 4.10 Metropolitan Subscribers are Open to Technology……………………………...33
Table 4.11 Subscribers who Accept Technology are Young and Wealthy……………….....33
Table 4.12 Predictor of Adoption Decision……………………………………………….....34
Table 4.13 Seeking Information to Ascertain of Risk……………………………………….34
Table 4.14 Unwanted Disclosure of Private Information……………………………………35
Table 4.15 Generating Trust among Uneducated Rural Subscribers………………………...35
Table 4.16 Online Transaction Reduces Cost……………………………………………….36
Table 4.17 Online Transaction Reduces Operation and Administrative Costs……………...36
Table 4.18 Credibility of Mobile Credit System…………………………………………….37
Table 4.19 Credit Availability can be affected by the Order Cycle………………………….37
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Table 4.20 Out of Stock Lead the Subscribers not to adopt the Credit System……………...38
Table 4.21 Costly if the Credit is out of Stock Situation Occurs frequently………………...38
Table 4.22 Increase the Volume of Utilization to Enjoy Economies of Scale………………38
Table 4.23 Lower the Cost to Attract Rural Population……………………………………..39
Table 4. 24 Lower the Cost of Maintenance and Upgrade…………………………………..39
Table 4.25 Service Provider should know the Subscribers Demands……………………….40
Table 4.26 Credit System should be reasonably Priced……………………………………...40
Table 4.27 Cost of Mobile Devices should not be too high………………………………….41
Table 4.28 Limitation of Speed and Memory………………………………………………..41
Table 4.29 Understanding Limitation before Implementing………………………………...42
Table 4.30 Limitation Increases Complexities………………………………………………42
Table 4.31 Navigational Efficiency………………………………………………………….43
Table 4.32 Threat of Losing Connectivity…………………………………………………...44
Table 4.33 Mobile Web Connections are slower…………………………………………….44
xii
LIST OF FIGURES
Figure 4.1 Market Exposure…………………………………………………………………34
Figure 4.2 Demographic Characteristics…………………………………………………….36
Figure 4.3 Security Concerns………………………………………………………………..38
Figure 4.4 Online Transaction……………………………………………………………….40
Figure 4.5 Credit Availability………………………………………………………………..42
Figure 4.6 Transaction Cost………………………………………………………………….44
Figure 4.7 Service Convenience……………………………………………………………..47
Figure 4.8 Technical Issues………………………………………………………………….49
Figure 4.9 Network Efficiency………………………………………………………………52
xiii
ABBREVIATIONS
ATM
Automated Teller Machine
GPRS
General Packet Radio Service
GPS
Global Positioning System
ICT
Information Communication Technology
MCS
Mobile Credit System
PWC
Price Waterhouse Coopers
SMS
Short Message Service
SPSS
Statistical Package for Social Science
TAM
Technology Acceptance Model
WAP
Wireless Application Protocol
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CHAPTER ONE
1.0 INTRODUCTION
1.1 Background of the Study
With the onset of the information age and its enormous power over production, world
business and commerce is now being undertaken on a transnational basis. Countries and
institutions are encouraging and are actually undertaking trade and business across national
and international borders. Information Communication Technology (ICT) diffusion is
changing the way in which organizations operate and/or compete. New ventures are being
created while existing business are being modified (Carr et al., 2010).
On technology adoption, several studies tried to explain by the conventional factors, such as
perceived benefits, organizational readiness, external pressures, changes in organizational
strategy, structure, management systems and human capital skills, and openness to external
sources of information (Carr et al., 2010). Many writers agree however that technological
adoption reduces the competitive advantage enjoyed by early adopters even though nonadopters are still disadvantaged by their inability to join the band wagon. Carr et al., (2010)
posit that there are both exogenous and endogenous factors influencing the adoption,
implementation, and the successful management of technologies.
Jayawardhena and Foley (2010) used The Network-Actor Theory to identify customers,
suppliers, and competitors as sources of information and influence on ICT adoption. Internal
influences include employees and management (Jayawardhena and Foley, 2010). They
emphasized on the unique role of management commitment and perceptions of ICT benefits
as an influence in Small Medium Enterprises ICT adoption. In his pioneering work in the
adoption of innovation, Toh (2007) led to the development of the Diffusion of Innovation
Theory. According to this theory, the decision to adopt an innovation depends on the
perception of the members of a social system regarding attributes of the innovation in
question, like relative advantage, compatibility, trial-ability, complexity and observability.
1
The first antecedent was the influence of acceptability factors on the adoption of mobile
credit system in Kenya where one of the most important contributing factors for adoption or
acceptance of mobile credit system is the creation of awareness among consumers for the
services or products (Pikkarainen et al., 2009). In this context, Simpson and Docherty (2010)
assert that consumers go through a process of knowledge, conviction, decision, and
confirmation before they are ready to adopt and use a product or service. Wu and Wang
(2005) emphasize the importance of awareness for the adoption of any new innovation.
Prabhudey and Sridhar (2008) indicate that there should be increasing promotional efforts on
the part of mobile service providers to create a greater awareness of mobile credit system and
its benefit.
In addition, for mobile credit system adoption service providers must consider a user’s
demographic characteristics to offer the correct range of service products. According to Siau
et al., (2009), demographic characteristics that describe typical electronic crediting customers
include young, affluent, and highly educated. Thus, a Finnish study (Ivatury, 2008) revealed
that crediting users are relative wealthy, highly educated and are in higher professions.
Ivatury (2008) find that affluent and highly educated groups generally accept technology
changes more readily.
Because mobile service providers have access to more sensitive information related to
personal interactions (for instance phone calls and SMS) and physical location (for example
global positioning system-GPRS), the issue of privacy is more relevant in adoption of mobile
credit system. According to Laurn and Lin (2007), people are concerned about unwanted
disclosure of their private information, or simply misuse of their information by the mobile
company collecting it. This dimension of risk include undisclosed capture of information
such as consumers’ borrowing habits.
The second antecedent was the influence of affordability factors on the adoption of mobile
credit system in Kenya where all business transactions require some elements of trust,
especially those conducted in uncertain environments (Delgado and Nieto, (2009). In order to
complete the mobile credit system purchase transaction, customers have to trust the online
business or overwhelming social complexity will cause them to avoid purchasing the service
2
(Prabhudey and Sridhar, 2008). In electronic commerce, trust can be viewed as a perceptual
belief or the level of confidence one expects from the other party during an online transaction
(Liu and Arnett, 2011). While consumers initially trust their e-vendors and have an idea that
adopting online service is beneficial to job performance or life style, they will eventually
believe that online service are useful (Prabhudey and Sridhar, 2008).
Mohammed and Kathy (2008) noted that some consumers have more ability to use crediting
technology and computer software for transferring credit than other consumers. Consumers
with increased computation ability may adopt internet crediting more easily and their ability
may also improve their efficiency in the use of online transaction. Tan and Teo (2007) find
that consumers who are non-adopters of internet crediting could be differentiated by their
low or poor computation proficiency and computer skills. Limayen et al., (2010) pointed out
that a person’s internet self-efficiency, such as internet skills, will affect the decision whether
or not to adopt mobile credit system.
In Germany, Mohammed (2008) found out that over 90% of the respondents were ready to
adopt to mobile credit service but over 37% of the respondents were ready to use mobile
credit service on condition that the transaction costs were nil. Approximately 23% of the
surveyed population was willing to pay up to Euro 10 yearly charges for using such services,
a further 30% up to Euro 5. The researchers further argue that to further lower the transaction
costs mobile service provider should increase the volume of utilization to enjoy economies of
scale. In addition to cost-saving, mobile service providers need to pay particular attention to
their pricing strategy with the objective to uneven the potential factors that encourage or
discourage its adoption.
The third and final antecedent was the influence of accessibility factors on the adoption of
mobile credit in Kenya where the mobile offers “anytime and anywhere” convenience not
only for communication but increasingly for mobile credit services, even for parts of the
world where traditional scratch card does not exist. Those mobile service providers who offer
mobile credit system know for a fact that consumers demand choice as to where and when
they connect, interact and transact (Kalakota and Robinson, 2009). Mobile service providers
also know that they need to offer combination of different mobile credit technologies to reach
their entire consumer base (Tiwari and Buse, 2009). Thus, price of a technology is an
3
important factor that influences the adoption of the technology. This is because in times of
increased competition, a distribution channel must organize business processes efficiently so
as to reduce distribution costs.
Complexity has a negative connection with the use of mobile credit system. Coursaris and
Hassanein (2008) stated that if a consumer perceives mobile credit system to be relatively
easy to use and understand, he or she will be more willing to use mobile credit system. By
designing in simplicity, a good mobile credit system application understands and works with
the limitations of a given mobile phone. Generalized solutions, such as those based on
Wireless Application Protocol (WAP) browsers, can suffer from poor implementations and
interfaces, resulting in a slow and cumbersome experience (Kalakota and Robinson, 2009).
Network interruptions pose a serious challenge to mobile credit system success. Coyle (2010)
noted that mobile web connections are generally slower than broadband connections. The
threat of losing connectivity in the middle of a transaction makes mobile credit system
inconvenient. For example consumers worry is if the information simply lost or it is cached
locally and then uploaded when the network becomes available. Service efficiencies has two
facets in mobile business (m-commerce) namely navigation and transaction processing
efficiencies (Limayem, Khalifa and Frini, 2010). Navigational efficiency is particular
important for mobile credit as the restrictive visual interface is usually regarded as a major
hindrance for its adoption (Lee and Park, 2006). One way to address this challenge is to
leverage multi-media input/output components such as speech interfaces (Davies et al.,
2007). Another important way of enhancing efficiency is personalization. That is, in mobile
credit system the ability to use the service wherever wanted enables immediate actions to
borrow or buy credit, which in turn saves time and thus is perceived as efficiency (Holbrook,
2009).
1.2 Statement of the Problem
Nysreen, Pedersen and Thorbjornsen (2010) argued that prestige credit includes among other
factors such as status and high standing peers and self-concept. According to Nysreen,
Pedersen and Thorbjornsen (2010), affluent and highly educated groups generally accept
changes more readily. Thus, highly educated consumers may be more likely to adopt mobile
4
credit services than low educated consumers. In addition, using mobile crediting gives these
consumers prestige among their peers. It is also part of the social scene of today’s technology
driven society. Laurn and Lin (2007) found that some customers simply prefer to deal
directly with a vendor instead of utilizing arms-length technology for example mobile top-up.
The examples tend to show that education level of consumers have a bearing on their
adoption of new technology. However the extent to which education level affects the
adoption of mobile credit system in Kenya is however not quite clear.
Mohammed and Kathy (2008) noted that some consumers have more ability to use crediting
technology and computer software for transferring credit than other consumers. Consumers
with increased computation ability may adopt internet crediting more easily and their ability
may also improve their efficiency in the use of online transaction. Mohammed (2008) found
that consumers who are non-adopters of internet crediting could be differentiated by their
low or poor computation proficiency and computer skills. Limayen, Khalif and Frini (2010)
pointed out that a person’s internet self-efficiency, such as internet skills, will affect the
decision whether or not to adopt mobile credit system.
It can be very costly if the out of stock situation is frequently or constantly occurring for
service provider, especially when one considers the risk of the total loss of loyal subscribers
who previously had been created by intense media-advertisements with large budget over the
long-run. Simply, subscribers and service providers responses to the out of stock situation
and the cost that a Airtel Company face as a result of out of stock be defined separately in
order to minimize both consumer and company losses (Mahajan and Muller, 2009).
Transaction processing efficiency in Kenya, mobile credit system is “kopa credo” the
transaction delivery time is not guaranteed since it is dependent on factors like congestion
and network strength in the area where the customer is located. Also if consumers find
registration and authentication procedures burdensome, adoption of this new channel will be
slow. On the other hand, if data security is compromised, negative publicity will quickly
show fears about the safety of crediting “over the air.”
5
1.3 Purpose of the Study
The general purpose of the study was to establish the factors influencing the adoption of
mobile credit system in Kenya.
1.4 Research Questions
1.4.1 How do acceptability factors influence the adoption of mobile credit system in Kenya?
1.4.2 How do affordability factors influence the adoption of mobile credit system in Kenya?
1.4.3 How do accessibility factors influence the adoption of mobile credit system in Kenya?
1.5 Importance of the Study
1.5.1 Mobile Service Provider
Mobile service providers like Airtel will use the study findings to understand how many
subscribers are using mobile credit services. Notably mobile credit system has to be
coordinated between service providers and subscribers.
1.5.2 Public
Users of mobile handsets will gain a better understanding of mobile credit system by
comparing to other payment systems in terms of costs, convenience, and complexity. The
findings of the study will lead to an increase of subscribers using credit system.
1.5.3 Environmental Cleanliness
The mobile credit system research findings will help both service providers and subscribers
from littering thus, leaving the environment clean and green.
1.5.4 Researchers and Academicians
Researchers and scholars will benefits from the study because they will use it for future
reference and learning material when researching on related topics. For academicians, this
research finding will make a contribution towards understanding the underlying ethical issues
of mobile credit system.
6
1.6 Scope of the Study
The population for the study comprised of Airtel Kenya’s employees where some of them
use mobile credit system from other mobile providers. Thus, study focused on the adoption
factors of mobile credit system instead of traditional top-up. The staffs were mainly
supervisors and subordinates whom were technology savvy. The study was done in the
month January to March 2014.
