FACTORS INFLUENCING THE ADOPTION OF ON-DEMAND CLOUD SERVICES BY ORGANISATIONS IN NIGERIA Janet Oyetolani OyeleyeInformation and Communications Technology Business Management INSTITUT MINES-TELECOM Ecole de Management Evry

FACTORS INFLUENCING THE ADOPTION OF ON-DEMAND CLOUD SERVICES BY ORGANISATIONS IN NIGERIA
Janet Oyetolani OyeleyeInformation and Communications Technology Business Management
INSTITUT MINES-TELECOM Ecole de Management
Evry, France
Supervisor
Pierre VialleSeptember 18, 2018

Declaration of OriginalityThis is to certify that the work is entirely my own and not of any other person, unless explicitly acknowledged (including citation of published and unpublished sources). The work has not previously been submitted in any form to INSTITUT MINES TELECOM Ecole de Management or to any other institution for assessment or for any other purpose.

Signed _________________________________________________
Date ___________________________________________________
AbstractOn-demand Cloud Services have become the norm today. Several organisations across the world maximize the full potential to effectively increase the efficiency in their respective workplaces, engage customers in a refreshing way, and to deal with existing information systems. Several factors would have to be put into consideration for business executives, before they think of embarking on the usage of on-demand cloud services, which has led to the realization of this study, to answer a question of what factors influence the adoption of on-demand cloud services by Nigerian organisations. The aim of this study is to correctly identify such factors and how they influence the decision making of senior executives to adopt this technology, and the perceived outcome that may spring up from such adoption. Data was collected via administered surveys, and the research model would be built upon the Technology – Organisation – Environment (TOE), PESTEL theoretical framework. Also, to be highlighted would be an interesting empirical result that indicates the factors that influences the adoption of on-demand cloud services.

Word CountNumber of Pages: 94
Number of Words:17748
Table of Contents TOC o “1-3” h z u Declaration of Originality PAGEREF _Toc525553014 h 1Abstract PAGEREF _Toc525553015 h 2Word Count PAGEREF _Toc525553016 h 3Table of Contents PAGEREF _Toc525553017 h 4List of Tables PAGEREF _Toc525553018 h 7List of Figures PAGEREF _Toc525553019 h 8CHAPTER 1 PAGEREF _Toc525553020 h 9INTRODUCTION PAGEREF _Toc525553021 h 91.1: Study Background PAGEREF _Toc525553022 h 91.1.1: Innovation PAGEREF _Toc525553023 h 91.1.2: Cloud Computing PAGEREF _Toc525553024 h 91.2: Aim and Objectives PAGEREF _Toc525553025 h 101.3: Study Scope PAGEREF _Toc525553026 h 101.4: Dissertation Structure PAGEREF _Toc525553027 h 11CHAPTER 2 PAGEREF _Toc525553028 h 12RESEARCH PROBLEM, THEORETICAL PERSPECTIVE AND METHODOLOGY PAGEREF _Toc525553029 h 122.1: Research Problem PAGEREF _Toc525553030 h 122.2: Theoretical perspective PAGEREF _Toc525553031 h 122.2.1: Organisational Adoption of Innovation PAGEREF _Toc525553032 h 132.3: Methodology PAGEREF _Toc525553033 h 132.3.1: Quantitative Research PAGEREF _Toc525553034 h 14CHAPTER 3 PAGEREF _Toc525553035 h 17BACKGROUND INFORMATION AND ANALYTICAL FRAMEWORK PAGEREF _Toc525553036 h 173.1: Background information PAGEREF _Toc525553037 h 173.1.1: Organisational Adoption of Innovation PAGEREF _Toc525553038 h 173.1.2: Technology – Organisation – Environment (TOE) Framework PAGEREF _Toc525553039 h 193.1.3: Diffusion of Innovation (DOI) Framework PAGEREF _Toc525553040 h 213.1.4: Previous Studies PAGEREF _Toc525553041 h 22
3.2: Design of analytical framework PAGEREF _Toc525553051 h 283.2.1: Questionnaire Format PAGEREF _Toc525553052 h 29CHAPTER 4 PAGEREF _Toc525553053 h 32INFORMATION DISPLAY (DESCRIPTIVE QUESTIONNAIRE DATA) PAGEREF _Toc525553054 h 324.1: Data Collection PAGEREF _Toc525553055 h 324.2: Developing Research Instrument PAGEREF _Toc525553056 h 334.3: Participants and Organisations Characteristics PAGEREF _Toc525553057 h 334.4: Decision Making Information PAGEREF _Toc525553058 h 374.5: Usage of On-demand Cloud Services PAGEREF _Toc525553059 h 384.6 Tabular Summary of Description Statistics of Participants and Organisations PAGEREF _Toc525553060 h 41CHAPTER 5 PAGEREF _Toc525553061 h 43ANALYSIS PAGEREF _Toc525553062 h 435.1: Descriptive Analysis PAGEREF _Toc525553063 h 43Technological Context PAGEREF _Toc525553064 h 435.1.1: Security and Trust PAGEREF _Toc525553065 h 435.1.2: Perception of Technology PAGEREF _Toc525553066 h 44Organisational Context PAGEREF _Toc525553067 h 465.1.3: Employees’ Opinions PAGEREF _Toc525553068 h 465.1.4: Skills and Resources PAGEREF _Toc525553069 h 475.1.5: Scope of Operations PAGEREF _Toc525553070 h 485.1.6: Willingness and Readiness PAGEREF _Toc525553071 h 495.1.7: Management Support (Decision Makers) PAGEREF _Toc525553072 h 50Environmental Context PAGEREF _Toc525553073 h 525.1.8: Competition PAGEREF _Toc525553074 h 525.1.9: Communication among Stakeholders PAGEREF _Toc525553075 h 535.2: Pearson Correlation Coefficient of Factors PAGEREF _Toc525553078 h 545.3: Categorisation of Open-Ended Responses using the TOE framework PAGEREF _Toc525553079 h 61CHAPTER 6 PAGEREF _Toc525553080 h 646.1: Interpretation and Explanation PAGEREF _Toc525553081 h 646.2: Limitations PAGEREF _Toc525553082 h 65CHAPTER 7 PAGEREF _Toc525553083 h 667.1: Conclusions and Recommendations PAGEREF _Toc525553084 h 66Appendices PAGEREF _Toc525553085 h 68Appendix 1: Preliminary Questionnaire (Pilot Study) PAGEREF _Toc525553086 h 68Appendix 2: Final Version of Questionnaire PAGEREF _Toc525553087 h 79References PAGEREF _Toc525553088 h 89
List of Tables TOC c “Table”
Identified elements of the TOE framework CITATION HOT15 l 2057 (HOTI, 2015)Previous studies of background information
Summary of Participants’ Characteristics
Summary of Organisations’ Characteristics
Descriptive Analysis of Perceived Security and Trust
Descriptive Analysis of Perception of Technology
Descriptive Analysis of Employees’ Opinions
Descriptive Analysis of Skills and Resources
Descriptive Analysis of Scope of Operations
Descriptive Analysis of Willingness and Readiness
Descriptive Analysis of Management Support
Descriptive Analysis of Competition
Descriptive Analysis of Stakeholders
Pearson Correlation Coefficient of Factors
List of FiguresTechnological–Organisational–Environmental (TOE) Analytical Framework. Human Systems Management Journal (2016)
PESTEL Analysis – External Business Environment (2014)
Using Market Research for Product Development (2016)
Technology, organisation, and, environment framework (TOE) CITATION Tor90 l 2057 (Tornatzky & Fleischer, 1990)Diffusion of innovations CITATION Rog95 l 2057 (Rogers E. , 1995)Framework Design
Questionnaire format diagram based on TOE framework culled from: CITATION Awa10 l 2057 (Awa, Nwibere, & Inyang, 2010)Pie chart depicting the various job positions held by respondents in their respective organisations
Pie chart of the various locations of respondents’ organisations
Pie chart of the duration of operation of respondents’ organisations
Pie chart showing the size of each respondent’s organisation on the basis of the number of employees
Pie chart depicting the main industries housing the economic activities of respondents’ organisations
Pie chart of decision making process of adopting IT and its services in respondents’ organisations
Pie chart of the usage of on-demand cloud services in respondents’ organisations
Pie chart depicting the duration of usage of on-demand cloud services
Bar chart depicting the type of on-demand cloud services in use in respondents’ organisations
Bar chart depicting the area of usage of On-demand Cloud Services
Analysis of respondents’ perception of Security and Trust in on-demand cloud services
Analysis of respondents’ indications of the influence of perception of technology
Analysis of respondents’ indications of the influence of employees’ opinions
Analysis of respondents’ indications of the influence of skills and resources
Analysis of respondents’ indications of the influence of scope of business operations
Analysis of respondents’ indications of the influence of willingness and readiness
Analysis of respondents’ indications of the influence of management support
Analysis of respondents’ indications of the influence of competition
Analysis of respondents’ indications of the influence of stakeholders
Categorisation of Open-Ended Responses using the TOE framework
Correlation formula CITATION Joh18 l 1036 (Dudovskiy, 2018)CHAPTER 1INTRODUCTION1.1: Study Background1.1.1: InnovationPer the BusinessDictionary.com, innovation is the process of translating an idea or invention into a good or service that creates value for which customers will pay. Such idea must be replicable at an economical cost and must satisfy a specific need. Furthermore, there are two broad kinds of innovations, and they include Evolutionary Innovation, which is just the increase of technological advancements or processes, and Revolutionary Innovation, which is often disruptive and new.

Several technological innovations have been developed and deployed in several areas of the world, such as Google Search, Social Media, Cloud Computing, etc., which have made it possible for business managers to seamlessly embrace the emerging trends of big data and analytics, mobile computing and social media for business. Robert LeBlanc(2014) states that for these executives, the cloud provides a quick and easy way to implement business process changes, finding new ways to engage with customers, and to effectively deal with their existing information technology investments. Following this, Cloud Computing would be the main innovative process to be focused on in this study, as it is aligned to the purpose and aim of this study.

1.1.2: Cloud ComputingM. Peter ; G. Timothy (2011) described cloud computing as a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. This cloud model is composed of five essential characteristics, which include On-demand Self-Service, Broad Network Access, Resource Pooling, Rapid Elasticity, and Measured Service. Furthermore, the model also includes three service models of Software as a Service, Platform as a Service, and Infrastructure as a Service, and four deployment models of Private cloud, Community cloud, Public cloud and Hybrid cloud.

In addition, as described by B. Vangie on Webopedia, cloud services being a disruptive kind of innovation are any service made available to users on demand via the Internet from a cloud computing provider’s servers as opposed to being provided from a company’s own on-premises servers. They are designed to provide easy, scalable access to applications, resources and services, and are fully managed by a cloud services provider. Examples of cloud services include online data storage and backup solutions, web-based email services, hosted office suites and document collaboration services, database processing, managed technical support services, etc.
1.2: Aim and ObjectivesThe aim of this study is to correctly identify the factors that influence the adoption of on-demand cloud services by organisations in Nigeria, how such factors influence the decision making of senior executives to adopt on-demand cloud services, and the perceived outcome that make spring up from the adoption. In addition, this study would bring into light how several factors positively or negatively affect decision-making in Nigerian Organisations to adopt on-demand cloud services, and if such factors also influence the usage of other services, apart from on-demand cloud services. It would also help inform upcoming entrepreneurs of such factors and how they could benefit from them, and finally be used as a guide for future studies that could be in relation to this study
The objectives that would enable the aim of this study to be achieved include the following:
Investigation of the concepts of On-demand Cloud Services and its application especially Software as a Service (SaaS) and Infrastructure as a Service (IaaS).

