Small and Medium Enterprises (SMEs) face numerous challenges in identification, setting up and making use of Information Technology (IT) as an enabler for business. Cloud computing could solve this problem by offering ready, low cost of entry IT solutions. Adoption of cloud computing among the SMEs in developing countries is however low due to a number of barriers as identified by previous studies. Over the years, research on adoption on innovation and technology has unveiled a number of theories on adoption which range from Individual level theories to Organizational level theories and even Market level theories. This study reviews the various theories, opting to use an organization level theory so that focus on the SME is emphasized. Analysis of literature renders this study to be based on the Technology- Organization-Environment (TOE) framework proposed by DePietro et al. (1990). This study reviews the current adoption levels of cloud computing and proposes a TOE based model for adoption of cloud-based services by SMEs in developing countries. The study employed literature review to determine the factors that are applicable for a model on adoption of cloud computing in the developing countries. Further, the study conducted a survey through a questionnaire to collect quantitative data to assist in determination of the most applicable model. Convenience sampling was employed due to the study’s constraints on time and budget. The study findings revealed that there is low adoption of cloud computing for business applications by SMEs in Nairobi County, hence confirms the need for the adoption model. Using Exploratory Factor Analysis (EFA) six components were extracted for the proposed model which include Relative Advantage, Accessibility, Organization Readiness and Size, Vendor Readiness, Regulations and Trading Partner Pressure, each with attributes required to ensure successful adoption of cloud computing. The model was validated through statistical analysis which confirms a largely reasonable level of fit for the indices and construct validity conducted through convergent and discriminant validity methods. Further, the model was subjected to experts’ analysis who concluded that the model is simple, applicable and fitting. The study finally proposes practical recommendations to governments and policy makers, educational institutions, software vendors and SMEs based on the model. Further research areas include subjecting the model to larger sample sizes to confirm its validity and the preparation of an implementation guideline. Keywords: Cloud computing, Small and medium enterprises, ICT Adoption, Nairobi Kenya

1. ADOPTION OF CLOUD-BASED SERVICES BY SMEs
IN DEVELOPING COUNTRIES: DEVELOPMENT OF A
T-O-E BASED MODEL
AMOS WACHANGA WAMBUGU

3. IT is integral to modern business
…. amongst other key business tasks
(Davenport, 2013; Ong et al, 2016; Tarutėa & Gatautis, 2014)
Information
Storage
Information
Retrieval
Communication Reporting

4. Small & Medium Enterprises
• SMEs play a significant role in the economy,
• but they have limited possibilities while
competing with the large enterprises,
• where low adoption of IT plays a big role
(Alshamaileh, 2013; Ong et al, 2016)

5. Research shows that there is low adoption of IT by
SMEs in developing economies such as Kenya
(KNBS, 2016; Agboh, 2015) Why?

6. Challenges of traditional IT environments
• the upfront setup costs and
• the increasingly complex
management issue of
• software,
• hardware
• and networking equipment
• which also require specialist staff
for implementing and
maintaining IT services.
(Alshamaila et al, 2013; Tarutėa et al, 2014; Ong et al, 2016)
Costs
Complexity

8. “a model for enabling very convenient, on-demand
network access to a shared pool of configurable
computing resources
such as networks, servers, storage, applications, and
services,
which can be rapidly provisioned and released with
minimal management effort or service provider
interaction”
(NIST, 2011, p. 2). Cloud Computing Definition

9. Reduced costs & Affordability
Setup time and effort
Simplicity
Scalability & Maintainability
Convenience and improved accessibility
(Ricketts, 2015)
Drivers
&
Benefits

10. SMEs could find cloud computing to be an
attractive solution
(Aljabre, 2012; Hassan et al, 2017)
Cloud-based services have been deemed to offer
reliable, reasonably priced IT services which
businesses need in this age.
(Carcary et al, 2014; Widyastuti & Irwansyah, 2018)

