Cloud computing adoption model for e-government implementation

Abstract

Cloud computing essentially is a scalable, flexible and pay-per-use model for the way IT services are delivered. It can significantly improve the way a government functions, the services it provides to its citizens and institutions. This paper aims to explore the factors that influence adopting cloud computing as a part of public sector organizations’ alternatives to implement e-government services. Based on two dominant theories in the field of adoption of new technology, the Fit-Viability Model and Diffusion of Innovation Theory, a model is proposed. Data were gathered using a structured questionnaire with a sample of 296 IT staff employed in public organizations in Yemen. The results clarify the need to consider factors affecting two dimensions, fit and viability, to make a decision to adopt cloud computing in an e-government context. The fitness of cloud computing to e-government tasks is affected by factors such as relative advantage, compatibility, trialability and security, but is not affected by the complexity of the technology. On the other hand, the viability is influenced by economic factors (return on investment and asset specificity) and technological readiness (IT infrastructure and IT policy and regulations), while the results do not support the relation between the organizational factors such as top management support and cloud knowledge and viability.

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Article
Cloud computing adoption model
for e-government implementation
Fathey Mohammed
Universiti Teknologi Malaysia, Taiz University
Othman Ibrahim
Universiti Teknologi Malaysia
Mehrbakhsh Nilashi
Universiti Teknologi Malaysia, Islamic Azad University (Lahijan Branch)
Ensaf Alzurqa
Taiz University
Abstract
Cloud computing essentially is a scalable, flexible and pay-per-use model for the way IT services are
delivered. It can significantly improve the way a government functions, the services it provides to its citizens
and institutions. This paper aims to explore the factors that influence adopting cloud computing as a part of
public sector organizations’ alternatives to implement e-government services. Based on two dominant
theories in the field of adoption of new technology, the Fit-Viability Model and Diffusion of Innovation
Theory, a model is proposed. Data were gathered using a structured questionnaire with a sample of 296
IT staff employed in public organizations in Yemen. The results clarify the need to consider factors affecting
two dimensions, fit and viability, to make a decision to adopt cloud computing in an e-government context.
The fitness of cloud computing to e-government tasks is affected by factors such as relative advantage,
compatibility, trialability and security, but is not affected by the complexity of the technology. On the other
hand, the viability is influenced by economic factors (return on investment and asset specificity) and tech-
nological readiness (IT infrastructure and IT policy and regulations), while the results do not support the
relation between the organizational factors such as top management support and cloud knowledge and
viability.
Keywords
cloud computing, e-government implementation, Yemen
Submitted: 7 March, 2016; Accepted: 31 May, 2016.
E-government tasks of providing services are driving the fit of cloud computing to public
sector organizations.
Introduction
Governments are continuously looking for ways to
improve their services. Therefore, there is a need for
restructuring processes and effectively using technol-
ogy to improve efficiency and effectiveness of the
business operations. Electronic government (e-gov-
ernment) is the term coined to describe the utilization
of information technology (IT), information and
Corresponding author:
Fathey Mohammed, Department of Information Systems, Faculty
of Computing, Universiti Teknologi Malaysia, 81310 UTM Johor
Bahru, Johor, Malaysia; Department of Information Technology,
Faculty of Engineering and Information Technology, Taiz
University, 6803, Taiz, Yemen.
Emails: alqadsi_ye@yahoo.com / fathey2@live.utm.my
Information Development
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ªThe Author(s) 2016
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DOI: 10.1177/0266666916656033
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communication technologies (ICTs), and other web-
based telecommunication technologies to improve
and/or enhance the efficiency and effectiveness of
service delivery in the public sector, business partners
and employees (Jeong, 2006). While ICT promises
lots of advantage for providing efficient government
services, it requires efforts for building infrastruc-
tures, changing processes, regulations establishment,
etc. On the other hand, increasing citizens’ access to
technology brings more expectations and demands on
government services. Hence, governments should be
proactive and plan new ways of improving services
and optimizing processes by effectively applying IT
advancements.
Cloud computing can provide a good basis to hope
that some of the traditional challenges can be
addressed. It may create a revolution in e-government
systems, in terms of cost saving and actual and pro-
fessional use of resources (Bansal et al., 2012; Naser
et al., 2012; Alshomrani and Qamar, 2013). Cloud
computing has created a revolution in the way ICT is
used by organizations and individuals, and cloud-
based applications in the public sector already have
established their effectiveness to meet the require-
ments and unexpected needs for resources (Tripathi
and Parihar, 2011). Many countries have launched
cloud computing for e-governance (Sharma et al.,
2012). Governments are seeking to enhance their
own ICT infrastructure and reduce ICT costs by
using cloud services. Many governments around the
globe are introducing cloud computing as a tool for
improving services, reducing costs, and increasing
effectiveness and efficiency in the public sector
(Kurdi et al., 2011). Several examples show that
cloud computing has become a strategic direction for
many public sector organizations and is already
being adopted in critical domains of the govern-
ment’s IT infrastructure in the world. For example,
USA.gov, which is the main federal government infor-
mation portal in the USA, suffered from issues such as
massive network traffic load, long downtime and
delays, and inefficient services. A cloud-based solution
(Terremark’s Enterprise Cloud service) is accounted to
be the proper option to handle these problems because
cloud computing can better deal with on-demand scal-
ability. The migration to the cloud has assistedthe Gen-
eral Services Administration (GSA) in reducing the site
upgrade time from 9 months to one day. Furthermore,
the downtime has been reduced by 99.9%. In terms of
costs, the GSA saved 72%annually by moving to the
cloud (Kundra, 2010).
In the literature, there are relatively few studies that
investigate cloud computing in the context of
e-government. Most of them mainly discuss the benefits
and challenges of cloud computing for e-government
(Mohammed and Ibrahim, 2015), while another group
of studies proposed a model or a framework to apply
cloud computing for e-government services imple-
mentation. However, most of these studies, whether
based on the cloud service model or existing
e-government models, suggest either layered models
(Liang, 2012; Li and Liu, 2014; Huang and Gu,
2013; Mukherjee and Sahoo, 2010; Chanchary and
Islam, 2011; Naseem, 2012; Bo, 2013), step-based
models (Song et al., 2013; Prasad and Atukuri,
2012; Naser et al., 2012; Rastogi, 2010; Wyld,
2010; Singh, 2012; Seo et al., 2014; Islam et al.,
2015), or component-based models (Singh and
Chandel, 2014; Nigam et al., 2015; Lee and Kim,
2013; Hana, 2013; Das et al., 2011; Kurdi et al.,
2011; Chandra and Bhadoria, 2012; Liang and Jin,
2013). There is a lack of studies investigating the
influencing factors on cloud computing adoption in
e-government context (Trivedi, 2013; Suo, 2013; Li
et al., 2013; Kuiper et al., 2014; Killaly, 2011; Shin,
2013; Sallehudin et al., 2015; Abeywickrama and
Rosca, 2015). This study therefore proposes a model
to explore the factors influencing public sector
organizations to adopt cloud computing to provide
e-government services.
