opportunities and challenges for adopting cloud ...

OPPORTUNITIES AND CHALLENGES FOR ADOPTING CLOUD COMPUTING AT UNIVERSITIES IN DEVELOPING COUNTRIES

AUTHORS Humphrey M. Sabi Doctoral Student, The ICT University Baton-Rouge, LA 70879, USA [email protected] CONFERENCE THEME: Harnessing ICT in Education for Global Competitiveness

VENUE: HILTON HOTEL, YAOUNDE

CONFERENCE PROCEEDINGS Vol. 6. ISBN: 978- 9956- 27- 030- X EDITOR Mayoka Kituyi CO-EDITORS Adekunle Okunoye, Charles Masango, Cosmas Nwokeafor, Kehbuma Langmia,Victor Mbarika

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The 6 Annual International Conference on ICT for Africa 2014 th 1-4 October, The ICT University, Yaoundé Cameroon

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ICT4AFRICA 2014 Conference Papers Paper No. 82, pp. 335-349

Doctoral Consortium Paper | Work in Progress

Opportunities and Challenges for Adopting Cloud Computing at Universities in Developing countries Received May 31st 2014, accepted June 31st 2014

Abstract. Cloud computing is a new computing paradigm that is revolutionising the way we access and use computer infrastructure and services. Developing countries are faced with challenging socioeconomic and political problems that limit their ability to invest in expensive information and communication infrastructures. Universities in these developing countries lag behind their western counterparts due to lack of leading edge technology required for teaching, collaboration and research. The purpose of this study is to investigate the level of awareness and diffusion of cloud computing within universities in the sub-Saharan region of Africa. Cloud computing offers a costeffective means for these universities to leapfrog the technological divide and become competitive at a global level with universities in western industrialised countries. We use triangulation of constructs from the diffusion of innovation theory and technology acceptance model to guide our study on factors impacting diffusion and adoption of cloud computing at universities in developing countries. The results of the study will attempt to develop an adoption model for cloud computing and will have far-reaching implications for university decision makers and academics on the future trend of cloud computing adoption for education at universities and other institutions in developing countries.

Keywords: Cloud computing, Educational technologies, Technology adoption, Diffusion of innovation, Sub-Sahara Africa, Developing countries

Introduction Information and communication technology (ICT) has been helping to shape the way in which businesses, organisations, academic institutions and governmental departments perform their operations over the last two decades (Commander, Harrison &Menezes-Filho, 2011; Dimelis&Papaioannou, 2011; Dawson, Heathcote& Poole, 2010). Businesses across the world have invested large sums of money on ICT infrastructure through the procurement of computer hardware and software to support their operations and improve their productivity and profit margins (Miyazaki, Idotaand Miyoshi, 2012; Spiezia, 2012). This investment in ICT has made many local businesses to extend their operations globally thereby increasing their customer base and profitability through the use of the internet. Many international and local organisations have also been investing in ICT to support their operations and remove management overheads inherent in manual processes (El Sayed and Westrup, 2003). Academic institutions are now making use of ICT for the effective and efficient delivery of courses, research, collaboration and managing study outcomes (Andersson, 2006; Tondeur, Van Braak and Valcke, 2007). Due to the costs involved in procuring, developing and maintaining large and costly ICT infrastructures, many businesses, organisations and governments in developing countries have been left behind in this technological process. Many academic institutions in developing countries are unable to afford the cost of running dedicated and custom-built ICT infrastructure necessary for managing teaching, assessment and research due to cost th


