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http://www.iaeme.com/IJMET/index.asp 484 [email protected]
International Journal of Mechanical Engineering and Technology (IJMET)
Volume 10, Issue 12, December 2019, pp. 484-511, Article ID: IJMET_10_12_048
Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=10&IType=12
ISSN Print: 0976-6340 and ISSN Online: 0976-6359
© IAEME Publication
EFFECT OF MOBILE MONEY TRANSFER
SCHEME ON THE ECONOMIC GROWTH OF
CAMEROON
Dr. Robinson Onuora Ugwoke, Michael Keneath Foleng and Obioma Vivian Ugwoke
Department of Accountancy, University of Nigeria Nsukka, Enugu Campus, Nigeria
ABSTRACT
This study evaluated the “Effect of Mobile Money Transfer Scheme on the
Economic Growth of Cameroon”. Mobile Money Transfer Scheme was the
independent variable, meanwhile Economic Growth was the dependent variable and
the proxies used were Gross Domestic Product (GDP) and Inflation Rate. The
population consisted of the two primary Mobile Money service providers in Cameroon
i.e. MTN Mobile Money and Orange Money. Explanatory Research Design was
employed in the Methodology. Secondary data was used for the study, and the data
were collected from the annual reports of Cameroon’s central bank (BEAC),IMF and
Knoema for Mobile Money, GDP and Interest Rate respectively. These data collected
wereanalysed using simple Linear Regression at 5% probability level of significance
with the aid of Statistical Package for the Social Sciences (SPSS), version 23.0. The
findings revealed that there is a weak positive insignificant correlation between
Mobile Money Transfer Scheme and Economic Growth in Cameroon i.e. GDP (r =
.162, alpha-significance is .520 at p > 0.05)and inflation rate (r = .385, alpha-
significance is .115 at p > 0.05). The research concludes that the reason why this
scheme does not currently have a material effect on Cameroon’s Economic Growth is
probably due to the fact that the industry is relatively new in the country, gradually
gaining grounds and so could potentially have a significant effect in the future. It
could also be due to the severe political instability plaguing Cameroon at the moment.
The study therefore recommends that the government should subsidise and also
implement policies which will favour the further penetration of mobile money service
even to the more remote parts of the country. It equally recommends that the
government should try to resolve the current, devastating political crisis so as to make
the environment safer to conduct business.
Keywords: Mobile Money, Mobile Money Transfer Service, Mobile Money Agent,
Economic Growth, Gross Domestic Product, Inflation, Life expectancy.
Cite this Article: Dr. Robinson Onuora Ugwoke, Michael Keneath Foleng and
Obioma Vivian Ugwoke, Effect of Mobile Money Transfer Scheme on the Economic
Growth of Cameroon. International Journal of Mechanical Engineering and
Technology 10(12), 2020, pp. 484-511.
http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=10&IType=12
Effect of Mobile Money Transfer Scheme on the Economic Growth of Cameroon
http://www.iaeme.com/IJMET/index.asp 485 [email protected]
1. INTRODUCTION
Access to financial services is a crucial boost to social and economic development of a
country. Until recently in Cameroon, such services focused on the formal banking sector,
which traditionally does not open branches in low income and rural areas as their returns there
would not be able to justify their substantial operating costs. This meant that only wealthy and
urban citizens could enjoy such privileges. The rapid growth of the mobile network industry
led to half the world‟s population having at least one mobile subscription by 2014 (GSMA
Intelligence, 2015), with a total number of mobile subscriptions worldwide reaching more
than 7 billion by the close of 2015 (Sanou, 2015). These mobile devices offered a distribution
technology for mobile financial services for the unbanked. The initial goal of mobile money
was to enable the unbanked persons to be able to carry out person-to-person (P2P) money
transfer transactions, which were previously done through relatively unsecured physical
means such as bus agencies as well as travelling relatives and friends. This service, therefore,
brought people from the cash-based, „unbanked‟ economy to the modern system of „book-
entry money‟. Thus began the era of „banking the unbanked‟ (Klein & Mayer, 2011).
Worldwide, mobile money service is available in 93 countries today. The service is fast
overtaking the banking sector with the number of registered accounts in the world increasing
by 31% to 411 million in 2015 compared to the previous year. Mobile money providers are
processing an average of 33 million transactions per day. Mobile money services offering
International Money Transfer (IMT) saw the volume of cross-border remittances increase by
52% in 2015, compared to the previous year (GSMA, 2015). Indeed, the World Bank (2016)
referred to mobile money as a “success story” that is also a “regulatory minefield”. Africa has
a vast potential for growth in the telecoms industry, especially as there is only 47%
penetration so far. One of the fastest growing areas in the telecom industry happens to be
mobile money. This service makes it possible for mobile phone users to send and receive
money anywhere by facilitating transactions through their mobile phones. This is essential
especially in Africa which has poor infrastructure, and a vast majority of the people do not
have bank accounts (Paelo, 2014). So far, Kenya is the country where mobile money service
is most successful as it has been there since 2007, especially with the advent of M-PESA
provided by the Vodafone-owned Safaricom mobile network which has the largest market
share in the country. Though not yet fully embraced, it is also present in Nigeria thanks to
MTN Mobile Money (working in partnership with GT Bank), Glo Xchange, Paga and so on.
In Cameroon, less than 20% of the population has a bank account whereas the penetration
of mobile telephony is estimated at 80% (Cameroonweb, 2015). This could be because the
cost of mobile phones is becoming more and more affordable over the years (currently as low
as 5,000FCFA or $10) and subscribers do not necessarily need an expensive Smartphone or
internet connection on their phones to be able to use mobile money. Some of these phones
take even up to 2 or 3 SIM cards at once, meaning a customer could have multiple mobile
money accounts using the same phone. Three Mobile Network Operators (MNOs) - MTN,
Orange and Nexttel are in Cameroon. Two of these currently provide mobile money services.
MTN Mobile Money (also called MoMo) began in 2010 and Orange Money followed a year
later in 2011. Nexttel Possa will be launched soon. According to Media Intelligence (2016),
as of June 2016, there were about 6.8 million mobile money subscribers in Cameroon, with
1.5 million active users.
Cameroon currently has a population of about 23 million, and the mobile money market is
continuing to proliferate in the country. For instance, Tabi (2018) posits that the Governor of
the Bank of Central African States (BEAC) signed authorisation in 2018 permitting
SociétéGénérale Cameroun (SGC) bank to partner with YUP Cameroun in launching its
mobile money services within 12 months. Worthy of note is the fact that SGC had initially
Dr. Robinson Onuora Ugwoke, Michael Keneath Foleng and Obioma Vivian Ugwoke
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launched a mobile money service called Monifone, but this was suspended in 2014 due to
competition and recurring conflicts with some telecommunication operators. This time
around, it has decided not to rely on any telecommunication operator, and instead chosen to
partner with YUP Cameroun (Business in Cameroon, 2018).Tabi (2018) states that official
statistics show that there are presently 34,114 mobile money service points in the CEMAC
(Economic and Monetary Community of Central Africa) zone, with Cameroon accounting for
70% (23,880) of them.