11. Cloud Computing Characteristics
Five essential characteristics
that need to exist in an IT
environment for it to be
considered as a Cloud.
(Mell & Grance, 2011; Erl, Mahmood, & Puttini, 2013).
on-demand
self-service,
broad
network
access,
resource
pooling,
rapid
elasticity,
measured
service

12. Cloud Computing Service Delivery Models
Cloud computing Illustration.
Adapted from (Oguntala, Abd-Alhameed, & Odeyemi, 2017)

13. Cloud Computing Deployment Models
Simplified Overview of Cloud Deployment Models.
Adapted from (Oguntala, Abd-Alhameed, & Odeyemi, 2017)

14. Cloud Computing Pricing Models
• Per user model especially for Software as a Service solutions or
• Based on usage of computing resources such as for Platform and
Infrastructure as a Service solutions
• Classified as Static Pricing or Dynamic Pricing (Kamra et al, 2012).
• Static pricing is where the cost per period of time is known and does not
change even if there’s more or less usage of the service e.g. Office 365
• Dynamic pricing is where there is a fixed charge and then based on usage, the
user attracts an extra cost e.g. Amazon Elastic Cloud (EC2)

15. Concerns on Cloud Computing
Information Security &
Privacy
Data residency and
legal jurisdiction
Regulatory compliance Vendor Lock-in
Business Continuity
due to Internet
Connectivity issues

16. SMEs Adoption from Literature
Developed Developing Kenya
92% of enterprises made use of
ICTs over the internet
(Eurostat, 2017).
Only 34% of SMEs makes use of
computers while 91% make use of
telephones
(Olise, Anigbogu, Edoko, & Okoli, 2014).
SMEs use of ICT devices is mainly
mobile phones (40.7%) only 9.5%
make use of computers
(KNBS, 2016)
Only about one in five makes use
of cloud computing with only 8%
of SMEs make use of private cloud
(Giannakouris & Smihily, 2016).
Only about 35% have access to the
internet and only 18% have
websites
(Onyedimewu & Kepeghom, 2013).
An average of 85.8% of SMEs had
access to internet at their
premises
(CA & KNBS, 2016).
Highest use of cloud-based
services by SMEs was email, office
software and storage of files and
databases
(Sánchez, 2016).
Only 36% of these indicated they
make use of SaaS or IaaS
(Hinde & Belle, 2012).
Fewer SMEs were using ICT for
specialized applications such as
orders management (20.9%) or
human resource management
(28.5%)
(CA & KNBS, 2016).

17. Adoption by SMEs in Kenya
• Main benefits indicated for adoption of cloud computing were
flexibility, cost savings, better scalability, disaster recovery and
complexity reduction
• Reason for not using ICT is it’s not needed by the business, is not
applicable for them or it’s too costly
• Main reasons highlighted for not using cloud computing by SMEs was
insufficient knowledge within the organization
(CA & KNBS, 2016).

18. Problem Statement
1. SMEs generally are constrained on budgets
2. Most SMEs then resolve to manual processes …….
3. Use of cloud services might become a key component for
success for SMEs
4. There is low adoption of IT, and more so, low adoption of
cloud computing by SMEs in Kenya and in Africa.
5. There is need to research a model that could lead to
adoption of cloud-based services by SMEs
(Venkatraman & Fahd, 2016; Widyastuti et al, 2018; Agboh, 2015; KNBS, 2016; Oguntala et al., 2017)

19. Considerations for such a model
• Many factors should be considered which include the Internal and
external environment the SME is operating in and the Technological
context as well as Organizational readiness of the SME.
• Theoretical frameworks as basis….
(Borgman, Bahli, Heier, & Schewski, 2013)

20. Theoretical frameworks on Adoption
Theory Level of Analysis Author
Technological Acceptance Model (TAM) Individual Davis (1989)
Diffusion of Innovation (DOI) Organization or Market Rogers (1995)
Theory of reasoned action (TRA) Individual Ajzen and Fishbein (1980)
Theory of planned behavior (TPB) Individual Ajzen (1985)
Unified Theory of Acceptance and Use of
Technology (UTAUT)
Individual Venkatesh et al. (2003)
Technology Organization Environment (TOE) Organization DePietro et al. (1990)
Should build on existing theories and frameworks that have been developed which
explain adoption of technology and innovations.