The following section reviews the related litera-
ture. In the next section, the theoretical background
will be established by reviewing the theories used to
adopt an innovation at the organization level. Then
the theoretical model is presented and its constructs
are elaborated. The fourth section describes the meth-
odology followed in this study. The fifth section rep-
resents and analyzes the results, followed by a
discussion of the findings. Finally, this paper ends
with the conclusion.
Related work
There are relatively few studies investigated the
factors influencing cloud computing adoption for
e-government services (Mohammed and Ibrahim,
2015). Ross (2010) evaluated the factors that influ-
ence an organization in a decision as to whether to
adopt cloud computing as a part of their strategic infor-
mation technology planning. The cost-effectiveness,
need, reliability, and perceived security effectiveness
of cloud computing were considered. A strong positive
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relationship was found between these four factors and
management’s intention to adopt cloud computing
technology. Shin (2013) examined the adoption of
cloud computing services in government agencies by
focusing on the key characteristics that affect beha-
vioral intention. By exploring the factors influencing
user perception of cloud computing, a theoretical
acceptance model was proposed. The Theory of Rea-
soned Action (TRA) and a modified Technology
Acceptance Model (TAM) were applied to propose the
model. The model was built upon the existing TAM by
integrating specific influencing factors such as avail-
ability, access, security, and reliability and was empiri-
cally verified by investigating the perceptions of users
working in public institutions. Results showed that
user intention and behavior are influenced by the per-
ceived features of cloud services.
Trivedi (2013) proposed a theoretical model for
cloud computing adoption in governments and large
enterprises. The proposed model helps organizations
understand what capabilities they need to develop to
adopt cloud computing. The Technology Organiza-
tion Environment (TOE) framework was applied to
identify technological, organizational and environ-
mental factors. The factors were identified from anal-
ysis of case studies and the literature on the TOE
framework. Hailu (2012) discussed the interest of IT
leaders in developing countries to adopt cloud com-
puting by evaluating their perceptions of the security
effectiveness, organizational need, reliability, and
cost-effectiveness of cloud-computing technology.
The results indicated that perceptions of security effec-
tiveness, need, reliability, and cost-effectiveness corre-
lated positively with IT leaders’ willingness to
recommend cloud-computing technologies.
Li et al. (2013), by analyzing the current situation
of e-government in China and cloud computing ser-
vices, discussed the potential of cloud computing for
e-government. By considering the practical issues
of implementing cloud e-government, the factors
influencing e-government implementation were
analyzed and an influence factor model was pro-
posed. Based on Gil-Garcia, factors influencing
cloud e-government implementation were classified
into five classes and 22 indicators. Suo (2013)
investigated the factors affecting successful imple-
mentation of cloud computing in organizations. A
qualitative method using interviews was used.
Based on theories from organization and manage-
ment studies, a taxonomy of both the success factors
and the challenges affecting cloud implementation is
proposed. Furthermore, to examine the influence of
cloud computing adoption on the IT function inside
the organization, a case study on two cloud models
was conducted. Using the gathered data and
grounded theory, a range of factors influencing
cloud implementation on the IT function was iden-
tified. These factors included the role and function-
ality, IT leadership, skills and jobs, IT staffing,
formal structure, and workplace culture.
Kuiper et al. (2014) described two models of fac-
tors influencing cloud adoption. A theoretical model
based on the general Diffusion of Innovation Theory
of Rogers was proposed. A system diagram to model
the European Commission perspective on public sec-
tor cloud adoption was then described. Results
showed that factors such as collaboration, traceability
and auditability, convincing IT managers, security
and legal issues, perception of the term ‘cloud’, and
risks of innovation factors (relative advantage, com-
patibility, complexity, observability and trialability)
all influence cloud adoption in the public sector.
Abeywickrama and Rosca (2015) investigated how
cloud computing enhances value in public service
delivery by conducting a case study of the central
public administration of Moldova. Based on the
DeLone and McLean information systems success
model, factors related to system quality, information
quality, service quality, intention to use, use and user
satisfaction were examined. Sallehudin et al. (2015)
explored the possible factors that could influence
cloud adoption decision in Malaysian public agencies.
They developed a model by integrating the Diffusion
of Innovation Theory (DOI) and characteristics of IT
personnel. The developed model was tested to deter-
mine the factors influencing cloud computing adop-
tion to enhance service delivery in Malaysian public
sector. The results showed that innovation attributes
(relative advantage and compatibility), and the human
factor (IT personnel knowledge) affect cloud comput-
ing adoption in the Malaysian public sector.
Theoretical foundation and proposed
model
The adoption process is a sequence of stages that the
adopter of an innovation (individual or other decision-
making unit) passes through before acceptance of a
new product, service or idea (Rogers Everett, 1995).
The outcome of the process is a decision (to adopt or
not to adopt), which is made by an entity on a specific
object in a particular context (Li, 2008). In the context
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of this study, the factors affecting the decision to
adopt (or not adopt) cloud computing (the object)
made by a public sector organization (the entity) in
the context of e-government, are investigated.
Based on our investigation, there is a lack of
empirical studies on the factors that influence the
decision to adopt cloud computing by public sector
organizations. However, e-commerce and e-business
models can be used to examine factors influencing the
adoption of electronic services in the public sector
(Lai and Pires, 2009; Carter and Be´langer, 2005). The
important theoretical perspectives include the Diffu-
sion of Innovation (DOI) theory (Rogers Everett,
1995), the Technology Organization Environment
(TOE) framework (Tornatsky and Fleischer, 1990)
and the Fit Viability Model (FVM) (Liang et al.,
2007). However, a single theory cannot be applied
to all types of innovations (Zmud, 1982). Hence, an
integrated model of theories is needed, to be applied
in determining the adoption process of specific types
of innovation (Zmud, 1982). A decision to adopt a
new technology to implement a system may involve
some risk. Therefore, developing a model that can
predict the applicability of a new technology in a
context will be valuable. The applicability of a tech-
nology or a system may concern not only the technol-
ogy characteristics, but also the readiness of the
context. FVM considers technological characteristics
(fit) and organizational readiness (viability), and is
extended from Task-Technology Fit, which uses fac-
tors that predict technology usage and performance
(Baas and van Rekom, 2010; Goodhue and Thomp-
son, 1995; Teo and Men, 2008). FVM can therefore
be used as a base for an integrated model for the
decision to adopt cloud computing for e-government
implementation.
FVM has been used specifically to address the
adoption of a new technology. It was proposed by
Liang (Tjan, 2001) for evaluating organizational
adoption of Internet initiatives. It has been used to
study mobile technology adoption (Liang and Wei,
2004; Liang et al., 2007), social software fitness for
decision task in organizations (Turban et al., 2011),
enterprise resource planning (ERP) adoption (Muham-
mad et al., 2013) and e-government maturity (Larosi-
liere and Carter, 2013). Analysis of the previous studies
shows that, depending on the context, researchers
define different factors to measure the fit dimension
(See Table 1).