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impediments. It has been shown that firm level productivity increases substantially for firms in developing countries that have invested in ICT (Commander et al., 2011). Due to the difficult economic situation of many developing countries, many of the universitiesin these countries cannot afford the upfront investment required to implement advanced ICT systems and hence are unable to benefit from the competitive and performance advantages gained through the use of ICTs for education compared to their western developed country counterparts. However, the advent of cloud computing and the opportunities it provides such as low upfront investment, dynamic scalability, ubiquitous network access and pay-as-you-go model(Mell&Grance, 2011) can help bridge this technological gap between developed and developing countries. The economic crisis and global financial challenges faced by western developed countries since the 2008 near collapse of the banking system has led to many educational establishments in western developed countries looking for cheaper alternatives for their ICT needs (Sultan, 2009). Cloud computing is providing these educational establishments and universities in western developed countries a cheaper and more costeffective alternatives to their ICT needs. Most universities in developing countries which do not have an established ICT network can therefore tap into this new computing paradigm to provide the required computing environment for their students and staffs. There are varying definitions of cloud computing mainly arising from the various vendors and providers. These definitions, though covering most of the underlying principles of cloud computing, are mostly geared towards marketing the services of the providers. Many providers of web services today try to sell them as cloud computing services. However aunified definition of cloud computing is provided by the national institute of standard and technology (NIST) in the USA which defines cloud computing as a model for enabling ubiquitous, convenient, on-demand network access to a sharedpool of configurable computing resources (e.g., networks, servers, storage, applications and services) thatcan be rapidly provisioned and released with minimal management effort or service provider interaction (Mell&Grance, 2011). A more commonly used technical definition describes cloud computing as clusters of distributedcomputers (largely vast data centres and server farms)which provide on-demand resources and services over a networkedmedium (usually the Internet) (Sultan, 2010). Cloud computing providers allow subscribers to dynamically scale up or scale down their use of the services based on their requirement using a metering capability to charge subscribers for actual usage only (Mell&Grance, 2011). Cloud computing opens up a new way in which businesses around the world can harness the power of the cloud infrastructure to benefit from high performing ICT systems without the initial capital investment required. This possibility offered by cloud computing has opened up a new way of provisioning IT systems remotely over the internet that can allow universities, firms and governments in developing countries to have access to the same advanced and powerful computer systems that are currently only available to large corporations and universities in developed countries thereby leapfrogging the technological divide.The cloud computing model is based on the idea of outsourcing corporate IT infrastructures to third party data centres with a shared pool of computing infrastructure, storage, and networking resources with services becoming accessible rapidly and on demand over the internet. The forecasted benefits include elastic and dynamic resource provisioning, simpler and automated administration of IT infrastructures, and sharing of nearly unlimited CPU, bandwidth, and storage space (Mell&Grance, 2011). This is made possible through resource virtualization, with scalability improvements and massive cost reductions in terms of infrastructure management. Cloud computing has five essential characteristics, three major service models and four deployment models (Mell&Grance, 2011). The characteristics include on-demand selfservice, broad network access, resource pooling, rapid elasticity and measured (or pay-asth


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you-go) service. On-demand self-service allows service consumers to unilaterally provision computing capabilities automatically without need for human interaction with the service provider. Broad network access allows service users the capability to access the services through heterogeneous thin and thick platforms such as mobile phones, laptops, tablets, personal digital assistants (PDAs) and personal computers (PCs) as long as they have internet access. Resource pooling allows service providers to make efficient use of computing resources by serving many customers across diverse geographical locations on the same computing platform in what is known as a multi-tenancy model. Rapid elasticity allows service users to quickly scale up or scale down required capabilities based on their requirements hence providing a seemingly unlimited flexibility. Meanwhile measured service entails the use of a metering capability by cloud systems to automatically control and optimize resource usage to ensure users only pay for resources used and provides transparent monitoring of system usage by both subscribers and providers. The cloud computing service models include software as a service (SaaS), platform as a service (PaaS) and infrastructure as a service (IaaS). SaaS provides consumers the capability to use software applications running on the provider‘s cloud infrastructure and can be accessed through any device with a web browser. PaaS provides a development environment with capability for users to create and run their own custom-made applications using programming languages and tools supported by the cloud service provider. IaaS allows the capability for users to manage processing power, storage, networks and other fundamental computing resources and forms the base on which PaaS and SaaS are supported. Cloud computing can be deployed in four main deployment models – private cloud, community cloud, public cloud and hybrid cloud. Private cloud deployment model is where the cloud infrastructure is operated solely for a single organisation on or off their premises and is similar to current private data centre models. Community cloud model is one in which several organisations with common goals share the same cloud infrastructure which can be managed by them or a third party provider. Public cloud model is the most common and costeffective model that is open to the general public and owned by an organisation selling cloud services. Meanwhile hybrid cloud infrastructures is a combination of two or more of the above cloud models (private, community or public) and are bound together by standardised or proprietary technologies that enables communication between the cloud models. Developing countries have often been left behind in the technology diffusion, adoption and implementation drive due to various bottlenecks that impact the adoption process. These impeding factors to technology adoption include infrastructure, knowledge, cost, government policies, education, user resistance and security concerns (Kshetri, 2010a; Svantesson& Clarke, 2010). The developing world‘s cloud computing sector is beginning to receive considerable attention from global and local IT players, national governments and international agencies with cloud provisioning companies like IBM establishing cloud computing centres in many developing countries such as China, India, Vietnam, Brazil, South Africa and South Korea (Kshetri, 2010b). Other global cloud providers such as Microsoft, Amazon, VMware, Salesforce, Dell and Parallels are actively searching for opportunities and implementing cloud computing centres in the developing world (Kshetri, 2010a). The rise in cloud computing adoption will open up a whole new future for developing countries which can benefit from the lower upfront cost and the elasticity of cloud computing services. Kshetri (2010b) further asserts that the developing economies can catch up with the West as the cloud allows them to have access to the same IT infrastructure, data centres and applications due to their ubiquitous nature. The cost of acquiring, implementing and maintaining a robust and reliable IT infrastructure is very high and has led to many universities in developing countries lagging behind in the technological advancements of ICT infrastructure used byuniversities in developed countries. th


