1.	Introduction
Many organizations in Africa have computerized their activities. Computerization of organization processes and activities has resulted to faster operations, efficiency and improvement in business transactions, quick reference among other benefits. On the other hand, computerization comes with a cost. Storage is required for data store, larger data centers, servers, cooling capacity in the data centers, system engineers, software licensing among others. As a result of this, organizations end up incurring a huge budget in the acquisition and maintenance of Information Technology (IT) infrastructure (Linthicum,2009).
Hormuud Telecom was established in April 2000. The company is based in Mogadishu, Somalia. Mogadishu is the national capital situated in the south-central Banaadir province. In the 2010’s, Hormuud launched a special ZAAD service for money transfers. The Al Shabaab militant group threatened to shut down the service in 2010 on the grounds that it was a simple way to channel funding to the former Transitional Federal Government (TFG).
As of 2014, Hormuud had 2 million subscribers, making it one of the largest telecommunication operators in Somalia. It’s main rivals are Nation Link Telecom and Telecom Somalia. The company employs more than 5,000 full-time and part-time staff with different specialties. Among these workers are telecommunication engineering, customer service, sales and marketing, and finance specialists. The organisation is geared towards business development and growth, in all areas of emerging technologies in the line of telecommunication and ICTs, including cloud computing. The company has 66 corporate customers in both private and public sector. 
Emerging information technology will be the key enabler for the rapid development of the private sector. Cloud computing is Internet-based computing, where computing tasks are assigned to a combination of remote connections, software and services on demand.  End-users no longer need expertise in, or control of the technology infrastructure "in the cloud," and this is why there's renewed optimism in the cloud business over emerging market in Africa.    
In December 2012, Hormuud launched its Tri-Band 3G service for mobile and internet clients. The first of its kind in the country, this 3G mobile telecommunications technology offers users a faster and more secure connection. In July 2014, Hormuud Telecom along with NationLink and Somtel signed an interconnection agreement. The cooperative deal was aimed to see the firms establish the Somali Telecommunication Company (STC), which was to allow their mobile clients to communicate across all three networks, 
According to Gartner (2009), many organizations want to venture into tasks that have high returns. On the other hand, if the level of activity of an organization reduces, a scale down of resources is also required without resulting to idle resources (Mitchell,2009). Organizations want to concentrate their resources on core business and not in supporting IT services hence the need to reduce unnecessary costs. Organizations are working towards reducing unnecessary IT costs and one way to deal with this is to adopt a technology that enables flexibility and scalability of IT infrastructure (Daryl, 2009). This can be made possible by accessing IT as a service. One technology that best supports this is cloud computing.
Gartner (2009) defines Cloud Computing as a means by which highly scalable and elastic technology-enabled services can be easily consumed over the Internet on an as-needed basis. According to Springboard Research (2010), Cloud Computing is a collection of IT enabled resources and capabilities that can be delivered via the internet as a service. In the cloud computing environment, working is through virtualized applications on a networked architecture. With Cloud Computing people work using appliances such as smart phones, iPhones, and laptops without necessarily installing applications on these devices.
According to Garner (2009), there are three models of cloud computing. The first model being software-as-a-Service (SaaS) which offers applications online, examples of such include Gmail or Google docs. Second model is Application Infrastructure Service which offers higher-level development environments which abstract the underlying technology and provide for scalability and rapid application development such as Google App Engine. The third model is hardware-as-a-Service (HaaS) which offers resources such as processing and storage services to a customer (user). The customer does not have to physically buy these services. Other authors such as Springboard refer to this model as System Infrastructure Services.
With increasing business complexity, companies are seeking innovative business models and specialized technologies to cater for customer demands (Khera, 2006). Cloud computing technology can provide companies with competitive advantage through cost reductions, simplified maintenance and management of applications across the enterprise, greatly extended scalability, agility, high availability, automation, large data storages and reliable backup mechanisms (King, 2009). According to Appistry (2009), companies can focus on their core business as opposed to concentrating their efforts on infrastructure scalability through the use of cloud computing infrastructure.
Companies can also accrue several benefits from cloud computing. Ellison (2010) classifies the primary benefits of migrating to cloud into two. The first relates to cost reduction, which cannot be achieved with a purely proprietary infrastructure. Companies can avoid purchasing costly infrastructure by outsourcing infrastructure from a third-party and manage all of their data and applications from a simple Web address on the Internet. The second benefit relates to scalability and flexibility. In this case companies will be able to pay for only what they need in terms of resources and capabilities.
Miller (2009) suggests that companies can make use of cloud computing in several key areas such as the identity management process in banking, thereby enhancing linking of identity information between accounts. This can significantly reduce costly provisioning, mitigate security loopholes and resolve traditional user issues caused by rigid application architecture. Enterprise content management is another candidate for Cloud Computing in companies specifically on customer interaction archival and search.
Companies can also enable transaction processing in the cloud through Extreme Transaction Processing (XTP) which pertains to a certain class of applications that need to handle large volumes of data that needs to be absorbed, correlated, and acted upon such as fraud detection, risk computation, and stock trade resolution. Other services that companies   can move to cloud computing includes managing schedules by using web-based scheduling whereby everyone places his/her schedule in the cloud thus enabling the meeting organizer to easily see who is available. E-mail archiving, e-mail security and document creation, back-office activities such as credit card processing, Foreign exchange among others can also be moved to cloud.
Cloud Computing Technology has met a lot of resistance in some industries particularly by financial services firms due to security concerns (Cloud Security Alliance, 2009). The idea of hosting sensitive information on the Internet is considered insecure by the companies. Other concerns include issues surrounding the down time of resources and servers offered by providers, response times from the cloud may be too slow for certain time-critical transaction management, undue dependency on cloud infrastructure providers, lack of visibility into the technology and lack of control over the application (Guilbert, 2009).
This study focused on companies   in selected African countries and the factors that affect the adoption of the cloud computing technology and also the effect of cloud computing technology on service delivery, flexibility, disaster recovery, systems security and reduction of cost on companies particularly in Kenya, South Africa, Egypt Nigeria and south Sudan.