21. Technology-Organization-Environment (TOE) Framework,
helps to analyze adoption of technological innovations
by firms and organizations
can provide a good basis and a good lens
(DePietro, Wiarda, & Fleischer, 1990)

22. Objective of the Study
The main objective of this study is:
To understand the adoption of cloud-based services by SMEs
within Nairobi County in Kenya, and based on the findings,
attempt to propose a model for the adoption of cloud-based
services as a solution to better IT strategy by the SMEs

24. Methodology
• Research Design - Design Science category of research
• Population and Sampling Design - Convenience sampling strategy
• Data Collection Methods - Quantitative methods founded on
questionnaire-based surveys since they are the most prevalent
methodologies
(Kumar, 2017;Alshamaileh, 2013; Carcary, Doherty, & Conway, 2014; Hassan et al, 2017)

25. Research Approach
Literature Review
Initial Model
(based on Theory)
Data Collection
Instruments
Development
Pilot Study
Review of the Data
Collection
Instruments
Data Collection
Data Analysis and
Model Update
Validation of Model
(Expert Reviews)
Final Model
Problem Definition

26. Tools & Techniques
• Data analysis was done through IBM SPSS Statistics version 23 and
IBM SPSS Amos version 21
• Statistical techniques including descriptive statistics, reliability
analysis and factor analysis
• Exploratory factor analysis (EFA) using Principal Component Analysis
(PCA) and varimax rotation was carried out.
(Pallant, 2007; Hair et al, 2010).

27. Methodology
• Reliability was tested using Internal Consistency (consistency of data
across responses) because Likert’s scale was largely used (Pallant, 2007)
• Cronbach alpha lower limit for this study was 0.7 which is deemed to
be moderately reliable (Hinton, 2004; Hair et al, 2010)
• Average Inter-Item Correlation used in the study was 0.2 to 0.5 (Clark &
Watson, 1995)
• Validity of the model was determined by experts review and
statistically using CFA to determine the model fit and verification of
the construct validity in terms of convergent and discriminant validity

29. TOE Framework
Source: DePietro et al. (1990)

30. Adaptation of TOE for Cloud Computing Adoption
Adaptation of TOE for cloud computing Adoption by SMEs

31. Factors influencing adoption by SMEs
Key Factors Influencing Adoption of ICT and Cloud Computing Illustrative Reference
Managerial support, cost of ICT, knowledge capacity of an SME Nduati et al (2015)
Top management support, technological readiness, organizations size, trading partner pressure,
competitive pressure, relative advantage, complexity of proposed technology
Makena (2013)
External support, competitive pressure, decision makers level of knowledge on cloud computing
and innovativeness, the employees cloud knowledge, information intensity, complexity,
compatibility, trialability, cost, security and privacy
Tehrani (2013)
Complexity, compatibility, trialability, cost and security Alam, (2009)
Perceived benefits, cost, ICT information and skill, Outdoor pressure, Government support Miraz & Habib (2016)
Relative advantage, uncertainty, geo-restriction, compatibility, trialability, size, top management
support, prior experience, innovativeness, industry, market scope, supplier efforts and external
computing support
Alshamaila,
Papagiannidis, & Li
(2013)
Relative advantage, top management support, firm size, competitive pressure, and trading partner Low, Chen, & Wu (2011)