Mobile money transfer has a lot of advantages. It improves efficiency and effectiveness by
increasing the speed, safety and frequency of payments and decreasing the cost, paper work,
and processes of sending and receiving money even on off days and odd hours. Besides,
security is also a significant benefit here, as it eliminates the risk of theft. Cash inflow to rural
areas can be enhanced too. Increased money flow from Urban to rural dwellers can greatly
enhance economic growth. Those who partner with telecom providers to offer financial
services are called Mobile Money Agents (MMA) in this paper. It examines the effect of the
Mobile Money Transfer Scheme on the Economic Growth of Cameroon proxy by GDP and
Inflation rate. It has five parts made up of introduction, literature review, methodology,
presentation and analysis of findings and conclusion and recommendations.
1.2. Statement of the Problem
The ability to carry out financial transactions through a mobile phone has attracted MNOs
(especially in developing countries) to the financial services industry. They were able to
penetrate the market due to their comparatively quicker service and lower charges, compared
to the formal banking sector. In the case of Cameroon, MTN and Orange now offer Mobile
Money Transfer (MMT) services called MTN Mobile Money and Orange Money
respectively. The third, Nexttel has plans to launch its own called Nexttel Possa. According to
Paelo (2014), Cameroon had more mobile money subscribers than bank account holders by
the end of 2013. OCHA (2016) indicates that MTN Mobile Money formed a partnership with
Afriland First Bank whereby MTN manages the technical platform as well as the marketing
and distribution network, while Afriland issues the e-money and ensures compliance with the
financial regulations. It goes on to say the services include person-to-person (P2P), bill
payment and purchasing of goods and services from authorised retailers but do not
substantiate on any of these aspects. Bahri-Damon (2015) posits that Mobile Money services
in Cameroon enable Cameroonians to send and receive money anywhere within and outside
the country as well as pay their electricity bills, cable bills, insurance premiums, university
tuition fees and taxes. It equally creates a medium through which they can buy train tickets,
flight tickets, airtime and fuel. It even enables them to do shopping in their authorised
supermarkets. Some companies pay salaries to their employees using this means. Though
these numerous activities are going on in the field of mobile money transfer in Cameroon, the
economy of the country has been at a comatose and no study has attempted to determine the
effect of the scheme on her economic growth. Langaa (2012) only explored the social impact
of mobile money and mobile electronic transfer services among rural farmers in the North
West Region of Cameroon. Therefore there exists a gap in literature in this field despite the
need to find a solution to poor economic growth in Cameroon.
1.3. Objectives of the Study and Accompanying Research Questions and
Hypotheses
The general objective of this study was to assess the effect of Money Mobile Transfer Scheme
on the Economic Growth of Cameroon. Its specific objectives were to: (i) establish the effect
of Mobile Money Transfer Scheme on the GDP of Cameroon and (ii) ascertain the effect of
Effect of Mobile Money Transfer Scheme on the Economic Growth of Cameroon
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Mobile Money Transfer Scheme on the inflation rate of Cameroon. These objectives gave rise
the following research questions viz.(i) Does Mobile Money Transfer Scheme have a
significant effect on the GDP of Cameroon? (ii) Does Mobile Money Transfer Scheme have a
significant effect on the inflation rate of Cameroon? In tandem with the above therefore the
following two hypotheses were formulated namely: (i) Mobile Money Transfer Scheme has a
significant effect on the GDP of Cameroon and (ii) Mobile Money Transfer Scheme has a
significant effect on the inflation rate of Cameroon. This research is expected to be of great
benefit to the government, researchers, mobile money service providers and the general
public. For instance, while the government would utilize the findings for effective policy
formulation to make the scheme acceptable to all and sundry in the country, researchers will
find in it usable data and reference point for further researches. We acknowledge and
appreciate the paucity of work done in this area about the Cameroonian economy due to the
newness of the introduction of the scheme in the country (2010) relative to Kenya and other
African countries. Notwithstanding the above limitation, the study focused on MTN Mobile
Money and Orange Money which occupy almost the entire market, it covered a period of 16
years (2002 – 2017), that is 8 years before the advent of Mobile Money, and then 8 years
after.
2. LITERATURE REVIEW
2.1. Conceptual Framework
2.1.1. Mobile Money Transfer
Mobile Money was relatively unknown over a decade ago starting only in 2001. But it is
probably the phenomenal growth since 2007 of Kenya‟s M-Pesa system that has brought
mobile money to international popularity. Mobile money refers to financial transaction
services potentially available to any mobile phone user, including the under banked and
unbanked global poor who are not a profitable target for commercial banks. According to
Diniz, Albuquerque and Cernev (2011), Mobile Money is a digital repository of electronic
money developed and implemented on a mobile device, allowing peer-to-peer transaction
between users of the same service provider. The services allow electronic money transactions
over a mobile phone (Ernst & Young, 2009) while GSMA (2010) opines that Mobile Money
is a service in which mobile phone is used to access financial services, giving rise to
movement of value from a mobile wallet through a mobile phone. Common mobile financial
services offered through the mobile phone include bill payment, account transfers, domestic
and international Person-to-Person transfers, proximity payments at the point of sale, and
remote payments to purchase goods and services. The Mobile Network Operators (MNOs)
partner with banking institutions to be able to render these mobile money services. Mobile
Money is not Mobile Banking. Mobile Money does not require an internet connection and
works merely with codes. On the other hand, Mobile Banking requires the subscriber to have
a bank account, a Smartphone, download the app on the phone, and use internet connection to
do the electronic money transfer. How it works according Aaron (2015) is that an
individual/customer sets up an electronic money account with the mobile money service
provider (after providing identity documents) and then deposits cash in exchange for
electronic money. This electronic money can be stored or withdrawn as cash, or transferred
via a coded secure text message to others, without the customer having a formal bank account.
He further asserts that the scheme enables the use of mobile phones to pay bills, remit funds,
deposit cash, and make withdrawals using e-money issued by banks as well as non-bank
providers such as telecommunication companies. This service currently exists in many
developing countries today and is proliferating, especially in Africa. It serves several people
without access to banking services, known as the unbanked. According to Buckley, Greenacre
Dr. Robinson Onuora Ugwoke, Michael Keneath Foleng and Obioma Vivian Ugwoke
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and Malady, (2015), it provides financial inclusion which has the potential of helping the
unbanked and low-income groups to save and borrow with a possible spiral effect of
investment in education and asset generating activities.
2.1.2. Economic Growth
Economic growth is an increase in the production and consumption of goods and services.
This happens when there is an increase in the multiplied product of population and per capita
consumption. The global economy grows as an integrated whole made up of agricultural,
extractive, manufacturing, and services sectors that require inputs and outputs. Economic
growth is often indicated by an increase in real Gross Domestic Product (GDP) or real Gross
National Product (GNP). Economic growth has been a primary, perennial goal of many
societies and most governments (Wilson et al., n.d). Haller (2012) posits that Economic
Growth is, in a limited sense, an increase of the national income per capita, and it has to do
with the analysis, especially in quantitative terms, of this process. It concentrates on the
functional relations between the endogenous variables; in the broader sense, it involves the
increase of the Gross Domestic Product(GDP), Gross National Product(GNP) and National
Income (NI), resulting in national wealth, including the production capacity, expressed in both
absolute and relative terms, per capita, as well as involving the structural modifications of
economy.
