1

1.0. Introduction
This chapter of the concept paper presents the background of the study, problem statement, purpose of the study, objectives of the study, research questions, scope of the study, significance of the study and the conceptual framework
1.1. Background to the study
Education is a gadget used to effect countrywide progress. Educational aims stand fixed out in the National Plan on Education in relations of their application to the requirements of the individual and society. The National Strategy is established with aims and objectives, to facilitate educational growth in the country. These goals and intents invite the school head to provide vital roles include enhancing real administrative skills and styles to manage colleges, improve job performance among teachers so students’ academic performance is boosted (Fika, I. Ibi, M. and Aji, B., 2015).
It is not astonishing that there is massive demand for operative administration of secondary schools. A good number of school heads haven’t considered the different administrative techniques which determine students’ academic performance in the country. Hence, some of them seem to find it tremendously problematic to successfully lead their schools (Akinnibagbe , 2002).
With the fast changing world, it is impossible for people of preferred managerial technique or type to embrace all knowledge, awareness or power to realise success (Muthondu G.W., 2007). These longstanding forms of management provide power and are a heading to one or few individuals involved in administrative positions. Leadership being gender requires that prospective leaders be trained to adapt to the fluctuating society and make an effort to teach and model different management techniques which will most effectually lead various institutions to achieve set goals.
Students’ performance in examinations is because of different factors; which include provision of physical facilities, classroom size, effective school discipline policies, administrative support and effective leadership. As several studies in Botswana, Nigeria, and Papua New Guinea concur to this (Muli, M.M , 2005). Good administration brings about necessary guidance in the school, clarity of direction and rewards to ensure effective performance of students.
1.2. Problem statement
Administration at work in educational institutions is a dynamic process where an individual is not only accountable for the group’s errands, but also actively seeks the cooperation and assurance of all the group members in achieving group goals in a particular perspective (Aji, B.M, 2014). Administration ensures that students perform to the maximum, since it ensures that tasks are accomplished and the responsible parties assigned for greater strengthening of the institution with emphasis put on recognition, service provision and motivation (Balunywa, W.S. , 2000). However, of all the above contributions of school administration towards academic performance, performance tends to be alarming and wanting in schools as a result of managerial techniques used which influences the organisational culture. Such managerial techniques are made of a set of attitudes, traits, and skills in the principals formed based on four factors: values, trusting employees, leadership orientation, and a sense of security shaped in important situations
1.3. Purpose of the study
The purpose of the study is to examine the effect of school administration on students’ academic performance in Isingiro District.
1.4. Objective of the study
i. To establish the different factors that affect students’ academic performance.
ii. To investigate the contribution of school administrators towards students’ academic performance
iii. To determine the possible measures to the challenges faced by students in Isingiro District.
1.5. Research questions
i. What are the different factors that affect students’ academic performance?
ii. What is the contribution of school administrators towards students’ academic performance?
iii. What are the possible measures to the challenges faced by students in Isingiro District?
1.6. Scope of the study
The study will focus on effect of school administrators on students’ academic performance The study is limited to school administrators as the independent variable and students’ academic performance as the dependent variable. The study will be carried out in Isingiro District. This so because of the limited finances and with government aided schools in place within the district whose academic performance tends to fluctuate each and every year.
1.7. Significance of the study
The results of this study will be valuable to researchers and scholars, as it would form a basis for further research. Scholars will use this study as a basis for discussions on school administration and students’ academic performance as it will provide the scholars with empirical studies that they will use in their studies. The study will also add to the body of knowledge in the education discipline by bridging the existing gap. This study will make several contributions to both knowledge building and practice improvement with several policies recommendation put forward
1.8. Conceptual framework
Independent Variable Dependent Variable

