Thesis MBIT Cloud computing

Offering Cloud computing through
the IT distribution Channel
To what extent is SME adoption influenced
by distribution Value Chain attributes?
MBA Business & IT Thesis
Final Version 1.06
Maarten J. Gubbens
September 17th
, 2015

MBA Business & IT Thesis Offering Cloud through IT the Distribution Channel
Nyenrode Business University, MBA Business & IT
To what extent is SME adoption influenced by the Distribution Value Chain?
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Abstract
The purpose of this executive MBA Business & IT Thesis research is to
understand to what extent IT Distribution Value Chain Attributes influence Small
and Medium size Enterprises Cloud computing adoption in the Netherlands.
To do so, a conceptual research model (Intention to Adopt) is proposed based on
the Diffusion Of Innovation theory and the Technology-Organization-Environment
framework applied in the IT Distribution Value Chain. The predictive adoption
model was tested empirically by means of an online questionnaire sent to
approximately 2.000 Dutch SME Business and IT decision makers of which 325
(16%) responded.
A Logistic regression analysis was performed on the panel data to evaluate the fit
of the research model. The Partial Least Squares method was applied to test the
research hypothesis. Surprisingly, adoption of Cloud computing at Dutch SMEs is
high, approximately 85%. Analyses indicate offering Multi-Vendor Cloud services
and Cloud Vetting will increase Cloud computing adoption at SMEs.
The research findings provide insights in the effectiveness of the IT Distribution
Value Chain with respect to offering Cloud computing services to SMEs. First the
traditional IT Distribution Value Chain is under pressure. In the perception of the
SME decision makers; Vendors need to address Cloud hygiene factors as privacy
and security, the position of Distribution is in need of change and the role of the
Value-Added Reseller to deliver value is highly questionable. Secondly, the IT
Distribution Value Chain is shortening. Offering aggregated Multi-Vendor Cloud
and Cloud Vetting to SMEs can be performed by both the Distribution and Value-
Added Resellers role in the IT Distribution Value Chain. Lastly, due to value chain
pressures and the resulting shorter IT Distribution Value Chain a new position is
emerging, that of a Cloud Services Brooker.

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List of Tables
Table 1: SME Cloud computing adoption obstacles…………………………………...31
Table 2: SME perceived business values of Cloud computing……………………….32
Table 3: Construct definitions and the hypothesized effect on SME adoption………38
Table 4: Original theories and constructs……………………………………………….39
Table 5: Reliability Validation Pilot Study……………………………………………….43
Table 6: Hygiene Factors alpha and inter-item correlation mean……..……………..43
Table 7: Aggregated Multi-Vendor Solutions alpha & inter-item correlation mean…44
Table 8: Cloud Vetting alpha and inter-item correlation mean……………………..…44
Table 9: Value-Added Services alpha and inter-item correlation mean……………..45
Table 10: KMO and Bartlett’s Test……………………………………………………….47
Table 11: Factor Analyses Final Results…………………………...………………......48
Table 12: Reliability of Constructs……………………………………………………….49
Table 13: Sample Characteristics………………………………………………………..51
Table 14: Personal Experience Characteristics..………………………………………52
Table 15: Company Characteristics………………………………………………….….52
Table 16: Adopted Cloud and How Many Employees Cross tabulation……………..52
Table 17: Descriptive Analyses (Security & Privacy)…………………………………..53
Table 18: Chi-Square Security & Privacy items and Likelihood of Adoption…….….54
Table 19: Descriptive Analyses (Aggregated Multi-Vendor Cloud computing)……...55
Table 20: Chi-Square Multi-Vendor and Likelihood of Adoption……………………...56
Table 21: Descriptive Analyses (Cloud Vetting)……………………………………..…56
Table 22: Chi-Square Cloud Vetting and Likelihood of Adoption………………….....57
Table 23: Descriptive Analyses (Value-Added Service)……………………………….58
Table 24: Chi-Square Value-Added Services and Likelihood of Adoption………......59
Table 25: Descriptive Analyses (Prior Cloud Experience)…………………………….60
Table 26: Chi-Square Prior Cloud Experience and Likelihood of Adoption……….…61
Table 27: Transforming Likelihood to Intention…………………………………………63
Table 28: Descriptive Analyses for Composite Scores…………………………...…...63
Table 29: Collinearity Results……………………………………………………….……64
Table 30: Overall Model Fit: Goodness of Fit………...……………………………...…64
Table 31: Hosmer and Lemeshow Test…………………………………………………65
Table 32: Classification Table………………………………………………………….…65
Table 33: Logistic Regression Results………………………………………………..…66
Table 34: Hypotheses Testing……………………………………………………………68
Table 35: Analyses Results……………………………………………………………….71

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List of Figures
Figure 1: The SME IT Distribution Channel……………………………………………..7
Figure 2: Traditional Channel Options…………………………………………………..12
Figure 3: Cloud Computing model according to NIST (2011)………………..….……15
Figure 4: Distribution Cloud computing Aggregation Value Chain Attributes……….19
Figure 5: Three IT channel Cloud computing scenarios……………………………….21
Figure 6: Diffusion of Innovation theory…………………………………………………23
Figure 7: TOE Framework………………………………………………………………...24
Figure 8: TOE Framework……………………………………………………………...…25
Figure 9: Gartner Hype Cycle 2014……………………………………………………...29
Figure 10: TOE Framework with attributes…………………………………………...…33
Figure 11: SME IT Distribution Channel……………………...…………………………34
Figure 12: Conceptual SME Adoption Model…………………………………………...34
Figure 13: Analyses of respondents’ perception about Cloud Security & Privacy….53
Figure 14: Analyses of respondents’ perception about Multi-Vendor Services……..55
Figure 15: Analyses of respondents’ perception about Cloud Vetting……………….57
Figure 16: Analyses of respondents’ perception about Value-Added Services…..…58
Figure 17: Analyses of respondents’ perception about Prior Cloud Experience……60
Figure 18: Weights of PLS analyses hypotheses testing……………………………...57
Figure 19: Path-coefficients of PLS analyses hypotheses testing…………………....68
Figure 20: Research Model Results……………………………………………………..70

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Table of Contents
Abstract..................................................................................................................................2
List of Tables ........................................................................................................................3
List of Figures.......................................................................................................................4
1. Introduction...............................................................................................................7
1.1 Business Problem............................................................................................. 8
1.2 Research Objective .......................................................................................... 9
1.3 Thesis Approach and Sections......................................................................... 9
2. Literature Review................................................................................................... 10
2.1 Subject Matter................................................................................................. 10
2.2 Channels of Distribution.................................................................................. 11
2.3 Cloud computing............................................................................................. 13
2.3.1 Federate Cloud and Cloud Services Brokerage................................. 18
2.3.2 Cloud Channel Business Models ....................................................... 18
2.3.3 Cloud Computing impact on Channel Structure................................. 20
2.4 Diffusion of Innovation and Adoption.............................................................. 22
2.4.1 Diffusion of Innovation........................................................................ 23
2.4.2 TOE Framework ................................................................................. 24
2.5 Conclusions for Research Purpose ................................................................ 26
3. Purpose .................................................................................................................... 27
3.1 Adoption Obstacles and Business Values...................................................... 27
3.1.1 SME adoption obstacles..................................................................... 27
3.1.2 SME Business Values of Cloud computing........................................ 30
3.1.3 IT Channel influence on obstacles and business values ................... 32
3.2 Aim.................................................................................................................. 33
3.3 Research Question......................................................................................... 34
3.4 Conceptual Model........................................................................................... 34
3.4.1 TOE Framework ................................................................................. 34
3.4.2 IT Distribution model .......................................................................... 35
3.4.3 Combined DOI, TOE and Distribution Model ..................................... 35
3.4.4 Hypotheses......................................................................................... 36
3.5 Summary and Next Step................................................................................. 39

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4. Methodology ........................................................................................................... 41
4.1 Sampling......................................................................................................... 42
4.1.1 Population........................................................................................... 42
4.1.2 Sampling Size .................................................................................... 42
4.1.3 Unit of Analyses ................................................................................. 42
4.2 Research Instrument ...................................................................................... 42
4.2.1 Survey Questionnaire......................................................................... 42
4.2.2 Questionnaire design ......................................................................... 43
4.2.3 Survey validation ................................................................................ 43
4.2.4 Online Survey..................................................................................... 46
5. Data Analyses......................................................................................................... 47
5.1 Factor Analyses .............................................................................................. 47
5.1.1 Common method bias ........................................................................ 51
5.2 Descriptive Analyses ...................................................................................... 52
5.2.1 Sample Characteristics ...................................................................... 52
5.2.2 SME Cloud computing Adoption ........................................................ 53
5.2.3 Vendor Constructs.............................................................................. 54
5.2.4 Distribution Constructs ....................................................................... 56
5.2.5 Value-Added Reseller construct......................................................... 59
5.2.6 SME Construct ................................................................................... 61
5.3 Logistic Regression ........................................................................................ 63
5.3.1 Intention to Adopt ............................................................................... 63
6. Hypothesis Testing ............................................................................................... 68
6.1 Partial Least Squares ..................................................................................... 68
6.1.1 Results of hypotheses testing using PLS analyses ........................... 71
6.2 Final Results ................................................................................................... 72
6.2.1 Supported Hypotheses....................................................................... 73
6.2.2 Non-supported hypotheses ................................................................ 74
6.2.3 Considerations ................................................................................... 75
6.3 Limitations and Future Research.................................................................... 76
6.3.1 Limitations .......................................................................................... 76
6.3.2 Future Research................................................................................. 77
7. Conclusions ............................................................................................................ 78
7.1 Theoratical Implications.................................................................................. 79
7.2 IT Channel Business Implications .................................................................. 80
References........................................................................................................................... 81
APPENDIX I, Preliminary Questionnaire...................................................................... 86
APPENDIX II, Final Questionnaire ................................................................................. 91
APPENDIX III, EFA ............................................................................................................. 98