According to Haller (2012), Economic Growth can be positive, zero or negative. It is
positive when the annual average rhythms of the macro-indicators (especially GDP) are
higher than the normal rhythms of growth of the population and negative when it vice versa.
Then it is zero when the annual average rhythms of growth of the macro-economic indicators
(especially GDP) are equal to those of the population growth. Essentially, Economic growth
is the sustained increase in the welfare of an economy together with the ongoing changes in
that economy's industrial structure; public health, literacy, and demography; and distribution
of income (Habane, 2012). The ultimate goal of Economic Growth is to increase the standard
of living of the citizens by making them sustainably wealthier.
Researchers attribute economic growth to several factors – economic and non economic.
Economic factors range from natural resources, capital formation, technological progress,
human resources, population growth, social overheads, and entrepreneurship to
transformation of traditional agricultural society. Each has varying degrees of impact on
economic growth (Boldeanu & Constantinescu, 2015; Onyinye, Idenyi & Ifeyinwa, 2017;
Muchdie et al., 2016; Arabi & Abdalla, 2013; Nwosu, Dike & Okwara, 2014; Biktemirova et
al., 2015; Afghah, Raoofi and Hoshyar, 2014; Odetola & Etumnu, 2013; Sertoglu, Ugural and
Bekun, 2017). On the other hand, non economic factors also affect economic growth and
these are political, education, urbanization and religion (Younis, Lin, Sharahili and
Selvarathinam, 2008; Kotaskova et al., 2018; Odit, Dookhan and Fauzel, 2010; Arouri,
Youssef, Nguyen-Viet and Soucat, 2014; and Campante & Yangizawa-Drott, 2013). Long
religious induced holidays and other religious bigotry affect economic growth negatively, for
instance.
2.1.3. Gross Domestic Product (GDP)
GDP is the total market value of all final goods and services produced by all the people and
companies in a country, within a given period. It does not matter if they are citizens or
foreign-owned companies, as long as they are located within the country's boundaries; the
government counts their production as GDP (Amadeo, 2018). Fresh Forex (n.d) posits that
GDP includes the market cost of all goods and services produced on the territory of the state
by all branches of the economy purposed for consumption, accumulation or exporting for a
year. GDP is different from Gross National Product (GNP); as the latter is the total value of
goods and services produced by citizens of a particular country, no matter which part of the
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world they reside in. Amadeo (2018) cites the World Bank for preferring to use Gross
National Income in place of GNP, as it gives a better picture and the difference between both
is insignificant. However, GDP per capita is the best index for comparing GDP between
countries as it is derived by dividing the GDP by the number of residents of a country. Real
GDP is usually lower than Nominal GDP, and most countries prefer to use it (Real GDP)
since it takes out the effects of inflation, exchange rates, and differences in population.
Essentially, GDP is an important economic analysis tool because it is the best instrument to
assess a country‟s economic health; it is a yardstick for several monetary policies.
2.1.4. Inflation Rate
Inflation rate according to Evans (2014) is a measure of a general increase of the price level in
an economy, as represented typically by a general price index, such as the Consumer Price
Index (CPI). The term indicates that many individual prices are rising simultaneously rather
than one or two isolated prices. The inflation rate has seven distinct thresholds viz: < 0%
Deflation; 0% - 2.5% Price Stability; 2.5% - 5% Moderate Inflation; 5% - 8% Serious
Inflation; 8% - 12% Self-Compounding Inflation; 12% - 20% Hyperinflation; and 20%+
Explosive Inflation. A healthy rate of inflation is considered positive since it encourages
consumption which can in turn stimulate the economy and create more jobs. However
inflation above the mild threshold is detrimental to economic growth and development.
2.2. Theoretical Framework
2.2.1. The Technology Acceptance Model (TAM)
TAM was postulated by Davis (1989). He believed that this model could be used as a tool to
predict acceptance of technology. This is because it demonstrates the relationship connecting
believe, attitude and action purpose. Holistically, TAM attempts to predict individuals‟
intentions toward using a technology based on its Perceived Usefulness (PU) and Perceived
Ease of Use (POEU). Accordingly, PU is the degree to which a person believes that using a
particular system would improve their job performance while PEOU is the degree to which a
person believes that using a particular system would be free of physical as well as mental
effort. Very importantly, some researches having individual‟s acceptance of mobile services
as their central research focus have used TAM to understand the adoption of different mobile
services (Hong et al., 2006; Bouwman et al., 2012; Pederson, 2003; Wang et al., 2006). This
suggests the possibility to predict users‟ acceptance and adoption of mobile services using
TAM constructs. However, Mathieson (1991) and Stern et al. (2007) argue that despite the
predictive ability of the constructs, they are not alone. In other words, PU and PEOU in TAM
are not sufficient enough to predict users‟ intentions.
2.2.2. Agency Theory
Jensen and Mecklin (1976) were among the propounders of the Agency Theory. The theory
focuses on the Principal – Agent - Delegation - of - Work paradigm using the contract. The
central idea here is that the Principal is too busy to do a given job and so hires an agent to
undertake the task for him. The issues that arise from this is that while the Principal and the
Agent work towards the same goal, they may not always have the same interest. The literature
on Agency Theory mostly focuses on methods and systems and their consequences that arise
to try to match the interests of both the Principal and the Agent. Mobile money transfer
definitely benefits from a proper analysis and application of the Agency theory as it
introduces middle men between the payer and receiver of money quite different from the bank
itself.
Dr. Robinson Onuora Ugwoke, Michael Keneath Foleng and Obioma Vivian Ugwoke
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2.2.3. Transaction Cost Theory
Transaction Cost Theory was initially developed by Coase (1937). He aimed at explaining
why certain activities, products, or services are carried out internally in firms, while others are
bought and sold in the marketplace. This implies that companies weigh the costs of
exchanging resources with the environment, against the bureaucratic costs of performing
activities in-house. If the external transaction costs are higher than the company‟s internal
bureaucratic costs, the company will be more profitable by performing activities in-house and
vice versa. Mobile money transfer definitely benefits from a proper analysis and application
of the transaction cost theory as it is envisaged that cost of transaction would drastically be
reduced through its adoption. Recall that Transaction Cost Theory recognizes that it is
unlikely to be economically optimal to obtain perfect knowledge and that even if this were
possible, the extra cost of obtaining extra information should be weighed against its extra
benefits (Baumol and Quandt, 1964). According to Williamson (1981), a transaction cost
occurs “when a good or serviceis transferred across a technologically separable interface".
From all the theories explained above, this study is anchored on the Theory of Technology
Acceptance Model (TAM). This is because a potential user‟s acceptance or rejection of this
new technology (mobile money) will depend on how easy and convenient they find it to use
the service. The more user-friendly the service, the more subscribers there will be and this
will therefore go a long way to enhance economic activities.