Analysis of secondary data gotten from online sources to investigate the rate of adoption of On-demand Cloud Services in the world, and how it cuts across several industries and continents.

Administration of surveys with the staff of Nigerian organisations, both SMEs and large corporations, in various industries to get first-hand data about their adoption of On-demand Cloud Services and the several factors that influenced their choices.
To analyse the data gotten using the Technology – Organisation – Environment (TOE) analytical frameworks.

Finally, to state the limitations, contributions, importance and application of this study.

1.3: Study ScopeDue to the scale of this study, the survey to be distributed will be limited to Nigerian organisations only, of roughly about 100 companies in different industries, which would be streamlined further during this study. This is to reduce the margin of error as much as possible, and since only a representative of a company needs to complete the survey, a reasonable amount of about 80 expected responses out of 100 would be good for this study. Also, the focus would be just on two service models of cloud services, which are Software as a Service, and Infrastructure as a Service. Secondary data to be collected online will need to be from 2010 till date, to be used to add to the data gotten primarily.
1.4: Dissertation StructureIn the following chapters, the main question, and sub-questions to be evaluated and solved would be included, alongside the theoretical perspective to be used and the methods to gather data and treat them. Further down this study, relevant literature reviews would be provided to give a summarised description of terminologies, and keywords to enable readers have a clear picture of where this study would be headed. Data gotten would be gathered and treated, and would also be assessed for validity. In depth analysis, would be done, results would be gotten to help answer the research questions, which would lead to the interpretations of results and discussion. Furthermore, limitations, contributions, importance, and applications of this study would be stated to show to readers the relevancy of this study and how it could be used for future purposes. It could also give a summarised insight of how the results gotten would be a good representation of Nigerian organisations. Finally, the conclusion of the work and recommendations for studies that could be conducted in this domain would be stated.

CHAPTER 2This chapter encompasses the problem to be researched via several research questions to be answered during this study. It would also describe the methods, theoretical perspectives that would be used for further investigation, and the analytical framework that would be used to analyse the data, which would be fully stated and delved into for more insights and knowledge. Also, the mode of data collection, coding, analysis, and interpretation would be described and statistical methods would therefore be used to show correlation between variables. Next, the research problem would follow.

RESEARCH PROBLEM, THEORETICAL PERSPECTIVE AND METHODOLOGY2.1: Research ProblemSeveral organisations in Nigeria have faced numerous challenges about owning their own on-site information system. Some are still battling with it, while some have decided to opt for the cloud information system. Several factors would have influenced some organisations to adopt on-demand cloud services either as their main information system or as a backup. This would not have occurred without business executives pondering on what decisions to make based on several factors that could influence their decisions. This reason led to the main question of this research problem, which is “what are the factors that influence the adoption of On-demand Cloud Services by organisations in Nigeria?”
In that same direction, below are the sub-questions to be put into consideration to help answer the main question. They are as follows:
Have companies in Nigeria adopted On-demand Cloud Services? How and why do they adopt such services?
Are several factors put into consideration by Nigerian business executives, when deciding to adopt On-demand Cloud Services? Why?
What are the adoption rates of On-demand Cloud Services by Nigerian Organisations?
What are the depths and widths of adoption of On-demand Cloud Services by Nigerian Organisations? What service and deployment model of On-demand Cloud Services is the most prominent?
2.2: Theoretical perspectiveTo conduct the analysis of the data to be gotten, the theoretical perspective of the Organisational Adoption of Innovation would be used for this study, which would be in turn followed by the methodology and the analytical frameworks to be used to analyse the data gotten via the methodological method.

2.2.1: Organisational Adoption of InnovationOrganisational innovation is a non-technological process innovation which encompasses the generation and/or the adoption of working and managing practices, techniques, processes and structures which take place in either the technical and social (or administrative) system, and which are new to the adopting firm and are intended to increase the efficiency and effectiveness of organisational process CITATION Dub17 p 26 l 2057 (Dubouloz, p. 26). It also shows how benefits might be anticipated or expected from changes by organisations due to adopting innovation. Organisational adoption of innovation might be actualized, and could be due to managerial choice, external conditions, performance gap due to internal efficiency, or stimulated by environmental change CITATION Dam06 l 2057 (Damanpour ; Scheneider, 2006).

Likewise, the adoption of technological innovations per itself may be described as consisting of a sequence of three stages: initiation, adoption and implementation. In the initiation stage the information about the technological innovation is gathered and evaluated. During the adoption stage a decision regarding the adoption of the technological innovation is made and in the third stage, if the decision goes in favour of the adoption, the implementation of the technological innovation in the enterprise takes place. CITATION HOT15 l 2057 (HOTI, 2015)This theoretical perspective would effectively show whether some Nigerian organisations have adopted on-demand cloud services, and how such adoption has benefitted them thus far. Also, it would further identify the factors that have influenced the adoption of on-demand services, and the stages at which some Nigerian organisations are currently at, based on the usage of on-demand cloud services.

2.3: MethodologyThis section would address which data, information to get, how the information would be collected and checked for reliability and relevancy, and how the data collected would be analysed, and checked.

First, organisations and industries would be used as units of analysis for comparison and analysis. Retail organisations, start-ups, medium and large companies in Nigeria would be selected. This would ensure the coherence and correspondence of data.

2.3.1: Quantitative ResearchA quantitative research purpose is to investigate “how much” or “how many” of a specific classification. A quantitative research divides the world into different clusters and out from that asks questions CITATION Sjö12 l 2057 (Sjöberg ; Svensson, 2012). In addition, a quantitative research is based on questions that are applied numerical values, which allows the theories to be measured. If the quality cannot be measured, there is no possibility to make a quantitative research. Quantitative research is usually used to investigate the numerical relationship between two or more measurable qualities CITATION Har98 l 2057 (J., 1998).

A primary method of quantitative mode of data collection via surveys would be conducted, with data from secondary sources such as on-demand cloud services usage across the globe in comparison to how it is being used in Nigeria, which would serve as a support that would be complementary to the primary method of data collection.

To make sure that the data gotten would be reliable and valid, several criteria would be followed, such as:
The years of publication, 2010 till date
References used, if applicable
Journals, books, press releases, articles, related theses, etc. from credible sources such as EBSCO, IEEE, Science direct, CompTIA, Statista, etc.
Reports from research companies such as the Gartner Research Company, institutions, and governmental departments.

A questionnaire survey would be used for collecting the primary data. The questionnaire survey would be sent via email to target companies. This is to ensure the questionnaire gets to the right person, so that answers given would be valid.

Lastly, a pilot study, which is a mini version of a full-scale study (also called ‘feasibility’ studies), as well as the specific pre-testing of a research instrument such as a questionnaire or interview schedule (Edwin R. van Teijlingen and H. Vanora, 2001) is important for a questionnaire survey. It would help estimate the duration of the main study and the level of response. It could further reduce the respondents’ possibility of misinterpreting questions and several mistakes not noticeable during creating and distributing the questionnaires. Relationships between factors and adoption would need correlation to be tested also. Software to be used was Stata, statistical application software.

Regarding the analytical framework, the methodology described above would enable the listed framework to be used, to analyse the data gotten from some Nigerian organisations regarding this study, and would further determine in-depth, how several factors whether, internal, external or technological have influenced business executives to adopt such services.

This includes:
The Technology – Organisation – Environment (TOE) framework (Tornatzky and Fleischer 1990), explains that three different contexts influence adoption decisions. First, the technological context includes the internal and external technologies that are relevant to the firm. Second, the organisational context refers to the characteristics and resources of the firm, including the firm’s size, degree of centralization, degree of formalization, managerial structure, human resources, amount of slack resources, and linkages among employees. Third and finally, the environmental context includes the size and structure of the industry, the firm’s competitors, the regulatory environment, the macroeconomic context, and the regulatory environment (C. Qing, B. Jeff, W. James & G. Vicky, 2012, p. 4-5). The following table further describes each element of the TOE framework.

Table 1: Identified elements of the TOE framework CITATION HOT15 l 2057 (HOTI, 2015)These three elements present “both constraints and opportunities for technological innovation” (Tornatzky and Fleischer 1990, p. 154). The TOE framework aligns with innovation diffusion theory (Rogers 1995), which explains that technological characteristics of the innovation, as well as internal and external characteristics of the organisation, are all related to the adoption of innovations (Zhu and Kraemer 2005). The TOE framework has been useful in investigating a wide range of innovations and contexts, and would be highly useful as the main analytical framework for this study to analyse the factors influencing the adoption of On-demand Cloud Services by Nigerian organisations, to determine which are controlled by technology, organisation and environment at which it operates.

In the following chapter, the background information of this study and the design of the analytical framework are further discussed.

CHAPTER 3In this chapter, a literature review was used as the anchor for the background information related to this study. A review of organisational adoption of innovation has been done to explicitly shed more light on how certain organisations adopt innovation and what governs their decisions. The concepts, categories, and several previous studies of TOE framework were further reviewed and defined to aid the design of the analytical frameworks previously stated.
BACKGROUND INFORMATION AND ANALYTICAL FRAMEWORK3.1: Background information3.1.1: Organisational Adoption of InnovationPer a study done by CITATION Mer13 l 2057 (Merono-Cerdan & Lopez-Nicolas, 2013), Organisational innovation comprises of the implementation of pronounce and important changes in business practices, the work environment of the concerned organisation, and external environment, which could be the related industry, competition or governmental policies. The main objectives that cajole organisations to adopt innovation have their foundations in knowledge sharing and innovation skills. As described previously, innovation is divided into process and product innovations, which have closed ties with organisational innovations.
Furthermore, organisational innovation could be said to occur internally or externally. Internally, it could comprise of the organisation’s certification, the quality of employees, strategies best known to the organisation, technologies used within the organisation to increase efficiency, amongst others CITATION Arm l 2057 (Armbruster, Bikfalyi, Kinkel, ; Lay, 2008). This is said to affect, whether positively or negatively, departments within the organisation, and the functions those departments undergo. Externally, organisational innovations involve external parties that revolve with the innovations. They include customers, suppliers, competitors, government, and other stakeholders. Subsequently, the emphasis is on the implementation, usage, and growth of organisational innovations in the organisation’s work environment, or external relations. Consequently, not only would the analysis on internal organisational innovations be done, but also the external organisational innovations.

Several studies, conference proceedings, and articles have been written on Information Technology (IT) adoption at both the organisational and individual stages. Organisational level studies have examined the procedure of adoption and diffusion of IT within an organisation, or those that revolve with it, which could presumably improve the organisation’s operations and strategies in relation to its stakeholdersCITATION GPe05 l 2057 (Pervan, Bajwa, ; Lewis, 2005). Likewise, many authors have studied a series of factors that influence IT adoption. Four major classes of IT adoption include the technological, organisational, environmental, and individual types of adoption, and how it influences the adoption, diffusion and usage of said innovations  CITATION Cha07 l 2057 (Chan ; Ngai, 2007). Technologically, alleged benefits, cost, intricacy and compatibility are crucial elements that influences and organisation or individual to adopt innovation. Organisationally, the physiognomies, magnitude, support from top management, current resources, and IT proficiency available at an organisation are said to determine the adoption of innovation, and its success  CITATION Jeo06 l 2057 (Jeon, Han, ; Lee, 2006). The environmental issues to consider could be competitive burden, demands from partners, suppliers and customers, provision from government, and environmental ambiguity. One major individual aspect of adopting innovation is the attitude towards the process or product. Also, the information available on such product or process innovation could strongly influence an individual’s decision to adopt such innovations.