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1. Introduction
Big Information Technology (IT) vendors as Microsoft, IBM, Oracle and HP sell
and deliver their products and services directly to corporations (large enterprises).
Small and Medium and Enterprises (SME) are supplied via the IT Distribution
Channel, i.e. indirectly via distribution organizations and value-added resellers,
see figure 1.
Figure 1, The SME IT Distribution Channel
With Cloud computing, a new computing and IT delivery paradigm, the traditional
IT Distribution channel is under pressure as IT vendors, now called Cloud
Services Providers (CSPs), tend to deliver Cloud computing services directly to
corporations and SMEs.
Cloud computing is an evolution of decades of research and development in
virtualization, distributed computing, utility computing, networking, internet
technology and software. Cloud computing offers a service oriented architecture,
reducing information technology overhead and increasing flexibility with reduced
cost of ownership and on-demand availability of functionality. Cloud computing is
often delivered as a Pay and Use service. Typical Cloud Service Providers
offering Cloud computing to businesses are, among others, Amazon, Google,
Microsoft Azure IBM SoftLayer and Salesforce.
Cloud computing has a drastic impact on how enterprises procure and deliver IT
services to end-users. IT Vendors and/or Cloud Service Providers sell directly to
corporations. These large enterprises have capabilities to adopt Cloud computing
with more ease than SMEs. According to Arend and Nebuloni (2014) 56% of
European SME businesses lack qualified staff. SME IT departments need to make
significant improvements before they fully can adopt cloud architectures. The
ability and hence likelihood for SMEs to successfully adopt Cloud is influenced by
the strategic design of the IT Distribution Channel.

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For SME’s the amount of Cloud computing offering is overwhelming. To be
successful SME’s have to combine Cloud offerings from several vendors in a
unionization of software, infrastructure and platform services from disparate
networks that can be accessed via the internet (Federated Cloud). The federation
of cloud resources is facilitated through network gateways that connect public or
external clouds, private or internal clouds (owned by a single entity) and/or
community clouds (owned by several cooperating entities); creating a hybrid
Cloud computing environment (Apprenda Cloud definitions, 2013). Federated
Clouds are being explored by SME’s. Cloud Federation introduces loss of (cost)
control, advanced payment models and SLA enforcement for SMEs.
Enterprise spending on Cloud Services has grown 25% in Europe over the past
12 months (Arend & Nebuloni, 2014). According to Arend and Nebuloni (2014)
61% of European Enterprises are struggling to educate employees to effectively
evaluate, negotiate contracts with, and manage relationships with cloud service
providers. Cloud computing, as a business-critical technology, is rapidly changing
the way SME’s evaluate, procure, and deploy IT assets. SME transition to Cloud
computing demands organizational change (People, Process, and Technology)
according to Arend and Nebuloni (2014).
1.1 Business Problem
The IT Distribution channel is under pressure, shortening. Cloud computing is
cannibalizing traditional Distribution organization’s business as ever more Cloud
computing is sold and delivered directly to SMEs. In a response the big 4 global IT
Distribution Organizations (Arrow Electronics, Avnet, Ingram Micro and Tech
Data) are in the process of building (aggregating) and offering Multi-Vendor Cloud
computing portfolio’s through the IT Channel to SMEs hence becoming a Cloud
Services Broker. For the purpose of this thesis a Cloud Services Broker is defined
as an organization that consults, mediates and facilitates the selection of Cloud
computing solutions on behalf of multiple vendors (Open Data Center Alliance,
2014). In the role of a Cloud Service Broker, the IT Distribution organization,
serves as an intermediate party between a Cloud Service Provider (Vendor) and
organizations reselling (Reseller) the provider's products and solutions to SME’s.
Attempts by Distribution to do so are not successful so far (O’Callaghan, 2012).
The urgent distributor’s business problem is the rapid decreasing impact of the
traditional IT Distribution Value Chain on SME Cloud computing and its adoption
resulting in diminishing turnover and vaporizing margins.

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1.2 Research Objective
The shorter IT Distribution Channel for Cloud Computing offers both a challenge
and an opportunity for Vendor, Distribution and Reseller organizations to
research, redefine and deliver capabilities targeted at SME’s to accelerate Cloud
adoption and grow or defend the market.
Cloud computing literature analysis revealed that technology-focused articles
outnumbered business focused ones (Yang & Tate, 2012). According to Yang and
Tate (2012) adoption of Cloud computing is a concern in the research community
and there is demand for research explaining Cloud computing with respect to
business issues as adoption in business-friendly language. This academic gap is
addressed in this MBA thesis research.
The aim of this study is to research SME Cloud computing adoption determinants
delivering elements (attributes) for Strategic Channel Design. The conceptual
research model used incorporates the IT Distribution Channel model in a
combination of the Technology, Organization and Environment framework
(Tornatzky & Fleischer, 1990) and the Diffusion Of Innovation theory (Rogers,
2003, 2010). A quantitative research among Dutch SME decision makers has
revealed SME adoption attributes for strategic IT channel redesign.
1.3 Thesis Approach and Sections
This thesis will deliver a detailed description of the research approach and its
findings. The academic literature review, section 2, describes the state of Cloud
computing in relation to SME adoption from a IT Channel perspective. Section 3
delivers the research aim, research question, conceptual research model (with the
new value chain attributes with measureable impact on SME Cloud adoption
through the IT distribution channel) as well as the hypothesis. The following
section deliver the methodology, data analyses, hypotheses testing (results) and
finally a conclusions section.

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2. Literature Review
This section of the paper describes the state of Cloud computing in relation to
SME adoption (diffusion of innovation) from the perspective of strategic IT
Distribution Channel Design. For the theoretical framework a search of
Information Systems (IS) database literature with keywords: innovation, adoption,
channel strategy and SME combined with the phrase “Cloud computing” has been
conducted. The search was aimed at peer-reviewed academic (scholarly) journal
publications. The literature reviews aim is to: (1) Establish a researchable SME
Cloud computing Adoption Model suitable for the IT Distribution Channel and (2)
Find and describe the attributes which affect the SME Cloud computing Adoption
Model success in the IT Distribution Channel
2.1 Subject Matter
The subject matter of this literature study review, in fact the thesis research, are
organizations which are too small (revenue) to be delivered by big IT Vendors
directly and hence are left to the distribution channel. The target organizations,
however, generate approximately 50% of the revenue of the big IT Vendors and
close to 100% of that of the Enterprise IT Distribution Channel (Resellers and
Distributors), they are therefore considered relevant as a subject matter.
Two definition approaches exist today to describe these types of organizations.
The European Community (EC) came up with a category for micro, small and
medium-sized enterprises (SMEs). According to the EC definition (Extract of
Article 2 of the Annex of Recommendation 2003/361/EC) an SME employ fewer
than 250 people, have an annual turnover equal or less than 50 million euro,
and/or an annual balance sheet total smaller than 43 million euro. In the USA this
category of companies is defined as a Small and Midsize Business (SMB). For IT
research purposes Gartner (2012) defines SMBs by employees and annual
revenue. Small businesses are defined as organizations with fewer than 100
employees and midsize enterprises are those organizations with 100 to 999
employees. Small Business is defined as organizations with less than $50 million
in annual revenue and Midsize Enterprise is defined as organizations that make
more than $50 million but less than $1 billion in annual revenue (Gartner, IT
Glossary, 2012).