It can be concluded that researchers measure fit-
ness by defining specific tasks or requirements, then
selecting a technology that has the features to deal
with these tasks. Hence, the level of fitness is pre-
dicted to be high. For example, in the study by Liang,
et al. (2007), all seven examined cases had high
degrees of fit. One of their findings was that they did
not use a reliable instrument for measuring the degree
of fit between task requirements and technology cap-
abilities. However, a review of the literature shows
that there is a relation between DOI factors (relative
advantage, compatibility, complexity and trialability)
and fit (See Table 2). Therefore, this study proposes
DOI factors to assess the fitness of cloud computing
(as an innovation) to implement e-government
services.
Research model and hypotheses
In this research, the fit viability model is adapted to
help to determine whether cloud computing is a viable
option for implementing e-government. Fit is defined
as the extent to which cloud computing is consistent
with the specific requirements of e-government. It is
measured by identifying the e-government related
tasks and requirements and assessing the impact of
cloud computing by using DOI factors. Viability mea-
sures the extent to which the public sector organiza-
tional environment is ready for cloud computing, as
well as the extent of the value-added potential of
using cloud computing in e-government. Accord-
ingly, the proposed model has two main dimensions:
fit and viability.
This study proposes the following hypotheses:
H1: The fit of cloud computing to the e-government
task characteristics positively influences public
organizations’ intention to adopt the technology.
H2: The viability of cloud computing positively
influences public organizations’ intention to adopt
the technology.
Fit. Fit is defined by Goodhue and Thompson (1995)
as the extent to which technology functionality
matches task requirements. Lippert and Forman
(2006) also defined fit as the extent to which a tech-
nology provides features and fits the requirements of
the task. According to Liang, et al. (2007), fit measures
the extent to which an advantage of a technology
matches the needs of the task. In the current study
context, fit is defined as the extent to which cloud
computing is consistent with the specific requirement
of e-government implementation. The fitness of cloud
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computing in the context of e-government is measured
by defining the e-government implementation tasks
and the innovative characteristics of cloud computing.
Task. Task characteristics refer to the task require-
ments within the organization (Liang et al., 2007).
Task requirements can be ability requirements or
behavior requirements (Zigurs and Buckland, 1998).
In the current study, the task construct assesses the
government-related requirements and actions that are
performed to provide e-services. In other words, it
examines government organizations’ computing
needs to implement e-government services. Establish-
ing online government services requires implement-
ing and upgrading the e-government system to match
task requirements that would otherwise be performed
offline (United Nations. Department of Economic and
Social Affairs, 2010; Krishnan and Teo, 2011). This
research therefore proposes the following hypothesis:
Table 1. FVM and adoption studies.
Resource The context Fit Factors Viability Factors
T.-P. Liang and
Wei (2004)
Mobile commerce applications Location-sensitive
Time-critical
Personal
The general economic
environment.
Social infrastructure
The readiness of the
organization.
T.-P. Liang et al.
(2007)
Adoption of mobile technology in
business
Mobility
Reachability
Economic feasibility
Technical infrastructure
Organizational factors
Turban et al.
(2011)
Enterprise Social Networking
Adoption
Information dissemination and
sharing
Communications
Collaboration and innovation
Training and learning
Knowledge management
Management activities and
Problem solving.
Economic feasibility
IT infrastructure
Organization readiness
Killaly (2011) Perceptions of cloud computing in the
(DoD - USA)
Computing platform
Computing needs
Cost
Organizational inertia
Fit
Turban, et al.
(2011)
Adopting Collaboration 2.0 Tools for
Virtual Group Decision Making
Information Sharing
Idea Generation and Innovation
Selection and Implementation of
the Solution
Economic Feasibility
IT Infrastructure
Organizational Readiness
Muhammad,
et al. (2013)
Cross-Cultural ERP Implementation Data format
Operating procedures
Output format
National factors
Political and Social
Economical
Environmental
Infrastructure /Technology
Organizational factors
Leadership
Management Style
Policies
Information Sharing
Training and Learning
Technical Staff
User Behavior
Larosiliere and
Carter (2013)
The Determinants Of E-Government
Maturity
Government-related task
characteristics
Government-related technology
characteristics
Economic
Organization
IT Infrastructure
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H1a: The e-government related task requirements
positively influence cloud computing technology
fitness for e-government services implementation.
Technology Characteristics: By considering the
technological factors influencing cloud computing
adoption in the literature (Alshamaila, 2013; Rieger
et al., 2013; Morgan and Conboy, 2013; Tehrani,
2013; Fu and Chang, 2015); relative advantage, com-
patibility, complexity, trialability and security are
proposed to measure the technology aspect of the fit-
ness of cloud computing for e-government services
implementation. In addition, reviewing the literature
demonstrates that there is a relationship between these
factors and the fit construct, as shown in Table 2.
Therefore, this research proposes the following
hypotheses:
H1b: Relative advantage positively influences the
fitness of cloud computing to organizations’ com-
puting needs to implement e-government services.
H1c: Compatibility has a positive impact on the
fitness of cloud computing to organizations’ com-
puting needs to implement e-government services.
H1d: Complexity negatively influences the fitness
of cloud computing to organizations’ computing
needs to implement e-government services.
H1e: Trialability positively influences the fitness
of cloud computing to organizations’ computing
needs to implement e-government services.
H1f: Security negatively influences the fitness of
cloud computing to organizations’ computing
needs to implement e-government services.
Viability. There are several factors that influence the
viability of an organization to adopt a new technology
(see Table 1). Liang and Wei (2004) measured viabi-
lity by examining economic feasibility, top manage-
ment support and IT infrastructure. To be viable to
implement e-government services, cloud computing,
as a new concept with open standards, may need more
knowledgeable users and support from top manage-
ment, as well as analysing its economic feasibility and
assessing the organization’s technological readiness.
From this standpoint, this research measures the via-
bility of cloud computing in public sector organiza-
tions by investigating economic feasibility,
organization and technological readiness.
Economic feasibility refers to the degree to which
the economic benefits of something to be made,
done, or achieved are greater than the economic
costs
1
. The economic feasibility determines whether
a particular technology/application is cost-effective,
which includes whether or not it reduces the cost,
and whether or not it provides an acceptable return
on investment (ROI). Hence, it includes two differ-
ent aspects; ROI and transaction costs. ROI assesses
the cost vs. benefit of the particular IT project to see
whether the investment can bring in adequate
returns. On the other hand, if transaction costs are
reduced, the willingness to use a technology can
increase. Factors influencing transaction cost may
differ from one technology to another. For example,
Liang, et al. (2007) identified asset specificity,
uncertainty and frequency in the context of mobile
technology adoption, while employee training cost,
compatibility cost and software/hardware mainte-
nance costs were discussed by Turban, et al. (2011)
for collaboration on the adoption of 2.0 tools. For
cloud computing adoption, asset specificity (Rieger
et al., 2013; Lian et al., 2014) and uncertainty
(Alshamaila et al., 2013; Nuseibeh, 2011) also have
been seen as influencing factors.