1.7 Limitations of the Study
There were also limitations and weakness in using questionnaires to conduct research
activities. This included on the fact respondents may have interpreted the questions in their
own ways without the direction of the interviewer. Meaning that questions may have had
different meanings for different respondents.
1.8 Definition of Terms
1.8.1 Adoption
Adoption is a decision to continue full use of an innovation. It is the uptake of something
new in this case mobile credit system (Wu and Wang, 2005).
1.8.2 Convenience
This refers to the extent to which mobile credit system makes it easier for subscriber to
access airtime credit (Berry et al., 2006). It offers efficiency of the services wherever one
needs it and wherever one is.
1.8.3 Innovation
This is the embodiment combination or synthesis of knowledge in original, relevant valued
new product, processes or services often leading to new marketable products and/or new
production and delivery systems and service (Carr, Hard and Trahant, 2010).
1.8.4 Mobile Credit System
This is part of many invention of telecommunication aimed at giving mobile phone users
unhinged access to communication (Delgado and Nieto, 2009).
7
1.9 Chapter Summary
This chapter provides background information of the study discussing the current
technological issues in mobile credit system and its concepts. The statement of the problem is
about the end results of technology adoption on how to access airtime credit, purpose of the
study was to identify factors influencing the adoption of mobile credit system in Kenya and
the research questions are what determine the influence of cost, convenience and complexity
on the adoption of mobile credit system in Kenya given. The chapter concludes by outlining
the importance of the study, its scope, and the definitions of the working terms.
The next chapter presents a review of the literature relevant to the research questions of the
study. Chapter three will present the research methodology to be used and chapter four will
present data analysis, while chapter five will present discussions, conclusions and
recommendations.
8
CHAPTER TWO
2.0 LITERETURE REVIEW
2.1 Introduction
This chapter reviews the previous studies that have been carried out on the various factors
that affect mobile credit system. They are based on the research questions highlighted in
chapter one. These include: determining the factors affecting acceptability factors,
affordability factors, and reliability factors on mobile credit system. The chapter summary
provides an outlines of the areas covered in this chapter and a description of what chapter
three covers.
2.2 Influence of Acceptability Factors on Mobile Credit System Adoption
2.2.1 Marketing Exposure
One of the most important contributing factors for adoption or acceptance of mobile credit
system is the creation of awareness among consumers for the services or products (Vijah,
Eitan and Srivastava, 2009). In this context, Laurn and Lin (2007) assert that consumers go
through a process of knowledge, conviction, decision, and confirmation before they are ready
to adopt and use a product or service. Legris, Ingham and Colleratte (2008) emphasize the
importance of awareness for the adoption of any new innovation. Vijah, Eitan and Srivastava
(2009) indicate that there should be increasing promotional efforts on the part of mobile
service providers to create a greater awareness of mobile credit system and its benefit.
Tiwari and Buse (2009) show that most customers do not know how to become an mobile
crediting user, how to use the technology, and hence feel insecure about credit facility
primarily due to a lack of marketing effort on the part of mobile service providers. Wallage
(2005) also studied the adoption of internet crediting in Australia, and found that security
concerns and lack of awareness stand out as the main reasons for the failure to adopt internet
crediting by sample respondents. Tan and Teo (2007) note that lack of awareness reduces the
adoption rate of internet crediting services in the Middle East. Creating greater awareness by
showing customers the benefits of using new system may encourage customers to adopt
internet crediting transactions.
9
In a study in a rural setting Laukkanen and Lauronen (2008), the villagers were made more to
be aware about mobile credit system and its usage through group meetings and training
sessions in order to generate trust among them for the new technology of mobile credit.
Effectiveness of the agent and/or merchant network in making people realize the usefulness
of mobile credit system by creating a trustworthy ground level infrastructure for mobile
credit system would contribute towards generating trust among the people. The villagers also
mentioned that once people around them started using mobile credit system, they would gain
more trust on the service and would like to use the same. Thus, peer feedback and social
influence was found to have a positive impact on the trust of the people on mobile credit
system.
Lassar et al, (2010) argued that since mobile credit system is one of the most technological
applications in terms of innovation, it is important that a strong understanding on how these
innovations would benefit is inculcated among clients. Thus, according to Luarn and Lin
(2007), an educated community is better at adopting new mobile technologies. This is
because rapid changes in mobile service environment, increased competition by new players,
product innovations, globalization and technological advancement have led to a market
situation where battle of customers is intense. In order to rise to the challenges, service
providers are even more interested to enhance their understanding of consumer behavior
patterns.
2.2.2 Demographic Characteristics
Demographic factors are frequently used as a basis for understanding consumer
characteristics (Pikkarainen et al, 2009). The popularity of using demographic factors is
attributed to the observed relationship between the consumption of certain products and
certain demographic factors. The demographic characteristics include age, sex, income,
occupation, education etc. But Tiwari and Buse (2009) argued that a service providers’
decision to provide mobile credit system depends on the characteristics of the market the
service provider serves, such as the demographic characteristics of potential customers as
well as whether the provider is located in a metropolitan area. Demographic characteristics
also play a vital role in understanding the buying behavior of consumer in different segments
and when the characteristics are identified, they enable companies to develop products and
10
services according to customer’s specific requirements, tastes, and preferences (Laurn and
Lin, 2007).
In addition, for mobile credit system adoption service providers must consider a user’s
demographic characteristics to offer the correct range of service products. According to Siau
et al. (2009), demographic characteristics that describe typical electronic crediting customers
include young, affluent, and highly educated. Thus, a Finnish study (Coursaris and
Hassanein, 2008) revealed that crediting users are relative wealthy, highly educated and are
in higher professions. Lassar et al. (2010) find that affluent and highly educated groups
generally accept technology changes more readily.
Studies have shown that most of mobile credit system users have traditionally had university
level education and higher professions (Legris et al., 2008). In their studies, Legris, Ingham
and Colleratte (2008), found during interview that people who adopted technology enabled
services like mobile phone and ATMs are educated and are more open and eager to adopt
mobile credit system. On the contrary, the villagers who had never used an ATM for top-up
were found to be reluctant to make the top-up request through their mobile phones rather
keying in the top-up voucher. This clearly showed that lack of technology readiness among
the rural population would be a barrier towards ensuring adoption of mobile credit system.
Coursaris and Hassanein (2008) argued that prestige credit includes among other factors such
as status and high standing peers and self-concept. According to Coursaris and Hassanein
(2008), affluent and highly educated groups generally accept changes more readily. Thus,
highly educated consumers may be more likely to adopt mobile credit services than low
educated consumers. In addition, using mobile crediting gives these consumers prestige
among their peers. It is also part of the social scene of today’s technology driven society.
Coursaris and Hassanein (2008) found that some customers simply prefer to deal directly
with a vendor instead of utilizing arms-length technology e.g. mobile top-up. The examples
tend to show that education level of consumers have a bearing on their adoption of new
technology. However the extent to which education level affects the adoption of mobile
credit system in Kenya is however not quite clear.
11
Vijay et al. (2009) also argues that consumer profiles of mobile credit system users are not
substantially different between one country and another, as most clients are young people
with a college education, a steady job, and income. Price Waterhouse Cooper’s (2000) state
that the typical mobile crediting customer is aged between 25 and 35 years has medium to
high income and likes to make his or her own credit technology adoption decisions. Wu and
Wang (2005) discovered that trust and education influence customers’ attitudes towards
using mobile credit system. In addition, Pikkarainen et al. (2009) study profiled the mobile
credit consumer and found that innovators normally belong to the high income group.
According to Babbie (2008), research relating to the customer adoption of innovation has
tended to concentrate on socio-demographic and psychographic attributes of potential
adopters. Even though these kinds of personal characteristics of a consumer have been found
to be predictors of adoption, an increasing body of research has demonstrated that it is the
perceived attributes of innovation itself rather than the personal characteristics that are the
stronger predictors of the adoption decision.
2.2.3 Security Concern
When adopting new products, customers face a dilemma between desirable and undesirable
consequences of the adoption and hence face a risky decision on security. This security risk
has two main elements: perceived risk grounded in concerns with regard for the technical
performance of the service delivery system; and perceived security associated with concerns
about personal privacy (Mohamed and Kathy, 2008). Privacy in mobile terminology is the
level of control that clients have over the timing, and circumstances of sharing oneself
physically, behaviorally, or intellectually with others. As an element of perceived security
risk, it is the extent to which technology-enabled services are perceived to be secure,
sufficiently safe and reliable to use (Ghosh and Swaminatha, 2010).
Because mobile service providers have access to more sensitive information related to
personal interactions (for instance phone calls and SMS) and physical location (for example
global positioning system-GPRS), the issue of privacy is more relevant in adoption of mobile
credit system. According to Ghosh and Swaminatha (2010), people are concerned about
unwanted disclosure of their private information, or simply misuse of their information by
12
the mobile company collecting it. This dimension of risk include undisclosed capture of
information such as consumers’ borrowing habits.
Adoption is a function of consumer innovativeness and this implies that perception of risk
may not have much to do with actual adoption. Nevertheless, it may lead to consumers
seeking more information to ascertain the level of risk, mitigate the perception of risk, or
manage the perceived risk. Previous studies have suggested that perceived risk may
negatively influence the decision to adopt new product, which may not be too obvious (Liu
and Arnett, 2011).
Liu and Arnett (2011) in their study on the adoption of electronic credit technologies by US
consumers postulate that with the development of market for mobile credit system services in
the United States, the protection of consumers from unauthorized access and potential
identity theft has been a concern for the mobile industry. According to Coursaris and
Hassanein (2008), consumer education also extends to ensuring that consumers understand
and are comfortable with the protections afforded under current regulation, network rules,
and industry practices related to individual mobile credit system. By using a statistical model
combining elements of the theories of diffusion and innovation and planned behavior to
predict mobile credit system take off in South Africa, Lassar et al. (2010) concluded that a
high level of perceived risk was the major barrier to adoption.
Security and trustworthiness of usage of service is one of the most important factors within
every target segments when deciding on a virtual service delivery channel. The effect of trust
has been identified along with other dimensions on the adoption of mobile credit system
(Holbrook, 2009). According to Ivatury (2008) found that when it came to monetary
transactions, villagers preferred channels that they trusted. In case of the rural un-educated
population, the complexity of trust was found to be twofold: first the trust of people on
technology and second, the trust on the credit services that is being offered. Thus, generating
trust on mobile credit system remains one of the major challenges for ensuring adoption of
offered services among the rural un-educated.
Basically, fear of a lack of security is recognized as an important factor impacting the
acceptance of mobile services. Luarn and Lin (2007), they found that perceived credibility
13
has a significant impact on the development of willingness to use mobile services.
Furthermore, Wu and Wang (2005) found that perceived credibility to be significantly related
to the technology acceptance of mobile credit system. The perceived credibility that people
have in a system which conclude that mobile transactions securely and maintain not deceive
them will affect their voluntary acceptance of mobile services. Since mobile credit system is
somewhat new, perceived credibility has the higher ability to predict and explain the
intention of users to adopt it.
2.3 Influence of Affordability Factors on Mobile Credit System Adoption
2.3.1 Online Transaction
All business transactions require some elements of trust, especially those conducted in
uncertain environments. In order to complete the mobile credit system purchase transaction,
customers have to trust the online business or overwhelming social complexity will cause
them to avoid purchasing the service (Ghosh and Swaminatha, 2010). In electronic
commerce, trust can be viewed as a perceptual belief or the level of confidence one expects
from the other party during an online transaction (Jayawardhena and Foley, 2010). While
consumers initially trust their e-vendors and have an idea that adopting online service is
beneficial to job performance or life style, they will eventually believe that online service are
useful (Liu and Arnett, 2010).
In particular, Prabhudey and Sridhar (2008) found that trust in an online shopping setting
explicitly indicated that trust is an antecedent of perceived usefulness. Trust also has a direct
influence on a customer’s behavioral intention to use the mobile credit system service (Wu
and Wang, 2005). Trust is one of the determinants of perceived usefulness especially in an
online environment. Simpson and Docherty (2010) also found that trust has a positive effect
on perceived usefulness in an electronic commerce setting.
Besides the perceived usefulness and perceived ease of use, the online transaction usage can
also be affected by the perceived credibility of mobile credit system. According to Wu and
Wang (2005), perceived credibility is usually impersonal and relies on reputation,
information, and economic reasoning. In the study of Luarn and Lin (2007) they found that
perceived credibility has significant impact on the development of willingness to use mobile
14
credit system. Thus, since mobile credit system is somewhat new, perceived credibility has
the higher ability to predict and explain the intention of users to adopt it. Therefore,
Jayawardhena and Foley (2010) showed that trust works as a mechanism for reducing
consumer’s perceived risk in online transactions.
A recent study of virtual electronic transactions showed that trust reduces perceived risk and
invigorates the usage of online electronic services (Siau et al., 2009). In contrast, Prabhudey
and Sridhar (2008) found that higher perceived risk decreases the level of trust toward the on
line transaction. In addition, Simpson and Docherty (2010) insisted that it was unclear
whether trust comes before perceived risk or otherwise. Whichever the case, it is clear that
online transaction is a concern to mobile credit system adoption by potential consumers. In
fact, people in high risk environments need to analyze situations. Since transactions in the
electronic virtual environment are processed virtually and people are not able to see the
process, users’ perception of the mobile credit system may think twice on the adoption.