1.2 Statement of the Problem

Companies in every industry   are facing a dynamic market, new technologies, economic uncertainties, fierce competition and more demanding customers. The dynamic nature of the companies market has presented an unprecedented set of challenges (Atos 2010). 
Carr (2009) surveyed companies in Africa, on awareness of the possibility of providing data processing and software applications as utility services over a public grid. The result revealed that cloud computing knowledge was limited to a fairly small set of IT specialists, and the term cloud computing was little known and rarely used. IT managers and suppliers, moreover, dismissed the entire idea of the Cloud as a pie-in-the-sky dream due to the security concerns. Therefore, there is need to know whether the know-how of cloud technology has hit African companies and gauge IT managers’ attitude on Cloud Computing.
Hormuud leads in Somalia’s telecommunication industry with state-of-the-art services, which include Mobile service (GSM), landlines and mobile linked internet services (such as GPRS, 3G and Hotspots). The organization covers Southern and Central regions of Somalia, and caters to people’s needs of communication. The organisation also runs a charity foundation (Hormuud Telecom Foundation) which was established to cater to Corporate Social Responsibilities (CSRs) with an agenda of poverty alleviation. 
East African firms in particularly Somalia businesses have not fully benefitted from the technology revolution because of the expensive upfront costs of buying hardware and software, and managing it on premise. More importantly, the lack of an IT skilled workforce prevented the development of supporting ecosystems of service providers, similar to those available in the industrialized world. There has been, and still is a perception that, Somalia is poised for the next wave of technology innovation where IT services will be instantly available to end users on request.
This research aimed at discovering the underlying factors that have affected the slow rate of adoption of cloud computing services in Somalia. 