2.3. Empirical Review
Nyasimi (2016) examined the effect of Mobile Money Transfers on Economic Growth in
Kenya. Her study employed Explanatory Research Design which concentrated on “why”
questions by developing casual explanations. The dependent variable was Economic Growth
for the year 2007 to 2015 while the independent variables were Mobile Money Transfer
Agents, Mobile Money Transfer Customer Enrolments, Mobile Money Transfer Transaction
Frequency and Mobile Money Transfer Deposit Value. These secondary data were collected
from the Central Bank of Kenya (CBK) as well as the Kenya National Bureau of Statistics
(KNBS) Reports. After regressing the data, there was no co-integration between Economic
Growth and Mobile Money Agents, Customers, Frequency of Transfer as well as the Value of
Money Transferred. Instead the VAR modelling impulse response showed that; Number of
Agents, Customers and Frequency of Transactions have a long run real shock on Economic
Growth while both interest rate and exchange rate impact it negatively. She recommended the
need to intensify the adaption of Mobile Money Transfer Services among those who have not
adopted them while the Kenya government should consider the Mobile Money Transfer when
drafting its policies.
Habane (2012) established the relationship between mobile money transfers and economic
growth in Kenya using descriptive research design and correlation analysis. The target
population included six mobile phone service providers in Kenya which also provide mobile
money transfer services. The total amounts transferred through the mobile for the past five
years was collected and then correlated with the economic growth proxy, Gross Domestic
Product, measured by change in GDP. Data were sourced from the Annual Financial
Statements of the Central Bank of Kenya and the Mobile Phone Companies, and Kenya
National Bureau of Statistics. The study revealed that the amount of money transacted
through Mobile Money Transfers increased steadily from KSh 0.06 billion in 2007 on its
launch to KSh 118.08 billion by 2012. The growth was driven by the convenience offered by
the service as the service does not require an individual to have a bank account in order to
transact. Customers also transacted business on Mobile Money Transfer platform from
anywhere thus offering convenience. But it also found that there was a positive though
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insignificant correlation between Economic Growth and Mobile Money Transfer in Kenya.
The study recommended that the policymakers should take Mobile Money Transfer into
account when drafting policies. This was due to the indirect relationship of Mobile Money
Transfer to Economic Growth through the provision of job opportunities, increased financial
deepening and financial inclusion.
Ssonko (2010) explored the role of Mobile Money Services (MMS) in enhancing
Financial Inclusion (FI). His study was inspired by the proliferation of mobile phones
amongst low-income earners, the prepaid billing system sensitive to users‟ incomes, embrace
of ICT by the government and the private sector that has enhanced e-commerce readiness of
Uganda, as well as the launch of three Mobile Money Services in the country. A Qualitative
Analysis of the web content of the three MMS providers was undertaken and concentrated on
issues related to services provided; transaction charges; number of registered customers;
number and volume of transactions; stakeholders; user interfaces and security; institutional
relationships; policy and regulation; as well as appropriateness of the current business
model(s). The results indicated that while the MMS has enormous potential to enhance FI, it
would require an open business model that involves all stakeholders to establish a truly
national solution. Moreover, the initial contribution of MMS to FI was in the enhancement of
money transfer by lowering the transaction costs for small volumes. He recommended that the
regulatory authorities need to establish a legal framework that does not suppress innovation
but ensures safety for customers‟ savings.
Mbogo (2010) investigated the success factors attributable to the use of mobile payments
by micro-business operators through a sample of 409 micro business entrepreneurs in Nairobi,
Kenya and applying the Theory of Technology Acceptance Model (TAM) which was
extended to include other factors to assist her in predicting success and growth in micro-
businesses. The results revealed that the convenience of the money transfer technology as
well as its accessibility, cost, support and security factors are related to behavioural intention
to use and actual usage of the mobile payment services by the micro businesses to improve
their success and growth.
Lee and Gardner (2010) analysed the impact of mobile phone penetration on economic
growth by estimating a fixed-effect dynamic panel model on 56 South Asian and Sub-Saharan
African countries from 1990 to 2008. Their results indicate that mobile phones are positively
correlated with economic growth and that the marginal contribution is even more significant
where the conventional fixed-line telecommunications infrastructure is poor.
Blauw and Franses (2011) also examined the impact of mobile telephone use on the
economic development of individual households in Uganda. They used unique cross-sectional
data obtained through personal interviews with heads of households (N=196) in Uganda.
Economic development was measured at the household level by the Progress out of Poverty
Index. They found strong evidence that mobile phone use positively impacts economic
development. Kamau (2012) established the relationship between agency banking and
financial performance of the banks in Kenya. An agency bank is a company or organisation
which acts in some capacity on behalf of another bank. Through a review of secondary data,
the study found that agency banking outlets had increased from 8,809 active agents in 2010 to
9,748 the following year, which is 2011. These specific agents facilitated a total volume of 8.7
million transactions which were valued at KSh 43.6 billion in 2011. Most of these
transactions mainly consisted of cash withdrawals and cash deposits carried out at the various
banking agency outlets. In their own case, Batista and Vicente (2012) designed and conducted
a field experiment to assess the impact of randomised mobile money dissemination in rural
Mozambique. For this purpose they benefited from the fact that mobile money was recently
launched in the country, allowing for the identification of a real control group. They found
Dr. Robinson Onuora Ugwoke, Michael Keneath Foleng and Obioma Vivian Ugwoke
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clear adherence to the services from administrative and behavioural data in the treatment
group. Financial literacy and trust outcomes were positively affected by the treatment. They
showed behavioural evidence that the availability of mobile money increased the marginal
willingness to remit. Finally, they observed that mobile money substitutes traditional
alternatives for both savings and remittances.
In a study in Uganda still, Kamukama and Tumwine (2012) found that Ugandan
commercial banks were in a liquidity crisis, having fallen short of the Bank of Uganda‟s
threshold ratio of 20% but mobile money services alone accounted for 36.7% of liquidity
variance. They recommended that commercial banks should partner or enter into a joint
venture with mobile money operators so as to expand their physical reach into poor and rural
areas.
Wanyonyi and Bwisa (2013) sought to determine if the use of Mobile Money Transfer
Services in the Kitale municipality of Kenya, a rural town setting, had resulted in the success
and growth of microenterprises. Their study was based on a survey of 36 microenterprises,
from three major sectors of the Kenyan economy; agriculture, service and processing sectors.
Microenterprises that were studied were those that had been in existence for more than five
years and had experienced business without Mobile Money Transfer (MMT), before 2007
(when MMT was not yet in Kenya), and after that with it. Their study used a questionnaire
and Chi-Square analysis and found out that mobile money transfer for business-to-business
(B2B) transactions when making purchases from suppliers, and customer-to-business (C2B)
transfers when customers buy from the business as well as debt collection for credit sales
contributed to improved performance of the micro-enterprises. Similar finding was made by
Kirui et al. (2013) in the Agricultural sector in Kenya where the use of mobile phone –based
money transfer significantly increased the level of annual household input use by $42,
household agricultural commercialisation by 37% and annual household income by $224.