Results from Siau et al. (2009) has shown that online transaction is the way forward in
reducing costs and remaining competitive in comparison with conventional crediting
practices. Mobile service providers in US, Europe and Asia have moved to online transaction
in their effort to cut costs while maintaining affordable customer service (Lee and Park,
2006). The desire to reduce both operational and administrative costs has driven mobile
providers to the internet crediting world. However, cost reduction is only realizable with an
increase in consumer adoption (Mohammed, 2008).
Mahajan and Muller (2009) noted that some consumers have more ability to use crediting
technology and computer software for transferring credit than other consumers. Consumers
with increased computation ability may adopt internet crediting more easily and their ability
may also improve their efficiency in the use of online transaction. Tan and Teo (2007) find
that consumers who are non-adopters of internet crediting could be differentiated by their
low or poor computation proficiency and computer skills. Shon and Swatman (2007) pointed
out that a person’s internet self-efficiency, such as internet skills, will affect the decision
whether or not to adopt mobile credit system.
15
2.3.2 Credit Availability
Availability may be defined as the extent to which customers are able to readily acquire and
use mobile credit system (Babbie, 2008). They argued that mobile service providers need to
explore alternative methods of delivering the mobile credit system to even the most isolated
communities like Northern Eastern Kenya. Babbie (2008) revealed that on-self availability of
the scratch cards remains a key challenge for all retailers, items that are out of stock result in
customer dissatisfaction. Thus, mobile credit system availability is important customer
service issues. European consumer’s rate mobile credit system as the third most important
after desire for more promotion i.e. lower prices and shorter queues. Consumer reaction to
out of stock range from product substitution to “voting with their feet,” and seeking mobile
credit s elsewhere. Most of the time mobile credit system availability may be affected by the
order cycle, lead time, and replenishment behaviors of seller.
According to Mahajan et al. (2009), out of stock can lead the consumer not to adopt the
credit system, delay the purchase, and substitute the credit system with the normal voucher
scratch cards. It is therefore quite important to service provider (Airtel) to ensure that she
does not give the consumers an opportunity to try what their competitors are offering by
ensuring that their retailers do not ran out of mobile credit system products. A strong but
hidden assumption behind marketing decisions is the availability of the mobile credit system
product being offered at a time and place relevant to the consumers.
Every program to introduce new product, build brand loyalty, and maintain market share
implies a high level of distribution system performance, an assumption not always warranted
(Niina, 2007). Niina (2007) continues to urge that because of possible out of stock,
manufacturers and retailers might also face retention costs of switching consumers in
addition to the high-inventory cost of precautionary stocking decision regarding the
frequently out of stock mobile credit system products. In other words out of stock costs are
generally hidden costs in many situations and are often difficult to detect by service provider
as argued by Niina (2007).
It can be very costly if the out of stock situation is frequently or constantly occurring for
service provider, especially when one considers the risk of the total loss of loyal subscribers
16
who previously had been created by intense media-advertisements with large budget over the
long-run. Simply, subscribers and service providers responses to the out of stock situation
and the cost that a Airtel Company face as a result of out of stock be defined separately in
order to minimize both consumer and company losses (Niina, 2007).
2.3.3 Transaction Cost
Mobile service providers which have had difficulty providing profitable services through
traditional channel to their clients, see mobile credit system as a “cardless top-up”, which
lowers the cost involved in serving customers (Ivatury, 2008). Technological development
has provided opportunities for mobile service providers to develop their services and offer
customers more flexibility. As a results Airtel have launched multiple service access methods
via delivery channels like ATM top-up, internet top-up and mobile phone top-up (Laukkanen
and Lauronen, 2008). Virtual top-up has brought into its fold a considerable group consumers
who formerly could be served only at too high cost (Lassar and Manolis, 2010). Holbrook
(2009) argued that one issue that drive top-up services is the cost efficiency pressures from
supply side. Because payment transaction cost vary, quite often cardless capability was built
into financial institution’s software platform, leaving maintenance and upgrades as the only
added costs.
According to Niina (2007), the cost of a payment transaction has direct effect on consumer
adoption if the cost is passed on to customers. Transaction costs should be low to make the
total cost of the transaction competitive. The transaction costs of borrowing airtime through
mobile credit system is only charged at 10% of the total requested amount (i.e. Ksh. 1 is
charged if one borrow Ksh. 10). In their studies in Kenya, mobile service providers (Airtel,
Safaricom, and Orange) found that the cost of availing the mobile credit system was a
common matter of concern among the villagers who were interviewed. People wanted to
know whether they would need to purchase a new sim-card for using mobile credit system
and were also eager to know the cost of transaction for availing this service. They became
aware and appreciated the fact that using mobile credit system would save them a lot of time,
effort and money that they currently spent for accessing airtime services through the existing
channels of delivery i.e. kiosks, supermarkets, and ATMs. Hence, cost of the mobile credit
system is an important factor that would determine the adoption of the services among the
17
rural population. Given the fact that majority of the rural population falls within the lower
income group, the total cost of availing the services need to be minimized for ensuring faster
adoption.
Vijay et al. (2009) stated that it was not viable for consumers to change their way of
performing their top-up tasks without offering a strong performance-to-price advantage. The
price of airtime services may have an opposite effect with respect to the adoption of virtual
top-up, which may result in consumers preferring the traditional top-up services (Laukkanen
and Lauronen, 2008). Provision of a lower service cost is a major benefit for users using
virtual mobile credit system and performing top-up transaction functions through a mobile
device. Thus, users would agree to pay a reasonable fee to use a service depending on the
credit cost and service provider. Therefore, value for money barrier may be another factor
influencing the adoption of mobile credit system (Luarn and Lin, 2007).
Arguably, a technology must be plausibly relative to alternatives for consumers to use the
novel technology. As Laukkanen and Lauronen (2008) puts it value barrier is responsible for
the failure of many new developments because of people’s perception that the cost of
adopting an innovation is far greater than ensuing benefits. Thus, if mobile credit system is
not being adopted it could be because it is not been reasonably priced compared to either
traditional voucher top-up, ATM top-up, and internet top-up (Holbrook, 2009). The
technology used for mobile transmission may increase or lower the cost of mobile credit
system as each technology has its own features which differ in costs. This cost impact in turn
may encourage or discourage adoption of mobile credit system.
In Germany, Tiwari and Buse (2009) found out that over 90% of the respondents were ready
to adopt to mobile credit service but over 37% of the respondents were ready to use mobile
credit service on condition that the transaction costs were nil. Approximately 23% of the
surveyed population was willing to pay up to Euro 10 yearly charges for using such services,
a further 30% up to Euro 5. The researchers further argue that to further lower the transaction
costs mobile service provider should increase the volume of utilization to enjoy economies of
scale. In addition to cost-saving, mobile service providers need to pay particular attention to
their pricing strategy with the objective to uneven the potential factors that encourage or
discourage its adoption.
18
2.4 Influence of Accessibility Factors on Mobile Credit System Adoption
2.4.1 Service Convenience
Berry et al. (2006) defined convenience as consumer’s effort and waiting time perception
related to buying or using products or service and comprises of decision, access, transaction,
benefit and post-benefit convenience. As cited by Berry et al. (2006) service convenience
consists of five constructs of (1) decision convenience (i.e. customers who desire a particular
performance devote time and effort to decide how to obtain the particular performance, (2)
access convenience (i.e. customer’s perceived time and effort expenditure to initiate service
delivery, (3) transaction convenience (i.e. customer’s perceived expenditure of time and
effort to effect a transaction, (4) benefit convenience i.e. customer’s perceived time and effort
expenditure to experience the service’s core benefit, and (5) post-benefit convenience i.e.
customer’s perceived time and effort expenditure when reinitiating contacts affirm after the
benefits stage of the services.
The mobile offers “anytime and anywhere” convenience not only for communication but
increasingly for mobile credit services, even for parts of the world where traditional scratch
card does not exist. Those mobile service providers who offer mobile credit system know for
a fact that consumers demand choice as to where and when they connect, interact and
transact (Berry, Seiders, and Grewal, 2006). Mobile service providers also know that they
need to offer combination of different mobile credit technologies to reach their entire
consumer base (Lee and Park, 2006). Thus, price of a technology is an important factor that
influences the adoption of the technology. This is because in times of increased competition,
a distribution channel must organize business processes efficiently so as to reduce
distribution costs.
Arguably, a technology must be plausibly priced relative to alternatives for consumers to use
the novel technology. As Laukkanen and Lauronen (2008) puts it value barrier is responsible
for the failure of many new developments because of people’s perception that the cost of
adopting an innovation is far greater than any ensuing benefits. Thus, if mobile credit system
is not being adopted it could be because it is not been reasonably priced compared to either
traditional top-up using scratch card. The technology used for mobile transmission may
19
increase or lower the cost of mobile credit system as each technology has its own features
which differ in costs. This cost impact in turn may encourage or discourage adoption of
mobile credit system.
Also according to Laukkanen and Lauronen (2008), when accessing mobile credit system, a
client has to enter passwords to access codes in order to log into the service. Thereafter, the
customer needs to punch in the subscriber’s number, the sum, and the pin number. This
complicated the process and such a complexity may lead to inconvenience and lead to
increase the feeling of uncertainty in mobile credit system. Thus, empirical studies on the
access of mobile technologies have found consistently positive relationships between
usefulness and to a lesser extent ease of use, and the adoption of a variety of specific
technologies, ranging from computer software to online transaction, may lead to adoption of
mobile credit system.
In the context of mobile credit system, transaction fees, access cost and equipment costs are
the three important cost components that make its use more expensive (Wu and Wang, 2005).
In mobile credit system there are three costs: normal costs associated with mobile phone
providers’ activities, the credit cost and charge, and the cellular phone cost. The cost of
mobile devices though a one off cost, makes mobile credit system as costly as other
crediting. If the cost of mobile devices is very high, this discourages credit users from
acquiring them hence impending the adoption of mobile crediting system (Mahajan and
Muller, 2009).
With the General Packet Radio Service (GPRS), the cost advantage is that the subscriber
pays for the volume of the transmitted data and not the time required in the process making it
the first technology that cannot only enable but also promote mobile credit system (Toh,
2007). In Philippines, for instance, domestic and international remittances offer a large
market given the large volume transacted and relative low cost of using SMS based mobile
credit system applications as compared to the high cost of current crediting and remittance
company alternatives (Tiwari and Buse, 2009).
20
2.4.2 Technical Issues
Issues like mobile credit security, mobile phone devices operations, and the pricing structure
exponentially increasing complexities in the mobile credit system. Although complexity and
compatibility are closely related, the distinction can be made that complexity has more to do
with real skills and abilities, whereas compatibility reflects attitudes towards innovations and
technology in general (Vijay et al., 2009). On the technical issues, there are key technical
requirements that must be present in order for two mobile credit system to be able to interconnect. This means the ability of a subscriber in one system to be able to send credit to
another subscriber on the other system (Holbrook, 2009).
Both systems must be able to adhere to the same message routing strategies. The credit
payment instruction issued on the one system should travel to the correct destination system
and then that the actual target account be identified (Holbrook, 2009). Also, there should be a
clear mechanism to clear the transaction and ultimately settle the transfers which must be
implemented in the same way by both systems. This is not a trivial issue as clearing and
settlement creates all kinds of liabilities that must be analyzed properly and catered for in the
selected system. Reconciliation and detection of discrepancies are also important (Holbrook,
2009).
On the other hand, one of the most difficult interconnected problems is the schema for the
management of uncompleted transactions. This type of situation would occur when the
originating system does not get any confirmation or declines message from the destination
system. The resolutions of such conflicts are extremely complex and thus, the design of rollbacks, pending transactions are very important. In addition, reporting systems must be agreed
on and load management must be well defined in order for the solution to work. Therefore, it
is important for industry specialists and technical suppliers that all the problems associated
with mobile credit system are well understood before implementation (Holbrook, 2007).
If a mobile subscriber is not adopting mobile credit system, it could be because their mobile
phone is not easy to operate or the commands are too complex due to their limitations which
diminish the usability and user-friendliness of mobile technologies (Siau et al., 2009). Other
typical limitations include small displays and keypads, limited transmission speed and
21
memory, and short battery life. Illiterate persons would be unable to utilize any text message
services. A solution in India that is being develop would utilized numeric messages rather
than alphabetic text, with different codes for different transactions (Ivatury, 2008). In the
instances where a large amount of data is entered data input method needs to be simplified
(Jin and Lee, 2011).
Complexity has a negative connection with the use of mobile credit system. Kalakota and
Robinson (2009) stated that if a consumer perceives mobile credit system to be relatively
easy to use and understand, he or she will be more willing to use mobile credit system. By
designing in simplicity, a good mobile credit system application understands and works with
the limitations of a given mobile phone. Generalized solutions, such as those based on
Wireless Application Protocol (WAP) browsers, can suffer from poor implementations and
interfaces, resulting in a slow and cumbersome experience (Kalakota and Robinson, 2009).
Past research indicates that the complexity of an innovation is more negatively to their rate of
adoption than any other characteristics of the innovation. According to Simpson and
Docherty (2010), the technology channel that mobile providers uses to roll out its mobile
credit system can be sophisticated to the level of discouraging potential adopters. Thus,
mobile providers employs any of the four technologies namely: the Interactive Voice
Response (IVR), Short Message Service (SMS), Wireless Access Protocol (WAP), and Stand
Alone Applications (SAA). Therefore, technical complexity is a major factor in any decisionmaking about the launch of new and innovative services like mobile credit system (Tiwari
and Buse, 2007).