Mediating/intervening variable

Source: Researcher 2018
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2.0. Literature review
Principalship is a critical management skill involving the ability to encourage group of people towards common goal. Leadership focuses on the development of followers and their needs. Managers exercising transformational administrative style focusing on the development of value system of employees, their motivational level and moralities with the development of their skills, (Sashkin, M. ;Sashkin, M. , 2003). Different administrative styles of a school principal which include initiative, consideration and participatory structure of management (Omolayo B., 2009)
Initiative structure of administration is the extent to which a principal defines managers and group member roles, initiates actions, organizes group activities and defines how task are to be accomplished by the group. A leader in this structure defines his goals and facilitates group movement toward them. This administrative style decides everything and tries to manipulate the followers into approving his ideas on how the school should function. A leader in this group does not give trust to any member of the group.
Lee (1995) reported that, the Initiative structure of management leadership style results in the group members reacting aggressively and apathetically in the work environment. This often results in unending industrial disputes in an organization hence affecting the overall achievement of the organizational goals and objectives. Mwalala, (2008) observed that Initiative structure and harsh climate leads to poor performance of students. Initiative structure of management, also known as autocratic leaders, provide clear expectations for what needs to be done, when it should be done, and how it should be done. There is also a clear division between the leader and the followers.
In their study, Lewin and Caillords (2001) found that participative administrator, also known as democratic leadership, is generally the most effective administrative style. Participatory structure leadership not only offers guidance to group members, but they are allowed to participate in the group and allow input from other group members. Hence, children in this group were less productive than the members of the Initiative structure group, but their contributions were of a much higher quality. Participative administrator encourages group members to participate, but retain the final say over the decision-making process. Group members feel engaged in the process and are more motivated and creative who in turn improve their performance as well as the performance of the organization.
3.0. Methodology
The researcher will adopt a descriptive research survey for this study it is suitable for the study as it gives the researcher the opportunity of obtaining respondents’ opinion from the entire population sampled. The total population of the study is made up of 5 Secondary Schools in Isingiro district during the 2015/2017 academic session. The population is chosen for investigation due to the researcher’s interest. Simple Random Sampling method will be used. The Schools in the Zone are grouped according the three divisions in the district. Simple Random Sampling method will be used to select the Schools under study. The sample size will comprise of 60 teachers from the secondary schools being studied which will constitute the number of questionnaires obtained after distribution.
A Structured Questionnaire will be used for data collection process. The questionnaire items will be validated to ascertain its suitability for use in data collection. The whole content of the questionnaire and its structure will critically be examined and corrections made where required and its reliability determined using a test –retest method.
Data collected will be edited upon the receipt of the questionnaires to ensure accuracy and consistency of the information given by the respondents. Data will be entered in the computer using a SPSS version 20, descriptive, principal component, correlation and regression analysis will be used to establish the relationship between the study variables. Responses from the questionnaire will be analysed using the descriptive statistics of frequency counts, percentage, and inferential statistics and descriptive statistics of frequency counts and percentages will also be used in analysing demographic variables and research questions.
For data analysis and presentation, the data collected will be edited and checked to ensure uniformity, accuracy, consistency and comprehensiveness. The structured questionnaires will be coded, questions grouped, tabulated and frequencies run according to the objectives of the study, the data will be analysed and the information presented using statistical frequency tables, graphs and pie – charts.
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4.0. References
Aji, B.M. (2014). Leadership styles of head of department and academic staff performance: Unpublished Master Dissertation, University of Maiduguri, Nigeria.
Akinnibagbe . (2002). The relationship between leadership and follower in-role performance and satisfaction with the leader: The mediating effects of empowerment and trust in the leader. Leadership and Organization Development Journal, 28,(1), 4-19.
Balunywa, W.S. . (2000). A hand Book of Business Management. Kampala: Ugandan Press. .
Fika, I. Ibi, M. and Aji, B. (2015). Leadership styles of head of department and academic staff performance in the University of Maiduguri: Maiduguri Journal of Education Studies, 8(1) 83-94.
Lee D. (1995). Leadership theory, application and skill development: USA: South- West College Publishing.
Lewin, K ; Caillords, f. (2001). Financing secondary education in development: strategic for sustainable growth: Paris International Institute for Education Planning. UNESCO.
Muli, M.M . (2005). Effects of Head Teachers Management Styles on Performance in Physics at K.S.C.E. Examination in Mutomo Division, Kitui District: Unpublished M. ed. Project, University of Nairobi.
Muthondu G.W. (2007). “Teachers’ Perception of Female Head Teachers’ Leadership Styles in Public Secondary School in Nairobi Province: Unpublished Master Dissertation, University of Nairobi. .
Mwalala D.B. . (2008). Influences of Head Teachers’ Leadership Styles on K.C.S.E Performance in Public Secondary School in Taita District: Unpublished Master Dissertation, University of Nairobi. .
Okumbe, J. A. (1998). Educational Management: Theory and Practice. Nairobi: Nairobi University Press.
Omolayo B. (2009). Effects of leadership styles on job related tension and psychological sense of community in work organization: case study of four organization in Lagos State, Nigeria: Bangladesh. E.J Social. 4,(2)133-157.
Sashkin, M. &Sashkin, M. . (2003). Leadership That Matters. San Francisco: BarrettKoehler Publishers Inc.