32. Development of the Questionnaire
Section Number of Questions Questions Adapted From
A: General Information Personal information (4 questions)
Basic organizational information (8 questions)
Alshamaila, Papagiannidis, and Li, (2013); Kumar,
(2017); McKinnie, (2016); Tehrani, 2013; Hassan,
Nasir, Khairudin, & Adon, (2017)
B: Current ICT Setup IT Personnel and Strategy (2 questions)
Software systems in place (2 questions)
Check on knowledge (1 question)
Alshamaila, Papagiannidis, and Li, (2013); Kumar,
(2017); McKinnie, (2016); Hassan, Nasir, Khairudin,
and Adon, (2017); Tehrani, 2013
C: ICT Use
(For those who didn't understand
what is cloud computing)
Technology factors (4 questions)
Organization factors (7 questions)
Environment factors (7 questions)
Rop, (2015); McKinnie, (2016); Mwai, (2016); and
Otieno, (2015); Tehrani, 2013
D: Cloud-based services at the
organization
(For those who understand what is
cloud computing)
Overview (4 questions)
Technology factors (19 questions)
Organization factors (9 questions)
Environment factors (12 questions)
Alshamaila, Papagiannidis, and Li, (2013); Kumar,
(2017); Rop, (2015); McKinnie, (2016); and Hassan,
Nasir, Khairudin, and Adon, (2017); Tehrani, 2013

34. Responses Received
Total Received Invalid Responses Valid Responses**
50 responses 3 from Government
2 from NGOs
45 responses
Valid Responses were from private businesses with less than 250 employees**

35. Demographics of Respondents
• Role
• Owner/Management category at 40% (n=18)
• Employees at 29% (n=13)
• IT Personnel at 20% (n=9)
• Gender
• Female were 17 (38%)
• Male were 28 (62%)
• Age- Groups
• “26-35 years” highest representation 31 respondents (69%)
• “greater than 45 years” was least represented (2%)

36. Demographics of Respondents
• There was an almost evenly
distributed representation of
organizations in relation to
the number of branches and
offices
• 9 responses (20%) did not
have any knowledge of
cloud-based services
• There was a correlation
between knowledge of cloud
computing and ICT
qualification Distribution of Respondents' by Number of Branches/Offices

37. Correlation between having qualification in ICT and
Knowledge of cloud-based services
Do you have any
qualification in ICT?
Do you know what
cloud-based services
are?
Do you have any
qualification in ICT?
Pearson Correlation 1 .433**
Sig. (2-tailed) .003
N 45 45
Do you know what cloud-
based services are?
Pearson Correlation .433** 1
Sig. (2-tailed) .003
N 45 45
**. Correlation is significant at the 0.01 level (2-tailed).

38. Correlation between ICT Complexity and ICT
Qualification
ICT is complicated, it’s
difficult to understand
what’s going on
Do you have any
qualification in
ICT?
ICT is complicated, it’s difficult
to understand what’s going on
Pearson Correlation 1 .726*
Sig. (2-tailed) .027
N 9 9
Do you have any qualification
in ICT?
Pearson Correlation .726* 1
Sig. (2-tailed) .027
N 9 9
*. Correlation is significant at the 0.05 level (2-tailed).

39. Reliable with Cronbach alpha of 0.903
No multicollinearity issue was found
(Cohen et al, 2003)

40. Automated Services in comparison to Cloud Deployed Services
Service
Automated Services Cloud Deployed Services
Frequency (n) Percentage Frequency (n) Percentage
Contact e.g. Email/Website 37 82% 31 86%
Accounting 27 60% 9 25%
Invoicing 22 49% 4 11%
CRM 21 47% 14 39%
HR 11 24% 3 8%
Payroll 20 44% 5 14%
Sales, Marketing 12 27% 6 17%
Manufacturing 0 0% 0 0%
Business Process Automation 15 33% 6 17%
Specialized Software 6 13% 4 11%
Inventory Management 10 22% 3 8%
Documents Management 18 40% 14 39%
Distribution (Supply Chain Management) 0 0% 0 0%(Similar to the CA&KNBS, 2016 paper)

41. Number of offices by amount paid for cloud computing

42. Satisfaction level by amount spent on cloud computing

43. Distribution of satisfaction levels based on amount
spent on cloud-based services
Positive significant (r=0.385; p<0.05) relationship,
although not a very strong relationship, between
the amount paid and level of satisfaction from
cloud-based services