1.3 Research Objectives

The aim of the study was to provide an understanding of the current status, trends and factors affecting the rate of adoption of cloud computing among companies in Somalia. It also aimed at providing recommendations with specific interventions needed to spur the growth of the companies sector. To achieve this goal, the following objectives guided the study:
(i)	To establish the effect of relative advantage on adoption of cloud computing   
(ii)	To establish complexity of technology affects the rate of adoption of cloud computing technology 
(iii)	 To determine an organization’s technological readiness affects the rate of adoption of cloud computing technology.
(iv)	To determine how vendor scarcity affect the rate of adoption of cloud computing technology.
1.4 Research questions

(i)	How does an organization relative advantage with regard to its competitors affect the rate of adoption of cloud computing services?
(ii)	How do emerging technologies influence the rate of adoption of cloud computing services?
(iii)	Does an organization’s technological readiness in terms of ICT investments determine the rate of adoption of cloud computing services?
(iv)	If there are multiple vendors giving different cloud computing options, how does this influence the rate of adoption of cloud computing? 
1.5	Hypotheses 
The research hypothesized as follows:
?	H0 - There is NO cause and effect relationship between independent variables and dependent variables 
?	H1- There is a cause and effect relationship between independent variables and dependent variables 


1.6 Justification  

The predecessor to cloud computing is grid computing, very similar in many ways. Grid computing are many computers combined to solve a common goal. The computers can be connected through different locations and by that forming a virtual computer that works to achieve a common goal (Bart et. al 2005). Whereas there is a lot of literature and research on grid computing, there is limited research on clod computing. 
Studying the factors affecting rate of adoption of cloud computing by the companies is an important issue for companies to discern. By examining the positive and negative issues relating to the rate of adoption cloud computing, this study can help organization in the East African countries especially Somalia, assess whether or not it is viable for companies to make a move to cloud computing.
All the experts now agree that the adoption of cloud computing offers tangible benefits in terms of flexibility and convenience. However, it cannot automatically be inferred from this that all the new economic models being ushered in by cloud computing systematically guarantee significant financial gains, whether for cloud providers or for cloud service users. The complexity of the cloud computing models thus far proposed is such that the corresponding economic analyses reach differing conclusions as to the costs involved and gains to be made following the adoption of a cloud computing model.
The frequent movement of data between company and cloud can also rack up the costs, particularly in terms of bandwidth consumption where transfer times are lengthy. As things currently stand, then, and aside from long-term storage and the frequent consultation of large volumes of data, it is clear that the other cloud computing services - SaaS, PaaS, NaaS, CaaS - are providing their adherents with very significant gains by comparison with in-house solutions.
Cloud computing has emerged in recent years however not much research has been done in this area in African countries. In many respects, African markets have opportunities to leap frog by adopting modern technologies that result in many benefits, such as cost cutting and speed of processing. Similar transformations have been observed in the uptake of mobile phones and mobile financial services in developing countries. 
For these technologies to be implemented appropriately and adopted, several critical elements must be in place. Governments must put in place supportive legal and regulatory frameworks, suppliers must make the technology available, technical people must have the right skills and consumers must have the right knowledge and attitude. 
Given the limited research at countries level, more so in emerging markets, this research was proposed to investigate the factors affecting the rate of cloud computing adoption among companies in Africa, a continent that has demonstrated leadership in developing and adoption of appropriate technological innovations. The study dug deep to understand the circumstances, challenges, opportunities and limitations facing the country in her quest to exploit cloud computing technologies. 
1.7 Scope

The study was limited to corporate clients of Hormuud Telecoms which is a Telecommunications company based in Somalia, headquartered I the city of Mogadishu. 

Chapter Summary
This chapter introduced the research topic and the organization that selected for the case study, Hormuud Telcom. It defined the research problem, the objectives of the research and the research questions to be answered. It further defined the justification and the scope of the study. 
 
CHAPTER TWO 
LITERATURE REVIEW 
2.	Introduction 
This chapter reviewed the literature that exists in the body of knowledge in cloud computing. It looked at three theories that affect technology and its adoption. From the theory, it looked at the empirical theory and the deployment of conceptual framework, which includes four independent and one dependent variable and finalizes with the operationalization of the variable. 
2.1.	Literature Review
In recent years companies have been notably adopting the cloud computing technology to make their services increasingly convenient. As a result cloud computing technology use has been on the rise across the globe, the technology can trace its innovation in Europe where most companies quickly adopted it. European companies can thus be termed as cloud computing technology innovators. Later cloud computing technology spread to America, American companies quickly adopted the technology with rest of the world following suit. Africa was the last to adopt cloud computing technology. 
In a global perspective Somalia falls under the public cloud services market in the Middle East and North Africa (MENA) region is projected to grow 17.1% in 2015 to a total of US$851 million, up from an estimated US$727 million in 2014, according to Gartner. Software as a service (SaaS), the largest segment of the cloud services market in MENA, is expected to grow 25 percent in 2015 to$US205.7 million. Gartner predicts that by 2018, total public cloud services spending in MENA region will rise to US$1.5 billion, with SaaS accounting for 28.3% of the market. In general, IT spending on public cloud services in the region is expected to reach $1.1 billion in 2017. Growth in the adoption of these applications can be attributed to the fact that providers such as Salesforce.com, Google, and Microsoft have only recently begun actively 12promoting these services in the region. 