They concluded that mobile phone-based money transfer services in rural areas help to
resolve a market failure that farmers face; access to financial services. Frederick (2014)
examined the effect of Mobile Money usage on microenterprise profits in Zambia. She
employed an instrumental variable strategy using the type of mobile operator as the
instrument to address the selection bias in adoption, as Mobile Money services are at the
disposal of everyone. In this urban context, she found initial evidence of positive net marginal
benefits for microenterprises using mobile money, and she calculated bounds that range
between 36% and 74% increase in profits. Her study helped to fill the gaps in the emerging
microenterprise and free money literature and offered guidance to public and private
policymakers regarding this market segment. According to Chale (2014), small and medium
enterprises in Tanzania use Mobile Money services in different ways for business purposes,
which include sales transactions, efficiency in the purchase of stock, receiving payment,
payment of goods and services, savings as well as money transfer that influenced their
business growth. This also is closely aligned to the position of Makee et al. (2014) who
maintain that there is indeed a positive effect of the mobile phone transfer services
innovations on enterprise performance among hair-dressing, carpentry and cloth making
industries in Kitale town in Kenya as well as Aker et al. (2014) to the effect that households
receiving free transfers had higher diet diversity and children consumed more meals per day
in Niger. On adoptability of mobile money transfer services, Etim (2014) carpeted Nigeria for
low use of mobile phones for money transfers. But Tsilizani (2015) paints a different picture
for Malawi where she says that mobile money transfer is well adopted and it has helped in
poverty alleviation. In like manner, in Ghana, Bampoe (2015) found that the adoption of
mobile money transfer is affected by factors as perceived usefulness, perceived trust, social
influence and competitive intensity and recommends that different parties of interest for
Effect of Mobile Money Transfer Scheme on the Economic Growth of Cameroon
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mobile money should recognise and address these factors to increase its use and encourage its
general acceptance.
Other notable researchers in this field include Peruta (2015) who investigated the adoption
patterns of Mobile Money in emerging developing countries such as Kenya, Tanzania,
Uganda, Burundi and Rwanda, Saliu (2015) and Bank of Ghana (2017) in Ghana; Similu and
Oloko (2015), Kirui and Onyuma (2015) and Soi (2018) in Kenya; Madila and Msamba
(2016) in Tanzania; Islam et al. (2016) in the East African countries of Kenya, Tanzania and
Uganda; Mawejje and Lakuma (2017); Munyoro et al. (2017) in Zimbabwe; These
researchers extensively linked Mobile Money Transfer Scheme and Economic Growth though
from different sectors of the varying economies and point to the success of Mobile money
transfer scheme. However, none of them, to the best knowledge of the researchers have
assessed the effect of Mobile Money Transfer Scheme on the Economic Growth of
Cameroon, and this constitutes the gap that this research has filled.
3. METHODOLOGY
Explanatory Research Design was adopted. It concentrates on “why” questions and answers
involving the development of causal explanations (De Vaus, 2001) thus establishing cause
and effect between variables (Mugenda and Mugenda 2003). It is for 16 year period from
2002 to 2017; 8 years pre and 8 years after the introduction the scheme in Cameroon. The
secondary data were collected from the Annual Reports of Cameroon‟s Central Bank i.e.
Bank of Central African States also called BEAC (2018) for mobile money data, IMF (2018)
for the GDP of Cameroon, Global Economy (2018) for the unemployment rate of Cameroon,
and Knoema (2018) for the inflation rate of Cameroon. The population consisted of the two
principal Mobile Money Operators in Cameroon, i.e. MTN Mobile Money and Orange
Money. Regression Analysis was used while the applicable model for each of the hypotheses
is specified below:
H01: GDP = f(MMTS)……………………………………………………… (1)
Where: GDP = Gross Domestic Product (dependent variable)
MMTS = Mobile Money Transfer Scheme (MMTS)
H02: IR = f(MMTS)…………………………………………………………… (2)
Where: IR = Inflation Rate (dependent variable)
MMTS = Mobile Money Transfer Scheme (MMTS)
4. DATA PRESENTATION AND ANALYSIS
Table 4.1: GDP of Cameroon from 2002 - 2017
Year GDP, Current Price
(bn US$)
GDP, Current PPP
(bn US$)
Real GDP Growth
(%)
2002 11.6 35.6 4.2
2003 14.6 38.0 4.6
2004 17.4 41.7 6.8
2005 18.0 43.9 2.0
2006 19.4 46.8 3.5
2007 22.4 50.4 4.9
2008 26.5 53.2 3.5
2009 26.1 54.8 2.2
2010 26.2 57.3 3.4
2011 29.4 60.9 4.1
2012 29.1 64.9 4.5
Dr. Robinson Onuora Ugwoke, Michael Keneath Foleng and Obioma Vivian Ugwoke
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Source: Open Data for Africa (2018) (citing IMF) Table 4.1 revealed
That the GDP of Cameroon experienced a fluctuation from 2002 to 2017. In other words,
it increased from 2002 – 2004 but declined in 2005. It rose again from 2006 – 2007 but
experienced a decline again in 2008 - 2009. In the same vein, it rose from 2010 to 2014 and
experienced another decline from 2015 – 2017. Therefore, the trend shows a persistent
fluctuation in the GDP of Cameroon. These trends are also shown by the time series plot
shown in Figure 4.1.
Figure 4.1: Showing the Trend in the GDP of Cameroon from 2002 – 2017
Source: Researcher‟s Analysis, 2018.
Table 4.2: Inflation Rate of Cameroon from 2002 - 2017
Year Inflation Rate (%) % Change
2002 2.8 -
2003 0.6 -78.6
2004 0.3 -50.0
2005 2.0 +566.7
2006 4.9 +145.0
2007 1.1 -77.6
2008 5.3 +381.8
2009 3.0 -43.4
2010 1.3 -56.7
2011 2.9 +123.1
2012 2.4 -17.2
2013 2.1 -12.5
2014 1.9 -9.5
2015 2.7 +42.1
2016 0.9 -66.7
2017 0.6 -33.3
Source: Knoema (2018)
2013 32.4 69.5 5.4
2014 35.0 74.9 5.9
2015 30.9 80.0 5.7
2016 32.2 84.6 4.5
2017 34.0 88.9 3.2
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Table 4.2 revealed that the inflation rate of Cameroon also experienced a fluctuation from
2002 to 2017. In other words, it declined from 2002 – 2004, then increased from 2005 – 2006
but fell again from 2008 – 2010. It increased in 2011 but dropped from 2012 – 2014. It rose in
2015 but descended from 2016 – 2017. This detail is expressed graphically in Figure 4.2
Figure 4.2: Showing the Trend in the Inflation Rate of Cameroon from 2002 - 2017
Source: Researcher‟s Analysis, 2018.