Table 2. DOI Factors and Fit.
Literature
Technology
Relative
Advantage Complexity Compatibility Trialability Fit The context
Goodhue and Thompson (1995) PP PIndividual performance
C.-C. Lee, Cheng, and Cheng (2007) PP PM-commerce
M. T. Dishaw and Strong (1999) PPSoftware maintenance
tool
Teo and Men (2008) PPKnowledge portals
Nance and Straub (1996) PPIT adoption
Lam, Cho, and Qu (2007) PP P PPIT adoption
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Asset specificity means identifying the assets that
an organization may need to implement a system or
adopt a new technology (Liang et al., 2007). Lian
et al. (2014) examined the costs of acquiring hardware
and software and of integration as factors influencing
cloud computing adoption in hospitals. In this
research, asset specificity can be defined as the cost
of physical (hardware, software, licensing and inte-
grating) and human (training and consulting) require-
ments to successfully implement cloud computing in
e-government services. Since cloud computing
reduces the costs of acquiring heavy infrastructures,
asset specificity can have a positive impact on the
viability of adopting cloud computing.
On the other hand, uncertainty refers to the state in
which an adopter has limited knowledge to describe
the results of using a new technology (Knight, 1921).
Liang et al. (2007) identified uncertainty as a factor
economically influencing the viability of mobile tech-
nology for organizations. In relation to cloud comput-
ing, questions as to where data is stored and how it is
handled increase the uncertainty. Alshamaila et al.
(2013) and Nuseibeh (2011) examined uncertainty
as a negative factor of cloud computing adoption. Due
to high risks, uncertainty increases transaction costs
(Miller, 1992). Subsequently, the uncertainty of cloud
computing in the public sector affects the transaction
costs, which economically influence the viability of
this technology in the context of e-government imple-
mentation. Therefore, the following hypotheses are
proposed:
H2a: Return On Investment (ROI) positively
influences the viability of cloud computing to
e-government implementation.
H2b: Asset specificity positively influences the
viability of cloud computing to e-government
implementation.
H2c: Uncertainty negatively influences the
viability of cloud computing to e-government
implementation.
The readiness of an organization to implement a
new system or use new technology is surely influ-
enced by a set of organizational factors. Top man-
agement support (Umble et al., 2003; Ang et al.,
2002) and IT knowledge of project team members
(Poon and Wagner, 2001; Umble et al., 2003) are
factors of this type. Liang et al. (2007) examined user
competence and top management support to measure
the viability of mobile technology in the context of
business organizations. Top management support and
employee training were defined by Turban et al.
(2011) to assess the readiness of an organization to
adopt social networking. With respect to cloud com-
puting, researchers have investigated the effect of fac-
tors such as top management support, cloud
knowledge and prior experience on private sector
adoption of cloud computing (Low et al., 2011; Teh-
rani, 2013). Top management support and cloud
knowledge are thus considered factors to be examined
on the viability of cloud computing for e-government
implementation. The following hypotheses are
proposed:
H2d: Top management support positively
influences the viability of cloud computing for
e-government implementation.
H2e: Cloud knowledge positively influences the
viability of cloud computing for e-government
implementation.
Technological readiness describes the organiza-
tional resources that influence the organization’s
decision to adopt a new technology. Organizational
resource include IT infrastructure and IT staff (Oli-
veira and Martins, 2010; Pan and Jang, 2008; Wang
and Hou, 2010; Zhu et al., 2006). Since there is a lack
of standards for using cloud computing, governments
need to promote and endorse open standards for the
cloud (Australian Academy of Technological
Sciences and Engineering , 2010). Low et al. (2011)
studied the influence of technology readiness on
cloud computing adoption decisions through asses-
sing readiness in term of IT infrastructure and IT
human resources. Tehrani (2013), Morgan and Con-
boy (2013), Saedi and Iahad (2013), Tan and Lin
(2012), Nuseibeh (2011), Borgman et al. (2013) and
Nkhoma and Dang (2013) examined the impact of
organizational readiness in terms of the effect of
human resources knowledge about cloud computing
on the adoption decision and on legal and privacy
issues. This study investigates the influence of tech-
nological readiness on the viability of cloud comput-
ing in terms of IT infrastructure, skills and policies. IT
infrastructure refers to the computing resources avail-
able within an organization (Mutula and Van Brakel,
2006). Whether the existing ICT infrastructure is
capable of running a new system or not has an impor-
tant influence on the decision to adopt this system.
Implementing a new information system within an
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organization requires skilled staff. The competence of
IT staff increases the chance of successful implemen-
tation of a new system. Adopting and implementing a
new system in an organization is also affected by
governmental and internal IT regulations and policies
(Alshehri and Drew, 2010). IT policy refers to gov-
ernments and organizational requirements in the form
of regulations, standards, guidelines, directives, or
laws, which treat information technology issues such
as accessibility, availability, security and privacy. The
following hypotheses are proposed:
H2f: IT Infrastructure positively influences the
perceived viability of cloud computing for provid-
ing e-government services.
H2g: IT Skills have a positive impact on the
perceived viability of cloud computing for
e-government services provision.
H2h: IT Policies positively influence the perceived
viabilityofcloudcomputingine-government
implementation.
Figure 1 presents the proposed model with the cor-
responding hypotheses.
Methodology
Many developing countries are facing fundamental
obstacles to implementing e-government initiatives,
such as lack of basic IT infrastructures, financial
Adoption
Fit
Viability
Task
Technology
Relative Advantage
Compatibility
Complexity
Trialability
Security
Organization
Top Mgt Support
Cloud Knowledge
Technological Readiness
IT Infrastructure
IT Skills
IT Policies
Economic
ROI
Asset Specificity
Uncertainty
H1
H2
H1a
H1b
H1c
H1d
H1e
H1f
H2a
H2b
H2c
H2d
H2e
H2f
H2g
H2h
Figure 1. The Proposed Theoretical Model.
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resources and IT experts (Rana et al., 2013; Al-
Rashidi, 2013; Bwalya and Mutula, 2015; Al-Wazir
and Zheng, 2012). For these countries – more specif-
ically, countries that are still in the early stages of
e-government development – as they invest in ICTs
to develop e-government systems, there is an increas-
ing need to exploit the opportunities created by cloud
computing. Because of such challenges, Yemen, as a
developing country still in the early stages of
e-government development (Al-Wazir and Zheng,
2012), needs to assess its ability to exploit the oppor-
tunities created by cloud computing to facilitate all
success factors and maximize the use of available
resources to implement e-government projects. To
verify the proposed model, data were collected from
staff members responsible for implementing and man-
aging e-government projects in public sector organi-
zations in Yemen. A questionnaire was used to collect
quantitative data.