2.4.3 Network Efficiency
In electronic service, consumption efficiency and convenience are in some way overlapping
concepts. The relation between them is obscure (Laukkanen and Lauronen, 2008). Berry,
Seiders and Grewal (2006) have defined efficiency as an aspect of convenience. On the
contrary, Holbrook (2009) has placed convenience under the concepts of efficiency.
However, these authors agree that efficiency refer to the perceived benefits customers receive
in relation to the good reception of network. This efficiency perception implies that consumer
perceives cognitively the benefits of network efficiency of mobile service provider. For
22
instance, a consumer who perceives that good network will enabled him or her to adopt to
mobile credit system compared to the bad network forms the perception of efficiency in him
or her (Laukkanen and Lauronen, 2008).
Network interruptions pose a serious challenge to mobile credit system success. Davies,
Moutinho and Curry (2007) noted that mobile web connections are generally slower than
broadband connections. The threat of losing connectivity in the middle of a transaction
makes mobile credit system inconvenient. For example consumers worry is if the information
simply lost or it is cached locally and then uploaded when the network becomes available.
Service efficiencies has two facets in mobile business (m-commerce) namely navigation and
transaction processing efficiencies (Limayem, Khalifa and Frini, 2010).
Navigational efficiency is particular important for mobile credit as the restrictive visual
interface is usually regarded as a major hindrance for its adoption (Lee and Park, 2006). One
way to address this challenge is to leverage multi-media input/output components such as
speech interfaces (Jayawardhena and Foley, 2010). Another important way of enhancing
efficiency is personalization. That is, in mobile credit system the ability to use the service
wherever wanted enables immediate actions to borrow or buy credit, which in turn saves time
and thus is perceived as efficiency (Lee and Park, 2006).
Transaction processing efficiency in Kenya, mobile credit system is “kopa credo” the
transaction delivery time is not guaranteed since it is dependent on factors like congestion
and network strength in the area where the customer is located. Also if consumers find
registration and authentication procedures burdensome, adoption of this new channel will be
slow. On the other hand, if data security is compromised, negative publicity will quickly
show fears about the safety of crediting “over the air.”
2.5 Chapter Summary
The chapter outlined the various factors by mobile service provider on the perception of
adoption of mobile credit system. The chapter describe the acceptability factors, affordability
factors, and accessibility factors affecting the adoption of mobile credit system. Thus, this
chapter focused on the literature review of recent studies done in the area of adoption of
mobile credit system. Therefore, the chapter established and demonstrated that the factors
23
(acceptability, affordability and accessibility) affecting adoption of mobile credit system may
help to solve research problem. The next chapter deals with the research methodology
whereby it will cover the methods and techniques that the researcher used to gather and
analyze the collected data.
CHAPTER THREE
3.0 RESEARCH METHODOLOGY
3.1 Introduction
This chapter covers the methodology and procedures that were used for collecting and
analyzing the data in the study. This chapter deals with the type of research design, the
population and sampling design, data collection method, and data analysis methods.
3.2 Research Design
Cooper and Schindler (2008), regards a research design as being a framework for generation
of evidence which is suited both to a certain set of criteria as well as to the research question
in which the research is interested. The research design used for this study was descriptive. It
facilitates the understanding of the characteristics associated with the subject population as
described by Cooper and Schindler (2008). It involves the observation and description of
variables as distributed in the population with the basic goal being the collection of
information about phenomena or variables within a population through the use of
questionnaires.
Descriptive research attempts to define the state of affairs as they exist at present. Thus,
descriptive research design was superior for this study, as it enabled the researcher to collect
comprehensive data on the population that was studied for the purpose of providing adequate
and relevant recommendations (Cooper and Schindler, 2008).
24
Descriptive research design requires some understanding of the nature of the problem which
in this case was to study the aim of increasing the understanding on the factors that influence
the adoption of mobile credit system. The objective of this type of design was to discover
whether a relationship exist between the variables and to describe the state of the variables.
The dependent variable of the study was adoption of mobile credit system and the
independent variables of the study were the influence of security factors, cost factors, and
convenience factors Cooper and Schindler, 2008).
3.3 Population and Sampling Design
3.3.1 Population
Cooper and Schindler (2008) define population as the total collection of elements about
which a researcher wishes to make some inferences. Thus, data collected and analyzed in
research is used to describe and make inferences about the population. Since the purpose of
this study was to establish the factors influencing the adoption of mobile credit system in
Kenya, the population in the study consisted of 350 Airtel Kenya employees (Mombasa Road
Headquarter) who were mainly supervisors and subordinates.
3.3.2 Sampling Design
3.3.2.1 Sampling Frame
Sampling frame is a list that constitutes the population. The basic idea of sampling is that by
selecting some of the elements in population, one can draw conclusion about the entire
population (Welman and Krugler, 2008). The research was conducted among the employees
at the Headquarter Office along Mombasa Road. The sampling frame of this study was
selected from a list of employees as provided by the Human Resource Department.
3.3.2.2 Sampling Technique
The study adopted a random sampling method. Random sampling gives a researcher a fair or
representative view of the entire population. In addition, this technique enables the researcher
to have an in-depth study and sight on the topic being studied (Merriam, 2009). Furthermore,
this sampling technique ensured selection of respondents with the requisite information to
25
address the specific research questions thereby enhancing the credibility and reliability of the
findings of the study.
3.3.2.3 Sample Size
A sample is a finite part of statistical population whose properties are studied to gain
information about the whole (Merriam, 2009). A sample size (n) of 350 employees was
selected. With random sampling, a research ensures representativeness of the sample size
because sufficient probability has been built into the sampling strategy (Welman and
Krugler, 2008). Sample size was directly proportional to the desired confidence level and
inversely proportional to the error that the researcher was prepared to accept.
At a confidence level of 95% and at a 5% degree of absolute error-accuracy of the estimate,
Corbetta (2006) recommends that for whatever size of the population N, if N ≥ 350 then with
350 cases ( n ꞊ 350), n is sufficient to provide estimates which are accurate to within ±5%
points. The researcher accepted a 5% degree of absolute error-accuracy.
n
=N
1 + N (e)
n =
350
= 77.78
1 + 350 (0.10)
Rounding of the answer to the nearest whole number came 78. Using this computation, data
was collected from a sample size of 78 respondents out of a population size of 350
respondents.
Table 3.1: Sample Size Distribution
Gender
Sample Size
Percentage of Sample Size
Male
36
46.15
Female
42
53.85
26
Total
78
100.00
3.4 Data Collection Methods
The data collection method using questionnaires was employed in this study. Questionnaires
are the most effective data collection tool for survey type of studies. The questionnaire was
adapted from earlier studies by Luarn and Lin, 2007; Nysveen et al., 2010; Pikkarainen et al.,
2009; and Wu and Wang, 2005. The questionnaire had closed ended questions. A five point
Likert-type Scale ranging from 1 (strongly agree) to 5 (strongly disagree) was used for all the
constructs (Babbie, 2008).
The primary data was collected through written questions that were presented to the
respondents in written form. It was administered by: sending questionnaires by mail with
clear instructions on how to answer the questions and asking for mailed responses, or hand
delivering questionnaires to respondents and collecting them later. The respondents
comprised of supervisors and subordinates. The questionnaire consisted of two sections:
section one was focusing on the respondent’s demographics; their gender, age, level of
education and income levels. Section two of the questionnaire was designed to rate some
statements pertaining to adoption of mobile credit system. The section was developed using
the key variables identified as factors influencing adoption of mobile credit system and the
questions relied on a Likert scale. The use of Likert scale presents a simple way of gauging
specific opinions and also enables the measurement of broader attitudes and values (Babbie,
2008).
The secondary data that related to the question on the correlation between adoption and
mobile credit system was obtained from Airtel Kenya by the Human Resource office
electronic mail.
Table 3.2: Secondary Data
Research Questions
Research Tool
RQ 1: The Effect of Acceptability Factors on 1A – Market Exposure, 1B – Demographic
Mobile Credit System.
Characteristics, 1C – Security Concerns
27
RQ 2: The Effect of Affordability Factors on
Mobile Credit System.
RQ 3: The Effect of Accessibility Factors on
Mobile Credit System.
2A – Online Transaction, 2B – Credit
Availability, 2C – Transaction Cost
3A – Service Convenience, 3B – Technical
Issues, 3C – Network Efficiency
3.5 Research Procedures
Pilot test is conducted to detect weakness in design and instrumentation and to provide proxy
data or selection of a probability sample (Cooper and Schindler, 2008). Pilot questionnaire
was prepared and administered to 10 staff of the Research and Development Department.
This acted as a pre-test questionnaire and any suggestions for improvement encountered
during the piloting process were incorporated in the final questionnaire. Final questionnaires
were distributed to the respondents via staff e-mail. This enhanced the speed of data
collection.
To improve the response rate, a cover letter explaining the reasons for the research, why the
research is important, why the recipient was selected, and a guarantee of the respondents’
confidentiality was provided. The questionnaire had clear instructions and an attractive
layout. Each completed questionnaire was treated as a unique case and a sequential number
given to each. As the researcher sent out the questionnaires in soft copies using his e-mail
address, it was very easy for the respondents to ask for any clarifications and prompt
responses were availed.
3.6 Data Analysis Methods
An in-depth quantitative analysis of the content of the responses were carried out and the
structured data was analyzed using Statistical Package for Social Science (SPSS) computer
software package. Thereafter, the data was cleaned to ensure completeness of the information
obtained. The collected data was statistically analyzed using Microsoft Excel program and
the Statistical Program for Social Science (SPSS) and presented in the tables and figures to
give clear picture of the findings at glance.
28
3.7 Chapter Summary
This chapter detailed the proposed research method, giving a description of the research
design, population and sampling design, data collection method, and research procedures.
The population and sample size were also discussed: justification was provided for the
sample size that was used in the end. This was pertinent as it is imperative that research
reflects the views of the Airtel Kenya employees. The chapter also indicated how the data
was analyzed using Excel and SPSS and presented on the form of tables and figures. The
next chapter presents the study results and findings based on the research questions.
CHAPTER FOUR
4.0 RESULTS AND FINDINGS
4.1 Introduction
This chapter addresses the results and findings on the factors influencing the adoption of
mobile credit system in Kenya. The findings are outline according to specific objectives of
the study. The findings are based on the responses from the questionnaires filled and
information gathered on the research questions. The specific objectives were to establish
firstly the influence of acceptability factors on the adoption of mobile credit system in
Kenya; secondly, the influence of affordability factors on the adoption of mobile credit
system in Kenya; and thirdly, the influence of accessibility factors on the adoption of mobile
credit system in Kenya. Out of the targeted 300 respondents, 78 responded to the
questionnaires on soft copy. This represented a 99.99% response rate.
4.2 General Information
The general information is organized in the following areas: gender, age, level of education
and average annual income.
4.2.1 Respondents Gender
29
Out of the 78 questionnaires filled, it was found out that the respondents’ gender sample
population comprised of 46% male and 54% female. The findings indicate that the female
were more compared to female respondents as indicated in Table 4.1 below.
Table 4.1: Respondents Gender
Gender
Male
Female
Total
Distribution
Frequency
36
42
78
Percentage
46.15
53.85
100.00
4.2.2 Respondents Age
The respondents were asked to indicate their age bracket. The findings indicated that, 40% of
the respondents were aged between 21 – 30 years, 35% were aged between 31 – 40 years,
18% were aged between 41 – 50 years, and 8% were aged over 50 years. The findings
indicate that most of the respondents were aged between 21 – 31 years.
Table 4.2 Respondents Age
Age
21 – 30 Years
31 – 40 Years
41 – 50 Years
51 and Over Years
Total
Distribution
Frequency
31
27
14
6
78
Percentage
39.74
34.62
17.95
07.69
100.00
4.2.3 Respondents Cross Tabulation of Gender against Age
The researcher sought to find if gender and age would affect the influence of respondents on
adoption of mobile credit system. Male aged between 21 – 30 years were 14 and female were
17, between 31 – 40 years 12 were male and 15 were female, between 41 – 50 years 6 were
male and 8 were female, and over 51 years 4 were male and 2 were female. The findings
30
indicate that female respondents aged between 21 – 30 years were more who would adopt
mobile credit system compared to male respondents. But male respondents aged over 51
years were more who would adopt mobile credit system. The results are summarized as
shown in Table 4.3.
Table 4.3: Cross Tabulation of Gender against Age
Gender
Male
Female
Total
21 – 30
14
17
31
Age of Respondents
31 – 40
41 – 50
12
6
15
8
27
14
Total
51 and Over
4
2
6
36
42
78
4.2.4 Level of Education
The respondents were asked to indicate their level of education. The findings indicated that,
2.56% of the respondents reached secondary, 14.10% of the respondents reached college, and
83.33% of the respondents reached university. The results indicate that most of the
respondents reached university. Refer to Table 4.4 which summarizes the findings of the
level of education of the respondents.
Table 4.4: Level of Education
Level of Education
Secondary
College
University
Total
Distribution
Frequency
2
11
65
78
Percentage
02.56
14.10
83.33
99.99
4.2.5 Average Annual Income
The researcher sought to find out the respondents’ average annual income. It was found out
that 2.56% of the respondents earned under Kshs. 25,000, 8.97% earned between Kshs.
25,000 to 45,000, and 16.67% earned between Kshs. 45,000 to 65,000, and 71.80% earned
31
above Kshs. 65,000. The findings indicate that most of the respondents earned over Kshs.
65,000. Table 4.5 gives a summary of the results.