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This cost impediments can be substantially reduced through the adoption and usage of cloud computing by universities in developing country which can in turn bring them up to the same competitive stature as their counterparts in western developed economies. Despite the possibilities offered through cloud computing such as low initialcapital investments and elasticity through which computing resources can be dynamically allocated and de-allocated according to the needs of the users, there is still little or no drive by universities in developing countries towards embracing this new technological innovation. There is still a very slow pace by universities in developing countries in adopting this technology that can offer them greater research, collaboration and teaching capabilities by leveraging the power of high performing IT infrastructures offered through cloud computing. Universities that do not embrace cloud computing risk forfeiting the benefits attributed to its novel educational-computing paradigm. The main problem to be investigated by this study is the limited awareness and adoption of cloud computing innovation and the factors impacting the adoption of cloud computing in education in developing countries with a particular focus on universities in sub-Saharan Africa. Given the opportunities available through cloud computing that can serve as a springboard for these universities in developing countries to leapfrog the technology divide and become competitive on the global stage with their western counterparts, it will be relevant to explore the various factors impacting cloud computingdiffusion and adoption. This will enable us to develop a model that can be used to inform university decision makers and facilitate the smooth adoption process of cloud computing for academic institutions. Africa has been chosen as the setting for this study because sub-Saharan African countries are lagging behind other developing countries like India and China in the Asian continent in the adoption of cloud computing (Kshetri, 2011). With the exception of South Africa which has adopted the use of cloud computing in some parts of its business sectors such as in the call centre business (Firth, 2009 as cited in Kshetri, 2010b), many African countries are yet to take advantage of this new and advanced technology which comes at a fraction of the capital investment required to own and maintain such systems in-house. Moreover, universities in the sub Saharan African region continue to use unintegrated computer systems which do not meet the standard required for global educational competitiveness, collaboration and research. The main objectives of this study is to examine and evaluate the awareness and adoption of cloud computing at universities in developing countries with the aim ofdeveloping an adoption model and informing the appropriate decision and policy makers to ensure a timely adoption and implementation of cloud computing at their universities. This study will seek to: a) Investigate level of awareness of cloud computing among university IT support staff, administration and computing academic staffat universities in Sub-Sahara Africa; b) Investigate the extent of diffusion of cloud computing atuniversities in Sub-Sahara Africa; c) Explore factors that impact the adoption and implementation of cloud computing at universities in Sub-Sahara Africa; d) Investigate how institutional culture, user perceptions, governmental policies and infrastructure impact the diffusion, adoption and implementation of cloud computing at universities in Sub-Sahara Africa and e) Develop a proposedadoption model that can be used for adoption of cloud computing at universities in sub-Sahara Africa. The purpose of this quantitative study will be to examine the level of awareness of cloud computing innovation by universityadministrations, computing academic staffs and ICT support staffs within universities in developing countries and their propensity to adopt this new computing innovation. Using triangulation of Roger‘s (1995) diffusion of innovation theory and Davis‘s (1989) technology acceptance model, we will carry out a survey with th