44. Source of Cloud-Based Services Information

45. Exploratory Factor Analysis

46. Component
Technology
Relative
Advantage
Environment
Vendor
Readiness
Organization
Readiness and
Size
1 2 3
T_RA_Easier - Using cloud-based services makes it
easier for us to do our job compared to traditional
on-premises software
.870
T_RA_Reliable - Cloud-based services are more
reliable than traditional on-premises software .848
T_SA_Secure - Cloud-based services are secure .805
T_Compa_Integrated - Cloud-based services can be
easily integrated into our existing IT infrastructure .615
T_RA_Efficiency - Using cloud-based services
improves operational efficiency, productivity and
quality of work in our organization
.606
E_VR_Info - Cloud Service Providers provide
enough information about the services .907
E_VR_Overall - Overall, Cloud Service Providers are
ready with the services they provide .852
E_VR_Capacity – Cloud Service Providers have
adequate capacity to run the services .828
O_TR_Ready - Our organization’s management is
ready to make use of cloud-based services .830
O_TR_Skills - Most of our employees possess
required skills to make use of cloud-based services .814
O_Size - Cloud-based services are applicable for us .688

47. Rotated Component Matrixa
Component
Environment
Regulations
Environment
Trading
Partner
Pressure
Technology
Accessibility
1 2 3 4 5 6
E_R_Gov - Government Regulations for our
business type allow use of cloud-based
services
.927
E_R_Industry - Our Industry Regulations allow
use of cloud-based services .871
E_TPP_Cust - Our customers have forced us to
make use of cloud-based services .871
E_TPP_Suppl - Our suppliers have forced us to
make use of cloud-based services .771
T_SA_Accessibility –
Our internet connectivity allows us to easily
use cloud-based services
.878
T_SA_Connection –
Accessibility on-the-go encouraged us to use
cloud-based services
.804
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 6 iterations.
Cont’d

49. Validity of the Model
Measurement Indices Model Value Recommended Value *
Chi-Square(χ2)/Degree of freedom(DF) 1.243 ≤ 3.000
Comparative Fit Index (CFI) 0.903 ≥ 0.9000
Goodness of Fit Index (GFI) 0.757 ≥ 0.9000
Adjusted Goodness of Fit Index (AGFI) 0.649 ≥ 0.8000
Tucker-Lewis Index (TLI) 0.876 ≥ 0.9000
Incremental Fit Index (IFI) 0.913 ≥ 0.9000
* Recommended values adapted from Kumar (2017)
Construct validity is evaluated by checking the discriminant and convergent validity of the model.

50. Convergent validity tests
CR AVE MSV
Trading Partner Pressure 0.806 0.676 0.153
Relative Advantage 0.841 0.524 0.166
Vendor Readiness 0.888 0.727 0.214
Readiness and Size 0.760 0.546 0.169
Regulations 0.900 0.819 0.106
Accessibility 0.778 0.637 0.214
Composite reliability (CR) values for each construct were found in the range of 0.760 to 0.900
indicating acceptable to a high level of convergent validity
(Hair, Anderson, Black, & Babin, 2010).
Average variance extracted (AVE) values were found in the range of 0.524 to 0.819 for all constructs.
These estimates are found above or equal to the threshold value of 0.50
(Fornell & Larker, 1981; Hair, Anderson, Black, & Babin, 2010).
The results confirm the convergent validity.