There are four arbitrators that strongly bind organisational innovation with IT adoption, they include: phase of innovation, type of innovation, type of organisation, and size of organisation. As for the innovation phase, the IT adoption in organisations can be divided into several phases straddling from policymaking to organisational processing. CITATION Ham12 l 2057 (Hameed, Counsell, & Swift, 2012) The phase before the adoption identifies the necessity for an innovation. Information gathering is done of the solution to substitute or complement the prevailing process or product. This is done to appraise the technology, deciding to accept or reject the innovation, and allotting resources towards its implementation. The phase, after which innovation has been adopted, includes the attainment of the innovation, users’ reception and organisational usage of the innovation.
The type of innovation could be influenced by the grade of adoption, to which innovation influences organisational efficiency and performance. Whether it is a good-producing organisation or a service-rendering organisation, private or public/governmental parastatals, these organisations adopt IT to boost the scope and scale of their products and services. Additionally, the size of an organisation makes a huge impact on how and why they would adopt innovation. For example, a large corporation could handle the usage of a product such as SAP systems, but a start-up company would not be able to handle the magnanimity of using such large intensive innovation. This could be largely determined by price, number of employees, the information available, etc.

Organisations may engage in innovation activity for several reasons, which should be identified via their economic objectives Based on a study done by CITATION Gua09 l 2057 (Guan, Yam, Tang, ; Lau, 2009), organisations may adopt innovation due to the following reasons such as: processes, products, markets, competences, quality, or the capacity to learn and to implement changes, which should be measured as accurate as possible. Other reasons could be opposition and opportunities for entering new markets. Organisations need to improve efficiency and productivity, and to make available innovation opportunities and an environment that will stimulate knowledge creation, input, and incorporation CITATION Alb03 l 2057 (Albers ; Brewer, 2003). This means that innovation activities aim to enhance knowledge sharing as an innovation objective. The upkeep, procurement, and fruition of an organisation’s abilities depend on its innovation objectives and strategies CITATION Bur01 l 2057 (Burgelman, Maidique, & Wheelwright, 2001).

3.1.2: Technology – Organisation – Environment (TOE) Framework CITATION Oli10 l 2057 (Oliveira & Martins, 2010) The TOE framework was developed in 1990 by CITATION Tor90 l 2057 (Tornatzky & Fleischer, 1990) to identify three aspects of an organisation’s context that influence the process of adopting and implementing a technological innovation. The three contexts include: technological context, organisational context, and environmental context.

Figure 4: Technology, organisation, and, environment framework (TOE) CITATION Tor90 l 2057 (Tornatzky ; Fleischer, 1990)Technological Context
The technological context describes both the internal and external technologies relevant to the firm. This includes current practices and equipment internal to the firm, as well as the set of available technologies external to the firm. The following steps below explain how implementing technological solutions can effectively increase an organisation’s efficiency CITATION Tor90 l 2057 (Tornatzky & Fleischer, 1990):
dimension of the innovation’s effectiveness,
deliberate on the social and organisational issues when planning and designing the implementation of the innovation,
make room for elasticity in the organisational design, and
support the innovation implementation with appropriate human resource development practices.

Organisational Context
CITATION Lia14 l 2057 (Lian, Yen, ; Wang, 2014)’s study described the organisational context’s representation on different organisational conditions such as relative advantage and operational benefits, top manager’s support, adequate resources and capabilities. The relative advantages will affect all types of businesses and impulse them to adopt new information technologies. Inclusively, the firm size, quality of employees, and amount of resources available are also relevant CITATION MPr99 l 2057 (Premkumar & Roberts, 1999).
Environmental Context
The environmental context encompasses how an organisation deals and communicates with the concerned industry, competitors, customers, suppliers, government, and other stakeholders. These stakeholders can influence how an organisation views the need to adopt an innovation, resources and capabilities needed to embark on trailing and installing innovation. They could also support or reject technological innovation, which could affect an organisation directly or indirectly. This context includes the government regulations, change in market, industry pressure CITATION Kua01 l 2057 (Kuan & Chau, 2001).
Additionally, the TOE framework has a solid theoretical base, and steady empirical support, although specific factors recognized within the three contexts may change across different studies CITATION Oli10 l 2057 (Oliveira & Martins, 2010). This framework is consistent with the diffusion of innovation theory CITATION Rog95 l 2057 (Rogers E. , 1995) in individual characteristics, and both the internal and external characteristics of an organisation, but the TOE framework includes the environment factor. The TOE framework enables the diffusion of innovation theory to enlighten the internal innovation diffusion of an organisation CITATION Hsu06 l 2057 (Hsu, Kraemer, & Dunkle, 2006).

CITATION Bos11 l 2057 (Bosch-Rekveldt, Jongkind, Mooi, Bakker, & Verbraeck, 2011)’s paper used the TOE (Technical, Organisational, and Environmental) framework to characterise project complexity in large engineering projects, which can be used to adapt the front–end development phase of engineering projects to the particular complexity. The framework showed the rudiments that contributed to project complexity from a theoretical ; practical perspective. CITATION Oli10 l 2057 (Oliveira ; Martins, 2010)’s study analysed the TOE framework by analysing the studies that used it and the studies that combined it with other theories such as: Diffusion of innovation, institutional theory, and the Iacovou, Benbasat, and Dexter model. Also, they considered the empirical study, and the difference between independent and dependent variables.

3.1.3: Diffusion of Innovation (DOI) FrameworkThe diffusion of innovation theory is arguably the bedrock of principal theoretical perspective on technology adoption at both individual and organisational levels CITATION Kap14 l 2057 (Kapoor, Dwivedi, & Williams, 2014). This theory is regarded as one of the most popular adoption models and theoretical frameworks used for studying the reception and diffusion of new technological innovations at an organisational level or by an individual. An in-depth usage of this theory could be the acceptance of innovations of all kinds. CITATION Rog03 l 2057 (Rogers E. M., 2003) further described that the alleged attributes of innovations are a vital description of the adoption rate of an innovation.
It is also a theory of how, why, and at what rate new concepts and technology cuts across cultures and development, at the individual and firm level CITATION Oli10 l 2057 (Oliveira & Martins, 2010). The diffusion of innovation theory perceives innovations as it is being communicated and traversed through certain channels over time and within a social classification CITATION Rog95 l 2057 (Rogers E. , 1995). It is commonly observed that the percentage of the populace adopting an innovation is roughly normally distributed over time due to individuals possessing diverse degrees of readiness to adopt innovations (Rogers 1995). This normal distribution gave rise to the development of a segregation of individuals into the following five classes of individual innovativeness: innovators, early adopters, early majority, late majority, laggards (Rogers 1995), as described in the previous chapter.
In organisations, innovation processes encompass several individuals, who may accept or reject a new idea of innovativeness, as compared to individuals. At organisational level, innovativeness is related to such independent variables as individual (leader) characteristics (attitude towards change), internal organisational structural characteristics (decision-making power), and external characteristics of the organisation (openness of system).

Figure 5: Diffusion of innovations CITATION Rog95 l 2057 (Rogers E. , 1995)3.1.4: Previous StudiesIntensive research and review have been done to gather several studies, which cover scientific journals, conference proceedings, articles, theses, etc., on the existing knowledge and information on the adoption of innovation, Technology – Organisation – Environment (TOE) and Diffusion of Innovation (DOI) frameworks. Abstracts and contents were read to check for relevance and validity. Almost all were from Business Source Complete and Science Direct using the keywords: technology-organisation-environment (TOE) framework, diffusion of innovation (DOI) by Rogers Everett, organisational adoption of innovation. The selection process was based on the year of publishing, country, type of study (qualitative or quantitative) and methodology, type of IS/IT being adopted, methodology, data, context of the study, focus and influencing factors CITATION HOT15 l 2057 (HOTI, 2015). The analysed articles are illustrated in a summary table as illustrated below.
Author(s) Topic IS/IT Adoption Type of study /Methods Country & Year Data & Context
1 CITATION Ang14 l 2057 (Angeles, 2014)Using the Technology-Organisation-Environment Framework for
Analyzing Nike’s “Considered Index” Green Initiative, a Decision
Support System-Driven System Green Initiative, a Decision
Support System-Driven System Qualitative (content analysis)/case study approach Canada, 2014 Nike corporate sustainability reports, journal articles, case study materials,
trade publication articles
2 CITATION Bra14 l 2057 (Bradford, Earp, ; Grabski, 2014)Centralized end-to-end identity and access management and ERP systems: A multi-case analysis using the Technology Organisation Environment framework Centralized end-to-end Identity and Access Management (CIAM) and ERP systems  Qualitative/Case study approach and focus interviews USA, 2014 2 case studies of Higher Educational Institutions and 24 interviews with personnels of the 2 Higher Educational Institutions
3 CITATION Bos11 l 2057 (Bosch-Rekveldt, Jongkind, Mooi, Bakker, ; Verbraeck, 2011)Grasping project complexity in large engineering projects: The TOE (Technical, Organisational and Environmental) framework Process engineering industry/ front–end development phase of projects Qualitative/Literature survey and case studies The Netherlands, 2011 18 interviews and 6 projects
4 CITATION deA16 l 2057 (de Araújo ; Novaes Zilber, 2016)What Factors Lead Companies to Adopt Social Media in their processes: Proposal and Test of a Measurement Model Social Media organisational adoption model Empirical study/survey Brazil, 2016 237 companies that incorporated social media into their business processes
5 CITATION Hal07 l 2057 (Halila, 2007)Networks as a means of supporting the adoption of organisational innovations in SMEs: the case of Environmental Management Systems (EMSs) based on ISO 14001 Environmental Management Systems (EMSs) Qualitative/ Interviews, participant-observations and studies of documentations in the network companies investigated Sweden, 2007 9 SMEs companies in Sweden
6 CITATION Ham12 l 2057 (Hameed, Counsell, ; Swift, 2012)A meta-analysis of relationships between organisational characteristics and IT innovation adoption in organisationsIT innovation adoption and implementation Qualitative/Meta-analysis of ten organisational factors UK, 2012 Findings of past studies (97) that had examined organisational attributes affecting IT adoption
7 CITATION Kap14 l 2057 (Kapoor, Dwivedi, ; Williams, 2014)Examining consumer acceptance of green innovations using innovation characteristics: A conceptual approach Green innovations (household solar innovation) A theory-based conceptual framework UK, 2014 Rogers’ Diffusion of Innovations theory, Tornatzky and Klein’s Meta-Analysis, and Moore and Benbasat’s Perceived Characteristics of Innovating theory
8 CITATION Kua01 l 2057 (Kuan & Chau, 2001)A perception-based model for EDI adoption in small businesses using a technology-organisation-environment framework Electronic Data Interchange (EDI) Quantitative/survey Hong Kong, 2001 575 small firms
9 CITATION Lia14 l 2057 (Lian, Yen, & Wang, 2014)An exploratory study to understand the critical factors affecting the decision to adopt cloud computing in Taiwan hospital Cloud computing technology Quantitative/Questionnaire Taiwan, 2014 60 hospital CIOs
10 CITATION Mah90 l 2057 (Mahajan, Muller, & Srivastava, 1990)Determination of Adopter Categories by Using Innovation Diffusion Models Personal computers Quantitative/Questionnaires Israel, USA, 1990 23 personal-computer-oriented publications of 860 respondents
11 CITATION Mak09 l 2057 (Makkonen, 2009)A Process Perspective on Organisational Innovation Adoption – A Qualitative Case StudyOrganisational innovation adoption from a process perspective Qualitative/single-case study Finland, 2009 10 thematic interviews of 8 informants
12 CITATION Mak16 l 2057 (Makkonen, Johnston, & Javalgi, 2016)A behavioural approach to organisational innovation adoptionTechnical infrastructures Qualitative/semi-structured interviews Finland, USA, 2016 31 interviews involving 28 informants (research directors, quality assurance managers, a CEO, and technology specialists)
13 CITATION Nar09 l 2057 (Naranjo-Gil, 2009)The influence of environmental and organisational factors on innovation adoptions: Consequences for performance in public sector organisationsAdoption of Technical and administrative innovations  Quantitative/Survey study and archival data from Spanish health care authorities Spain. 2009 112 CEOs in Spanish Public Hospitals
14 CITATION Oli10 l 2057 (Oliveira & Martins, 2010)Information Technology Adoption Models at Firm Level: Review of LiteratureInformation Systems Review and description of studies for adoption models at firm levels. Portugal, 2010 Papers that used TOE, and TOE with other theoretical models
15 CITATION MPr99 l 2057 (Premkumar & Roberts, 1999)Adoption of new information technologies in rural small businessesEmail, online data access, internet access, EDI Qualitative/Interviews USA, 1999 78 Organisations in rural areas
16 CITATION Ray17 l 2057 (Raynard, 2017)Understanding Academic E-books Through the Diffusion of Innovations Theory as a Basis for Developing Effective Marketing and Educational StrategiesElectronic books (e-books)  Qualitative/Literature Review Canada, 2017 Library Information and Technology Abstracts, EbscoHost Academic Search Complete, and the University of Manitoba’s integrated catalogue
17 CITATION Uns09 l 2057 (Unsworth, Sawang, Murray, ; Sorbello, 2009)Developing an integrative model for understanding innovation adoptionTheoretical-based integrative model of organisational innovation adoption. Quantitative/Questionnaires Australia, 2009 Managing Director of 864 organisations from business register databases
18 CITATION Vas16 l 2057 (Vassil, Solvak, Vinkel, Trechsel, ; Alvarez, 2016)The diffusion of internet voting. Usage patterns of internet voting in Estonia between 2005 and 2015E-voting (internet) Quantitative/surveys Estonia, 2016 1000 respondents of internet voters, ballot-paper voters, and non-voters from 8 e-enabled elections in Estonia
19 CITATION Won05 l 2057 (Wonglimpiyarat ; Yuberk, 2005)In support of innovation management and Roger’s Innovation Diffusion theoryManagement of research and development (R&D) projects Qualitative/Case study and interviews Thailand, 2005 Interviews with key staff members of 54 companies located in the Bangkok Metropolitan Region (BMR)
Table 2: Previous studies of background information
Furthermore, a summary of some of the studies, including findings and opinions can be seen below in paragraphs.