Questionnaire design
The questionnaire for this research was mainly
based on the constructs identified from technology
adoption theories (Diffusion of Innovation Theory
and Fit Viability Model) and the cloud computing
adoption literature. The questionnaire consisted of
two parts to measure respondent’s perceptions
towards the two suggested dimensions of the pro-
posed model; Fit and Viability. In each part, each
question reflected a measurement construct under
the corresponding dimension (see Appendix I). The
first part (Technological) adapts diffusion of inno-
vation construct scales to measure how cloud com-
puting matches with the tasks of e-government
implementation. The second part (Organizational)
includes items requesting respondents to express
their views regarding the extent to which their
organization is ready for cloud computing, and
comprises three measures: economic feasibility,
organizational factors and technological readiness.
For each scale, the measurement dimensions and
the items are identified. The detailed list of items
used in the questionnaire, and the related refer-
ences, are shown in Table 3. To avoid response
bias, reverse phrase is applied in a few items (For
example: COMPLX1, COMPLX5 and IT_PL-
OCY4) in the questionnaire. All items are based
on a 5-point Likert scale, where 1 ¼‘strongly dis-
agree’ and 5 ¼‘strongly agree’. Nominal scale is
used in the demographics section.
Validity and reliability
For this research, content validity, pre-testing and
pilot study were used as ways to test the validity and
reliability of the questionnaire. To ensure content
validity, five senior researchers assessed whether the
items in each construct represented the entire range of
possible items that should be covered. The question-
naire was modified based on their comments. As a
result, measurement of the fit construct was modified
to reflect its alignment with the task construct.
Accordingly, the number of items in the fit construct
was raised to five. Pre-testing was conducted to
improve the validity and the clarity of the question-
naire. A sample of colleagues and experts from the
field were asked to comment on the wording and
clarity of the questionnaire. For further clarity, to
minimize redundancy and misunderstanding, ensure
the appropriateness of the questions and question-
naire flow, a pilot study was carried out. For the pilot
study, an electronic version of the questionnaire was
developed and data was collected from IT staff in
five public organizations in Yemen. Twenty-six
valid responses were analyzed for scale reliability,
as a result of which some items (ADV5, COMPLX4,
COMP5, TRIAL4, KNWG1, ROI1, ASSET4,
IT_INFRA1, IT_SKILLS5, IT_POLICY4, VIABI-
LITY3, and ADP4) were dropped to ensure high
reliability.
Data collection
The sample of the study is IT staff in public orga-
nizations in Yemen. Since organizations may have
different characteristics and different experience of
e-services provision, the researcher intentionally
identified and contacted 50 organizations of differ-
ent kinds. The researcher visited each organization
and explained the questionnaire to the IT manager.
Then, either the IT manager or the researcher dis-
tributed questionnaires among the most informed
IT staff. After two days or more, the researcher
collected the completed questionnaires. A total of
380 questionnaires were distributed and 296 were
returned (77.9%).
Results and analysis
Descriptive statistics
Table 4 presents the respondents’ profile statistics.
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Model analysis
To verify the model, partial least squares (PLS) was
used for the data analysis. PLS follows a two-stage
analytical procedure, measurement model assessment
and structural model assessment (Anderson and Gerb-
ing, 1988). In the measurement model assessment the
reliability and validity of the constructs are examined.
Structural model assessment aims to verify the model
hypotheses.
Measurement model. In this stage, the measurement
scales should be assessed and their reliability and
validity verified. All items are identified as reflective
indicators (Lian et al., 2014; Muhammad et al., 2013;
Table 3. Questionnaire items development based on literature.
Construct
No. of
Items Dimensions Reference
Task 3 Servicing citizens - Internal operations -
Exchanging and sharing information
(Killaly, 2011; Liang et al., 2007)
Relative advantage 5 Cost – Quality – Access to latest technology (Alshamaila, 2013; Tehrani, 2013; Moore and
Benbasat, 1991; Espadanal, 2012; Gupta
et al., 2013; Ross, 2010)
Complexity 5 Ease of Implementation – clarity – Easy to
learn - Time
(Alshamaila, 2013; Moore and Benbasat, 1991;
Espadanal, 2012; Ifinedo, 2011; Tehrani,
2013)
Compatibility 5 Fit with work norms – Integrating with
existing systems – Requiring for technical
changes
(Alshamaila, 2013; Moore and Benbasat, 1991;
Espadanal, 2012; Ifinedo, 2011; Ross, 2010;
Tehrani, 2013)
Trialability 4 Trialling before actual use – adequacy of
testing time – availability for trial
(Alshamaila, 2013; Moore and Benbasat, 1991;
Tehrani, 2013)
Security 5 Adequacy of security techniques – Data
Protection – Data privacy and
confidentiality
(Tehrani, 2013; Hailu, 2012)
Fit 4 Task requirements alignment – Systems
adaptability – Alignment with computing
needs
(Killaly, 2011; Zigurs and Buckland, 1998; Baas
and van Rekom, 2010; Goodhue, 1998)
Top Management
Support
5 Interest – leadership – engagement -
commitment
(Bennett and Savani, 2011; Espadanal, 2012;
Ifinedo, 2011), (Shah Alam et al., 2011; Lian
et al., 2014)
Cloud knowledge 5 Basic knowledge (structure – types- models –
requirements) – Understanding the
benefits and challenges
(Tehrani, 2013)
ROI 6 Investment in new infrastructure – Time and
effort – Maintenance costs - hiring IT
expertise – Training costs
(Espadanal, 2012; Gupta et al., 2013; Tehrani,
2013)
Asset specificity 4 Need for special equipment/ special expertise (Liang et al., 2007)
Uncertainty 3 Unpredictability of performance - unreliability -
Ambiguity
(Alshamaila, 2013)
IT infrastructure 5 Necessary technical requirements - Internet
connection - computational capabilities
(Bennett and Savani, 2011; Espadanal, 2012;
Killaly, 2011; Liang et al., 2007)
IT Skills 5 Employees’ IT literate / IT-related skills – IS/IT
staff capabilities (analysis-implementation)
(Bennett and Savani, 2011; Espadanal, 2012;
Ifinedo, 2011; Liang et al., 2007)
IT Policies 4 Security rules, policies - Privacy laws -
Standard legislations - legal protection
(Alshamaila, 2013; Espadanal, 2012)
Viability 3 Sufficient of current resources – efficient
transformation
(Liang et al., 2007; Killaly, 2011)
Adoption 4 Recommending – Evaluating/ Planning –
initiating the applying process
(Espadanal, 2012; Ross, 2010)
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Liang et al., 2007; Tehrani, 2013; Killaly, 2011;
Alshamaila et al., 2013). Therefore, item loading was
considered. The measurement model was revised by
repeatedly dropping items with loading less than 0.4
(Hair et al., 2014). As a result, IT_INFRA4, IT_IN-
FRA5, UNCERT1, IT_POLICY3 and ROI2 were
dropped. To examine the reliability of the reflective
measures, three indicators, internal consistency, com-
posite reliability and Cronbach’s alpha were used
(Hair et al., 2014). As shown in Table 5, item loadings
for all constructs’ indicators are above the minimum
cutoff point of .50 (Gefen et al., 2000), which indi-
cates satisfactory internal consistency. In terms of
composite reliability, all values were above 0.7 (Hair
et al., 2014; Chin, 2010) as shown in Table 5. Cron-
bach’s alpha also exceeds the minimum criteria of 0.6
(Hair, 2010) for all constructs (See also Table 5).