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1

1.Introduction to the Logistic Regression Model
Introduction
Fitting the Logistic Regression Model

Testing for the Significance of the Coefficients
Confidence Interval Estimation

2.The Multiple Logistic Regression Model
Introduction
The Multiple Logistic Regression Model
Fitting the Multiple Logistic Regression Model
Testing for the Significance of the Model
Confidence Interval Estimation

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3. Interpretation of the Fitted Logistic Regression Model
Introduction
Dichotomous Independent Variable
Polychotomous Independent Variable
Continuous Independent Variable
Multivariable Models
Presentation and Interpretation of the Fitted Values

4.Model-Building Strategies and Methods for Logistic Regression
Introduction
Purposeful Selection of Covariates
CASE STUDY.
APPENDIX

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REVIEW –
Types of Data & Measurement Scales: Nominal, Ordinal, interval, and ratio.
These are simply ways to categorize different types of variables.
Nominal- Nominal scales are used for labeling variables, without any quantitative value. A good way to remember all of this is that “nominal” sounds a lot like “name” and nominal scales are kind of like “names” or labels. Examples of nominal variables include region, zip code, or gender of individual or religious affiliation. The nominal scale can also be coded by the researcher in order to ease out the analysis process, for example; M=Female, F= Female, etc.

Ordinal -This level of measurement involves ordering or ranking the variable to be mea¬sured, it is the order of the values is what’s important and significant, but the differences between each one are not really known. For example, is the difference between “OK” and “Unhappy” the same as the difference between “Very Happy” and “Happy?” We cant say. Ordinal scales are typically measures of non-numeric concepts like satisfaction, happiness, discomfort, etc.

Interval- The interval level of measurement not only classifies and orders the measurements, but it also specifies that the distances between each interval on the scale are equivalent along the scale from low interval to high interval. For example, on a standardized intelligence measure, a 10-point difference in IQ scores has the same meaning anywhere along the scale. Thus, the difference in IQ test scores between 80 and 90 is the same as the difference between 110 and 120. However, it would not be correct to say that a person with an IQ score of 100 is twice as intelligent as a person with a score of 50. The reason for this is because intelligence test scales (and other similar interval scales) do not have a true zero that represents a complete absence of intelligence.

Ratio -In this level of measurement, the observations, in addition to having equal intervals, can have a value of zero as well. The zero in the scale makes this type of measurement unlike the other types of measurement, although the properties are similar to that of the interval level of measurement. In the ratio level of measurement, the divisions between the points on the scale have an equivalent distance between them.
The four data types
Attribute Nominal Ordinal Interval Ratio
Name2 Categorical Sequence Equal Interval Ratio
Name3 Set Fully ordered, rank ordered Unit size fixed Zero or ref.pt fixed
Statistics Count, Mode, chi-squared + median, rank order correlation + ANOVA, mean, SD + Logs??
Example1 Set of participants makes of car order of finishing a race centigrade scale Degrees Kelvin or absolute
Types of relativity A?B A;B |(A-B)| ; |(C-D)| ?
Types of absolute The identity of individual entities order, sequence intervals, differences ratios, proportions