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university administration, ICT support staffs, computing academic staffs and decision makers to assess the level of awareness of cloud computing and their willingness to adopt cloud computing innovation. A survey instrument will be used to measure the perceived attributes of cloud computing such as compatibility, complexity, observability, relative advantage, trialability, risk, cost, ease of use and usability in relation to the dependent variable intention to adopt and use cloud computing. A quantitative research approach has been chosen over structured interviews for this study because it will allow us to efficiently measure the responses of a large sample from universities in many countries in sub-Sahara Africa in a cost-effective and efficient manner (Creswell, 2009). Quantitative research is rooted in positivist/postpositivist worldview of determinism where causes determine effects or outcomes(Creswell, 2013).We will use quantitative analysis approach in this study to understand the relationship between the independent variables about cloud computing innovation perception and the dependent variable of intent to use cloud computing. Using a quantitative approach will therefore allow us to collate and present the views of universityadministration, computing academic staffs, ICT support staffs and decision makers regarding cloud computing innovation and deductively propose a diffusion and adoption model based on the study findings. In order to address the problem regarding cloud computingawareness and adoption in developing countries and achieve the objectives of this study, we seek to answer the following questions: 1. What is the level of awareness of cloud computing technology among university administration, computing academic staffs and university ICT support staffs in developing countries? 2. What is the extent of diffusion, adoption and implementation of cloud computing atuniversities in sub-Saharan Africa? 3. What factors impactthe adoption and implementation of cloud computing at universitiesin the sub-Sahara Africa? 4. What are user perceptions of the novelty of cloud computing within universities in sub-Sahara Africa? Using the postpositivist deterministic research paradigm of quantitative research, we advance the following hypotheses which are a priori assumptions that will be statistically tested in the data analysis to help us answer the research questions outlined above. The hypotheses will address the relationship between the independent variables encompassing the perceived attributes of cloud computing and diffusion of innovation theory with the dependent variable ―intent to adopt and use cloud computing technology‖. The survey questions will be designed based on previous technology adoption studies and will focus on getting responses for each of the variables identified in the hypotheses. The first hypothesis (H1) theorises that greater awareness and knowledge of the attributes, characteristics and provisions of cloud computing by universitystaffs and decision makers will lead to adoption of cloud computing innovation H1: Awareness is correlated with the intent to adopt and use cloud computing The second hypothesis (H2) theorises that the perceived cost involved in adopting and implementing cloud computing will impact the decision by universitystaffs and decision makers to adopt the innovation H2: Cost is correlated with the intent to adopt and use cloud computing The third hypothesis (H3) theorises that the level of risk and data security perceived by the universitystaffs and decision makers will impact the adoption of cloud computing innovation. H3: Risk is correlated with the intent to adopt and use cloud computing