Figure 1 - Middle East and North Africa Region
 
2.2.	Theoretical review
An innovation is an idea, practice, or object that is perceived as new by an individual or other unit of adoption” (Rogers, 1995, ).There are various factors  that affect an innovation adoption , these factors can also be likened to the factors that potentially affect companies in adopting  cloud computing technology. The factors are taken primarily from the diffusion of innovation theory (Rogers, 1995). The theory identifies four variables that have a profound influence on the rate of an innovation adoption including: technological factors of innovation, type of innovation decision, communication channels and nature of social system. Among these Rogers (1995) argues that perceived attributes of innovation is an important predictor of innovation adoption intention. 
2.2.1.	Chou’s Theory of Cloud Computing: The 5-3-2 Principle
According to Yung Chou (2011), the adoption of cloud computing is based on what he described as the 5-3-2 principle. The 5-3-2 principle of cloud computing describes the 5 essential characteristics, 3 delivery methods, and 2 deployment models of cloud computing. 
The essential characteristics are: On demand self service, Ubiquitous network access, Location transparent resource pooling, Rapid elasticity and Measured service with pay per use.
The three delivery methods are: Software as a service where users consume the cloud. SaaS is a model where an application is available on demand. It is the most common form of cloud computing delivered today. Platform as a service where users leverage the cloud. PaaS is a platform available on demand for development, testing, deployment and on-going maintenance of applications without the cost of buying the underlying infrastructure and software environments and Infrastructure as a service where users become part of the cloud.  IaaS is an IT environment with an ability for a subscriber to on demand provision infrastructure. This infrastructure is, for example, delivered with virtual machines in which a subscriber maintains the OS and installed applications, while the underlying fabric is managed by a service provider
The two deployment methods are; Private cloud inside your data and Public cloud by service providers
2.2.2.	Technology Acceptance Model

TAM is an adaptation of the Theory of Reasoned Action (TRA) to the field of IS. TAM posits that perceived usefulness and perceived ease of use determine an individual's intention to use a system with intention to use serving as a mediator of actual system use. Perceived usefulness is also seen as being directly impacted by perceived ease of use
One of the well-known models related to technology acceptance and use is the technology acceptance model (TAM), originally proposed by Davis in 1986. TAM has proven to be a theoretical model in helping to explain and predict user behavior of information technology (Legris, Ingham, & Collerette, 2003). TAM is considered an influential extension of theory of reasoned action (TRA), according to Ajzen and Fishbein (1980). Davis (1989) and Davis, Bagozzi, and Warshaw (1989) proposed TAM to explain why a user accepts or rejects information technology by adapting TRA. TAM provides a basis with which one traces how external variables influence belief, attitude, and intention to use. Two cognitive beliefs are posited by TAM: perceived usefulness and perceived ease of use. According to TAM, one’s actual use of a technology system is influenced directly or indirectly by the user’s behavioral intentions, attitude, perceived usefulness of the system, and perceived ease of the system. TAM also proposes that external factors affect intention and actual use through mediated effects on perceived usefulness and perceived ease of use. Figure 1 depicts the original TAM (Davis, 1989).
Figure 2 - Technology Acceptance Model
 