Hence, the measurement model provided sufficient
evidence in terms of reliability.
Table 4. Respondents Profile Statistics.
Variable Frequency Percent
Education (degree) Bachelor of
Science
228 78.1
Diploma. 13 4.5
Master. 49 16.8
PhD 1 0.3
Missed Value 1 0.3
Field of Study CS 102 34.9
Eng 60 20.5
IS 8 2.7
IT 104 35.6
Management 6 2.1
Missed Value 12 4.1
The Respondent’s
Role
Consultant 6 2.1
DBA 4 1.4
Instructor 2 0.7
ITM 80 27.4
Network
Engineer
87 29.8
Programmer 9 3.1
System Analyzer 36 12.3
Technician 47 16.1
Missed Value 21 7.2
Experience Less than 1 year 7 2.4
1-5 years 104 35.6
5-10 years 91 31.2
More than
10 years
87 29.8
Missed Value 3 1.0
Table 5. Measures: reliability and validity.
Item
Loading AVE
Composite
Reliability
Cronbach’s
Alpha
Adoption 0.7185 0.6492 0.8466 0.726
0.8446
0.8474
Relative
Advantage
0.7403 0.5933 0.8531 0.7711
0.8347
0.6978
0.8009
Asset
Specificity
0.8757 0.5348 0.768 0.6065
0.7518
0.5218
Complexity 0.6148 0.6158 0.8633 0.7852
0.8446
0.8038
0.852
Compatibility 0.7396 0.5676 0.8393 0.7484
0.7853
0.8112
0.6697
FIT 0.7132 0.5274 0.8477 0.7767
0.7586
0.7596
0.6629
0.7325
IT
Infrastructure
0.9256 0.8443 0.9156 0.8159
0.9121
IT Policy 0.8919 0.7288 0.8428 0.6328
0.8137
IT_ Kills 0.6719 0.5545 0.7871 0.6104
0.7078
0.8433
Cloud
Knowledge
0.8807 0.7756 0.9325 0.9037
0.8789
0.8495
0.9125
ROI 0.7842 0.5233 0.8107 0.7399
0.8301
0.5332
0.7105
Security 0.8835 0.7164 0.9265 0.9005
0.885
0.7842
0.8456
0.8295
TASK 0.8071 0.6592 0.853 0.7422
0.8179
0.8108
TMS 0.7819 0.7049 0.9225 0.8991
0.813
0.8166
(continued)
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In terms of convergent validity, the average var-
iance extracted (AVE) values meet the minimum cri-
terion of .50 (Hair et al., 2014), which means that for
each construct, the items share more than 50%of their
variance (Table 5). For discriminant validity, the
square root of AVE for each construct is greater than
the inter-correlations of the construct with other con-
structs in the research model (Chin, 2010; Komiak
and Benbasat, 2006) (Table 6). This indicates that
each construct is distinct and captures phenomena not
represented by other constructs. Thus, the measure-
ment model is satisfactory in terms of reliability and
validity.
Structural model. The structural model was assessed
using significance of the path coefficients, which
indicate the strengths of relationships between
dependent and independent variables, and the R
2
value, which represents the amount of variance
explained by independent variables. The R
2
values
for the dependent variables Fit, Viability and
Adoption are 0.564, 0.688 and 0.325 respectively.
These values reveal that the innovation factors with
task characteristics explain about 56%of the fitness
of cloud computing to implement e-government ser-
vices, while about 69%of the viability is explained
by factors such as ROI, uncertainty, TMS, IT infra-
structure and IT skills. Also, one third of the decision
to adopt cloud computing is contributed by fit and
viability. In addition, to obtain t-values, the signifi-
cance of each path was estimated using a PLS boot-
strapping method utilizing 500 resamples (Chin,
1998). Table 7 presents the results of the hypotheses
testing. The results showed that hypotheses H1c,
H2c, H2d, H2e and H2g are rejected while the rest
are accepted.
Discussion
The fit viability model has been examined to investi-
gate the factors that influence the decision to adopt a
technology by organizations. According to this
model, to make a decision to adopt a new technology
in an organization, the technology’s characteristics
should fit with the tasks that it is to be adopted for,
and the organization should also have the capabilities
to implement the technology. Previous fit viability
model studies measured the fitness of a technology
by identifying the tasks that can be done by the tech-
nology. Therefore, the fitness was always high. In this
study, the researcher examined the fit dimension
regardless of the specific characteristics of the tech-
nology. The innovation technology factors from dif-
fusion of innovation theory were used to measure
the fitness of cloud computing to implement
e-government services. The viability dimension was
examined using the FVM factors along with a consid-
eration of the context of e-government. The results
show that all fit dimension hypotheses were sup-
ported, except the influence of the complexity of
cloud computing on its fitness to implement
e-government services. Further, although hypothesis
H1f (Security !Fit) is supported, the related path
coefficient has a positive value opposite to the pro-
posed direction of the hypothesis. This interprets the
positive direction of the measurement used for the
security scale in the instrument (See Appendix I). The
results also show that the fitness of cloud computing
to e-government tasks has a medium effect on the
adoption decision (Path coefficient 0.35) (Suhr,
2008).
Four hypotheses related to the factors influencing
the viability of cloud computing to implement
e-government were rejected. They are the influence
of IT skills, cloud knowledge, top management sup-
port and uncertainty on viability. However, almost
70%of the viability was explained by these factors
and others (ROI, asset specificity, IT infrastructure,
and IT regulations and policies). Moreover, the effect
of viability on the decision to adopt cloud computing
was medium with path coefficient 0.32.
Conclusion
Due to the main characteristics of cloud computing
such as measured services, elasticity and resource
polling, it may be considered as an ideal solution to
the challenges related to cost, lack of compatibility
and lack of IT skills. However, to adopt cloud
Table 5. (continued)
Item
Loading AVE
Composite
Reliability
Cronbach’s
Alpha
0.8621
0.9176
Trialability 0.8642 0.6073 0.8215 0.7236
0.773
0.6911
Uncertainty 0.5066 0.6248 0.7507 0.6039
0.9965
Viability 0.9072 0.823 0.9029 0.7849
0.9072
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Table 6. Discriminant Validity (AVE and Correlations).