Probability
P=(outcomes of interest)/( all possible outcomes )
Odds= (p(occurring ))/(p(not occurring))= p/((1-p)) =The odds of an event are the number of events / the number of non-events.
Odds ratio- odds ratio is a ratio of two odds.
Odds ratio = odds1/odds0
Odds ratio = ((p1/(1-p1)))/((p0/(1-p0)))

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Introduction to the Logistic
Regression Model

INTRODUCTION
Logistic regression is the appropriate regression analysis to conduct when the dependent variable (y)is dichotomous (binary) such as “yes” or “no”, “1” or “2”, “A” or “B” or “c”. Logistic regression allows one to predict a discrete outcome, such as group membership, from a set of variables that may be continuous, discrete, dichotomous, or a mix of any of these. Generally, the dependent variable is dichotomous, such as male/female, smoker/non¬-smoker or success/failure like all regression analyses, the logistic regression is a predictive analysis. Logistic regression is used to describe data and to explain the relationship between one dependent binary variable and one or more nominal, ordinal, interval or ratio-level independent variables. The logistic regression model is the most frequently used regression model for the analysis of these data. The independent variables are often called covariates.
What distinguishes a logistic regression model from the linear regression model is that the outcome variable in logistic regression is categorical. This difference between logistic and linear regression is reflected both in the form of the model and its assumptions. Once this difference is accounted for, the methods employed in an analysis using logistic regression follow, more or less, the same general principles used in linear regression. Thus, the techniques used in linear regression analysis motivate our approach to logistic regression.
There are three primary uses of logistic regression:
Prediction of group membership and outcome.
The goal is to correctly predict the category of the outcome of individual cases. Thus, the research question asked is whether an outcome can be predicted from a selected set of independent variables. For instance, in epidemiologi¬cal studies, can the development of lung cancer be predicted from the incidence and duration of smoking as well as from demographic variables such as gender, age, and social and economic status (SES)?
2. Logistic regression provides knowledge of the relationships and strengths among the variables.
The goal is to identify which independent vari¬ables predict the outcome, that is, increase or decrease the probabil¬ity of the outcome or have no effect. For example, does inclusion of information about the incidence and duration of smoking improve prediction of lung cancer, and is a particular variable associated with an increase or decrease in the probability that a case has lung cancer? These parameter estimates (the coefficients of the predictors included in a model) can also be used to calculate and interpret the odds ratio. For instance, what are the odds that a person has lung cancer at age 65, given that he has smoked 10 packs a day for the past 30 years?
3.Classification of cases.
The goal is to understand how reliable the logistic regression model is in classifying cases for whom the effect is known. For instance, how many people with or without lung can¬cer are diagnosed correctly? The researcher establishes a cut point of say .5, and then asks, for instance: How many people with lung cancer are correctly classified if everyone with a predicted probabil-ity more is diagnosed as having lung cancer?
Why will other regression procedure not work?
Simple linear regression is one quantitative variable predicting another.
Multiple regression is a simple linear regression with more independent variables.
Nonlinear regression is still two quantitative variables, but the data is curvilinear.
Running a typical linear regression in some way has major problems since binary data does not have a normal distribution which is a condition needed for most other types of regression.
Example1: Table 1.1 lists the age in years (AGE), and presence or absence of
Evidence of significant coronary heart disease (CHD) for 100 subjects in a hypothetical
Study of risk factors for heart disease. The table also contains an identifier
Variable (ID) and an age group variable (AGEGRP). The outcome variable is CHD,
Which is coded with a value of “0” to indicate that CHD is absent, or “1” to indicate
That it is present in the individual. In general, any two values could be used, but
We have found it most convenient to use zero and one. We refer to this dataset as the CHDAGE data.