The study done by CITATION Nar09 l 2057 (Naranjo-Gil, 2009) showed how although technical and administrative innovations have received much academic curiosity in recent years, the understanding of why some organisations adopt these innovations and others do not is still immature. Furthermore, how technical and administrative innovations affect an organisation’s performance was examined, and results gotten suggested that environmental and organisational factors have inconsistent effects on the adoption of administrative and technical innovations in public sector organisations. Also, it was stated per their findings that high adopters of both types of innovations were more sensitive to environmental factors than organisational factors.
In the words of CITATION Hal07 l 2057 (Halila, 2007), “despite their large numbers, most Small and Medium Organisations (SMEs) have little knowledge of or interest in environmental questions and generally have difficulties when it comes to integrating environmental aspects into their activities”. The author also emphasized that one way for SMEs to shift from a reactive to a proactive environmental behaviour was to adopt environmental innovations. In this study, a description of how SMEs can use a network as a basis for initiating environmental work was done.

The study of CITATION Mak09 l 2057 (Makkonen, 2009) analysed the organisational innovation adoption from a process viewpoint. A qualitative single-case study based on ten thematic interviews within the food-processing industry was done and the results gotten emphasized the entrenched nature of organisational innovation adoption, as it was intertwined with the organisation’s previous and current business processes and future goals.

CITATION Ham12 l 2057 (Hameed, Counsell, & Swift, 2012)’s study showed how the adoption of IT in organisations is influenced by technology, organisation, environment, and individuals. They performed a meta-analysis of ten organisational factors and amassed their findings to determine the scale and direction of the relationship between organisational factors and the adoption of IT innovation. They also found out that organisational enthusiasm was the most significant characteristic, while formalization, and centralization were insignificant characteristics.
CITATION deA16 l 2057 (de Araújo ; Novaes Zilber, 2016)’s study was done to comprehend which factors influenced organisations to use social media to attain outcomes. They gathered data using a survey of 237 companies, and used the structural equation modelling technique to analyse the data. Their results disclosed that the comparative advantage of social media was important factors to social media, which was in line with organisational adoption. They also found out that big companies with more formalized organisational structure (OS) were more ready to adopt social media than small ones with no formal OS.

Lastly, the study done by CITATION Mak16 l 2057 (Makkonen, Johnston, & Javalgi, A behavioral approach to organizational innovation adoption, 2016), scrutinized four qualitative cases, which uncovered organisations’ core essentials that shape the adoption behaviours and activities for adoption, such as identifying goals and technical infrastructure, business relationships, and key individuals.

3.2: Design of analytical frameworkThis section displays the intended design of the frameworks for this study. This is strongly in line with the background information of the frameworks described above, and how it will be used to gather, analyse and display the results for this study.
Figure 6: Framework Design
1171091182353146679756375Organisational Adoption of innovation
0Organisational Adoption of innovation
2421750617Literature Review
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19217894714TOE Framework
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3813810229235Technological Innovation Decision Making
00Technological Innovation Decision Making
32397706781634260946069019764325618Technology Context
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572071513017500532187113398534338432317531952517121909Organisation Context
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19196056985Environment Context
0Environment Context
3244086214630
2573153117290
4264174244496002582545196215313817066675Questionnaire
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209809979375Discussions
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3.2.1: Questionnaire FormatThe questionnaire to be created to collect the necessary information needed for this study would be based majorly on the TOE framework. This is to make sure that the purpose of this study is not defeated; the time constraint would not hinder the realisation of this study, which would make for efficient and prompt analysis to be done.

The technological, organisational and environmental factors that influence the adoption of on-demand cloud services to be gotten, would be analysed based on the questionnaire that is modelled after the framework. That is, the questionnaire would be tailored made to glaringly point out these factors, to make it easy to analyse and discuss results. Please find below a diagram of what the questionnaire would be based on, and how it would be designed based on the points stated below. Also, included is a summary of each factor of the TOE framework, which guided the construction of the questionnaire.

2251710236855Organisation
-Scope of Business Operation
-Firm Size
-Organisation Mission
-Facilitating Conditions
-Individual Difference Factors
-Social Influence or Subjective Norms
00Organisation
-Scope of Business Operation
-Firm Size
-Organisation Mission
-Facilitating Conditions
-Individual Difference Factors
-Social Influence or Subjective Norms
27355809969500
647705080000422910119380Technology
-Perceived Usefulness
-Perceived Ease of Use
-Perceived Behavioural Control
-Perceived Service Quality
– Intensity of Use
-Width of Use
00Technology
-Perceived Usefulness
-Perceived Ease of Use
-Perceived Behavioural Control
-Perceived Service Quality
– Intensity of Use
-Width of Use
31280101041404278630172720Environment
-Consumer Readiness
-Competitive Pressure
-Trading Partners’ Readiness
-Perceived Trust
00Environment
-Consumer Readiness
-Competitive Pressure
-Trading Partners’ Readiness
-Perceived Trust
41109901270000
2297430184150Adoption Drivers
Adoption Formation
Adoption Behaviour
Post Adoption Behaviour
Post Adoption Results
0Adoption Drivers
Adoption Formation
Adoption Behaviour
Post Adoption Behaviour
Post Adoption Results

1885950187960003989070187960
Figure 7: Questionnaire format diagram based on TOE framework culled from: CITATION Awa10 l 2057 (Awa, Nwibere, ; Inyang, 2010)Among the questions in the questionnaire, the question(s) based on perceived usefulness of on-demand cloud services would serve as a medium to find out the user’s (prospective or not) subjective probability that using a such services will increase job related productivity, performance, effectiveness, and/or profitability within organisational. Other questions would be based on the following explanations of perceived factors as seen in the previous diagram:
Perceived ease of use question(s) would measure the user’s assessment of how complex or easy is it to use on-demand cloud services, and perceived behavioural question(s) would serve as a medium to find out the analysed skills, opportunities, and resources required for using the services, and whether these resources were enough to aid the easy usage of these services, or inadequate.

Perceived service quality question(s) would find out the perceived quality of on-demand cloud services’ image in business users’ eyes or the overall assessment of their judgment about the superiority or excellence of it. They may include reliability, responsiveness, etc.
Questions based on depth and width of use of on-demand cloud services would investigate the intensity of usage of these services, and what areas in the organisation these services are being used.

Consumer readiness/willingness question(s) would find out how organisations were willing to adopt on-demand cloud services, and perceived trust question(s) would serve as a medium to find out the trust level of using such services and how it is strongly linked to willingness/readiness to adopt it.
Question(s) based on Competitive pressure would find out whether the usage of on-demand cloud services was due to responding to competitive pressure or not, and would show whether organisations exploited the environment to their own advantage or set the industry pace to the disadvantage of opponents.

Question(s) based on Trading partner readiness would find out how the usage or adoption status of on-demand cloud services in the organisations’ value chain, and how it affected their decision in adopting the services.
Scope of business operations questions would be based on the external and internal co-ordination costs of a firm and its business scope, such as administrative complexities, stock holding costs, etc.
Individual difference factors questions would find out how the role of decision makers in organisations impacted the adoption of on-demand cloud services.

Questions based on organisation missions would find out the definitive statements about an organisation’s philosophy, character, identity, etc., based on its history, current preferences, available resources, distinctive competence, etc., and how these impacted the decision of adopting on-demand cloud services.

Question(s) based on the organisation’s size would find out how influential it was to the adoption of on-demand cloud services.
Facilitating conditions question(s) would find out the enabling conditions created for innovations to be adopted.
Social influences/subjective norms question(s) would find out the if and how an organisation’s image/social status influenced the decision of adopting On-demand Cloud Services
A sample question can be seen below:
Did competitive pressure influence the adoption of on-demand cloud services?
Yes
No
If yes, on a scale of 1 to 5 select the degree of influence competitive pressure had on the adoption of on-demand cloud services in your organisation
High Influence
High to Medium Influence
Medium Influence
Medium to Low Influence
Low influence
More questions created can be found on the questionnaire. In chapter 4, the necessary results gotten from the questionnaire would be displayed via pie charts and histogram.

CHAPTER 4INFORMATION DISPLAY (DESCRIPTIVE QUESTIONNAIRE DATA)In this chapter, some studies are highlighted that used only the TOE framework to understand different IT adoptions and how the variables were analysed, methods used, data and context of empirical studies are presented in a tabular form. This would give an insight of how the variables gotten for this study would be analysed, and the methods used in analysing these variables. Additionally, the results obtained will be presented, responses gotten from the survey conducted, graphical and statistical analysis will be presented too. From the presentation of the raw data results, to analysing these results no stones will be left unturned to arrive at a substantial conclusion for this study. The survey conducted online was sent to various personnel; one personnel per organisation, 152 responses were gotten and 133 responses were used due to the invalidity of some of the responses.