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Small and medium size organizations play a pivotal role in the European
economy. According to the European Commission SMEs provide a major source
of entrepreneurial skills, innovation and employment (European Commission,
2003). In the European Union of 25 member countries some 23 million SMEs
provide around 75 million jobs and represent 99% of all enterprises.
The literature review will continue with the EC SME definition as the subject
matter (research target group) for establishing a qualitative researchable Cloud
computing Adoption Model. According to Tan, Chong, Lin and Eze (2009)
appropriate IT can help SMEs to become more efficient. SMEs have less
tolerance in bearing cost and risk of adopting new innovations (Malecki, 1977).
High costs and risks involved in IT projects prevent SMEs to invest in or adopt
new technology (Tehrani, 2013). Cloud computing is an innovation which help
SMEs to overcome issues as cost and risk.
2.2 Channels of Distribution
Cloud computing is a new phenomenon in Enterprise IT distribution.
Technological advances as Cloud computing, demanding businesses rather than
IT departments, global competition and shifts in the workforce (work-life-balance
and population demographics) are affecting distribution channels (Wade &
Hulland, 2004). These changes demand a strategic perspective on the (re)design
of IT distribution and forces vendors, distributors and resellers to reconsider
fundamental assumptions about how they reach their markets (SME’s) through
the Enterprise IT Channel. Change in distribution is slow due to persistent inertia.
Anderson, Day and Rangan (1997) see a growing mismatch in vendors overall
strategy and its means of distribution, incremental approaches cannot cope due to
overwhelming pressure to change. Cloud computing urges IT Vendors to
(re)assess their channel design and to consider alternatives. Enterprise IT
Distribution organizations should implement creative combinations (federated
cloud) of value-adding instruments that increases the potential competitive
advantages in and for the channel.
In traditional distribution channels IT Vendors transfer responsibility from one
layer to the next. Vendors gain access to enterprise customers directly (option 1)
or via a one-step intermediates like wholesale (option 2) or through distribution
(option 3), see figure 2 on the next page. With Cloud computing the role of the
distributor is under pressure and new forms of direct channels are emerging.

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However the role of distribution as an intermediary in retail (option 3 and 3a) is
also threatened in certain markets. Nevertheless this research has concentrated
on the role of distribution in option 3 and 3b (Vendor-distribution-VAR-SME).
Figure 2, Traditional Channel Options
With Cloud computing IT distribution channels compress as vendors have chosen
to go directly to large enterprises as well as to SME’s (Global Technology
Distribution Council, 2013). The role of distribution is therefore challenged and
channels drift out of alignment, leading to conflict, reevaluation and rapid change.
According to Eisenhart and Zbaracki (1992) there is no time to determine whether
an initial channel design is effective, they refer to such an environment as “high
velocity”.
How should IT distribution manage the SME channel in a high velocity
environment as Cloud computing? Research in strategic decision making
indicates that the traditional exhaustive and inclusive planning model does not
work, effective firms sacrifice thorough planning for experimental action (Anderson
et al., 1997). For Cloud computing distribution to SMEs this implies experimenting
with vendors, value added resellers and SME’s in different ways to reach the
market. Thus, According to Anderson et al. (1997), building the channel
incrementally to be able to alter the channel configuration when it becomes
evident that wrong choices were made or as buyer behavior becomes predictable
and/or Cloud computing services assume accepted configurations.

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For Cloud computing the Enterprise IT distribution channel has to be reassessed
and redesigned. The objective of designing the channel strategy is to enhance
effective delivery of the customer value proposition. According to Shuanghong
(2009), the channel, in the support role, must meet the requirements of:
Effectiveness (address end-users requirements), Coverage (address the value
of a vendor’s offering), Cost-efficiency (gain greater strategic effectiveness due
to the multiplier effect that distribution has on increasing the impact on marketing
variables) and Adaptability (incorporate possible new services and emergent
channel innovation).
According to Anderson et al. (1997) Strategic Channel Design must adhere to the
following principles: (1) Align channel with competitive strategy (design the Cloud
computing SME channel from the market back anticipating target market
requirements, building competitive barriers and establishing a superior value
chain and operating model), (2) Compose channel functions into integrated
collections of functions (creatively combining functions to reduce cost, improve
responsiveness and disperse over several players in the channel), (3) Invest in
learning (firms in high-velocity environments should create a portfolio of options
and experiment to explore channel design by trial and error) and (4) Translate
strategic choices into programs and controls for monitoring channel performance
(manage learning, set performance standards, correct mistakes and control cycle
in the design process);
Now that the strategic characteristics of the distribution channel(s) with respect to
design and options are set the next paragraph will return to the definition of Cloud
computing, the impact on the channel and the role of the SME in it.
2.3 Cloud computing
Cloud computing is a concept where applications and files are hosted on a “cloud”
consisting of thousands of computers and servers, all linked together and
accessible via the Internet delivering functionality to enterprises and/or consumers
(Buyya, Yeo, Venugopal, Broberg & Brandic, 2009). Cloud computing does not
have a clear and definite definition in academic literature. For simplicity purposes
it can be put as: “cloud is a pool of virtualized computer resources”. This research
proposal uses the 2011 NIST definition of Cloud computing (Mell & Grance,
2011).

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Cloud computing is a next generation platform that provides dynamic resource
pools, virtualization, scalability and high availability (Somani, Lakhani & Mundra,
2010). The term Cloud computing describes both a platform and a type of
application use. A Cloud computing platform dynamically provisions, configures,
reconfigures, and de-provisions of servers as needed (Dasmalakar, 2014).
Cloud applications are extended to be accessible through the Internet (Joshi &
Rana, 2011). Cloud computing uses large data centers and powerful servers to
host and deliver Cloud Services, accelerating and driving the adoption
of innovations as it comprises an automated framework that can deliver
standardized services quickly and cheaply (Gudadhe, Desmukh & Dhande, 2013).
According to Gudaghe et al. (2013) Cloud computing infrastructure allows
enterprises to achieve more efficient use of their IT hardware and software
investments increasing profitability by improving resource utilization, cost is driven
down through pooling resources into large clouds and delivering resources only
as those resources are needed.
Cloud computing is 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 (Mell & Grance, 2011). Cloud computing is perceived as the fifth utility
to join electricity, gas, water and telephony (Buya et al., 2009).
For SMEs Cloud computing promises to deliver tangible business benefits, often
at much lower cost as they only pay for the resources needed, offering good
return on investment of their limited resources. In turn they can focus on what truly
delivers value to their customers and results in a competitive advantage
(Alshamaila et al., 2013).

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According to Mell and Grance (2011) the NIST cloud model is composed of five
essential characteristics, three service models, and four deployment models, see
figure 3.
Figure 3, Cloud computing model according to NIST (2011)
Five essential characteristics of the NIST Cloud computing model are: (1) On-
demand self-service. An SME should unilaterally be able to provision computing
capabilities as needed automatically without requiring human interaction with each
service provider. (2) Broad network access. Capabilities are available over the
network and accessed through standard mechanisms that ensure use by
heterogeneous client platforms. (3) Resource pooling. Computing resources are
pooled to serve multiple consumers in a multi-tenant model, with different physical
and virtual resources dynamically assigned and reassigned according to
consumer demand. The customer has in general no control over the location of
the resources provided (location independence) but probably is able to specify
location at a higher level of abstraction (e.g., country, region, or datacenter). (4)
Rapid elasticity. Capabilities can be elastically provisioned and released to scale
rapidly outward and inward commensurate with demand. To the SME for provision
available Cloud computing capabilities appear to be limitless and can be
appropriated in any quantity at any time. (5) Measured service. Cloud computing
platforms automatically control and optimize resources leveraging a metering
capability at some level of abstraction appropriate to the type of service. Resource
usage is monitored, controlled, and reported, providing transparency for both the
provider and SME of the utilized service.

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According to Hassan and Hub (2013) Cloud computing is deployed in the
following four deployment categories: (1) Private clouds are built for the
exclusive use of one client, providing the utmost control over data, security, and
quality of service. The company owns the infrastructure and has control over how
applications are deployed on it. Private clouds may be deployed in an enterprise
datacenter, and they also may be deployed at a colocation facility. Private clouds
can be built and managed by a company’s own IT organization or by a cloud
provider. This model gives companies a high level of control over the use of cloud
resources while bringing in the expertise needed to establish and operate the
environment. (2) Community cloud. The cloud infrastructure is provisioned for
exclusive use by a specific community of consumers from organizations that have
shared concerns (e.g., mission, security requirements, policy, and compliance
considerations). It may be owned, managed, and operated by one or more of the
organizations in the community, a third party, or some combination of them, and it
may exist on or off premises. (3) Public clouds are run by third parties, and
applications from different customers are likely to be mixed together on the
cloud’s servers, storage systems, and networks. Public clouds are most often
hosted away from customer premises, and they provide a way to reduce customer
risk and cost by providing a flexible, even temporary extension to enterprise
infrastructure. If a public cloud is implemented with performance, security, and
data locality in mind, the existence of other applications running in the cloud
should be transparent to both cloud architects and end-users. Indeed, one of the
benefits of public clouds is that they can be much larger than a company’s private
cloud might be, offering the ability to scale up and down on demand, and shifting
infrastructure risks from the enterprise to the cloud provider, if even just
temporarily. (4) Hybrid clouds combine both public and private cloud models.
They can help to provide on-demand, externally provisioned scale. The ability to
augment a private cloud with the resources of a public cloud can be used to
maintain service levels in the face of rapid workload fluctuations. Hybrid clouds
introduce the complexity of determining how to distribute applications across both
a public and private cloud. Among the issues that need to be considered is the
relationship between data and processing resources. If the data is small, or the
application is stateless, a hybrid cloud can be much more successful than if large
amounts of data must be transferred into a public cloud for a small amount of
processing.