CHD

AGE(Years)
FIG 1.1

A scatterplot of the data in Table 1.1 is given in Figure 1.1.
In this scatterplot, all points fall on one of two parallel lines representing the
absence of CHD (y = 0) or the presence of CHD (y = 1). There is some tendency
for the individuals with no evidence of CHD to be younger than those with evidence
of CHD. While this plot does depict the dichotomous nature of the outcome variable
quite clearly, it does not provide a clear picture of the nature of the relationship
between CHD and AGE.
The main problem with Figure 1.1 is that the variability in CHD at all ages is
large. This makes it difficult to see any functional relationship between AGE and
CHD. One common method of removing some variation, while still maintaining
the structure of the relationship between the outcome and the independent variable,
is to create intervals for the independent variable and compute the mean of the
outcome variable within each group. We use this strategy by grouping age into the
categories (AGEGRP) defined in Table 1.1. Table 1.2 contains, for each age group,
the frequency of occurrence of each outcome, as well as the percent with CHD present.
Table 1.2
Age group n Absent Present Mean
20–29 10 9 1 0.1
30–34 15 13 2 0.133
35–39 12 9 3 0.25
40–44 15 10 5 0.333
45–49 13 7 6 0.462
50–54 8 3 5 0.625
55–59 17 4 13 0.765
60–69 10 2 8 0.8
Total 100 57 43 0.43

FIG 1.2Age(years)

By examining Table 1.2, a clearer picture of the relationship begins to emerge. It
shows that as age increases, the proportion (mean) of individuals with evidence of
CHD increases. Figure 1.2 presents a plot of the percent of individuals with CHD
versus the midpoint of each age interval. This plot provides considerable insight
into the relationship between CHD and AGE in this study, but the functional form
for this relationship needs to be described. The plot in this figure is similar to what
one might obtain if this same process of grouping and averaging were performed
in a linear regression. We note two important differences.

Some important facts:-

The dependent variable in logistic regression follows the Bernoulli distribution having an unknown probability, p.
Bernoulli distribution is just a special case of the Binomial distribution where n=1 (just one trial)
Success is “1” and failure is “0”.
The probability of success is “p” and failure is “q=1-p”.
In logistic regression, we are estimating an unknown p, for any given linear combination of the independent variables.
Therefore, we need to link together our independent variable to essentially the Bernoulli distribution, that link is called the logit.

The first difference concerns the nature of the relationship between the outcome
and independent variables.

In any regression problem, the key quantity is the mean value of the outcome variable, given the value of the independent variable. This quantity is called the conditional mean and is expressed as “E(Y|x)” where Y
Denotes the outcome variable and x denotes a specific value of the independent
Variable. The quantity E (Y|x) is read “the expected value of Y, given the value x”.
In linear regression, we assume that this mean may be expressed as an equation. This expression implies that it is possible for E (Y|x) to take on any value as x
Ranges between ??and +?.The column labeled “Mean” in Table 1.2 provides an estimate of E (Y|x). We assume, for purposes of exposition, that the estimated values plotted in Figure 1.2are close enough to the true values of E (Y|x) to provide a reasonable assessment of the functional relationship between CHD and AGE. With a dichotomous outcome variable, the conditional mean must be greater than or equal to zero and less than or equal to one (i.e., 0 ?E (Y|x) ?1). This can be seen in Figure 1.2. In addition,
The plot shows that this mean approaches zero and one “gradually”. The change in
The E (Y|x) per unit change in x becomes progressively smaller as the conditional
Mean gets closer to zero or one. The curve is said to be S-shaped and resembles a
The plot of the cumulative distribution of a continuous random variable. Thus, it should
Not seem surprising that some well-known cumulative distributions have been used
To provide a model for E (Y|x) in the case when Y is dichotomous. The model we use is based on the logistic distribution.
In order to simplify notation, we use the quantity ?(x) = E (Y|x) to represent
The conditional mean of Y given x when the logistic distribution is used. The
The specific form of the logistic regression model we use is:
?(x) = e^(?0+?1x)/(1+e^(?0+?1x) )(1.1)