Source: 
TAM postulates that the Attitude toward Using (A) to exploit technology by a person is critically dependent on two variables, the perceived usefulness (Perceived Usefulness) and the perceived ease of use (Perceived Ease of Use). The Perceived Usefulness (U) is the subjective sensation of the person, that the use of any technology improves his job performance. The Perceived Ease of Use (E) in turn measures the perception of the person, how much - or rather how little - effort to learn the use of new technology is connected. Furthermore, the intention to use (is Intention to Use, BI ) depends on the Perceived Usefulness and Attitude .
TRA and TAM, both of which have strong behavioural elements, assume that when someone forms an intention to act, that they will be free to act without limitation. In practice constraints such as limited ability, time, environmental or organisational limits, and unconscious habits will limit the freedom to act
2.2.2.1.	Critique to theory
The theory has been criticized that it would be subject to an innovations positivism by it was characterized by a positive attitude technologies against and would be in the consideration of grounds for refusal in the use of technology negative characteristics of innovation ignore.
While the technology acceptance model is considered robust model, it has been criticized for being too simple to explain complex psychological processes such behavior and technology acceptance. The complex successor models TAM2 and TAM3 by contrast criticized for being too complex and too inflexible to explain the behavior and technology adoption by users reliably
2.2.3.	Diffusion of innovations Theory
Diffusion of innovations is a theory that seeks to explain how, why, and at what rate new ideas and technology spread. Everett Rogers, a professor of communication studies, popularized the theory in his book Diffusion of Innovations; the book was first published in 1962.
Figure 3 - Diffusion of Innovation Theory
 Source 
Another element of Everett Rogers’ theory that may illuminate the process adoption problem is the five stages of innovation adoption are: Knowledge – someone is first exposed or becomes aware of an innovation but has limited information or understanding about it. Persuasion – the exposed person expresses interest and gets more information and details which helps them begin to form and opinion of the innovation. Decision – the person understands the innovation and the change it creates, which leads to making a decision on whether to adopt or reject the innovation. Implementation – the innovation is put to use with varying degrees of actual usage depending on applicability and benefits for each individual. Confirmation – the person evaluates the results of using the innovation and makes a final decision to continue using it.