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The fourth hypothesis (H4) theorises that the level at which university staffs and decision makers perceived the relative advantage of cloud computing will impact the adoption of cloud computing innovation. H4: Relative advantage is correlated with the intent to adopt and use cloud computing The fifth hypothesis (H5) theorises that the level of compatibility of cloud computing with existing systems and operations perceived by the university staff and decision makers will impact the adoption of cloud computing innovation. H5: Compatibility is correlated with the intent to adopt and use cloud computing The sixth hypothesis (H6) theorises that the lower the level of complexity of cloud computing perceived by the university staffs and decision makers, the higher the propensity for them to adopt cloud computing. H6: Complexity will negatively impact the intent to adopt and use cloud computing The seventh hypothesis (H7) theorises that the degree to which university staffs and decision makers perceive the observability of cloud computing results will impact the adoption of cloud computing innovation H7: Observability is correlated with the intent to adopt and use cloud computing The eighth hypothesis (H8) theorises that the extent of experimentation or trialability of cloud computing by the university staff and decision makers will impact the adoption of cloud computing innovation. H8: Trialability is correlated with the intent to adopt and use cloud computing The ninth hypothesis (H9) theorises that the level of results demonstrable though use of cloud computing perceived by the university staffs and decision makers will impact the adoption of cloud computing innovation. H9: Results demonstrable is correlated with the intent to adopt and use cloud computing The tenth hypothesis (H10) theorises that ease of use of cloud computing perceived by the university staffs and decision makers will impact the adoption of cloud computing innovation. H10: Ease of use is correlated with the intent to adopt and use cloud computing The eleventh hypothesis (H11) theorises that usefulness of cloud computing perceived by the university staffs and decision makers will impact the adoption of cloud computing innovation. H11: Usefulness is correlated with the intent to adopt and use cloud computing Cloud computing is a very new and emerging technology that is still being adopted and implemented by universities, firms and governments in western industrialised countries. This research will contribute to the body of knowledge for the on-going IS research into cloud computing diffusion and adoption in developing countries. It will provide a focus for subSahara Africa as a whole which continues to be highly under-researchedwithin the IS cycles (Mbarika&Okoli, 2005). It will allow: a) The identification of the concept and framework of cloud computing diffusion and adoption in the context of least developed countries taking into consideration their operating environments; b) The generation of awareness amongst universities in developing countries towards the role cloud computing can play in opening up new avenues for their IT requirements, research and collaboration; c) The provision of useful knowledge on factors that can impact cloud computing adoption and propose strategies to mitigate them; and d) The support and enrichment of theory and development of a model for cloud computing adoption that can be applicable in similar contexts. This study will focus on the adoption and implementation of cloud computing services in the context of universities in countries of the sub-Saharan region of Africa with focus on five th


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countries - Cameroon, Nigeria, Uganda, Kenya and Ghana. We believe that the socio-cultural and economic situation of these five countries will be representative of the general situation inherent in many sub-Saharan African countries hence providing valid insights into the phenomenon of cloud computing adoption in universities within the region. We will look at universities that are already using cloud computing services as well as those that are still lagging behind or are unaware of this new technology and its benefits.

Literature Review Developing countries have perpetually faced varying challenges in their socio-economic and political development leading to low investments in advance technologies. African countries of the sub Saharan region have seen development and integration with the global economy limited due to lack of high performing technological infrastructures. However recent reviews about the economic status of sub-Saharan Africa have shown more optimism towards economic growth, poverty reduction, increased per capita income and life expectancy (Devarajan and Fengler, 2013). The un-exploited economic situation of African countries has opened new avenues for foreign investment with notably China and India driving a new trend for investment in Africa through commodity exports and infrastructural developments (Cheru and Obi, 2010). Adoption of innovation in various business sectors is pivotal in the attainments of high turnovers and productivity margins. However smaller firms and enterprises in Africa have been limited to leverage new technologies for their operations due to cost and other socio-economic and political limitations (Hounkonnou et al., 2012). The last part of the 20th century saw a slow pace of adoption of innovation in developing countries compared to the spread of innovation in western industrialised countries. However, the emergence of ICTs has led to accelerated rate of innovation diffusion to developing countries with the potential of supporting development strategies that can help leapfrog the digital divide between developed and developing countries (Steinmueller, 2001). Steinmueller (2001) also asserts that achieving the benefits of leapfrogging by implementing technology transfer strategies is not straightforward as it sounds due to various challenges. These challenges include adaptation of the technology to local needs, costs, skills required for its effective operation and limitations due to local market dynamics. The limited knowledge and expertise on imported ICTs also leads to failed implementations thus preventing developing countries from benefiting from the technologies (Moens, Broerse, Gastand Bunders, 2010). Even though ICT innovation places a lot of emphasis on the socioeconomic context, there are significant epistemological differences regarding the process used for such innovation adoption (Avgerou, 2010). Innovation diffusion must be backed by institutional willingness and support to ensure successful adoption. The government must develop policies that support innovation while decision makers within governmental agencies and private enterprises must also provide the needed resources and funding required ensuring success (McGrath and Maiye, 2010). ICT innovations in developing countries have gathered pace over the last decade even though the nature of such innovations is still a key concern for IS researchers (Lyytinen and Rose 2003b). Such innovations have been largely context-specific with a focus on selected issues and challenges without looking at the actual nature and evolution of the ICT innovation. Furthermore the path and manner in which ICT innovation is undertaken and the interaction between local context and the needs of the beneficiaries of the innovation have to be taken in to consideration (Rai, Chatterjee and Sarker, 2011). The World Bank Group is encouraging and helping developing countries to adopt ICTs to drive innovations and productivity that can eventually lead to poverty reduction, economic growth and improved accountability and governance (World Bank, 2013). Fundamental and successful innovation through the use of th


