A Contingency Model for Assessing Cloud Composite Capabilities
Noel Carroll
1
, Markus Helfert
2
and Theo Lynn
1
1
Irish Centre for Cloud Computing and Commerce, Business School, Dublin City University, Dublin, Ireland
2
School of Computing, Dublin City University, Dublin, Ireland
Keywords: Cloud Computing, Contingency Model, Service Quality, Composite Capabilities, Cloud Service Index.
Abstract: Cloud computing present new economic and flexible business and technological models. The explosive
uptake of cloud solutions has fuelled the growth of cloud service providers (CSP). However, recent
development show that within the field of cloud computing there is often too much focus on technology
solutions but little insight on cloud exploitation and service analytics from a business perspective. To
support CSPs and cloud users, it is critical that sourcing decisions are informed to align cloud strategy and
service capabilities. In this paper we present a contingency model which supports the assessment of cloud
composite capabilities. While we develop an understanding of the research gaps which exists throughout
academic and industry literature, the contribution of this paper is the introduction of our contingency model
which forms the initial development of the Cloud Service Index (CSI). The CSI is a basis to assess cloud
composite capabilities.
1 INTRODUCTION
Cloud computing focuses on the how IT enables
greater business value through increased
technological capacity and capabilities. As business
subscribe or rent additional capabilities, IT
capabilities are extended on an ‘on-demand’ basis
from applications to additional storage. There are
numerous definitions of cloud computing. One of
the most accepted definitions comes from Mell and
Grance (2009) at the National Institute of Standards
and Technology (NIST). They define cloud
computing as a “model for enabling convenient, on-
demand network access to a shared pool of
configurable computing resources (e.g., networks,
servers, storage, applications, and services) that
can be rapidly provisioned and released with
minimal management effort or service provider
interaction.” This suggest that cloud computing
allows users to utilise IT resources and capabilities
when required. The fundamental benefits of cloud
computing is its ability to share resources on-
demand at considerably reduced costs. This has led
to the explosive uptake of cloud computing.
According to the latest Cisco report, “cloud is now
on the IT agenda for over 90% of companies, up
from just over half of companies (52%) last year”
(Cisco CloudWatch Report, 2012). However,
measuring the value of Cloud service capabilities
through a systematic manner can become a very
complex process, particularly for small-to-medium
sized enterprises (SMEs).
In this paper, we discuss the initial work on
developing a contingency model to assess cloud
capabilities. Our research addresses the following
research question: ‘how can we measure the
contributory business value of cloud service
capabilities in SMEs?’ Examining the complexity
and value of ‘the cloud’ offers immense
opportunities through service analytics (i.e.
measuring performance). Thus, understanding how
cloud resource may be assessed for ‘on-demand’
services requires a contingency model to assist in
the strategic alignment of business and IT
resources.
2 LITERATURE REVIEW
The focus of our research focuses on is cloud
computing in SMEs. We have examined how SMEs
are considered the backbone of economies (Europa,
2012) and cloud computing presents them with a
level playing field in terms of availing of IT
resources and capabilities. However, considering
the complexity of today’s service environment,
515
Carroll N., Helfert M. and Lynn T..
A Contingency Model for Assessing Cloud Composite Capabilities.
DOI: 10.5220/0004375805150519
In Proceedings of the 3rd International Conference on Cloud Computing and Services Science (CLOSER-2013), pages 515-519
ISBN: 978-989-8565-52-5
Copyright
c
2013 SCITEPRESS (Science and Technology Publications, Lda.)
SMEs cannot afford to accept the status quo of
service operations and therefore must have some
clear business analytics objective to reach. Without
clear metric objectives, organisations are almost
destined for disaster since the allocation of
resources may not have responded to the demand
exerted from outside of the organisation. This
places greater emphasis on the need to assess
service capabilities in terms of quality and
performance.
2.1 Cloud Value Co-creation
Value co-creation is concerned with the strategic
and mutual approach to generating value between a
service provider and a customer. Cloud computing
comprises of four main layers within the cloud
stack. These layers include Business Process-as-a-
Service (BPaaS), Software-as-a-Service (SaaS),
Platform-as-a-Service (PaaS), Infrastructure-as-a-
Service (IaaS), and an overarching management
layer which, in a real world scenario, would operate
in most of the layers of service provision. We
identify the need to assess the business value of
each layer in the cloud stack and the relational
dynamics of service metrics between each layer.