Rogers (2003) noted that incomplete adoption and non-adoption do not form this adopter classification. Only adopters of successful innovations generate this curve over time. In this normal distribution, each category is defined using a standardized percentage of respondents. For instance, the area lying under the left side of the curve and two standard deviations below the mean includes innovators who adopt an innovation as the first 2.5% of the individuals in a system.
For Rogers (2003), innovators were willing to experience new ideas. Thus, they should be prepared to cope with unprofitable and unsuccessful innovations, and a certain level of uncertainty about the innovation. Also, Rogers added that innovators are the gatekeepers bringing the innovation in from outside of the system. “They may not be respected by other members of the social system because of their venturesomeness and close relationships outside the social system. Their venturesomeness requires innovators to have complex technical knowledge”.
 The Early Adopters Compared to innovators, early adopters are more limited with the boundaries of the social system. Rogers (2003) argued that since early adopters are more likely to hold leadership roles in the social system, other members come to them to get advice or information about the innovation. In fact, “leaders play a central role at virtually every stage of the innovation process, from initiation to implementation, particularly in deploying the resources that carry innovation forward” (Light, 1998). Thus, as role models, early adopters’ attitudes toward innovations are more important. Their subjective evaluations about the innovation reach other members of the social system through the interpersonal networks. Early adopters’ leadership in adopting the innovation decreases uncertainty about the innovation in the diffusion process. Finally, “early adopters put their stamp of approval on a new idea by adopting it” (Rogers, 2003).
The early majority according to Rogers (2003) have a good interaction with other members of the social system, they do not have the leadership role that early adopters have. However, their interpersonal networks are still important in the innovation-diffusion process. As Figure 3 shows, the early majority adopts the innovation just before the other half of their peers adopts it. As Rogers stated, they are deliberate in adopting an innovation and they are neither the first nor the last to adopt it. Thus, their innovation decision usually takes more time than it takes innovators and early adopters. Similar to the early majority, the late majority includes one-third of all members of the social system who wait until most of their peers adopt the innovation. Although they are skeptical about the innovation and its outcomes, economic necessity and peer pressure may lead them to the adoption of the innovation. To reduce the uncertainty of the innovation, interpersonal networks of close peers should persuade the late majority to adopt it. Then, “the late majorities feel that it is safe to adopt” (Rogers, 2003).
As Rogers (2003) stated, laggards have the traditional view and they are more skeptical about innovations and change agents than the late majority. As the most localized group of the social system, their interpersonal networks mainly consist of other members of the social system from the same category. Moreover, they do not have a leadership role. Because of the limited resources and the lack of awareness-knowledge of innovations, they first want to make sure that an innovation works before they adopt. Thus, laggards tend to decide after looking at whether the innovation is successfully adopted by other members of the social system in the past. Due to all these characteristics, laggards’ innovation-decision period is relatively long.
In addition to these five categories of adopters, Rogers (2003) further described his five categories of adopters in two main groups: earlier adopters and later adopters. Earlier adopters consist of innovators, early adopters, and early majority, while late majority and laggards comprise later adopters. Rogers identifies the differences between these two groups in terms of socioeconomic status, personality variables, and communication behaviors, which usually are positively related to innovativeness. For instance, “the individuals or other units in a system who most need the benefits of a new idea (the less educated, less wealthy, and the like) are generally the last to adopt an innovation” (Rogers, 2003)
2.2.4.	Critique to theory
As the writer suggests Innovation of technology theory explains and predicts the rate of innovation diffusion which depends on factors like availability of information concerning technology e.g. relative advantage and compatibility, adopters’ properties like past experiences etc. this is in contrast with what is generally known of diffusion.
Rogers placed the contributions and criticisms of diffusion research into four categories: pro-innovation bias, individual-blame bias, recall problem, and issues of equality. The pro-innovation bias, in particular, implies that all innovation is positive and that all innovations should be adopted. Cultural traditions and beliefs can be consumed by another culture's through diffusion, which can impose significant costs on a group of people. Also the one-way information flow from sender to receiver is another weakness of this theory. The message sender has a goal to persuade the receiver, and there is little to no reverse flow. The person implementing the change controls the direction and outcome of the campaign. In some cases, this is the best approach, but other cases require a more participatory approach. In  environments where the adopter is receiving information from many sources and is returning feedback to the sender, a one-way model is insufficient and multiple communication flows need to be examined. 
Rogers also defines an adopter category as a classification of individuals within a social system on the basis of innovativeness. In the book Diffusion of Innovations, Rogers suggests a total of five categories of adopters in order to standardize the usage of adopter categories in diffusion research. The adoption of an innovation follows an S curve when plotted over a length of time. The categories of adopters are: innovators, early adopters, early majority, late majority and laggards In addition to the gatekeepers and opinion leaders who exist within a given community, change agents may come from outside the community. Change agents bring innovations to new communities– ?rst through the gatekeepers, then through the opinion leaders, and so on through the community. In contrast not all products follow this route as they are diffused to the market especially in technology where other forces contribute to adoption, such forces may include political forces or cultural forces where technology becomes an important element especially in business whereby those companies who do not adopt new technology are forced into extinction due to growing competition especially when the technology lowers the cost of production. In this case all companies are forced to adopt the new technology by some external powerful forces.
Diffusion theory also doesn’t consider the economic environment in a certain market because the application of innovation diffusion theory in developing countries is different from how the theory can be applied in developing or developed markets. All these markets have different economic environments plus some other factors. In markets where spending power is high diffusion of technology becomes easier because as the economic situation of consumers improves their demand for sophisticated products proportionally increases. In this line of argument economic situation of target market ought to be a factor to be considered when determining the rate of technology diffusion.
Also innovation when we look at the theory from business and marketing perspectives, we must contend that it needs to be organized around attributes of both the innovations and the organizations adopting them. The notion of static categories of adopters, maintains that anyone can be an innovator if innovations are matched with organizations targeted for adoption. , hence anybody offering this market and infrastructure approach, require focusing monetary and personnel resources on a small number of people, the category traditionally considered innovators. It’s recommended to use marketing techniques to target appropriate innovations to specific segments of farmers.