Table 4.5: Average Annual Income
Income Level
Distribution
Frequency
2
7
13
56
78
Under 25,000
25,000 to 45,000
45,001 to 65,000
Above 65,000
Total
Percentage
02.56
08.97
16.67
71.80
100.00
4.2.6 Respondents Cross Tabulation of Gender against Level of Education
The researcher sought to find if gender and level of education would affect the influence of
respondents on adoption of mobile credit system. 1 male respondent reached secondary level
3 reached college level, and 32 reached university level. 1 female reached secondary level, 8
reached college level, and 33 reached university level. The findings indicate that female
respondents who went to college and university were more compared to male respondents.
Table 4.6 gives a summary of the results.
Table 4.6: Cross Tabulation of Gender against Level of Education
Gender
Male
Female
Total
Secondary
1
1
2
Level of Education
College
3
8
11
Total
University
32
33
65
36
42
78
4.3Influence of Acceptability Factors on Adoption of Mobile Credit System
Respondents were asked to rate how they perceived the adoption of mobile credit system on
marketing exposure, demographic characteristics, and security concerns. They were asked to
32
tick the appropriate response from a five Likert Scale: Strongly Disagree (1); Disagree (2);
Neutral (3); Agree (4); and Strongly Agree (5).
4.3.1 Marketing Exposure
4.3.1.1 Create Awareness of Mobile Credit System
The researcher sought to find out if promotional effort can create awareness of mobile credit
system. The findings indicate that of the 78 respondents 1.28% strongly disagreed with the
statement, 3.85% disagreed, 7.69% did not give their view on the statement by ticking
neutral, 44.87% agreed, and 42.31% strongly agreed with the statement. Table 4.7 gives a
summary of the results.
Table 4.7: Create Awareness
Create Awareness
Strongly Disagree
Disagree
Neutral
Agree
Strongly Agree
Total
Distribution
Frequency
1
3
6
35
33
78
Percentage
01.28
03.85
07.69
44.87
42.31
100.00
4.3.1.2 Create Awareness through Training Session
The researcher sought to find out if in rural setting awareness can be created through training
session and group meeting. The findings indicate that of the 78 respondents, 5.13% strongly
disagreed with the statement, 8.97% disagreed, and 2.56% did not give their views by ticking
neutral. But 43.59% agreed and 39.74% strongly agreed with the statement. Table 4.8 gives a
summary of the results.
Table 4.8: Create Awareness through Training Session
33
Training Session
Strongly Disagree
Disagree
Neutral
Agree
Strongly Agree
Total
Distribution
Frequency
4
7
2
34
31
78
Percentage
5.13
8.97
2.56
43.59
39.74
100.00
4.3.2.3 Understand Subscriber Behavior Patterns
The researcher aimed at establishing if training session help to understand subscriber
behavior patterns. The findings indicates that of the 78 respondents, 5.13% strongly
disagreed, 1.28% disagreed, and 12.82% ticked neutral. But 37.18% agreed and 43.59%
strongly agreed with the statement. The results are illustrated in Table 4.9.
Table 4.9: Understand Subscriber Behavior Patterns
Behavior Patterns
Strongly Disagree
Disagree
Neutral
Agree
Strongly Agree
Total
Distribution
Frequency
4
1
10
29
34
78
Percentage
5.13
1.28
12.82
37.18
43.59
100.00
Figure 4.1 summarized the percentages of factors which influence the market exposure on
acceptability factors. Create awareness (44.9%) played a major role in market exposure
compared to the behavior patterns (43.6%) and training sessions (43.6%) on the adoption of
mobile credit system.
34
Market Exposure
Behavior
Patterns
[VALUE]%
Create
Awareness
[VALUE]%
Training Sessions
[VALUE]%
Create Awareness
Training Session
Behavior Patterns
Figure 4.1: Market Exposure
4.3.2.1 Metropolitan Subscriber are Open to Technology
The researcher sought to find out if metropolitan subscribers are open to technology change
compared to the villagers. The findings indicates that of the 78 respondents, 8.97% strongly
disagreed, 6.41% disagreed and 35.89% were undecided and tick neutral. But 38.46% agreed
and 10.26% strongly agreed. The results are summarized in Table 4.10.
Table 4.10: Metropolitan Subscriber are Open to Technology
Metropolitan Subscriber
Strongly Disagree
Disagree
Neutral
Agree
Strongly Agree
Total
Distribution
Frequency
7
5
28
30
8
78
Percentage
8.97
6.41
35.89
38.46
10.26
100.00
4.3.2.2 Subscribers who Accept Technology are Young and Wealthy
35
The researcher sought to find out if subscriber who accept technology are young, highly
educated, and wealthy. The findings indicates that of the 78 respondents, 1.28% strongly
disagreed, 1.28% disagreed, and 6.41% were undecided and ticked neutral. But 39.74%
agreed and 51.28% strongly agreed. The results are summarized in Table 4.11.
Table 4.11: Subscribers who Accept Technology are Young and Wealthy
Young Subscribers
Strongly Disagree
Disagree
Neutral
Agree
Strongly Agree
Total
Distribution
Frequency
1
1
5
31
40
78
Percentage
1.28%
1.28%
6.41%
39.74%
51.28%
100.00
4.3.2.3 Young and Wealth Characteristics are Predictor of Adoption Decision
The researcher sought to find out if young, education and wealth characteristics are predictor
of adoption decision. The findings indicates that of the 78 respondents, 1.28% strongly
disagreed, 3.85% disagreed, and 1.28% were undecided and ticked neutral. But 39.74%
agreed and 53.85% strongly agreed. The results are summarized in Table 4.12.
Table 4.12 Predictor of Adoption Decision
Predictor of Adoption
Strongly Disagree
Disagree
Neutral
Agree
Strongly Agree
Total
Distribution
Frequency
1
3
1
31
42
78
Percentage
1.28
3.85
1.28
39.74
53.85
100.00
Figure 4.2 summarized the percentages of factors which influence the demographic
characteristics on acceptability factors. Adoption decision (53.9%) played a major role in
36
demographic characteristics compared to the young and wealthy (51.3%) and metropolitan
subscribers (38.5%) on the adoption of mobile credit system.
Demographic Characteristics
53.90%, 37%
38.50%, 27%
51.30%, 36%
Metropolitan Subscriber
Young and Wealthy
Adoption Decision
Figure 4.2: Demographic Characteristics
4.3.3 Security Concerns
4.3.3.1 Seeking Information to Ascertain level of Risk
The researcher sought to find out on seeking information to ascertain level of risk. The
findings indicates that of the 78 respondents, 6.41% strongly disagreed, 11.54% disagreed,
and 2.56% were undecided and ticked neutral. But 38.46% agreed and 41.03% strongly
agreed. The results are summarized in Table 4.13.
Table 4.13 Seeking Information to Ascertain Level of Risk
Seeking Information
Strongly Disagree
Disagree
Neutral
Agree
Distribution
Frequency
5
9
2
30
37
Percentage
6.41
11.54
2.56
38.46
Strongly Agree
Total
32
78
41.03
100.00
4.3.3.2 Unwanted Disclosure of Private Information
The researcher sought to find out on unwanted disclosure of private information. The
findings indicates that of the 78 respondents, 2.56% strongly disagreed, 7.69% disagreed, and
23.08% were undecided and ticked neutral. But 32.05% agreed and 34.62% strongly agreed.
The results are summarized in Table 4.14.
Table 4.14 Unwanted Disclosure of Private Information
Private Information
Strongly Disagree
Disagree
Neutral
Agree
Strongly Agree
Total
Distribution
Frequency
2
6
18
25
27
78
Percentage
2.56
7.69
23.08
32.05
34.62
100.00
4.3.3.3 Generating Trust among Uneducated Rural Subscribers
The researcher sought to find out on generating trust among uneducated rural subscribers.
The findings indicates that of the 78 respondents, 1.28% strongly disagreed, 1.28%
disagreed, and 3.85% were undecided and ticked neutral. But 55.13% agreed and 38.46%
strongly agreed. The results are summarized in Table 4.15.
Table 4.15: Generating Trust among Uneducated Rural Subscribers
Generating Trust
Strongly Disagree
Disagree
Neutral
Agree
Strongly Agree
Distribution
Frequency
1
1
3
43
30
38
Percentage
1.28
1.28
3.85
55.13
38.46
Total
78
100.00
Figure 4.3 summarized the percentages of factors which influence the security concerns on
acceptability factors. Generating trust (55.1%) played a major role in security concerned
compared to the seeking information (41.0%) and private information (34.6%) on the
adoption of mobile credit system.
Security Concerns
Generating Trust
[VALUE]
Seeking
Information
[VALUE]
Private
Information
[VALUE]
Seeking Information
Private Information
Generating Trust
Figure 4.3: Security Concerns
4.4 Influence of Affordability Factors on Adoption of Mobile Credit System
4.4.1 Online Transaction
4.4.1.1 Online Transaction reduces Cost
The researcher sought to find out if online transaction reduces cost on mobile credit system.
The findings indicates that of the 78 respondents, 2.56% strongly disagreed, 7.69%
disagreed, and 23.08% were undecided and ticked neutral. But 32.05% agreed and 34.62%
strongly agreed. The results are summarized in Table 4.16.
Table 4.16: Online Transaction Reduces Cost
Online Transaction
Distribution
Frequency
39
Percentage
Strongly Disagree
Disagree
Neutral
Agree
Strongly Agree
Total
2
6
18
25
27
78
2.56
7.69
23.08
32.05
34.62
100.00
4.4.1.2 Online Transaction Reduces Operational and Administrative Costs
The researcher sought to find out if online transaction reduces cost on mobile credit system.
The findings indicates that of the 78 respondents, 11.54% strongly disagreed, 14.10%
disagreed, and 25.64% were undecided and ticked neutral. But 33.33% agreed and 15.38%
strongly agreed. The results are summarized in Table 4.17.
Table 4.17: Online Transaction Reduces Operation and Administrative Costs
Operational Costs
Strongly Disagree
Disagree
Neutral
Agree
Strongly Agree
Total
Distribution
Frequency
9
11
20
26
12
78
Percentage
11.54
14.10
25.64
33.33
15.38
100.00
4.4.1.3 Credibility of Mobile Credit System
The researcher sought to find out on the credibility of mobile credit system. The findings
indicates that of the 78 respondents, 2.56% strongly disagreed, 3.85% disagreed, and 12.82%
were undecided and ticked neutral. But 46.15% agreed and 34.62% strongly agreed. The
results are summarized in Table 4.18.
Table 4.18 Credibility of Mobile Credit System
Credibility
Strongly Disagree
Disagree
Neutral
Distribution
Frequency
2
3
10
40
Percentage
2.56
3.85
12.82
Agree
Strongly Agree
Total
36
27
78
46.15
34.62
100.00
Figure 4.4 summarized the percentages of factors which influence the online transaction on
affordability factors. Credibility (46.2%) played a major role in online transaction compared
to the cost reduction (34.6%) and operational cost (33.3%) on the adoption of mobile credit
system.
Online Transaction
Credibility
[VALUE]
Cost
Reduction
[VALUE]
Operational Cost
[VALUE]
Cost Reduction
Operational Cost
Credibility
Figure 4.4: Online Transaction
4.4.2 Credit Availability
4.4.2.1 Credit Availability can be affected by the Order Cycle
The researcher sought to find out on the credibility of mobile credit system. The findings
indicates that of the 78 respondents, 2.56% strongly disagreed, 3.85% disagreed, and 12.82%
were undecided and ticked neutral. But 46.15% agreed and 34.62% strongly agreed. The
results are summarized in Table 4.19.
Table 4.19: Credit Availability can be affected by the Order Cycle
Order Cycle
Strongly Disagree
Distribution
Frequency
20
41
Percentage
25.64
Disagree
Neutral
Agree
Strongly Agree
Total
25
3
18
12
78
32.05
3.85
23.08
15.38
100.00
4.4.2.2 Out of Stock Lead the Subscriber not to adopt the Credit System
The researcher sought to find out on the out of stock credibility of mobile credit system. The
findings indicates that of the 78 respondents, 6.41% strongly disagreed, 5.13% disagreed, and
1.28% were undecided and ticked neutral. But 47.44% agreed and 39.74% strongly agreed.
The results are summarized in Table 4.20.
Table 4.20: Out of Stock Lead the Subscribers not to adopt the Credit System
Out of Stock
Strongly Disagree
Disagree
Neutral
Agree
Strongly Agree
Total
Distribution
Frequency
5
4
1
37
31
78
Percentage
6.41
5.13
1.28
47.44
39.74
100.00
4.4.2.3 Costly if the Credit is out of Stock Situation Occurs Frequently
The researcher sought to find out if it is costly if the credit is out of stock situation occurs
frequently. The findings indicates that of the 78 respondents, 12.8% strongly disagreed,
19.23% disagreed, and 42.31% were undecided and ticked neutral. But 15.38% agreed and
10.26% strongly agreed. The results are summarized in Table 4.21.
Table 4.21: Costly if the Credit is out of Stock Situation Occurs Frequently
Costly Situations
Strongly Disagree
Disagree
Neutral
Distribution
Frequency
10
15
33
42
Percentage
12.82
19.23
42.31
Agree
Strongly Agree
Total
12
8
78
15.38
10.26
100.00
Figure 4.5 summarized the percentages of factors which influence the credit availability on
affordability factors. Out of stock (47.4%) played a major role in credit availability compared
to the (42.4%) and order cycle (32.1%) on the adoption of mobile credit system.