Table 4.3: Showing the GDP and Inflation Rate of Cameroon BEFORE the Mobile Money Transfer
Scheme
Year MMTS GDP
(%)
Inflation
Rate
(%) Volume of
Transactions
Value of
Transactions
(FCFA)
Value of
Transactions
(US $)
2002 - - - 4.2 2.8
2003 - - - 4.6 0.6
2004 - - - 6.8 0.3
2005 - - - 2.0 2.0
2006 - - - 3.5 4.9
2007 - - - 4.9 1.1
2008 - - - 3.5 5.3
2009 - - - 2.2 3.0
Table 4.3 revealed that the GDP and the inflation rate fluctuated (increased and decreased)
repeatedly from 2002 – 2009, that is before the Mobile Money Transfer Scheme. These
details are expressed graphically in Figure 4.3
Dr. Robinson Onuora Ugwoke, Michael Keneath Foleng and Obioma Vivian Ugwoke
http://www.iaeme.com/IJMET/index.asp 496 [email protected]
Figure 4.3: Showing the Trend in the GDP and Inflation Rate of Cameroon BEFORE the Mobile
Money Transfer Scheme
Source: Researcher‟s Analysis, 2018.
Table 4.4: Showing the GDP and Inflation Rate of Cameroon AFTER the Mobile Money Transfer
Scheme
Year MMTS GDP
(%)
Inflation
Rate
(%)
Volume of
Transacti
ons
Value of
Transactions
(FCFA)
Value of
Transactio
ns (US $)
2010 6,478 461,973 807 3.4 1.3
2011 35,359 1,485,327,981 2,594,110 4.1 2.9
2012 605,691 14,326,108,301 25,020,405 4.5 2.4
2013 2,131,267 106,919,195,069 186,733,305 5.4 2.1
2014 6,991,176 280,112,364,451 489,213,443 5.9 1.9
2015 13,768,731 307,589,225,761 537,201,507 5.7 2.7
2016 49,831,982 887,783,935,214 1,550,505,765 4.5 0.9
2017 210,276,929 3,412,970,418,636 5,960,718,706 3.2 0.6
NOTE: 1 FCFA = 0.00174649 US $
1 US $ = 572,578 FCFA
Table 4.4revealed that GDP of Cameroon increased from 2010 – 2014 but decreased from
2015 – 2017. However, the inflation rate of Cameroon fluctuated repeatedly from 2010 –
2017.These results are supported by the time series plot shown in Figure 4.4
GDP INFLATION RATE
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Figure 4.4: Showing the Trend in the GDP and Inflation Rate of Cameroon AFTER the Mobile
Money Transfer Scheme
Source: Researcher‟s Analysis, 2018.
4.2. Test of Hypotheses
4.2.1. Test of Hypothesis One
H0: Mobile Money Transfer Scheme does not have a significant effect on the GDP of
Cameroon.
H1: Mobile Money Transfer Scheme has a significant effect on the GDP of Cameroon.
To test the hypothesis, the data on Tables 4.3 and 4.4were used. The goal was to
determine if the Mobile Money Transfer Scheme has a significant effect on the GDP of
Cameroon.
Regression Test for Hypothesis One
Table 4.5
Model Summaryb
Model r r2 Adjusted r
2
Std. The error of the
Estimate
1 .162a .026 -.035 1.25265
a. Predictors: (Constant), MOBILE MONEY TRANSFER SCHEME
b. Dependent Variable: GDP
Source: SPSS Analysis, 2018
Table 4.6
GDP INFLATION RATE
Coefficientsa
Model
Unstandardized Coefficients
Standardised
Coefficients
T Sig. B Std. Error Beta
1 (Constant) 4.337 .320 13.546 .000
MOBILE MONEY
TRANSFER SCHEME -8.522E-11 .000 -.162 -.658 .520
a. Dependent Variable: GDP
Source: Researcher‟s SPSS Analysis, 2018
Dr. Robinson Onuora Ugwoke, Michael Keneath Foleng and Obioma Vivian Ugwoke
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Result: The result of the regression model shows that there is no statistically significant
relationship between mobile Money Transfer Scheme and the GDP of Cameroon (i.e. p >
0.05, @ 0.05 significant level). It also indicates that the strength or magnitude of the
relationship between the Mobile Money Transfer Scheme and the GDP of Cameroon is feeble
even though it is positive (r = .162). Also, the coefficient of determination (r2) which shows
the variance explained between Mobile Money Transfer Scheme and the GDP of Cameroon
indicates 2.6% shared variance (i.e. r2 expressed as a percentage). This implies that the
Mobile Money Transfer Scheme explains only 2.6% of the variance in the GDP. Furthermore,
the coefficient of the variable vis-à-vis Mobile Money Transfer Scheme (that is
0.00000000008522 with a p-value of .520) is also not significant when related with the GDP
of Cameroon for the period under study. Thus, the effect of the Mobile Money Transfer
Scheme on the GDP of Cameroon is not significant.
Decision Rule: If the p-value is less than 0.05, reject the null hypothesis and accept the
alternate hypothesis.
Decision: The result of the regression model shows that there is no statistically significant
relationship between Mobile Money Transfer Scheme and the GDP of Cameroon (r = .162,
alpha-significance is .520 at p > 0.05). Therefore, we accept the null hypothesis which states
that Mobile Money Transfer Scheme does not have a significant effect on the GDP of
Cameroon.
4.2.2. Test of Hypothesis Two
H0: Mobile Money Transfer Scheme has no significant effect on the inflation rate of
Cameroon
H1: Mobile Money Transfer Scheme has a significant effect on the inflation rate of Cameroon
To test the hypothesis, the data on Tables4.3 and 4.4were used. The goal was to determine
if the Mobile Money Transfer Scheme has a significant effect on the inflation rate of
Cameroon.
Regression Test for Hypothesis Two
Table 4.7
Model Summaryb
Model R r2 Adjusted r
2
Std. The error of the
Estimate
1 .385a .148 .095 1.42650
a. Predictors: (Constant), MOBILE MONEY TRANSFER SCHEME
b. Dependent Variable: INFLATION RATE
Source: Researcher‟s SPSS Analysis, 2018
Table 4.8
Source: Researcher‟s SPSS Analysis, 2018
Coefficientsa
Model
Unstandardized Coefficients
Standardised
Coefficients
T Sig. B Std. Error Beta
1 (Constant) 2.469 .365 6.770 .000
Mobile Money Transfer
Scheme -2.462E-10 .000 -.385 -1.668 .115
a. Dependent Variable: INFLATION RATE
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Result: The result of the regression model shows that there is no statistically significant
relationship between the Mobile Money Transfer Scheme and the inflation rate of Cameroon
(i.e. p > 0.05 at 0.05 significance level). It also indicates that the strength or magnitude of the
relationship between mobile Money Transfer Scheme and the inflation rate in Cameroon is
feeble even though it is positive (r = .385). Also, the coefficient of determination (r2) which
shows the variance explained between Mobile Money Transfer Scheme and the inflation rate
of Cameroonindicates 14.8% shared variance (i.e. r2 expressed as a percentage). This implies
that the Mobile Money Transfer Schemehelps to explain only 14.8% of the variance inthe
inflation rate of Cameroon. This is also an insignificant amount of variance explained.