ADP ADV ASSET CMPLX CMPT FIT
IT_
INFRA
IT_
POLICY
IT_
SKILL KNWLG ROI SEC TASK TMS TRL UNCRT Viability
ADP 0.8057
ADV 0.3947 0.7702
ASSET 0.2625 0.3287 0.7313
CMPLX 0.3175 0.5414 0.1609 0.7847
CMPT 0.3652 0.3242 0.0614 0.4886 0.7534
FIT 0.4897 0.6263 0.2052 0.5168 0.5587 0.7262
IT_INFRA 0.3069 0.1943 0.0939 0.3428 0.3181 0.3211 0.9189
IT_POLICY 0.0752 0.0105 0.0298 0.1115 0.0633 0.0478 0.1577 0.8537
IT_SKILL 0.3117 0.1741 0.1116 0.2320 0.2055 0.2402 0.3216 0.0337 0.7447
KNWLG 0.3342 0.2264 0.2526 0.3277 0.2140 0.2078 0.2378 0.1522 0.4316 0.8807
ROI 0.1533 0.2482 0.1890 0.3620 0.2940 0.2555 0.1893 0.0405 0.1541 0.1512 0.7234
SEC 0.4385 0.4663 0.2612 0.3905 0.2836 0.4256 0.2678 0.1523 0.1330 0.4145 0.2456 0.8464
TASK 0.2327 0.4889 0.1499 0.3393 0.2043 0.4254 0.1324 0.0581 0.0702 0.0658 0.1219 0.2821 0.8119
TMS 0.4006 0.0917 0.1131 0.1488 0.2041 0.1652 0.3688 0.0652 0.2769 0.1459 0.0491 0.1893 0.0809 0.8396
TRL 0.0891 0.2526 0.0798 0.2147 0.1766 0.2849 0.0556 0.1323 0.2020 0.0081 0.1893 0.0879 0.2197 0.1202 0.7793
UNCRT 0.1799 0.1534 0.0857 0.1350 0.0884 0.1407 0.1579 0.2124 0.0682 0.0399 0.1029 0.0701 0.0431 0.1062 0.1339 0.7905
Viability 0.4680 0.2980 0.2040 0.3566 0.3343 0.4137 0.8106 0.0639 0.3438 0.2634 0.1971 0.2732 0.1828 0.3288 0.0337 0.1386 0.9072
13
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computing in public sector organizations, many orga-
nizational factors, in addition to the technological,
should be investigated. Hence, it is important to
evaluate the applicability of cloud computing to
e-government implementation through measuring
the fitness of cloud computing to the task of
e-government, while the readiness of public sector
organizations to adopt this innovation should also be
measured. FVM was therefore selected to examine the
factors influencing cloud computing adoption for the
purpose of e-government implementation. Because
cloud computing is considered as an innovation, and
most previous studies used the diffusion of innova-
tions theory to investigate the factors affecting its
adoption at the organizational level, this study applied
this theory to measure the fitness of cloud computing
through examining its technological factors. Hence,
this study contributes to the literature by extending
the FVM to measure the fit of a technology regardless
of its specific attributes, as well as empirically
examining the FVM of a new technology (cloud
computing). Consequently, it contributes to the under-
standing of cloud computing adoption in the
e-government context.
Regarding the implications for practice, the find-
ings suggest that the e-government tasks of providing
services and the technology itself are driving the fit of
cloud computing to public sector organizations. On
the other hand, economic factors (ROI and asset spe-
cificity) and organizations’ technical readiness (IT
infrastructure and IT policies) are affecting the
viability of cloud computing for e-government imple-
mentation. In addition, the degree of fit has more
effect than viability in the decision to adopt cloud
computing, while fit and viability together describe
just 33%of the adoption decision.
To conclude, cloud computing can offer a real
opportunity for public sector organizations to effec-
tively provide e-services. The study model can help
policy makers and those who supervise e-government
to evaluate this new and up-to-date technology as they
designing the roadmap for e-government.
Although this study contributes significantly to
both academia and practice, it is limited in some
ways. Firstly, the data was collected from public orga-
nizations in Yemen, which may be not a perfect envi-
ronment and where the diffusion of cloud computing
is still low. Secondly, the sample size is relatively
small. Therefore more research is suggested for the
future. Researchers may conduct research using data
from other countries, and studies should be conducted
in developed as well as developing countries. Then,
comparisons may be considered.
Appendix I: The Instrument Items
Task
E-government ensures providing effective ser-
vices to citizens.
E-government enhances the organization inter-
nal operations’ performance.
Table 7. Hypotheses results.
Hypothesis Relation Path Coefficient T-Statistics Supported
H1 FIT -> Adoption 0.3573 6.1782 Yes
H2 Viability -> Adoption 0.3202 5.6379 Yes
H1a TASK -> FIT 0.1122 3.0731 Yes
H1b Relative Advantage -> FIT 0.3605 6.6566 Yes
H1c Compatibility -> FIT 0.3482 8.2841 Yes
H1d Complexity -> FIT 0.0568 0.8991 No
H1e Trialability -> FIT 0.0869 1.9091 Yes
H1f Security -> FIT 0.0973 2.0399 Yes
H2a ROI -> Viability 0.0640 1.6889 Yes
H2b Asset Specificity -> Viability 0.1320 3.1673 Yes
H2c Uncertainty -> Viability 0.0310 0.8415 No
H2d TMS -> Viability 0.0101 0.2906 No
H2e Cloud Knowledge -> Viability 0.0193 0.4256 No
H2f IT Infrastructure -> Viability 0.7634 21.0698 Yes
H2g IT Skill -> Viability 0.0627 1.3993 No
H2h IT Policy -> Viability 0.0675 1.7188 Yes
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E-government helps exchanging and sharing
information between organizations effectively.
Relative advantage
Providing e-services over the cloud will lower
the costs.
Using cloud computing would make it easier for
organizations to implement e-government
services.
Cloud computing allows using the latest version
of the technology.
Using cloud computing would improve the qual-
ity of the work organizations do
Overall, using cloud computing would be advan-
tageous for e-government implementation
Complexity
It is easy to get cloud computing services to do
what organization officials want them to do.
Interacting with the cloud computing services is
not clear and difficult to understand.
Learning to operate on cloud computing services
cannot be easy for employees.
It takes too much time for IT staff if they want to
use cloud computing to do their normal
duties.
Overall, cloud services are easy to use.
Compatibility
Using cloud computing fits well with the way
organization usually performs.
Cloud can easily be integrated into existing IT
infrastructure.
Cloud computing is compatible with the systems
that are already in use.
Using cloud computing services does not require
many technical changes.
Using cloud computing services is compatible
with all aspects of organization work.
Trialability
Before deciding to use any of cloud computing
services, it can be tested properly.