Credit Availability
Costly Situations
[VALUE]
Order Cycle [VALUE]
Out of Stock [VALUE]
Order Cycle
Out of Stock
Costly Situations
Figure 4.5: Credit Availability
4.4.3 Transaction Cost
4.4.3.1 Increase the Volume of Utilization to enjoy Economies of Scale
The researcher sought to find out on increasing the volume of utilization to enjoy the
economies of scale. The findings indicates that of the 78 respondents, 6.41% strongly
disagreed, 2.56% disagreed, and 29.49% were undecided and ticked neutral. But 35.89%
agreed and 25.64% strongly agreed. The results are summarized in Table 4.22.
Table 4.22: Increase the Volume of Utilization to enjoy Economies of Scale
Volume of Utilization
Distribution
Frequency
43
Percentage
Strongly Disagree
Disagree
Neutral
Agree
Strongly Agree
Total
5
2
23
28
20
78
6.41
2.56
29.49
35.89
25.64
100.00
4.4.3.2 Lower the Cost to Attract Rural Population
The researcher sought to find out on lowering the cost to attract rural population. The
findings indicates that of the 78 respondents, 2.56% strongly disagreed, 1.28% disagreed, and
3.85% were undecided and ticked neutral. But 28.21% agreed and 64.10% strongly agreed.
The results are summarized in Table 4.23.
Table 4.23: Lower the Cost to Attract Rural Population
Attract Rural Population
Strongly Disagree
Disagree
Neutral
Agree
Strongly Agree
Total
Distribution
Frequency
2
1
3
22
50
78
Percentage
2.56
1.28
3.85
28.21
64.10
100.00
4.4.3.3 Lower the Cost of Maintenance and Upgrade
The researcher sought to find out on the lowering the cost of maintenance and upgrade. The
findings indicates that of the 78 respondents, 5.13% strongly disagreed, 16.67% disagreed,
and 24.36% were undecided and ticked neutral. But 41.03% agreed and 12.82% strongly
agreed. The results are summarized in Table 4.24.
Table 4.24: Lower the Cost of Maintenance and Upgrade
Maintenance and Upgrade
Strongly Disagree
Disagree
Distribution
Frequency
4
13
44
Percentage
5.13
16.67
Neutral
Agree
Strongly Agree
Total
19
32
10
78
24.36
41.03
12.82
100.00
Figure 4.6 summarized the percentages of factors which influence the transaction cost on
affordability factors. Rural population (64.1%) played a major role in transaction cost
compared to the maintenance and upgrade (41.0%) and economies of scale (35.9%) on the
adoption of mobile credit system.
Transaction Cost
Maitanence and
Upgrade [VALUE]
Economies of
Scale [VALUE]
Rural Population
[VALUE]
Economies of Scale
Rural Population
Maintenance and Upgrade
Figure 4.6: Transaction Cost
4.5 Influence of Accessibility Factors on Adoption of Mobile Credit System
4.5.1 Service Convenience
4.5.1.1 Service Provider should know the Subscribers Demands
The researcher sought to find out if service provider should know the subscribers demands.
The findings indicates that of the 78 respondents, 1.28% strongly disagreed, 2.56%
disagreed, and 8.97% were undecided and ticked neutral. But 42.31% agreed and 44.87%
strongly agreed. The results are summarized in Table 4.25.
45
Table 4.25: Service Provider should know the Subscribers Demands
Subscribers Demands
Strongly Disagree
Disagree
Neutral
Agree
Strongly Agree
Total
Distribution
Frequency
1
2
7
33
35
78
Percentage
1.28
2.56
8.97
42.31
44.87
100.00
4.5.1.2 Credit System should be Reasonably Priced
The researcher sought to find out if credit system should be reasonably priced. The findings
indicates that of the 78 respondents, 1.28% strongly disagreed, 1.28% disagreed, and 1.28%
were undecided and ticked neutral. But 21.79% agreed and 78.21% strongly agreed. The
results are summarized in Table 4.26.
Table 4.26 Credit System should be Reasonably Priced
Reasonably Priced
Strongly Disagree
Disagree
Neutral
Agree
Strongly Agree
Total
Distribution
Frequency
1
1
1
17
61
78
Percentage
1.28
1.28
1.28
21.79
78.21
100.00
4.5.1.3 Cost of Mobile Devices should not be too high
The researcher sought to find out on the cost of mobile devices. The findings indicates that of
the 78 respondents, 6.41% strongly disagreed, 12.82% disagreed, and 46.15% were
undecided and ticked neutral. But 21.79% agreed and 12.82% strongly agreed. The results
are summarized in Table 4.27.
Table 4.27: Cost of Mobile Devices should not be too high
Mobile Devices
Distribution
Frequency
46
Percentage
Strongly Disagree
Disagree
Neutral
Agree
Strongly Agree
Total
5
10
36
17
10
78
6.41
12.82
46.15
21.79
12.82
100.00
Figure 4.7 summarized the percentages of factors which influence the service convenience on
accessibility factors. Reasonably priced (78.2%) played a major role in service convenience
compared to the mobile devices (46.2%) and subscriber demands (44.9%) on the adoption of
mobile credit system.
Service Convenience
Mobile
Devices
[VALUE]
Subscriber
Demands
[VALUE]
Reasonably Priced
[VALUE]
Subscriber Demands
Reasonably Priced
Mobile Devices
Figure 4.7: Service Convenience
4.5.2 Technical Issues
4.5.2.1 Limitation of Speed and Memory
The researcher sought to find out on the limitation of speed and memory. The findings
indicates that of the 78 respondents, 23.08% strongly disagreed, 29.49% disagreed, and
3.85% were undecided and ticked neutral. But 25.64% agreed and 17.94% strongly agreed.
The results are summarized in Table 4.28.
Table 4.28: Limitation of Speed and Memory
47
Speed and Memory
Strongly Disagree
Disagree
Neutral
Agree
Strongly Agree
Total
Distribution
Frequency
18
23
3
20
14
78
Percentage
23.08
29.49
3.85
25.64
17.94
100.00
4.5.2.2 Understanding Limitations before Implementing
The researcher sought to find out on the understanding of limitations before implementation.
The findings indicates that of the 78 respondents, 7.69% strongly disagreed, 6.41%
disagreed, and 12.82% were undecided and ticked neutral. But 31.74% agreed and 33.33%
strongly agreed. The results are summarized in Table 4.29.
Table 4.29: Understanding Limitation before Implementing
Understanding Limitation
Strongly Disagree
Disagree
Neutral
Agree
Strongly Agree
Total
Distribution
Frequency
6
5
10
31
26
78
Percentage
7.69
6.41
12.82
31.74
33.33
100.00
4.5.2.3 Limitation Increases Complexities
The researcher sought to find out if limitation increases complexities. The findings indicates
that of the 78 respondents, 11.54% strongly disagreed, 14.10% disagreed, and 1.28% were
undecided and ticked neutral. But 28.21% agreed and 44.87% strongly agreed. The results
are summarized in Table 4.30.
Table 4.30: Limitation Increases Complexities
Complexities
Distribution
Frequency
48
Percentage
Strongly Disagree
Disagree
Neutral
Agree
Strongly Agree
Total
9
11
1
22
35
78
11.54
14.10
1.28
28.21
44.87
100.00
Figure 4.8 summarized the percentages of factors which influence the technical issues on
accessibility factors. Complexities (44.9%) played a major role in technical issues compared
to the limitation (33.3%) and speed and memory (29.5%) on the adoption of mobile credit
system.
Technical Issues
Complexities
[VALUE]
Speed and
Memory
[VALUE]
Limitation
[VALUE]
Speed and Memory
Limitation
Complexities
Figure 4.8: Technical Issues
4.5.3 Network Efficiency
4.5.3.1 Navigational Efficiency
The researcher sought to find out on the navigational efficiency. The findings indicates that
of the 78 respondents, 15.38% strongly disagreed, 25.64% disagreed, and 25.64% were
undecided and ticked neutral. But 20.51% agreed and 12.82% strongly agreed. The results
are summarized in Table 4.31.
Table 4.31: Navigational Efficiency
49
Navigational Efficiency
Strongly Disagree
Disagree
Neutral
Agree
Strongly Agree
Total
Distribution
Frequency
12
20
20
16
10
78
Percentage
15.38
25.64
25.64
20.51
12.82
100.00
4.5.3.2. Threat of Losing Connectivity
The researcher sought to find out on the threat of losing connectivity. The findings indicates
that of the 78 respondents, 10.26% strongly disagreed, 10.26% disagreed, and 24.36% were
undecided and ticked neutral. But 37.18% agreed and 17.95% strongly agreed. The results
are summarized in Table 4.32.
Table 4.32: Threat of Losing Connectivity
Losing Connectivity
Strongly Disagree
Disagree
Neutral
Agree
Strongly Agree
Total
Distribution
Frequency
8
8
19
29
14
78
Percentage
10.26
10.26
24.36
37.18
17.95
100.00
4.5.3.3 Mobile Web Connections are slower
The researcher sought to find out if mobile web connections are slower. The findings
indicates that of the 78 respondents, 7.69% strongly disagreed, 5.13% disagreed, and 38.46%
were undecided and ticked neutral. But 34.62% agreed and 14.10% strongly agreed. The
results are summarized in Table 4.33.
Table 4.33: Mobile Web Connections are slower
50
Mobile Web
Distribution
Frequency
6
4
30
27
11
78
Strongly Disagree
Disagree
Neutral
Agree
Strongly Agree
Total
Percentage
7.69
5.13
38.46
34.62
14.10
100.00
Figure 4.9 summarized the percentages of factors which influence the network efficiency on
accessibility factors. Slow connections (38.5%) played a major role in technical issues
compared to the losing connectivity (37.2%) and navigational efficiency (25.6%) on the
adoption of mobile credit system.
Network Efficiency
Slow
Connections
[VALUE]
25.64%, 25%
Losing
Connectivity
[VALUE]
Navigational Efficiency
Losing Connectivity
Slow Connections
Figure 4.9: Network Efficiency
4.6 Chapter Summary
This chapter has presented the findings of the data analysis. The data analysis was done by
breaking down factors identified through the data collected into simpler coherent parts in line
with the purpose of the study in order to derive meanings. The tabulated data was analyzed
quantitatively by calculating various percentages, while descriptive data was analyzed
qualitatively by organizing collected data into meaningful notes. The presentation of the
51
results of quantitative analysis was in form of frequency tables and pie-charts so as to
highlight the results and to make it more illustrative and easier to understand and interpret,
while the results of qualitative analysis was provided in form of explanatory notes. The next
chapter presents a summary of the findings as well as discussions and conclusions.
52
CHAPTER FIVE
5.0 DISCUSSION, CONCLUSION, AND RECOMMENDATIONS
5.1 Introduction
This chapter addresses the results and findings on the factors influencing the adoption of
mobile credit system in Kenya. The findings were outlined according to specific objectives of
the study. The findings were based on the responses from the questionnaires filled and
information gathered on the research questions. The researcher provided a discussion on the
findings of the research as compared to the findings in the literature review based on the
specific objectives. Thus, this means that this chapter presents summary of research
objectives, findings, conclusions, and recommendations for further study.
5.2 Summary
The purpose of this study was to identify factors influencing the adoption of mobile credit
system in Kenya and the research questions of the study were: to establish the influence of
acceptability factors on the adoption of mobile credit system in Kenya; to establish the
influence of affordability factors on the adoption of mobile credit system in Kenya; and to
establish the influence of accessibility factors on the adoption of mobile credit system in
Kenya.
The research design was descriptive in nature. The dependent variable of the study was
adoption of mobile credit system while the independent variables were acceptability,
affordability, and accessibility factors. The research was conducted among Airtel Kenya
employees – Mombasa Road Headquarters’ Office. The sampling frame from this study was
selected from a list of 350 employees as provided by the Human Resources office electronic
mails. A sample of 78 employees was targeted to represent the population of interest. This
represented 99.99% response rate. The data gathered was edited and transformed into a
quantitative form through coding. It was then entered into a computer. Univariate analysis
like frequency distribution was adopted in the study. The analyzed data was presented inform
of tables. SPPS was used to aid in data analysis.
53
The findings of this study were that majority of the mobile credit system users perceived that
their adoption of mobile credit system was being affected by the three specific objectives:
acceptability factors; affordability factors; and accessibility factors. The findings indicate that
these three specific objectives were the main factors influencing the adoption of mobile
credit system in Kenya.
With regards to acceptability factors and adoption of mobile credit system in Kenya, the first
major findings of the study was that the questions which were asked included amongst others
on marketing exposure, demographic characteristics, and security concerns. On marketing
exposure, 44.87% agreed that promotional efforts can create awareness of mobile credit
system; on demographic characteristics, 53.85% agreed that age, education and wealth
characteristics are predictor of adoption decision; and on security concerns, 55.13% agreed
that mobile provider should generate trust among uneducated rural subscribers. Thus, the
researcher concluded that perceived security concerns has the higher ability to predict and
explain the intention of users to adopt mobile credit system in Kenya.
With respect to affordability factors and adoption of mobile credit system in Kenya the
second major findings of this study indicated that the questions which were asked included
amongst others on online transaction, credit availability, and transaction cost. On online
transaction, 46.15% of respondents agreed that credibility was everything if it comes to
mobile credit system; on credit availability, 47.44% of respondents agreed that out of stock
lead the subscribers not to adopt the credit system; on transaction cost, 64.10% of the
respondents strongly agreed that the cost of transaction to be lowered to attract rural
population. Thus, the researcher concluded that transaction cost has the higher ability to
predict and explain the intention of users to adopt mobile credit system in Kenya.