A transformation of ?(x) that is central to our study of logistic regression is the logit transformation. This transformation is defined, in terms of ?(x), as:
g(x) = ln (?(x))/(1-?(x))
g(x) = ?0+ ?1x. (1.1*)
The importance of this transformation is that g(x) has many of the desirable properties
Of a linear regression model. The logit, g(x), is linear in its parameters, May
Be continuous, and may range from ??to +?, depending on the range of x.
The second important difference between the linear and logistic regression
Models concern the conditional distribution of the outcome variable. In the linear
Regression model we assume that an observation of the outcome variable may be
Expressed as y = E (Y|x) + ?. The quantity ? is called the error and expresses an
Observation’s deviation from the conditional mean. The most common assumption
Is that ? follows a normal distribution with mean zero and some variance that is
Constant across levels of the independent variable. It follows that the conditional
Distribution of the outcome variable given x is normal with mean E (Y|x), and a
The variance that is constant. This is not the case with a dichotomous outcome variable.
In this situation, we may express the value of the outcome variable given x
As y = ?(x) + ?. Here the quantity ? may assume one of two possible values. If
y = 1 then ? = 1 ??(x) with probability ?(x), and if y = 0 then ? = ??(x) with
Probability 1 ??(x). Thus, ? has a distribution with mean zero and variance equal
To ?(x) 1 ??(x). That is, the conditional distribution of the outcome variable
Follows a binomial distribution with probability given by the conditional mean,
?(x).

In summary, we have shown that in a regression analysis when the outcome
Variable is dichotomous:
1. The model for the conditional mean of the regression equation must be bounded between zero and one. The logistic regression model, ?(x), given
In equation (1.1), satisfies this constraint.
2. The binomial, not the normal, distribution describes the distribution of the
Errors and is the statistical distribution on which the analysis is based.
3. The principles that guide an analysis using linear regression also guide us in
Logistic regression.
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1

1.0 INTRODUCTION
The Study Aims to find out consumer awareness and adoption of Islamic banking products in Mogadishu-Somalia as statistical shows the number of users of Islamic banking are increasing last years with different user of Muslims and non-Muslims with that increasing on the other hand if we compare number of users of both conventional and Islamic banking conventional banking users are larger this leads to many contributing factor so in Somalia
The study is driven to achieve three main objectives which are:
1. to investigate the extent of consumer awareness in Islamic banking products provide by Islamic banks in Mogadishu,
2. Second is to examine the level of adoption in Islamic banking products in Mogadishu
3. To find out the relationship between consumer awareness and Adoption of Islamic banking products in Mogadishu.
According to above objectives the research questions will be:
1. What is extent that consumers aware of Islamic banking products in Mogadishu-Somalia?
2. What is level of adoption of Islamic banking products in Mogadishu-Somalia?
3. What is the relationship between consumer awareness and adoption of Islamic banking Products in Mogadishu-Somalia?

The Main Objective if this study is to find out consumer awareness and adoption of Islamic banking products in Mogadishu Somalia by identifying the degree that consumers live Mogadishu aware that Islamic banks in Mogadishu provide Islamic banking products if they are aware of much they know and describe about that Islamic products and also study aims to examine that degree of adoption of Islamic banking products in Mogadishu the capital city of Somalia how much consumers adopted the Islamic banking products provide Islamic banks operate in Mogadishu the motivate of this study is that since 1991 Somalia were lack of strong government which have strong institutions that operate significantly so from that period Islamic banks who individually established began to operate in Mogadishu and there is no string central bank that monitors, the study also aims to examine how much customer’s trust on Islamic banks in Mogadishu-Somalia.