According to Orand (2010), the main issue with IT
is the inability to improve service provision due to
a lack of ‘proper’ measurements. There is often a
mismatch in IT personnel’s ability to address the
business needs as business demands more for IT
support and functionality. Thus, the alignment of IT
and business is often only experienced as an
organisation matures (Luftman, 2003) to support
evolving strategies. However, this is no longer the
case in cloud computing. While there is often a lot
of discussion surrounding business and
IT capabilities, consider for a moment that
business do not ‘want’ IT but rather, they
want the ‘service’ which is provided by IT.
Figure 1: Cloud Value Co-creation.
We describe this as cloud value co-creation, i.e., the
alignment of business objectives and IT capabilities
to supports organisations ability to generate value
(Figure 1). IT is a cost, and yet it enables business
value. Thus, we are interested in the output of a
service and the capabilities employed to reach the
desired output. What is of immense interest here is
the ability to assess cloud capabilities in delivering
the desired output though service metrics. We
consider the cloud to be a value co-creation
environment to support service maturity.
This allows us to examine the generation and
on-going realisation of mutual organisational-
customer value through the affordance of additional
IT capabilities provided by cloud computing
initiatives.
2.2 Cloud Service Quality
Service quality can be measured and created
through the utilisation of service capabilities.
Within the cloud service environment,
organisations rely on service quality through the
successful alignment of business objectives and IT
capabilities to co-create value. The concept of
‘service’ and ‘quality’ has received much attention
across business and information systems literature.
However, based on our analysis, we posit the need
to evolve their meaning in a cloud computing
context as we prescribe an alternative view through
a ‘sourcing maturity model’. For example, Kang
and Bradley (2002; p. 153) define service quality as
“an abstract and elusive construct because of three
features unique to the service delivery –
intangibility, heterogeneity and inseparability of
production and consumption”. This is particularly
interesting when we consider the co-creation
relationship between the service provider and user
in generating business value within a cloud
computing context.
Within a cloud computing context, service
quality relies on the tangible resources which often
rely on representative agents of resource provision
rather than heterogeneous consumption. Therefore,
we would attempt to define service quality in a
cloud context as ‘the orchestration of resources
which contribute towards value co-creation actions
that align the required IT and governance
resources to support business objectives on-
demand while satisfying customer requirements’.
This introduces a tangible relationship for service
quality which is measurable within a cloud
services. We argue that service quality must have a
business value which is enabled through the
alignment of IT and business architecture. This may
be measured through performance metrics and
service capability maturity.
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2.3 Service Capabalitiy Maturity
There are five main maturity levels within the
capability maturity model (CMM):
1. Initial: undocumented starting point.
2. Repeatable: documented process to allow the
process to be repeated.
3. Defined: confirmation of process becoming
standardised.
4. Managed: agreed metrics to evaluate the
process performance.
5. Optimising: managing the improvement of the
process.
These levels provide a holistic view of process
maturity. Within each phase there are key process
areas which examine the goals, commitment,
ability, measurement, and verification as they reach
greater maturity. Ultimately, these steps were
designed to improve performance through
quantitative process-improvement objectives.
However, one of the biggest criticisms of adopting
CMM model is the cost and time associated
(Herbsleb and Goldenson, 1996) with adopting it
assessment activities (training and appraisal).
Considering our focus lies with SMEs, we
emphasise the need to develop an inexpensive and
easily adoptable framework which is particularly
interesting when applied to in a cloud context.
3 THE PROMISE OF MATURITY
MODELS
Maturity models have been very prominent through
information systems management literature. A
maturity model may be described as a systematic
service assessment which provides a model to
understand an organisations capability maturity of
business processes. A maturity model is
specifically used to inform and support decisions
and reduce risk in management strategies. A CMM
comprises of five key factors which must be
considered in the assessment including:
1. The Maturity Levels: presents a scale of one to
five, where five is the ideal maturity state.
2. Key Process Areas: clusters specific business
process or activities which are considered
important to achieve a business goal.
3. Goals: goals of individual processes and to
what extent they are realised indicates the
capability and maturity of an organisation.
4. Common Features: describe the practices
which implement a process centred on
performance mechanisms.
5. Key practices: the infrastructure and practice
which contribute to the process.
The main objective of developing a capability
assessment is to provide some level of
measurement which can generate data to support
decision-making. These measurements can support
managers determine a process status and its
effectiveness when executed by their cloud
strategy. There are a number of essential
measurements which are associated with cloud and
overlooked in the existing capability maturity
models. We posit that their traditional approach of
“a one size fits all” is no longer valid for the
dynamic nature of cloud computing.