2.3.	Conceptual Framework

Independent Variables						Dependent Variable






2.4.	Operationalization of the Variables

Relative Advantage - Relative Advantage is defined as the level to which a Technological factor has more benefits that disadvantages to the organizations. It is logical for organizations to weigh the benefits that are expected to come from adopting an innovation. It has been reported that the probability of adopting a new technology increases when firms perceive a relative advantage in that innovation. Cloud computing promises various advantages to organizations that adopt it such as speed of business communication, efficient coordination among firms, better customer communication, and access to market information mobilization.
Complexity - Complexity can be defined as the perceived degree of difficulty of understanding and using a technology. It is related to time taken to perform tasks, integration with cloud infrastructure, efficiency of data transfer, system functionality, and interface design. The probability of adopting a new innovation will be less likely if it is seen as more challenging to use.
Technological Readiness - The technological readiness means the readiness of infrastructure and IT human resources which influence the adoption of a new technology. Reference referred to technological infrastructure as “installed network technologies, and enterprise systems, which provide a platform on which the cloud computing application can be built”. IT human resources are considered the sources of knowledge and skills that are needed to implement cloud computing related IT applications
Vendor Scarcity - Vendor scarcity refers to the lack of reputable and qualified cloud service vendors in the cloud service market. Given that cloud service is still a fairly new market, existing knowledge or experience about the cloud service may be limited. Having a limited number of vendors may in turn result in lower service quality. Thus, the availability of enough vendors with good reputation improves the organization’s confidence in cloud services and lets them make a positive trusting attitude towards cloud services transformation.
2.5.	Chapter Summary 
According to the technology diffusion theory the rate of adoption of cloud computing technology is dependent on the four  factor  that is, technological factors  of innovation, type of innovation decision, communication channels, environmental social factors. All these factors are at play when determining the rate of adoption of cloud computing technology; they can hinder or accelerate the rate of adoption of cloud computing technology. It analysis the empirical research and develops a conceptual framework. 
 

CHAPTER THREE 
RESEARCH METHODOLOGY 
3.	Introduction
This chapter provides a discussion of the research methodology. It discusses the research design used in the survey. It also discusses the population of study, data collection methods as well as data analysis and presentation methods employed. It describes the methods used to collect data in this study and explains their appropriateness to the exploration of the four research questions outlined at the end of the previous chapter.
3.1.	Research Design
Research design is often confused with choice of research method – the decision to use qualitative or quantitative methods, for example, or to use face-to-face interviews rather than telephone.
Given the scarcity of empirical work in the area of cloud computing adoption and the need to obtain rich data, the study will be exploratory in nature, and therefore, a case study approach will therefore be considered appropriate (Yin, 2003; Marshall and Rossman, 1989). Case studies are useful for exploring areas where existing knowledge is limited (Eisenhardt, 1989) and are also valuable in generating an understanding of a particular situation (Yin, 2003). 
3.1.1.	Population
The research study was based on a population of 66 corporate clients who have adopted or have the potential to adopt the cloud computing services. These are currently corporate customers of Hormuud Telecom. Form here, a sample of 20 respondents was required, being 30% of the total number in the sampling frame. 
3.1.2.	Sampling
In order to estimate the sample size, at least 30% of the total population is representative (Borg and Gall, 2003). Thus, 30% of the accessible population is enough for the sample size.
A sample is a smaller group or sub-group obtained from the accessible population (Mugenda and Mugenda, 1999). This subgroup is carefully selected so as to be representative of the whole population with the relevant characteristics. 
Each member or case in the sample is referred to as subject, respondent or interviewees. Sampling is a procedure, process or technique of choosing a sub-group from a population to participate in the study (Ogula, 2005). 
The study applied both random sampling procedures to obtain the respondents for questionnaires. 