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Cloud Service Providers deliver services segmented into three service models: (1)
SaaS is software that is developed and hosted by the SaaS vendor and which the
SME or customer accesses over the Internet. Unlike traditional applications that
users install on their computers or servers, SaaS software is owned by the vendor
and runs on computers in the vendor’s data center (or a colocation facility). A
single instance of the software runs on the cloud and services multiple end users
or client organizations. In general, all customers of a SaaS vendor use the same
software: these are one-size-fits-all solutions. (2) PaaS provides virtualized
servers on which customers can run applications without having to worry about
maintaining the operating systems, server hardware, load balancing or computing
capacity. A PaaS environment provides compute power by providing a runtime
environment for application code. Therefore the unit of deployment is a package
that contains application code or some compiled version of the application code.
Another capability of PaaS environments is that scale can be specified via
configuration and provided automatically by the environment. (3) IaaS offers
compute power, storage, and networking infrastructure as a service via the public
internet. An IaaS customer is a software owner that is in need of a hosting
environment to run its software. IaaS vendors use virtualization technologies to
provide compute power, the unit of deployment is a virtual machine.

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2.3.1 Federate Cloud and Cloud Services Brokerage
Federated Cloud refers to the unionization of software, infrastructure and
platform services from disparate networks that can be accessed by a client via the
internet (Apprenda Cloud definitions, 2014). Federation of cloud computing is
facilitated by connecting public clouds and private clouds creating a hybrid Cloud
computing environment. Federation of Cloud computing allows SMEs to optimize
IT services delivery by choosing the best cloud services provider (flexibility, cost &
availability of services) to meet particular business or technological needs.
Federation enables SMEs to run applications on the most appropriate Cloud
computing environment allowing SMEs to distribute compute workloads around
the globe, move data between disparate networks and implement innovative
security models for user access to cloud resources. An SME obstacle in adoption
of federated Cloud computing is the difficulty in brokering connectivity between
the SME and Cloud providers. Cloud Services Brokerage (CSB) plays an
intermediary role in cloud computing (Gartner, 2012). CSBs make it easier for
SMEs to consume and maintain cloud services, particularly spanning multiple
providers. According to Lheureux and Plummer (2012) to facilitate cloud
consumption, IT Distribution organizations and VARs will need to adopt, accept
the need and existence of, the CSB role. This presents opportunities and
challenges for SMEs and Vendors.
2.3.2 Cloud Channel Business Models
Big Vendors and/or Cloud Service Providers have positioned three business
models to sell to, through and with the IT Distribution Channel: (1) Cloud Builder:
a model where the reseller can utilize the vendor technology to build the cloud in a
private, hybrid or public offering.(2) Cloud Provider: a model where the reseller
can actually build the cloud on their premise, then offer the vendor’ solutions
(SaaS, IaaS and PaaS). (3) Cloud Reseller: a model for the traditional resale
agent to offer the annuity oriented cloud solutions on a stock keeping unit (sku)
basis.
IT Distribution organizations have adopted the above Cloud Channel Business
models. Distributors have supported the Cloud Builder model as it is similar to the
role of the distributor’s transactional support of hardware and software sku sale.
Resellers are supporting private cloud deployments as it represents an extension
of their current customer relationship and role of technology reselling. Value Add
Resellers are, however, evaluating and adopting practices to be able to support
other cloud business models.

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Building Cloud computing provisioning services the reseller, traditionally, focus on
the risk versus reward challenges as investments are significant to build the
infrastructure and maintain the environment for their customer, the SME.
Reselling cloud offerings needs vetting to ensure the reseller’s trusted
relationships stay intact or increase in value. Resellers see opportunity for the
distributor to both vet vendor cloud offerings and build scale with hosting models
(O’Challaghan, 2012).
The role of IT Distribution Organizations in Cloud computing will not fundamentally
change due to cloud implementations. Vendors will continue to focus on product
innovation and growth expansion into additional product and service areas
(O’Challaghan, 2012). According to the Global Technology Distribution Council
(2012), the distributor’s role will potentially be expanded with the addition of
services to manage a virtual warehouse, electronic software delivery, cloud-
aggregated billing and other financial and technical support “white label” services
for vendors and resellers. The distribution Cloud computing value chain (old and
new) is depicted in figure 4 below.
Figure 4, Distribution Cloud computing Aggregation Value Chain Attributes

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The roles in the Cloud computing value chain are: (1)Vendors’ core competency
will remain product research and development. Vendor monetizes the Cloud
computing service’s sale through transaction gross margin and limited warranty
services. (2) Distributors provide warehousing, financing and go-to-market
capabilities to sell Cloud computing. Distributors monetize the sale through
transaction gross margin, manufacturer rebates, inventory turns and Return on
Working Capital. (3) Resellers represent the local extension of the manufacturer
relationship, providing high-touch value / customer management. Resellers
monetize the sale through transaction gross margin, vendor rebates and value-
added services. (4) SME’s are expect to order Cloud computing services
accompanied with a single SLA and develop IT departments skills and expertise
to source, manage and deliver business project rather than IT platforms.
2.3.3 Cloud Computing impact on Channel Structure
According to Diamdi, Dubey, Pleasance and Vora (2011), traditional leading IT
vendors like IBM, HP, Cisco, Microsoft and Oracle are working towards providing
a variety of Cloud computing platforms and application services. Complementing
the Big Vendor Cloud computing services are a growing group of new companies
aggressively developing solutions on these Cloud computing services (Zhou,
Zhang, Zeng & Qian, 2010).
The traditional IT distribution channel is the vendor’s eco-system that puts
products and solutions in the hands of non-corporate customers (SMEs). The
SME market, the vast majority of big vendor opportunities, can only be addressed
through the IT distribution channel as selling direct is not economical. As big IT
vendors are dependent on the IT channel to maintain their position in the SME
market, it makes sense that these companies are trying to find ways to engage
and involve the channel in their cloud computing efforts. Cloud Computing has
tremendous impact on the existing IT distribution channel structure. The
distribution industry has for decades survived on a two-tier system consisting of
master distributors and a large reseller base. In the SME market this value added
reseller base is large. Distributors aggregate products and handle the logistics of
distribution and promotion to resellers. The resellers handle the final sale to SMEs
and provide installation, integration, customization and maintenance services.

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SME targeted Cloud computing drive change in the IT distribution channel
structure. According to a Techaisle Channel Perspective whitepaper (2014) three
scenarios, depicted in figure 5, can be distinguished depending upon the number
and complexity of cloud services SME demands: (1) Single Cloud; Customer is in
need of a single Cloud computing platform comprising of a few Cloud computing
services. For SMEs this is a viable scenario. Integration and customization needs
are minimal at best. (2) Single Cloud with integration; Customer is in need of a
few services but integration with existing systems and customization is essential.
SME does not have the necessary capabilities. Resellers of cloud services can
capitalize on such SME opportunities. (3) Multiple Clouds on subscription;
Customer is in need of multiple cloud subscription services without the
administrative burden (inhibitor of adoption) of managing multiple vendors.
Integration and customization needs are not significant. This emerging scenario
fits SMEs as SMEs grow to depend on Cloud computing services.
Scenario three is potentially the most disruptive for the channel. It suggests the
need for a Cloud Services Broker (CSB) that has the capability to aggregate,
integrate and syndicate cloud services to resellers and SMEs.
Figure 5, Three IT channel Cloud computing scenarios (Techaisle, 2014)
The CSB is an independent entity whose primary role is to integrate and deliver
Cloud computing services that best serves the SME customer base. A CSB is well
positioned to replace the IT distributor in the traditional IT value chain. The CSB
as an emerging entity challenges the current channel structure and impacts the
channel’s competencies at its roots.
Cloud has a deep impact on the SME distribution channel, the next paragraph
addresses the SME diffusion of Cloud computing adoption and introduces
individual characteristics (decision maker) and firm level adoption models.

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2.4 Diffusion of Innovation and Adoption
Adoption of Cloud computing is growing rapidly (McCue, 2014). Big solutions
vendors, among others: IBM, Microsoft, HP and Cisco, are offering Cloud
Services to the corporate market directly and have neglected the SME or
commercial market.
Arend and Nebuloni (2014) find that 56% of European businesses lack qualified
staff to effectively support cloud projects (Ahorlu, 2014). IT departments still need
to make significant improvements before they fully embrace cloud architectures.
European SME’s are struggling to upskill their employees to effectively evaluate,
negotiate contracts with, and manage relationships with cloud service providers
(Arend & Nebuloni, 2014). Cloud computing is becoming a business-critical
technology and changing how SME’s evaluate, procure, and deploy IT assets.
The transition to Cloud computing requires change throughout SME’s - in people,
process, and technology. 70% of IT staff need to learn how to make effective use
of automation, self-service, and orchestration tools (Arend & Nebuloni, 2014).
According to Ahorlu (2014), the push from Cloud Service Providers might start
running out of steam if IT buyers and line-of-business owners are not assessed in
their cloud maturity level and then helped to systematically tackle hurdles to
adoption.
To find a solution for the “Cloud computing SME adoption” business problem it is
vital to understand the attributes of IT adoption and study frequently used
theoretical models and frameworks. Many models of adoption and theories have
been used in IT research (Wade & Hulland, 2004). This thesis research was
interested in theories and models with respect to technology adoption. The most
popular are the technology acceptance model (TAM) (Davis, 1986), theory of
planned behavior (TPB) (Ajzen, 2011), unified theory of acceptance and use of
technology (UTAUT) (Venkatesh, Morris, Davis & Davis, 2003), diffusion of
innovation (Rogers, 2010), and the TOE framework (Tornatzky & Fleischer, 1990).
For the purpose of SME Cloud computing adoption (firm-level) this research
considered only the Diffusion of Innovation and Technology, Organization and
Environment frameworks.