4 TOWARDS A CONTINGENCY
MODEL
The concept of quality has long been on the
management agenda and is still ranked amongst the
more important factors which influence service
performance and strategy. There is a clear
relationship between ‘quality’ and ‘value’. We have
argued that the concept of quality taken from
management science literature is no longer
prevalent in a cloud computing context. To support
our argument we try to redefine service and service
quality through our contingency model and service
composite capabilities and explain how quality is a
co-creating activity between organisations that
unpack and exchange service capabilities.
Therefore this alters the responsibility for quality
within a distributed cloud service ecosystem. We
have categorised cloud metrics into BPaaS, SaaS,
PaaS, and IaaS. Within each level, we can classify
service metric depending on the managerial level:
1. Strategic Cloud Service View: resource
allocation.
2. Tactical Cloud Service View: IT alignment of
business processes.
3. Operational Cloud Service View:
performance measures.
From each perspective, we consider it important to
encapsulate the relationship between service and
technology and how it co-creates value within an
organisational context. Figure 2 illustrates a high
level conceptual model which demonstrates the
relationship between technological quality metrics
and service quality metrics in the establishment of
our Cloud Service Index (CSI). It also lists some
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examples of the broad technological and service
categories which will be examined within the cloud
service stack.
Figure 2: Establishing the Cloud Service Index.
In the cloud strategy, the primary difference
between the CSI and service quality is the focus on
value in the context of how cloud computing
increases business value through sourcing
additional service capabilities. The service quality
indicators will examine cloud service provision in
the context of subjective and objective quality
criteria. The quality criteria will represent end-user
perception and requirements analysis of service
quality. In addition, the technical performance
measures are primarily concerned with what the IT
infrastructure provides through the requested
functions and performance (i.e. execution). The
CSI relates the quality factors of the cloud service
to the business strategy and organisational goals.
We define service quality as the difference between
customer’s perception of the expected benefit and
the realised benefit which emerged from a service,
or:
Cloud Service Quality = Expected Benefit –
Realised Benefit
Therefore, quality is an attribute result of an
emerging relationship between consumer
expectation and service provision upon which we
can build metrics to derive a value for quality.
Within each of the service lifecycle phases, we are
undertaking a layered analysis of the cloud stack
(Figure 3) to identify specific metrics which
include the following criteria:
1. Metrics must be actionable (i.e. influence what
action managers must take);
2. Supports service trending which allows us to
flag weak service performance;
3. Supports catalog data to examine processes and
how they align with SLAs;
4. Have some industry baseline to benchmark
against;
5. Reflect successes, problems, and failures to
facilitate a ‘learning’ performance business
intelligence system.
Figure 3: Cloud Service Index Model.
Having assessed the various cloud capabilities, our
assessment focuses on presenting the results with
regards the cloud service lifecycle in terms of
process readiness:
1. Strategy Readiness (SR): focuses on how the
cloud will align with the organisational strategy
while understanding the general demands to
benefit from the promise of the cloud.
2. Design Readiness (DR): balancing service
requirements with service capabilities.
3. Transition Readiness (TR): moving the service
into operation through service provisions.
4. Operation Readiness (OR): examining effective
and efficient service operations to (re)align the
cloud strategy.
5. Continuous Improvement Readiness (CIR):
monitors the governance and critical success
factors (metrics and KPIs) to report on service
capabilities throughout the cloud lifecycle.
We can model the service maturity through a
snapshot where cloud service providers and users
may view their readiness towards cloud solutions
(see Figure 4). The model represents a conceptual
view of service capabilities and customer
experience. It offers an exemplary solution towards
reporting cloud capabilities to SME managers.
Each phase in the cloud lifecycle is scored (out of
5) to indicate it readiness to offer/avail of cloud
solutions (0=not ready; 5=ready). In this example,
we demonstrate how the OR and CIR presents us
with an indication that these are areas of concern
because the score below the value-added curve and
therefore warrants immediate attention in these
specific areas. This suggests that managers could
investigate the suitability of cloud service
capabilities to support these two phases and
improve the service maturity.
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Figure 4: Example of Reporting through CSI.
5 CONCLUSIONS
This paper highlights that this is our initial step to
establishing the CSI contingency model. There are
many other challenges ahead. In terms of the CMM
approach, we are positioned at the ‘defined’ phase
where we are currently defining metrics to evaluate
cloud process performance. We envisage that this
work will address some of the key issues identified
throughout cloud computing literature. The CSI
will also indicate areas where organisations are
strongest and examine which service functions may
be of concern when compared to peer-
organisations, i.e. benchmarking. We will also
explore the visualisation of service brokerage
through network analysis techniques to add greater
transparency on value co-creation (for example,
Carroll et al., 2012). Through the development of
the CSI, we can assist organisations improve their
cloud services through the assessment of their
cloud capabilities, quality and performance using
the contingency model.
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