4.1: Data Collection
In this work, a total of 37 questions were asked to respondents to collate the factors that influenced the adoption of on-demand cloud services by Nigerian organisations via a survey tool called “Google Forms”, which with its user friendly interface and smooth gliding feature from one question to another, enabled ease of answering the questions, as mentioned by some of the respondents. These questions were grouped into four sections, which are listed and explained below:
Demographics:
This section was created to determine the job positions of the respondents. This was critical in determining how the job positions would have any effects in any factor that could influence the adoption of said technology;
Decision Making:
This section catered to find out if respondents were inclined to be in a position to make any decision that could be influential in being termed as factors of adopting on-demand cloud services.

Usage of Highlighted Technology:
This section was created to find if respondents’ organisations were currently making use of any form of on-demand cloud services, duration of usage, type of highlighted technology, depth and width of usage, usage experience, usage satisfaction and quality.

Factors Influencing the Adoption of On-Demand Cloud Services:
Questions in this section were posed to find out which factors influenced the adoption of said technology, and the degree of influence.

Access to respondents in various organisations was made via LinkedIn, which was the suitable platform to send questionnaires, as it was unable to visit Nigeria to personally distribute the questionnaire. Over 200 messages were sent to people who were fully, or had a hand in the adoption of any technological innovation in their respective organisations. These categories would be thoroughly described with information collected further in this chapter.

4.2: Developing Research InstrumentThis research was dependant on several studies to build up the questionnaire around the theoretical framework in this paper. A pilot study was conducted to evaluate the questionnaire, which in turn predicted the appropriate sample size and was used to improve the study design prior to the performance of this research project. It was sent out to 10 participants, whose responses guided the final development of the questionnaire, based on mistakes to be corrected, questions to be altered, removed or added, and provided an estimated average amount of time to be spent answering the questionnaire. These changes were made to the questionnaire before it was finally sent out to a wide spectrum of respondents. This questionnaire consisted of a five-point Likert scale, value “1” represented the least correlation to the respondents’ responses, and value “5” represented the most correlation to the respondents’ responses. The questionnaire comprised of a combination of open and closed questions; open question were created to properly collate and analyse said factors influencing the adoption of on-demand cloud services, and closed questions were created to get accurate information regarding respondents’ demographical information and technology usage. This questionnaire was validated through an evaluation conducted by an academic expert in strategy and innovation, Pierre Vialle, before it was sent out to receive responses from respondents.

4.3: Participants and Organisations CharacteristicsThis section of this chapter would display the characteristics of the participants and their respective organisations, which includes the positions held in their respective organisations, locations of said organisations, sizes of organisations, etc. Results are displayed below using several pie charts to depict the data gotten via the questionnaire accurately and visually.

Job Positions of Participants:

Figure 8: Pie chart depicting the various job positions held by respondents in their respective organisations
As seen in the chart above, 20% of the respondents are Operations Managers, 18% are Chief Executive Officers and IT Administrators of their organisations, 7% of them are General Managers, while 3% are Field staff members. Other job positions include Program Officers, Digital Analysts, Developers, Research Officers, etc., all have three to one respondents each assigned to those positions that filled the questionnaire.

Location of Organisation:
1679296top
Figure 9: Pie chart of the various locations of respondents’ organisations

As seen above, 76% of the respondents indicated that their organisations’ location is situated in Lagos state, 13% in Federal Capital Territory (FCT) also known as the state where a city situated in it, called Abuja is known as the capital of Nigeria. Additionally, 13% of the respondents indicated that their organisations are situated in Ogun state, 12 % in Oyo State, while just 2% of the respondents indicated that their organisations are situated in Delta state. Other states include Kogi state, Rivers State, Cross Rivers state, Benue state, etc.

Duration of Operation:

Figure 10: Pie chart of the duration of operation of respondents’ organisations
As depicted in the chart above, 35% of the respondents indicated that their organisations have been in operation for more than 13 years, 25% of the respondents said that their organisations have been operating for 1 to 3 years, while 19% of the respondents indicated that their organisations have been in operation for 3 to 7 years. Additionally, 9% of the respondents’ organisations have been in operation not amounting to a year, 7% between 7 to 10 years, and 6% between 10 to 13 years.

Size of Organisation

Figure 11: Pie chart showing the size of each respondent’s organisation on the basis of the number of employees
The chart above depicts the total number of respondents’ data regarding the size of their organisations, in regards to the number of employees. 37% of the respondents indicated that the size of their organisations is large, which means that the total number of employees is about 250 and above. 30% of the respondents indicated that their organisations are small scale, 23% are medium scale, and 10% indicated the presence of just a sole owner.

Industry

Figure 12: Pie chart depicting the main industries housing the economic activities of respondents’ organisations
Each respondent indicated their organisations’ economic activities being centred in the various industries as illustrated in the chart above. 53% of the respondents indicated their organisations’ economic activities were centred in the tertiary industry, 19% in governmental activities, 12% in the quaternary industry, 12% in the secondary industry, and 4% in the primary industry.

4.4: Decision Making InformationAs a necessity, it was highly important to find out if respondents had any hand in decisions made to adopt on-demand cloud services in their respective organisations. Questions series in this section were posed to determine the respondents who would continue to answer the questionnaire. Results gotten are displayed below using pie chart.

Figure 13: Pie chart of decision making process of adopting IT and its services in respondents’ organisations
The pie chart above made known two important things. The first is the percentage of respondents who attested to making decisions or partaking in the decision making process of adopting any form of Information Technology (IT) and its services, which connotes a total of 80%. The second important thing is that the 80% of the respondents moved beyond this decision making question of the questionnaire, as they are the target sample audience suitable for this research, and whose responses are highly important and suitable to determine the outcome of this research. Some notable reasons why some respondents indicated that they do not take part in any decision making process of adopting IT and its services include the following:
No authorisation to do so
New Employee
Not in the IT department
Dictation of superiors (strong hierarchy culture)
4.5: Usage of On-demand Cloud Services
As previously described, this section of the questionnaire was created to find if the respondents’ organisations were currently making use of any form of on-demand cloud services, duration of usage, type of highlighted technology, depth and width of usage, usage experience, usage satisfaction and quality, etc. The responses provided such great insights as to how organisations in Nigeria use on-demand cloud services. Also, this section weeded out responses that were not favourable, as they were not needed anymore because of the absence of on-demand cloud services in some of the respondents’ organisations.

Figure 14: Pie chart of the usage of on-demand cloud services in respondents’ organisations
Surprisingly, only 39% of the respondents attested to the usage of on-demand cloud services in their organisations. 13% still needed to know more about on-demand cloud services to attribute their response to a suitable Yes/No answer, while 20% of the respondents chose not to respond to this question. As stated in the introductory message of the questionnaire, the aim was not to forceful acquire data from respondents, but for them to wilfully respond to the questionnaire. 39% of these respondents were certain to continue answering the questionnaire, while 13% of the respondents were asked to refer to the main description of the questionnaire to fully understand the technological concept at hand. Regardless, 13% of the respondents insisted on not moving forward with the questionnaire. Some notable responses as to why some respondents’ organisations do not make use of any on-demand cloud service, which could be considered factors influencing the decision not to adopt on-demand cloud service include:
No need to use it
Highly sensitive information that could be vulnerable if stored in the cloud
Cost of usage
Difficult concept to grasp
No encouragement for accepting changes as regards to IT and its services
Level of work does not demand its usage
Mismanagement of technology
No maintenance culture, etc.

Management
Government Legislation
Insufficient data services
Other important technological investments
In addition to asking respondents about their organisations’ usage of on-demand cloud services, questions were asked to find out about the duration of said usage, type of on-demand cloud services in use, the departments where on-demand services are being used, the intensity of said usage, factors that prompted the intensity of usage, areas where on-demand cloud services are being used, usage experience and satisfaction, and quality of on-demand cloud services in their respective organisations. These would be treated differently and appropriately portrayed using charts to point out important information needed for this research.

Duration of Usage of On-demand Cloud Services

Figure 15: Pie chart depicting the duration of usage of on-demand cloud services
As depicted above, 52% of the respondents attested to the usage of on-demand cloud services 1 to 9 years into their organisations’ operations. 34% of the respondents answered that the usage of on-demand was in line with their organisations’ operations since the inception, while 14% of the respondents attested to the fact that their organisations’ have been using on-demand cloud services for over 10 years.

Type of On-demand Cloud Services

Figure 16: Bar chart depicting the type of on-demand cloud services in use in respondents’ organisations
As seen above, 42% of the respondents indicated that their organisations subscribed to the services of an Infrastructure as a Service to provide processing, storage, networks, etc. where they deploy and run their software. 31% of the respondents indicated that their organisations have access to applications via a software distribution model known as Software as a Service. The least representative percentage of 27% of respondents indicated that their organisations use hardware and software tools needed for the development and hosting of their own applications via the cloud (platform). The information provided here can be translated that majority of the organisations whose respondents filled the questionnaire would rather subscribe to services readily available via the cloud than develop theirs.

Area of Usage of On-demand Cloud Services

Figure 17: Bar chart depicting the area of usage of On-demand Cloud Services
As depicted above, over half of the respondents indicated that the IT department in their organisations make use of on-demand cloud services the most compared to other departments. Production department was indicated to be the least area of concentration where on-demand cloud services is being used, while a significant amount of respondents indicated that on-demand cloud services is being used in all departments in their organisations.

These information relating to the factors influencing the adoption of on-demand cloud services collected and collated via the questionnaire would be thoroughly analysed in the following chapter using various analytical models and results would be discussed to shed more light to the analysis.

4.6 Tabular Summary of Description Statistics of Participants and OrganisationsDue to the irrelevancy of other characteristics apart from the job position, which was needed for this study, other characteristics were not posed into questions for participants to respond to them. Job position characteristics of the participants are summarized below
Participants Characteristics Size Percent
Job Position Operation Manager 27 20%
Chief Executive Officer 24 18%
IT Administrator 24 18%
General Manager 9 7%
Field Staff Member 4 3%
Management Development Officer 3 2%
Sales Officer 3 2%
Teaching Staff 3 2%
Audit Manager 2 2%
Software Developer 2 2%
HR/Admin Officer 2 2%
Research Officer 2 2%
Others 28 27%
Total 133 100%
Table 3: Summary of Participants’ Characteristics
Additionally, the organisations’ characteristics are summarised below in a tabular form.

Sample Characteristics Size Percent
Location Lagos State 71 53%
Federal Capital Territory (FCT) 18 14%
Ogun State 18 14%
Oyo State 14 11%
Other States 12 8%
Total 133 100%
Duration of Operation Under 1 Year 12 9%
1 – 3 Years 33 25%
3 – 7 Years 25 19%
7 – 10 Years 9 7%
10 – 13 Years 8 6%
Over 13 Years 46 34%
Total 133 100%
Size of Organisation Small Scale (10 – 49 Employees) 40 30%
Medium Scale (50 – 249 Employees) 31 23%
Large Scale (Over 250 Employees) 49 37%
One Man Company 13 10%
Total 133 100%
Industry Primary Industry (Extraction of Raw Materials) 5 4%
Secondary Industry (Manufacture & Processing of RW) 16 12%
Tertiary Industry (Provision of Any Form of Service) 71 53%
Quaternary Industry (Science, Research & Tech) 16 12%
Government Parastatal 25 19%
Total 133 100%
Table 4: Summary of Organisations’ Characteristics
In the next chapter, the results of the data analysis would be described in details, with the various analytical methods used.

CHAPTER 5ANALYSISIn this chapter, the results gotten from the data analysed via the questionnaire using Stata will be displayed and discussed. First, each independent variable (factor) would be descriptively analysed, then analysed using the correlation analysis method, Pearson correlation coefficient, to analyse and compare how each factor influences each other to adopt on-demand cloud services. Additionally, the factors provided by the respondents would be categorised separately using the TOE framework.