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ICTs in developing countries can only be achieved through an integrated approach that takes into consideration cultural context and organisational needs (Ponte and Cullen, 2013). Cloud computing has emerged as a new computing paradigm that will revolutionised the way we buy and use computing equipment and services. The concept of cloud computing can be traced back to 1969 when Leonard Kleinrock who was the chief scientist of the original Advanced Research Projects Agency Network (ARPANET) said that computer networks were at their infancy then but that as they develop and become more sophisticated, they will probably become computer utilities like other utilities such as gas, water and electricity (Buyya, Yeo, Venugopal, Broberg and Brandic, 2008). The framework for the delivery of cloud computing has been commoditized in a manner similar to that proposed by Leonard Kleinrock. Other computing paradigms that have tried in the past to deliver utility computing included cluster computing, Grid computing and remote hosting of data centres (Uzoka, Akinnuwesi, Olabiyisi&Demilade, 2012; Buyya, Yeo and Venugopal, 2009). Using this model of provision of computing services as a utility allows for efficient, effective and optimized usage of computing resources thereby reducing cost to both providers and users (Monroy, Arias and Guerrero, 2013). Internet infrastructure and connectivity today is providing a ubiquitous access to shared applications, software and hardware across the globe allowing users to quickly and easily get information whenever and wherever they might be. Cloud computing is taking this ubiquitous access to another level be providing users with access not just to information available on the internet but also to their business applications and network wherever there is internet connectivity using any compatible device such as a personal computer, laptop, tablet or smartphone. Users can have access to programs, storage, applications, processing and software development environments that are providedthrough cloud computing (Rahimli, 2013). Developing countries have always lagged behind in technological innovation due to varying factors that impact adoption and usage of advance technologies. Some of these factors include infrastructure, cost, economic and political stability as well as availability of qualified personnel to implement and support such advance systems. Cloud computing innovation is widely seen as a solution to the innovation adoption problems faced by developing countries and will offer developing country firms the opportunity to access and use advance technologies available to western developed countries hence making them competitive on the global stage (Kshetri, 2011). Even though there are still factors that can impact the adoption and usage of cloud computing in developing countries such as outages (system availability), security, performance, compliance, compatibility, integration and cost; most of these factors are not relevant to consumers and users of cloud computing services (Kim, Kim, Lee, Lee, 2009). The cloud computing model and providers ensure highly scalable and resilient systems that users and organisations in developing countries cannot afford on individual basis. The pay-as-you-go payment model provided by cloud computing makes it easy for all types of businesses to engage and start using cloud computing with minimal upfront investments. This model together with the scalability and flexibility attributes of cloud computing has led to many firms and governments in developing countries taking advantage of it to enhance their performance and transform their operations (Kshetri, 2011). The digital divide between developed and developing countries suggests that there is inequality in access to ICT resources, inequality in capability to exploit ICT and inequality in the outcomes from ICT usage (Purkayastha and Braa, 2013). The adoption and usage of cloud computing will eliminate these inequalities and provide a platform for developing country firms and organisations to bridge the digital divide and become competitive at the global stage. Cloud computing will enable even the health sector in developing countries to bridge th