2.0 LITERATURE REVIEW

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RELATED STUDIES ABOUT CUSTOMER AWARENESS AND ISLAMIC BANKING
(Khan ; Asghar, 2012) investigated customer awareness and adoption of Islamic banking in Pakistan, the researchers used descriptive study by adopting 32 Questionnaire stuffs focusing on 5main variables which are financial service, service knowledge, sharia based, customer service, targeting demographic factors like age, gender and education, the study concluded that most of Pakistan people are positive attitude and enough awareness towards the Islamic banking this result can be satisfactory according to Pakistan the country is developing in Islamic banking and becoming a hub to Islamic banks which encouraged the investors in Pakistan to put their money in Islamic banks shifting from conventional banks which will have positive impact on Pakistan Financial sector coming future.
This Study Is important to my research because, the researchers focused on same variables like my research is intended to do and secondly they applied the same methodology I would like to use but I think they found out that that Pakistan people are aware of Islamic banking which is the reason that Pakistan people have Islamic culture and long term of government in the result of peaceful transition of power which different from my study area Somalia which suffered from civil war and lack of strong government institutions since 1991 when the military rule was ousted by rebel groups.
(Alsoud, 2013), in Kuwait studied customer awareness and satisfaction of Islamic retail products, the study used descriptive study and distributed 150 Kuwait customers a questionnaire asking whether they are ware of some Islamic products provide by Islamic banks operate in Kuwait, and how satisfied they are towards Islamic retail products they receive from Islamic banks in Kuwait, the researchers found that Kuwait customers are demanding not only compliance of sharia from but other traditional things customer request from banks the study also found that Kuwait aware of some Islamic products like Islamic credit cards but not aware of all Islamic products, on the other hand the level of satisfactory which the study aimed to explore have found it quite higher in with in the Kuwait customers in general.
In my opinion this study looks another dimension which is examining the level of satisfaction of Kuwait customers towards Islamic retail products which good part but this dimension comes after customer uses and adopts Islamic banking products provide by Islamic banks in Kuwait where the study is conducted.
(Sarbo, 2016) studied the influence of consumer’s awareness on Islamic banking in Nairobi, Kenya. The study examined how the customer’s awareness influences the Islamic banking in Nairobi County, the study used descriptive design and questionnaire by number of 196 customers live in Nairobi the capital city of Kenya, the researchers found that 64% of respondents aware of Islamic banking but only aware of basic Islamic banking like credit account and debit accounts but not the famous ones like Murabaha,Musharakah and Mudharabah.

This study is conducted east Africa where my study location is more over Kenya and Somalia are neighbors so study showed that Kenyan consumers are not that aware of Islamic banking but according to culture and religion which is the country is different from each other, Kenya most of population is Christian but in Somalia 100% of the population are Muslim which can lead to different result if the study is conducted in Mogadishu.
(Mahdzan, Zainudin, ; Au, 2017) researched the adoption of Islamic banking services in Malaysia to test the level of understand of Islamic banking and factors effecting the adoption if Islamic banking, the study used descriptive design by distributing Questionnaire to 200 MBA working student who have been selected in famous public university in Malaysia, the studied concluded that the level of understanding is below average and the Islamic banking concepts and perceived knowledge have significant effect on the adoption of Islamic banking The preliminary findings show that the respondents’ self-reported level of understanding on Riba and Shariah concepts is above average; however, their understanding on concepts such as ijarah,Mudharabah, musharakah, and Murabaha appears to be low.
This study focused factors that affect the adoption of Islamic banking in Malaysia and the level of understanding which are good dimension that needs the attention but I would recommend as further studies.
(Saini, Bick, & Abdulla, 2011) investigated consumer awareness and usage of Islamic banking products in South Africa, the researchers examined the level of consumer awareness and the use of Islamic banking products, the study used non probability sampling method and distributed 250 Questionnaire to Consumers in South Africa to test level of awareness in South Africa, the study found that Muslims are aware of Islamic banks, but their rate of use is low, as Muslim customers regard efficiency, lower bank charges, the availability of automatic teller machines and an extensive branch network as important factors when it comes to choosing a bank, rather than religious motivations for compliance with Islamic conventions. It was concluded that, if Islamic banks wanted to attract and retain customers and remain relevant in the South African context, they would have to develop relevant strategies designed to meet customers’ needs. Religion as the sole motivation for choosing Islamic banking is inadequate.

2.3 CONCEPTUAL FRAME WORK OF THE STUDY

Figure2.1

INDEPENDENT VARIABLE DEPENDENT VARIABLE

Primary data (2018)

The above conceptual framework explains in figure the variables of the study independent variable which customer awareness and dependent variable which is adoption of Islamic banking products and personal characters that intervening Variable.