In relation to accessibility factors and adoption of mobile credit system in Kenya, the third
and last major findings of this study indicated that the questions which were asked included
amongst others on service convenience, technical issues, and network efficiency. On service
convenience, 78.21% of the respondents strongly agreed that credit system should be
reasonably priced; on technical issues, 44.87% of respondents strongly agreed that limitation
increases complexities thus reduces the possibility of adoption; and on network efficiency,
38.46% of the respondents were neutral on the speed of mobile web connections. Thus, the
54
researcher concluded that service convenience has the higher ability to predict and explain
the intention of users to adopt mobile credit system in Kenya.
5.3 Discussion
5.3.1 Influence of Acceptability Factors on Mobile Credit System Adoption
The findings of this study indicated that majority of the respondents agreed with the
statement that there is a relationship between marketing exposure and mobile credit system
adoption. The results indicated that 44.87% agreed with the fact that promotional efforts can
create awareness of mobile credit system and its benefit. This is supported by Tiwari and
Buse (2009) who said that one of the most important contributing factors for adoption or
acceptance of mobile credit system is the creation of awareness among consumers for the
service or products. Tiwari and Buse (2009) also asserted that consumers go through a
process of knowledge, conviction, decision, and confirmation before they are ready to adopt
and use a product or service.
Wallage (2005) also studied the adoption of internet crediting in Australia, and found that
security concerns and lack of awareness stand out as the main reasons for the failure to adopt
internet crediting by sample respondents. Tan and Teo (2007) note that lack of awareness
reduces the adoption rate of internet crediting services in the Middle East. Creating greater
awareness by showing customers the benefits of using new system may encourage customers
to adopt internet crediting transactions.
In a study in a rural setting Laukkanen and Lauronen (2008), the villagers were made more to
be aware about mobile credit system and its usage through group meetings and training
sessions in order to generate trust among them for the new technology of mobile credit.
Effectiveness of the agent and/or merchant network in making people realize the usefulness
of mobile credit system by creating a trustworthy ground level infrastructure for mobile
credit system would contribute towards generating trust among the people. The villagers also
mentioned that once people around them started using mobile credit system, they would gain
more trust on the service and would like to use the same. Thus, peer feedback and social
influence was found to have a positive impact on the trust of the people on mobile credit
system.
55
In this study it was established that majority of the respondents agreed with the statement that
there is a relationship between demographic characteristics and mobile credit system
adoption. The results indicated that 53.85% of the respondents agreed with the fact that
demographic factors are frequently used as a basis for understanding consumer
characteristics (Laurn and Lin, 2007). The popularity of using demographic factors is
attributed to the observed relationship between the consumption of certain products and
certain demographic factors. The demographic characteristics include age, sex, income,
occupation, education etc. (Kotler, 2002). But Pikkarainen et al. (2009) argued that a service
providers’ decision to provide mobile credit system depends on the characteristics of the
market the service provider serves, such as the demographic characteristics of potential
customers as well as whether the provider is located in a metropolitan area. Demographic
characteristics also play a vital role in understanding the buying behavior of consumer in
different segments and when the characteristics are identified, they enable companies to
develop products and services according to customer’s specific requirements, tastes, and
preferences (Laurn and Lin, 2007).
Coursaris and Hassanein (2008) argued that prestige credit includes among other factors such
as status and high standing peers and self-concept. According to Coursaris and Hassanein
(2008), affluent and highly educated groups generally accept changes more readily. Thus,
highly educated consumers may be more likely to adopt mobile credit services than low
educated consumers. In addition, using mobile crediting gives these consumers prestige
among their peers. It is also part of the social scene of today’s technology driven society.
Coursaris and Hassanein (2008) found that some customers simply prefer to deal directly
with a vendor instead of utilizing arms-length technology e.g. mobile top-up. The examples
tend to show that education level of consumers have a bearing on their adoption of new
technology. However the extent to which education level affects the adoption of mobile
credit system in Kenya is however not quite clear.
This study established that majority of the respondent, 55.13% agreed with the statement that
security and trustworthiness of usage of service is one of the most important factors within
every target segments when deciding on a virtual service delivery channel. The effect of trust
has been identified along with other dimensions on the adoption of mobile credit system
56
(Mattila, 2003). This is supported by Rajanish and Sujoy (2011) who found that when it
came to monetary transactions, villagers preferred channels that they trusted. In case of the
rural un-educated population, the complexity of trust was found to be twofold: first the trust
of people on technology and second, the trust on the credit services that is being offered.
Thus, generating trust on mobile credit system remains one of the major challenges for
ensuring adoption of offered services among the rural un-educated.
Security and trustworthiness of usage of service is one of the most important factors within
every target segments when deciding on a virtual service delivery channel. The effect of trust
has been identified along with other dimensions on the adoption of mobile credit system
(Mattila, 2003). According to Rajanish and Sujoy (2011) found that when it came to
monetary transactions, villagers preferred channels that they trusted. In case of the rural uneducated population, the complexity of trust was found to be twofold: first the trust of people
on technology and second, the trust on the credit services that is being offered. Thus,
generating trust on mobile credit system remains one of the major challenges for ensuring
adoption of offered services among the rural un-educated.
5.3.2 Influence of Affordability Factors on Mobile Credit System Adoption
Research findings of this study established that 46.15% of the respondents agreed that
credibility was everything if it comes to mobile credit system adoption. According to Wang
et al. (2006), perceived credibility is usually impersonal and relies on reputation, information,
and economic reasoning. This is supported by Luarn and Lin (2005) who found that
perceived credibility has significant impact on the development of willingness to use mobile
credit system. Thus, since mobile credit system is somewhat new, perceived credibility has
the higher ability to predict and explain the intention of users to adopt it. Therefore,
Jarvenpaa and Todd (2008) showed that trust works as a mechanism for reducing consumer’s
perceived risk in online transactions.
According to Grant and Fernie (2008), out of stock can lead the consumer not to adopt the
credit system, delay the purchase, and substitute the credit system with the normal voucher
scratch cards. It is therefore quite important to service provider (Airtel) to ensure that she
does not give the consumers an opportunity to try what their competitors are offering by
57
ensuring that their retailers do not ran out of mobile credit system products. A strong but
hidden assumption behind marketing decisions is the availability of the mobile credit system
product being offered at a time and place relevant to the consumers.
Every program to introduce new product, build brand loyalty, and maintain market share
implies a high level of distribution system performance, an assumption not always warranted
(Kucuk, 2008). Kucuk (2008) continues to urge that because of possible out of stock,
manufacturers and retailers might also face retention costs of switching consumers in
addition to the high-inventory cost of precautionary stocking decision regarding the
frequently out of stock mobile credit system products. In other words out of stock costs are
generally hidden costs in many situations and are often difficult to detect by service provider
as argued by Kucuk (2008).
The study also showed that respondents, 47.44% agreed that out of stock lead subscribers not
to adopt the credit system. According to Grant and Fernie (2008), out of stock can lead the
consumer not to adopt the credit system, delay the purchase, and substitute the credit system
with the normal voucher scratch cards. It is therefore quite important to service provider
(Airtel) to ensure that she does not give the consumers an opportunity to try what their
competitors are offering by ensuring that their retailers do not ran out of mobile credit system
products. A strong but hidden assumption behind marketing decisions is the availability of
the mobile credit system product being offered at a time and place relevant to the consumers.
According to Grant and Fernie (2008), out of stock can lead the consumer not to adopt the
credit system, delay the purchase, and substitute the credit system with the normal voucher
scratch cards. It is therefore quite important to service provider (Airtel) to ensure that she
does not give the consumers an opportunity to try what their competitors are offering by
ensuring that their retailers do not ran out of mobile credit system products. A strong but
hidden assumption behind marketing decisions is the availability of the mobile credit system
product being offered at a time and place relevant to the consumers.
Every program to introduce new product, build brand loyalty, and maintain market share
implies a high level of distribution system performance, an assumption not always warranted
(Kucuk, 2008). Kucuk (2008) continues to urge that because of possible out of stock,
58
manufacturers and retailers might also face retention costs of switching consumers in
addition to the high-inventory cost of precautionary stocking decision regarding the
frequently out of stock mobile credit system products. In other words out of stock costs are
generally hidden costs in many situations and are often difficult to detect by service provider
as argued by Kucuk (2008).
The study found out that 64.10% of the respondents agreed that the cost of a payment
transaction had direct effect on consumer adoption if the cost is passed on to rural population
customers. According to Mallat (2007), transaction costs should be low to make the total cost
of the transaction competitive. In his study, Mallat (2007) suggested that cost of availing the
mobile credit system was a common matter of concern among the villagers. Villagers wanted
to know whether they would need to purchase a new sim-card for using mobile credit system
and were also eager to know the cost of transaction for availing this service. They became
aware and appreciated the fact that using mobile credit system would save them a lot of time,
effort and money that they currently spent for accessing airtime services through the existing
channels of delivery i.e. kiosks, supermarkets, and ATMs. Hence, cost of the mobile credit
system is an important factor that would determine the adoption of the services among the
rural population. Given the fact that majority of the rural population falls within the lower
income group, the total cost of availing the services need to be minimized for ensuring faster
adoption.
Mobile service providers which have had difficulty providing profitable services through
traditional channel to their clients, see mobile credit system as a “cardless top-up”, which
lowers the cost involved in serving customers (Ivatury and Mas, 2008). Technological
development has provided opportunities for mobile service providers to develop their
services and offer customers more flexibility. As a results Airtel have launched multiple
service access methods via delivery channels like ATM top-up, internet top-up and mobile
phone top-up (Laukkanen and Pasanen, 2007). Virtual top-up has brought into its fold a
considerable group consumers who formerly could be served only at too high cost (Datta,
Pasa, and Schnitker, 2005). Mattila and Pento (2002) argued that one issue that drive top-up
services is the cost efficiency pressures from supply side. Because payment transaction cost
59
vary, quite often cardless capability was built into financial institution’s software platform,
leaving maintenance and upgrades as the only added costs.
5.3.3 Influence of Accessibility Factors on Mobile Credit System Adoption
This study established that 78.21% of the respondents agreed that credit system should be
reasonably priced. Arguably, a technology must be plausibly priced relative to alternatives
for consumers to use the novel technology. As Laukkanen et al (2007) puts it value barrier is
responsible for the failure of many new developments because of people’s perception that the
cost of adopting an innovation is far greater than any ensuing benefits. Thus, if mobile credit
system is not being adopted it could be because it is not been reasonably priced compared to
either traditional top-up using scratch card. The technology used for mobile transmission
may increase or lower the cost of mobile credit system as each technology has its own
features which differ in costs. This cost impact in turn may encourage or discourage adoption
of mobile credit system.
In the context of mobile credit system, transaction fees, access cost and equipment costs are
the three important cost components that make its use more expensive (Wu and Wang, 2005).
In mobile credit system there are three costs: normal costs associated with mobile phone
providers’ activities, the credit cost and charge, and the cellular phone cost. The cost of
mobile devices though a one off cost, makes mobile credit system as costly as other
crediting. If the cost of mobile devices is very high, this discourages credit users from
acquiring them hence impending the adoption of mobile crediting system (Chavidi and
Mulabagula, 2004).
With the General Packet Radio Service (GPRS), the cost advantage is that the subscriber
pays for the volume of the transmitted data and not the time required in the process making it
the first technology that cannot only enable but also promote mobile credit system (Toh,
2002). In Philippines, for instance, domestic and international remittances offer a large
market given the large volume transacted and relative low cost of using SMS based mobile
credit system applications as compared to the high cost of current crediting and remittance
company alternatives (Agabin, 2007).
60
In this study 44.87% of the respondents strongly agreed that limitation increases
complexities. Complexity has a negative connection with the use of mobile credit system.
Kolodinsky, Hogarth and Hilgert (2004) stated that if a consumer perceives mobile credit
system to be relatively easy to use and understand, he or she will be more willing to use
mobile credit system. By designing in simplicity, a good mobile credit system application
understands and works with the limitations of a given mobile phone. Generalized solutions,
such as those based on Wireless Application Protocol (WAP) browsers, can suffer from poor
implementations and interfaces, resulting in a slow and cumbersome experience (Kalakota
and Robinson, 2002).
Past research indicates that the complexity of an innovation is more negatively to their rate of
adoption than any other characteristics of the innovation. According to Anil, Ting, Moe and
Jonathan (2008), the technology channel that mobile providers uses to roll out its mobile
credit system can be sophisticated to the level of discouraging potential adopters. Thus,
mobile providers employs any of the four technologies namely: the Interactive Voice
Response (IVR), Short Message Service (SMS), Wireless Access Protocol (WAP), and Stand
Alone Applications (SAA). Therefore, technical complexity is a major factor in any decisionmaking about the launch of new and innovative services like mobile credit system (Tiwari
and Buse, 2007).
The study also found out that 38.46% of the respondents were neutral on the speed of mobile
web connections. Network interruptions pose a serious challenge to mobile credit system
success. Drummond (2008) noted that mobile web connections are generally slower than
broadband connections. The threat of losing connectivity in the middle of a transaction
makes mobile credit system inconvenient. For example consumers worry is if the information
simply lost or it is cached locally and then uploaded when the network becomes available.
Service efficiencies has two facets in mobile business (m-commerce) namely navigation and
transaction processing efficiencies (Limayem, Khalifa and Frini, 2006).