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2.4.1 Diffusion of Innovation
Diffusion of Innovation (DOI) is a theory that determines at what rate new ideas
and technology spread through cultures operating at the individual and firm level
(Oliveira & Martins, 2011). The DOI theory sees innovations as being
communicated through certain channels over time in a particular social system
(Rogers, 2010). Individuals possess different degrees of willingness to adopt
innovation. This is approximately normally distributed over time. According to
Rogers (2010), segmenting this normal distribution leads to the segregation of
individuals into the following five categories of individual innovativeness (from
earliest to latest adopters): innovators, early adopters, early majority, late majority,
laggards.
The IT innovation process on firm level and SME in particular is much more
complex. At the firm level diffusion of innovations are related to variables as
individual characteristics (leader), internal organizational structural characteristics
and external characteristics of the organization: Individual characteristics describe
the leader attitude toward change, Internal characteristics of organizational
structure and External characteristics of organizational refers to system openness.
Figure 6, Diffusion of Innovation theory
The DOI theory (Rogers, 2010), depicted in figure 6, has been adopted and
applied since the early application of IT research. It has been used in studies of e-
business, enterprise resource planning, web-sites, intranet and material
requirements planning. No studies have been found using DOI on Cloud
computing on firm level.

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2.4.2 TOE Framework
The Technology, Organization and Environment (TOE) framework was developed
by DiP’ietro, Wiarda and Fleisher (1990).
Figure 7, TOE Framework
The TOE framework is comparable with Rogers’ Diffusion of Innovation theory
(emphasizing individual characteristics, and both the internal and external
characteristics of the organization as driver for organizational innovativeness).
The TOE framework adds a new and crucial component, the environmental
context. Technical innovation is constrained by the environmental context but also
offers opportunities. The TOE framework is better suited to explain intra-firm
innovation, the subject of the research paper, than Rogers’ innovation diffusion
theory.
The TOE framework is a firm-level theory. It represents one segment of the
innovation process, i.e. how the firm context influences the adoption and
implementation of innovations (Baker, 2011). Based on this framework, the
technology innovation adoption process is influenced by three aspects of an
enterprise’s context: (1)Technological context, representing the internal and
external technologies related to the organization; both technologies that are
already in use at the firm, as well as those that are available in the marketplace
but not currently in use. These technologies may include either equipment,
practice or Cloud computing services, (2) Organizational context, relating to the
resources and characteristics of the firm and (3) Environmental context, referring
to the arena in which a firm conducts its business; it can be related to surrounding
elements such as industry, competitors and the presence of cloud service
providers. These three contexts present both constraints and opportunities for
technological Innovation (Tornatzky & Fleischer, 1990).

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These elements, constructs, influence the firm’s level of technological innovation
as depicted in figure 8 below.
Figure 8, TOE Framework
The three TOE constructs with its attributes will now be described with reference
to previous research by Alshamaila, Papagiannidis and Li (2012) in Cloud
computing adoption by SME’s in the north east of England.
Technological context and its attributes in the adoption of IT innovation: (1)
Relative advantage, the central indicator of new IT innovation and refers to “the
degree to which an innovation is perceived as being better than the idea it
supersedes” (Rogers, Diffusion of Innovation, p. 229, 2003). (2) Uncertainty: the
extent to which the results of using an innovation are insecure (Ostlund, 1974;
Fuchs, 2005). (3) Compatibility: “the degree to which an innovation is perceived
as consistent with the existing values, past experiences, and needs of potential
adopters” (Rogers, Diffusion of Innovation, p. 240, 2003). (4) Complexity: “the
degree to which an innovation is perceived as relatively difficult to understand and
use” (Rogers, Diffusion of Innovation, p. 257, 2003). (5) Trialability: “The degree to
which an innovation may be experimented with on a limited basis” (Rogers,
Diffusion of Innovation, p. 258, 2003).

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Organizational context: (1) Size: The size of the company. (2) Top management
support: devoting time to the (IT) programmes in proportion to its cost and
potential, reviewing plans, following up on results and facilitating the management
problems involved with integrating ICT with the management process of the
business (Young & Jordan, 2008). (3) Innovativeness: the extent to which a client
adopts innovations earlier than other members of the same social context (Rogers
& Shoemaker, 1971). (4) Prior technology experience: the extent of a user’s
experience with previous similar technologies (Heide & Weiss, 1995; Lippert &
Forman, 2005).
Environmental context: (1) Competitive pressure Refers to: the degree of
pressure felt by the firm from competitors within the industry (Oliveira & Martins,
2010). (2) Industry Refers to: the sector to which the business belonged (Yap,
1990; Goode & Stevens, 2000). (3) Market scope refers to: the horizontal extent
of a company’s operations (Zhu et al., 2003). (4) Supplier computing support: the
supplier activities that can significantly influence the probability that an innovation
will be adopted (Frambach et al., 2002).
The TOE model has attracted criticism due to the context attributes that already
have been empirically tested on other IT innovations, i.e. a limited set of attributes
to choose from. This has also been applicable to earlier research from Alshamaila
et al. (2012). A sub-question of the research on Cloud computing SME Adoption
Determinants is to find a better suited set of, to be tested, attributes more
applicable for SME Cloud adoption in the IT distribution channel.
2.5 Conclusions for Research Purpose
Academic literature study on the influence of the IT Distribution Channel on Cloud
computing adoption at SMEs suggests that: (1) in a high velocity environment, as
Cloud computing, the Channel Strategy and Design demands experimental
action, i.e. building the new and shorter channel incrementally to be able to alter
the channel when it becomes evident that wrong choices were made. (2) the
Cloud Channel is in need of a new business model, an Cloud Services Broker
entity whose primary role is to integrate and deliver Cloud computing services that
best serves the SME customer base. (3) that Federation of Cloud allow SMEs to
optimize IT services by choosing the best (multiple) Cloud Services Providers to
meet business needs. (4) that the human factor is driving diffusion of innovation at
SMEs. (5) and the TOE framework is applicable for research purposes but context
attributes are limited to choose from. In the next section the above conclusions
are taken as input for the research question and the design the conceptual model.

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3. Purpose
Cloud computing potentially represents a paradigm shift in IT delivery models.
Traditional IT demand and supply management issues are in need of academic
re-examination and exploration (Yang & Tate, 2012). For SMEs, Cloud computing
promises to deliver business benefits (lower cost for pay per use IT resources)
offering good return on investment on limited resources and in turn SMEs can
focus on delivering customers value resulting in a competitive advantage
(Alshamaila et al., 2013). Big IT vendors and/or Cloud Service Providers deliver
Cloud computing directly to corporations and large enterprises. SMEs are left to IT
Distribution organizations that have experimented with Cloud computing offerings
through the IT channel with little success. Therefore it was high time to research
how success can be influenced positively in determining the factors that will
enhance Strategic IT Channel design. This thesis research shall contribute in the
decision making process for SME’s for adopting Cloud Computing services
through the IT Distribution Channel. This purpose section of the thesis will
introduce SME adoption Obstacles and Business Values (drivers) which have an
influence on the IT Channel. From the SME Cloud computing adoption
Obstacles and Business Values the Research Question, Conceptual Model and
Hypotheses are derived.
3.1 Adoption Obstacles and Business Values
Cloud computing has attracted interest from SMEs. However, apart from business
advantages, SMEs face adoption challenges. This paragraph analyses the SME
Cloud computing business values and adoption obstacles resulting in an overview
of factors that might be influenced by the IT Distribution Channel.
3.1.1 SME adoption obstacles
SMEs planning to adopt Cloud computing face obstacles such as specifying
service requirements; how to deal with unexpected outages; cost implications of
failure and uncertainty about cloud providers’ ability to meet service level
agreements (SLA). Benson, Akella, Shaikh and Sahu (2011) analysed the
obstacles Enterprises face in adopting Cloud computing and categorized them
into five technical categories: (1) Application, (2) Virtual Infrastructure, (3)
Image Management, (4) Performance and (5) Connectivity.