3.1.3.	Description of Data Collection Instruments
This research method was used for field survey and the survey instrument was a structured questionnaire. According to Gil (2010, p. 35), the surveys "are characterized by direct questioning people whose behavior is wished to know. Basically, it proceeds in order to request information from a significant group of people around the problem studied and then, through a quantitative analysis, obtain the conclusions regarding the collected data".
Additionally, the questionnaires were used for the following reasons: a) its potentials in reaching out to a large number of respondents within a short time, b) able to give the respondents adequate time to respond to the items, c) offers a sense of security (confidentiality) to the respondent and d) it is objective method since no bias resulting from the personal characteristics (as in an interview) (Owens, 2002).
The questionnaire is divided into the main areas of investigation except the first part which captures the demographic characteristics of the respondents. Other sections are organized according to the major research objectives.
3.1.4.	Validity and Reliability of Research Instruments
3.1.4.1.	Validity
Validity refers to the degree to which evidence and theory support the interpretation of test scores entailed by use of tests. The validity of instrument is the extent to which it does measure what it is supposed to measure. According to Mugenda and Mugenda (1999), Validity is the accuracy and meaningfulness of inferences, which are based on the research results. It is the degree to which results obtained from the analysis of the data actually represent the variables of the study. 
The research instrument was validated in terms of content and face validity. 5 questionnaires will be issued for this purpose. The content related technique measures the degree to which the questions items reflected the specific areas covered.
The preparation of the questionnaire used as a research tool for primary data collection followed the following schedule: (a) Preparation of questions and pilot survey instrument pre-test with five IT professionals and (b) Preparation and application of the definitive survey instrument which presents the relation among the specific proposed objectives and the construct validity of the research
3.1.4.2.	Reliability
Reliability is the ability of a research instrument to consistently measure characteristics of interest over time. It is the degree to which a research instrument yields consistent results or data after repeated trials. If a researcher administers a test to a subject twice and gets the same score on the second administration as the first test, then there is reliability of the instrument (Mugenda and Mugenda, 1999).  Reliability is concerned with consistency, dependability or stability of a test (Nachmias and Nachmias, 1996). The researcher will measure the reliability of the questionnaire to determine its consistency in testing what they are intended to measure. The test re-test technique will be used to estimate the reliability of the instruments. This will involve administering the same test twice to the same group of respondents who have been identified for this purpose. 

3.2.	Data Analysis Methods
Descriptive statistics such as mean, frequencies and proportions will be used to analyze the collected data. Information was presented in form of tables and charts. Data was analyzed with  Statistical Package for Social Sciences (SPSS) program.
3.2.1.	Multivariate Analysis Of Variance (MANOVA
A one-way multivariate analysis of variance (MANOVA) was used to analyze the data because a MANOVA takes into consideration the correlation among the dependent variables while controlling for the overall alpha level (Tabachnick & Fidell, 2007). Various conventions exist to determine the number of participants per cell to conduct a MANOVA. Suggestions about the optimal participants range from the minimum of 20 participants per group to six to ten times the number of dependent variables (Swanson & Holton, 2005). 
At minimum, the number of participants per group must exceed the number of dependent variables (Swanson & Holton). For this study, the number of participants per group exceeded the number of dependent variables. A p < .05 level of significance will be used for all analyses in the study to determine if the null hypotheses can be rejected. The effect size will be calculated using the Eta squared statistic and interpreted based on Cohen’s (1988). Preliminary analyses to examine the assumptions of no extreme outliers, normality, linearity singularity was also be conducted.
3.2.2.	Correlation Analysis 
Since there are 4 independent variables, multivariate regression model was used. This is used when there is more than one independent variable and as such the regression line cannot be visualized in the two dimensional space, but can be computed just as easily.
 In general then, multiple regression procedures will estimate a linear equation of the form:
Y = a + b1*X1 + b2*X2 + ... + bp*Xp
This formula has the property that the prediction for Y is a straight-line function of each of the X variables, holding the others fixed, and the contributions of different X variables to the predictions are additive.  The slopes of their individual straight-line relationships with Y are the constants b1, b2, …, bp, the so-called coefficients of the variables.

3.3.	Ethical Concerns
The researcher explained to the respondents about the research and that the study was for academic purposes only. It was made clear that the participation was voluntary and that the respondents were free to decline or withdraw any time during the research period. Respondents were not coerced into participating in the study. The participants had informed consent to make the choice to participate or not. They were guaranteed that their privacy will be protected by strict standard of anonymity. 
3.4.	Chapter Summary
This chapter has discussed the methodology to be used in the study and the rationale for its use. It has given the details the participants in the study and the selection criteria used, the overviews the data collection process and also the timeline for completion of each stage of the study. It discusses how the data will be analysed and also the ethical considerations of the research and its potential problems and limitations.