Furthermore, the coefficient of the variable vis-à-vis Mobile Money Transfer Scheme (that is
0.0000000002462 with a p-value of .115) is also not significant when related with the
inflation rate of Cameroon for the period under study. This implies that the Mobile Money
Transfer Scheme cannot be used solely to predict the inflation rate of Cameroon as other
factors account for the remaining 85.2% of the variance unaccounted for.
Decision Rule: If the p-value is less than 0.05, reject the null hypothesis and accept the
alternate hypothesis.
Decision: The result of the regression model shows that there is no statistically significant
relationship between Mobile Money Transfer Scheme and the inflation rate of Cameroon (r =
.385, alpha-significance is .115 at p > 0.05). Therefore, we accept the null hypothesis which
states that Mobile Money Transfer Scheme has no significant effect on the inflation rate of
Cameroon.
4.3. Discussion of Results
For hypothesis one, the result of the test of hypothesis indicated that Mobile Money Transfer
Scheme has no significant effect on the GDP of Cameroon (r = .162, alpha-significance is
.520 at p > 0.05).Also, the value of r-squared implied that Mobile Money Transfer Scheme
only helps to explain only 2.6% of the variance in the GDP of Cameroon, and therefore
insignificant. This result or finding can be supported with the study of Nyasimi (2016) in
Kenya who revealed that there was no significant influence of Mobile Money Transfer
Scheme on Economic Growth. She also revealed that mobile money transfer deposit value
had a positive but insignificant relationship with economic growth in Kenya. This further
aligned with the result of Mawejje and Lakuma (2017) who found out that mobile money had
only a moderate positive effect in the Ugandan economy, partly because the service was
relatively new in the country and had not yet gained substantial ground in the economy.
In a similar vein, the study also agrees with the findings of Wilkison and Sundelelowotz
(2007) who argued that there are direct and indirect links between the exponential growth of
mobile telephony and the rate of economic growth in Africa. A possible explanation for this
finding could be seen in the work of Eriksson (2010) who stated that even though Mobile
Money Transfer Scheme could have positive effects with economic growth, such benefits
could be stifled by regulatory and initial investment barriers that could prevent the widespread
adoption of mobile money. In a practical sense, this could be said to be the situation in
Cameroon where the government has not deemed it fit to subsidise the development of more
local mobile money infrastructure or to adopt policies that would enable the formation of a
decentralised network of trusted mobile money agents.
For hypothesis two, the result indicated that the Mobile Money Transfer Scheme has no
significant effect on the inflation rate of Cameroon (r= .385, alpha-significance is .115 at p >
0.05). Besides, the value of the r-squared also implies that Mobile Money Transfer Scheme
helps to explain only 14.8% of the variance in the inflation rate in Cameroon, therefore
insignificant. This finding can be corroborated with the study of Habane (2012) who revealed
Dr. Robinson Onuora Ugwoke, Michael Keneath Foleng and Obioma Vivian Ugwoke
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that there was a weak positive insignificant correlation between economic growth (inflation
rate) and mobile money transfer in Kenya as explained by the Pearson correlation coefficient
of +0.027 which was very low with the significance two-tailed test figure being 0.966 which
was greater than 0.05.
A possible explanation could be adduced from the findings of Pat (2018) who stated that
the major causes of inflation in Cameroon are increased money supply, increased input costs
of raw materials and wages as well as staggering exchange rate which has made the
Cameroon currency (FCFA) to become less valuable relative to foreign currency and has also
made imports to be more expensive toCameroonian consumers while simultaneously making
Cameroonian‟s exports cheaper to foreign consumers.
Generally, Mobile Money is an essential tool for poverty reduction since it bridges the gap
that exists between banks and poor households. Most banks do not find it economically
attractive to make banking infrastructure and financial services available in poor
communities. This is because high transaction costs relative to small transaction value sizes
make it unprofitable for banks to service this population. Similarly, poor people can be
reluctant to access formal financial services as a result of the inconvenience and high cost
involved in accessing these services relative to the more local and informal alternatives they
have traditionally used. Besides, some of them even mistrust these formal banking
institutions.For these reasons, around 2.5 billion adults in the world today are excluded from
the formal financial system and are subject to „financial exclusion.‟ This group of people are
referred to as the „unbanked.‟ Providing the unbanked with access to financial services,
known as „financial inclusion‟, is now recognised as an essential mechanism for alleviating
poverty and enhancing a country‟s broader economic development. Financial inclusion aims
to provide the unbanked,low-income households and small business with a range of financial
services which they can use to facilitate their consumption and protect themselves against
„economic shocks‟, such as illness, accidents, theft, and unemployment. An economic shock
can be severely detrimental to the unbanked‟s already vulnerable financial position, making it
more difficult for them to move out of poverty. In many less developed countries, economic
shocks can take a wide variety of forms beyond traditional financial or economic crisis; they
can also be health-related emergencies, crop failures, livestock deaths, and farming-equipment
expenses. Financial inclusion also aims to help the unbanked and low-income groups to save
and borrow which in turn can enable them to invest in education and asset generating
activities, such as enterprises. (Buckley, Greenacre and Malady, 2015)
5. SUMMARY OF FINDINGS, CONCLUSION AND
RECOMMENDATIONS
5.1. Summary of Findings
1. There was no significant relationship between Mobile Money Transfer Scheme and GDP in
Cameroon.
2. There was no significant relationship between Mobile Money Transfer Scheme and
Inflation Rate of Cameroon..
5.2. Conclusion
The importance of the Mobile Money Transfer Scheme in third world countries, where bank
accounts are unaffordable to most people, cannot be overemphasised. However given the fact
that this industry is a relatively new phenomenon in Cameroon, it has not yet attained the
level whereby it can have a significant effect on the economic growth of Cameroon. The
result of the study attests to this fact. One of the reasons that could account for this is due to
the current,severe political crisis in Cameroon which is slowing down most businesses.
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5.3. Recommendations
1. The government of Cameroon needs to implement policies which will favour the further
penetration of the Mobile Money operations right into the remote interior parts of the country.
They can do so by giving incentives to MTN, Orange and Nexttel to install network poles in
those areas so that the use of Mobile Money can reach the places.
2. The government of Cameroon needs to resolve the current political crisis in the North West
Region and South West Region to facilitate the penetration of the Mobile Money services into
the interior villages of those regions. This will enable the service to be enjoyed by a greater
audience and thus boost the economic growth of the nation.
3. Increase in the tele-density of Cameroon. This could be done by subsidising the cost of
mobile phones to make it even more affordable to a wider population of the country.
4. The government needs to do more to ensure a more constant and stable supply of
electricity. This is because mobile phones are powered by electricity, and so blackouts will
only help to reduce the frequency of mobile money transactions as the phone‟s battery will be
running flat.
5. The government also needs to invest more in education so as to improve the literacy rate of
the country. This because the more literate the populace is, the greater exposure they will have
and thus will be able make better use of technology such as mobile money.