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the gap in health information systems that are only available in developed countries, making them readily available to developing countries to enable processing and analysis of Big Data (Purkayastha and Braa, 2013). Capital investment into expensive IT implementations has largely limited developing country firms from embracing new technology since many firms both in developed and developing countries underutilize the IT systems (Marston, Li, Bandyopadhyay, Zhang and Ghalsasi, 2011). However the opportunities offered through cloud computing will reduce these capital investments and firms only pay for the services they use thereby reducing their IT expenditures. Educational establishments around the world have seen their budgetary allocations cut due to the prevailing global economic situation (Sultan, 2010). This has led to many schools, university and college administrators looking at cost cutting measure to remain competitive within their industry (Sultan, 2010). Cloud computing and its flexible operational model has emerged as a real alternative to the cost-cutting measures required by these educational establishments without impacting their performance and service offerings. Moreover cashstrapped educational establishments and new institutions that are still being setup will find the benefits, scalability and pay-as-you-go cost structure in the cloud computing model more appealing (Sultan, 2010). In addition to cutting cost of ICT implementation and support, educational establishment are also attracted to the ubiquitous nature of the cloud computing model which can allow for easy collaboration between faculty and students anywhere and at any time compared to the current campus only access model that exists in many establishments. Key functions such as file storage, file synchronization, document creation and collaboration using applications such as Google Apps, Zoho and Microsoft Office 365 provide educators with a more flexible alternative to campus-based systems (Aaron & Roche, 2011). Using cloud computing systems removes the need to have dedicated support staff on campus, eliminate geographical constraints of only accessing the system when on campus and mitigate overheads involved in installing, upgrading and configuring hardware and software required to keep the system up-to-date (Aaron & Roche, 2011). The cloud computing providers take care of the management of the system and users can access it from anywhere in the world using an internet browser on any compatible device that is connected to the internet. Many educational establishments are also taking advantage of the cloud model to manage their course work online using applications such as Moodle and Blackboard (Blue &Tirotta, 2011). Some establishments have even gone further to use the cloud for course delivery through systems such as Webex and Skype which are all based on a cloud computing infrastructure.

Theoretical and Conceptual framework Adoption of innovation has been the subject of many research studies in areas of medicine, computing and management science (Ozdemir&Trott, 2009). Many theories like the diffusion of innovation (DOI) theory (Rogers, 2003) and technology acceptance model (TAM) (Davis, 1989) have been used to explain customer behaviours towards the adoption of innovation in various disciplines including information systems. DOI sees innovation as being communicated through certain channels over time and within a particular social system. Individuals and organisations are seen as having varying degrees of willingness to adopt innovation and the portion of the population willing to adopt innovation is approximately normally distributed over time (Rogers, 1995). The DOI theory segregates individuals into 5 categories based on their innovativeness: innovators, early adopters, early majority, late majority and laggards (Rogers, 1995). DOI was originally introduced in the 1960s to study adoption of innovation within the agricultural sector (Rogers, 2003). However despite this theory focusing on issues of innovativeness, it doesn‘t look at the behavioural aspects that th


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affect individual willingness to adopt any innovation. Although theories such as the Theory of reasoned action (TRA) (Fishbein and Ajzen, 1975), Theory of planned behaviour (TPB) (Ajzen, 1991) and TAM are all intention-based theories derived from social psychology, TAM was specifically adapted from TRA to apply to the field of information system. TAM posits that perceived usefulness and perceived ease of use determines an individual‘s intention to use a system. Davis (1989) defines perceived usefulness as the degree to which a person using a particular system would enhance his or her performance. Even if the usage of a system can improve performance, it is also important to evaluate the effort it will take to use such a system. Hence perceived ease of use, which is defined as the degree to which a person believes that using a particular system would be free of effort, is as important as perceived usefulness in the decision to use a system (Davis 1989;Davis, Bagozzi&Warshaw, 1989;Venkatesh& Davis, 1996). There are other factors that might impact user decision to adopt and use a system such as cost of the system but research has shown that the two determinants that are especially important are perceived usefulness and perceived ease of use of the system (Davis, 1989). We will useDOI as the main theoretical framework underpinning our research. The reason for choosing DOI to investigate the diffusion and adoption of cloud computing is because of the novelty of cloud computing which is still at the early stages of diffusion in developing countries. Even though Moore and Benbasat (1991) had used Davis‘s (1989) TAM to integrate the perceived usefulness construct with relative advantage of the DOI and perceived ease of use construct with the complexity attribute of DOI (Rogers, 2003; Moore and Benbasat, 1991), we will extend the DOI with constructs of TAM to measure the intention of users to adopt cloud computing based on its perceived usefulness and ease of use. TAM has the ability to explain individual behavioural intentions to use a system based on its usefulness and ease of use and has been widely used by many IS researchers to explain adoption of many internet based technologies such as general use of the world wide web, internet shopping (e-shopping, e-commerce) and internet banking both in developed and developing countries (Gefen, Karahanna& Straub, 2003; Gefen& Straub, 2000, Awamleh, C Fernandes; Moon and Kim, 2001). Little research has been done by integrating constructs from TAM and DOI theories to explain user adoption and usage of new technology in developing countries. Our research will focus on developing a holistic understanding of adoption and usage of cloud computing in developing countries by integrating the key constructs of TAM and DOI theory. Using a triangulation of the two theories will provide a new perspective and offer more insights into the perception, adoption and usage of cloud computing in developing countries. Based on our theoretical model and the hypothesis postulated above, we propose the following conceptual model that will guide our research study. The model maps the relationships between the theoretical constructs (independent variables impacting the adoption and usage of new technology) and the dependent variable ―intention to adopt and use‖. Figure 1: Cloud Computing Adoption Model