5.1: Descriptive AnalysisIn this section, each concerned independent variable gotten from the results via the questionnaire, its mean, standard deviation and frequency results are displayed in a tabular form and categorised according to the concerned TOE framework to be used in the analysis and interpretation phase.

Technological ContextAs proffered by the TOE framework model, the respondents were asked to evaluate each of the three attributes based on the suggested factors that could be influential in whatever form of intensity. Also, in order to look beyond the boundaries set by the model, they were also asked to identify any other technological factors that could have influenced the decision to adopt on-demand cloud services, which would be later further dealt with in this chapter. This section’s sole focus would be the factors that were technologically attributed using to the TOE framework, as seen below.

5.1.1: Security and TrustThe first factor suggested by the TOE framework was security and trust. It refers to the preservation of confidentiality, integrity and availability of information; in addition, other properties such as authenticity, accountability, non-repudiation and reliability can also be involved CITATION Pea12 l 1036 (Pearson, 2012). Respondents were asked: “An important aspect of on-demand cloud services is security/trust. On a scale of 1 to 5, what is the degree of influence security had in the decision to adopt such service(s)?” They all responded depending on the degree of influence, on how security and trust in on-demand cloud services has allowed their organisation to adopt or want to adopt the said technology. Below, a tabular summary of the descriptive analysis and a graphical representation of data got from the questionnaire, regarding security and trust of on-demand cloud services.

Table 5: Descriptive Analysis of Perceived Security and Trust
To note, a 1 to 5 Likert scale was used to measure the degree of influence of the listed factors and not just this factor. This gave rise to the minimum value of 1 and the maximum value of 5. On an average, respondents’ indication of security and trust in on-demand cloud services, which could have influenced the adoption of said technology came at 3.692. This indicates that security and trust had a mid-level influence on the decision to adopt on-demand cloud services.

22586412580437Likert Scale Value (Ascending Order)
00Likert Scale Value (Ascending Order)
1290453636377Frequency0Frequency
Figure 18: Analysis of respondents’ perception about Security and Trust in on-demand cloud services
It can be seen that 27 out of 52 respondents, which is more than half of the respondents, believe strongly in the security and trust of on-demand cloud services, which led to them signifying that this factor influenced them to adopt this said technology. Additionally, the graph above is left skewed or negative skewed because the “tail” of the distribution points to the left, which produces a negative skewness value. This means, as indicated before, respondents view security and trust in on-demand cloud services and how it would influence the adoption of said technology in a relatively positive way.
5.1.2: Perception of TechnologyThe second factor suggested by the TOE framework was perception of technology. It refers to the degree to which a technological factor is regarded, understood, or interpreted. Respondents were asked: “On a scale of 1 to 5, how would you rate the diffusion of on-demand cloud services and the current perception of the rate of innovation adoption in your organisation?” They all responded depending on the degree of influence, on how the perception of innovation in general and specifically on-demand cloud services, has allowed their organisation to adopt or want to adopt the said technology. Below, a tabular summary of the descriptive analysis and a graphical representation of data gotten from the questionnaire are displayed.

Table 6: Descriptive Analysis of Perception of Technology
As depicted above, respondents’ indication of this factor influencing the adoption of said technology came at 3.692, same as the previous factor. This indicates that the perception of technology had a mid-level to high influence on the decision to adopt on-demand cloud services.

22618702387600Likert Scale Value (Ascending Order)
00Likert Scale Value (Ascending Order)
1470025527050Frequency0Frequency
Figure 19: Analysis of respondents’ indications of the influence of perception of technology
It can be seen that 25 out of 52 respondents, believe strongly on how their perception of technology and specifically on-demand cloud services led to them signifying it influenced them to adopt the said technology. Additionally, the graph above is left skewed or negative skewed because the “tail” of the distribution points to the left, which produces a negative skewness value. This means, as indicated before, respondents’ perception of technology and on-demand cloud services influenced the adoption of said technology in a relatively positive way.
Organisational ContextNext is the organisational context. This model suggested employees’ opinions, available skills, resources and opportunities, scope of business operations, willingness and readiness to adopt and management support as the key attributes. Participants were asked to evaluate each one of them and identify any other organisational factors that could have influenced the decision to adopt on-demand cloud services. This section’s sole focus would be the factors that were organisationally attributed using to the TOE framework, as seen below.

5.1.3: Employees’ OpinionsThe first factor suggested by the TOE framework is employees’ opinions. It means the views or statement of advices offer by employees in regards to what should and should not be done to provide greater benefit to the business. Respondents were asked: “Employees are said to sometimes voice out their opinions regarding the internal workings of an organisation. How did this influence the decision to adopt on-demand cloud services in your organisation?” They all responded depending on the degree of influence, on how employees’ opinions have allowed their organisation to adopt or want to adopt the said technology. Below, a tabular summary of the descriptive analysis and a graphical representation of data gotten from the questionnaire are displayed.

Table 7: Descriptive Analysis of Employees’ Opinions
Averagely, respondents’ indication of employees’ opinions on how they could influence the adoption of on-demand cloud services is shown as 3.346, which shows a mid-level influence on the decision to adopt said technology.

21875752186305Likert Scale Value (Ascending Order)
00Likert Scale Value (Ascending Order)
1511876535097Frequency0Frequency
Figure 20: Analysis of respondents’ indications of the influence of employees’ opinions
As seen above, it can be said that this factor does not have a strong influence on the adoption of on-demand cloud services, compared to the factor of the perception of security and trust. Only 9 respondents signified that the employees’ opinions greatly influenced the decision to adopt on-demand cloud services.
5.1.4: Skills and ResourcesThe second factor is the available skills, resources and opportunities currently on ground in organisations, which could have prompted the decision to adopt on-demand cloud services. These skills, resources and opportunities available must have been adequate enough to cater for the need to adopt said technology. Respondents were asked: “Skills, opportunities and resources must be strategically analysed before deciding to adopt on-demand cloud services. How much influence did these factors have in the adoption on-demand cloud services?” They all responded depending on the degree of influence, on how this factor has allowed their organisation to adopt or want to adopt the said technology. Below, a tabular summary of the descriptive analysis and a graphical representation of data gotten from the questionnaire are displayed.

Table 8: Descriptive Analysis of Skills and Resources
Averagely, respondents’ indication of the available skills, resources and opportunities on how they could influence the adoption of on-demand cloud services is shown as the mean of 3.635, which shows a mid-level to high influence on the decision to adopt said technology.

1471295528955Frequency0Frequency21869402235835Likert Scale Value (Ascending Order)
00Likert Scale Value (Ascending Order)

Figure 21: Analysis of respondents’ indications of the influence of skills and resources
As depicted above, respondents’ responses were an indication of how prominent the available skills and resources were influential to the decision to adopt on-demand cloud services. More than half of the respondents indicated that these factors had a relatively strong influence in adopting on-demand cloud services. Only a few respondents indicated that this factor had little or no influence on the decision to adopt said technology.

5.1.5: Scope of OperationsThe third factor is scope of operations. It refers to every activity performed by that business including sales, services, product developments, marketing and contracts. Basically, business scope refers to all daily operations of the business, particularly those activities required to secure revenue CITATION Ref18 l 1036 (Reference, 2018). Respondents were asked: “Regardless of the scope of your business operations, how much influence did it have on the adoption on on-demand cloud services?” They all responded depending on the degree of influence, on how this factor has allowed their organisation to adopt or want to adopt the said technology. Below, a tabular summary of the descriptive analysis and a graphical representation of data gotten from the questionnaire are displayed.

Table 9: Descriptive Analysis of Scope of Operations
Based on the mean value, which is approximately 4, it can be deduced that respondents indicated that this factor had a high level of influence in pushing for the adoption of on-demand cloud services. With more evidence shown in the standard deviation, with a value of 0.634, the scope of business operations has shown that it is one of the most pressing factors to influence the adoption of on-demand cloud services.

21164552200910Likert Scale Value (Ascending Order)
00Likert Scale Value (Ascending Order)
1423670478790Frequency0Frequency
Figure 22: Analysis of respondents’ indications of the influence of scope of business operations
It can be seen that 39 out of 52 respondents, believe strongly on how the scope of their organisations’ business operations led to them signifying that this factor influenced them to adopt the said technology. Additionally, the graph above is left skewed or negative skewed because the “tail” of the distribution points to the left, which produces a negative skewness value. This means, as indicated before, respondents’ perception of technology and on-demand cloud services influenced the adoption of said technology in a relatively positive way.
5.1.6: Willingness and ReadinessThe fourth factor suggested by the TOE framework is the willingness and readiness to adopt said technology, regardless of the current situation of their respective organisations. Participants were asked: “How willing and ready was your organisation towards the adoption of on-demand cloud services?” They all responded depending on the degree of influence, on how this factor has allowed their organisation to adopt or want to adopt the said technology. Below, a tabular summary of the descriptive analysis and a graphical representation of data gotten from the questionnaire are displayed.

Table 10: Descriptive Analysis of Willingness and Readiness
Based on the mean value, which is 4.11, it can be deduced that respondents indicated that this factor had a high level of influence in pushing for the adoption of on-demand cloud services, could be translated to employees being open minded and receptive to changes in the usage of innovative technology. With more evidence shown in the standard deviation, with a value of 0.784, the willingness and readiness of employees has shown that it is one of the most pressing factors to influence the adoption of on-demand cloud services.

22688552159635Likert Scale Value (Ascending Order)
00Likert Scale Value (Ascending Order)
1644650483870Frequency0Frequency
Figure 23: Analysis of respondents’ indications of the influence of willingness and readiness
It can be seen that 41 out of 52 respondents, believe strongly on how the scope of the willingness and readiness of employees led to them signify that this factor influenced them to adopt the said technology. Additionally, the graph above is left skewed or negative skewed because the “tail” of the distribution points to the left, which produces a negative skewness value. This means, as indicated before, respondents’ perception of technology and on-demand cloud services influenced the adoption of said technology in a relatively positive way.
5.1.7: Management Support (Decision Makers)The final factor suggested by the TOE framework is the support of management and how effective its prevalence is regarding the decision to adopt any form of innovative technology, specifically on-demand cloud services. Respondents were asked: “As the main decision maker or part of the decision making team, the organisation relies on you to make swift, reliable decisions towards the betterment of the organisation. How subtle or great was your influence in the decision making process of adopting on-demand cloud services in your organisation?” They all responded depending on the degree of influence, on how this factor has allowed their organisation to adopt or want to adopt the said technology. Below, a tabular summary of the descriptive analysis and a graphical representation of data gotten from the questionnaire are displayed.

Table 11: Descriptive Analysis of Management Support
On an average, respondents’ indication of the support of management on how they could influence the adoption of on-demand cloud services is shown as the mean of 3.692, which shows a mid-level to high influence on the decision to adopt said technology. With the standard deviation value of 1.094, it can be said that the selection of values to reflect the degree of influence of adopting said technology were not exactly spread out, as almost the same number of respondents chose the values that represented the mid-level to high degree of influence.

21901152214880Likert Scale Value (Ascending Order)
00Likert Scale Value (Ascending Order)
1558925508635Frequency0Frequency
Figure 24: Analysis of respondents’ indications of the influence of management support
As depicted above, respondents’ responses were an indication of how prominent and prevalence the support of management was influential to the decision to adopt on-demand cloud services. More than half of the respondents indicated that these factors had a relatively strong influence in adopting on-demand cloud services. Only a few respondents indicated that this factor had little or no influence on the decision to adopt said technology.