Navigational efficiency is particular important for mobile credit as the restrictive visual
interface is usually regarded as a major hindrance for its adoption (Lee and Benbasat, 2003).
One way to address this challenge is to leverage multi-media input/output components such
as speech interfaces (Fan, Saliba, Kendall and Newmarch, 2005). Another important way of
61
enhancing efficiency is personalization. That is, in mobile credit system the ability to use the
service wherever wanted enables immediate actions to borrow or buy credit, which in turn
saves time and thus is perceived as efficiency (Lee, McGoldrick, Keeling and Doherty,
2007).
Transaction processing efficiency in Kenya, mobile credit system is “kopa credo” the
transaction delivery time is not guaranteed since it is dependent on factors like congestion
and network strength in the area where the customer is located. Also if consumers find
registration and authentication procedures burdensome, adoption of this new channel will be
slow. On the other hand, if data security is compromised, negative publicity will quickly
show fears about the safety of crediting “over the air.”
5.4 Conclusion
5.4.1 Influence of Acceptability Factors on Adoption of Mobile Credit System
This study established that when adopting new products, customers face a dilemma between
desirable and undesirable consequences of the adoption and hence face a risky decision on
security. This security risk has two main elements: perceived risk grounded in concerns with
regard for the technical performance of the service delivery system; and perceived security
associated with concerns about personal privacy. Privacy in mobile terminology is the level
of control that clients have over the timing, and circumstances of sharing oneself physically,
behaviorally, or intellectually with others. Also security and trustworthiness of usage of
service is one of the most important factors within every target segments when deciding on a
virtual service delivery channel. The effect of trust has been identified along with other
dimensions on the adoption of mobile credit system. For example, when it came to monetary
transactions, villagers preferred channels that they trusted. In case of the rural un-educated
population, the complexity of trust was found to be twofold: first the trust of people on
technology and second, the trust on the credit services that is being offered. Thus, generating
trust on mobile credit system remains one of the major challenges for ensuring adoption of
offered services among the rural un-educated. Therefore, the study concluded that perceived
security risk was all about the extent to which technology-enabled services were perceived to
be secure, sufficiently safe and reliable to use.
62
5.4.2 Influence of Affordability Factors on Adoption of Mobile Credit System
The study established that it was not viable for consumers to change their way of performing
their top-up tasks without offering a strong performance-to-price advantage. The price of
airtime services may have an opposite effect with respect to the adoption of virtual top-up,
which may result in consumers preferring the traditional top-up services. Provision of a lower
service cost is a major benefit for users using virtual mobile credit system and performing
top-up transaction functions through a mobile device. Mobile service providers who have had
difficulty providing profitable services through traditional channel to their clients, see mobile
credit system as a “cardless top-up”, which lowers the cost involved in serving customers.
Technological development has provided opportunities for mobile service providers to
develop their services and offer customers more flexibility. As a results Airtel have launched
multiple service access methods via delivery channels like ATM top-up, internet top-up and
mobile phone top-up. Virtual top-up has brought into its fold a considerable group consumers
who formerly could be served only at too high cost hence one issue that drive top-up services
is the cost efficiency pressures from supply side. Because payment transaction cost vary,
quite often cardless capability was built into financial institution’s software platform, leaving
maintenance and upgrades as the only added costs. The study concluded that, users would
agree to pay a reasonable fee to use a service depending on the credit cost and service
provider. Thus, value for money barrier may be another factor influencing the adoption of
mobile credit system.
5.4.3 Influence of Accessibility Factors on Adoption of Mobile Credit System
The research study also found out that mobile offered “anytime and anywhere” convenience
not only for communication but increasingly for mobile credit services, even for parts of the
world where traditional scratch card did not exist. Those mobile service providers who offer
mobile credit system knew for a fact that consumers demanded choice as to where and when
they had to connect, interact, and transact. Mobile service providers also knew that they
needed to offer combination of different mobile credit technologies to reach their entire
consumer base. Furthermore, technology must be plausibly priced relative to alternatives for
consumers to use the novel technology because the value barrier is responsible for the failure
of many new developments because of people’s perception that the cost of adopting an
63
innovation is far greater than any ensuing benefits. Thus, if mobile credit system is not being
adopted it could be because it is not been reasonably priced compared to either traditional
top-up using scratch card. The technology used for mobile transmission may increase or
lower the cost of mobile credit system as each technology has its own features which differ in
costs. This cost impact in turn may encourage or discourage adoption of mobile credit
system. Therefore, the study concluded that, price of a technology was an important factor
that influenced the adoption of the technology. This was because in times of increased
competition, a distribution channel organized business processes efficiently so as to reduce
distribution costs.
5.5 Recommendation
5.5.1 Recommendation for Improvement
5.5.1.1 Influence of Acceptability Factors on Mobile Credit System Adoption
When adopting new products, customers face a dilemma between desirable and undesirable
consequences of the adoption and hence face a risky decision on security. This security risk
has two main elements: perceived risk grounded in concerns with regard for the technical
performance of the service delivery system; and perceived security associated with concerns
about personal privacy. Hence, as an element of perceived security risk, privacy is the extent
to which technology-enabled services are perceived to be secure, sufficiently safe and
reliable to use. Thus, because mobile service providers have access to more sensitive
information related to personal interactions (for instance phone calls and SMS) and physical
location (for example global positioning system-GPRS), people are concerned about
unwanted disclosure of their private information, or simply misuse of their information by
the mobile company collecting it. Therefore, the study recommends that mobile company
should not disclosed capture of information such as consumers’ borrowing habits since
privacy protection seems to be critical for potential adopters.
5.5.1.2 Influence of Affordability Factors on Mobile Credit System Adoption
It is not viable for consumers to change their way of performing their top-up tasks without
offering a strong performance-to-price advantage. The price of airtime services may have an
opposite effect with respect to the adoption of virtual top-up, which may result in consumers
64
preferring the traditional top-up services. Provision of a lower service cost is a major benefit
for users using virtual mobile credit system and performing top-up transaction functions
through a mobile device. Thus, users would agree to pay a reasonable fee to use a service
depending on the credit cost and service provider. Therefore, to further lower the transaction
costs mobile service provider the study recommends that mobile company should increase
the volume of utilization to enjoy economies of scale. In addition to cost-saving, mobile
service provider need to pay particular attention to their pricing strategy with the objective to
uneven the potential factors that encourage or discourage its adoption.
5.5.1.3 Influence of accessibility Factors on Mobile Credit System Adoption
Complexity has a negative connection with the use of mobile credit system. That is if a
consumer perceives mobile credit system to be relatively easy to use and understand, he or
she will be more willing to use mobile credit system. Thus, by designing in simplicity, a
good mobile credit system application should be invented so that mobile subscribers can
understand and work with the limitations of a given mobile phone. Therefore, the study
recommends that mobile service provider should make use of General Packet Radio Service
(GPRS), the cost advantage is that the subscriber pays for the volume of the transmitted data
and not the time required in the process making it the first technology that cannot only enable
but also promote mobile credit system.
5.5.2 Recommendation for Further Research
The study did not examine all the stakeholders involved in the research study due to time
limit and financial constraints posing a comparison challenge. However a representative
sample was obtained from the study population and in depth analysis of the factors was done
thus ensuring that generalization of the study findings were possible. Furthermore, the
location of this study is only confined to Airtel Kenya employees at Kenya Headquarter in
Nairobi. Thus, the sample and its responses may not be a representation of the beliefs and
intention of Kenya towards using mobile credit system. Future research can improve on this
limitation by increasing the sample size and performing future research across different
respondents.
65
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APPENDICES
APPENDIX I: COVER LETTER
Kisila Eric Joseph
P.O Box 14634 – 00800
Nairobi
Dear Respondent,
RE: REQUEST FOR YOUR PARTICIPATION IN MY ACADEMIC RESEARCH
I am the above student currently pursuing a course towards conferment of Executive Master
of Science in Organization Development (EMOD) from United States International
University – Africa.
In partial fulfilment of the requirements of the award of the degree, I am conducting research
to determine the effect of adoption on mobile credit system in Kenya. You (full time
employees) have been randomly selected to participate in this study. Participation is
voluntary and I will spare a few minutes of your time to fill in the blanks of the attached list
of questions to the best of your knowledge. Kindly complete all sections of the questionnaire
to enable me complete the study. Please note that the information you provide will be treated
as confidential, and will only be used for purpose of this research.
The findings of this study will inform the Airtel management to facilitate on decision making
towards the adoption of mobile credit system. The final report will be shared with all
stakeholders, with priority given to participants full time employees. The response is targeted
from senior managers who are involved in leadership, strategy and governance, and research
and development practitioner within the organization.
Your participation in this study will be highly appreciated.
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Yours Sincerely,
Kisila Eric Joseph
APPENDIX II: QUESTIONNAIRE
SECTION A: GENERAL INFORMATION
Please respond to the questions below by ticking in the boxes provided
1. Gender: Male
Female
2. Age:
21 – 30 Years
31 – 40 Years
41 – 50 Years
Over 50 Years
3. Level of Education:
Secondary
College
University
4. Annual Income (Kshs):
Under 25,000
25,001 – 45,000
45,001 – 65,000
Over
65,000
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SECTION B: RESEARCH TOPIC
FACTORS AFFECTING THE ADOPTION OF MOBILE CREDIT SYSTEM
To what extend do you agree or disagree with the following statements? Please indicate your
response by putting an X to each item using the scale of strongly disagree to strongly agree.
Research Question I: The Effect of Acceptability Factors on Mobile Credit System
A) Marketing Exposure
Strongly
Disagree
1) Mobile service provider
should increase promotional
efforts to create awareness of
mobile credit system and its
benefits.
2) In rural setting awareness
should be made through group
meeting and training session to
generate trust for mobile credit
system.
3) An educated community is
better at adopting new mobile
technologies which will help
service provider to understand
subscriber behavior patterns.
B)Demographic
Strongly
Characteristics
Disagree
1) Subscriber who are located in
a metropolitan areas are open
and eager to adopt mobile credit
system compared to villagers.
2) Subscriber who accept
technology changes are relative
young,
highly
educated,
Disagree
Neutral
Agree
Strongly
Disagree
Disagree
Neutral
Agree
Strongly
Disagree
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wealthy, and are in higher
professions.
3) Attributes of innovation itself
rather than the
personal
characteristics that are the
stronger predictors of the
adoption decision.
C) Security Concerns
Strongly
Disagree
1) Adoption of mobile credit
system may lead to seeking
more information to ascertain
the level of risk and mitigate the
perception of risk.
2) Subscribers are concerned
about unwanted disclosure of
their private information such as
consumer’s borrowing habits.
3) Generating trust on mobile
credit system is one challenges
for ensuring adoption of offered
services among the rural uneducated.
Disagree
Neutral
Agree
Strongly
Agree
Research Question II: The Effect of Affordability Factors on Mobile Credit System
A) Online Transaction
Strongly
Disagree
Disagree
1) Online transaction is the way
forward in reducing costs and
remaining
competitive
in
comparison with conventional
crediting practices.
2) The desire to reduce both
operational and administrative
costs
has
driven
mobile
providers to internet crediting
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Neutral
Agree
Strongly
Agree
world.
3) Online transaction usage can
also be affected by the perceived
credibility of mobile credit
system.
B) Credit Availability
Strongly
Disagree
1) Mobile credit system
availability may be affected by
the order cycle, lead time, and
replenishment behaviors of
service provider.
2) Out of stock can lead the
subscriber not to adopt the credit
system, delay the purchase, and
substitute the credit system with
the normal voucher scratch card.
3) Its very costly if the out of
stock situation is frequently or
constantly occurring for service
provider.
C) Transaction Cost
Strongly
Disagree
1) To further lower the
transaction costs mobile service
providers should increase the
volume of utilization to enjoy
economies of scale.
2) Transaction cost should be
low to attract the larger rural
population and ensuring faster
adoption.
3) Cost efficiency of mobile
credit system can be achieved
only when cost of maintenance
and upgrade is reduced by
mobile service provider.
Disagree
Neutral
Agree
Strongly
Agree
Disagree
Neutral
Agree
Strongly
Agree
73
Research Question III: The Effect of Accessibility Factors on Mobile Credit System
A) Service Convenience
Strongly
Disagree
Neutral
Agree
Strongly
Agree
Disagree
Neutral
Agree
Strongly
Agree
Disagree
1 Mobile service provider
should know the subscriber
demand choice as to where and
when they connect, interact, and
transact.
2 If mobile credit system is not
being adopted it could be
because it is not been reasonably
priced compared to scratch card.
3 If the cost of mobile devices is
very high, this discourages credit
users from acquiring them hence
impending the adoption of
mobile credit system.
B Technical Issues
Strongly
Disagree
1) Adoption of mobile credit
system may not happen either
because of limitation like small
displays and keypads, limited
transaction, speed and memory,
and short battery life.
2) It’s important for industry
specialists
and
technical
suppliers that all the problems
associated with mobile credit
system are well understood
before implementation.
74
3) Issues like mobile credit
security, mobile phone devices
operations and pricing structure
exponentially
increasing
complexities in the mobile credit
system.
C) Network Efficiency
1) Navigational efficiency is
important for mobile credit as
the restrictive visual interface is
regarded as hindrance for its
adoption.
2) The threat of losing
connectivity in the middle of a
transaction makes mobile credit
system inconvenient.
3) Mobile web connections are
generally slower than broadband
connections.
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