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Methodology Our study will use a quantitative cross-sectional survey strategywhose intention is not to measure causation. Quantitative research has been extensively used in psychology and social studies due to its positivist/post-positivist paradigm that allows researchers to use experimental and correlational techniques to test theory using quantitative data (Gelo,Braakmann and Benetka, 2008). Using a quantitative approach will allow us to gain deeper insights into the process of diffusion and adoption of cloud computing in a developing countries context through survey questionnaires that will cover a larger population. This study will target public and private universitiesin fivesub-Sahara African countries (Cameroon, Nigeria, Uganda, Kenya and Ghana) to investigate the level of awareness of cloud computing and their willingness to adopt and use cloud computing for their educational needs. These five countries have been chosen because little research has been done on cloud computing adoption at universities in these countries. They also serve as a macrocosm of the socio-political, cultural and economic representation of the sub-Saharan African region covering both English and French speaking regions as well as East and West African regions. A sample is a sub-section of a population that that exhibits characteristics common to the general population from which they are drawn. Given the novelty of the cloud computing paradigm and the purpose of the study, we will use a purposive sampling method in order to identify universities and individuals within those universities that can participate in this study. Purposive sampling allows the researcher to tailor their sample in a manner that will provide the best information required to answer the research questions. We will seek to use university ICT support staffs,computing academic staffs, senior administrative staffs and key decision makers within the university in order to gather a cross-sectional view of cloud th


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computing awareness and adoption perceptions. The unit of analysis for the study will be university staffs representing the various categories of staffs identified above. Data will be collected through self-designed questionnaires that will be self-administered by the researcher as well as using web-based survey tools. A pre-test survey will be carried out to validate the constructs in our instrument before engaging in the final data collection phase. Invitations to participate in the study will be sent to all selected universities via email or post and selected members of staff will be sent the link to the online survey questionnaire or handed physical copies by the researcher or his assistant. Measurements will use a5-point Likert Scale ranging from 1=strongly disagree to 5=strongly agree. Profile data will be collected in section one of the questionnaire whilebackground data about the participating universities and countries will be gathered from records at the participating universities and online to back our reporting of the study results. We will employ descriptive statistics to present the profile data and determine their impact on the results. Exploratory factor analysis using SPSS for windows will be used to reduce the variables into a smaller number that have an effect on the adoption of cloud computing in universities. Multiple regression analysis will then be used to validate our model and test the hypotheses propounded above.

Conclusion The purpose of this research is to investigate the level of awareness and adoption of cloud computing within universities in developing countries with a focus on sub-Sahara Africa. We will set out to investigate the essential factors that impact diffusion, adoption and usage of cloud computing in sub-Sahara African universities. Our proposed model will use triangulation of constructs from two theories (DOI and TAM) to investigate user perception of cloud computing and their behavioural intention to adopt and use cloud computing. It is relevant to investigate the extent of diffusion and adoption as well as organizational factors that influence the implementation and usage of cloud computing in this region. It is hoped that the findings of this study will lead to the development of an adoption model for cloud computing at universities in developing countries. The findings will also be beneficial to universities in the region as well as firms, organisations and governmental departments in developing countries. The study will also contribute to the body of knowledge on cloud computing diffusion and adoption research.

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