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The above obstacles hinder the adoption of Cloud computing. Zardari, Bahsoon
and Ekart (2012) believe that for SMEs “evaluating pre adoption choices at early
stages is a cost-effective strategy to mitigate risks for probable losses due to
wrong or uninformed selection decisions”.
According to Zardari et al. (2012) SMEs should identify obstacles and their
importance through understanding the consequences on the Cloud computing
adoption process, the obstacle prioritization analyses. From the obstacle
prioritization analyses appropriate tactics should be derived to handle, manage,
and solve the problems. SMEs considering to adopt Cloud computing should
identify and understand the properties of the problems (obstacles) that they will
face in introducing Cloud computing. SME’s obstacles for Cloud computing are:
(1) Knowledge and Experience; IT departments struggle with supporting Cloud
projects, management skills and finding and recruiting qualified staff is an
obstacle for adoption (Arend & Nebuloni, 2014). According to Arend and Nebuloni
(2014) the transition to Cloud computing requires change throughout the
organization (people, process and technology). The Cloud, Changing the
Business Ecosystem report finds that lack of alignment of IT procurement and
line-of-businesses is the course of the inability to adopt Cloud Services to drive
business innovation and competitive advantage (Parakala & Udhas, 2010). (2)
Lack of Control; According to Sen (2013) enterprises may not have the kind of
control over data or performance of applications they need, or the ability to
auditor the change-processes and policies under which users must work, and
monitoring and maintenance tools are still immature. (3) Data Loss and Privacy;
72% of SME’s distrust cloud service providers to obey data protection laws and
regulations (Oltsik & Ponemon, 2014). Data Breach: The Cloud Multiplier Effect in
European Countries, according to Oltsik and Ponemon (2014), found widespread
mistrust with 84% of respondents doubting that their cloud service providers
would notify them if their intellectual property or confidential information was
breached. Data security and privacy breaches, as well as regulatory and legal
compliance are significant obstacles to overcome for SMEs to adopt Cloud
computing. Data loss is a real risk, SMEs risk losing data by being locked into
proprietary formats and may lose control of data because tools are inadequate
(Sen, 2013). (4) Standardization; It is almost impossible to tailor SLAs to the
speciﬁc needs of SMEs, standards are immature and things change very rapidly
in the cloud. According to Sen (2013) compensation for downtime may be
inadequate and SLAs are unlikely to cover concomitant damages.

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(5) Cost; Cloud computing project Business Case and ROI difficulties with respect
to impact, costs and benefits (i.e. demonstrating success of cloud projects) are
hindering adoption. Calculating cost savings is also not straightforward. SaaS
deployments are cheaper initially than in-house installations and future costs are
predictable after 3-5 years of monthly fees, however, SaaS may prove more
expensive over-all (Sen, 2013). (6) Interoperability; Cloud Service Providers use
different technologies and different standards. Storage infrastructure is different
among typical datacenters. Hence SME’s cannot migrate applications to the cloud
ad expect them to run, much work is involved in moving an application into the
cloud as is involved in moving it from an existing server to a new one (Parakala &
Udhas, 2010). (7) Latency and Performance Issues; Cloud computing latency
and performance issues might prevent SMEs to adopt Cloud computing. The
Cloud Service Providers, controlling the infrastructure, will share and reallocate
machines which may affect run times introducing unwanted latency and
performance problems (Catteddu & Hogben, 2009). Findings of the 2011 Cloud
Computing seminar: SaaS work best for non-strategic, non-mission-critical
processes that are simple and standard and not highly integrated with other
business systems. Customized applications may demand an in-house solution,
but SaaS makes sense for applications that have become commoditized.

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3.1.2 SME Business Values of Cloud computing
The Cloud computing model can mature to a dynamic compute infrastructure
offering advantages for start-ups, SMEs and corporates alike (Gartner, Arend &
Nebuloni, 2014).
Figure 9, Gartner Hype Cycle 2014.
Cloud computing has established best practices and represents SME Business
Values (Milak, 2009): (1) Elasticity; Cloud computing need to scale with workload
demands (performance, compliance & service levels). Elasticity refers to scaling
of Cloud computing, i.e. ready to scale up, but also scale down as workloads
diminish in order to not run up the cost of deploying in the cloud. (2) Pay-as-you-
go; The metered cost appeals to SMEs. Cloud vendors, supporting many
customers, can lower the per-unit cost to each customer. IT is remotely managed
and maintained, typically for a monthly fee. (3) Availability; SME’s expect Cloud
Computing to be up and running every minute of every day. Data is in the cloud
i.e. at some remote location so it can easily be access from any location at any
time. (4) Reliability; Cloud vendors promise availability without interruption of
server failures and data loss.

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(5) Lower Capital and Operational Expenditure; Capital expenditure is reduced
by shifting compute and storage to the Cloud Service Provider who is able to
provide service at a lower cost and due to the reduced cost of software by
subscription. IT at SMEs is reduced as the maintenance is performed by the
Cloud Service Provider. (6) Reduce run time; more compute may be allocated
dynamically to perform application jobs quicker. (7) Agility; SMEs use the cloud
to reduce the inherent risk in purchasing IT with respect to workload balancing.
Cloud Services Providers have often abundant infrastructure which can absorb
the growth and workload spikes of individual customers hence reducing the
ﬁnancial risk SMEs face and can respond to the sudden growth in market.

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3.1.3 IT Channel influence on obstacles and business values
Table 1 list the SME Cloud computing adoption Obstacles accompanied with a
mapping where in the IT Distribution Channel these obstacles best be addressed
according to the Distribution Cloud Computing Aggregation Value Chain Attributes
(figure 4).
SME Cloud Adoption
Obstacle
IT Distribution
Value Chain
Attribute to address to
eliminate adoption barrier
1 Capabilities: Lack of
Strategy, Planning &
Execution (Arend &
Nebuloni, 2014)
Reseller (H4);
Value-Added
Services
Resellers ability to add unique features
and functionality to the commodity Cloud
computing services
2 Knowledge & Experience
(Ahorlu, 2014)
Reseller (H4);
Value-Added
Services
computing services
3 Lack of Control (Sen, 2013) Distribution (H2);
Aggregated Multi-
Vendor Solutions
Distribution’s ability to an aggregate
multi-vendor Cloud Computing services
portfolio into a white-label portal.
4 Data Loss & Privacy (Sen,
2013)
Vendor (H1);
Security & Privacy
The degree to which the vendor has
addressed security and privacy issues
(hygiene)
5 Standardization (Sen, 2013) Distributor (H3);
Cloud Vetting
The degree to which distribution is able
to combine multiple vendor Cloud
computing services into tested SME
solutions with a single SLA.
6 Cost; Cloud computing
project Business Case and
ROI calculations (Sen, 2013)
Reseller (H4);
Value-Added
Services
computing services
7 Interoperability (Parakala &
Udhar, 2010)
Distribution (H2);
Aggregated Multi-
Vendor Solutions
8 Latency & Performance
(Catteddu & Hogben, 2009)
Distribution (H3);
Cloud Vetting
Distributors ability to aggregate Cloud
Computing into a white-label offering with
a single SLA
9 Single SLA (Sen, 2013) Distribution (H3);
Cloud Vetting
The degree to which distribution is able
to combine multiple vendor Cloud
computing services into tested SME
solutions with a single SLA.
10 Ability to combine Distribution (H2);
Aggregated Multi-
Vendor Solutions
Table 1, SME Cloud computing adoption obstacles

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Table 2 presents the overview of the SME perceived Business Values of Cloud
computing with a mapping where in the IT Distribution channel these business
values can be leveraged.
SME Business Value IT Distribution
Value Chain
How to leverage by Value
Chain
1 Elasticity (Milak, 2009) Vendor (H1);
Security & Privacy
addressed hygiene factors as …..,
security and privacy issues (hygiene)
2 Availability & Reliability (Milak,
2009)
Vendor (H1);
Security and Privacy
addressed security and privacy
issues (hygiene)
3 Agility (Milak, 2009) Distribution (H2);
Aggregated Multi-
Vendor Solutions
multi-vendor Cloud Computing
services portfolio into a white-label
portal.
Table 2, SME perceived business values of Cloud computing
3.2 Aim
The research objective has been to study the extent of the IT Distribution
Channel’s influence on SME Cloud computing adoption in the Netherlands. The
research examined which value chain attributes have an effect on Cloud
computing adoption decisions at the firm level and to what extent these attributes
can be influenced by the IT Distribution Channel. Ontologically the research
considered the SME as the unit of analysis. As the research was a quantitative
study of the channel’s influence on adoption attributes, the DOI theory and the
TOE framework seemed to be appropriate as an exploratory work (epistemology).
This enabled a deeper analyses of the SME Cloud computing adoption
determinants. The influence of the IT Distribution Channel on the adoption
determinants was studied by a quantitative approach.