Environmental ContextThe last area of exploration is the environmental context. The suggested attributes were competition and communication amongst stakeholders. Once again, the participants were asked to evaluate them and identify any other environmental factors that they had to consider in anyway how such factors were influential in adopting on-demand cloud services in their respective organisations. This section’s sole focus would be the factors that were environmentally attributed using to the TOE framework, as seen below.

5.1.8: CompetitionThe first factor suggested by the TOE framework is competition. Relative advantage and relevancy could be strong fuels to prompting organisations to adopt on-demand cloud services. Respondents were asked: “An organisation is often not without competition, how did this influence the decision to adopt on-demand cloud services?” They all responded depending on the degree of influence, on how this factor has allowed their organisation to adopt or want to adopt the said technology. Below, a tabular summary of the descriptive analysis and a graphical representation of data gotten from the questionnaire are displayed.

Table 12: Descriptive Analysis of Competition
On an average, respondents’ indication of how competition could influence the adoption of on-demand cloud services is shown as the mean of 3.596, which shows a mid-level to high influence on the decision to adopt said technology. With the standard deviation value of 1.089, it can be said that the selection of values to reflect the degree of influence of adopting said technology were not exactly spread out, as almost the same number of respondents chose the values that represented the mid-level to high degree of influence.

22574252185035Likert Scale Value (Ascending Order)
00Likert Scale Value (Ascending Order)
1526540509270Frequency0Frequency

Figure 25: Analysis of respondents’ indications of the influence of competition
As depicted above, respondents’ responses were an indication of how prominent and prevalence the support of management was influential to the decision to adopt on-demand cloud services. More than half of the respondents indicated that these factors had a relatively medium to strong influence in adopting on-demand cloud services. Only a few respondents indicated that this factor had little or no influence on the decision to adopt said technology.

5.1.9: Communication among StakeholdersThe second factor suggested by the TOE framework is the communication among stakeholders. This could be said to mean how customers, media; government, etc., could, one way or the other communicates with various Nigerian organisations on the usage of on-demand cloud services. Participants were asked: “Communication among stakeholders is essential for an organisation’s relevancy and growth. How did this influence the adoption of on-demand cloud services?” They all responded depending on the degree of influence, on how this factor has allowed their organisation to adopt or want to adopt the said technology. Below, a tabular summary of the descriptive analysis and a graphical representation of data gotten from the questionnaire are displayed.

Table 13: Descriptive Analysis of Stakeholders
Respondents’ indication of how competition could influence the adoption of on-demand cloud services is shown as the approximate mean value of 4, which shows a high influence on the decision to adopt said technology. With the standard deviation value of 0.826, it can be said that the selection of values to reflect the degree of influence of adopting said technology were quite spread out.
23018752515870Likert Scale Value (Ascending Order)
00Likert Scale Value (Ascending Order)
1440815716915Frequency0Frequency

Figure 26: Analysis of respondents’ indications of the influence of stakeholders
As depicted above, respondents’ responses were an indication of how prominent and prevalence the support of management was influential to the decision to adopt on-demand cloud services. More than half of the respondents indicated that these factors had a relatively strong influence in adopting on-demand cloud services. Only a few respondents indicated that this factor had little or no influence on the decision to adopt said technology. Additionally, the graph above is left skewed or negative skewed because the “tail” of the distribution points to the left, which produces a negative skewness value. This means that this factor influenced the adoption of said technology in a relatively positive way.

5.2: Pearson Correlation Coefficient of FactorsMethods of correlation and regression can be used in order to analyse the extent and the nature of relationships between different variables. Correlation analysis is used to understand the nature of relationships between two individual variables CITATION Joh18 l 1036 (Dudovskiy, 2018). Correlation coefficient ‘r’ is calculated using the formula below:

Figure 28: Correlation formula CITATION Joh18 l 1036 (Dudovskiy, 2018)Where: ‘x’ and ‘y’ are the variable values and ‘n’ is size of the sample
The correlation coefficient value can be interpreted in the following ways:
If ‘r’ is equal to 1, then there is perfect positive correlation between two values;
If ‘r’ is equal to -1, then there is perfect negative correlation between two values;
If ‘r’ is equal to zero, then there is no correlation between the two values
Quite often, except between the correlation of the same value, a perfect value of 1 or 0 is not always gotten. What could be achievable would be the closer the value of ‘r’ is to 1, the higher positive correlation is between two values. If the value of ‘r’ is less than 0, the closer it is to – 1, the greater the negative correlation between two values. If ‘r’ is equal to zero, then as described earlier, there is no correlation between the two values.

Correlation analysis as a research method could be very advantageous. It allows for the analysis of data from many subjects simultaneously. Correlation analysis can also study a wide range of variables and their interrelations. To also note, findings of correlation do not always indicate cause and effect relationships. A typical type of correlation coefficient of taking the ratio of the sample of the two variables to the product of the two standard deviations and illustrates the strength of linear relationships CITATION Joh18 l 1036 (Dudovskiy, 2018) is the Pearson Correlation Coefficient. Pearson’s correlation coefficient is also a measure of the strength of the association between the two variables CITATION UWE18 l 1036 (UWE, 2018).

Correlation is an effect size and so we can verbally describe the strength of the correlation using the guide that Evans (1996) suggests for the absolute value of r for both negative and positive correlation:
From -.00 to -.19 = “very weak”||From .00 to .19 = “very weak”
From -.20 to -.39 = “weak”||From .20 to .39 = “weak”
From -.40 to -.59 = “moderate”||From .40 to .59 = “moderate”
From -.60 to -.79 = “strong”||From .60 to .79 = “strong”
From -.80 to -1.0 = “very strong”||From .80 to 1.0 = “very strong”
The validity of Pearson correlation calculations are based on several assumptions:
data is at a continuous level
data values are independent of each other
a linear relationship is assumed when calculating Pearson’s coefficient of correlation
observations are random samples from normal distributions
These assumptions were used as yardsticks to check if the data gotten via the questionnaire satisfied the conditions above before proceeding to calculate the Pearson’s coefficient of correlation. Below is the Pearson’s coefficient of correlation calculated, which is followed be the explanations for each variable matched against each other. Each variable would be treated differently and analysed to view what form of relationship it has with other variables, whether positive or negative.

Table 14: Pearson Correlation Coefficient of Factors
Correlation of Security and Trust with Other Factors
As seen above, the first correlation for this factor done with employees’ opinions, another factor, with the highest positive correlation value of 0.4247, which shows a moderate positive value. This stems from how employees’ views and opinions of on-demand cloud services, which could influence the adoption of this innovative technology, could be driven by their views and knowledge on the security and trust of this technology. The deeper their knowledge and opinions about security and trust of on-demand cloud services, the larger the effects of voicing and standing by their opinions to adopt this technology and vice versa. This correlation had the largest correlation value amongst others correlated against security and trust, which include:
Skills, resources and opportunities, with a positive correlation value of 0.3760 coming in as the second strongest correlation value.
Scope of business operations, with a positive correlation value of 0.3664
Willingness and readiness to adopt, with a positive correlation of 0.1541, the lowest correlation value, which could be termed as very weak.

Management support, with a positive correlation of 0.2423
Competition, with a positive correlation of 0.3074
Communication among stakeholders, with a positive correlation value of 0.2754
Perception of innovative technology, with a positive correlation value of 0.3542
Correlation of Perception of Technology with Other Factors
Secondly, the first correlation of this factor with “communication among stakeholders”, another factor, with the highest positive correlation value of 0.4497, shows a moderate positive value. This stems from how the communication among stakeholders about their perception about on-demand cloud services could have influenced the adoption of this innovative technology. The higher the frequency of stakeholders’ communication with these organisations, the quicker it is to adopt this technology by organisations and vice versa. This correlation had the largest correlation value amongst others correlated against this factor, which include:
Security and trust, with a positive correlation value of 0.3542 coming in as the second strongest correlation value.
Skills, resources and opportunities, with a correlation value of -0.1181. The only negative correlation, which is very weak and show very little correlation with the treated factor.
Scope of business operations, with a positive correlation value of 0.2407
Willingness and readiness to adopt, with a positive correlation of 0.1636
Management support, with a positive correlation of 0.0575, the lowest correlation value, which could be termed as very weak.

Competition, with a positive correlation of 0.1349
Communication among stakeholders, with a positive correlation value of 0.2754
Employees’ opinions, with a positive correlation value of 0.1958
Correlation of Employees’ Opinions with Other Factors
The factor with the highest positive correlation with employees’ opinions is skills, resources and opportunities, with the value of 0.5304 showing a moderate positive value. This stems from how employees’ views and opinions of on-demand cloud services could be driven by the available skills, resources and opportunities. Without them, employees might have little or no opinion to offer as regards to adopt on-demand cloud services. This correlation had the largest correlation value amongst correlation values, which include:
Security and Trust, with a positive correlation value of 0.4247, coming in as the second strongest correlation value.

Scope of business operations, with a positive correlation value of 0.4192
Willingness and readiness to adopt, with a positive correlation of 0.0117, the lowest correlation value, which could be termed as very weak.

Management support, with a positive correlation of 0.3270
Competition, with a positive correlation of 0.2371
Communication among stakeholders, with a positive correlation value of 0.1492
Perception of innovative technology, with a positive correlation value of 0.1958
Correlation of Skills and Resources with Other Factors
As described above, skills, resources, etc., has the highest positive correlation with employees’ opinions, with the value of 0.5304, a moderate positive value. This correlation had the largest correlation value amongst correlation values, which include:
Security and trust, with a positive correlation value of 0.3760 coming in as the second strongest correlation value.
Scope of business operations, with a positive correlation value of 0.3156
Willingness and readiness to adopt, with a positive correlation of 0.0172, the lowest correlation value, which could be termed as very weak.

Management support, with a positive correlation of 0.2060
Competition, with a positive correlation of 0.2119
Communication among stakeholders, with a positive correlation value of 0.2262
Perception of innovative technology, with a correlation value of -0.1181. The only negative correlation, which is very weak and show very little correlation with the treated factor.

Correlation of Scope of Operations with Other Factors
This factor had the strongest value of 0.4192, which shows a moderate positive value. This stems from how employees’ views and opinions of on-demand cloud services, which could influence the adoption of this innovative technology, could be driven by the bulk of workload to be eased by the usage of this technology in their various departments, available manpower, etc. The bigger the scope of business operations is for any organisation, the louder the employees’ opinions, to make their work efficient and fast and vice versa. This correlation had the largest correlation value amongst correlation values, which include:
Security and trust, with a positive correlation value of 0.3664 coming in as the second strongest correlation value.
Skills, resources and opportunities, with a positive correlation value of 0.3156
Willingness and readiness to adopt, with a positive correlation of 0.1138, the lowest correlation value, which could be termed as very weak.

Management support, with a positive correlation of 0.2405
Competition, with a positive correlation of 0.2249
Communication among stakeholders, with a positive correlation value of 0.2161
Perception of innovative technology, with a positive correlation value of 0.2407
Correlation of Willingness and Readiness with Other Factors
Throughout, this factor had the least correlation values across the board when correlated with other factors. The highest positive correlation value of 0.2531, although a weak positive value, is from the correlation with communication among stakeholders. Other correlation values with other factors include:
Competition, with a positive correlation of 0.2153 coming in as the second strongest correlation value.

Skills, resources and opportunities, with a positive correlation value of 0.0172
Scope of business operations, with a positive correlation value of 0.1138
Employees’ opinions, with a positive correlation of 0.0117, the lowest correlation value, which could be termed as very weak.

Management support, with a correlation of -0.0876. The only negative correlation, which is very weak and show very little correlation with the treated factor.