51. Discriminant Validity
Trading
Partner
Pressure
Relative
Advantage
Vendor
Readiness
Readiness and
Size Regulations Accessibility
Trading Partner Pressure 0.822
Relative Advantage 0.084 0.724
Vendor Readiness 0.391 0.339 0.852
Readiness and Size -0.290 0.065 0.251 0.739
Regulations 0.325 0.318 0.308 0.083 0.905
Accessibility 0.109 0.408 0.463 0.411 0.262 0.798
Note: Bold values across the diagonal indicate the square root of AVE
The square root of the AVE of each construct should be
larger than all the cross-correlations between the construct
and other constructs which is the case for as shown in
(Fornell & Larker, 1981; Hair, Anderson, Black, & Babin, 2010; Kumar,
2017).
The results of these tests show that each
construct nominated in this study is
different from other, thereby confirming
the discriminant validity.

52. Validity of the Model through Expert Reviews
• SME owner highlighted a key strength was that the model was not
looking at only one facet of the ICT adoption
• Cloud computing expert suggested a weakness could be the
applicability of the model for different types of services but a strength
was it encapsulated most of the key areas that she found to be
applicable in real life situations
• They advised the model should be further adapted into steps which
can be a guideline for SMEs that provides a set of instructions

54. Objective 1: Current Adoption Levels of Cloud-Based Services
• ICT was widely available, there was still low use of ICT for day to day
operations by businesses (CA & KNBS, 2016)
• Communication, that is email and website, was the service mainly
used on the cloud (86%)
• Larger SMEs generally spent more on cloud-based services as
compared to smaller organizations which mainly make use of free
services.
• There was a tendency that the more money an organization spent on
cloud-services, the more satisfied the SME would be.
• In summary, it was concluded that there was low use of ICT and
cloud computing especially for business applications with the
highest use being communication such as email and websites.
• This conclusion also informs the need for theoretical models that
could ensure successful adoption of cloud computing by SMEs

55. [Revisiting] Model based on Literature Review
Adaptation of TOE for cloud computing Adoption by SMEs

56. Objective 2: Final TOE based model

57. Objective 3: Validation of the model
• Statistical analysis of the data showed a largely reasonable level of fit
for the indices. This indicates good model fit.
• Construct validity was also confirmed where both convergent and
discriminant validity was confirmed.
• The statistical analysis in general concluded that the model was valid
and a good fit for its purpose.
• The experts in general concluded that the model was valid and a good
fit for cloud computing adoption by the SMEs.
• The experts advised the need for a guideline.

58. Conclusions
• Based on the exploratory nature and the small sample size in
this study, I’m not suggesting the findings to be generalizable.
• Nonetheless, the insights gained from this study can provide
some interesting findings and basis for further research on
cloud computing adoption

59. SMEs can make use of the model proposed

60. Cloud providers, schools and other interested parties
should invest in dissemination of information on cloud
computing

61. Cloud service providers should invest in business
applications that offer both free & premium services to
cater for smaller & larger organizations

62. Governments and policy makers can also make use of
the model to determine the best environment for
enabling SMEs to adopt cloud computing

63. This research contributes to knowledge of ICT adoption
& cloud computing

64. Limitations, Recommendations and Future Directions
Limitation Recommendations and Future Directions
Constraints on time and budget convenience
sampling was used in this study. This sampling
methodology meant that the results may not
be generalized
Conduct an external validity which may have representation
from various types of organization, in different environments
and with different types of representatives as well as a larger
sample size
SMEs in different contexts may be affected by
different factors which was not accounted for
The model can be refined to a more generalized model which
can be applicable to different environments
Need for an implementation guideline Implementation guideline highlighting the expected
conditions, factors and issues that could help an SME,
governments and other interested parties to be able to use
the model to embrace cloud-based services
Applicability to other types of organizations Investigated further in relation to different types of industries
and organization sizes to determine its usability even for
large enterprises, non-governmental organizations and
governments
Combination of theories to cover more Theories at different analysis levels could help improve the
model to cover more dimensions

65. Publications
• Refereed Conference Paper
• Wambugu, Amos Wachanga and Ndiege, Joshua Rumo (2018).
Harnessing cloud computing by small and medium enterprises in
Kenya. In: 12th Egerton University International Conference, 2018
Njoro, Kenya

66. Amos Wachanga Wambugu
wachanga@sibasi.com