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3.3 Research Question
Determining the factors (attributes) that influence SME Cloud computing adoption
should enable the IT distribution channel to predict the rate of adoption of Cloud
Computing. The research question of the study was:
To what extent is Intention to SME Adoption influenced by distribution value
chain attributes?
3.4 Conceptual Model
3.4.1 TOE Framework
The TOE framework (Tornatzky & Fleischer, 1990) identified three aspects of an
enterprise's context that influence the process by which it adopts and implements
IT innovation in a: technological, organizational and an environmental context.
Figure 10, TOE Framework with traditional attributes

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3.4.2 IT Distribution model
Rearranging the traditional IT channel options (3 & 3b), see figure 2, for SMEs
from vertical to horizontal delivers the SME IT Distribution Channel as depicted
below in figure 11.
Figure 11, SME IT Distribution Channel
3.4.3 Combined DOI, TOE and Distribution Model
Figure 12 depicts the attributes that in the combined research model influence the
Intention of SME Cloud Computing adoption. The attributes or variables are
categorized in four groups: technology factors, organizational factor environmental
attributes, and human factors. The constructs come from the Diffusion on
Innovation theory, Technology Organization Environment framework and the SME
IT distribution model as discussed in chapter two.
Figure 12, Conceptual SME Cloud Adoption Model

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Technology factors are attributes of the Vendor which influence the adoption
decision (Research & Development, Innovation, Security & Privacy and
Distribution Aggregation Leverage). As an extension of the SME, organization
factors are influenced by the Reseller’s attributes (Value-Added Services,
Customer Relationship and Channel Services Leverage). Environment factors are
those that are outside of the organization (SME) and are influenced by the SME IT
distribution Channel (Multi-Vendor Solutions, Cloud Vetting, External Support
and Aggregation). The human factors are Innovativeness of the decision
maker(s) and the SME employee’s Cloud computing knowledge or
experience.
To study the influence of the IT Distribution channel on the SME Cloud computing
adoption the conceptual or research model will consisted of six variables
(attributes) that might influence the decision to adopt. These six variables were:
(1) Security & Privacy, (2) Aggregated Multi-Vendor Solutions, (3) Cloud Vetting,
(4) Value-Added Services, (5) Innovativeness and (6) Prior Cloud Computing
Experience.
3.4.4 Hypotheses
In this paragraph of the research design the attributes of the combined TOE
framework perspectives (Technology, Environment & Organization) and the DOI
theory Individual Characteristics perspective (Human Factors) are presented in
the IT distribution context (vendor, distributor, value-added reseller & SME)
delivering the six constructs with their hypothesis.
Technology (Vendor)
Hygiene factors (availability, accessibility, reliability, security & privacy): In
the context of Cloud computing hygiene is defined as “the degree to which Cloud
computing is perceived as being more applicable than other computing
paradigms. Cloud Service Providers claim that they are able to deliver a more
applicable computing platform than SMEs deliver themselves. According to Oltsik
and Ponemon (2014) 72% of SMEs distrust Cloud Service providers to obey data
protection laws and regulations. Oltsik and Ponemon (2014) found 84% of SME
respondents distrust Cloud Service Providers with of intellectual property or
confidential information. Milak (2009) found that SMEs expect Cloud computing to
be up and running every minute of every day (availability), that information is
accessible from any location at any time and that Cloud computing is delivered
without interruption of server failures and data loss.

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It is hypothesized that tot addressing hygiene factors or managing perception by
vendors will introduce Cloud computing adoption obstacles for SMEs.
H1, Not addressing hygiene factors in Cloud computing services has a
negative relation with SME adoption of Cloud computing
Environment (Distribution)
Aggregated Multi-Vendor Solutions: SMEs are in need of multiple Cloud
computing subscription services without the administrative burden of managing
multiple vendors (Techaisle, 2014). This is an emerging scenario as SMEs grow
to depend on Cloud computing. This scenario is however disruptive for the
traditional IT channel as it suggests the need for a Cloud Services Broker with the
capability to aggregate and syndicate multiple-vendor services via VARs and
SMEs. The Cloud Services Broker’s role, as an independent entity, is to integrate
and deliver Cloud computing services via a white-label portal that best fit the
SMEs needs. It is hypothesized that the new role for distribution as a Cloud
Services Broker in Cloud computing will accelerate SME adoption.
H2, Aggregating Multi-Vendor Cloud Computing services into a white-labelled
platform by distribution is positively related SME adoption
Cloud Vetting: Benson et al. (2011) analyzed obstacles enterprises face in
adopting Cloud computing, among others latency, performance and
interoperability hinder adoption. Cloud Service Providers use different
technologies and standards. SMEs cannot migrate applications to the Cloud and
expect them to run. Interoperability is an issue that can be addressed by the
Cloud Services Broker, distribution’s new role, assisting SMEs in pre-testing
workload migrations. It is almost impossible to tailor SLAs to the specific needs of
SMEs, standards are immature and developments go rapidly in the cloud.
According to Sen (2013) compensation for downtime may be inadequate and
SLAs are unlikely to cover concomitant damages. According to O’Challagan
(2012) the distributor’s role in Cloud computing distribution will potentially be
expanded with the addition of services as Cloud Vetting. Cloud Vetting is defined
as “the guarantee the Cloud Services Broker provides a combined multi-vendor
Cloud computing service is trusted: tested, reliable, interoperable and delivered
with a single SLA.”.

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It is hypothesized that bundled Cloud computing services specific for the SME
market need to be vetted by distribution before successful deployment.
H3, The ability to deliver trusted combined and supported Cloud Computing
services with a single SLA is positively related to SME Adoption
Organization (Reseller)
Value-Added Services: According to Chau and Hui (2001) the size and structure
of SMEs limits their capabilities with respect to internal IT support. SMEs should
rely on external parties as Value-Added Resellers instead. In this context external
support is defined as “the perceived necessity of external support offered by
Value-Added Resellers”. According to the Techaisle Channel Perspective
whitepaper (2014) SMEs do not have the necessary capabilities to integrate
Cloud computing services with existing systems (customization) and that resellers
of cloud services can capitalize on such SME opportunities. It is hypothesized that
higher level of external support by VARs increases the chance of Cloud
computing adoption by SMEs.
H4, The VARs ability to add unique features and capabilities to the Cloud
Computing service positively affects the likelihood of SME adoption
Human Factor (SME)
Innovativeness: Thong and Yap (1995) define innovativeness as “the level of
decision makers’ preference to try solutions that have not been tried out and are
therefore risky”. Level of innovativeness range from the person’s ability to perform
tasks in a better way to the person’s ability to perform the task differently (Tehrani,
2013). It is therefore hypothesized that senior managers who prefer to perform
tasks differently are more innovative and hence usually adopt new technologies.
SME whose decision makers are innovative are more likely to adopt Cloud
computing.
H5: Senior management’s innovativeness is positively related to the adoption
of Cloud computing

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Prior Cloud computing experience: Indicated in Diffusion of Innovation theory,
having knowledge about a specific innovation is the first step in its adoption
process. Leadership knowledge has a positive impact on adoption of IT (Thong,
1999). An SME whose employees have more knowledge of innovation face less
resistance against adoption of new technologies (Tehrani, 2013). According to
Thong (1999) there is empirical evidence that shows a positive relationship
between employees’ IT knowledge and the decision to adopt IT. In the context of
Cloud computing the following hypotheses is adopted and modified from (Tehrani,
2013):
H6: Key employee’s knowledge (business and IT) of Cloud Computing is a
predictor for early SME adoption
3.5 Summary and Next Step
Table 3 provides a detailed summary and definition of each construct and the
hypothesized effect on diffusion of Cloud Computing and the SME Adoption
Decision.
Variable Definition of Variable Effect on
Decision
H1 V: Hygiene
factors
The degree to which the vendor has addressed
hygiene factors as availability, reliability,
accessibility, security and privacy issues
(dissatisfier)
Negative
H2 D: Aggregated
Multi-Vendor
Solutions
Distribution’s ability to an aggregate multi-vendor
Cloud Computing services portfolio into a white-
label portal.
Positive
H3 D: Cloud Vetting The degree to which distribution is able to
combine multiple vendor Cloud computing
services into tested SME solutions with a single
SLA.
Positive
H4 R: Value-Added
Services
Resellers ability to add unique features and
functionality to the commodity Cloud computing
services
Positive
H5 S: Innovativeness The level of decision makers’ preference to try
solutions that have not been tried out; and
therefore are risky
Positive
H6 S: Prior Cloud
Experience
Decision makers’ knowledge about Cloud
computing
Positive
S: Intention to
Adopt
SME Current status (adopted or not adopted
Cloud computing)
Dependant
Variable
Table 3, Construct definitions and the hypothesized effect on SME adoption

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Table 4, below, maps each applied construct to the original theories used in the
combined SME Cloud computing adoption research model.
Original Theory IT Channel Role: Variable Construct in original
theory
Diffusion of Innovation SME: Innovativeness Human factors / Change
AgentSME: Prior Cloud computing
experience
Technology Organization
Environment Framework
Vendor: Security & Privacy Technological Context
Distribution: Aggregated
Multi-Vendor Solutions
Environmental Context
Distribution: Cloud Vetting &
SLA
Value-Added Reseller: Value-
Added Services
Organizational Context
Table 4, Original theories and constructs
Not all constructs from the original theories have been tested in the SME Cloud
computing adoption research. Only the constructs and variables that are
applicable to the context of SME Cloud computing adoption were used in this
research.
In the next section of the thesis, Methodology, the conceptual model is put to the
test by means of an on-line survey.