Security and trust, with a positive correlation value of 0.1541
Perception of innovative technology, with a positive correlation value of 0.1636
Correlation of Management Support with Other Factors
Throughout, this factor had the least correlation values across the board when correlated with other factors. The highest positive correlation value of 0.3270, although a weak positive value, is from the correlation with employees’ opinions. Quite reasonable, as the management staff would seldom act except prompted by employees via feedbacks, meetings, etc., on the usage of on-demand cloud services, which could invoke their decisions to adopt the technology. Other correlation values with other factors include:
Security and trust, with a positive correlation of 0.2423 coming in as the second strongest correlation value.
Skills, resources and opportunities, with a positive correlation value of 0.2060
Scope of business operations, with a positive correlation value of 0.2405
Willingness and readiness to adopt, with a correlation of -0.0876. The only negative correlation, which is very weak and show very little correlation with the treated factor.

Competition, with a positive correlation value of 0.2868
Communication among stakeholders, with a positive correlation value of 0.2200
Perception of innovative technology, with a positive correlation value of 0.0575, the lowest correlation value, which could be termed as very weak.

Correlation of Competition with Other Factors
Competition could give rise to the incessant pleas from various stakeholders to organisations to step up and adopt on-demand cloud services, which gave rise to the highest positive correlation value of 0.4265, which shows a moderate positive value. Competitors could be regarded as an organisation’s stakeholder and that could also drive the need to adopt this technology. Other correlation values with other factors include:
Security and trust, with a positive correlation of 0.3074 coming in as the second strongest correlation value.
Skills, resources and opportunities, with a positive correlation value of 0.2119
Scope of business operations, with a positive correlation value of 0.2249
Willingness and readiness to adopt, with a positive correlation of 0.2153
Management support, with a positive correlation of 0.2868
Employees’ opinions, with a positive correlation value of 0.2371
Perception of innovative technology, with a positive correlation value of 0.1349, the lowest correlation value, which could be termed as very weak.

Correlation of Communication among Stakeholders with other Factors
As described previously, the perception of technology has the highest positive correlation of value 0.4497, which shows a moderate positive value, with this factor. This stems from how the communication among stakeholders about their perception about on-demand cloud services could have influenced the adoption of this innovative technology. The higher the frequency of stakeholders’ communication with these organisations, the quicker it is to adopt this technology by organisations and vice versa. Other correlation values with other factors include:
Competition, with a correlation of 0.4265 coming in as the second strongest correlation value.
Skills, resources and opportunities, with a positive correlation value of 0.2262
Scope of business operations, with a positive correlation value of 0.2161
Willingness and readiness to adopt, with a positive correlation of 0.2531
Management support, with a positive correlation of 0.2200
Employees’ opinions, with a positive correlation value of 0.1492, the lowest correlation value, which could be termed as very weak.

Security and trust, with a positive correlation value of 0.2754
5.3: Categorisation of Open-Ended Responses using the TOE frameworkBased on the TOE framework developed in 1990 by CITATION Tor90 l 2057 (Tornatzky & Fleischer, 1990), the categorisation of the open-ended responses provided by the respondents to identify three aspects of an organisation’s context that influence the process of adopting and implementing a technological innovation are diagrammatically displayed below. The three contexts include: technological context, organisational context, and environmental context.
411817867759Organisation:
Ease of Operations
Business Growth
Resource Sharing
Internal Business Collaboration
Ease of Internal Communication
Business Agility
0Organisation:
Ease of Operations
Business Growth
Resource Sharing
Internal Business Collaboration
Ease of Internal Communication
Business Agility
-11620567310Environment:
Technology Advancement
Provision of on-demand cloud services to other companies
Industry direction
Trendiness of cloud computing
00Environment:
Technology Advancement
Provision of on-demand cloud services to other companies
Industry direction
Trendiness of cloud computing

21894851434620
2189480184150034645606477000
2465705107315Technological Innovation Decision Making
00Technological Innovation Decision Making

34645601955800014748701932200
3095625127000
1597815252347Technology:
Automation
Data Storage and Management
Flexibility and Scalability of Technology
Decentralisation
0Technology:
Automation
Data Storage and Management
Flexibility and Scalability of Technology
Decentralisation

Figure 27: Categorisation of Open-Ended Responses using the TOE framework
Technological Context
Respondents were asked, “Apart from the aforementioned probable factors that may have influenced the adoption of on-demand cloud services in your organisation, please list other factors that could have prompted the decision of adoption.” Most respondents responded to this question, while only a few chose to skip this question, because they noted that the aforementioned probable factors were the most important or only factors that may have influenced the adoption of on-demand cloud services in their respective organisations. Other factors mentioned by respondents in this context include the following:
Automation of work, rather than the manual way of doing work.

Data Storage and Management as opposed to the on-site storage of data, which could lead to the vulnerability of data.

Flexibility and Scalability of Technology, which could accommodate for more changes in technology usage and more adoption of more innovations.

Decentralisation of usage of technology and storage of data.

Organisational Context
Respondents were asked, “Apart from the aforementioned probable factors that may have influenced the adoption of on-demand cloud services in your organisation, please list other factors that could have prompted the decision of adoption.” Most respondents responded to this question, while only a few chose to skip this question, because they noted that the aforementioned probable factors were the most important or only factors that may have influenced the adoption of on-demand cloud services in their respective organisations. Other factors mentioned by respondents in this context include the following:
Ease of Operations
Business Growth
Resource Sharing
Internal Business Collaboration
Ease of Internal Communication
Business Agility
Environmental Context
Respondents were asked, “Apart from the aforementioned probable factors that may have influenced the adoption of on-demand cloud services in your organisation, please list other factors that could have prompted the decision of adoption.” Most respondents responded to this question, while only a few chose to skip this question, because they noted that the aforementioned probable factors were the most important or only factors that may have influenced the adoption of on-demand cloud services in their respective organisations. Other factors mentioned by respondents in this context include the following:
Technology Advancement, which could mean the need to use more technological advanced innovations for whatever reasons.

Provision of on-demand cloud services to other companies as regards to being the provider and not just the user of this technology. This could be financially driven.

Industry direction as regards to being just a follower of trends and not the creator or early adopter.

Trendiness of cloud computing. This reason could be attributed to late adopters who do not take the initiative to adopt innovations, but wait for other organisations to do so. This could be seen as organisations reducing the risks to the bare minimum when embarking on the journey to adopt innovation.

CHAPTER 66.1: Interpretation and ExplanationAlthough tasking, the factors influencing the adoption of on-demand cloud services in Nigeria were found, collated and highlighted in the previous chapter. These factors were also categorized according to the TOE framework to properly determine the context of the factors, if they were technologically, organisationally or environmentally driven. The aim of this study is to identify the factors that impact the adoption of on-demand cloud services.

First of all, the fact that knowledge of on-demand cloud services plays an important role in decision to adopt said technology is highly evident on how it influenced the responses from participants. The results demonstrated that having information about whether on-demand cloud services is advantageous, complex, costly or secure is not as influential in decision making as to having information about how their scope of business operations, available skills, resources and opportunities, amongst other factors in the organisational context. Decision makers should have detailed knowledge about different aspects of cloud computing such as its structure, different types, etc., because companies that are more conservative than individuals, do not spend money on new technologies unless they have thorough knowledge about the technology.
As seen in this study, most organisations in Nigeria are not early adopters of innovation. As widely known, Nigeria is not among the top innovative countries, which has affected the need to be either innovative or early adopters of innovation. Government parastatals are the least innovative organisations in Nigeria, filled with processes done manually. Most Nigerian organisations also suffer from nepotism and management staff members who are not proactive, which contributed to the current lack of innovation in the country. There is also no diversity in technology in the country. Most technological related fields are primarily focused in software development, network and securities. Previously noted, the organisational context, rather than the technological context as seen in other studies is the backbone of any form of innovation in any Nigerian organisation. With skills, resources, opportunities and scope of business operations as the highest drivers of any form of innovation, vastly noticed in Nigerian organisation.

In this research the final number of variables was nine and number of total responses was 152, 52 accounting for the users of on-demand cloud services. This limitation significantly influences the results’ reliability. More studies and researches in different environment are to be done to cater for the diversity in results.
6.2: LimitationsSome respondents were not particularly interested in filling the questionnaire, they voiced out that they had no time to do so, or did not understand the concept of on-demand cloud services. Also, the size of the sample size proved to be a problem due to the availability and responsiveness of respondents. Great significant results were not gotten, but good enough results to deduce, conclude and give constructive recommendations were gotten. Also, the sample was collected from Nigerian organisations, which makes the results not being applicable to organisations in different parts of the world. Although some countries might have similar reception to technological inventions, this does not mean that they would have similar rates of diffusion, reception and acceptance, which further cements why the results would not be applicable in other countries.
In all, the results of this study cannot be generalised, as mentioned before, it is specifically applicable to Nigerian organisations. Performing further research in this field of study is highly recommended. Other researchers should test and confirm the proposed conceptual model in other contexts. Longitudinal study is also useful, which defines whether companies who currently use on-demand cloud services, continue their usage; and they are satisfied with the service. It is always recommended that researchers improve the models that are proposed, by adding or removing construct from the model.

CHAPTER 77.1: Conclusions and RecommendationsThe decision to adopt any form of innovation is not a simple process. As seen in this study, adoption is influenced by various factors. In the context of on-demand cloud services, quite a number of researches have been conducted, but in the general scope of sense and not geographically targeted. This prompted the focus of this research to be limited to just Nigerian organisations. As interesting as this maybe, Nigeria as an emerging market, it was necessary to investigate the factors that influenced the adoption of not just any technological invention, but specifically, on-demand cloud services. This research used the TOE framework to correctly identify and categorise the factors influencing the adoption of on-demand cloud services by Nigerian organisations.
Competition and communication amongst stakeholders were the suggested factors of the environmental context. Employees’ opinions, available skills, resources and opportunities, scope of business operations, willingness and readiness to adopt innovative technology by employees and management support were the factors of the organisational context. Security, privacy and trust in technology and perception of technological innovations were the suggested factors of the technological context. These factors suggested by the TOE framework have shown to be paramount in influencing the decision to adopt on-demand cloud services. With new technologies, there is a reoccurring need in understanding the organizational adoption of technological innovation, its dimensions and characteristics. The TOE framework has proven that it is able to provide insights for researchers and practitioners interested in this topic and has been useful in investigating a wide range of innovations and contexts.
After analysis of the factors, it was found that the organisational context of the scope of business operations and the willingness, readiness to adopt on-demand cloud services have the greatest impact on organisations. This study also summarises the key factors derived and other factors vaguely explained by respondents. It was also noticed that this framework (TOE) was significant in gaining insights into the usage of not just on-demand cloud services, but other technological innovations.
Additionally, this study uses the TOE framework to rank and group of the factors influencing the adoption on-demand cloud services in various organisations, and conducts analyses. After analysis of the factors, it can be found that the organisational context has the greatest impact on Nigerian organisations, followed by organization and environment. This is quite different from studies done by other researchers, who write on how the technological context had the greatest impact on organisations. This could be due to the difference in geolocation and socio-political views.
This research work was not just targeted at the technological aspects of on-demand cloud services adoption, hence the TOE framework used to investigate the organisational and environmental factors. At this point of time, until on-demand cloud services’ failure cases start to emerge, most of the respondents have a high expectation of this technological innovation and those who have not adopted on-demand cloud services due to one or two reasons are hopeful in the nearest future to do so. In the future, organisations, regardless of location should perform studies like this, to predict what technological invention would be highly beneficial to their success rates.

AppendicesAppendix 1: Preliminary Questionnaire (Pilot Study)

Appendix 2: Final Version of Questionnaire
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