It is essential to be able to try out cloud services
properly before deciding whether it fits with
organization’s tasks or not.
It is essential to be able to try cloud services (on
a trial basis) for long enough to see what they
can do.
Cloud computing is available to adequately test
and run various e-government services.
Security
Cloud computing provide sufficient security
controls.
The security systems built into the cloud com-
puting services are strong enough to protect
organization data.
Cloud providers maintain the privacy and con-
fidentiality of organization data.
Cloud providers’ servers and data centers are
secure.
Overall, cloud computing technology is more
secure than traditional computing methods.
Fit
Organization’s computing task requirements to
implement e-government services closely
align with cloud services.
Cloud computing will satisfy organization’s com-
puting needs to implement e-government
services.
Cloud computing would be a good way to
share and exchange information between
organizations.
Current e-government applications can be easily
adapted to the cloud.
It seems to be that cloud computing fits
with organization requirements to provide
e-government services.
Top Management Support
Top management is interested in the use of cloud
computing technologies in our operations.
Theorganization’stopmanagement provides
strong leadership and engages in the process
when it comes to information systems.
Top management encourages using new emer-
ging technology to provide e-services.
Top management understands the benefits of
cloud computing technology.
The top management supports the implementa-
tion of e-service using cloud computing.
Cloud Knowledge
Employees have basic knowledge about cloud
computing.
IT staff have good knowledge about the under-
lying structure of cloud computing.
IT staff have good knowledge about the benefits
of using cloud computing
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IT staff have good knowledge about various
cloud computing models (SaaS, PaaS and
IaaS) and types (Public, Private, Community
and Hybrid)
Overall, IT staff have good knowledge about
cloud computing.
ROI
Cloud computing decreases the investment in
new infrastructure
Deployment process of cloud computing involves
a negligible amount of time and effort
Cloud computing decreases the cost of system
maintenance
Cloud computing eliminates hiring expensive IT
expertise in-house
Employees can use cloud computing to perform
their work better with no need of more
training.
The benefits of cloud computing are greater than
the costs of its adoption
Asset Specificity
Special hardware/software is needed to use
cloud computing.
Employees with special expertise need to be
hired to adopt cloud computing.
To process organization data, cloud services pro-
viders would have to make substantial invest-
ments in equipment and software tailored to
organization’s needs.
The use of cloud technology reduce the need for
physical asset on-hand
Uncertainty
Cloud computing services might not perform
well and create problems with our IT
operations.
Cloud computing services servers may not per-
form well and may not support our IT opera-
tions effectively.
The pay-as-use model of payment not clear and
makes it difficult to justify cost and benefits.
IT infrastructure
The organization has the necessary technical
requirements to using cloud computing
systems.
The organization has a good Internet connection
speed.
The organization is mature in using the Internet
and related technology.
The organization needs to improve its computa-
tional capabilities.
The organization needs cloud computing tech-
nology to meet its IT needs.
IT Skills
Within this organization, managers at all levels
are IT literate.
This organization has high levels of IT-related
skills and technical knowledge.
IS department know the business process well
enough to identify the required applications.
IS staff has the ability in supporting cloud com-
puting system development
Within the organization there are the necessary
skills to using cloud computing services
IT Policies
There is a lack of security rules, policies and
privacy laws.
Due to differences in legislation, organization
might lose control of data if it used cloud
computing services provided from a supplier
hosting data outside the country.
There is no legal protection in the use of cloud
computing
The laws and regulations that exist nowadays
are sufficient to protect the use of cloud
computing.
Viability
The organization’s capabilities and current
resources support cloud computing.
The organization can efficiently move comput-
ing needs to cloud computing.
Cloud computing is viable to implement
e-government services in the organization.
Cloud Computing Adoption
It is recommended to use cloud computing
approaches in the organization.
It is anticipated that the organization will adopt
cloud computing in the near future.
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The organization plans to evaluate and adopt
cloud computing.
The organization is currently engaged at the ini-
tial stage of cloud computing adoption.
Note
1. (Cambridge Business English Dictionary)
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About the authors
Fathey Mohammed is currently a PhD student in the
Information Service System Innovation Research Group
(ISSI RG) at Universiti Teknologi Malaysia in Skudai,
Johor, Malaysia. He received his BSc in Computer Engi-
neering from Isfahan University, Isfahan, Iran, in 2003. He
received his MSc degrees in Information Technology Engi-
neering from the Tarbiat Modarres University, Tehran,
Iran. His research interests E-government, E-business,
Cloud Computing, HCI, and Information System project
management. Contact: Department of Information Sys-
tems, Faculty of Computing, Universiti Teknologi Malay-
sia, 81310 UTM Johor Bahru, Johor, Malaysia. Email:
alqadsi_ye@yahoo.com / fathey2@live.utm.my
20 Information Development
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Othman bin Ibrahim is Associate Professor at the Infor-
mation Systems Department, Faculty of Computing, Uni-
versiti Teknologi Malaysia (UTM). He obtained his BSc
(Computer Science) in 1997 from Universiti Teknologi
Malaysia and MSc (Information Technology) in 1999
from Universiti Kebangsaan Malaysia. He received his
PhD in Computation, in 2004 from University of Man-
chester Institute of Science and Technology (UMIST).
He has been involved in many projects since 2007,
such as Web Based Matchmaking System, and Identi-
fication of Risk Analysis Standards as a Method in
Malaysia E-Government Security. Contact: UTM
Career Centre, Blok L51, Pejabat Hal Ehwal Maha-
siswa, Universiti Teknologi Malaysia, Skudai. Email:
othmanibrahim@utm.my
Mehrbakhsh Nilashi received his PhD degree in Com-
puter Science in the Faculty of Computing, University
Teknologi Malaysia in 2014. Now he is a postdoctoral
fellow in the University Teknologi Malaysia. His
research is mainly in the field of Soft Computing,
Machine Learning, Big Data, Multi-criteria Decision
Making, Information Retrieval, Recommender Systems
with a special focus on Multi-Criteria Recommender
Systems, Health Information Technology and Decision
Support Systems. His contributions have been published
in prestigious peer-reviewed journals and international
conferences. Contact: Department of Information Sys-
tems, Faculty of Computing, Universiti Teknologi
Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia.
Email: nilashidotnet@hotmail.com
Ensaf Al-Zurqa received the BS, MS and PhD degrees
from the Department of Information Technology, Faculty
of Computers and Information System, Cairo University,
Egypt, in 2002, 2005 and 2009, respectively. She is cur-
rently an associate professor in the IT Department, Faculty
of Engineering and Information Technology, Taiz Univer-
sity, Yemen. Her research interests include wireless sensor
networks, speech and speaker recognition, digital image
processing and virtual reality. Contact: Department of
Information Technology, Faculty of Engineering and Infor-
mation Technology, Taiz University, 6803, Taiz, Yemen.
Email: ensafzurqa@gmail.com
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