The significant contribution to knowledge in this study is filling the existing gap observed in
literature, which is absence of documented research on the effect of Mobile Money Transfer
Scheme on the Economic Growth of Cameroon.
5.4. Suggested Areas for Further Studies
This study is by no means exhaustive. The researcher has noted other possible areas for
further studies such as: Mobile Money Transfer Scheme vis-viz Interest Rate in Cameroon.
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Effect of Mobile Money Transfer Scheme on the Economic Growth of Cameroon
http://www.iaeme.com/IJMET/index.asp 507 [email protected]
APPENDIX
Regression
Notes
Output Created 20-OCT-2018 19:14:59
Comments
Input Data C:\Users\DELL\Desktop\MICHAEL'S
ANALYSIS\ANALYSIS FOR
MICHAEL - SPSS.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data
File 18
Missing Value Handling Definition of Missing User-defined missing values are treated
as missing.
Cases Used Statistics are based on cases with no
missing values for any variable used.
Syntax REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R
ANOVA COLLIN TOL ZPP
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT GDP
/METHOD=ENTER VOT
/SCATTERPLOT=(*ZRESID
,*ZPRED).
Resources Processor Time 00:00:02.54
Elapsed Time 00:00:11.22
Memory Required 1420 bytes
Additional Memory Required
for Residual Plots 240 bytes
[DataSet1] C:\Users\DELL\Desktop\MICHAEL'S ANALYSIS\ANALYSIS FOR MICHAEL -
SPSS.sav
Variables Entered/Removeda
Model Variables Entered
Variables
Removed Method
1 MOBILE MONEY
TRANSFER SCHEMEb
. Enter
a. Dependent Variable: GDP
b. All requested variables entered.
Model Summaryb
Model r r2 Adjusted r
2 Std. Error of the Estimate
1 .162a .026 -.035 1.25265
a. Predictors: (Constant), MOBILE MONEY TRANSFER SCHEME
b. Dependent Variable: GDP
Dr. Robinson Onuora Ugwoke, Michael Keneath Foleng and Obioma Vivian Ugwoke
http://www.iaeme.com/IJMET/index.asp 508 [email protected]
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression .678 1 .678 .432 .520b
Residual 25.106 16 1.569
Total 25.784 17
a. Dependent Variable: GDP
b. Predictors: (Constant), MOBILE MONEY TRANSFER SCHEME
Coefficientsa
Model
Unstandardized
Coefficients
Standardised
Coefficients
t Sig. B Std. Error Beta
1 (Constant) 4.337 .320 13.546 .000
MOBILE MONEY
TRANSFER SCHEME
-8.522E-
11 .000 -.162 -.658 .520
Coefficients
a
Model
Correlations Collinearity Statistics
Zero-order Partial Part Tolerance VIF
1 (Constant)
MOBILE MONEY
TRANSFER SCHEME -.162 -.162 -.162 1.000 1.000
a. Dependent Variable: GDP
CollinearityDiagnosticsa
Model Dimension
Eigen
Value
Condition
Index
Variance Proportions
(Constant)
MOBILE MONEY
TRANSFER SCHEME
1 1 1.387 1.000 .31 .31
2 .613 1.504 .69 .69
a. Dependent Variable: GDP
Residuals Statisticsa
Minimum Maximum Mean Std. Deviation N
Predicted Value 3.6171 4.3370 4.2556 .19977 18
Residual -2.33699 2.46301 .00000 1.21525 18
Std. Predicted Value -3.196 .408 .000 1.000 18
Std. Residual -1.866 1.966 .000 .970 18
a. Dependent Variable: GDP
Effect of Mobile Money Transfer Scheme on the Economic Growth of Cameroon
http://www.iaeme.com/IJMET/index.asp 509 [email protected]
Charts
REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA COLLIN TOL ZPP
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT EMPLOYMENT
/METHOD=ENTER VOT
/SCATTERPLOT=(*ZRESID ,*ZPRED).
Regression
Notes
Output Created 20-OCT-2018 19:22:57
Comments
Input Data C:\Users\DELL\Desktop\MICHAEL'
S ANALYSIS\ANALYSIS FOR
MICHAEL - SPSS.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working
Data File 18
Missing Value Handling Definition of Missing User-defined missing values are
treated as missing.
Cases Used Statistics are based on cases with no
missing values for any variable used.
Dr. Robinson Onuora Ugwoke, Michael Keneath Foleng and Obioma Vivian Ugwoke
http://www.iaeme.com/IJMET/index.asp 510 [email protected]
Syntax REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R
ANOVA COLLIN TOL ZPP
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT INFLATION
/METHOD=ENTER VOT
/SCATTERPLOT=(*ZRESID
,*ZPRED).
Resources Processor Time 00:00:00.48
Elapsed Time 00:00:01.81
Memory Required 1420 bytes
Additional Memory
Required for Residual Plots 240 bytes
Variables Entered/Removeda
Model Variables Entered Variables Removed Method
1 MOBILE MONEY TRANSFER
SCHEMEb
. Enter
a. Dependent Variable: INFLATION RATE
b. All requested variables entered.
Model Summaryb
Model r r2 Adjusted r
2 Std. Error of the Estimate
1 .385a .148 .095 1.42650
a. Predictors: (Constant), MOBILE MONEY TRANSFER SCHEME
b. Dependent Variable: INFLATION RATE
a. Dependent Variable: INFLATION RATE
b. Predictors: (Constant), MOBILE MONEY TRANSFER SCHEME
Coefficientsa
Model
Unstandardized Coefficients
Standardised
Coefficients
t Sig. B Std. Error Beta
1 (Constant) 2.469 .365 6.770 .000
MOBILE MONEY
TRANSFER SCHEME -2.462E-10 .000 -.385 -1.668 .115
ANOVAa
Model
Sum of
Squares Df Mean Square F Sig.
1 Regression 5.662 1 5.662 2.782 .115b
Residual 32.558 16 2.035
Total 38.220 17
Effect of Mobile Money Transfer Scheme on the Economic Growth of Cameroon
http://www.iaeme.com/IJMET/index.asp 511 [email protected]
Coefficientsa
Model
Correlations Collinearity Statistics
Zero-order Partial Part Tolerance VIF
1 (Constant)
MOBILE MONEY
TRANSFER SCHEME -.385 -.385 -.385 1.000 1.000
CollinearityDiagnosticsa
Model Dimension Eigenvalue
Condition
Index
Variance Proportions
(Constant)
MOBILE MONEY
TRANSFER SCHEME
1 1 1.387 1.000 .31 .31
2 .613 1.504 .69 .69
Residuals Statisticsa
Minimum Maximum Mean Std.
Deviation
N
Predicted Value .3891 2.4686 2.2333 .57709 18
Residual -2.16856 2.83144 .00000 1.38391 18
Std. Predicted
Value
-3.196 .408 .000 1.000 18
Std. Residual -1.520 1.985 .000 .970 18
a. Dependent Variable: INFLATION RATE
Charts
a. Dependent Variable: INFLATION RATE
a. Dependent Variable: INFLATION RATE