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4. Methodology
The research on IT Channel Value Chain Attributes of SME Cloud computing
Adoption was a predictive study on the SME Intention to adopt. This research
tried to determine which IT Channel attributes influence SMEs’ Intention to Adopt
Cloud computing using a positive research paradigm.
To study SME Cloud computing Adoption the TOE framework as an extension of
Rogers’ Diffusion Of Innovation theory was applicable due to the firm level focus.
The TOE framework has received criticism in earlier IT adoption research due to
the limited list of attributes that have been tested empirically (Ramdani & Kawalek,
2007, 2008).
The research aim was to find SME Cloud computing adoption attributes in the IT
Distribution Channel applicable as building blocks in a Strategic Channel Design
effort. In an intermediate step a literature review has been conducted (thesis
sections 2 and 3) to find better suited and more applicable attributes for the TOE
framework in an SME Cloud computing Adoption setting. The new found attributes
were described in hypotheses and incorporated in the combined TOE Framework
and Diffusion Of Innovation theory. As a second step a quantitative research by
surveying the indirect IT distribution channel using the “SME Cloud computing
Adoption TOE Framework” by means of a questionnaire was conducted.
The questionnaire has been designed to identify relationships among the IT
Distribution Channel participants (vendor, distribution, value-add reseller & SME)
and measure the impact, if any, on SME Cloud computing Adoption. Questions
include: organizational background, awareness of Cloud computing, the IT
distribution model, role of the vendor, distributor, reseller and SME and measure
the impact of the TOE adoption attributes.
The questionnaire used was adopted from earlier published research in the field
of SME Cloud adoption (Tehrani, 2013) and virtual worlds (Yoon & George, 2013).
In addition to the standard questions one of the four constructs (environment) and
accompanied items has been modified specifically to the IT distribution context of
SME Cloud computing adoption.

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4.1 Sampling
A distinction has been made between (1) have already adopted Cloud computing
(innovators and early adopters), (2) intend to adopt Cloud computing in the next 6-
18 months (early and late majority) and those that have no intentions or plans in
the foreseeable future (laggards). Participants: Key staff in the adoption and
decision making process through the IT distribution channel
4.1.1 Population
Population refers to the total number of interested entities in the research, it will
be the collection of individuals for which this study is required to make inferences
about (Albright, Wiston & Zappe, 2010). This research’s population came from a
database consisting of a variety of 6.000 SME’s in the Netherlands.
4.1.2 Sampling Size
Intention was to sample 4.000 Dutch SMEs being served by the Dutch IT
Distribution Channel. An organization size of 250 or less was therefore a
prerequisite. The study needed approximately 300 participating respondents to be
relevant.
4.1.3 Unit of Analyses
Unit of analyses refer to the individuals, i.e. the SME employees, who were
approached for this research (Diamatopoulos & Schegelmilch, 2000). The SME
employees approached were senior level IT managers and business directors of
the SMEs, the decisions makers.
4.2 Research Instrument
This paragraph describes the instruments used in the research.
4.2.1 Survey Questionnaire
The developed questionnaire aimed to capture the SME respondents’ opinion
about Cloud computing, and other factors influencing the decision to adopt Cloud
computing. The very first draft of the questionnaire consists of items trying to
measure the research model’s constructs.

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4.2.2 Questionnaire design
According to Moore and Benbasat (1991), in order to measure each construct, at
least three items should be created. Items were developed based on a five point
Likert type scale.
The questionnaire was constructed from earlier validated research using the TOE
framework in SME Cloud adoption (Tehrani, 2013), Technology Impact on Cloud
Computing (Ramagoffy, 2012) and Virtual World adoption (Yoon 2009). The
majority of the items included in the preliminary questionnaire were adopted from
already published articles and research by Tehrani (2013), Chau and Hui (2001),
Thong (1999), Thong and Yap (1995) and Moore and Banbasat (1991). In the
preliminary questionnaire (see Appendix I, Preliminary Questionnaire ) four
perspectives were used (Technology, Environment, Organization and Human
factors). The Technology, Organizational and Human constructs were adopted
from validated questionnaires but modified (wording) to accommodate business
language in the Cloud computing IT distribution setting. The Environment
construct was adopted and changed considerably (see the text in red) to cater for
items reflecting the Aggregated Multi-Vendor and Cloud Vetting attributes.
4.2.3 Survey validation
For the TOE Environment perspective new constructs and items have been
designed accompanied with relevant questions. In order to increase the usability
and content validity of the item’s questions the Environment part of the
questionnaire have been validated by a panel of experts. To check the clarity of
the questions 3 Cloud experts from Tech Data and 2 business executives have
been approached to perform this task. Based on their feedback some minor
modifications have been made to the items, i.e. the survey questions.
A pilot study was necessary to validate the reliability of the questionnaire. For the
pilot study 10 SME’s have been invited. Participants were senior IT staff and
business decision makers. The validity of the pilot study’s questionnaire was
tested by the inter-item Correlation Mean and Cronbach’s Alpha. Cronbach’s
alpha is a measure for internal consistency, see table 5 on the next page. It
indicates whether different items measuring a specific construct actual do. An alfa
greater than 0.7 is acceptable (Kline, 1999). The inter-item correlation mean will
need to be above 0.3.

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Construct Number of
items
Inter-item
Correlation
Mean
Cronbach’s
Alpha
Hygiene Factors 11 0,166 0,674
Aggregates Multi-Vendor
Solutions
7 0,287 0,729
Cloud Vetting 5 0,332 0,7
Value-Added Services 6 0,281 0,702
Innovativeness 3 0,174 0,621
Prior Cloud computing
experience
4 0,473 0,725
Table 5, Reliability Validation Pilot Study
Only one construct (Prior Cloud computing experience) delivered the desired
internal consistency and inter-item correlation. The other constructs needed
adjustments.
Hygiene Factors were measured using 11 statements:
1. Cloud computing provides a secure service
2. Cloud Providers’ services and data are secure
3. The network carrying our data to Cloud providers’ datacenter is secure
4. Cloud providers maintain the privacy of our data we are using
5. Overall I do not have any concern about the security and privacy of cloud
computing services
6. Relevant Cloud computing services are available to our organization
7. Cloud computing services offered are reliable
8. Cloud computing can elastically be provisioned and released to scale demand
9. Security and Privacy concerns will stop me adopting cloud computing service
10. The patriot act will stop me from adopting US Cloud computing services
11. A local or Dutch based Cloud provider will take away obstacles or concerns
Removing items 9, 10 and 11 increased the alpha and inter-item correlation mean
to acceptable levels.
Construct Number of
items
Inter-item
Correlation Mean
Cronbach’s
Alpha
Hygiene Factors 8 0,41 0,864
Table 6, Hygiene Factors alpha and inter-item correlation mean

Page 45 of 99
Aggregates Multi-Vendor Solutions were measured using 7 statements:
1. Lack of control of data and/or application performance and the ability to
change processes and policies using Cloud Service is an obstacle for adoption
2. The availability of aggregated Multi-Vendor Cloud Solutions will increase the
agility 45orm y organization
3. A choice from Multi-Vendor Cloud Solutions is a must
4. Portal access to an established portfolio of white-labelled Cloud services is
essential for adoption
5. Aggregated Cloud services offering by a trusted party will speed-up adoption
6. Offering interoperability between Cloud computing platforms will allow for
early adoption
7. Ability to combine multi-vendor Cloud services is essential for adoption
Removing item 1 increased the alpha and inter-item correlation mean to
acceptable levels.
Construct Number of
items
Inter-item
Correlation Mean
Cronbach’s
Alpha
Aggregated Multi-
Vendor Solutions
6 0,355 0,77
Table 7, Aggregated Multi-Vendor Solutions alpha and inter-item correlation mean
Cloud Vetting was measured using 5 statements:
1. Unwanted latency and performance issues of Cloud platforms will stop me from
adopting Cloud computing
2. The lack of standardized Cloud computing services and practices is an obstacle
for adopting Cloud computing
3. A Cloud Service Broker testing relevant combinations of aggregated Cloud
services specific to our organization’s industry will increase the likelihood of
adoption
4. A trusted party delivering and guaranteeing the integrity of specific aggregated
Cloud services will take away a barrier for adoption
5. A single Cloud computing SLA from a Cloud Services Broker will take away a
barrier for adoption
Removing items 1 and 2 increased the alpha and inter-item correlation mean to
acceptable levels.
Construct Number of
items
Inter-item
Correlation Mean
Cronbach’s
Alpha
Cloud Vetting 3 0,54 0,776
Table 8, Cloud Vetting alpha and inter-item correlation mean

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